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WO2024223837A1 - Dna-paint related materials and methods - Google Patents

Dna-paint related materials and methods Download PDF

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Publication number
WO2024223837A1
WO2024223837A1 PCT/EP2024/061562 EP2024061562W WO2024223837A1 WO 2024223837 A1 WO2024223837 A1 WO 2024223837A1 EP 2024061562 W EP2024061562 W EP 2024061562W WO 2024223837 A1 WO2024223837 A1 WO 2024223837A1
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Prior art keywords
nucleic acid
molecule
acid sequence
target
imaging
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French (fr)
Inventor
Eduard UNTERAUER
Heinrich GRABMAYR
Mahipal GANJI
Joschka HELLMEIER
Isabelle BAUDREXEL
Jürgen Schmied
Sebastian Strauss
Ralf Jungmann
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Ludwig Maximilians Universitaet Muenchen LMU
Max Planck Gesellschaft zur Foerderung der Wissenschaften
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Ludwig Maximilians Universitaet Muenchen LMU
Max Planck Gesellschaft zur Foerderung der Wissenschaften
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Publication of WO2024223837A1 publication Critical patent/WO2024223837A1/en
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays

Definitions

  • the present invention relates to the field of detection and quantification of targets, for example via PAINT, and single cell analysis, particularly spatial intermolecular single-cell omics.
  • targets for example via PAINT
  • single cell analysis particularly spatial intermolecular single-cell omics.
  • documents including patent applications and manufacturer’s manuals are cited.
  • the disclosure of these documents, while not considered relevant for the patentability of this invention, is herewith incorporated by reference in its entirety. More specifically, all referenced documents are incorporated by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference.
  • spatial single-cell omics In what is generally known as “spatial single-cell omics”, there are many technologies, represented by multiple companies (e.g. Akoya, 10x Genomics), which aim at identifying proteins and nucleic acids, by direct hybridization of fluorophore-labeled oligonucleotides, or by enzymatic processes like rolling-circle amplification. However, these known technologies are concerned with cell-to-tissue context and use the single cell as the base unit.
  • spatial intermolecular single-cell omics refers to the spatial context of targets within a single cell, i.e. the intermolecular spatial relationships within a single cell.
  • the resolution of the known technologies mentioned above cannot be increased in a straightforward manner to reach intermolecular spatial information, due to basic effects like prohibitive probe size, diffusion limits, etc.
  • the present invention provides an approach to spatial intermolecular single-cell omics based on fluorescence imaging.
  • the fluorescence imaging technique and related molecules, compositions and kits comprise independent inventive aspects that may be used for other imaging applications than spatial intermolecular single-cell omics.
  • Major progress has been made in the field of fluorescence imaging over the years.
  • Super- resolution techniques, such as STED, STORM, PALM and PAINT were developed to overcome the diffraction limit of light microscopy, generally known to be approximated by Ernst Abbe’s formula.
  • DNA-PAINT (with the sub-forms Exchange-PAINT, SPEED-PAINT and qPAINT) is a super-resolution technique that breaks the optical diffraction limit by temporally separating fluorescence signals from targets that are locally unresolvable in non-super-resolution fluorescence microscopy, e.g. confocal microscopy. The signals are localized timely separated, and the image is reconstructed from the localized data. This concept is also known as single-molecule localization microscopy, in short SMLM.
  • SPEED-PAINT describes optimized sequences and buffer conditions for up to 100x speed increase compared to non-optimized sequences, meaning the acquisition time is drastically decreased.
  • the drawback of SPEED-PAINT is its limitation to only a few (currently 6) suitable imager sequences and thus targets.
  • An object of the present invention is to provide increased plexing capabilities in PAINT. Another object of the present invention is PAINT-related speed optimization. Another object of the present invention is to provide techniques and methods for spatial intermolecular single-cell omics.
  • the present invention relates in a first aspect to a single-stranded nucleic acid molecule, comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence, and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.
  • the first nucleic acid sequence may be capable of stronger associating with its complementary nucleic acid sequence (e.g. via specific hybridization) than the second nucleic acid sequence under the same conditions (e.g.
  • the term “associating” refers to binding strength between the first or second nucleic acid sequence, respectively, and its complementary nucleic acid sequence.
  • the first nucleic acid sequence may be capable of stronger binding to its complementary nucleic acid sequence than the second nucleic acid sequence under the same conditions.
  • nucleic acid sequence compositions can be selected by routine means.
  • single-stranded nucleic acid molecule in accordance with the present invention may refer to single stranded DNA or RNA.
  • DNA deoxyribonucleic acid
  • DNA deoxyribonucleic acid
  • RNA ribonucleic acid
  • the nucleic acid molecule may also be modified by many means known in the art.
  • Non-limiting examples of such modifications include methylation, "caps", substitution of one or more of the naturally occurring nucleotides with an analog, and internucleotide modifications such as, for example, those with uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphoroamidates, carbamates, etc.) and with charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.).
  • Nucleic acid molecules in the following also referred as polynucleotides and/or oligonucleotides, may contain one or more additional covalently linked moieties, such as, for example, proteins (e.g., nucleases, toxins, antibodies, signal peptides, poly-L-lysine, etc.), intercalators (e.g., acridine, psoralen, etc.), chelators (e.g., metals, radioactive metals, iron, oxidative metals, etc.), and alkylators.
  • the polynucleotides may be derivatized by formation of a methyl or ethyl phosphotriester or an alkyl phosphoramidate linkage.
  • nucleic acid mimicking molecules known in the art such as synthetic or semi-synthetic derivatives of DNA or RNA and mixed polymers.
  • nucleic acid mimicking molecules or nucleic acid derivatives according to the invention include phosphorothioate nucleic acid, phosphoramidate nucleic acid, 2’-O- methoxyethyl ribonucleic acid, morpholino nucleic acid, hexitol nucleic acid (HNA), peptide nucleic acid (PNA) and locked nucleic acid (LNA) (see Braasch and Corey, Chem Biol 2001, 8: 1).
  • LNA is an RNA derivative in which the ribose ring is constrained by a methylene linkage between the 2’-oxygen and the 4’-carbon.
  • nucleic acids containing modified bases for example thio-uracil, thio-guanine and fluoro-uracil.
  • the target complementary nucleic acid sequence may be a portion of a target molecule to be detected, e.g., in a single cell. In this case, the single-stranded nucleic acid molecule may directly hybridize to the target molecule.
  • the target complementary nucleic acid sequence may be a portion of a primary binder.
  • the primary binder is a molecule that specifically binds the target molecule.
  • the single-stranded nucleic acid molecule may indirectly bind to the target molecule via the primary binder.
  • Hybridization as used herein is the process in which two complementary single-stranded nucleic acid molecules bind together to form a double-stranded molecule. The bonding is dependent on the appropriate base-pairing across the two single-stranded molecules. Whether or not two complementary single-stranded nucleic acid molecules bind together to form a double-stranded molecule also depends on the hybridization condition, that is the sum of environmental factors influencing hybridization. Examples of environmental factors that may affect hybridization include temperature, the concentration of one or more salts and pH. This is also referred to in the prior art as the “stringency” of hybridization.
  • the stringency is determined by the hybridization temperature and the salt concentration in the hybridization buffer, whereby high temperature and low salt is more stringent as only perfectly matched hybrids will be stable.
  • a pH that is too alkaline may cause the strands to separate; too acidic and they may be forced together.
  • the length and the GC content of nucleic acid molecules influence hybridization. Generally, binding occurs under more stringent conditions for long nucleic acid molecules and nucleic acid molecules with a high GC content and binding occurs under less stringent conditions for short nucleic acid molecules and/or nucleic acid molecules with a high AT content.
  • the determination of ideal hybridization conditions for two given single-stranded nucleic acid molecules to bind together to form a double-stranded molecule is a matter of routine in the field of molecular biology.
  • the ideal hybridization conditions are estimated from the calculation of the melting temperature (Tm) of the double-stranded molecule. At the Tm, half of the sequence is double stranded and half of the sequence is single stranded.
  • Tm melting temperature
  • the Tm for short probes 14 – 20 base pairs
  • Tm 4°C x number of G/C pairs + 2°C x number of A/T pairs
  • the hybridization temperature (annealing temp) of oligonucleotide probes is generally approximately 5°C below the melting temperature.
  • the requirement that the first nucleic acid sequence is capable of specifically hybridizing to a target complementary nucleic acid sequence means that under the selected or envisioned hybridization conditions the first nucleic acid sequence only hybridizes to the target complementary nucleic acid sequence and to no other nucleic acid molecule that might be present, for example, in a sample.
  • the requirement that the second nucleic acid sequence is capable of transiently binding to a complementary nucleic acid sequence means that under the selected or envisioned hybridization conditions the second nucleic acid sequence binds together with the complementary nucleic acid sequence to form a double-stranded molecule transiently, whereby transiently means only momentarily or briefly.
  • a binding interaction with a mean duration of shorter than 30 seconds is deemed transient, while a duration of longer than 1 hour is deemed stable.
  • the intermediate duration between 30 seconds and 1 hours may be designated “semi-stable”.
  • DNA hybridization and de-hybridization is a first order chemical reaction and therefore the binding duration follows an exponential distribution.
  • the values for the binding duration given above are the mean values of the distribution. It follows for the above that in the context of the first aspect of the invention the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence (i.e. via specific hybridization) than the second nucleic acid sequence (i.e. via transient binding only).
  • the complementary nucleic acid sequence is labeled by an imaging molecule.
  • labeled means that the imaging molecule is connected or linked to the complementary nucleic acid sequence, preferably via covalent chemical bonds.
  • the first nucleic acid sequence is capable of stably hybridizing to its target complementary nucleic sequence. “Stably hybridizing” in the context of the present invention has to be held distinct from the above described “transient binding”.
  • “Stably hybridizing” means that under the selected or envisioned hybridization conditions the first nucleic acid sequence hybridizes to the target complementary nucleic acid sequence thereby forming a double-stranded molecule that remains stably in the double-stranded form and does not dissociate again into single-strands.
  • the present invention relates in a second aspect to a hybridization complex, wherein the nucleic acid molecule is specifically hybridized to a target complementary nucleic acid sequence.
  • the first nucleic acid sequence of the single stranded nucleic acid molecule hybridizes to a target complementary nucleic acid sequence thereby forming a double-stranded region within the nucleic acid molecule.
  • the target nucleic acid sequence is complementary to the first nucleic acid sequence. This means that it comprises or consists of a nucleic acid sequence that is complementary to the first nucleic acid sequence.
  • This complementary nucleic acid sequence may have a length of 6nt to 150nt, preferably 10nt to 50 nt, more preferably 12 nt to 30 nt.
  • the target nucleic acid sequence is preferably an exogenous nucleic acid sequence, for example, an in vitro synthesized or produced nucleic acid sequence. However, the target nucleic acid sequence may also be or be a part of a naturally occurring nucleic acid sequence, such as mRNA.
  • the target complementary nucleic acid sequence is conjugated to a binding molecule, wherein the binding molecule is preferably a nucleic acid sequence, a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer.
  • a binding molecule is a compound being capable of binding to a target molecule.
  • the binding molecule preferably specifically binds to the target molecule. Specific binding designates that the binding molecule does not or essentially does not bind to other molecules, e.g. in the context of a sample, than the target molecule.
  • the target molecule of the binding molecule may be any kind of molecule that is potentially present in a sample, e.g.
  • the sample can be, for example, a cellular sample or a body sample, such as a body liquid or tissue sample.
  • the cellular sample may be from cultured cells or may have been obtained from a subject, e.g. via a biopsy.
  • the body liquid is preferably a blood sample (whole blood, serum or plasma).
  • the binding molecule is a nucleic acid sequence it may bind to a target nucleic acid via hybridization.
  • the binding of the binding molecule to the target molecule may be directly or indirectly, wherein indirectly means that there may be one or more further binding partners between the binding molecule and the target molecule.
  • indirectly means that there may be one or more further binding partners between the binding molecule and the target molecule.
  • the binding molecule is an antibody the antibody might be a biotin labelled antibody that binds to a streptavidin labelled antibody and the streptavidin labelled antibody binds to the target molecule.
  • the "small molecule” as used herein is preferably an organic molecule.
  • Organic molecules relate or belong to the class of chemical compounds having a carbon basis, the carbon atoms linked together by carbon-carbon bonds.
  • Organic compounds can be natural or synthetic.
  • the organic molecule is preferably an aromatic molecule and more preferably a heteroaromatic molecule.
  • aromaticity is used to describe a cyclic (ring-shaped), planar (flat) molecule with a ring of resonance bonds that exhibits more stability than other geometric or connective arrangements with the same set of atoms.
  • Aromatic molecules are very stable, and do not break apart easily to react with other substances.
  • a heteroaromatic molecule at least one of the atoms in the aromatic ring is an atom other than carbon, e.g.
  • the molecular weight is preferably in the range of 200 Da to 1500 Da and more preferably in the range of 300 Da to 1000 Da.
  • the "small molecule" in accordance with the present invention may be an inorganic compound. Inorganic compounds are derived from mineral sources and include all compounds without carbon atoms (except carbon dioxide, carbon monoxide and carbonates). Preferably, the small molecule has a molecular weight of less than about 2000 Da, or less than about 1000 Da such as less than about 500 Da, and even more preferably less than about 200 Da, or amu.
  • the size of a small molecule can be determined by methods well-known in the art, e.g., mass spectrometry.
  • the small molecules may be designed, for example, based on the crystal structure of the target molecule, where sites presumably responsible for the biological activity can be identified and verified in in vivo assays such as in vivo high-throughput screening (HTS) assays.
  • the term “protein” as used herein is interchangeably with the term “polypeptide” and describes linear molecular chains of amino acids, including single chain proteins or their fragments. The protein may also be a peptide.
  • the term “peptide” as used herein describes a group of molecules consisting of up to 49 amino acids, whereas the term “polypeptide” (also referred to as "protein”) as used herein preferably describes a group of molecules consisting of at least 50 amino acids.
  • (poly)peptides may further form oligomers consisting of at least two identical or different molecules.
  • the corresponding higher order structures of such multimers are, correspondingly, termed homo- or heterodimers, homo- or heterotrimers etc..
  • antibody as used in accordance with the present invention comprises, for example, polyclonal or monoclonal antibodies. Furthermore, also derivatives or fragments thereof, which still retain the binding specificity to the target are comprised in the term "antibody”.
  • Antibody fragments or derivatives comprise, inter alia, Fab or Fab’ fragments, Fd, F(ab')2, Fv or scFv fragments, single domain VH or V-like domains, such as VhH or V-NAR-domains, as well as multimeric formats such as minibodies, nanobodies, diabodies, tribodies or triplebodies, tetrabodies or chemically conjugated Fab’-multimers (see, for example, Harlow and Lane “Antibodies, A Laboratory Manual”, Cold Spring Harbor Laboratory Press, 198; Harlow and Lane “Using Antibodies: A Laboratory Manual” Cold Spring Harbor Laboratory Press, 1999; Altshuler EP, Serebryanaya DV, Katrukha AG.2010, Biochemistry (Mosc)., vol.
  • the multimeric formats in particular comprise bispecific antibodies that can simultaneously bind to two different types of antigen.
  • the first antigen can be found on a protein of interest.
  • the second antigen may, for example, be a tumor marker that is specifically expressed on cancer cells or a certain type of cancer cells.
  • Bispecific antibodies formats are Biclonics (bispecific, full length human IgG antibodies), DART (Dual-affinity Re- targeting Antibody) and BiTE (consisting of two single-chain variable fragments (scFvs) of different antibodies) molecules (Kontermann and Brinkmann (2015), Drug Discovery Today, 20(7):838-847).
  • antibody also includes embodiments such as chimeric (human constant domain, non-human variable domain), single chain and humanised (human antibody with the exception of non-human CDRs) antibodies.
  • chimeric human constant domain, non-human variable domain
  • single chain humanised (human antibody with the exception of non-human CDRs) antibodies.
  • Various techniques for the production of antibodies are well known in the art and described, e.g. in Harlow and Lane (1988) and (1999) and Altshuler et al., 2010, loc. cit.
  • polyclonal antibodies can be obtained from the blood of an animal following immunisation with an antigen in mixture with additives and adjuvants and monoclonal antibodies can be produced by any technique which provides antibodies produced by continuous cell line cultures. Examples for such techniques are described, e.g.
  • Harlow E and Lane D Cold Spring Harbor Laboratory Press, 1988; Harlow E and Lane D, Using Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, 1999 and include the hybridoma technique originally described by Köhler and Milstein, 1975, the trioma technique, the human B-cell hybridoma technique (see e.g. Kozbor D, 1983, Immunology Today, vol.4, 7; Li J, et al.2006, PNAS, vol.103(10), 3557) and the EBV-hybridoma technique to produce human monoclonal antibodies (Cole et al., 1985, Alan R. Liss, Inc, 77-96).
  • recombinant antibodies may be obtained from monoclonal antibodies or can be prepared de novo using various display methods such as phage, ribosomal, mRNA, or cell display.
  • a suitable system for the expression of the recombinant (humanised) antibodies may be selected from, for example, bacteria, yeast, insects, mammalian cell lines or transgenic animals or plants (see, e.g., US patent 6,080,560; Holliger P, Hudson PJ.2005, Nat Biotechnol., vol.23(9), 11265).
  • techniques described for the production of single chain antibodies see, inter alia, US Patent 4,946,778) can be adapted to produce single chain antibodies specific for an epitope of a target.
  • antibody mimetics refers to compounds which, like antibodies, can specifically bind antigens, but which are not structurally related to antibodies. Antibody mimetics are usually artificial peptides or proteins with a molar mass of about 3 to 20 kDa.
  • an antibody mimetic may be selected from the group consisting of affibodies, adnectins, anticalins, DARPins, avimers, nanofitins, affilins, Kunitz domain peptides, Fynomers®, trispecific binding molecules and prododies.
  • affibody refers to a family of antibody mimetics which is derived from the Z-domain of staphylococcal protein A. Structurally, affibody molecules are based on a three-helix bundle domain which can also be incorporated into fusion proteins. In itself, an affibody has a molecular mass of around 6kDa and is stable at high temperatures and under acidic or alkaline conditions. Target specificity is obtained by randomisation of 13 amino acids located in two alpha-helices involved in the binding activity of the parent protein domain (Feldwisch J, Tolmachev V.; (2012) Methods Mol Biol.899:103-26).
  • adnectin (also referred to as “monobody”), as used herein, relates to a molecule based on the 10th extracellular domain of human fibronectin III (10Fn3), which adopts an Ig- like ⁇ -sandwich fold of 94 residues with 2 to 3 exposed loops, but lacks the central disulphide bridge (Gebauer and Skerra (2009) Curr Opinion in Chemical Biology 13:245-255).
  • Adnectins with the desired target specificity can be genetically engineered by introducing modifications in specific loops of the protein.
  • the term “anticalin”, as used herein, refers to an engineered protein derived from a lipocalin (Beste G, Schmidt FS, Stibora T, Skerra A.
  • Anticalins possess an eight-stranded ⁇ -barrel which forms a highly conserved core unit among the lipocalins and naturally forms binding sites for ligands by means of four structurally variable loops at the open end.
  • Anticalins although not homologous to the IgG superfamily, show features that so far have been considered typical for the binding sites of antibodies: (i) high structural plasticity as a consequence of sequence variation and (ii) elevated conformational flexibility, allowing induced fit to targets with differing shape.
  • DARPin refers to a designed ankyrin repeat domain (166 residues), which provides a rigid interface arising from typically three repeated ⁇ -turns. DARPins usually carry three repeats corresponding to an artificial consensus sequence, wherein six positions per repeat are randomised. Consequently, DARPins lack structural flexibility (Gebauer and Skerra, 2009).
  • avimer refers to a class of antibody mimetics which consist of two or more peptide sequences of 30 to 35 amino acids each, which are derived from A-domains of various membrane receptors and which are connected by linker peptides.
  • binding of target molecules occurs via the A-domain and domains with the desired binding specificity can be selected, for example, by phage display techniques.
  • the binding specificity of the different A- domains contained in an avimer may, but does not have to be identical (Weidle UH, et al., (2013), Cancer Genomics Proteomics; 10(4):155-68).
  • a “nanofitin” (also known as affitin) is an antibody mimetic protein that is derived from the DNA binding protein Sac7d of Sulfolobus acidocaldarius.
  • Nanofitins usually have a molecular weight of around 7kDa and are designed to specifically bind a target molecule, by randomising the amino acids on the binding surface (Mouratou B, Béhar G, Paillard-Laurance L, Colinet S, Pecorari F., (2012) Methods Mol Biol.; 805:315-31).
  • the term “affilin”, as used herein, refers to antibody mimetics that are developed by using either gamma-B crystalline or ubiquitin as a scaffold and modifying amino-acids on the surface of these proteins by random mutagenesis. Selection of affilins with the desired target specificity is effected, for example, by phage display or ribosome display techniques.
  • affilins have a molecular weight of approximately 10 or 20kDa.
  • the term affilin also refers to di- or multimerised forms of affilins (Weidle UH, et al., (2013), Cancer Genomics Proteomics; 10(4):155-68).
  • a “Kunitz domain peptide” is derived from the Kunitz domain of a Kunitz-type protease inhibitor such as bovine pancreatic trypsin inhibitor (BPTI), amyloid precursor protein (APP) or tissue factor pathway inhibitor (TFPI).
  • BPTI bovine pancreatic trypsin inhibitor
  • APP amyloid precursor protein
  • TFPI tissue factor pathway inhibitor
  • Kunitz domains have a molecular weight of approximately 6kDA and domains with the required target specificity can be selected by display techniques such as phage display (Weidle et al., (2013), Cancer Genomics Proteomics; 10(4):155-68).
  • the term "Fynomer®” refers to a non-immunoglobulin-derived binding polypeptide derived from the human Fyn SH3 domain. Fyn SH3-derived polypeptides are well-known in the art and have been described e.g. in Grabulovski et al. (2007) JBC, 282, p.
  • the target complementary nucleic acid sequence may be conjugated to the binding molecule in any suitable manner.
  • the conjugation between the target complementary nucleic acid sequence and the binding molecule can be covalent or non-covalent.
  • the target complementary nucleic acid sequence is conjugated to the binding molecule via a linker, wherein the linker preferably comprises biotin and one of avidin or streptavidin.
  • the target complementary nucleic acid sequence is covalently coupled to the binding molecule via NHS- chemistry or site-specific labeling via click chemistry.
  • the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence is based on the formation of more hydrogen bonds than the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule.
  • a nucleic acid molecule labeled by an imaging molecule, which transiently binds another nucleic acid molecule is known as an ‘imager’ or ‘imager strand’.
  • the ‘imager’ or ‘imager strand’ preferably comprises or consists of a complementary nucleotide sequence having a length of 5 to 13 nucleotides that carries the imaging molecule. Any combination of suitable melting temperatures (Tm) that provide for the appropriate difference in association strength of the first nucleic acid sequence with its complementary nucleic acid sequence on the one hand and the second nucleic acid sequence with the complementary nucleic acid sequence being labeled by an imaging molecule on the other hand is contemplated.
  • Tm melting temperatures
  • the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence has a melting temperature of between 25°C and 90°C, preferably between 45°C and 85°C and most preferably between 62°C and 78°C.
  • the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of between 8°C and 22°C, preferably 12°C to 18°C and most preferably 14°C and 16° C.
  • the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence may have a melting temperature of between 45°C and 85°C and the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of 12°C to 18°C.
  • the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence may have a melting temperature of between 62°C and 78°C and the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of 14°C to 16°C.
  • the imaging molecule may be of any suitable type.
  • the imaging molecule is a fluorescent molecule.
  • Preferred fluorescent molecules are fluorescent proteins or fluorescent dyes.
  • the fluorescent dye is preferably a component selected from Atto, Alexa Fluor or Cy dyes.
  • the fluorescent protein is preferably GFP or YFP.
  • other detectable types of imaging molecules may also be used, for example a radionuclide.
  • the radionuclide is preferably either selected from the group of gamma-emitting isotopes, more preferably 99m Tc, 123 I, 111 In, and/or from the group of positron emitters, more preferably 18 F, 64 Cu, 68 Ga, 86 Y, 124 I, and/or from the group of beta-emitter, more preferably 131 I, 90 Y, 177 Lu, 67 Cu, 90 Sr, or from the group of alpha-emitter, preferably 213 Bi, 211 At.
  • the nucleic acid sequence being labeled by an imaging molecule may have any suitable length that is suitable to achieve the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.
  • the nucleic acid sequence being labeled by an imaging molecule has a length of 4 to 10 nucleotides. This short length is particularly advantageous for providing achieving the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule.
  • the first nucleic acid sequence may have any suitable length that is suitable to achieve the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.
  • the first nucleic acid sequence may have a length of 4 to 30 nucleotides, preferably 16 to 24 nucleotides. This length is longer than the short length nucleic acid sequence being labeled by an imaging molecule and is particularly advantageous for providing for achieving the specific, preferably stable hybridization of the first nucleic acid sequence to the target complementary nucleic acid sequence.
  • the first nucleic acid sequence may have a GC-content of 45%- 55%, preferably 50%. Both the length and the GC-content of the first nucleic acid sequence may be used, individually or in combination, to advantageously provide for the specific, preferably stable hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence. As discussed above, the binding strength may thus be adjusted as appropriate, in order to ensure that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence (i.e. via specific hybridization) than the second nucleic acid sequence (i.e. via transient binding only). As discussed above, appropriate hybridization conditions and nucleic acid sequence compositions can be selected by routine means.
  • the second nucleic acid sequence of the single-stranded nucleic acid molecule may have any suitable length, for example a length of 4 to 10 nucleotides for the use of traditional DNA- PAINT probes, or 8-36 nucleotides for the use with speed-optimized DNA-PAINT probes.
  • the nucleic acid molecule further comprises a toehold seed, whereby the specific hybridization between the first nucleic acid sequence and its complementary target nucleic acid sequence can be disconnected via toehold mediated strand displacement.
  • Toehold mediated strand displacement is generally known in the art; see, for example, Yurke et al.
  • protector strand that is hybridized to a complementary nucleic acid sequence (called original strand), with another nucleic acid strand (called invading strand).
  • the original strand comprises an overhanging region (called toehold) that is not hybridized to the protector strand.
  • the invading strand is complementary to the original strand, including the toehold.
  • the invading strand first binds to the toehold. Branch migration of the invading strand causes the replacement of the protector strand.
  • an invading strand may be added to the hybridization complex in which the nucleic acid molecule is specifically hybridized to the target complementary nucleic acid sequence, but in which the nucleic acid molecule has an overhanging toehold that is not hybridized to the target complementary nucleic acid sequence.
  • the invading strand may replace the target complementary nucleic acid sequence, i.e. may hybridize to the nucleic acid molecule instead of the target complementary nucleic acid sequence.
  • the nucleic acid molecule may be separated from the target complementary nucleic acid sequence. This enables, i.a. removing the nucleic acid molecule from a sample, e.g. by washing it out.
  • the present invention relates in a third aspect to a plurality of nucleic acid molecules of the first aspect of the invention or hybridization complexes of the second aspect of the invention, wherein the nucleic acid molecules comprise: different first nucleic acid sequences that differ from each other in that they are capable of specifically and stably hybridizing to different target complementary nucleic acid sequences; and/or different second nucleic acid sequences that differ from each other in that they are capable of transiently binding to different complementary nucleic acid sequences optionally being labeled by at least two, at least three, at least four, or at least five different imaging molecules, wherein preferably each of the different target complementary nucleic acid sequences forms a cognate pair with a different imaging molecule.
  • each target complementary nucleic acid can be distinguished from all other target complementary nucleic acid sequences in the plurality of target complementary nucleic acid sequences by a distinct imaging molecule when, both, the imaging molecule and target complementary nucleic acid sequence are bound to the nucleic acid molecule or the hybridization complex of the invention.
  • each target complementary nucleic acid sequence can be identified by a different imaging molecule.
  • the different complementary nucleic acid sequences may be structurally different and/or the different target complementary nucleic acid sequences may be structurally different.
  • the second nucleic acid sequences may be capable of transiently binding to the same type of complementary nucleic acid sequences.
  • the first nucleic acid sequences may be capable of specifically and stably binding to the same type of target complementary nucleic acid sequences.
  • the second nucleic acid sequences and preferably the nucleic acid molecules may be orthogonal.
  • orthogonal is used herein for two or more entities that are sufficiently different so that two corresponding binding partners specifically bind to them, i.e. without binding among each other or to the one or more non-corresponding entities.
  • Orthogonal is also used herein for two or more entities that are sufficiently different to specifically bind to two corresponding binding partners, i.e. without binding among each other or to the one or more non-corresponding binding partners.
  • specificity it may also be used as a complementary term to specificity.
  • the second nucleic acid sequences comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12.
  • sequences (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n are known as so-called SPEED-sequences known from the SPEED-PAINT technique; see Strauss and Jungmann (2020), Nature Methods, 17:789–791.
  • SPEED-sequences allow a design of tunable hybridization kinetics and demonstrated up to 100-fold faster imaging compared to classical DNA-PAINT. Besides being faster these sequences enable imaging at lower imager concentration, leading to reduced background and thus increased signal-to-noise. Since 6 SPEED sequences are available 6-plex experiments can be designed for multiplexing.
  • the present invention relates in a fourth aspect to a kit or composition
  • a kit or composition comprising (a) the nucleic acid molecule or the hybridization complex or the plurality of nucleic acid molecules or hybridization complexes of the above aspects of the invention and at least one complementary nucleic acid sequence being labeled by an imaging molecule (also termed herein type A kit or composition); or (b) one or more single-stranded nucleic acid molecules comprising a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence and a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, one or more target complementary nucleic acid sequences capable of specifically hybridizing to the first nucleic acid sequence of the one or more single-stranded nucleic acid molecules, and one or more complementary nucleic acid sequences being labeled by an imaging molecule capable of transiently binding to the second nu
  • the one or more single stranded nucleic acid molecules may have any of the properties described herein in relation to single-stranded nucleic acid molecules, with the proviso that the first nucleic acid sequence may be capable of reversibly, preferably transiently, binding to a target complementary nucleic acid sequence.
  • the type B kit or composition is not necessarily limited by the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.
  • a desired higher amount of single- stranded nucleic acid sequences being bound to the target complementary nucleic acid sequence may be implemented by selecting the higher appropriate amount of single-stranded nucleic acid molecules compared to the amount of complementary nucleic acid sequence being labeled by an imaging molecule, taking into account the particular binding kinetics of all three types of molecules.
  • the excess of single-stranded nucleic acid molecules as compared to complementary nucleic acid sequences being labeled by an imaging molecule ensures that more first nucleic acid sequences are hybridized to their target complementary nucleic acid sequences than second nucleic acid sequences being bound to their complementary nucleic acid sequences being labeled by an imaging molecule.
  • the excess amount in connection with the type B kit or composition the essentially same technical effect is achieved as by the proviso that first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence in connection with the type A kit or composition.
  • Two or more complementary nucleic acid sequences being labeled by an imaging molecule may have different imaging molecules, particularly imaging molecules that may be distinguished in the detection technique.
  • the fluorescent imaging molecules may have different excitation/emission-spectra that are distinguishable in fluorescence microscopy.
  • the present invention also relates in a fifth aspect to a method of detecting a target molecule in a sample, comprising: a. contacting the sample with the nucleic acid molecule or the kit or composition of the aspects of the invention as described above; b.
  • the sample optionally contacting the sample with a target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds the target molecule in the sample; c. contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the nucleic acid molecule or composition of (a); and d. detecting the imaging molecule in the sample, thereby detecting the biological target molecule in the sample.
  • the order of method steps may be any order unless explicitly stated otherwise.
  • steps a), b) and c) of the above method may be any order. It may be advantageous to have the order step b) before step a), and optionally step d) after step c) after step a). It may also be advantageous to have the steps in alphabetical order.
  • the target molecule in the sample is a nucleic acid sequence in the sample and the binding molecule is a nucleic acid being complementary to the nucleic acid sequence in the sample
  • the target molecule in the sample is a protein or peptide or polysaccharide in the sample and the binding molecule is a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer, specifically binding to the protein or peptide or polysaccharide in the sample
  • the target molecule in the sample is a nucleic acid sequence comprising a/the target complementary nucleic acid sequence .
  • Method step b) may not be required for cases in which the single-stranded nucleic acid molecules directly bind to target molecules, i.e. in which the one or more target complementary nucleic acids are a part of the one or more target molecules.
  • the present invention relates in a sixth aspect to a method of detecting two or more target molecules in a sample, comprising a. contacting the sample with a first nucleic acid molecule or composition of any preceding claim and a second nucleic acid molecule or composition of any preceding claim; b.
  • the method is carried out sequentially, wherein the sample is first contacted with the first nucleic acid molecule or the composition, the first target complementary nucleic acid sequence, and the first complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the first target molecule in the sample, and then the sample is contacted with the second nucleic acid molecule or the composition, the second target complementary nucleic acid sequence, and the second complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the second biological target molecule in the sample, wherein preferably the first complementary nucleic acid sequence being labeled by an imaging molecule is removed before the second complementary nucleic acid sequence being labeled by an imaging molecule is added; (ii) the imaging molecule of the first complementary nucleic acid sequence being labeled by an imaging molecule is different from the imaging molecule of the
  • the two or more target molecules in a sample are at least 10 target molecules and the method comprises in step (a) at least 10 different nucleic acid molecules or compositions, in step (b) at least 10 different target complementary nucleic acid sequences, and optionally in step (c) at least 10 different first complementary nucleic acid sequences being labeled by an imaging molecule; preferably at least 30 target molecules and the method comprises in step (a) at least 30 different nucleic acid molecules or compositions, in step (b) at least 30 different target complementary nucleic acid sequences, and optionally in step (c) at least 30 different first complementary nucleic acid sequences being labeled by an imaging molecule; more preferably at least 60 target molecules and the method comprises in step (a) at least 60 different nucleic acid molecules or compositions, in step (b) at least 60 different target complementary nucleic acid sequences, and optionally in step (c) at least 60 different first complementary nucleic acid sequences being labeled by an imaging molecule
  • the method is particularly useful for a large number of different target molecules in the sample.
  • different target molecules may mean different types of target molecules.
  • the at least 10, at least 30, at least 60 or at least 100 target molecules in the sample are distinguished from each other in step (d) by one or more of method options (i) to (iii), preferably option (iii) as described in the above preferred embodiment and further comprising e. mapping the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; and f.
  • the methods according to the invention may include labeling all targets with the same type of imager and thus quantifying the number of targets in a localization cluster.
  • the codebook may be adaptively provided in response to the number of unresolved target molecules. This may be advantageous for handling cases in which two or more target molecules are so close to each other that assigning the sequential imager binding in the different imaging rounds to the target molecules individually is impossible –only the sum of the two identification sequences will be detected. Hence, there is no way of correct identification.
  • a solution is to first determine the largest number of target molecules that cannot be separated in localization, and adapt the codebook accordingly, such that one can still tell the identity of the target molecules present.
  • the adaption of the codebook is combinatorics that is generally known in the art.
  • one option is to target all target complementary nucleic acids with a single stranded nucleic acid which reversible bind the same imager and use qPAINT as known in the art to determine the number of target complementary nucleic acids in each localization cluster (as described in the section methods/image analysis below), and create a codebook that distinguishes all combinations of target complementary single stranded nucleic acids up to the maximum number of molecules per localization cluster if the analysis is done beforehand, or if the analysis of the number of target complementary single stranded nucleic acids is done after the planned experiment, by analyzing the potentially confused or wrongly assigned codebook entries, and adding rounds of single stranded nucleic acid addition and imaging, thus extending the codes, to disambiguate the analysis.
  • targets present may be one of the tuples (2, 3), (2, 4), (3, 4), (4, 4). Therefore, to disambiguate the results, two rounds may be added to identify targets 2-4, to make the full codes for example 1: ‘10000’, 2: ‘01010’, 3: ‘00101’, 4: ‘01100’.
  • the present invention also relates to a method including method step iii) related to the codebook including: (v) if applicable: removing the single-stranded nucleic acid molecules from a previous round of the following steps from the target molecules, preferably using buffer conditions, toehold-mediated strand displacement, or heat; (iv) contacting the sample with all single-stranded nucleic acid molecules assigned to one of the N positions of all identification sequences and the complementary nucleic acid sequence(s) being labeled by an imaging molecule; (iiv) identifying and localizing the imaging molecules in the sample; (iiiv) repeating step (v), (iv) and (iiv) for all other N-1 identification sequence positions.
  • (ix) for one or more locations in the sample generating a detection sequence having a length of N positions corresponding to the N positions of the identification sequence, wherein each position of the detection sequence is either assigned with an identified imaging molecule of the corresponding round of steps (iv) and (iiv) or, if no imaging molecule was detected, with a gap; (x) for each location of step (ix): by comparing the detection sequence of the location with the identification sequences of the codebook of step c), identifying the target type detected at the location.
  • the second nucleic acid sequences of the different single-stranded nucleic acid sequences are orthogonal.
  • detecting the imaging molecule(s) and thus imager(s) in the sample may include performing detection steps of DNA-PAINT (Schnitzbauer et al. (2017), Nature Protocols, 12:1198–1228).
  • DNA-PAINT the transient association of the fluorophore to a target molecule is mediated by the pairing of short ( ⁇ 10 nucleotides) complementary DNA sequences:
  • a docking strand comprising a nucleic acid sequence is coupled to the target molecule, usually through an antibody, nanobody, aptamer or other high affinity probe and an imager strand carries a fluorophore (i.e. the imaging molecule).
  • the imaging strand carrying the fluorophore is free to diffuse in the imaging buffer.
  • DNA hybridization i.e. binding of the imaging strand to the docking strand
  • the fluorophore is transiently immobilized near the target molecule, and thus excited by the laser light, typically in Total Internal Reflection Fluorescence (TIRF) or highly inclined and laminated optical sheet (HiLO) configuration, however light sheet and spinning disk microscopies have been used as well.
  • TIRF Total Internal Reflection Fluorescence
  • HiLO highly inclined and laminated optical sheet
  • the emitted light can then be captured by the camera as a diffraction limited flash.
  • any of the nucleic acid probes described above may comprise two, three, four, five or six second nucleic acid sequences. This may enable binding of two, three, four, five or six complementary nucleic acid sequence being labeled by an imaging molecule.
  • the second nucleic acid sequences may be the same and thus provide for binding with the same type of imaging molecule, or different thus providing for binding to different complementary nucleic acid sequences being labeled by an imaging molecule, preferably with different imaging molecules. Thus, different combinations of intensities and/or colors of imaging molecules may be created and/or used. Additionally or alternatively, contacting the sample with the different complementary nucleic acid sequences being labeled by an imaging molecule at different times allows for the usage of a larger barcoding space. Additionally or alternatively, no target molecule is labeled with more than five, preferentially two, more preferentially one single-stranded nucleic acid molecule(s).
  • one or more single-stranded nucleic acid molecule is capable of binding exactly one type of imaging molecule via its complementary nucleic acid sequence being labeled by this imaging molecule. Additionally or alternatively, different single stranded nucleic-acid molecules may be capable of binding different types of imaging molecules. Alternatively, one or more nucleic acid probes may be capable of binding multiple types of imaging molecules. In some embodiments, one or more single-stranded nucleic acid molecule is capable of binding exactly one type of complementary nucleic acid sequence being labeled by an imaging molecule. Additionally or alternatively, different single stranded nucleic-acid molecules may be capable of binding different types of complementary nucleic acid sequence being labeled an imaging molecule.
  • one or more single-stranded nucleic acid molecule is capable of binding multiple types of complementary nucleic acid sequence being labeled an imaging molecule.
  • the sample may comprise one or more cells and/or a cell lysate and/or purified components, all e.g. from cell culture, a biopsy, and/or a liquid biopsy, for example primary patient cells (from healthy tissue, immune cells, cancer), fresh frozen (FF) tissue, formalin-fixed paraffin embedded (FFPE) tissue, or extracts from liquid biopsy (e.g. cells or exosomes), or spheroids, or organs-on a chip, all from human or animal.
  • the methods may include drift correction.
  • the term complementary is used as follows: The complementarity may be 100% or less, for example between 80% and 100%, or more preferably between 90% and 100%.
  • the methods may include: determining the binding kinetics of at least one, preferably all, first nucleic acid sequences to their target complementary nucleic acid sequences, and/or second nucleic acid sequences to their complementary nucleic acid sequences being labeled by an imaging molecule; and taking the determined binding kinetic into account for detection step d).
  • the target complementary nucleic acid sequence may be a portion of a secondary binder (not to be confused with secondary probe/label).
  • the secondary binder is a molecule that specifically binds to a molecule, termed the binding molecule, which may specifically bind to the target molecule.
  • the binding molecule may specifically bind to the target molecule.
  • the single-stranded nucleic acid molecule may indirectly bind to the target molecule via the secondary binder and the binding molecule.
  • Secondary binders may be comprised of a nucleic acid molecule and a secondary binding molecule.
  • a secondary binding molecule is a molecule specifically binding a primary binding molecule as described herein above.
  • Secondary binding molecules are generally known in the art. For example, they may be secondary antibodies. Secondary antibodies are antibodies which specifically bind to antibodies produced from the respective animals. For example ‘anti-rat’ secondary antibodies bind to antibodies from rats.
  • secondary binding molecules may be nanobodies.
  • the invention further relates to a hybridization complex comprising the target complementary nucleic acid, the nucleic acid molecule, and a blocking strand.
  • a blocking strand also termed “blocker strand” or “blocker” is generally known in the art as a single stranded nucleic acid that may hybridize to a different single stranded nucleic acid molecule with one or more reaction partners that have a lower affinity than the blocking strand to the different single stranded nucleic acid molecule (see e.g. doi: 10.1021/acs.nanolett.9b02565). It can be used to block the interactions of the different single stranded nucleic acid molecule with its reaction partner(s).
  • a blocking strand may comprise the complementary sequence to a docking sequence (e.g. the second nucleic acid sequence of the nucleic acid molecule), to prevent the transient interaction of an imager with the second nucleic acid sequence of the nucleic acid molecule.
  • a docking sequence e.g. the second nucleic acid sequence of the nucleic acid molecule
  • the number of nucleotides of a blocking strand is 2-30 nucleotides, preferably 5-20 nucleotides, more preferably 10-15 nucleotides longer than the competitive interaction partner, and they should cover the whole sequence targetable by the interaction partner. Their total length is 15-40 nucleotides.
  • imager strands R1-R6 (SeqID No 3533- 3538) are 6-7 nucleotides long, while the blocking strands used (SeqID No 3498-3526) are 19-20 nucleotides long and cover the complete docking sequences (SeqID No 3527-3532).
  • blocking strands may fulfill the following requirements: * blocking strands should cover the whole region targeted by their competitor; * blocking strands should stably bind to the region targeted by their competitor; * blocking strands should not be displaced by their competitors (i.e. bind much more stably and thus be longer).
  • the approach of using blocking strands is especially useful when a target molecule is designed to provide signal in only one round of imaging, as described in this example.
  • the single stranded nucleic acid molecule can be provided to the sample before the respective imaging round, thereby activating the target molecule, and the blocker can be provided after the imaging round, thereby deactivating the target molecule.
  • the present invention also relates to a method of analyzing a sample, comprising: a. localizing with increasing preference at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; e.
  • Point Pattern Analysis is a collection of methods to extract information from point patterns, especially the spread or distribution of each of the target molecules over the space of the sample, preferably on the cell surface or within the volume of the cells. It provides a number of metrics that describe the type of association and association probability between different target molecules.
  • Point pattern analysis is the study of point patterns, the spatial arrangements of points in space, herein generally in 3-dimensional space.
  • Nearest Neighbour Analysis measures the spread or distribution of each of the target molecules over the space of the sample, preferably on the cell surface or within the volume of the cells. It provides a numerical value that describes the extent to which the different the target molecules are clustered or uniformly spaced. This in turn results in an interaction pattern of the different target molecules.
  • Different target molecules that are found to co-localize or almost co-localize likely bind to each other in the context of the sample, e.g. within cells.
  • Such interaction partners are bona fide targets for the modification of biological processes with samples, e.g. within cells or between cells.
  • the method further comprises binding molecule evaluation and development.
  • binding molecule evaluation and development it is beneficial to first characterize binders that are later used for labelling target molecules. This characterization can then be integrated into the data evaluation as a calibration.
  • the method of analyzing a sample uses one or more single-stranded nucleic acid molecules and/or one or more hybridization complexes as defined herein above.
  • the method uses hybridization complexes as defined herein above, wherein the target complementary nucleic acid sequences of the hybridization complexes are conjugated to different binding molecules that are capable of specifically binding to the at least 10, at least 30, at least 60 or at least 100 target molecules.
  • the hybridization complexes as defined herein above are particularly advantageous for the method of analyzing a sample because different hybridization complexes are conjugated to different binding molecules that are capable of specifically binding to the at least 10, at least 30, at least 60 or at least 100 target molecules, and can be easily designed, thereby achieving a high multiplex level.
  • the second nucleic acid sequences of the single-stranded nucleic acid molecules and/or the complexes comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12.
  • (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12, are the six speed- optimized sequences R1-R6 (doi: 10.1038/s41592-020-0869-x). Due the speed-optimization these sequences are ideally suitable for multiplexed applications, such as the method of analyzing a sample.
  • the method of analyzing a sample is preferably conducted via multiple subsequent imaging rounds and the speed-optimized sequences are particularly advantageous for achieving the subsequent imaging rounds in short time.
  • only one of the second nucleic acid sequences is present per imaging round.
  • only two, only three, only four, only five, only six or more different second nucleic acid sequences are present and addressable per imaging round.
  • only one type of the first nucleic acid sequences is present per imaging round.
  • only two, only three, only four, only five, only six or more different first nucleic acid sequences are present and addressable per imaging round.
  • a present single stranded nucleic acid may be turned non-addressable by blocking using a “blocker strand” (also termed “blocking strand”).
  • the method comprises binding molecule preselection, preferably bare binding molecule preselection. To preselect probably appropriate binding molecules from a given pool of potential binding molecules to a target molecule of interest, their specificity may be screened. Briefly, this may be done by colocalizing fluorescence signal originating from the target molecule of interest (or a moiety fused to it) with a fluorescence signal related to a secondary binder known to bind the bare binding molecule.
  • “Secondary binder” refers to a molecule that specifically binds a binding molecule/primary binder.
  • the method comprises binding molecule specificity testing. After successful preselection, a short DNA oligonucleotide can be conjugated onto the bare binding molecule, to make it what is called a primary binder herein. The short DNA oligonucleotide can be conjugated to the binding molecule using site-specific DNA conjugation.
  • the method comprises primary binder labelling efficiency testing.
  • primary binder labelling efficiency may be tested.
  • the method comprises primary binder labelling efficiency determination, e.g. via 2-plex Exchange-PAINT imaging or any of the methods described herein.
  • binding molecule characterization e.g. binding molecule specificity testing and/or primary binder labelling efficiency testing
  • any cell line may be used, e.g. CHO, BSC1, HeLa.
  • the method comprises sample preparation for multiplexed immune receptor DNA-PAINT.
  • samples may be prepared for imaging in any suitable way. This can be done in multiple ways and also depends on the sample type.
  • the method comprises preparation of functionalized planar supported lipid bilayers (SLBs).
  • the method comprises cell preparation for multiplexed immune receptor DNA-PAINT imaging.
  • the method comprises data acquisition, e.g. multiplexed molecular imaging of target molecules.
  • the method comprises multiplexed cellular imaging of cellular proteins.
  • the method comprises data evaluation, e.g.
  • the method comprises image analysis, e.g. postprocessing of a/the raw super-resolution imaging data. It may provide for getting from multiple single channel transient binding movies to one multiplexed molecular map, which specifies the localizations of all target molecules detected in the sample.
  • the method comprises data analysis, wherein data is aggregated to elucidate the direct interaction patterns present in the sample. This step corresponds to getting from the multiplexed molecular map to one or more direct interaction patterns present in the sample.
  • a direct interaction pattern describes a set of target molecules commonly found in close proximity, and optionally probability distributions of their relative distances.
  • the method comprises selecting one or more binding molecule candidates having a size of 25nm or less, preferably 12.5nm or less, for example 12.4nm or 4nm.
  • the binding molecules may be small enough to allow for generation of direct interaction patterns precise enough for meaningful insights for drug development or diagnostics.
  • the method comprises selecting one or more binding molecule candidates having an affinity as measured in bulk measurements (e.g. SPR, Octet, FRET-based assays) of KD ⁇ 200nM, preferably ⁇ 20nM.
  • the step of localizing at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample may comprise super-resolution imaging, preferably super-resolution fluorescence microscopy, preferably DNA-PAINT, optionally including any of its improvements such as Exchange-PAINT, RESI, and/or the methods described herein.
  • a sample in any of the embodiments herein may be one or more cell lines, primary patient cells (from healthy tissue, immune cells, cancer), fresh frozen (FF) tissue, formalin-fixed paraffin embedded (FFPE) tissue, or extracts from liquid biopsy (e.g.
  • the target complementary nucleic acid sequence e.g. in the form of DNA
  • the target complementary nucleic acid sequence may be conjugated to a binding molecule site-specifically, preferably by using sortase, C-terminal cysteine, N-terminal serine, threonine, or artificial aminoacids.
  • a molecular density of target-molecule-fused reference molecules may be less than 5000 molecules/ ⁇ m 2 , preferably less than 500 molecules/ ⁇ m 2 , more preferably less than 50 molecules/ ⁇ m 2 .
  • target-molecule-fused reference molecules may be more than 0.01 molecules/ ⁇ m 2 , preferably more than 0.1 molecules/ ⁇ m 2 , more preferably more than 1 molecule/ ⁇ m 2 .
  • Target-molecule-fused reference molecules are easily-taggable reference molecules, which are genetically fused to target molecules.
  • the cells when developing binding molecules for the target molecule CD-80, the cells can be CD-80 non-expressing or knock-out cells and transfected with a GFP-CD80 fusion.
  • GFP can serve as a reference molecule, for which a well-characterized anti-GFP nanobody is available and can be used in the characterization of the binding molecules for the target molecule.
  • the method is configured for rational drug design and comprises one or a combination of the following: • Indication selection, e.g. melanoma, non-small cell lung cancer (NSLC); • Target and off-target sample selection, e.g.: o Healthy, normal vs cancerous tissue, cancer vs dendritic cell, T cell & DC vs T cell & cancerous tissue; and/or o FACS-sorted samples; • Visualization of target molecule identification, e.g.: o Membrane proteins (e.g.
  • CD80 & CD86 may be often ⁇ 15nm apart from each other in the target sample, but rarely in off-target sample, with the total number of CD80 and CD86 being similar in target and off-target samples.
  • the method may comprise one or a combination of the following: • Drug development comprising: o Developing mono-, bi- or multivalent primary binder (antibody-based or similar primary binder; optional: biocompatible scaffold for organizing target binders) based on the direct interaction pattern, and use cooperativity for binding to investigate if target proteins are present on pathogenic cells/tissue, such that a single binding domain does not bind stably but the specific neighbourhood configuration (or most of it) is required for prolonged, stable binding, thereby generating specificity between target and off-target o using the neighborhood configuration distances, angles and configurations (hetero- and homo-oligomerisation) as described by the direct interaction pattern to guide the drug molecule design (see, for example, Bila et al, J Am Chem Soc
  • the method is configured for hit-to-lead and/or lead optimization, and comprises one or a combination of the following: • Target and off-target sample selection; • Selection of the molecular targets potentially involved o Use Proteins appearing in biochemical hypotheses and general knowledge of the indication involved; Also add candidate drug as a visualization target, preferably each binding domain separately, to elucidate its position in the interaction pattern • creating a direct interaction pattern, optionally including: o Define an optimization metric based on the direct interaction pattern: e.g.
  • fraction of colocalized binding domains to target molecules, and drug molecule target vs off- target binding fully characterize recorded protein map consisting of multiple different proteins or protein epitopes and binding epitopes and extract key parameters such as distances, angles, molecular orientations, oligomerisation, cluster contributions (cluster size, cluster shape, cluster density, amount of clusters, protein ratios, protein motifs), reduction of dimensionality (e.g. UMAP) for key parameter elucidation; • Data acquisition and evaluation of samples with multiple optimization candidates as target molecules; • Scoring of optimization candidates, resulting in one optimal candidate; • Based on the direct interaction pattern, optimization of mono- and multivalent binder geometry, e.g.
  • Fig.1 schematically illustrate a single-stranded nucleic acid molecule according to the invention and related concepts and methods
  • Fig.2 schematically illustrates a single-stranded nucleic acid molecule according to the invention and related concepts and methods
  • Fig.3 illustrates a proof of principle experiment
  • Fig.4 schematically illustrates aspects of methods including a codebook
  • Fig.5 schematically illustrates aspects of methods including a codebook.
  • Fig.6 shows a false color image of neurons imaged with a 29-plex imaging method according to the present invention
  • Fig.7 shows zoom-ins from a whole-cell view, via DNA-PAINT localization Data, to single target molecule localization data obtained from
  • Fig.8 shows Specificity
  • Fig.9 shows Labeling Efficiency
  • Fig.10 shows preparation steps for functionalized planar glass-supported lipid bilayers
  • Fig.11 shows multiplexed single-protein imaging of immune checkpoint receptors
  • Fig.12 shows a direct interaction analysis for dendritic cells and that multiplexed spatial receptor pattern analysis reveals novel key interaction motifs in dendritic cells
  • Fig.13 shows a direct interaction analysis for cancer cells and that the absence of costimulatory receptors drives formation of PD-L1/MHC-I clusters in B16-F10.
  • Fig.14 shows that CD80 presence interferes with MHC-I/PD-L1 clustering irrespective of cell type.
  • Fig.15 Workflow of analysis pipeline. Schematic outline of workflow for the analysis of 6- plex receptor point patterns on cells. Following image post-processing, whole-cell datasets were utilized for further analysis. In the first step, global receptor correlations were determined by a modified version of Ripley’s K function. Ripley’s K curve was calculated for all 36 possible pairwise receptor combinations and compared to the results of complete spatial randomness (CSR). The normalized Ripley’s K curves were then integrated and averaged from all individual cells of a single cell type and corresponding condition.
  • CSR complete spatial randomness
  • a correlation matrix for all 36 possible receptor combinations was generated to distinguish between clustered or dispersed receptor patterns, as well as random receptor distributions.
  • NBD nearest neighbor distance
  • single channel data was compared to CSR simulations to determine the percentage of homo- interactions.
  • Hetero-interactions were evaluated via correlation of cross-channel data to corresponding CSR simulations.
  • the percentage of receptor interactions, as well as the corresponding interaction distance were defined.
  • global DBSCAN analysis was used to identify receptor motifs within the upper limit of evaluated receptor interaction distances in clustered regions containing at least three receptors.
  • DBSCAN was applied to both experimental and simulated 6-plex data, ignoring receptor identities at this stage.
  • Detected receptor motifs for experimental and CSR simulated data were sorted based on their receptor identities within each cluster, leading to 63 unique cluster IDs. Comparison of experimental and CSR simulated data allowed for the extraction of multiple different parameters. All readout parameters are highlighted in blue.
  • Fig.16 Multiplexed spatial receptor pattern analysis of non-stimulated MutuDCs.
  • A DNA-PAINT image of non-stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors.
  • Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions as in Fig.12B.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • Non-clustered areas were compared to a CSR distribution of target receptors on the same cell surface.
  • Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • G Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct motifs (Motif 1 – MHC-I/CD80/PD-L1, Motif 2 – MHC-I/CD86/PD-L1. The motifs represent 2.0% ⁇ 1.3%, 1.9% ⁇ 1.2% of all clusters, respectively.
  • FIG.18 Multiplexed spatial receptor pattern analysis of 6 hours stimulated MutuDCs.
  • A DNA-PAINT image of 6 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 4 distinct motifs (Motif 1 – CD86/CD80/PD-L1, Motif 2 – CD80, Motif 3 – MHC-I/CD80/PD-L1, Motif 4 – MHC-I/CD86/CD80/PD-L1. The motifs represent 5.9% ⁇ 2.1%, 0.7% ⁇ 0.3%, 4.5% ⁇ 1.1%, 5.1% ⁇ 2.9% of all clusters, respectively.
  • FIG.19 Multiplexed spatial receptor pattern analysis of 12 hours stimulated MutuDCs.
  • A DNA-PAINT image of 12 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 distinct motifs (Motif 1 – MHC-I/CD80/PD-L1, Motif 2 – MHC-I/CD86/PD-L1, Motif 3 – MHC-II/CD80). The motifs represent 1.7% ⁇ 0.5%, 1.7% ⁇ 0.8%, 1.1% ⁇ 0.9% of all clusters, respectively.
  • FIG.20 Multiplexed spatial receptor pattern analysis of 24 hours stimulated MutuDCs.
  • A DNA-PAINT image of 24 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 6 distinct motifs (Motif 1 – CD86/CD80/PD-L1, Motif 2 – CD80/PD-L1, Motif 3 & Motif 4 – MHC-I/CD80/PD-L1, Motif 5 & Motif 8 – MHC- I/CD86/PD-L1, Motif 6 – MHC-I/CD86/CD80, Motif 7 – MHC-I/CD86/PD-L2).
  • the motifs represent 1.9% ⁇ 0.6%, 0.5% ⁇ 0.1%, 2.3% ⁇ 0.7%, 4.5% ⁇ 1.7%, 3.1% ⁇ 0.8%, 0.5% ⁇ 0.2% of all clusters, respectively.
  • Data is shown as mean ⁇ 95% confidence interval of three independent experiments and 10 cells, * p ⁇ 0.05; *** p ⁇ 0.001; n.s., not significant)
  • Fig.21 Multiplexed spatial receptor pattern analysis of specific peptide-MHC-I complexes on MutuDCs fed ovalbumin protein.
  • A DNA-PAINT image of MutuDCs stimulated for 6h with CpG and IFN ⁇ , while being fed ovalbumin, showing receptor positions of the imaged immune checkpoint receptors.
  • A DNA-PAINT image of non-stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • Fig.23 Multiplexed spatial receptor pattern analysis of 3 hours stimulated B16-F10 cells.
  • A DNA-PAINT image of 3h IFN ⁇ stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key motif (Motif 1 – MHC-I/PD-L1). The motif represents 29.2% ⁇ 3.9% of all clusters, respectively. (Data is shown as mean ⁇ 95% confidence interval of three independent experiments and 11 cells, *** p ⁇ 0.001; n.s., not significant) Fig.24. Multiplexed spatial receptor pattern analysis of 6 hours stimulated B16-F10 cells.
  • A DNA-PAINT image of 6h stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • F Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • Non-clustered areas were compared to a CSR distribution of target receptors on the same cell surface.
  • Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • G Quantitative analysis of the key receptor motifs from (F) reveals 3 independent receptor motifs (Motif 1 – MHC-I/MHC- II/PD-L1, Motif 2 – MHC-II, Motif 3 – MHC-I/MHC-II/CD86/PD-L1).
  • the receptor motifs represent 30.0% ⁇ 3.1%, 3.6% ⁇ 1.1%, 3.3% ⁇ 1.2% of all clusters, respectively.
  • Data is shown as mean ⁇ 95% confidence interval of three independent experiments and 10 cells, ** p ⁇ 0.01; *** p ⁇ 0.001; n.s., not significant
  • Fig.26 Multiplexed spatial receptor pattern analysis of 24 hours stimulated B16-F10 cells.
  • A DNA-PAINT image of 24h stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 independent receptor motifs (Motif 1 – MHC-I/MHC- II/PD-L1, Motif 2 – MHC-I/MHC-II/CD86/PD-L1). The receptor motifs represent 2.3% ⁇ 1.2%, 0.6% ⁇ 0.5% of all clusters, respectively.
  • FIG.27 Multiplexed spatial receptor pattern analysis of specific OVA peptide-MHC-I complexes on B16-F10 cells.
  • A DNA-PAINT image of B16-F10 cells that transgenically express OVA protein stimulated for 6h with IFN ⁇ showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key receptor motif (Motif 1 – MHC-I/PD-L1). The receptor motif represents 29.0% ⁇ 3.5% of all clusters, respectively. (Data is shown as mean ⁇ 95% confidence interval of three independent experiments and 6 cells, *** p ⁇ 0.001; n.s., not significant) Fig.28. Validation of CD80 KO cDC1 cells.
  • cDC1 cells were then differentiated from the bone marrow in Flt3 cultures prior to stimulation with 500nM CpG1826 + 100U/ml IFN ⁇ for 6 hours and sorting as in (A) for subsequent imaging experiments. Verification of CD80 deletion on cDC1 cells by flow cytometric staining is shown. Fig.29. Multiplexed spatial receptor pattern analysis of 6 hours stimulated wild-type cDC1 cells.
  • A DNA-PAINT image of 6 h stimulated cDC1 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 distinct receptor motifs (Motif 1 & Motif 2 – CD86/CD80/PD-L1, Motif 3 – MHC-I/CD86/CD80, Motif 4 & Motif 5 – MHC- I/CD86/CD80/PD-L1). The motifs represent 9.3% ⁇ 1.9%, 0.4% ⁇ 0.1%, 19.8% ⁇ 0.6% of all clusters, respectively.
  • FIG.30 Multiplexed spatial receptor pattern analysis of 6 hours stimulated CD80 KO cDC1 cells.
  • A DNA-PAINT image of 6 h stimulated CD80 KO cDC1 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 – MHC-I/PD- L1, Motif 2 – MHC-I/CD86/PD-L1). The motifs represent 46.6% ⁇ 3.0%, 5.3% ⁇ 1.7% of all clusters, respectively. (Data is shown as mean ⁇ 95% confidence interval of two independent experiments and 11 cells, *** p ⁇ 0.001; n.s., not significant) Fig.31.
  • A DNA-PAINT image of non-stimulated cDC1 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • F Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • A DNA-PAINT image of non-stimulated CD80 KO cDC1 cells showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • Fig.33 Multiplexed spatial receptor pattern analysis of 6 hours stimulated CD80 KO MutuDCs.
  • A DNA-PAINT image of 6 h stimulated CD80 KO MutuDCs showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot.
  • Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 & Motif 2 & Motif 4 – MHC-I/PD-L1, Motif 3 – MHC-I/CD86/PD-L1). The motifs represent 19.3% ⁇ 2.7%, 5.7% ⁇ 2.6% of all clusters, respectively.
  • FIG.34 Validation of CD80-overexpressing B16-F10 cell lines.
  • B16-F10 cells were retrovirally transduced using MSCV-mCD80 (IRES-mCherry) retroviral vectors to overexpress either wild-type CD80 or mutant CD80-L107E.
  • mCherry expressing cells were then sorted by flow cytometry to generate stably transduced cell lines. Representative flow cytometric plots showing mCherry versus CD80 staining are included for all 3 cell lines.
  • Fig.35 Validation of CD80-overexpressing B16-F10 cell lines.
  • A DNA-PAINT image of 6 h stimulated CD80- overexpressing B16-F10 cell showing receptor positions of the imaged immune checkpoint receptors.
  • B Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • F Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • F Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • the receptor motif represents 2.1% ⁇ 0.5%, 27.5% ⁇ 3.7%, 14.6% ⁇ 2.3% of all clusters, respectively.
  • Data is shown as mean ⁇ 95% confidence interval of two independent experiments and 12 cells, *** p ⁇ 0.001; n.s., not significant
  • Fig.38 Multiplexed spatial receptor pattern analysis of Abatacept-treated CD80 KO MutuDCs.
  • A DNA-PAINT image of 6h stimulated CD80 KO MutuDCs showing receptor positions of the imaged immune checkpoint receptors. Cells were treated with Abatacept during the last 10 minutes of the overall time.
  • C Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis.
  • D NND analysis yields quantitative information about directly interacting receptor species.
  • E Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface.
  • (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted.
  • (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key receptor motif (Motif 1 – MHC-I/CD86/PD-L1). The receptor motif represents 35.0% ⁇ 3.9% of all clusters, respectively. (Data is shown as mean ⁇ 95% confidence interval of two independent experiments and 12 cells, *** p ⁇ 0.001; n.s., not significant) Fig.39. Receptor interaction distances.
  • DNA origami discs were either non-functionalized (“empty”) or functionalized either with pMHC only or a combination of pMHC and PD-L1. The latter were arranged into clusters that are either closely spaced (“close”, ⁇ 14 ⁇ ⁇ ) or widely (“far”, ⁇ 28 ⁇ ⁇ ) spaced.
  • B Agarose gel analysis (2% agarose) of the DNA origami disc library either functionalized (+ Ligands) or non-functionalized (No Ligand) with pMHC and/or PD-L1 molecules showing properly folded and purified samples. Cy5 signal characterizes assembled DNA origami disc and SYBR Safe signal represents all DNA-based samples. A delayed sample migration indicates successful attachment of the ligands.
  • the Examples illustrate the present invention. Examples The present invention provides, i.a., nucleic acid based molecules, kits and compositions and methods for target detection, particularly for multiplexed imaging. The present invention provides for an application in a biological environment, particularly a single cell, particularly intermolecular single cell multiomics. Intermolecular single cell multiomics provides for localizing a relatively large number of target molecules of a single cell, particularly via super-resolution fluorescence microscopy, particularly PAINT.
  • Figure 1 schematically illustrates a target 1 and a single-stranded nucleic acid molecule 2, the single-stranded nucleic acid molecule 2 comprising a first nucleic acid sequence 4 and a second nucleic acid sequence 6 that differs from the first nucleic acid sequence.
  • the first nucleic acid sequence is capable of specifically hybridizing to a target complementary nucleic acid sequence 10 and the second nucleic acid sequence 6 is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule 20.
  • the single-stranded nucleic acid molecule 2 may comprise a toehold seed 8 as shown, but is not so limited.
  • the toehold seed 8 may be used for separating these molecules via toehold mediated strand displacement, i.e. by contacting the hybridized molecules with an invader strand as explained above.
  • the target complementary nucleic acid 10 may be part of a primary binder 14.
  • the primary binder 14 may further comprise a binding molecule 12.
  • the target complementary nucleic acid 10 may be conjugated to the binding molecule 12 in any suitable way, see above.
  • the binding molecule 12 may be capable of selectively binding the target molecule 1.
  • the target molecule 1 may be a protein and the binding molecule 12 may be a corresponding antibody.
  • the target molecule 1 may comprise a nucleic acid sequence, e.g. DNA, and the binding molecule 12 may be a complementary nucleic acid sequence.
  • the primary binder 14 may be a nucleic acid strand comprising two domains, one is the target complementary nucleic acid sequence 10 and the other one is the binding molecule 12.
  • the target complementary nucleic acid 10 may alternatively be part of the target molecule (not illustrated).
  • the single-stranded nucleic acid molecule 2 directly hybridizes to the target molecule via the first nucleic acid sequence 4, without any primary binder 14 in between.
  • the complementary nucleic acid sequence being labeled by an imaging molecule 20 may comprise one or more fluorescent imaging molecules 22, for example Alexa488, Cy3b, and/or Atto 647N, and a nucleic acid sequence 24 that is complementary to the second nucleic acid sequence 6.
  • the hybridization kinetics between these two sequences (this hybridization being termed HybB) and the hybridization kinetics between the first nucleic acid sequence 4 and the target complementary nucleic acid sequence 10 (this hybridization being termed HybA) may be chosen such that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. This may include an appropriate selection of conditions.
  • HybA may be a reversible but stable binding
  • HybB is a transient binding
  • the melting temperatures, the GC content and/or the length of the sequences i.e. the number of hybridizing base pairs
  • the appropriate binding behavior may be achieved.
  • HybA and HybB may be selected to have similar binding kinetics when the ratio is 1:1. With such tool at hand, a detection method as explained above may be performed.
  • Figure 2 illustrates an exemplary method according to the invention.
  • Inset a) of Figure 2 shows a target molecule 1 bound to a primary binder 14 via a binding molecule 12.
  • the target molecule 1 may be comprised in a single cell that was appropriately prepared for fluorescence imaging as generally known in the art or an artificially created sample such as a surface with DNA origami, etc. Examples for sample preparation are given below.
  • the target molecule 1 may be contacted with the primary binder 14, which may be present in solution.
  • the binding of the target molecule 1 to the primary binder 14 as well as the binding molecule 12 to the target complementary nucleic acid strand 10 are stable under the given conditions.
  • the single-stranded nucleic acid strand 2 is added and contacted with the complementary primary binder 14.
  • the first nucleic acid sequence 4 of the single-stranded nucleic acid strand 2 hybridizes with the target complementary nucleic acid sequence 10 of the primary binder 12, the result being shown in inset b) of Figure 2. Under the given conditions this hybridization (HybA) may be stable.
  • An imaging buffer including the complementary nucleic acid sequence being labeled by an imaging molecule 20 is added, resulting in insert c), which shows transient binding of the second nucleic acid strand 6 of the nucleic acid molecule 2 to the complementary nucleic acid sequence being labeled by an imaging molecule 20.
  • the binding constant kon may be 10 7 1/Ms, 10 8 1/Ms, 10 9 1/Ms (with the unit 1/Ms being equivalent to liter/(mol*s) ) and the unbinding constant koff may be 3/s, 1/s, 10/s, or 50/s.
  • the amount of the added molecules i.e.
  • the order of the step of contacting the sample/target molecule 1 with the primary binder and thus the target complementary nucleic acid sequence 10 and binding molecule 12, the step of contacting the sample/target molecule 1 with the single-stranded nucleic acid molecule, and the step of contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule 20 under appropriate conditions may be performed in any order.
  • DNA-PAINT image acquisition or any other suitable acquisition, may be carried out.
  • the imaging molecule 22 provides for a signal in an image when bound to the target molecule 1 (ON-state), here via the nucleic acid molecule 2 and optionally the primary binder 14.
  • ON-state the imaging molecule 22
  • OFF-state the imaging molecule 22
  • Switching between ON- and OFF-states is a stochastic process for each target molecule in the sample.
  • the binding may be controlled to enable a sufficiently low number of target molecules 2 in the ON-state in each acquired image to be able to localize the individual target molecules 2 within one image without disturbing signals from neighbors at a distance that is classically unresolvable.
  • a sufficiently large number of target molecules 2 will be present as a signal in one of the images.
  • all the signals as derived from the individual images may be added and analyzed to localize the individual target molecules 2.
  • a final data set which includes the positions of the localized target molecules 2 may be created and visualized as appropriate, e.g. plotted and/or shown in a single image.
  • a drift marker may be added to the sample prior to starting the image acquisition.
  • a drift marker may be any suitable marker known in the art, for example one or more gold beads, fluorescent beads, or fluorescent dyes immobilized to a fix reference in the sample such as a surface of a cover slip or imaging chamber, channel, or well.
  • the drift marker has a shape and/or combination of spectrally distinguishable fluorescence dyes that enables identification and correction for drift in all three dimensions.
  • an unbinding step may be performed, e.g. as illustrated in Fig.2 d).
  • the unbinding step may comprise any of the techniques mentioned above. Particularly, and as illustrated in Fig.2, it may include adding an invader strand 30 corresponding to the toehold seed 8 and the first nucleic acid sequence 4.
  • the invader strand 30 may hybridize with the first nucleic acid sequence 4 and remove the single- stranded nucleic acid molecule 2 from the primary binder 14.
  • the invader strand 30 may remove the single-stranded nucleic acid molecule 2 from the target molecule 1.
  • the complex of the single-stranded nucleic acid molecule 2 and the invader strand 30 is removed from the sample, e.g. by washing with an appropriate buffer solution.
  • the unbinding step may include applying heat and/or buffer conditions to the sample that support and/or enable dissociation of the single-stranded nucleic acid molecule 2 from the corresponding primary binder 14 and/or target molecule 1.
  • An unbinding step may also be done before the first detection round in the sample with an unspecific technique, e.g. heat and/or buffer conditions, to ensure that the target molecules 1 are free to bind the single-stranded nucleic acid molecules according to the invention in the first detection round.
  • an unspecific technique e.g. heat and/or buffer conditions
  • Another option is to use different imaging molecules 22 that are distinguishable in the applied detection method, e.g. have distinguishable fluorescence spectra. This enables detecting the different target molecules 1 in the same detection round.
  • the different complementary nucleic acid sequences being labeled by an imaging molecule 20 may specifically bind to the different single stranded nucleic acid sequences 2 for the different target molecules 1.
  • the different complementary nucleic acid sequences being labeled by an imaging molecule 20 may be orthogonal and the second nucleic acid strands 6 of the different single stranded nucleic acid sequences 2 may be orthogonal.
  • the primary binders 14 need to be orthogonal with respect to their binding molecule 12 and with respect to their target complementary nucleic acid sequence 10 in order to allow for specific detection of the two or more target molecules 1.
  • Contacting the sample with the two or more orthogonal primary binders 14 may be a single step in which all orthogonal primary binders 14 are added to the sample at once, preferably in a step upstream the cycle of detection rounds. This means that the sample may first be contacted with all primary binders 14, and then the cycle of M detection rounds starts with the first detection round by contacting the sample with the first single-stranded nucleic acid molecule as described above.
  • adding one or more but not all orthogonal primary binders 14 may be a step upstream of the detection cycle, and the rest of the orthogonal primary binders 14 may be added to the sample as part of one or more detection rounds as described above.
  • the orthogonal primary binders 14 may be grouped for addition to the sample according to reaction condition requirements and/or constraints that are given by the way of coding the labeling of the different target molecules 1 (see also explanations relating to codebook below).
  • Primary binders 14 may not be required for target molecules 1 that may be directly bound by single-stranded nucleic acid molecules 2, for example for target molecules comprising the same type of nucleic acid as the single-stranded nucleic acid molecule 2.
  • the target complementary nucleic acid sequence 10 is a domain of the target molecule 2. Accordingly, the step of contacting such target molecules 1 with primary binders 14 may be omitted. Alternatively, it is also possible to have one or more additional intermediate binding molecules (not shown) between the primary binder 14 and the single-stranded nucleic acid molecule 2. In this respect reference is made to the above provided example of biotin and streptavidin-labelled antibodies.
  • the step of adding a single-stranded nucleic acid molecule 2 to the sample may be performed at the same time as adding the target complementary nucleic acid sequence 14. Particularly, the nucleic acid molecule 2 may be contacted with the target complementary nucleic acid sequence 14 before adding both to the sample.
  • the nucleic acid molecule 2 may be hybridized to the target complementary nucleic acid sequence 14 when it is added to the sample.
  • the complementary nucleic acid sequences being labeled by an imaging molecule 20 may be added to the sample together with the corresponding nucleic acid molecules 2 and/or primary binders. However, it is preferred to add the complementary nucleic acid sequences being labeled by an imaging molecule 20 after the optional addition of the corresponding primary binders 14 and the corresponding nucleic acid molecules 2, particularly after some incubation time in order to allow for the primary binders 14 and the nucleic acid molecules 2 to stably bind.
  • the first nucleic acid sequence 4 of the single-stranded nucleic acid molecule 2 may have a length that is appropriate for the respective application.
  • the length may be as short as possible to avoid unnescessary long reaction time, and as long as necessary to provide for the required specificity and number of orthogonal sequences.
  • the first nucleic acid sequence 4 may have a length of 4 to 30 nucleotides, preferably 16 to 24 nucleotides.
  • the first nucleic acid sequence may have a GC-content of 45%- 55%, preferably 50%. The GC-content may be used to set the binding kinetics as appropriate.
  • the second nucleic acid sequence 6 of the single-stranded nucleic acid molecule 2 may have a length of
  • the target nucleic acid sequence in a primary binder may have a length of 6nt to 150nt, preferably 10nt to 50 nt, more preferably 12 to 20 nt, for example 21 nt.
  • the nucleic acid sequence being labeled by an imaging molecule may have any suitable length.
  • the nucleic acid sequence being labeled by an imaging molecule may have a length of 4 to 10 nucleotides.
  • the length of any of the above mentioned nucleic acid sequences may also be used, within the constraints of specificity and orthogonality, for setting the binding kinetics to the complementary nucleic acid sequence.
  • the present invention provides for several advantages.
  • the invention uses two hybridizations, HybA and HybB.
  • HybA of the first nucleic acid sequence 4 of the single stranded nucleic acid molecule 2 to the target complementary nucleic acid sequence 10 of or bound to the target molecule 1 provides for a high degree of multiplexing.
  • the number of available orthogonal first sequences 4 is high enough to enable detection of a large number of different target molecules 1 in one sample, e.g. proteins in a single cell.
  • the imaging molecule 22 is indirectly bound to the target molecule 1 via the complementary nucleic acid sequence being labeled by an imaging molecule 20 and the second nucleic acid sequence 6 of the single-stranded nucleic acid molecule 2, i.e. HybB.
  • the degree of multiplexing is decoupled form the binding kinetics of the complementary nucleic acid sequence being labeled by an imaging molecule 20 via HybB.
  • the second nucleic acid sequences 6 comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12.
  • the sequences known from the SPEED- PAINT technique for example the sequences known from the SPEED- PAINT technique.
  • the complementary nucleic acid sequences being labeled by an imaging molecule 20 have complementary sequences. This enables speed-optimized acquisition of images in the detection rounds. Nevertheless, the multiplexing may be 20 to 30, i.e.
  • the single-stranded nucleic acid molecule 2 may comprise more than 1 second nucleic acid sequences 6.
  • the single-stranded nucleic acid molecule 2 may comprise 2 or more,3 or more, 4 or more, 5 or more, or 6 or more second nucleic acid sequences 6. This may enable binding of a corresponding number of complementary nucleic acid sequences being labeled by an imaging molecule 20.
  • At least some of the second nucleic acid sequences of one single-stranded nucleic acid molecule 2 may be orthogonal. This may enable binding of orthogonal complementary nucleic acid sequences being labeled by an imaging molecule 20 and thus different imaging molecules 22.
  • the same type of imaging molecules 22 may be bound to one nucleic acid molecule 2 via the same type of second nucleic acid sequence 6 or via different types of second nucleic acid sequence 6. Additionally or alternatively, multiplexing may be increased by binding more than one primary binder 14 per target molecule 1. Particularly, 2 or more, 3 or more, 4 or more, 5 or more, or 6 or more primary binders 14 and thus corresponding single stranded nucleic acid molecules 2 may be used per target molecule 1. However, for both cases multiple second nucleic acid sequences 6 per single-stranded nucleic acid molecule and multiple primary binder 14 per target molecule 1 care needs to be taken not to corrupt the binding kinetics of HybA and/or HybB.
  • the singe-stranded nucleic acid molecules 2 according to the invention provide for another advantage: Using them as imager targets and washing them off after a detection round stabilizes imager accessibility of the target molecule 1. Nucleic acid strands that hybridize to complementary nucleic acid sequences being labeled by an imaging molecule 20 tend to get corrupted by photodamage in prolonged DNA-PAINT experiments [doi:10.3390/molecules23123165]. Therefore, replacing the single-stranded nucleic acid molecules in between detection rounds provides for fresh single-stranded nucleic acid molecules in the subsequent detection round, thus increasing the reliability.
  • Figure 3 illustrates a proof of principle experiment. Insert A shows the design of an experiment according to the present invention. Insert B shows an exemplary image of the respective experiment.
  • DNA origami is a technique generally known in the art. The term is used for DNA that is artificially designed to fold into a specific structure or shape. In short, a long scaffold strand and multiple small so called staple strands are designed. When the staple strands bind to the scaffold strand the scaffold is forced into a desired shape. It is useful to know that DNA origami may provide for very well defined structures with defined lengths and distances in between certain points, which can be simply addressed with DNA strands by extending staple strands, such that single-stranded nucleic acid sequences are present at the respective positions. Therefore, DNA origami is often used as a microscopy standard or ruler.
  • DNA-origami was chosen to have an overall shape approximating a cuboid, with a significant number of helices lying essentially parallel, and a thickness of only one layer of helices.
  • the comic in figure 3 shows a top view of a DNA-origami cuboid 40 including the addressable staple strand positions 42 of the structure, represented by hexagons (only one of them is referenced with reference sign 42). Some of the positions 42 were designed to be a first type of target molecules 1. Some of the positions 42, represented by dotted hexagons, were designed to be regular DNA-PAINT targets as known in the art. A pattern was created by providing the target molecules 1 of the first type at 12 positions 42 with a spacing of 20 nm, indicated by cross hatch, and the regular DNA-PAINT targets at clusters of positions 42 in the four corners of the shown top of the cuboid 40.
  • a second type of these DNA origami were created, using different extension sequences at the cross hatch positions (second type of target molecules 1), acting as target complementary nucleic acid sequences as described herein below.
  • a first DNA origami structure was designed to comprise a first target complementary nucleic acid sequence 10 at the cross hatched positions and a second DNA origami structure was designed to comprise a second target complementary nucleic acid sequence 10 at the cross hatched positions.
  • the DNA-origami were immobilized on a surface of a cover slip of a reaction chamber.
  • Labelling (i.e. in this case attaching a docking strand) of the regular DNA-PAINT targets (in this case the respective positions of the scaffold) at the dotted positions 42 for standard DNA-PAINT was done during DNA origami folding by extending the respective staple strand sequences with a docking strand sequence.
  • the first and second DNA origami comprising the first and second target complementary nucleic acid sequences 10 were mixed and immobilized on a surface of a cover slip of a reaction chamber. Detection occurred in three detection rounds, a first detection round using standard DNA-PAINT to localize the DNA origami using the regular DNA-PAINT targets (dotted positions in Fig.3), and a second and third detection round according to the present invention for the first and second type target molecule 1 represented by cross hatched positions.
  • the sample i.e.
  • the first and second DNA origami which already incorporated standard DNA PAINT docking strands at the dotted positions and a first and second primary binder 14, respectively, at the cross hatched positions, was contacted in the first imaging round with an imager capable of transiently binding the standard DNA PAINT docking strands and a standard DNA PAINT dataset was recorded.
  • a first single-stranded nucleic acid molecule comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to the first target complementary nucleic acid sequence 10 and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence, was added to the sample. Then, an imaging buffer comprising the complementary nucleic acid sequence being labeled by an imaging molecule 20 was added. Then, detection of the imaging molecule 22 was performed according to PAINT.
  • a third detection round was performed for the second target complementary nucleic acid sequence 10 with the second nucleic acid molecule 2 but with the same type of complementary nucleic acid sequence being labeled by an imaging molecule 20.
  • the two DNA origami structures and their different target molecule types 1 were distinguished via their imaging time (second detection round or third detection round). The result of the experiment is shown in the false color picture in panel B of Fig.3. The top row shows signal from a first DNA origami structure and the bottom row shows signal from a second DNA origami structure.
  • the left, second to left and second to right columns show the signal for the first, second and third imaging round, respectively, and the right column shows the overlayed signal.
  • the duration of an experiment with the technique described so far still increases for every additional target molecule 1, i.e. the more target molecules to be detected the longer the overall detection time.
  • another aspect of the present invention is related to a specific way of barcoding the target molecules 1, which will be described in the following with reference to Fig.4.
  • Figure 4 shows a scheme for up to 1820-plex single protein imaging in 16 sequential detection rounds.
  • the numbers are only exemplary and depend on the length of the used barcodes and whether a single type of imager and primary binders with exactly one target complementary nucleic acid sequence 10 are used (as in the scheme), or more, which would of course further increase the plexing-number (see above).
  • the concept is as follows: To every type of target molecule 1 a unique barcode (i.e. identification sequence) having M ordered positions is assigned. Every position represents one detection round and is assigned with either “to be imaged” or “not to be imaged”. Thus, the identification sequence defines for every one of the M detection rounds, whether the corresponding type of target molecule 1 is to be labeled and detected according to the invention described above or not.
  • the identification sequences assigned to the target molecules 1 are referred to as a codebook.
  • “to be imaged” is represented by “1”
  • “not to be imaged” is represented by “0”.
  • a unique single-stranded nucleic acid molecule 2 is used, as represented by the library in Fig.4B.
  • the types of single-stranded nucleic acid molecules 2 have orthogonal first nucleic acid sequences 4 but the same second nucleic acid sequence and are consequently configured to bind the same nucleic acid sequence labeled by an imaging molecule 20, i.e. the same type of imaging molecule 22.
  • FIG.4C the pictures schematically illustrate a portion of a cell is schematically illustrated by the thick black curved lines (note that this is an exemplary environment).
  • Target molecules 1 of different types are represented by hexagons with different numbers (T1, T4, T5 and T8).
  • Figure 4C depicts three of 16 detection rounds, namely round 1 in the left column, round 2 in the middle column and round 16 in the right column.
  • the top row of Fig.4C schematically depicts the nucleic acid strands that are present/bound.
  • the bottom row shows schematic illustrations of what is detected in imaging.
  • detection round M all target molecules 1 that have been assigned with “to be imaged” in that detection round M as set out in their identification sequence are detected as described herein.
  • detection round 1 left column of fig.4C
  • target molecules T5 and T8 are labelled, as indicated by the black stars representing an imaging molecule 22.
  • Target molecules of type T5 and T8 are bound by corresponding primary binders (represented by the short black straight line extending from the hexagons; note the different degrees of abstraction of the drawn molecules in different figures as explained in Fig.4A), which present target specific target complementary nucleic acid sequences.
  • Secondary binders (long black line) with first nucleic acid sequences corresponding to the respective target complementary nucleic acid sequences have been added to the sample and are hybridized to the target complementary nucleic acid sequence.
  • the secondary probes present the same second nucleic acid sequences capable of transiently binding to the complementary nucleic acid sequence being labelled by an imaging molecule (short black line with black star). It must be noted that, due to this transient binding, the “labelling” is still of the stochastic nature that is the basic principle of the PAINT technique. After imaging, the result of which is represented in the bottom left comic of Fig.
  • the secondary probes and the nucleic acid sequences being labelled by an imaging molecule of the previous round are removed via toehold mediated replacement and washing.
  • primary binders are added such that all target molecule types are bound to corresponding primary binders before starting the imaging rounds and adding the first secondary probes. As already explained, this is optional; the primary binders may also be added individually in the individual detection rounds, e.g. together with the secondary probes.
  • the imaging data collected in the different imaging rounds is processed, particularly aligned.
  • the imaging data in combination with the knowledge, which target types are labelled in which imaging round i.e. the data from the codebook
  • the individual target types may not only imaged in high resolution but also be identified.
  • the maximum ratio of identification sequence positions being “1” over the total number of identification sequence positions may be chosen to allow for error correction.
  • the ratio may be from 1/16 to 10/16, or from 1/10 to 7/10, or from 1 ⁇ 4 to 3/4.
  • specific rules may be set, defining the minimum number of “0”-positions between neighboring “1”-positions, for example at least 1, at least 2, or at least 3 “0”-postions between neighboring “1”-positions or vice versa at least 1, at least 2, or at least 3 “1”-postions between neighboring “0”-positions. This allows for certain error correction because false negative or false positive detection events may be excluded.
  • the present invention relates to a method of mapping the localization of different target molecules within a sample, preferably within cells of the sample.
  • an interaction pattern of the different target molecules based on their localization may be performed, preferably by nearest neighbor-based analysis.
  • this aspect of the present invention is an additional and independent invention. It is independent of the specific imaging technique and tools, particularly of the imaging techniques and tools mentioned above. Any imaging technique that provides for the required imaging quality may be used. Nevertheless, the imaging techniques and tools mentioned above are among the preferred ones. Molecularly resolved, multiplexed image data may be used to rationally inform the choice and further development of drugs.
  • the image data may not only be used for improving quantification of the target molecules by counting instead of using fluorescence intensity as a metric, but also the key information of target molecule maps such as distances, angles, molecular orientations, oligomerisation, cluster contributions (cluster size, cluster shape, protein ratios, protein motifs) may be used for characterizing differences between samples for better patient stratification as well as developing multivalent drugs that specifically target one type of molecular key motif, thereby increasing specificity and sensitivity in treated patients while reducing/inhibiting undesired side effects as well as significantly reducing development time and costs.
  • target molecule maps such as distances, angles, molecular orientations, oligomerisation, cluster contributions (cluster size, cluster shape, protein ratios, protein motifs) may be used for characterizing differences between samples for better patient stratification as well as developing multivalent drugs that specifically target one type of molecular key motif, thereby increasing specificity and sensitivity in treated patients while reducing/inhibiting undesired side effects as well as significantly reducing development time
  • the present invention provides to-date unprecedented biomedical understanding and insight into basic principles of cell-cell communication and thus not only in the development of novel multi-specific drugs but also in the characterization of the mechanism of action of already existing as well as novel drugs by being able to characterize the spatial organization of key target molecules (e.g. proteins) at molecular resolution, determining specificity and efficiency of target drug, and molecular reorganization at the nanoscale in response to different drug doses. Thus, it may guide the design of dosing, toxicity and drug combination studies, as well as for patient stratification.
  • This aspect of the present invention is most relevant to drugs that specifically bind to target molecules, e.g. therapeutic and diagnostic antibodies or other binders (e.g. nanobody, aptamer, scF V ).
  • the present invention may enable study potential reorganization of the molecular architecture, thus enabling mode of action or biomarker investigations.
  • Indications most relevant to the present invention for drug development are those targetable with therapeutic antibodies, such as cancer, autoimmune diseases, migraine and various infectious diseases (e.g. HIV, HPV, Ebola, Alzheimer’s disease, Crohn’s disease, multiple sclerosis, rheumatic arthritis, as well as cancers like melanoma, non-small cell lung cancer, breast cancer, and others).
  • DNA origami sample preparation was done in a 6-channel u-slide (Ibidi Cat.: 80607).
  • the chamber was washed with 5 ml of 2xSSC buffer and 1 ml of B+ buffer until 1 ml of imager solution was applied for and between imaging rounds. After all imaging rounds for one set of secondary labels, the chamber was incubated with dehybridization buffer for 15 min. More explicitly, after immobilization of the 42 DNA origami in one chamber and one DNA origami each in 42 chambers and washing all chambers with buffers B+ and 2xSSC, secondary labels 1, 2, 3, 4, 5, and 6 were incubated at a concentration of 100 nM each in hybridization buffer for 15 min.
  • the chamber was washed with 5ml of 2xSSC and 1ml B+ buffer, and flushed with 1ml R1 imager solution (500 pM R1 in Buffer B+ with 7 ⁇ g/ml PCD (Sigma-Aldrich cat. P8279-25UN), 0,4 mg/ml PCA (Sigma-Aldrich 37580-25g-f), and 0,3 mg/ml Trolox (Sigma-Aldrich 238813-1G)).
  • DNA-PAINT imaging (10000 frames at exposure of 100 ms with an illumination of 25 mW at 561nm) of this imager, the chamber was washed with 3ml B+ buffer, and 1ml R2 imager solution was applied.
  • the secondary labels (single stranded nucleic acid molecules) were stripped off by flushing 1ml of 100nm secondary label-specific toehold strands (as listed in Table 1, 600nm in total) in dehybridization buffer into the chamber and incubating for 15 min, followed by injecting 1ml of new secondary labels at 100 nM each in hybridization buffer and incubating for 15 min. Subsequently, the chamber was washed with 5ml of 2xSSC and 1ml B+ buffer, and flushed with 1ml R1 imager solution, repeating the rounds of imagers.
  • Tables 1 and 2 list the sequences and codes used, and Figure 5 shows representative results, where the single- DNA origami samples could be used to confirm the correct identification of DNA origami structures based on their codes.
  • a code for a given location is created by a binary number with as many digits as DNA-PAINT imaging rounds are performed (in this case 42), where a ‘0’ denotes no imager binding in the respective location in the respective imaging round, while a ‘1’ denotes registered imager binding at that location.
  • DNA-PAINT imaging rounds are taken in chronological order, so for example the third DNA-PAINT imaging round (of imager R3 in the present case) of the second secondary labeling round (II) is the 9 th DNA- PAINT imaging round in total, so it is represented in the 9 th digit of the code.
  • 210 different DNA origamis were designed using 1220nm spaced positions of target complementary sequences, as described herein above. Secondary labels (single-stranded nucleic acid molecules) for a target complementary sequence were used in four secondary label hybridization rounds each, while they were absent in six secondary label hybridization rounds, leading to codebook entries as described in Table 3.
  • the sample was washed three times with 1 ml PBS, and a 1:3 dilution of Gold nanoparticles (90nm Standard Gold Nanoparticles, Cytodiagnostics, Inc., cat.: G-90-100) were incubated for 5 min as fiducial markers for drift correction.
  • Gold nanoparticles 90nm Standard Gold Nanoparticles, Cytodiagnostics, Inc., cat.: G-90-100
  • Target complementary nucleic acids were conjugated to the C-terminus of their respective secondary binding molecules (in this case secondary nanobodies) according to table 4 and 5 (Mouse: FluoTag- X2 anti-Mouse Ig kappa light chain, NanoTag Biotechnologies; Rabbit: FluoTag-X2 anti- Rabbit IgG, NanoTag Biotechnologies; Synaptotagmin: FluoTag-X2 anti-Synaptotagmin 1, NanoTag Biotechnologies, PSD95: sdAb anti-PSD95, NanoTag Biotechnologies) to form secondary binders: First, buffer was exchanged to 1 ⁇ PBS + 5 mM EDTA, pH 7.0 using Amicon centrifugal filters (10k Da molecular weight cut-off) and free cysteines were reacted with 20-fold molar excess of bifunctional maleimide-DBCO linker (Sigma Aldrich, cat: 760668) for 2-3 hours on ice.
  • Unreacted linker was removed by buffer exchange to PBS using Amicon centrifugal filters. Azide-functionalized DNA was added with 3-5 molar excess to the DBCO-nanobody and reacted overnight at 4°C. Unconjugated nanobody and free azide-DNA was removed by anion exchange using an ⁇ KTA Pure liquid chromatography system equipped with a Resource Q 1 ml column. Nanobody-DNA concentration was adjusted to 5 ⁇ M (in 1xPBS, 50% glycerol, 0.05% NaN3) and stored at -20°C.
  • Target molecule labeling with these secondary binders was done by performing a preincubation of the antibody (binding molecule) with their respective secondary binder in 10 ul antibody incubation buffer at room temperature for 2 h, for each target molecule separately. After the preincubation time, a large excess (molar ratio of 1:2) of unlabeled secondary nanobody was introduced and incubated for 5 min. Subsequently, the six antibody-to-secondary-binder complexes corresponding to the first round of secondary label (single-stranded nucleic acid molecule) hybridization were pooled in 300 ul antibody incubation buffer and incubated in the neuron sample for 90 min.
  • the sample was washed five times with 1ml PBS and once with 1 ml Buffer C followed by a postfixation with 2.4% paraformaldehyde in PBS for 7 min. Afterwards, the sample was washed three times with 1ml PBS and once with 1ml 2xSSC buffer and the secondary label (nucleic acid molecule) hybridization for barcoding round 1 was carried out according to table 4 with 100 nM of each secondary label for 20 min (totalling 600 nM). Finally, the sample was washed five times with 1ml 2xSSC buffer and once with 1ml buffer C.
  • the sample was then flushed with 1ml R1 imager solution (concentration as described in Table 4, in buffer C with 7 ⁇ g/ml PCD (Sigma-Aldrich cat. P8279-25UN), 0,4 mg/ml PCA (Sigma-Aldrich 37580-25g-f), and 0,3 mg/ml Trolox (Sigma-Aldrich 238813-1G)).
  • 1ml R1 imager solution concentration as described in Table 4, in buffer C with 7 ⁇ g/ml PCD (Sigma-Aldrich cat. P8279-25UN), 0,4 mg/ml PCA (Sigma-Aldrich 37580-25g-f), and 0,3 mg/ml Trolox (Sigma-Aldrich 238813-1G)).
  • DNA-PAINT imaging 15 mW at 561nm HILO illumination
  • the chamber was washed with 1ml buffer C, and 1ml R2 imager solution was applied.
  • the secondary labels (single-stranded nucleic acid molecules) were blocked by flushing 1ml 100 nM blocking strands per second nucleic acid sequence to block binding of the complementary nucleic acid sequences being labeled by an imager (corresponding to the six imagers previously imaged, totalling 600 nM) in hybridization buffer into the chamber and incubating for 15 min, followed by injecting 1ml of new secondary labels at 100 nM each in hybridization buffer and incubating for 15 min.
  • Fig 6 shows the results of the described 29-plex SUM-PAINT experiment mapping the 3D protein distribution of a single neuron with all the 29 channels color-coded and overlayed.
  • Fig 6 B The gallery with the respective protein distribution and localization precision can be seen in Fig 6 B. Summing up the acquisition time of single channels and adding the transition time between barcoding rounds the whole 29-plex single molecule atlas can be acquired in only 30h of total experimental time. Vendor Cat.
  • T cells may be very relevant in other applications, especially for characterization of the mode of action of drugs, or for biomarker discovery and validation. Other target molecules than proteins are also contemplated.
  • One of the most important interfaces for cell-cell communication is the cell surface.
  • the receptor-ligand interactions engaged during a cell-cell encounter can trigger signalling cascades that have a profound downstream impact upon cell behaviour and differentiation. This is particularly important in the immune system, where the outcome of cellular interactions can dictate whether an immune response is initiated, the nature of the ensuing response, and whether an existing response is sustained or extinguished.
  • DCs dendritic cells
  • Surface encounter of specific peptide-MHC antigen complexes alongside co-stimulatory molecules, such as CD80 and CD86, can drive both initial T cell differentiation into effector cells, and subsequent expansion and differentiation of an established effector T cell response.
  • antigen encounter in the context of excessive immune checkpoint molecules, such as PD-L1 and PD-L2 can both block initial T cell priming, and restrain effector T cell function and differentiation.
  • T cell stimulatory capacity of a given cell type has classically been defined by the absolute cell surface levels of key immunomodulatory ligands.
  • the native, single protein spatial organisation of immune regulatory molecules on the cell surface, and how this spatial organisation contributes to T cell control remains poorly understood. This represents a major knowledge gap, particularly given that protein clustering is a well-defined determinant of surface protein signalling and function. Progress has been limited in this area due to a lack of technologies capable of concurrent spatial mapping of multiple proteins at the single molecule level.
  • Target molecule of interest
  • Binding molecule the affinity molecule/antibody/antibody mimetic/.... to tag the target molecule
  • Secondary binder an affinity molecule tagging the binding molecule, either directly fluorescently labeled, or labeled with a DNA-PAINT docking sequence
  • Binding molecule evaluation and development is an optional aspect of the present invention. This also applies to all of its sub-aspects. As mentioned above, for optimal sample analysis, it may be beneficial to first characterize binding molecules and/or primary binders that are later used for labelling target molecules. This characterization can then be integrated into the data evaluation as a calibration.
  • a protein fusion of the target molecule of interest e.g. MHC-I, MHC-II, CD86, CD80, PD-L1, PDL2
  • reference targets for reference labelling e.g. epitope tags like ALFA-tag, Halo- tag, SNAP tag, SPOT tag, FLAG-tag, His-Tag, sortase tag or fluorescent proteins
  • unnatural amino acid labeling or gene editing e.g. CRISPR
  • binding molecule and secondary binder binding according to version 1 (preferably if the cells express a single target molecule): o Incubate a binding molecule with the cell sample to let it attach to the target molecule; o Incubate a secondary binder against the binding molecule in the cell sample, where the secondary binder comprises a modification for stable or transient fluorescence (e.g. fluorophore modification, or DNA oligomer conjugation comprising a docking sequence for a complementary nucleic acid sequence being labelled by an imaging molecule, preferably a DNA-PAINT imager).
  • a modification for stable or transient fluorescence e.g. fluorophore modification, or DNA oligomer conjugation comprising a docking sequence for a complementary nucleic acid sequence being labelled by an imaging molecule, preferably a DNA-PAINT imager.
  • Binding molecule and secondary binder binding according to version 2 (preferably if the cells express multiple target molecules each with a unique reference target) – as an alternative to step 5, version 1: o Incubate a secondary binder against the binding molecule in bulk solution, where the secondary binder comprises a modification for stable or transient fluorescence (e.g. fluorophore modification, or DNA oligomer conjugation comprising a docking sequence for a complementary nucleic acid sequence being labelled by an imaging molecule, preferably a DNA-PAINT imager). O Incubate the complex of secondary binder and binding molecule against the sample, to let it attach to the target molecule. Optional steps: 1. Add fiducial markers, e.g. gold nanoparticles. 2.
  • CHO cells were transfected with a single receptor construct (one of the following set in each sample: mEGFP-ALFA-MHC-I, mEGFPALFA-MHC-II, mEGFP-ALFA-CD86, mEGFP-ALFA-CD80, meGFP-ALFA-PD-L1, meGFP- ALFA-PDL2) for binding molecule characterization using Lipofectamine LTX as specified by the manufacturer.
  • CHO cells were allowed to express mEGFP-ALFA-receptors for 16–24 h.
  • Binding molecules (one each for the corresponding samples: CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2) and ALFA-tag nanobody (as reference binders) were diluted in blocking buffer and added at a final concentration of 50nM each for 90min at 24°C. Unbound binding molecules and reference binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min.
  • Fluorescently labelled secondary antibodies targeting binding molecules were dissolved in blocking buffer and added at a final concentration of 100nM each for 60min at 24°C (to the corresponding sample: CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2). Unbound secondary antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post- fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS.
  • Binding molecule preselection Imaging Required steps: 1. Perform at least two-plex fluorescence imaging of the sample cells, by using two-plex DNA-PAINT or optionally by chromatic splitting using dichroic mirrors or filters, preferentially using light sheet, confocal, epifluorescence or TIRF microscopy. Continuation of the Specific Example: Prior to image acquisition, all fluorophores (e.g.
  • CHO-mEGFP-ALFA-MHC-I, CHO-mCherry- CD80 were deactivated by a high intensity bleach pulse (488 nm, 150 mW at the sample plane, for 1 minute).
  • Cellular imaging was conducted via two subsequent conventional DNA- PAINT imaging rounds (i.e. detection rounds) using distinct imagers for each binding molecule with only one of the imagers present at a time.
  • Cy3B as the imaging molecule
  • Cy3B-conjugated imagers were dissolved in Buffer Z and imager solution was added to the sample to perform conventional DNA-PAINT measurements. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected, followed by incubation of Buffer X for 5min.
  • Binding molecule preselection determination Required steps: 1. Quantify the number of secondary binder molecules (e.g. secondary nanobody) bound to binding molecules, which in turn are bound to their target molecules. This can be done with various accuracies and precisions, e.g.
  • o Using diffraction-limited fluorescence intensity of secondary binder in the test sample as a proxy for secondary binder molecule numbers, optionally in the image region confirmed to be covered by a cell; o Using diffraction-limited fluorescence intensity of the corresponding imager of secondary binder in the test sample, colocalizing with fluorescent protein signal as a proxy for secondary binder molecule numbers; o Performing conventional DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to secondary binder molecule localizations. Using secondary binder molecule counts as a proxy for secondary binder molecule numbers, optionally only those that are in an image region confirmed to be covered by a cell.
  • o Using diffraction-limited fluorescence intensity of secondary binder in the control sample as a metric, optionally in the image region confirmed to be covered by a cell; o Using diffraction-limited fluorescence intensity of the corresponding imager of secondary binder in the test sample as a proxy, optionally in the image region confirmed to be covered by a cell; o Performing DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to secondary binder molecule localizations. Using secondary binder molecule counts as a proxy, optionally only those that are in an image region confirmed to be covered by a cell. 3. Calculate a metric for bare binding molecule selection, e.g.
  • binding molecule density was evaluated by correlating respective integrated bulk intensity values to the background-corrected intensity value of a clearly defined monomeric single fluorophore and further calculating corresponding ratios between control and test sample as well as ratios between secondary binder and reference binder density in test sample only.
  • binding molecule selection may be done via conventional DNA-PAINT.
  • DNA-PAINT based determination of binding molecule selection raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments.
  • DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Binding molecule selection was evaluated by counting the number of secondary binder signals in control and test sample within the cell or optionally, in the image region confirmed to be covered by a cell, determining underlying secondary binder density and further corresponding ratios between control and test sample as well as ratios between secondary binder and reference binder density in test sample only. 1.2.
  • a short DNA oligo target complementary nucleic acid
  • a short DNA oligo target complementary nucleic acid
  • the primary binder shows little off-target binding. This can be characterized by primary binder specificity testing.
  • 1.2.1 Sample preparation for primary binder specificity imaging (Version 1). Required steps: 1. Seed cells that do not endogenously express proteins of interest on a microscopy slide. Split into control and test sample. 2. Transfect cells in test sample with a receptor construct, leading them to express a protein fusion of the target molecule (e.g.
  • an epitope tag e.g. Alfa-tag, Halo-tag, FLAG-tag, His-Tag, sortase
  • a fluorescent protein – or CRISPR edit e.g. Alfa-tag, Halo-tag, FLAG-tag, His-Tag, sortase
  • a fluorescent protein – or CRISPR edit e.g. Alfa-tag, Halo-tag, FLAG-tag, His-Tag, sortase
  • a fluorescent protein – or CRISPR edit e.g. Alfa-tag, Halo-tag, FLAG-tag, His-Tag, sortase
  • a fluorescent protein – or CRISPR edit e.g. Alfa-tag, Halo-tag,
  • meGFP-ALFA-MHC-I meGFP-ALFA-MHC-I
  • meGFP-ALFA-MHC-II meGFP-ALFA-CD86
  • meGFP-ALFA-CD80 meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2
  • Lipofectamine LTX Lipofectamine LTX as specified by the manufacturer.
  • CHO cells were allowed to express meGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100U/ml Penicilin and 100 ⁇ g/ml Streptomycin followed by fixation.4% PFA solution was preheated to 37°C before addition to the cells.
  • Non-transfected CHO cells served as a reference.
  • DNA-conjugated antibodies CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2
  • primary binders were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound primary binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min.
  • MutuDC 1940 wt were seeded on ibidi 8 Well high Glass Bottom chambers at a density of 20000 cells per cm 2 several hours prior fixation.
  • MutuDC1940 KO (MutuDC1940 MHC-I KO, MutuDC1940 MHC-II KO, MutuDC1940 CD86 KO, MutuDC1940 CD80 KO, MutuDC1940 PD-L1 KO, MutuDC1940 PD-L2 KO) cells served as a reference.
  • DNA-conjugated antibodies (CD80, CD86, MHC-I/MHC-I OVA, MHC-II, PD-L1, PD-L2) were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS. Subsequently, 250 ⁇ l of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS.
  • DNA-PAINT imaging for primary binder specificity determination (Version 1). This step may be performed after Version 1 of sample preparation for primary binder specificity imaging.
  • meGFP-ALFA-MHC-I meGFP-ALFA-MHC-I
  • meGFP-ALFA-MHC-II meGFP- ALFA-CD86, meGFP-ALFA-CD80, meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2
  • was conducted via imaging single target receptors using distinct imagers for each primary binder Prior to imaging a high intensity bleach pulse was applied until no residual signal from meGFP was observable. Cy3b-conjugated imager strands were dissolved in Buffer Z and imager solution was added to the sample to perform DNA-PAINT measurements. 1.2.4 DNA-PAINT imaging for primary binder specificity determination (Version 2). This step may be performed after Version 2 of sample preparation for primary binder specificity imaging.
  • Imager Imager Cluster Table 6 Imaging parameters for primary binder specificity imaging.
  • Underlying primary binder density was evaluated by correlating respective integrated and background corrected bulk intensity values to the background-corrected intensity value of a clearly defined monomeric single fluorophore and further calculating corresponding ratios between control and test sample.
  • raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso).
  • Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments.
  • gold particles were also used to align all rounds for 2-plex Exchange-PAINT experiments.
  • DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centred around local maxima were identified and grouped (assigned a unique identification number). Primary binder specificity was evaluated by counting the number of binder signals in control and test sample within the cell or alternatively, in the image region confirmed to be covered by a cell, determining underlying binder density and further corresponding ratios between control and test sample. 1.3. Primary binder labelling efficiency testing For sample analysis, it may be beneficial to correct the observed data for incomplete binding of primary binders to target molecules. For example, primary binder labelling efficiency may be tested.
  • Sample preparation for primary binder labelling efficiency imaging Required steps: 1. Seed cells that do not endogenously express proteins of interest (i.e. target molecules) on a microscopy slide. 2. Transfect cells with a receptor construct which leads them to express a protein fusion of the target molecule of interest (e.g. MHI-I, MHC-II, CD86, CD80, PD-L1, PD-L2), and one or more targets for reference labeling (e.g. epitope tags like Alfa-tag, Halo-tag, FLAG-tag, His- Tag, sortase or fluorescent proteins) – or CRISPR edit. 3. Incubate cells until target receptor construct will be expressed. 4. Cell fixation and permeabilization. 5. Incubate primary binders. 6.
  • targets for reference labeling e.g. epitope tags like Alfa-tag, Halo-tag, FLAG-tag, His- Tag, sortase or fluorescent proteins
  • the one or more reference targets in either case being conjugated to a DNA-oligo comprising a docking sequence for a complementary nucleic acid sequence being labeled by an imaging molecule orthogonal to other complementary nucleic acid sequence being labeled by imaging molecules used, where the different epitope tags and or fluorescent proteins may be tagged with the same or different docking sequences; those must be orthogonal to the docking sequence used in 5.
  • CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers the day prior to transfection at a density of 15000 cells per cm2.
  • CHO cells were transfected with a single receptor construct (meGFP-ALFA-MHC-I, meGFP-ALFA-MHC-II, meGFP-ALFA-CD86, meGFPALFA-CD80, meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2) for primary binder characterization using Lipofectamine LTX as specified by the manufacturer.
  • CHO cells were allowed to express eGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100U/ml Penicilin and 100 ⁇ g/ml Streptomycin followed by fixation.4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX- 100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C.
  • DNA-conjugated antibodies (CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2) – primary binders – and ALFA-tag nanobody – reference binders – were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound primary and reference binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS.
  • Cellular imaging was conducted via two subsequent imaging rounds using distinct imagers for each primary binder (Table 7) with only one of the imagers present at a time. Cyb3-labeled imagers were dissolved in Buffer Z and imager solution was added to the sample to perform DNA-PAINT measurements. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5min. Then, the next imager solution was introduced. Imaging parameters are listed in detail in Table 7.
  • Image Clust Clust Table 7 Imaging parameters for binder labelling efficiency imaging.
  • primary binders will be selected which specifically tag target molecules.
  • the number of primary binders found to be bound to target molecules can be corrected for the labelling efficiency of the reference labels (this is more beneficial if only one reference label is used; with more reference labels, the overall labelling efficiency gets closer to 100%, which renders the correction less beneficial).
  • Diffraction-limited analysis option For Diffraction-limited based determination of binder labelling efficiency, background corrected integrated fluorescence intensity values were determined from raw fluorescence data for cellular experiments. Prior to integration, fiducial markers (e.g. gold particles) were used to align channels of target binder and reference in the test sample of 2-plex experiments.
  • DNA-PAINT analysis alternative For DNA-PAINT based determination of primary binder labelling efficiency, raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Further gold particles were used to align all subsequent imaging rounds for 2- plex Exchange-PAINT experiments.
  • DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number).
  • Primary binder labelling efficiency was determined as follows: 1. Cross-nearest neighbour distances were determined for primary binder and reference binder, corresponding histograms were plotted and colocalizing contributions of primary binder and reference binder were determined based on comparison of experimental and simulated data. Underlying labelling efficiency was extracted by simulating different contributions of randomly distributed monomers (reference) and dimers (target + reference) and fitting respective contributions to optimally fit distribution of experimental data.
  • Respective cut-off distance was based on the molecular size of used target primary binder and reference primary binder accounting for the underlying binding epitope of both, target and reference primary binder.
  • Cross-nearest neighbour distances were determined for primary and reference binders, corresponding histograms were plotted and colocalizing contributions of primary and reference binders were determined based on comparison of experimental and simulated data. Underlying labelling efficiency was extracted by a multi-gaussian fit of simulated CSR distribution of dimers only (target + reference) or monomers only (reference) to optimally fit experimental data.
  • Off-reference and Off-target binding was accounted for by randomly removing signals according to percentage of non-specific binding from respective region of interest with the labeling efficiencies determined beforehand. 2.
  • Sample preparation for multiplexed immune receptor DNA-PAINT For the analysis of target molecule patterns, samples have to be prepared for imaging. This can be done in multiple ways and also depends on the sample type.
  • Vesicle refers to a spheroid compartment of aqueous solution with a lipid bilayer as a boundary and a diameter smaller than 100 ⁇ m.
  • Glass coverslips were plasma cleaned for 3 min and attached to the bottom side of an 8-well chamber slide. Coverslips were incubated with a fivefold diluted vesicle solution for 10 min at 24°C, before they were extensively rinsed with PBS. For specific and efficient cell attachment sequential two-step incubation procedure is required. First, SLBs were incubated with DNA-modified lipids (e.g. cholesterol) at a final concentration of 100nM for 60min at 30°C followed by washing excessive lipids off with PBS.
  • DNA-modified lipids e.g. cholesterol
  • FIG. 10 shows the general concept of the preparation steps according to a preparation method, exemplarily performed in the Specific Example: 1. Contacting a coverslip, preferentially a glass coverslip, with an aqueous solution comprising SUVs. 2. This results in a supported lipid bilayer (SLB) supported by the coverslip. 3.
  • histidine-tagged adhesion proteins e.g. His 10 -tag ICAM-1
  • HBSS HBSS supplemented with 2% FBS, 2mM CaCl2 and 2mM MgCl2 prior cell seeding.
  • Figure 10 shows the general concept of the preparation steps according to a preparation method, exemplarily performed in the Specific Example: 1. Contacting a coverslip, preferentially a glass coverslip, with an aqueous solution comprising SUVs. 2. This results in a supported lipid bilayer (SLB) supported by the coverslip. 3.
  • SLB supported lipid bilayer
  • Incubate sample with primary binders against multiple target molecules Incubate sample with primary binders against multiple target molecules.
  • stimulatory reagents e.g. CpG1826, IFN ⁇ , ovalbumin
  • fiducial markers e.g. gold nanoparticles
  • Post-fixation was performed for 5min at ambient temperatures using distinct post-fixation buffers for different kinds of binding molecules (e.g.2% paraformaldehyde in PBS for antibody-based imaging).
  • samples Prior to addition of gold fiducials, samples were washed with PBS. Subsequently, 250 ⁇ l of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS.
  • 3 Data acquisition describes the imaging procedure for multiplexed molecular imaging of targets.
  • 3.1 Multiplexed cellular imaging of immune receptors Required steps: 1. Flush imager solution for the current target molecule into the sample.
  • Imager concentration 1pM to 10nM, more preferably 50pM to 500pM; in Buffer, preferably buffer Z, B+ or C+, most preferably buffer Z. 2. Acquire movie of transient imager binding events; exposure time 1ms to 1s, more preferably 50ms-200ms; number of frames 5.000-100.000, more preferably 10.000-30.000. 3. Repeat steps 1&2 for all target molecules. Optional steps: 1. Deactivation of remaining fluorophores, e.g. GFP, for example via high-energy epifluorescent illumination, such as 150 mW at 488 nm for 1 min. 2. Wash sample between imaging rounds, e.g. to deplete sample of imagers of preceding imaging round. 3.
  • Cy3b) imager strands (also called complementary nucleic acid sequence being labeled by an imager) were dissolved in Buffer Z and the imager solution was added to the sample to perform DNA- PAINT measurements.
  • the result of DNA-PAINT measurements was raw imaging data in the 5 form of transient binding movies. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5min. Then, the next imager solution was introduced. Imaging parameters for DNA-PAINT cell experiments are listed in detail in Table 3. 10 Power (at Ima er Ima er Ima er Cluster AGAGAGA 20 20 a e : mmune ecep o magng pa ame e s. 4.
  • the raw imaging data needs to be processed. This is described here.
  • 4.1 Image analysis Here, the raw super-resolution imaging data is postprocessed.
  • the step describes getting from multiple single channel transient binding movies to one multiplexed molecular map, which specifies the localizations of all target molecules detected in the sample.
  • Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Subsequently, the centers of the localization groups were calculated as weighted mean by employing the squared inverse localization precisions as weights. These centers represent the single-protein positions of the respective imaging round. Merging localizations of all rounds yields the final multiplexed DNA-PAINT data/image (multiplexed molecular map).
  • the data needs to be aggregated to elucidate the direct interaction patterns present in the sample. This step describes getting from the multiplexed molecular map to one or more direct interaction patterns present in the sample.
  • a direct interaction pattern describes a set of target molecules commonly found in close proximity, and optionally probability distributions of their relative distances.
  • Ripley The interaction of receptor molecules was assessed via modified version of Ripley’s K function [as disclosed, e.g., at https://rss.onlinelibrary.wiley.com/doi/10.1111/j.2517- 6161.1977.tb01615.x].
  • Ripley K function as ⁇ ⁇ ⁇ where ⁇ is the density of points, ⁇ ⁇ ⁇ ⁇ points within radius ⁇ of the i-th point ⁇ ⁇ , and the sum is taken over ⁇ points.
  • the cell area was determined via the total area of bins constituting the cell, and the density by the total number of points divided by the cell area.
  • For normalization of Ripley’s K curves we subtracted the obtained mean from each curve, and normalized the data such that the 2.5 and 97.5 percentiles corresponded to values of –1 and 1, respectively.
  • the 95% confidence interval of the integral for complete spatial randomness is given by [ ⁇ ⁇ , ⁇ ], where ⁇ is the length of the integration interval.
  • the patternbar in the corresponding Figures indicates integral values scaling from 2000 (max. clustering) to -2000 (max. dispersion).
  • NND Nearest-Neighbor Distance
  • the surface density of each receptor species is calculated from the number of receptors found on the cell surface area. The latter is measured via masking the multiplexed DNA-PAINT image. Then, a completely spacially randomly (CSR) distributed dataset is generated for each receptor species at the respective experimental protein density. The data points of the CSR distribution are placed within an area defined by the cell outline. Subsequently, the DBSCAN analysis is performed on the “in silico” dataset in the same way as for the experimental data. Finally, the properties of protein clusters in the cell and in the CSR scenario can be compared.
  • Dominating receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean ⁇ 95% CI). Frequencies of all 63 receptor motifs were then compared to respective CSR distribution and tested for significance. In analogy, receptor contributions for all significant receptor motifs were quantified, compared to respective CSR distributions and tested for significance.
  • FIG.11B shows an Exchange-PAINT schematic.
  • Exchange-PAINT uses orthogonal strands linked to target molecules 1 (here: receptors) and sequential imaging and washing of complementary imagers for multiplexed super-resolution; see Jungmann et al., Nature Methods, 11:313–318 (2014).
  • a legend of the symbols indicating the different types of receptors is provided in Figs.11D and 11E.
  • the multiplexed Exchange-PAINT imaging was used to simultaneously map the MHC class I (H2-K b protein) and MHC class II molecules, the co-stimulatory receptors CD86 and CD80 and the inhibitory checkpoint receptors PD-L1 and PD-L2 on the surface of individual mouse conventional DCs (MutuDC cDC1 cell line) and melanoma cells (B16-F10 cells) at the single protein level.
  • sequential imaging with different imager strands labelled with the same fluorophore (Cy3b) was utilized to visualize respective immune checkpoint receptors simultaneously on target cells, overcoming the classical diffraction limit using DNA-PAINT as a single-molecule localization technique.
  • FIG.11C shows the 6-plex Exchange-PAINT image of a MutuDC stimulated for 6 h with CpG and IFN ⁇ .
  • the zoom-ins in the right column depict diffraction- limited vs. super-resolution representation (top vs. middle).
  • Subsequent spot analysis allows digitization of receptor molecules and reveals their molecular arrangement (bottom).
  • individual receptors could be clearly identified in the respective DNA-PAINT image.
  • Figure 11D shows morphological changes of MutuDC stimulated with CpG and IFN ⁇ over 24 h at molecular resolution.
  • Zoom-ins (bottom row) at multiple different time-points (0 h, 3 h, 6 h, 12 h, 24 h) reveal the spatial details of key receptor interactions.
  • Quantitative analysis of morphology and the underlying receptors enabled measurement of absolute cell surface area15 (Fig.11E, table 9) and absolute receptor density (Fig.11F, table 9) over time (24 h stimulation), with a peak overall receptor density observed at 6 hours after beginning of stimulation in MutuDC (for the mean over multiple experiments).
  • (G) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. A dominating exemplary motif is shown in the zoom-in.
  • Figure 12C shows a correlation matrix for all 36 possible receptor combinations, which allows classification into “clustered”, “random” or “dispersed” receptor distributions.
  • Fig.12D which shows 10 different interactions.
  • nearest neighbor distance analysis See Fig.12E: Representative whole-cell analysis of first nearest neighbor distances (short: NND) of MHC-I to CD80 receptors (black) and PD-L2 (white) are shown in histograms with a total area of 1. The fit of simulations to these data is shown as solid and dashed lines, respectively.
  • Fig.12F This allowed us to determine the percentage of molecular interactions for each receptor pair (Fig.12F), accounting for the underlying labeling efficiency of the target binders (fig.9).
  • the numbers represent the fraction of receptors in the group that interact with the receptors on the x-axis. For example, 49% of the MHC-I molecules (left-most cluster in Fig. 12F) are in an interaction cluster with CD80. Prior to stimulation most molecules were not clustered and only minor CD86/CD86 and CD80/CD80 interactions were observed (Fig.16). Starting at 3 h and becoming most prominent at 6h stimulation, there was a substantial remodelling of receptor interactions, particularly interactions involving CD80 (Fig.12F, Fig.16 - 20).
  • PD-L2 was either randomly distributed or mildly dispersed from other molecules, while MHC-II was strongly dispersed away from all other imaged molecules. This may imply that MHC-II and PD-L2 are genuinely dispersed away from protein-protein interactions, or that they are recruited into other complexes containing factors that are not being imaged. Nevertheless, small MHC- II clusters were observed at 12h after stimulation (Fig. 19), although they dispersed again at 24h (Fig.20). We next wished to determine if clusters containing 3 or more proteins could be identified. By applying DBSCAN to the receptor centers in the multiplexed DNA-PAINT data independent of the receptor identity, key receptor motifs could be identified (Fig.12G, Fig.18).
  • FIG.12G Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors.
  • a dominating exemplary motif is shown in the zoom-in of fig.12G.
  • Quantification of receptor motifs identified CD86/CD80/PD-L1 hetero-trimers and MHC-I/CD86/CD80/PD-L1 hetero-tetramers as dominating interaction motifs on dendritic cells stimulated for 6 hours (see Figure 12H, the motif represents 5.9% ⁇ 2.1% of all clusters; black filled bars represent the number of respective receptors expected to occur in the area of the motive due to a spatially random distribution based on their observed density, without motif interaction; white (empty) bars represent the observed receptors in the cluster; only contributors that have statistically significant values are included in the motif.
  • MutuDCs were stimulated for 6h with CpG and IFN ⁇ in the presence of the model protein antigen ovalbumin, and instead of imaging total MHC-I, we specifically measured MHC-I (H-2K b protein) bound to the SIINFEKL ovalbumin peptide antigen using a specific detection antibody. Similar receptor motifs were identified containing peptide antigen-bound MHC-I molecules, indicating that specific, cross-presented antigens acquired by DCs can be incorporated into these surface motifs (Fig. 21). Collectively, these data support a surface architecture on DCs that favours T cell activation, and identify a large number of previously unknown surface protein interactions of key immune molecules. 5.3.
  • FIG.13A shows morphological changes of stimulated B16-F10 cells (see table 9 for the number of cells analyzed) over 24 h. Zoom-ins (bottom row) at multiple different time-points (0 h, 3 h, 6 h, 12 h, 24 h) reveal key receptor interaction patterns.
  • Figure 13C shows the dynamics of overall receptor density and respective species contributions in B16-F10s (solid outline) vs. MutuDCs (dashed outline) over 24 h stimulation. Overall receptor density is almost identical for dendritic and cancer cells, significant differences can be observed for individual receptor densities. Consistent with this visible difference in surface architecture, the pairwise clustering patterns between proteins on melanoma cells (Fig. 13D: Correlation matrix of all 36 possible receptor combinations allows identification of key receptor interactions in B16-F10 cells.) was noticeably different from that of DCs (Fig.12C), with a strong enrichment of PD-L1 and MHC- I clustering and a paucity of CD80 interactions (likely due to its low surface abundance) (Fig.13D).
  • FIG.13E is a visualization of corresponding receptor interactions in B16-F10 cells (top) using a river plot, which reveals homo-interactions of MHC-I, MHC-II and PD-L1. Most significant however are MHC-I/PD-L1 hetero-interactions.
  • a comparison to MutuDCs (bottom) reveals downregulation of costimulatory receptors (mainly CD80) as driving force for formation of MHC-I/PD-L1 clusters.
  • FIG.13F,G shows the quantification of dominating receptor interactions in B16- F10 and a comparison to MutuDC (if possible) shows significant differences in MHC-I, MHC-II and PD-L1 interactions.
  • Figure 13G shows exemplary zoom-ins of key receptor interactions in B16-F10. Data are shown as the mean ⁇ 95% confidence interval. *** p ⁇ 0.001.
  • MHC-I and PD-L1 homodimers were seen on melanoma cells, and unlike on DCs where MHC-II was exclusively non-clustered, MHC-II showed pronounced self-association on melanoma cells.
  • Quantification of key receptor motifs similarly identified MHC-I/PD-L1 clusters as the dominant receptor motif on melanoma cells. Strikingly, even on non-stimulated B16-F10 cells, MHC-I/PD-L1 heteroclusters accounted for 26.4% of all detected clusters, and at 6h this increased to almost 60% of all identified receptor motifs detected on the cell surface (Fig.22-24).
  • MHC-II was recruited to MHC-I/PD-L1 clusters (Fig. 25-26).
  • B16-F10 line used for imaging transgenically expressed ovalbumin we could again determine if specific peptide antigen-MHC-I complexes similarly incorporate into these structures, and we found comparable PD-L1 clustering with antigen-bound MHC-I as for total MHC-I (Fig. 27).
  • Fig. 27 there are profound differences in surface organisation of immune checkpoint molecules on melanoma cells relative to DCs, with structures that favour PD-L1 recruitment into the synapse upon antigen recognition enriched on cancer cells.
  • FIG 14A top row, schematic sketches of our understanding of relevant receptor interactions (MHC-I - CD80 - PD- L1; with the receptor identified by signs according to the legend) as revealed by the analysis presented herein are illustrated for cDC1, cDC1 CD80 knock out, B16-F10 and CD80 overexpressing B16-F10 either expressing the physiological costimulatory receptor CD80 or the L107E mutant CD80, lacking its PD-L1 binding domain.
  • Crossed-out receptors indicate knock-out, upward-facing arrows annotated with ‘High’ indicate overexpression, downward- facing arrows annotated with ‘Low’ indicate a low expression level, an encircled minus indicates a noninteracting mutant.
  • Horizontal arrows indicate interaction where one-sided arrows indicate that the interaction is driven by one receptor (e.g. in the case of CD80 overexpression, the interaction between CD80 and PD-L1 is driven by CD80).
  • Fig.14A morphological differences are shown for all cells together with corresponding zoom-ins to illustrate key receptor interactions at molecular resolution at 6 h stimulation. Strikingly, CD80 deletion was sufficient to precipitate MHC-I/PD-L1 clustering on DCs at levels that were comparable to those observed on melanoma cells (Fig.14B, Fig.29, 30).
  • FIG 14B receptor interactions are visualized via a circle plot; Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis), with this pattern already detectable prior to DC stimulation (Fig.31-32). Similar patterns were seen in wild-type (Fig.16, 18, 21) and CD80 knock-out MutuDCs (Fig. 33). Conversely, retroviral over-expression of CD80 on melanoma cells (Fig. 34) completely disrupted MHC-I/PD-L1 clustering alongside reinstating PD-L1/CD80 interactions (Fig.14B).
  • CTLA4-Ig fusion reagents such as Abatacept
  • CD80-blocking immunosuppressive reagents that are used in the clinic to treat autoinflammatory conditions, such as rheumatoid arthritis, juvenile idiopathic arthritis, and psoriatic arthritis. While the predominant mode of action is presumed to be due to CD80 blockade, CTLA4-Ig fusion proteins are also known to disrupt the interaction between CD80 and PD-L1, suggesting that PD-L1 liberation may contribute to clinical efficacy.
  • CD80 strongly interacts with MHC-I and PD-L1 in untreated dendritic cells (as shown on the left side in a cartoon, as a representative cluster, and in the interaction network, where the interaction arrows between PD-L1 and CD80 as well as MHC-I and CD80 are strong).
  • PD-L1 is released from CD80 and clusters with MHC-I (as shown on the rights side in a cartoon, as a representative cluster, and in the interaction network, where the interaction of PD-L1 with CD80 is much lower than on the left, but a strong interaction of PD-L1 with MHC- I can be observed).
  • MHC-I/PD-L1 clustering While DCs showed relatively low levels of MHC-I/PD-L1 clustering (typical distance between MHC-I and PD-L1 of about 30 nm), B16-F10 cells displayed a significant increase in MHC/PD-L1 heterodimers (typical distance between MHC-I and PD-L1 of about 15 nm) which could limit T cell activation (left).
  • MHC-I and PD- L1 were arranged into clusters separated by either a small (close, 10-15 nm) or larger (far, 25- 30 nm) distance, as observed on B16-F10 and DCs, respectively (middle).
  • DNA origami structures presenting no (empty) or only stimulatory ligands (pMHCs) were included for comparison (right).), and interrogated the primary T cell response.
  • MHC-II molecular family member MHC-I, where clustering supports prolonged signal propagation in CD8+ T-cells.
  • the direct interaction map shows strong interactions between PD-L1 and MHC-I in the cancer cell, while this interaction is weak in the dendritic cell due to the presence of CD80.
  • PD-L1 and CD86 and CD80 interact strongly in the dendritic cell and hardly in the cancer cell, even though all molecules are present on the membrane of both cells.
  • cytotoxic bi- or multispecific compounds against PD-L1 and MHC-I together with MHC-II could be developed to specifically kill cancer cells by recruiting not only CD4+ Helper T-cells but also CD8+ Killer T-cells followed by further recruitment of multiple different immune cells.
  • Discussion The higher order clustering of immune-regulatory ligands on the cell surface likely plays a critically important role in guiding T cell immunity, but our understanding of this aspect of immune cell biology is very limited.
  • melanoma cells were enriched for higher order immunosuppressive MHC-I/PD-L1 aggregates, and other motifs that were not present at significant levels on DCs.
  • CD80 was the key determinant of this surface architecture switch, as it was both necessary and sufficient to prevent MHC-I/PD-L1 aggregation.
  • similar surface restructuring could be precipitated by a clinically approved immunosuppressive agent that disrupts the CD80/PD-L1 interaction (Abatacept), implicating surface remodelling as a contributing factor to clinical efficacy. Overall, this implicates changes in surface architecture as an important regulator of T cell immunity.
  • Nanoscale surface organisation has typically been inferred through biochemical approaches, like co-immunoprecipitation, indirect methods such as Fluorescence Energy Resonance Transfer (FRET), or through structural studies. While these existing methodologies are powerful tools for probing protein-protein association, they are unable to spatially resolve complex clustering at the single protein level on the cell surface, particularly those clusters involving 3 or more species.
  • Super-resolution imaging has the potential to address this knowledge gap, however prior methodologies were not sufficiently resolved or multiplexed. While current state-of-the -art super-resolution techniques have made major effort to break the molecular resolution barrier, the present invention overcame these limitations, and the value of higher-plex single protein imaging was illustrated.
  • PD-1 engagement inhibits T cells through recruitment of the phosphatase SHP2, which can inhibit signalling downstream of both CD28 and TCR engagement (see https://pubmed.ncbi.nlm.nih.gov/16227604/; https://pubmed.ncbi.nlm.nih.gov/22641383/; https://pubmed.ncbi.nlm.nih.gov/28280247/; https://pubmed.ncbi.nlm.nih.gov/28280249/).
  • CD80 as a central regulator of MHC-I/PD-L1 interactions that disrupts clustering by binding to PD-L1.
  • CD80-bound PD-L1 is sequestered away from other proteins, which in turn has significant downstream effects on surface organisation.
  • CD80 stripping may also contribute to regulatory T cell-mediated immunosuppression.
  • CD80 in addition to co-stimulation through CD28, and inhibition of PD-L1, CD80 also remodels the cell surface into a configuration that favours T cell activation. More broadly, our results highlight the importance of considering spatial organisation in drug design. We demonstrate that the cell surface is in a finely tuned equilibrium, where small perturbations can drastically remodel surface architecture. We find that blocking reagents, such as those used for immunotherapy in autoimmunity and cancer, can mimick these effects. In the case of Abatacept, the surface changes precipitated by target binding will likely augment the intended immunosuppressive activity of this clinical agent. 7.
  • Diagnostics development The results from the data analysis may be further used for drug development.
  • DNA oligonucleotides modified with DBCO-PEG4 and Cy3B were ordered from IDT and MWG Eurofins.
  • Sticky-slide 8 well chambers (80808) were purchased from Ibidi and glass slides (10756991) were purchased from Marienfeld. Double-sided tape (665D) was ordered from Scotch. FBS Good (P40-37500) was purchased from PAN Biotech. FBS Advanced (FBS-11A, heat inactivated) was ordered from Capricorn Scientific. His 10 -tag ICAM-1 (50440-M08H) was purchased from Biozol.
  • EndoFit TM ovalbumin (vac-pova) and Primocin® (ant-pm-05) were obtained from InvivoGen.
  • CpG1826 was ordered from Pfizer.
  • Recombinant murine IFN ⁇ (315-05) was purchased from PeproTech.
  • Ninety- nanometer gold nanoparticles (G-90-100) were ordered from Cytodiagnostics.
  • High-binding 96-well plates (3361) were obtained from Corning.
  • EasySep TM Mouse CD8+ T Cell Isolation Kit (19853) was purchased from STEMCELL Technologies. Buffer Recipes: • Buffer X: 1x PBS, 500mM NaCl. • Buffer Y: 1x PBS, 1mM EDTA, 0.01% Tween-20.
  • Buffer Z 1x PBS, 1 mM EDTA, 500 mM NaCl (pH 7.4), 0.01%Tween-20 supplemented with 1x Trolox.
  • Blocking buffer 1x PBS, 1mM EDTA, 0.02% Tween-20, 0.05% NaN3, 2% BSA, 0.05 mg/ml sheared salmon sperm DNA.
  • Trolox 100x Trolox was made by adding 100 mg Trolox to 430 ⁇ l of 100% methanol and 345 ⁇ l of 1 M NaOH in 3.2 ml water.
  • Hybridization buffer 4xSSC 10% Dextrane sulfate 10% ethylanecarbonate 0.04% Tween20 ⁇
  • Dehybridization buffer 2xSSC 10% Dextrane sulfate 20% ethylanecarbonate 0.04% Tween20 ⁇
  • Blocking Buffer 2 1x PBS, 3% BSA 0.25% Triton 0.05 mg/ml sheared salmon sperm DNA (DNA-PAINT) microscope setup: Fluorescence imaging was carried out on an inverted microscope (Nikon Instruments, Eclipse Ti2) with the Perfect Focus System, applying an objective-type TIRF configuration equipped with an oil-immersion objective (Nikon Instruments, Apo SR TIRF 100x, NA 1.49, Oil).
  • a 560- nm laser (MPB Communications, 1 W) was used for excitation.
  • the laser beam was passed through a cleanup filter (Chroma Technology, ZET561/10) and coupled into the microscope objective using a beam splitter (Chroma Technology, ZT561rdc). Fluorescence was spectrally filtered with an emission filter (Chroma Technology, ET600/50m and ET575lp) and imaged on an sCMOS camera (Andor, Zyla 4.2 Plus) without further magnification, resulting in an effective pixel size of 130 nm (after 2 x 2 binning). The readout rate was set to 540 MHz. Images were acquired by choosing a region of interest with a size of 512 x 512 pixels.
  • SiteClickTM antibody-DNA conjugation Prior functionalization unconjugated antibodies (MHC-I, MHC-I OVA, MHC-II, PD-L2) were concentrated to 1mg/ml in Tris, pH 7.0, by using Amicon centrifugal filters (50,000 MWCO). For each conjugation 200 ⁇ g of respective antibody were used. Azide-modified antibodies were produced following manufacturer’s protocol. Azido-modified antibodies were reacted with 10x molar excess of DBCO- functionalized DNA (R2 - MHC-II, R5 - MHC-I/MHC-I OVA and R6 - PD-L2) in Tris, pH 7.0, overnight at 25°C, 300rpm.
  • DBCO- functionalized DNA R2 - MHC-II, R5 - MHC-I/MHC-I OVA and R6 - PD-L2
  • Unreacted DNA was removed by buffer exchange to PBS using Amicon centrifugal filters (50,000 MWCO). Unconjugated antibody were removed by anion exchange chromatography using an ⁇ KTA pure system equipped with a Resource Q 1-ml column and antibody concentration was adjusted to 5 ⁇ M. DNA-conjugated antibodies were stored at 4°C. Enzymatic antibody-DNA conjugation:Prior functionalization unconjugated antibodies (CD80, CD86, PD-L1) were concentrated to 1mg/ml in TBS + 0.05% Tween20 by using Amicon centrifugal filters (50,000 MWCO). For each conjugation 100 ⁇ g of respective antibody were used.
  • PNGase 0.6 U
  • mTG 1.2 U
  • an 80-fold molar excess of bifunctional azide-PEG3- amine linker were added to the antibody and reacted for 16h at 37°C, 300rpm.
  • Enzymes and excessive linker were removed by buffer exchange to PBS using Amicon centrifugal filters (50,000 MWCO).
  • Azido-modified antibodies were reacted with 10x molar excess of DBCO- functionalized DNA (R1 – CD86, R3 – CD80 and R4 - PD-L1) overnight at 25°C, 300rpm.
  • Unreacted DNA and unconjugated antibody were removed by anion exchange chromatography using an ⁇ KTA pure system equipped with a Resource Q 1-ml column and antibody concentration was adjusted to 5 ⁇ M.
  • DNA-conjugated antibodies were stored at 4°C.
  • ALFA-tag nanobody-DNA conjugation ALFA-tag nanobodies were conjugated as described previously1. Unconjugated nanobodies were thawed on ice, then 20-fold molar excess of bifunctional DBCO-PEG4-Maleimide linker was added and reacted for 2 h on ice. Unreacted linker was removed by buffer exchange to PBS using Amicon centrifugal filters (10,000 MWCO).
  • the DBCO-modified nanobodies were reacted with 5xmolar excess of azide- functionalized DNA (R3, R4) overnight at 4°C. Unconjugated protein and free DNA were removed by anion exchange chromatography using an ⁇ KTA pure system equipped with a Resource Q 1-ml column. Preparation of functionalized planar SLBs.
  • Vesicles containing 98% 1-palmitoyl-2-oleoyl-sn- glycero-3-phosphocholine (POPC) and 2% 1,2-dioleoyl-sn-glycero-3-[N(5-amino-1- carboxypentyl)iminodiaceticacid]succinyl[nickel salt] (Ni-DOGS NTA) were prepared at a total lipid concentration of 0.5mg ml-1 as described ( J. B. Huppa, et al., TCR-peptide-MHC interactions in situ show accelerated kinetics and increased affinity.
  • PBS phosphate-buffered saline
  • MutuDC1940 KO MHC-I KO, MHC-II KO, CD86 KO, CD80 KO, PD-L1 KO, PD-L2 KO cells served as a reference.
  • Cells were stimulated using 500 nM CpG1826 + 100 U/ml IFN ⁇ ⁇ ovalbumin for 6 hours at 37°C.
  • the 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS.
  • Target binding molecules (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti- CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C. Unbound antibodies (binding molecules) were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min.
  • DNA origami preparation For folding DNA origami, 10 nM single-stranded DNA scaffold from M13p18 bacteriophages (SeqID No 3541, Tilibit GmbH cat.
  • the folded origami structures were then purified from excess staples using 100 kDa MWCO centrifugal filters.
  • Purified origami structures were stored in buffer C (PBS, 500 mM NaCl) at -20 °C until usage.
  • buffer C PBS, 500 mM NaCl
  • Sample preparation for binding molecule labeling efficiency imaging CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers (Cat.No: 80807) the day prior to transfection at a density of 15 ⁇ 10 4 cells/well.
  • CHO cells were transfected with a single receptor construct (mEGFP-ALFA-MHC-I, mEGFP-ALFA-MHC-II, mEGFP- ALFA-CD86, mEGFP-ALFA-CD80, mEGFP-ALFA-PD-L1, mEGFP-ALFA-PD-L2) at a time for binding molecule characterization using Lipofectamine LTX as specified by the manufacturer.
  • CHO cells were allowed to express mEGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100 U/ml Penicillin + 100 ⁇ g/ml Streptomycin followed by fixation.
  • Target binding molecules (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti-CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C followed by addition of ALFA-tag nanobody, dissolved in blocking buffer and added at a final concentration of 500 pM for 60 min at 24°C.. Unbound binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to the addition of gold fiducials, samples were washed with PBS.
  • DNA-conjugated antibodies (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti-CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C. Unbound antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to the addition of gold fiducials, samples were washed with PBS.
  • DNA origami discs (Fig. S32) were assembled as previously [https://pubmed.ncbi.nlm.nih.gov/34613711/].
  • the staples corresponding to the functionalization points were extended at their 3′ end with a 21-nucleotide overhang of docking strands (SeqID No 3527-3532).
  • Cy3B-conjugated imager strands were dissolved in Buffer Z and 600 ⁇ l of the imager solution was added to the sample to perform DNA-PAINT measurements. Imaging parameters are listed in detail in table 6. 2-plex Exchange-PAINT imaging for binding molecule labeling efficiency determination. Prior image acquisition, all fluorophores (e.g. CHO-mEGFP-ALFA-MHC-I) were deactivated by a high intensity bleach pulse. Cellular imaging was conducted via two subsequent imaging rounds using distinct imagers for each binding molecule (table 12) with only one of the imagers present at a time. Cy3B-conjugated imager strands were dissolved in Buffer Z and 600 ⁇ l of the imager solution was added to the sample to perform DNA-PAINT measurements.
  • fluorophores e.g. CHO-mEGFP-ALFA-MHC-I
  • Imaging parameters are listed in detail in table 7. Multiplexed cellular imaging of immune receptors. Prior image acquisition, all fluorophores (B16-F10 CD80- mCherry, B16-F10 CD80 (L107E)-mCherry) were deactivated by a high intensity bleach pulse.
  • Multiplexed cellular imaging was conducted via six subsequent imaging rounds using the six imagers R1-R6 (as published in https://pubmed.ncbi.nlm.nih.gov/32601424/ ) with only one of the imagers present at a time. Cy3B- conjugated imager strands were dissolved in Buffer Z and 600 ⁇ l of the imager solution was added to the sample to perform DNA-PAINT measurements. In between imaging rounds, the sample was washed with 2 ml PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5 min. Then, the next imager solution was introduced. Imaging parameters for DNA-PAINT cell experiments are listed in detail in table 8. Microscope setup.
  • Fluorescence imaging was carried out on an inverted microscope (Nikon Instruments, Eclipse Ti2) with the Perfect Focus System, applying an objective-type TIRF configuration equipped with an oil- immersion objective (Nikon Instruments, Apo SR TIRF ⁇ 100, NA 1.49, Oil).
  • a 560-nm laser MPB Communications, 1 W was used for excitation.
  • the laser beam was passed through a cleanup filter (Chroma Technology, ZET561/10) and coupled into the microscope objective using a beam splitter (Chroma Technology, ZT561rdc).
  • the readout rate was set to 540 MHz. Images were acquired by choosing a region of interest with a size of 512 ⁇ 512 pixels. Detailed imaging conditions for the respective experiments are shown in table 6-8. Image analysis. Raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso).
  • Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Gold particles were also used to align all rounds for multiplexed Exchange-PAINT experiments.
  • DNA- PAINT data were analyzed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Subsequently, the centers of the localization groups were calculated as weighted mean by employing the squared inverse localization precisions as weights. Merging localizations of all rounds yields the final multiplexed DNA-PAINT image. Data analysis – Binding molecule specificity.
  • Binding molecule specificity was evaluated by counting the number of binding molecule signals (circular clusters of localizations centered around local maxima) in stimulated wild-type MutuDC1940 and MutuDC1940 KO samples within the cell area, determining underlying binding molecule density and further corresponding ratios between MutuDC1940 and MutuDC1940 KO samples. Data analysis - Labeling efficiency.
  • NND nearest neighbor distance
  • the algorithm of the simulation can be summarized as follows: 1. Parameters. Density of target monomers: number of target monomers per unit area. Density of reference monomers: number of reference monomers per unit area. Density of target-reference dimers: number of dimers per unit area. Dimer distance: expected distance between reference and target molecule including the labeling construct. Uncertainty: variability in the position of each molecule due to labeling and localization errors.
  • the total density for target and reference is set to match the respective experimentally observed densities in each channel.
  • Simulation of monomers a set of spatial coordinates with CSR distribution and given density are drawn.
  • Simulation of dimers a set of spatial coordinates with CSR distribution are drawn, representing the center of each dimer. For each dimer center two positions are generated with a random orientation and a distance with expected value Dimer distance. The position of each pair of molecules are drawn taking into account the Uncertainty parameter (drawn from a gaussian distribution).
  • NND are calculated on the subset of detectable molecules.
  • CHO cells CCL-61, ATCC were cultured in GibcoTM Ham's F-12K (Kaighn's) Medium, supplemented with 10% Fetal Bovine Serum (FBS) (11573397, Gibco), 100 U/ml Penicillin, 100 ⁇ g/ml Streptomycin.
  • FBS Fetal Bovine Serum
  • Murine dendritic cell line (MutuDC 1940) was cultured in IMDM supplemented with 10% FBS, 100 ⁇ M ⁇ - Mercaptoethanol (Gibco), 100U/ml Penicilin, 100 ⁇ g/ml Streptomycin and 1.32 mM Glutamax (Gibco).
  • Murine melanoma B16-F10melanoma cell line was cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% FBS, 100 U/ml Penicillin and 100 ⁇ g/ml Streptomycin. Cells were maintained and passaged using Accutase solution. Cloning.
  • DMEM Modified Eagle Medium
  • Receptor c-terminally tagged with ALFA-tag and mEGFP (MHC-I-ALFA-mEGFP, MHC-II-ALFA- mEGFP, CD86-ALFA-mEGFP, CD80-ALFA-mEGFP, PD-L1-ALFA-mEGFP, PD-L2-ALFA-mEGFP) were individually cloned into pcDNATM3.1 (+) Mammalian Expression Vector (Invitrogen Cat. No. V79020).
  • Retroviral Transduction of B16-F10 The retroviral transduction involved the production of murine stem cell virus (MSCV) followed by the transduction of the B16-F10 cell line.
  • MSCV murine stem cell virus
  • MSCV vectors were produced in HEK293GP cells by co-transfection of packaging plasmids containing mCD80 L107E (IRES-mCherry), mCD80 (IRES- mCherry) and empty vector with the VSV-G envelope glycoprotein, using Lipofectamine 2000 (Invitrogen) for 6 hours. Following five days of culture in DMEM supplemented with 10% FBS, 100 U/ml Penicillin, 100 ⁇ g/ml Streptomycin and 1.32 mM Glutamax (Gibco), virus-containing supernatant was collected, filtered and frozen at -20°C.5 ⁇ 10 4 B16-F10 cells were seeded in each well of 6-well plate.
  • the cells were transduced with 2ml of viral supernatant and 8 ug/ml polybrene (Sigma Aldrich) followed by 1-hour centrifugation at 500G. This was done twice a day for three days for a total of five viral hits.
  • CD80-mCherry expression was validated by FACS and mCherry positive B16-F10 cells were sorted using FACSAria Fusion Flow Cytometer (BD Biosciences). Flow cytometry data was analyzed using FlowJo (v10.8.1, Treestar). CRISPR/Cas9 gene editing.
  • the CRISPR editing protocol for the MutuDC1940, B16-F10 and primary bone marrow dendritic cells was adapted from https://pubmed.ncbi.nlm.nih.gov/32152070/.
  • sgRNAs targeting the murine MHC-I, MHC-II, CD86, CD80, PD-L1, PD-L2 and non-targeting Ctrl genes were obtained from Integrated DNA Technology (sequences of each target sgRNA is described in table 11).
  • 5 ⁇ 10 6 cells were electroporated for the MutuDC1940 cell lines (MHC-I, MHC-II, CD86, CD80, PD-L1, PD-L2 and non-targeting Ctrl), for the B16-F10 cell lines (CD80 and non-targeting Ctrl) and for the primary bone marrow cells (CD80 and non-targeting Ctrl), and 100 ⁇ 10 6 cells were electroporated for the primary bone marrow cells.
  • sgRNA/Cas9 RNP complex formation Cas9 protein and sgRNA were combined and incubated at room temperature for 10 minutes.
  • Bone marrow cells were collected from the femurs and tibia of C57BL/6 mice. Red blood cells were lysed using RBC lysis buffer (Ammonium Chloride) for 2 minutes and washed in media. Cells were filtered and spun down at 200G for 7 minutes. Cells were counted and divided for KO experiments using sgRNAs targeting the murine CD80 and nontargeting Ctrl sgRNA (Integrated DNA Technologies).
  • primary bone marrow cells were cultured at 1.5 ⁇ 10 6 cells/ml for 8 days in RPMI 1640 media (ThermoFisher Scientific) supplemented with 1.32 mM Glutamax, 10% FBS, 90 ⁇ M ⁇ -mercaptoethanol, 100 U/ml Penicillin, 100 ⁇ g/ml Streptomycin and 150 ng/ml Flt3L (BioXCell). Following 8 days of culture, cells were stimulated using 500 nM CpG1826 (Pfizer) + 100 U/ml IFN (Peprotech) for 6 hours in suspension.
  • both unstimulated and stimulated cells were incubated in Fc Block (BD Biosciences) for 10 minutes on ice, stained for 30 minutes using B220 (PE, Clone R836B2, BD #553090), SIRP ⁇ (BV510, Clone P84, BD #740159), CD11c (BV785, Clone N418, BioLegend #117336), CD24 (BUV395, Clone M1/69, BD #744471) antibodies.
  • B220- CD11c high CD24 high SIRP ⁇ low cDC1s were then sorted using FACSAria Fusion Flow Cytometer (BD Biosciences).
  • CD80 deletion was validated on a separate sample using CD80 (APC, Clone 16-10A1, BioLegend #104714) antibody. Following the sort, the cells were washed in media and resuspended in Hank’s Balanced Salt Solution (HBSS) ((H8264-500ML, Sigma Aldrich).) supplemented with 2% FBS, 2 mM CaCl2 and 2 mM MgCl2 and seeded onto the SLB. I m r S Imager name Imager Table 12. Imager sequences used for binder characterization. It is emphasized that in the examples for a method of mapping the localization of different target molecules within a sample DNA-PAINT was used as the imaging method.
  • HBSS Hank’s Balanced Salt Solution
  • DNA-PAINT shows advantageous characteristics for the analyzing of direct interaction patterns, due the combination of molecular resolution and high degree of multiplexing. Nonetheless, other super-resolution fluorescence imaging techniques, such as dSTORM, may be used as well. It is also emphasized that not only the conventional DNA-PAINT techniques may be used but also the inventive concepts presented in this disclosure. With respect to molecular resolution it is stressed that the analysis in the methods of mapping the localization of different target molecules within a sample according to the present invention analysis is preferably based on data with such a high resolution that proteins that touch each other can be spatially separated, i.e. the resolution is about 5 nm. This means that imaging data can be interpreted as and transformed into primary binder localizations or target molecule positions. Nonetheless, cluster analysis of lower-resolution imaging data and/or analysis of imager localizations may also be used and yield usable results.

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Abstract

The present invention relates to a single-stranded nucleic acid molecule, comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence, and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.

Description

New PCT-Patent Application Max-Planck-Gesellschaft; Ludwig-Maximilians-Universität München; Vossius Ref.: AG2105 PCT S5 DNA-PAINT related materials and methods The present invention relates to the field of detection and quantification of targets, for example via PAINT, and single cell analysis, particularly spatial intermolecular single-cell omics. In this specification, a number of documents including patent applications and manufacturer’s manuals are cited. The disclosure of these documents, while not considered relevant for the patentability of this invention, is herewith incorporated by reference in its entirety. More specifically, all referenced documents are incorporated by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference. In what is generally known as “spatial single-cell omics”, there are many technologies, represented by multiple companies (e.g. Akoya, 10x Genomics), which aim at identifying proteins and nucleic acids, by direct hybridization of fluorophore-labeled oligonucleotides, or by enzymatic processes like rolling-circle amplification. However, these known technologies are concerned with cell-to-tissue context and use the single cell as the base unit. In the present invention “spatial intermolecular single-cell omics” refers to the spatial context of targets within a single cell, i.e. the intermolecular spatial relationships within a single cell. The resolution of the known technologies mentioned above cannot be increased in a straightforward manner to reach intermolecular spatial information, due to basic effects like prohibitive probe size, diffusion limits, etc. The present invention provides an approach to spatial intermolecular single-cell omics based on fluorescence imaging. The fluorescence imaging technique and related molecules, compositions and kits comprise independent inventive aspects that may be used for other imaging applications than spatial intermolecular single-cell omics. Major progress has been made in the field of fluorescence imaging over the years. Super- resolution techniques, such as STED, STORM, PALM and PAINT were developed to overcome the diffraction limit of light microscopy, generally known to be approximated by Ernst Abbe’s formula. For PAINT, which stands for “points accumulation for imaging in nanoscale topography”, several sub-forms have been developed, some of which are of interest in the context of this invention. DNA-PAINT (with the sub-forms Exchange-PAINT, SPEED-PAINT and qPAINT) is a super-resolution technique that breaks the optical diffraction limit by temporally separating fluorescence signals from targets that are locally unresolvable in non-super-resolution fluorescence microscopy, e.g. confocal microscopy. The signals are localized timely separated, and the image is reconstructed from the localized data. This concept is also known as single-molecule localization microscopy, in short SMLM. In DNA-PAINT, the timely separation and thus the spatial separation is achieved via transient binding of fluorescently labeled oligonucleotides (called imagers) to their target-bound complements. The technique has recently been refined in several ways. Relevant for the topic at hand is, e.g., qPAINT, which achieves target quantification by analyzing the binding kinetics of imagers even of targets unresolvable in DNA-PAINT. Exchange-PAINT enables sequential multiplexing by performing multiple rounds of imaging using different imagers that bind different target oligonucleotides. The amount of plex in the initial Exchange-PAINT implementation scales linearly in time. SPEED-PAINT describes optimized sequences and buffer conditions for up to 100x speed increase compared to non-optimized sequences, meaning the acquisition time is drastically decreased. The drawback of SPEED-PAINT is its limitation to only a few (currently 6) suitable imager sequences and thus targets. Detailed descriptions of these techniques may be found in the following publications, which are incorporated herein by reference in their entirety: ● DNA-PAINT, doi:10.1021/nl103427w ● Exchange-PAINT, doi:10.1038/nmeth.2835 ● qPAINT, doi:10.1038/nmeth.3804 ● SPEED-PAINT, doi:10.1038/s41592-020-0869-x An object of the present invention is to provide increased plexing capabilities in PAINT. Another object of the present invention is PAINT-related speed optimization. Another object of the present invention is to provide techniques and methods for spatial intermolecular single-cell omics. The present invention relates in a first aspect to a single-stranded nucleic acid molecule, comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence, and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. The first nucleic acid sequence may be capable of stronger associating with its complementary nucleic acid sequence (e.g. via specific hybridization) than the second nucleic acid sequence under the same conditions (e.g. via transient binding only). In this connection the term “associating” refers to binding strength between the first or second nucleic acid sequence, respectively, and its complementary nucleic acid sequence. Hence, the first nucleic acid sequence may be capable of stronger binding to its complementary nucleic acid sequence than the second nucleic acid sequence under the same conditions. To achieve this appropriate hybridization conditions and nucleic acid sequence compositions can be selected by routine means. The term “single-stranded nucleic acid molecule” in accordance with the present invention may refer to single stranded DNA or RNA. In this regard, "DNA" (deoxyribonucleic acid) means any chain or sequence of the chemical building blocks adenine (A), guanine (G), cytosine (C) and thymine (T), called nucleotide bases, that are linked together on a deoxyribose sugar backbone. DNA can have one strand of nucleotide bases, or two complimentary strands which may form a double helix structure. "RNA" (ribonucleic acid) means any chain or sequence of the chemical building blocks adenine (A), guanine (G), cytosine (C) and uracil (U), called nucleotide bases, that are linked together on a ribose sugar backbone. The nucleic acid molecule may also be modified by many means known in the art. Non-limiting examples of such modifications include methylation, "caps", substitution of one or more of the naturally occurring nucleotides with an analog, and internucleotide modifications such as, for example, those with uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphoroamidates, carbamates, etc.) and with charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.). Nucleic acid molecules, in the following also referred as polynucleotides and/or oligonucleotides, may contain one or more additional covalently linked moieties, such as, for example, proteins (e.g., nucleases, toxins, antibodies, signal peptides, poly-L-lysine, etc.), intercalators (e.g., acridine, psoralen, etc.), chelators (e.g., metals, radioactive metals, iron, oxidative metals, etc.), and alkylators. The polynucleotides may be derivatized by formation of a methyl or ethyl phosphotriester or an alkyl phosphoramidate linkage. Further included are nucleic acid mimicking molecules known in the art such as synthetic or semi-synthetic derivatives of DNA or RNA and mixed polymers. Such nucleic acid mimicking molecules or nucleic acid derivatives according to the invention include phosphorothioate nucleic acid, phosphoramidate nucleic acid, 2’-O- methoxyethyl ribonucleic acid, morpholino nucleic acid, hexitol nucleic acid (HNA), peptide nucleic acid (PNA) and locked nucleic acid (LNA) (see Braasch and Corey, Chem Biol 2001, 8: 1). LNA is an RNA derivative in which the ribose ring is constrained by a methylene linkage between the 2’-oxygen and the 4’-carbon. Also included are nucleic acids containing modified bases, for example thio-uracil, thio-guanine and fluoro-uracil. The target complementary nucleic acid sequence may be a portion of a target molecule to be detected, e.g., in a single cell. In this case, the single-stranded nucleic acid molecule may directly hybridize to the target molecule. Alternatively, the target complementary nucleic acid sequence may be a portion of a primary binder. The primary binder is a molecule that specifically binds the target molecule. In this case, the single-stranded nucleic acid molecule may indirectly bind to the target molecule via the primary binder. Hybridization as used herein is the process in which two complementary single-stranded nucleic acid molecules bind together to form a double-stranded molecule. The bonding is dependent on the appropriate base-pairing across the two single-stranded molecules. Whether or not two complementary single-stranded nucleic acid molecules bind together to form a double-stranded molecule also depends on the hybridization condition, that is the sum of environmental factors influencing hybridization. Examples of environmental factors that may affect hybridization include temperature, the concentration of one or more salts and pH. This is also referred to in the prior art as the “stringency” of hybridization. The stringency is determined by the hybridization temperature and the salt concentration in the hybridization buffer, whereby high temperature and low salt is more stringent as only perfectly matched hybrids will be stable. A pH that is too alkaline may cause the strands to separate; too acidic and they may be forced together. Also the length and the GC content of nucleic acid molecules influence hybridization. Generally, binding occurs under more stringent conditions for long nucleic acid molecules and nucleic acid molecules with a high GC content and binding occurs under less stringent conditions for short nucleic acid molecules and/or nucleic acid molecules with a high AT content. The determination of ideal hybridization conditions for two given single-stranded nucleic acid molecules to bind together to form a double-stranded molecule is a matter of routine in the field of molecular biology. The ideal hybridization conditions are estimated from the calculation of the melting temperature (Tm) of the double-stranded molecule. At the Tm, half of the sequence is double stranded and half of the sequence is single stranded. For example, the Tm for short probes (14 – 20 base pairs) may be calculated as follows Tm = 4°C x number of G/C pairs + 2°C x number of A/T pairs The hybridization temperature (annealing temp) of oligonucleotide probes is generally approximately 5°C below the melting temperature. The requirement that the first nucleic acid sequence is capable of specifically hybridizing to a target complementary nucleic acid sequence means that under the selected or envisioned hybridization conditions the first nucleic acid sequence only hybridizes to the target complementary nucleic acid sequence and to no other nucleic acid molecule that might be present, for example, in a sample. The requirement that the second nucleic acid sequence is capable of transiently binding to a complementary nucleic acid sequence means that under the selected or envisioned hybridization conditions the second nucleic acid sequence binds together with the complementary nucleic acid sequence to form a double-stranded molecule transiently, whereby transiently means only momentarily or briefly. For the field of DNA-PAINT and also preferably in accordance with the invention, a binding interaction with a mean duration of shorter than 30 seconds is deemed transient, while a duration of longer than 1 hour is deemed stable. The intermediate duration between 30 seconds and 1 hours may be designated “semi-stable”. DNA hybridization and de-hybridization is a first order chemical reaction and therefore the binding duration follows an exponential distribution. The values for the binding duration given above are the mean values of the distribution. It follows for the above that in the context of the first aspect of the invention the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence (i.e. via specific hybridization) than the second nucleic acid sequence (i.e. via transient binding only). In accordance with the above item (b) the complementary nucleic acid sequence is labeled by an imaging molecule. In this connection “labeled” means that the imaging molecule is connected or linked to the complementary nucleic acid sequence, preferably via covalent chemical bonds. In accordance with a preferred embodiment of the first aspect the first nucleic acid sequence is capable of stably hybridizing to its target complementary nucleic sequence. “Stably hybridizing” in the context of the present invention has to be held distinct from the above described “transient binding”. “Stably hybridizing” means that under the selected or envisioned hybridization conditions the first nucleic acid sequence hybridizes to the target complementary nucleic acid sequence thereby forming a double-stranded molecule that remains stably in the double-stranded form and does not dissociate again into single-strands. The present invention relates in a second aspect to a hybridization complex, wherein the nucleic acid molecule is specifically hybridized to a target complementary nucleic acid sequence. As discussed above, in the hybridization complex, the first nucleic acid sequence of the single stranded nucleic acid molecule hybridizes to a target complementary nucleic acid sequence thereby forming a double-stranded region within the nucleic acid molecule. The target nucleic acid sequence is complementary to the first nucleic acid sequence. This means that it comprises or consists of a nucleic acid sequence that is complementary to the first nucleic acid sequence. This complementary nucleic acid sequence may have a length of 6nt to 150nt, preferably 10nt to 50 nt, more preferably 12 nt to 30 nt. The target nucleic acid sequence is preferably an exogenous nucleic acid sequence, for example, an in vitro synthesized or produced nucleic acid sequence. However, the target nucleic acid sequence may also be or be a part of a naturally occurring nucleic acid sequence, such as mRNA. In accordance with a preferred embodiment of the second aspect of the invention the target complementary nucleic acid sequence is conjugated to a binding molecule, wherein the binding molecule is preferably a nucleic acid sequence, a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer. A binding molecule is a compound being capable of binding to a target molecule. The binding molecule preferably specifically binds to the target molecule. Specific binding designates that the binding molecule does not or essentially does not bind to other molecules, e.g. in the context of a sample, than the target molecule. The target molecule of the binding molecule may be any kind of molecule that is potentially present in a sample, e.g. a macromolecule, a protein or peptide, a nucleic acid, a polysaccharide, lipid, or artificially added objects like organic or inorganic nanoparticles. The sample can be, for example, a cellular sample or a body sample, such as a body liquid or tissue sample. The cellular sample may be from cultured cells or may have been obtained from a subject, e.g. via a biopsy. The body liquid is preferably a blood sample (whole blood, serum or plasma). As explained above, in the case where the binding molecule is a nucleic acid sequence it may bind to a target nucleic acid via hybridization. The binding of the binding molecule to the target molecule may be directly or indirectly, wherein indirectly means that there may be one or more further binding partners between the binding molecule and the target molecule. Hence, for example, in case the binding molecule is an antibody the antibody might be a biotin labelled antibody that binds to a streptavidin labelled antibody and the streptavidin labelled antibody binds to the target molecule. The "small molecule" as used herein is preferably an organic molecule. Organic molecules relate or belong to the class of chemical compounds having a carbon basis, the carbon atoms linked together by carbon-carbon bonds. The original definition of the term organic related to the source of chemical compounds, with organic compounds being those carbon-containing compounds obtained from plant or animal or microbial sources, whereas inorganic compounds were obtained from mineral sources. Organic compounds can be natural or synthetic. The organic molecule is preferably an aromatic molecule and more preferably a heteroaromatic molecule. In organic chemistry, the term aromaticity is used to describe a cyclic (ring-shaped), planar (flat) molecule with a ring of resonance bonds that exhibits more stability than other geometric or connective arrangements with the same set of atoms. Aromatic molecules are very stable, and do not break apart easily to react with other substances. In a heteroaromatic molecule at least one of the atoms in the aromatic ring is an atom other than carbon, e.g. N, S, or O. For all above-described organic molecules the molecular weight is preferably in the range of 200 Da to 1500 Da and more preferably in the range of 300 Da to 1000 Da. Alternatively, the "small molecule" in accordance with the present invention may be an inorganic compound. Inorganic compounds are derived from mineral sources and include all compounds without carbon atoms (except carbon dioxide, carbon monoxide and carbonates). Preferably, the small molecule has a molecular weight of less than about 2000 Da, or less than about 1000 Da such as less than about 500 Da, and even more preferably less than about 200 Da, or amu. The size of a small molecule can be determined by methods well-known in the art, e.g., mass spectrometry. The small molecules may be designed, for example, based on the crystal structure of the target molecule, where sites presumably responsible for the biological activity can be identified and verified in in vivo assays such as in vivo high-throughput screening (HTS) assays. The term “protein” as used herein is interchangeably with the term “polypeptide” and describes linear molecular chains of amino acids, including single chain proteins or their fragments. The protein may also be a peptide. The term “peptide” as used herein describes a group of molecules consisting of up to 49 amino acids, whereas the term “polypeptide” (also referred to as "protein") as used herein preferably describes a group of molecules consisting of at least 50 amino acids. The group of peptides and polypeptides are referred to together by using the term "(poly)peptide". (Poly)peptides may further form oligomers consisting of at least two identical or different molecules. The corresponding higher order structures of such multimers are, correspondingly, termed homo- or heterodimers, homo- or heterotrimers etc.. The term “antibody” as used in accordance with the present invention comprises, for example, polyclonal or monoclonal antibodies. Furthermore, also derivatives or fragments thereof, which still retain the binding specificity to the target are comprised in the term "antibody". Antibody fragments or derivatives comprise, inter alia, Fab or Fab’ fragments, Fd, F(ab')2, Fv or scFv fragments, single domain VH or V-like domains, such as VhH or V-NAR-domains, as well as multimeric formats such as minibodies, nanobodies, diabodies, tribodies or triplebodies, tetrabodies or chemically conjugated Fab’-multimers (see, for example, Harlow and Lane "Antibodies, A Laboratory Manual", Cold Spring Harbor Laboratory Press, 198; Harlow and Lane “Using Antibodies: A Laboratory Manual” Cold Spring Harbor Laboratory Press, 1999; Altshuler EP, Serebryanaya DV, Katrukha AG.2010, Biochemistry (Mosc)., vol. 75(13), 1584; Holliger P, Hudson PJ.2005, Nat Biotechnol., vol.23(9), 1126). The multimeric formats in particular comprise bispecific antibodies that can simultaneously bind to two different types of antigen. The first antigen can be found on a protein of interest. The second antigen may, for example, be a tumor marker that is specifically expressed on cancer cells or a certain type of cancer cells. Non-limiting examples of bispecific antibodies formats are Biclonics (bispecific, full length human IgG antibodies), DART (Dual-affinity Re- targeting Antibody) and BiTE (consisting of two single-chain variable fragments (scFvs) of different antibodies) molecules (Kontermann and Brinkmann (2015), Drug Discovery Today, 20(7):838-847). The term "antibody" also includes embodiments such as chimeric (human constant domain, non-human variable domain), single chain and humanised (human antibody with the exception of non-human CDRs) antibodies. Various techniques for the production of antibodies are well known in the art and described, e.g. in Harlow and Lane (1988) and (1999) and Altshuler et al., 2010, loc. cit. Thus, polyclonal antibodies can be obtained from the blood of an animal following immunisation with an antigen in mixture with additives and adjuvants and monoclonal antibodies can be produced by any technique which provides antibodies produced by continuous cell line cultures. Examples for such techniques are described, e.g. in Harlow E and Lane D, Cold Spring Harbor Laboratory Press, 1988; Harlow E and Lane D, Using Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, 1999 and include the hybridoma technique originally described by Köhler and Milstein, 1975, the trioma technique, the human B-cell hybridoma technique (see e.g. Kozbor D, 1983, Immunology Today, vol.4, 7; Li J, et al.2006, PNAS, vol.103(10), 3557) and the EBV-hybridoma technique to produce human monoclonal antibodies (Cole et al., 1985, Alan R. Liss, Inc, 77-96). Furthermore, recombinant antibodies may be obtained from monoclonal antibodies or can be prepared de novo using various display methods such as phage, ribosomal, mRNA, or cell display. A suitable system for the expression of the recombinant (humanised) antibodies may be selected from, for example, bacteria, yeast, insects, mammalian cell lines or transgenic animals or plants (see, e.g., US patent 6,080,560; Holliger P, Hudson PJ.2005, Nat Biotechnol., vol.23(9), 11265). Further, techniques described for the production of single chain antibodies (see, inter alia, US Patent 4,946,778) can be adapted to produce single chain antibodies specific for an epitope of a target. Surface plasmon resonance as employed in the BIAcore system can be used to increase the efficiency of phage antibodies. As used herein, the term “antibody mimetics” refers to compounds which, like antibodies, can specifically bind antigens, but which are not structurally related to antibodies. Antibody mimetics are usually artificial peptides or proteins with a molar mass of about 3 to 20 kDa. For example, an antibody mimetic may be selected from the group consisting of affibodies, adnectins, anticalins, DARPins, avimers, nanofitins, affilins, Kunitz domain peptides, Fynomers®, trispecific binding molecules and prododies. These polypeptides are well known in the art and are described in further detail herein below. The term “affibody”, as used herein, refers to a family of antibody mimetics which is derived from the Z-domain of staphylococcal protein A. Structurally, affibody molecules are based on a three-helix bundle domain which can also be incorporated into fusion proteins. In itself, an affibody has a molecular mass of around 6kDa and is stable at high temperatures and under acidic or alkaline conditions. Target specificity is obtained by randomisation of 13 amino acids located in two alpha-helices involved in the binding activity of the parent protein domain (Feldwisch J, Tolmachev V.; (2012) Methods Mol Biol.899:103-26). The term "adnectin" (also referred to as “monobody”), as used herein, relates to a molecule based on the 10th extracellular domain of human fibronectin III (10Fn3), which adopts an Ig- like β-sandwich fold of 94 residues with 2 to 3 exposed loops, but lacks the central disulphide bridge (Gebauer and Skerra (2009) Curr Opinion in Chemical Biology 13:245-255). Adnectins with the desired target specificity can be genetically engineered by introducing modifications in specific loops of the protein. The term "anticalin", as used herein, refers to an engineered protein derived from a lipocalin (Beste G, Schmidt FS, Stibora T, Skerra A. (1999) Proc Natl Acad Sci U S A.96(5):1898- 903; Gebauer and Skerra (2009) Curr Opinion in Chemical Biology 13:245-255). Anticalins possess an eight-stranded β-barrel which forms a highly conserved core unit among the lipocalins and naturally forms binding sites for ligands by means of four structurally variable loops at the open end. Anticalins, although not homologous to the IgG superfamily, show features that so far have been considered typical for the binding sites of antibodies: (i) high structural plasticity as a consequence of sequence variation and (ii) elevated conformational flexibility, allowing induced fit to targets with differing shape. As used herein, the term "DARPin" refers to a designed ankyrin repeat domain (166 residues), which provides a rigid interface arising from typically three repeated β-turns. DARPins usually carry three repeats corresponding to an artificial consensus sequence, wherein six positions per repeat are randomised. Consequently, DARPins lack structural flexibility (Gebauer and Skerra, 2009). The term “avimer”, as used herein, refers to a class of antibody mimetics which consist of two or more peptide sequences of 30 to 35 amino acids each, which are derived from A-domains of various membrane receptors and which are connected by linker peptides. Binding of target molecules occurs via the A-domain and domains with the desired binding specificity can be selected, for example, by phage display techniques. The binding specificity of the different A- domains contained in an avimer may, but does not have to be identical (Weidle UH, et al., (2013), Cancer Genomics Proteomics; 10(4):155-68). A “nanofitin” (also known as affitin) is an antibody mimetic protein that is derived from the DNA binding protein Sac7d of Sulfolobus acidocaldarius. Nanofitins usually have a molecular weight of around 7kDa and are designed to specifically bind a target molecule, by randomising the amino acids on the binding surface (Mouratou B, Béhar G, Paillard-Laurance L, Colinet S, Pecorari F., (2012) Methods Mol Biol.; 805:315-31). The term “affilin”, as used herein, refers to antibody mimetics that are developed by using either gamma-B crystalline or ubiquitin as a scaffold and modifying amino-acids on the surface of these proteins by random mutagenesis. Selection of affilins with the desired target specificity is effected, for example, by phage display or ribosome display techniques. Depending on the scaffold, affilins have a molecular weight of approximately 10 or 20kDa. As used herein, the term affilin also refers to di- or multimerised forms of affilins (Weidle UH, et al., (2013), Cancer Genomics Proteomics; 10(4):155-68). A “Kunitz domain peptide” is derived from the Kunitz domain of a Kunitz-type protease inhibitor such as bovine pancreatic trypsin inhibitor (BPTI), amyloid precursor protein (APP) or tissue factor pathway inhibitor (TFPI). Kunitz domains have a molecular weight of approximately 6kDA and domains with the required target specificity can be selected by display techniques such as phage display (Weidle et al., (2013), Cancer Genomics Proteomics; 10(4):155-68). As used herein, the term "Fynomer®" refers to a non-immunoglobulin-derived binding polypeptide derived from the human Fyn SH3 domain. Fyn SH3-derived polypeptides are well-known in the art and have been described e.g. in Grabulovski et al. (2007) JBC, 282, p. 3196-3204, WO 2008/022759, Bertschinger et al (2007) Protein Eng Des Sel 20(2):57-68, Gebauer and Skerra (2009) Curr Opinion in Chemical Biology 13:245-255, or Schlatter et al. (2012), MAbs 4:4, 1-12). The target complementary nucleic acid sequence may be conjugated to the binding molecule in any suitable manner. The conjugation between the target complementary nucleic acid sequence and the binding molecule can be covalent or non-covalent. In accordance with a further preferred embodiment of the second aspect of the invention, the target complementary nucleic acid sequence is conjugated to the binding molecule via a linker, wherein the linker preferably comprises biotin and one of avidin or streptavidin. In accordance with an alternative embodiment of the second aspect of the invention, the target complementary nucleic acid sequence is covalently coupled to the binding molecule via NHS- chemistry or site-specific labeling via click chemistry. In accordance with a preferred embodiment of the first and second aspect of the invention, the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence is based on the formation of more hydrogen bonds than the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule. In the art, a nucleic acid molecule labeled by an imaging molecule, which transiently binds another nucleic acid molecule is known as an ‘imager’ or ‘imager strand’. The ‘imager’ or ‘imager strand’ preferably comprises or consists of a complementary nucleotide sequence having a length of 5 to 13 nucleotides that carries the imaging molecule. Any combination of suitable melting temperatures (Tm) that provide for the appropriate difference in association strength of the first nucleic acid sequence with its complementary nucleic acid sequence on the one hand and the second nucleic acid sequence with the complementary nucleic acid sequence being labeled by an imaging molecule on the other hand is contemplated. In accordance with a preferred embodiment of the first and second aspect of the invention the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence has a melting temperature of between 25°C and 90°C, preferably between 45°C and 85°C and most preferably between 62°C and 78°C. Additionally or alternatively, the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of between 8°C and 22°C, preferably 12°C to 18°C and most preferably 14°C and 16° C. Particularly, the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence may have a melting temperature of between 45°C and 85°C and the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of 12°C to 18°C. Preferably, the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence may have a melting temperature of between 62°C and 78°C and the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule may have a melting temperature of 14°C to 16°C. The imaging molecule may be of any suitable type. In accordance with a preferred embodiment of the first and second aspect of the invention, the imaging molecule is a fluorescent molecule. Preferred fluorescent molecules are fluorescent proteins or fluorescent dyes. The fluorescent dye is preferably a component selected from Atto, Alexa Fluor or Cy dyes. The fluorescent protein is preferably GFP or YFP. However, other detectable types of imaging molecules may also be used, for example a radionuclide. The radionuclide is preferably either selected from the group of gamma-emitting isotopes, more preferably 99mTc, 123I, 111In, and/or from the group of positron emitters, more preferably 18F, 64Cu, 68Ga, 86Y, 124I, and/or from the group of beta-emitter, more preferably 131I, 90Y, 177Lu, 67Cu, 90Sr, or from the group of alpha-emitter, preferably 213Bi, 211At. The nucleic acid sequence being labeled by an imaging molecule may have any suitable length that is suitable to achieve the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. In accordance with a preferred embodiment of the first and second aspect of the invention the nucleic acid sequence being labeled by an imaging molecule has a length of 4 to 10 nucleotides. This short length is particularly advantageous for providing achieving the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule. Similarly, the first nucleic acid sequence may have any suitable length that is suitable to achieve the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. In accordance with a preferred embodiment of the first and second aspect of the invention the first nucleic acid sequence may have a length of 4 to 30 nucleotides, preferably 16 to 24 nucleotides. This length is longer than the short length nucleic acid sequence being labeled by an imaging molecule and is particularly advantageous for providing for achieving the specific, preferably stable hybridization of the first nucleic acid sequence to the target complementary nucleic acid sequence. Additionally or alternatively, the first nucleic acid sequence may have a GC-content of 45%- 55%, preferably 50%. Both the length and the GC-content of the first nucleic acid sequence may be used, individually or in combination, to advantageously provide for the specific, preferably stable hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence. As discussed above, the binding strength may thus be adjusted as appropriate, in order to ensure that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence (i.e. via specific hybridization) than the second nucleic acid sequence (i.e. via transient binding only). As discussed above, appropriate hybridization conditions and nucleic acid sequence compositions can be selected by routine means. The second nucleic acid sequence of the single-stranded nucleic acid molecule may have any suitable length, for example a length of 4 to 10 nucleotides for the use of traditional DNA- PAINT probes, or 8-36 nucleotides for the use with speed-optimized DNA-PAINT probes. In accordance with a preferred embodiment of the first and second aspect of the invention the nucleic acid molecule further comprises a toehold seed, whereby the specific hybridization between the first nucleic acid sequence and its complementary target nucleic acid sequence can be disconnected via toehold mediated strand displacement. Toehold mediated strand displacement is generally known in the art; see, for example, Yurke et al. (2000), Nature, 406(6796):605–608 and Simmel et al. (2019), Chem. Rev.2019, 119, 10, 6326–6369. In short, it is a tool to replace one nucleic acid strand (called protector strand) that is hybridized to a complementary nucleic acid sequence (called original strand), with another nucleic acid strand (called invading strand). The original strand comprises an overhanging region (called toehold) that is not hybridized to the protector strand. The invading strand is complementary to the original strand, including the toehold. The invading strand first binds to the toehold. Branch migration of the invading strand causes the replacement of the protector strand. Accordingly, in the present invention, an invading strand may be added to the hybridization complex in which the nucleic acid molecule is specifically hybridized to the target complementary nucleic acid sequence, but in which the nucleic acid molecule has an overhanging toehold that is not hybridized to the target complementary nucleic acid sequence. Thus, the invading strand may replace the target complementary nucleic acid sequence, i.e. may hybridize to the nucleic acid molecule instead of the target complementary nucleic acid sequence. In this way, the nucleic acid molecule may be separated from the target complementary nucleic acid sequence. This enables, i.a. removing the nucleic acid molecule from a sample, e.g. by washing it out. The present invention relates in a third aspect to a plurality of nucleic acid molecules of the first aspect of the invention or hybridization complexes of the second aspect of the invention, wherein the nucleic acid molecules comprise: different first nucleic acid sequences that differ from each other in that they are capable of specifically and stably hybridizing to different target complementary nucleic acid sequences; and/or different second nucleic acid sequences that differ from each other in that they are capable of transiently binding to different complementary nucleic acid sequences optionally being labeled by at least two, at least three, at least four, or at least five different imaging molecules, wherein preferably each of the different target complementary nucleic acid sequences forms a cognate pair with a different imaging molecule. In this connection the term “cognate pair” means that each target complementary nucleic acid can be distinguished from all other target complementary nucleic acid sequences in the plurality of target complementary nucleic acid sequences by a distinct imaging molecule when, both, the imaging molecule and target complementary nucleic acid sequence are bound to the nucleic acid molecule or the hybridization complex of the invention. Hence, each target complementary nucleic acid sequence can be identified by a different imaging molecule. In accordance with the third aspect of the invention the different complementary nucleic acid sequences may be structurally different and/or the different target complementary nucleic acid sequences may be structurally different. Further in accordance with the third aspect of the invention, in case the nucleic acid molecules comprise different first nucleic acid sequences that differ from each other in that they are capable of specifically and stably hybridizing to different target complementary nucleic acid sequences, the second nucleic acid sequences may be capable of transiently binding to the same type of complementary nucleic acid sequences. Further in accordance with the third aspect of the invention, in case the nucleic acid molecules comprise different second nucleic acid sequences that differ from each other in that they are capable of transiently binding to different complementary nucleic acid sequences, the first nucleic acid sequences may be capable of specifically and stably binding to the same type of target complementary nucleic acid sequences. In accordance with a preferred embodiment of the third aspect of the invention the second nucleic acid sequences and preferably the nucleic acid molecules may be orthogonal. The term “orthogonal” is used herein for two or more entities that are sufficiently different so that two corresponding binding partners specifically bind to them, i.e. without binding among each other or to the one or more non-corresponding entities. Orthogonal is also used herein for two or more entities that are sufficiently different to specifically bind to two corresponding binding partners, i.e. without binding among each other or to the one or more non-corresponding binding partners. In other words, in addition to including specificity it may also be used as a complementary term to specificity. For example, molecule A specifically binds to molecule B and molecule C specifically binds to molecule D. Then, molecules A and C are orthogonal, and B and D are orthogonal. In accordance with a further preferred embodiment of the third aspect of the invention the second nucleic acid sequences comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12. Particularly, the sequences (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n are known as so-called SPEED-sequences known from the SPEED-PAINT technique; see Strauss and Jungmann (2020), Nature Methods, 17:789–791. The SPEED-sequences allow a design of tunable hybridization kinetics and demonstrated up to 100-fold faster imaging compared to classical DNA-PAINT. Besides being faster these sequences enable imaging at lower imager concentration, leading to reduced background and thus increased signal-to-noise. Since 6 SPEED sequences are available 6-plex experiments can be designed for multiplexing. The present invention relates in a fourth aspect to a kit or composition comprising (a) the nucleic acid molecule or the hybridization complex or the plurality of nucleic acid molecules or hybridization complexes of the above aspects of the invention and at least one complementary nucleic acid sequence being labeled by an imaging molecule (also termed herein type A kit or composition); or (b) one or more single-stranded nucleic acid molecules comprising a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence and a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, one or more target complementary nucleic acid sequences capable of specifically hybridizing to the first nucleic acid sequence of the one or more single-stranded nucleic acid molecules, and one or more complementary nucleic acid sequences being labeled by an imaging molecule capable of transiently binding to the second nucleic acid sequence of the one or more single- stranded nucleic acid molecules, wherein in the kit or composition the amount of the single-stranded nucleic acid molecules is at least equal to, preferably at least 10-times or100-times or 1000-times the amount of the complementary nucleic acid sequences being labeled by an imaging molecule (also termed herein type B kit or composition). In the type B kit or composition the one or more single stranded nucleic acid molecules may have any of the properties described herein in relation to single-stranded nucleic acid molecules, with the proviso that the first nucleic acid sequence may be capable of reversibly, preferably transiently, binding to a target complementary nucleic acid sequence. However, it is to be understood that the type B kit or composition is not necessarily limited by the proviso that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. In the type B kit or composition, at a given point in time, a desired higher amount of single- stranded nucleic acid sequences being bound to the target complementary nucleic acid sequence, particularly in comparison to the amount of complementary nucleic acid sequences being labeled by an imaging molecule being bound to the single-stranded nucleic acid sequences, may be implemented by selecting the higher appropriate amount of single-stranded nucleic acid molecules compared to the amount of complementary nucleic acid sequence being labeled by an imaging molecule, taking into account the particular binding kinetics of all three types of molecules. The excess of single-stranded nucleic acid molecules as compared to complementary nucleic acid sequences being labeled by an imaging molecule ensures that more first nucleic acid sequences are hybridized to their target complementary nucleic acid sequences than second nucleic acid sequences being bound to their complementary nucleic acid sequences being labeled by an imaging molecule. Hence, by the excess amount in connection with the type B kit or composition the essentially same technical effect is achieved as by the proviso that first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence in connection with the type A kit or composition. Two or more complementary nucleic acid sequences being labeled by an imaging molecule may have different imaging molecules, particularly imaging molecules that may be distinguished in the detection technique. For example, in the case of fluorescent imaging molecules, the fluorescent imaging molecules may have different excitation/emission-spectra that are distinguishable in fluorescence microscopy. The present invention also relates in a fifth aspect to a method of detecting a target molecule in a sample, comprising: a. contacting the sample with the nucleic acid molecule or the kit or composition of the aspects of the invention as described above; b. optionally contacting the sample with a target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds the target molecule in the sample; c. contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the nucleic acid molecule or composition of (a); and d. detecting the imaging molecule in the sample, thereby detecting the biological target molecule in the sample. Generally, the order of method steps may be any order unless explicitly stated otherwise. The order of steps a), b) and c) of the above method may be any order. It may be advantageous to have the order step b) before step a), and optionally step d) after step c) after step a). It may also be advantageous to have the steps in alphabetical order. In accordance with a preferred embodiment of the fifth aspect of the invention (a) the target molecule in the sample is a nucleic acid sequence in the sample and the binding molecule is a nucleic acid being complementary to the nucleic acid sequence in the sample, or (b) the target molecule in the sample is a protein or peptide or polysaccharide in the sample and the binding molecule is a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer, specifically binding to the protein or peptide or polysaccharide in the sample, or (c) the target molecule in the sample is a nucleic acid sequence comprising a/the target complementary nucleic acid sequence . Method step b) may not be required for cases in which the single-stranded nucleic acid molecules directly bind to target molecules, i.e. in which the one or more target complementary nucleic acids are a part of the one or more target molecules. The present invention relates in a sixth aspect to a method of detecting two or more target molecules in a sample, comprising a. contacting the sample with a first nucleic acid molecule or composition of any preceding claim and a second nucleic acid molecule or composition of any preceding claim; b. contacting the sample with a first target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the first nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds a first target molecule in the sample, and a second target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the second nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds a second target molecule in the sample, c. contacting the sample with a first complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the first nucleic acid molecule or the composition of (a) and contacting the sample with a second complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the second nucleic acid molecule or the composition of (a); and d. detecting the imaging molecules, thereby detecting the first and second biological target molecules in the sample, wherein optionally (i) the method is carried out sequentially, wherein the sample is first contacted with the first nucleic acid molecule or the composition, the first target complementary nucleic acid sequence, and the first complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the first target molecule in the sample, and then the sample is contacted with the second nucleic acid molecule or the composition, the second target complementary nucleic acid sequence, and the second complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the second biological target molecule in the sample, wherein preferably the first complementary nucleic acid sequence being labeled by an imaging molecule is removed before the second complementary nucleic acid sequence being labeled by an imaging molecule is added; (ii) the imaging molecule of the first complementary nucleic acid sequence being labeled by an imaging molecule is different from the imaging molecule of the second complementary nucleic acid sequence being labeled by an imaging molecule, wherein the imaging molecules are preferably spectrally distinct fluorescent molecules; and/or (iii) the method additionally comprises the provision of a codebook that unambiguously maps a unique identification sequence to the first target molecule in the sample and another unique identification sequence to the second target molecule in the sample, wherein preferably each identification sequence has N ordered positions, and each position is assigned with either a gap or one or more second nucleic acid sequences ; whereby by options (i) to (iii) the first and the second target molecule in the sample can be distinguished in step (d); and/or wherein optionally (iv) the hybridization complex between the first target complementary nucleic acid sequence and the first nucleic acid sequence of the first nucleic acid molecule or composition is dissociated before the formation of the hybridization complex between the second target complementary nucleic acid sequence and the first nucleic acid sequence of the second nucleic acid molecule or composition, wherein the dissociation is preferably achieved by stringent buffer conditions, toehold-mediated strand displacement, or heat; and/or (iv.b) the second nucleic acid sequence of the first nucleic acid molecule or composition is blocked with a complementary blocking strand. In accordance with a preferred embodiment of the sixth aspect of the invention the two or more target molecules in a sample are at least 10 target molecules and the method comprises in step (a) at least 10 different nucleic acid molecules or compositions, in step (b) at least 10 different target complementary nucleic acid sequences, and optionally in step (c) at least 10 different first complementary nucleic acid sequences being labeled by an imaging molecule; preferably at least 30 target molecules and the method comprises in step (a) at least 30 different nucleic acid molecules or compositions, in step (b) at least 30 different target complementary nucleic acid sequences, and optionally in step (c) at least 30 different first complementary nucleic acid sequences being labeled by an imaging molecule; more preferably at least 60 target molecules and the method comprises in step (a) at least 60 different nucleic acid molecules or compositions, in step (b) at least 60 different target complementary nucleic acid sequences, and optionally in step (c) at least 60 different first complementary nucleic acid sequences being labeled by an imaging molecule; and most preferably at 100 least target molecules target molecules and the method comprises in step (a) at least 100 different nucleic acid molecules or compositions, in step (b) at least 100 different target complementary nucleic acid sequences, and optionally in step (c) at least 100 different first complementary nucleic acid sequences being labeled by an imaging molecule. The method is particularly useful for a large number of different target molecules in the sample. Herein, different target molecules may mean different types of target molecules. In accordance with a more preferred embodiment of the sixth aspect of the invention the at least 10, at least 30, at least 60 or at least 100 target molecules in the sample are distinguished from each other in step (d) by one or more of method options (i) to (iii), preferably option (iii) as described in the above preferred embodiment and further comprising e. mapping the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; and f. optionally determining the interaction pattern of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of (e), preferably by nearest neighbor-based analysis. The methods according to the invention may include labeling all targets with the same type of imager and thus quantifying the number of targets in a localization cluster. The codebook may be adaptively provided in response to the number of unresolved target molecules. This may be advantageous for handling cases in which two or more target molecules are so close to each other that assigning the sequential imager binding in the different imaging rounds to the target molecules individually is impossible –only the sum of the two identification sequences will be detected. Hence, there is no way of correct identification. However, a solution is to first determine the largest number of target molecules that cannot be separated in localization, and adapt the codebook accordingly, such that one can still tell the identity of the target molecules present. The adaption of the codebook is combinatorics that is generally known in the art. For example, one option is to target all target complementary nucleic acids with a single stranded nucleic acid which reversible bind the same imager and use qPAINT as known in the art to determine the number of target complementary nucleic acids in each localization cluster (as described in the section methods/image analysis below), and create a codebook that distinguishes all combinations of target complementary single stranded nucleic acids up to the maximum number of molecules per localization cluster if the analysis is done beforehand, or if the analysis of the number of target complementary single stranded nucleic acids is done after the planned experiment, by analyzing the potentially confused or wrongly assigned codebook entries, and adding rounds of single stranded nucleic acid addition and imaging, thus extending the codes, to disambiguate the analysis. More specifically for example, if there are four target complementary nucleic acids in the codebook, which have the codes 1: ‘100’, 2: ‘010’, 3: ‘001’, and 4: ‘011’, all localization clusters incorporate one target complementary nucleic acid but one, which incorporates two, with the analysis result ‘011’, the targets present may be one of the tuples (2, 3), (2, 4), (3, 4), (4, 4). Therefore, to disambiguate the results, two rounds may be added to identify targets 2-4, to make the full codes for example 1: ‘10000’, 2: ‘01010’, 3: ‘00101’, 4: ‘01100’. The present invention also relates to a method including method step iii) related to the codebook including: (v) if applicable: removing the single-stranded nucleic acid molecules from a previous round of the following steps from the target molecules, preferably using buffer conditions, toehold-mediated strand displacement, or heat; (iv) contacting the sample with all single-stranded nucleic acid molecules assigned to one of the N positions of all identification sequences and the complementary nucleic acid sequence(s) being labeled by an imaging molecule; (iiv) identifying and localizing the imaging molecules in the sample; (iiiv) repeating step (v), (iv) and (iiv) for all other N-1 identification sequence positions. (ix) for one or more locations in the sample: generating a detection sequence having a length of N positions corresponding to the N positions of the identification sequence, wherein each position of the detection sequence is either assigned with an identified imaging molecule of the corresponding round of steps (iv) and (iiv) or, if no imaging molecule was detected, with a gap; (x) for each location of step (ix): by comparing the detection sequence of the location with the identification sequences of the codebook of step c), identifying the target type detected at the location. Optionally, the second nucleic acid sequences of the different single-stranded nucleic acid sequences are orthogonal. In the methods described herein, detecting the imaging molecule(s) and thus imager(s) in the sample may include performing detection steps of DNA-PAINT (Schnitzbauer et al. (2017), Nature Protocols, 12:1198–1228). In DNA-PAINT the transient association of the fluorophore to a target molecule is mediated by the pairing of short (<10 nucleotides) complementary DNA sequences: Generally, a docking strand comprising a nucleic acid sequence is coupled to the target molecule, usually through an antibody, nanobody, aptamer or other high affinity probe and an imager strand carries a fluorophore (i.e. the imaging molecule). The imaging strand carrying the fluorophore is free to diffuse in the imaging buffer. Upon DNA hybridization (i.e. binding of the imaging strand to the docking strand) the fluorophore is transiently immobilized near the target molecule, and thus excited by the laser light, typically in Total Internal Reflection Fluorescence (TIRF) or highly inclined and laminated optical sheet (HiLO) configuration, however light sheet and spinning disk microscopies have been used as well. The emitted light can then be captured by the camera as a diffraction limited flash. By adjusting sequence, concentration, ratio of the DNA strands, and composition of the imaging buffer, at each time point only a few fluorophores will be imaged, enabling stochastic super resolution microscopy. Optionally, localization is done at a resolution < 20nm, preferably < 10nm, most preferably < 5nm. So far, the invention has been described by referring to one second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule per single- stranded nucleic acid molecule. However, according to an aspect of the invention, any of the nucleic acid probes described above may comprise two, three, four, five or six second nucleic acid sequences. This may enable binding of two, three, four, five or six complementary nucleic acid sequence being labeled by an imaging molecule. The second nucleic acid sequences may be the same and thus provide for binding with the same type of imaging molecule, or different thus providing for binding to different complementary nucleic acid sequences being labeled by an imaging molecule, preferably with different imaging molecules. Thus, different combinations of intensities and/or colors of imaging molecules may be created and/or used. Additionally or alternatively, contacting the sample with the different complementary nucleic acid sequences being labeled by an imaging molecule at different times allows for the usage of a larger barcoding space. Additionally or alternatively, no target molecule is labeled with more than five, preferentially two, more preferentially one single-stranded nucleic acid molecule(s). In some embodiments, one or more single-stranded nucleic acid molecule is capable of binding exactly one type of imaging molecule via its complementary nucleic acid sequence being labeled by this imaging molecule. Additionally or alternatively, different single stranded nucleic-acid molecules may be capable of binding different types of imaging molecules. Alternatively, one or more nucleic acid probes may be capable of binding multiple types of imaging molecules. In some embodiments, one or more single-stranded nucleic acid molecule is capable of binding exactly one type of complementary nucleic acid sequence being labeled by an imaging molecule. Additionally or alternatively, different single stranded nucleic-acid molecules may be capable of binding different types of complementary nucleic acid sequence being labeled an imaging molecule. In some embodiments, one or more single-stranded nucleic acid molecule is capable of binding multiple types of complementary nucleic acid sequence being labeled an imaging molecule. The sample may comprise one or more cells and/or a cell lysate and/or purified components, all e.g. from cell culture, a biopsy, and/or a liquid biopsy, for example primary patient cells (from healthy tissue, immune cells, cancer), fresh frozen (FF) tissue, formalin-fixed paraffin embedded (FFPE) tissue, or extracts from liquid biopsy (e.g. cells or exosomes), or spheroids, or organs-on a chip, all from human or animal. The methods may include drift correction. For all nucleic acid sequences the term complementary is used as follows: The complementarity may be 100% or less, for example between 80% and 100%, or more preferably between 90% and 100%. According to an additional aspect of the methods described herein, the methods may include: determining the binding kinetics of at least one, preferably all, first nucleic acid sequences to their target complementary nucleic acid sequences, and/or second nucleic acid sequences to their complementary nucleic acid sequences being labeled by an imaging molecule; and taking the determined binding kinetic into account for detection step d). In some embodiments of the invention the target complementary nucleic acid sequence may be a portion of a secondary binder (not to be confused with secondary probe/label). The secondary binder is a molecule that specifically binds to a molecule, termed the binding molecule, which may specifically bind to the target molecule. In this case, the single-stranded nucleic acid molecule may indirectly bind to the target molecule via the secondary binder and the binding molecule. Secondary binders may be comprised of a nucleic acid molecule and a secondary binding molecule. A secondary binding molecule is a molecule specifically binding a primary binding molecule as described herein above. Secondary binding molecules are generally known in the art. For example, they may be secondary antibodies. Secondary antibodies are antibodies which specifically bind to antibodies produced from the respective animals. For example ‘anti-rat’ secondary antibodies bind to antibodies from rats. Similarly, 'anti-mouse’, ‘anti- rabbit’, ‘anti-goat’, ‘anti-guineapig’, ‘anti-sheep’, and 'anti-donkey’ specifically bind antibodies from the respective species. Analogously, secondary binding molecules may be nanobodies. For the invention described herein, it is preferable to pre-incubate primary binding molecules with their respective secondary binders individually and purify the constructs of primary binding molecules bound to secondary binders, before incubating either with the sample or primary or secondary binding molecules targeting different target molecules, to avoid cross binding of primary binding molecules and secondary binders designed to target different target molecules. The invention further relates to a hybridization complex comprising the target complementary nucleic acid, the nucleic acid molecule, and a blocking strand. A blocking strand (also termed “blocker strand” or “blocker”) is generally known in the art as a single stranded nucleic acid that may hybridize to a different single stranded nucleic acid molecule with one or more reaction partners that have a lower affinity than the blocking strand to the different single stranded nucleic acid molecule (see e.g. doi: 10.1021/acs.nanolett.9b02565). It can be used to block the interactions of the different single stranded nucleic acid molecule with its reaction partner(s). In relation to the invention described herein, a blocking strand may comprise the complementary sequence to a docking sequence (e.g. the second nucleic acid sequence of the nucleic acid molecule), to prevent the transient interaction of an imager with the second nucleic acid sequence of the nucleic acid molecule. This way, a target molecule that has been imaged can be deactivated for subsequent imaging rounds, thus ensuring no signal related to the target molecule is created in these subsequent rounds. The number of nucleotides of a blocking strand is 2-30 nucleotides, preferably 5-20 nucleotides, more preferably 10-15 nucleotides longer than the competitive interaction partner, and they should cover the whole sequence targetable by the interaction partner. Their total length is 15-40 nucleotides. For example, imager strands R1-R6 (SeqID No 3533- 3538) are 6-7 nucleotides long, while the blocking strands used (SeqID No 3498-3526) are 19-20 nucleotides long and cover the complete docking sequences (SeqID No 3527-3532). Generally, blocking strands may fulfill the following requirements: * blocking strands should cover the whole region targeted by their competitor; * blocking strands should stably bind to the region targeted by their competitor; * blocking strands should not be displaced by their competitors (i.e. bind much more stably and thus be longer). This is an alternative or additional method to toehold-mediated displacement of the single stranded nucleic acid molecule for controlling the signal related to target molecules. The approach of using blocking strands is especially useful when a target molecule is designed to provide signal in only one round of imaging, as described in this example. In this case, the single stranded nucleic acid molecule can be provided to the sample before the respective imaging round, thereby activating the target molecule, and the blocker can be provided after the imaging round, thereby deactivating the target molecule. The present invention also relates to a method of analyzing a sample, comprising: a. localizing with increasing preference at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; e. mapping the localization of each of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; and f. optionally determining the interaction pattern of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of (e), preferably by point pattern analysis, more preferably by nearest neighbor-based analysis. Point Pattern Analysis is a collection of methods to extract information from point patterns, especially the spread or distribution of each of the target molecules over the space of the sample, preferably on the cell surface or within the volume of the cells. It provides a number of metrics that describe the type of association and association probability between different target molecules. Point pattern analysis (PPA) is the study of point patterns, the spatial arrangements of points in space, herein generally in 3-dimensional space. Nearest Neighbour Analysis measures the spread or distribution of each of the target molecules over the space of the sample, preferably on the cell surface or within the volume of the cells. It provides a numerical value that describes the extent to which the different the target molecules are clustered or uniformly spaced. This in turn results in an interaction pattern of the different target molecules. Different target molecules that are found to co-localize or almost co-localize likely bind to each other in the context of the sample, e.g. within cells. Such interaction partners are bona fide targets for the modification of biological processes with samples, e.g. within cells or between cells. The modification of biological interaction processes in turn may be useful for drug development and the treatment of diseases. Hence, in some embodiments of the method above, the method further comprises binding molecule evaluation and development. For optimal sample analysis, it is beneficial to first characterize binders that are later used for labelling target molecules. This characterization can then be integrated into the data evaluation as a calibration. In some embodiments of any of the methods above, the method of analyzing a sample uses one or more single-stranded nucleic acid molecules and/or one or more hybridization complexes as defined herein above. In this connection it is particularly preferred that the method uses hybridization complexes as defined herein above, wherein the target complementary nucleic acid sequences of the hybridization complexes are conjugated to different binding molecules that are capable of specifically binding to the at least 10, at least 30, at least 60 or at least 100 target molecules. The hybridization complexes as defined herein above are particularly advantageous for the method of analyzing a sample because different hybridization complexes are conjugated to different binding molecules that are capable of specifically binding to the at least 10, at least 30, at least 60 or at least 100 target molecules, and can be easily designed, thereby achieving a high multiplex level. In this connection it also particularly preferred that the second nucleic acid sequences of the single-stranded nucleic acid molecules and/or the complexes comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12. (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12, are the six speed- optimized sequences R1-R6 (doi: 10.1038/s41592-020-0869-x). Due the speed-optimization these sequences are ideally suitable for multiplexed applications, such as the method of analyzing a sample. The method of analyzing a sample is preferably conducted via multiple subsequent imaging rounds and the speed-optimized sequences are particularly advantageous for achieving the subsequent imaging rounds in short time. In some embodiments of any of the methods above only one of the second nucleic acid sequences is present per imaging round. In some embodiments, only two, only three, only four, only five, only six or more different second nucleic acid sequences are present and addressable per imaging round. In some embodiments of any of the methods above only one type of the first nucleic acid sequences is present per imaging round. In some embodiments, only two, only three, only four, only five, only six or more different first nucleic acid sequences are present and addressable per imaging round. A present single stranded nucleic acid may be turned non-addressable by blocking using a “blocker strand” (also termed “blocking strand”). In some embodiments of any of the methods above, the method comprises binding molecule preselection, preferably bare binding molecule preselection. To preselect probably appropriate binding molecules from a given pool of potential binding molecules to a target molecule of interest, their specificity may be screened. Briefly, this may be done by colocalizing fluorescence signal originating from the target molecule of interest (or a moiety fused to it) with a fluorescence signal related to a secondary binder known to bind the bare binding molecule. This way, no conjugation of DNA to the potential binding molecule is necessary and it can be used as supplied, i.e. bare. “Secondary binder” refers to a molecule that specifically binds a binding molecule/primary binder. In some embodiments of any of the methods above, the method comprises binding molecule specificity testing. After successful preselection, a short DNA oligonucleotide can be conjugated onto the bare binding molecule, to make it what is called a primary binder herein. The short DNA oligonucleotide can be conjugated to the binding molecule using site-specific DNA conjugation. This is advantageous for two reasons: firstly, care can prevent the short DNA oligonucleotide from interfering with the binding molecule epitope and thus help maintain a good labeling efficiency; and secondly, the stoichiometry can be controlled, to make sure to have one DNA oligo on one binding molecule. For a good sample analysis, it is advantageous that the primary binder shows little off-target binding. This can be characterized by binder specificity testing. In some embodiments of any of the methods above, the method comprises primary binder labelling efficiency testing. For sample analysis, it may be beneficial to correct the observed data for incomplete binding of the binding molecules to target molecules. For example, primary binder labelling efficiency may be tested. In some embodiments of any of the methods above, the method comprises primary binder labelling efficiency determination, e.g. via 2-plex Exchange-PAINT imaging or any of the methods described herein. For binding molecule characterization, e.g. binding molecule specificity testing and/or primary binder labelling efficiency testing, any cell line may be used, e.g. CHO, BSC1, HeLa. In some embodiments of any of the methods above, the method comprises sample preparation for multiplexed immune receptor DNA-PAINT. For the analysis of target molecule patterns, samples may be prepared for imaging in any suitable way. This can be done in multiple ways and also depends on the sample type. In some embodiments of any of the methods above, the method comprises preparation of functionalized planar supported lipid bilayers (SLBs). Especially for live-cell analysis, which may be useful for the elucidation of mode of action, it is beneficial to use biomimetic environments. For example, supported lipid bilayers may be used. Other approaches could include embedding cells in a 3D matrix, such as in agarose or PDMS. In some embodiments of any of the methods above, the method comprises cell preparation for multiplexed immune receptor DNA-PAINT imaging. In some embodiments of any of the methods above, the method comprises data acquisition, e.g. multiplexed molecular imaging of target molecules. In some embodiments of any of the methods above, the method comprises multiplexed cellular imaging of cellular proteins. In some embodiments of any of the methods above, the method comprises data evaluation, e.g. processing of raw super-resolution imaging data. In some embodiments of any of the methods above, the method comprises image analysis, e.g. postprocessing of a/the raw super-resolution imaging data. It may provide for getting from multiple single channel transient binding movies to one multiplexed molecular map, which specifies the localizations of all target molecules detected in the sample. In some embodiments of any of the methods above, the method comprises data analysis, wherein data is aggregated to elucidate the direct interaction patterns present in the sample. This step corresponds to getting from the multiplexed molecular map to one or more direct interaction patterns present in the sample. A direct interaction pattern describes a set of target molecules commonly found in close proximity, and optionally probability distributions of their relative distances. Parameter specification In some embodiments of any of the methods above, the method comprises selecting one or more binding molecule candidates having a size of 25nm or less, preferably 12.5nm or less, for example 12.4nm or 4nm. The binding molecules may be small enough to allow for generation of direct interaction patterns precise enough for meaningful insights for drug development or diagnostics. In some embodiments of any of the methods above, the method comprises selecting one or more binding molecule candidates having an affinity as measured in bulk measurements (e.g. SPR, Octet, FRET-based assays) of KD ≤ 200nM, preferably ≤ 20nM. In some embodiments of any of the methods of analyzing a sample above, the step of localizing at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample may comprise super-resolution imaging, preferably super-resolution fluorescence microscopy, preferably DNA-PAINT, optionally including any of its improvements such as Exchange-PAINT, RESI, and/or the methods described herein. A sample in any of the embodiments herein may be one or more cell lines, primary patient cells (from healthy tissue, immune cells, cancer), fresh frozen (FF) tissue, formalin-fixed paraffin embedded (FFPE) tissue, or extracts from liquid biopsy (e.g. cells or exosomes), or spheroids, or organs-on a chip, all from human or animal. In any of the embodiments described herein, the target complementary nucleic acid sequence, e.g. in the form of DNA, may be conjugated to a binding molecule site-specifically, preferably by using sortase, C-terminal cysteine, N-terminal serine, threonine, or artificial aminoacids. In some embodiments of any of the methods of analyzing a sample above, a molecular density of target-molecule-fused reference molecules may be less than 5000 molecules/μm2, preferably less than 500 molecules/μm2, more preferably less than 50 molecules/μm2. Additionally, the molecular density of target-molecule-fused reference molecules may be more than 0.01 molecules/µm2, preferably more than 0.1 molecules/µm2, more preferably more than 1 molecule/µm2. Target-molecule-fused reference molecules are easily-taggable reference molecules, which are genetically fused to target molecules. For example, when developing binding molecules for the target molecule CD-80, the cells can be CD-80 non-expressing or knock-out cells and transfected with a GFP-CD80 fusion. Here, GFP can serve as a reference molecule, for which a well-characterized anti-GFP nanobody is available and can be used in the characterization of the binding molecules for the target molecule. In some embodiments of any of the methods of analyzing a sample above, the method is configured for rational drug design and comprises one or a combination of the following: • Indication selection, e.g. melanoma, non-small cell lung cancer (NSLC); • Target and off-target sample selection, e.g.: o Healthy, normal vs cancerous tissue, cancer vs dendritic cell, T cell & DC vs T cell & cancerous tissue; and/or o FACS-sorted samples; • Visualization of target molecule identification, e.g.: o Membrane proteins (e.g. CD86, PD-L1, PD-L2, CD80, MHC-I, MHC-II); o Intracellular membrane-associated or membrane-recruited proteins (e.g. Lck, Grb2, LAT); and/or o Organelle-associated or – recruited proteins; • extracting key molecular parameters from target molecule map at single molecule resolution (maximally simple neighborhood configuration), wherein preferably at least some of the target molecules are proteins; • distinguishing target and off-target samples according to the key molecular parameters. For example, CD80 & CD86 may be often <15nm apart from each other in the target sample, but rarely in off-target sample, with the total number of CD80 and CD86 being similar in target and off-target samples. One or more candidate neighborhood configurations may be explored for drug development, for example 2, 3, 4, 5, 6, 10, 20, 50, 100, 500 or more candidate neighborhood configurations may be explored for drug development. In some embodiments of any of the methods of analyzing a sample above being configured for rational drug design the method may comprise one or a combination of the following: • Drug development comprising: o Developing mono-, bi- or multivalent primary binder (antibody-based or similar primary binder; optional: biocompatible scaffold for organizing target binders) based on the direct interaction pattern, and use cooperativity for binding to investigate if target proteins are present on pathogenic cells/tissue, such that a single binding domain does not bind stably but the specific neighbourhood configuration (or most of it) is required for prolonged, stable binding, thereby generating specificity between target and off-target o using the neighborhood configuration distances, angles and configurations (hetero- and homo-oligomerisation) as described by the direct interaction pattern to guide the drug molecule design (see, for example, Bila et al, J Am Chem Soc.2022 Nov 30; 144(47): 21576–21586 wherein such a configuration of binders was put onto a DNA- Origami); o Optionally, using a biocompatible scaffold such as DNA origami as a pegboard to test drug molecule designs; o Optionally, performing super-resolution imaging, e.g. one of the methods described herein, on drug candidates, visualizing binding domains and underlying detailed architecture, to characterize the interaction radius; o Attaching marker domains to a multivalent primary binder for pharmacological potency; o Modifying one or more multivalent primary binders with additional drugs, enzyme- activating drugs, cytotoxic drugs, radioactive drugs and/or pathological markers; o using hit-to-lead / lead optimization procedure steps; o Imaging of fluorescently labeled multivalent primary binders representing a direct interaction pattern of cell surface proteins and correlating the imaging signal with imaging signals of intracellular tags with known biochemical pathways to evaluate the mode of action of drugs that influence signalling of the direct interaction pattern; o Detailed quantification of underlying configurations, e.g. degree of oligomerisation, preferred geometrical configuration, based on simulations accounting for non-optimal, i.e. <100%, labelling efficiency; o Optionally, relative comparison of nanoscale molecular distributions of healthy and pathogenic cells/tissue for drug development and optimization; In some embodiments of any of the methods of analyzing a sample above, the method is configured for hit-to-lead and/or lead optimization, and comprises one or a combination of the following: • Target and off-target sample selection; • Selection of the molecular targets potentially involved o Use Proteins appearing in biochemical hypotheses and general knowledge of the indication involved; Also add candidate drug as a visualization target, preferably each binding domain separately, to elucidate its position in the interaction pattern • creating a direct interaction pattern, optionally including: o Define an optimization metric based on the direct interaction pattern: e.g. fraction of colocalized binding domains to target molecules, and drug molecule target vs off- target binding, fully characterize recorded protein map consisting of multiple different proteins or protein epitopes and binding epitopes and extract key parameters such as distances, angles, molecular orientations, oligomerisation, cluster contributions (cluster size, cluster shape, cluster density, amount of clusters, protein ratios, protein motifs), reduction of dimensionality (e.g. UMAP) for key parameter elucidation; • Data acquisition and evaluation of samples with multiple optimization candidates as target molecules; • Scoring of optimization candidates, resulting in one optimal candidate; • Based on the direct interaction pattern, optimization of mono- and multivalent binder geometry, e.g. orientation and/or linker length; The invention will be further described with reference to the Figures. Fig.1 schematically illustrate a single-stranded nucleic acid molecule according to the invention and related concepts and methods; Fig.2 schematically illustrates a single-stranded nucleic acid molecule according to the invention and related concepts and methods; Fig.3 illustrates a proof of principle experiment; Fig.4 schematically illustrates aspects of methods including a codebook; Fig.5 schematically illustrates aspects of methods including a codebook. Fig.6 shows a false color image of neurons imaged with a 29-plex imaging method according to the present invention; Fig.7 shows zoom-ins from a whole-cell view, via DNA-PAINT localization Data, to single target molecule localization data obtained from; Fig.8 shows Specificity; Fig.9 shows Labeling Efficiency; Fig.10 shows preparation steps for functionalized planar glass-supported lipid bilayers; Fig.11 shows multiplexed single-protein imaging of immune checkpoint receptors; Fig.12 shows a direct interaction analysis for dendritic cells and that multiplexed spatial receptor pattern analysis reveals novel key interaction motifs in dendritic cells; Fig.13 shows a direct interaction analysis for cancer cells and that the absence of costimulatory receptors drives formation of PD-L1/MHC-I clusters in B16-F10. Fig.14 shows that CD80 presence interferes with MHC-I/PD-L1 clustering irrespective of cell type. Fig.15. Workflow of analysis pipeline. Schematic outline of workflow for the analysis of 6- plex receptor point patterns on cells. Following image post-processing, whole-cell datasets were utilized for further analysis. In the first step, global receptor correlations were determined by a modified version of Ripley’s K function. Ripley’s K curve was calculated for all 36 possible pairwise receptor combinations and compared to the results of complete spatial randomness (CSR). The normalized Ripley’s K curves were then integrated and averaged from all individual cells of a single cell type and corresponding condition. Based on the resulting pairwise correlation coefficients, a correlation matrix for all 36 possible receptor combinations was generated to distinguish between clustered or dispersed receptor patterns, as well as random receptor distributions. In the next step, a nearest neighbor distance (NND) based analysis was used to quantify the percentage of receptor interactions. To this end, single channel data was compared to CSR simulations to determine the percentage of homo- interactions. Hetero-interactions were evaluated via correlation of cross-channel data to corresponding CSR simulations. As a readout, the percentage of receptor interactions, as well as the corresponding interaction distance, were defined. Finally, global DBSCAN analysis was used to identify receptor motifs within the upper limit of evaluated receptor interaction distances in clustered regions containing at least three receptors. To accomplish this, DBSCAN was applied to both experimental and simulated 6-plex data, ignoring receptor identities at this stage. Detected receptor motifs for experimental and CSR simulated data were sorted based on their receptor identities within each cluster, leading to 63 unique cluster IDs. Comparison of experimental and CSR simulated data allowed for the extraction of multiple different parameters. All readout parameters are highlighted in blue. Fig.16. Multiplexed spatial receptor pattern analysis of non-stimulated MutuDCs. (A) DNA-PAINT image of non-stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions as in Fig.12B. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). For non-stimulated MutuDC receptor motifs occur only randomly. (Data is shown as mean ± 95% confidence interval of four independent experiments and 13 cells, , *** p < 0.001; n.s., not significant) Fig.17. Multiplexed spatial receptor pattern analysis of 3 hours stimulated MutuDCs. (A) DNA-PAINT image of 3 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct motifs (Motif 1 – MHC-I/CD80/PD-L1, Motif 2 – MHC-I/CD86/PD-L1. The motifs represent 2.0% ± 1.3%, 1.9% ± 1.2% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of five independent experiments and 10 cells, , *** p < 0.001; n.s., not significant) Fig.18. Multiplexed spatial receptor pattern analysis of 6 hours stimulated MutuDCs. (A) DNA-PAINT image of 6 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 4 distinct motifs (Motif 1 – CD86/CD80/PD-L1, Motif 2 – CD80, Motif 3 – MHC-I/CD80/PD-L1, Motif 4 – MHC-I/CD86/CD80/PD-L1. The motifs represent 5.9% ± 2.1%, 0.7% ± 0.3%, 4.5% ± 1.1%, 5.1% ± 2.9% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, *** p < 0.001; n.s., not significant) Fig.19. Multiplexed spatial receptor pattern analysis of 12 hours stimulated MutuDCs. (A) DNA-PAINT image of 12 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 distinct motifs (Motif 1 – MHC-I/CD80/PD-L1, Motif 2 – MHC-I/CD86/PD-L1, Motif 3 – MHC-II/CD80). The motifs represent 1.7% ± 0.5%, 1.7% ± 0.8%, 1.1% ± 0.9% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, * p < 0.05; ** p < 0.01; *** p < 0.001; n.s., not significant) Fig.20. Multiplexed spatial receptor pattern analysis of 24 hours stimulated MutuDCs. (A) DNA-PAINT image of 24 h stimulated MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 6 distinct motifs (Motif 1 – CD86/CD80/PD-L1, Motif 2 – CD80/PD-L1, Motif 3 & Motif 4 – MHC-I/CD80/PD-L1, Motif 5 & Motif 8 – MHC- I/CD86/PD-L1, Motif 6 – MHC-I/CD86/CD80, Motif 7 – MHC-I/CD86/PD-L2). The motifs represent 1.9% ± 0.6%, 0.5% ± 0.1%, 2.3% ± 0.7%, 4.5% ± 1.7%, 3.1% ± 0.8%, 0.5% ± 0.2% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, * p < 0.05; *** p < 0.001; n.s., not significant) Fig.21. Multiplexed spatial receptor pattern analysis of specific peptide-MHC-I complexes on MutuDCs fed ovalbumin protein. (A) DNA-PAINT image of MutuDCs stimulated for 6h with CpG and IFN ^^, while being fed ovalbumin, showing receptor positions of the imaged immune checkpoint receptors. In this experiment, an antibody that specifically detects MHC-I complexed to the SIINFEKL OVA peptide was used to enable imaging of cross-presented antigen rather than pan-MHC-I imaging. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. I Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 4 distinct receptor motifs (Motif 1 – CD80/PD-L1, Motif 2 – CD86/CD80/PD-L1, Motif 3 – MHC-I/CD80/PD-L1, Motif 4 – MHC-I/CD86/CD80/PD-L1). The receptor motifs account for 18.0% ± 2.5%, 1.5% ± 0.6%, 3.2% ± 0.7%, 0.4% ± 0.2% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 8 cells, *** p < 0.001; n.s., not significant) Fig.22. Multiplexed spatial receptor pattern analysis of non-stimulated B16-F10 cells. (A) DNA-PAINT image of non-stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key motif (Motif 1 – MHC-I/PD-L1). The motif represents 26.4% ± 3.1% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, *** p < 0.001; n.s., not significant) Fig.23. Multiplexed spatial receptor pattern analysis of 3 hours stimulated B16-F10 cells. (A) DNA-PAINT image of 3h IFN ^^ stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key motif (Motif 1 – MHC-I/PD-L1). The motif represents 29.2% ± 3.9% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 11 cells, *** p < 0.001; n.s., not significant) Fig.24. Multiplexed spatial receptor pattern analysis of 6 hours stimulated B16-F10 cells. (A) DNA-PAINT image of 6h stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals one dominating key motif (Motif 1 & Motif 2 & Motif 3 & Motif 4 – MHC-I/PD-L1). The motif represents 58.2% ± 4.2% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, *** p < 0.001; n.s., not significant) Fig.25. Multiplexed spatial receptor pattern analysis of 12 hours stimulated B16-F10 cells. (A) DNA-PAINT image of 12h stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 independent receptor motifs (Motif 1 – MHC-I/MHC- II/PD-L1, Motif 2 – MHC-II, Motif 3 – MHC-I/MHC-II/CD86/PD-L1). The receptor motifs represent 30.0% ± 3.1%, 3.6% ± 1.1%, 3.3% ± 1.2% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, ** p < 0.01; *** p < 0.001; n.s., not significant) Fig.26. Multiplexed spatial receptor pattern analysis of 24 hours stimulated B16-F10 cells. (A) DNA-PAINT image of 24h stimulated B16-F10 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 independent receptor motifs (Motif 1 – MHC-I/MHC- II/PD-L1, Motif 2 – MHC-I/MHC-II/CD86/PD-L1). The receptor motifs represent 2.3% ± 1.2%, 0.6% ± 0.5% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, ** p < 0.01; *** p < 0.001; n.s., not significant) Fig.27. Multiplexed spatial receptor pattern analysis of specific OVA peptide-MHC-I complexes on B16-F10 cells. (A) DNA-PAINT image of B16-F10 cells that transgenically express OVA protein stimulated for 6h with IFN ^^ showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key receptor motif (Motif 1 – MHC-I/PD-L1). The receptor motif represents 29.0% ± 3.5% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 6 cells, *** p < 0.001; n.s., not significant) Fig.28. Validation of CD80 KO cDC1 cells. (A) Sort strategy for cDC1 cells isolation from mouse bone marrow Flt3 cultures. Live cells were split into cDCs and pDCs based on relative B220 and MHC-II expression (top plots). Within the cDC gated cells, CD11clow cells were excluded, and cDCs were split into cDC1 and cDC2 based on relative SIRPa and CD24 expression, with cDC1 cells sorted for imaging. (B) Prior to Flt3 culture, mouse bone marrow was electroporated with Cd80 (or control) sgRNA/Cas9 RNPs to delete the C80 gene. cDC1 cells were then differentiated from the bone marrow in Flt3 cultures prior to stimulation with 500nM CpG1826 + 100U/ml IFNγ for 6 hours and sorting as in (A) for subsequent imaging experiments. Verification of CD80 deletion on cDC1 cells by flow cytometric staining is shown. Fig.29. Multiplexed spatial receptor pattern analysis of 6 hours stimulated wild-type cDC1 cells. (A) DNA-PAINT image of 6 h stimulated cDC1 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 distinct receptor motifs (Motif 1 & Motif 2 – CD86/CD80/PD-L1, Motif 3 – MHC-I/CD86/CD80, Motif 4 & Motif 5 – MHC- I/CD86/CD80/PD-L1). The motifs represent 9.3% ± 1.9%, 0.4% ± 0.1%, 19.8% ± 0.6% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 11 cells, *** p < 0.001; n.s., not significant) Fig.30. Multiplexed spatial receptor pattern analysis of 6 hours stimulated CD80 KO cDC1 cells. (A) DNA-PAINT image of 6 h stimulated CD80 KO cDC1 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 – MHC-I/PD- L1, Motif 2 – MHC-I/CD86/PD-L1). The motifs represent 46.6% ± 3.0%, 5.3% ± 1.7% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 11 cells, *** p < 0.001; n.s., not significant) Fig.31. Multiplexed spatial receptor pattern analysis of non-stimulated wild-type cDC1 cells. (A) DNA-PAINT image of non-stimulated cDC1 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 4 distinct receptor motifs (Motif 1 – MHC-II/CD86/CD80, Motif 2 – MHC-II/CD86/CD80/PD-L1, Motif 3 – MHC-I/CD86/CD80/PD-L1). The motifs represent 13.0% ± 1.1%, 8.3% ± 1.8%, 3.6% ± 0.7%, 2.9% ± 1.5% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 14 cells, *** p < 0.001; n.s., not significant) Fig.32. Multiplexed spatial receptor pattern analysis of non-stimulated CD80 KO cDC1 cells. (A) DNA-PAINT image of non-stimulated CD80 KO cDC1 cells showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 – PD-L1, Motif 2 – MHC-I/CD86/PD-L1). The motifs represent 31.8% ± 2.0%, 7.8% ± 1.4% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 18 cells, *** p < 0.001; n.s., not significant) Fig.33. Multiplexed spatial receptor pattern analysis of 6 hours stimulated CD80 KO MutuDCs. (A) DNA-PAINT image of 6 h stimulated CD80 KO MutuDCs showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 & Motif 2 & Motif 4 – MHC-I/PD-L1, Motif 3 – MHC-I/CD86/PD-L1). The motifs represent 19.3% ± 2.7%, 5.7% ± 2.6% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of three independent experiments and 10 cells, *** p < 0.001; n.s., not significant) Fig.34. Validation of CD80-overexpressing B16-F10 cell lines. B16-F10 cells were retrovirally transduced using MSCV-mCD80 (IRES-mCherry) retroviral vectors to overexpress either wild-type CD80 or mutant CD80-L107E. mCherry expressing cells were then sorted by flow cytometry to generate stably transduced cell lines. Representative flow cytometric plots showing mCherry versus CD80 staining are included for all 3 cell lines. Fig.35. Multiplexed spatial receptor pattern analysis of 6 hours stimulated CD80- overexpressing B16-F10 cells. (A) DNA-PAINT image of 6 h stimulated CD80- overexpressing B16-F10 cell showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 4 distinct receptor motifs (Motif 1 – CD86/CD80/PD-L1, Motif 2 & Motif 3 – MHC-I/CD80/PD-L1, Motif 4 – MHC- I/CD86/CD80, Motif 5 – MHC-I/CD86/CD80/PD-L1). The motifs represent 1.6% ± 0.2%, 20.3% ± 1.7%, 3.4% ± 0.4%, 8.5% ± 1.1% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 10 cells, *** p < 0.001; n.s., not significant) Fig.36. Multiplexed spatial receptor pattern analysis of 6 hours stimulated L107E mutant CD80-overexpressing B16-F10 cells. (A) DNA-PAINT image of 6 h stimulated L107E mutant CD80-overexpressing B16-F10 cell showing receptor positions of the imaged immune checkpoint receptors. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 2 distinct receptor motifs (Motif 1 & Motif 3 – MHC- I/CD80/PD-L1, Motif 2 – MHC-I/PD-L1). The motifs represent 14.8% ± 2.4%, 24.7% ± 5.5% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 10 cells*** p < 0.001; n.s., not significant) Fig.37. Multiplexed spatial receptor pattern analysis of Abatacept-treated MutuDCs. (A) DNA-PAINT image of 6h stimulated MutuDCs cells showing receptor positions of the imaged immune checkpoint receptors. Cells were treated with Abatacept during the last 10 minutes of the overall time. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals 3 distinct receptor motifs (Motif 1 – MHC-I/CD86/CD80/PD-L1, Motif 2 – MHC- I/PD-L1, Motif 3 – MHC-I/MHC-II/CD86/\PD-L1). The receptor motif represents 2.1% ± 0.5%, 27.5% ± 3.7%, 14.6% ± 2.3% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 12 cells, *** p < 0.001; n.s., not significant) Fig.38. Multiplexed spatial receptor pattern analysis of Abatacept-treated CD80 KO MutuDCs. (A) DNA-PAINT image of 6h stimulated CD80 KO MutuDCs showing receptor positions of the imaged immune checkpoint receptors. Cells were treated with Abatacept during the last 10 minutes of the overall time. (B) Correlation matrix for all 36 possible receptor combinations allows classification into “clustered”, “random” or “dispersed” receptor distributions. (C) Receptor interactions visualized via a circle plot. Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines colored by their corresponding receptor identity with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis. (D) NND analysis yields quantitative information about directly interacting receptor species. (E) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. Overall contributions of “clustered” vs. “non-clustered” areas were compared to a CSR distribution of target receptors on the same cell surface. (F) Receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean +- 95% CI). Respective key receptor motifs are highlighted. (G) Quantitative analysis of the key receptor motifs from (F) reveals a single key receptor motif (Motif 1 – MHC-I/CD86/PD-L1). The receptor motif represents 35.0% ± 3.9% of all clusters, respectively. (Data is shown as mean ± 95% confidence interval of two independent experiments and 12 cells, *** p < 0.001; n.s., not significant) Fig.39. Receptor interaction distances. (A) Representative whole-cell analysis of first nearest neighbor distances of MHC-I and PD-L1 receptors on MutuDC. Characteristic receptor interaction distances (dinteraction) are highlighted. (B) Matrix of receptor interaction distances for all possible protein-protein combinations. Average distances are given in nm. Fig.40. DNA origami disc library design and characterization. (A) (Left) Each white dot represents the 36 possible positions on the top side of the DNA origami disc available for ligand functionalization. (Right) the open circles indicate the positions for pMHC and the open squares for PD-L1 molecules. DNA origami discs were either non-functionalized (“empty”) or functionalized either with pMHC only or a combination of pMHC and PD-L1. The latter were arranged into clusters that are either closely spaced (“close”, ^^~14 ^^ ^^) or widely (“far”, ^^~28 ^^ ^^) spaced. (B) Agarose gel analysis (2% agarose) of the DNA origami disc library either functionalized (+ Ligands) or non-functionalized (No Ligand) with pMHC and/or PD-L1 molecules showing properly folded and purified samples. Cy5 signal characterizes assembled DNA origami disc and SYBR Safe signal represents all DNA-based samples. A delayed sample migration indicates successful attachment of the ligands. (C) Immobilization of DNA origami discs on culture plates for T cell proliferation assays. Quantification of DNA origami disc immobilization level was performed by measuring Cy5 intensity levels in each well. As a control, wells with PBS 1x were used. A similar plate coating level was observed across all samples. The Examples illustrate the present invention. Examples The present invention provides, i.a., nucleic acid based molecules, kits and compositions and methods for target detection, particularly for multiplexed imaging. The present invention provides for an application in a biological environment, particularly a single cell, particularly intermolecular single cell multiomics. Intermolecular single cell multiomics provides for localizing a relatively large number of target molecules of a single cell, particularly via super-resolution fluorescence microscopy, particularly PAINT. Figure 1 schematically illustrates a target 1 and a single-stranded nucleic acid molecule 2, the single-stranded nucleic acid molecule 2 comprising a first nucleic acid sequence 4 and a second nucleic acid sequence 6 that differs from the first nucleic acid sequence. The first nucleic acid sequence is capable of specifically hybridizing to a target complementary nucleic acid sequence 10 and the second nucleic acid sequence 6 is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule 20. The single-stranded nucleic acid molecule 2 may comprise a toehold seed 8 as shown, but is not so limited. When the nucleic acid molecule 2 is hybridized to the target complementary nucleic acid sequence 10, the toehold seed 8 may be used for separating these molecules via toehold mediated strand displacement, i.e. by contacting the hybridized molecules with an invader strand as explained above. As shown in Figure 1, the target complementary nucleic acid 10 may be part of a primary binder 14. The primary binder 14 may further comprise a binding molecule 12. The target complementary nucleic acid 10 may be conjugated to the binding molecule 12 in any suitable way, see above. The binding molecule 12 may be capable of selectively binding the target molecule 1. For example, the target molecule 1 may be a protein and the binding molecule 12 may be a corresponding antibody. The target molecule 1 may comprise a nucleic acid sequence, e.g. DNA, and the binding molecule 12 may be a complementary nucleic acid sequence. In this case, the primary binder 14 may be a nucleic acid strand comprising two domains, one is the target complementary nucleic acid sequence 10 and the other one is the binding molecule 12. The target complementary nucleic acid 10 may alternatively be part of the target molecule (not illustrated). In this case, the single-stranded nucleic acid molecule 2 directly hybridizes to the target molecule via the first nucleic acid sequence 4, without any primary binder 14 in between. As shown in both Figures 1 and 2, the complementary nucleic acid sequence being labeled by an imaging molecule 20 may comprise one or more fluorescent imaging molecules 22, for example Alexa488, Cy3b, and/or Atto 647N, and a nucleic acid sequence 24 that is complementary to the second nucleic acid sequence 6. The hybridization kinetics between these two sequences (this hybridization being termed HybB) and the hybridization kinetics between the first nucleic acid sequence 4 and the target complementary nucleic acid sequence 10 (this hybridization being termed HybA) may be chosen such that the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence. This may include an appropriate selection of conditions. For example, HybA may be a reversible but stable binding, while HybB is a transient binding. In order to achieve this, the melting temperatures, the GC content and/or the length of the sequences (i.e. the number of hybridizing base pairs) may be set as explained above. Also, by, additionally or alternatively, setting the amount of single-stranded nucleic acid molecule 2 (of the same type) compared to the amount of the target complementary nucleic acid sequences 10 (of the same type), the appropriate binding behavior may be achieved. When using the ratio of amounts, HybA and HybB may be selected to have similar binding kinetics when the ratio is 1:1. With such tool at hand, a detection method as explained above may be performed. Figure 2 illustrates an exemplary method according to the invention. Inset a) of Figure 2 shows a target molecule 1 bound to a primary binder 14 via a binding molecule 12. The target molecule 1 may be comprised in a single cell that was appropriately prepared for fluorescence imaging as generally known in the art or an artificially created sample such as a surface with DNA origami, etc. Examples for sample preparation are given below. In order to arrive at the target molecule 1 being bound to the primary binder 14, the target molecule 1 may be contacted with the primary binder 14, which may be present in solution. The binding of the target molecule 1 to the primary binder 14 as well as the binding molecule 12 to the target complementary nucleic acid strand 10 are stable under the given conditions. The single-stranded nucleic acid strand 2 is added and contacted with the complementary primary binder 14. The first nucleic acid sequence 4 of the single-stranded nucleic acid strand 2 hybridizes with the target complementary nucleic acid sequence 10 of the primary binder 12, the result being shown in inset b) of Figure 2. Under the given conditions this hybridization (HybA) may be stable. An imaging buffer including the complementary nucleic acid sequence being labeled by an imaging molecule 20 is added, resulting in insert c), which shows transient binding of the second nucleic acid strand 6 of the nucleic acid molecule 2 to the complementary nucleic acid sequence being labeled by an imaging molecule 20. This means that the complementary nucleic acid sequence being labeled by an imaging molecule 20 binds and unbinds to the second nucleic acid strand according to the binding kinetics of HybB. For example, the binding constant kon may be 1071/Ms, 1081/Ms, 1091/Ms (with the unit 1/Ms being equivalent to liter/(mol*s) ) and the unbinding constant koff may be 3/s, 1/s, 10/s, or 50/s. The amount of the added molecules, i.e. single-stranded nucleic acid molecule 2, primary binder 14 and complementary nucleic acid sequence being labeled by an imaging molecule 20 may be, in addition to the considerations regarding the binding kinetics, chosen according to the sample of interest and/or the expected amount of target molecule 1 in the sample. It is emphasized that the order of the step of contacting the sample/target molecule 1 with the primary binder and thus the target complementary nucleic acid sequence 10 and binding molecule 12, the step of contacting the sample/target molecule 1 with the single-stranded nucleic acid molecule, and the step of contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule 20 under appropriate conditions may be performed in any order. However, it is preferred that the step of contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule 20 is the last of the three steps. Now DNA-PAINT image acquisition, or any other suitable acquisition, may be carried out. In DNA-PAINT the imaging molecule 22 provides for a signal in an image when bound to the target molecule 1 (ON-state), here via the nucleic acid molecule 2 and optionally the primary binder 14. When the imaging molecule 22 is not bound to the target molecule 1, it does not provide for a signal that will be counted (OFF-state). Switching between ON- and OFF-states is a stochastic process for each target molecule in the sample. Via the binding kinetics of HybA and HybB the binding may be controlled to enable a sufficiently low number of target molecules 2 in the ON-state in each acquired image to be able to localize the individual target molecules 2 within one image without disturbing signals from neighbors at a distance that is classically unresolvable. By acquiring an image sequence with a sufficient number of images sufficiently separated in time, a sufficiently large number of target molecules 2 will be present as a signal in one of the images. By taking all images into account, all the signals as derived from the individual images may be added and analyzed to localize the individual target molecules 2. A final data set which includes the positions of the localized target molecules 2 may be created and visualized as appropriate, e.g. plotted and/or shown in a single image. In order to be able to correct for drift between the images of an image sequence, a drift marker may be added to the sample prior to starting the image acquisition. Such a drift marker may be any suitable marker known in the art, for example one or more gold beads, fluorescent beads, or fluorescent dyes immobilized to a fix reference in the sample such as a surface of a cover slip or imaging chamber, channel, or well. Preferably the drift marker has a shape and/or combination of spectrally distinguishable fluorescence dyes that enables identification and correction for drift in all three dimensions. After one detection round M (M being an integer that counts through the detection rounds), another detection round M+1 according to inserts a) b) and c) of Figure 2 with a different target molecule 2, meaning a different type of target molecule 2 (and/or a different set of target molecules), may follow. A sufficient number of detection rounds may be performed until all different target molecules of interest are detected. In order to prepare the sample after detection round M for the next detection round M+1, an unbinding step may be performed, e.g. as illustrated in Fig.2 d). The unbinding step may comprise any of the techniques mentioned above. Particularly, and as illustrated in Fig.2, it may include adding an invader strand 30 corresponding to the toehold seed 8 and the first nucleic acid sequence 4. Via toehold mediated strand displacement the invader strand 30 may hybridize with the first nucleic acid sequence 4 and remove the single- stranded nucleic acid molecule 2 from the primary binder 14. In cases of direct binding of the single-stranded nucleic acid molecule 2 to the target molecule 1, i.e. where no primary binder 14 is present, the invader strand 30 may remove the single-stranded nucleic acid molecule 2 from the target molecule 1. Preferably, the complex of the single-stranded nucleic acid molecule 2 and the invader strand 30 is removed from the sample, e.g. by washing with an appropriate buffer solution. Additionally or alternatively, the unbinding step may include applying heat and/or buffer conditions to the sample that support and/or enable dissociation of the single-stranded nucleic acid molecule 2 from the corresponding primary binder 14 and/or target molecule 1. An unbinding step may also be done before the first detection round in the sample with an unspecific technique, e.g. heat and/or buffer conditions, to ensure that the target molecules 1 are free to bind the single-stranded nucleic acid molecules according to the invention in the first detection round. In order to detect different target molecules 1 several options for detecting them in a distinguishable manner are in accordance with the present invention. As already explained, they may be detected in subsequent detection rounds. Another option is to use different imaging molecules 22 that are distinguishable in the applied detection method, e.g. have distinguishable fluorescence spectra. This enables detecting the different target molecules 1 in the same detection round. The different complementary nucleic acid sequences being labeled by an imaging molecule 20 may specifically bind to the different single stranded nucleic acid sequences 2 for the different target molecules 1. Stating it the other way around, the different complementary nucleic acid sequences being labeled by an imaging molecule 20 may be orthogonal and the second nucleic acid strands 6 of the different single stranded nucleic acid sequences 2 may be orthogonal. In case two or more different target molecules 1 are to be detected, the primary binders 14 need to be orthogonal with respect to their binding molecule 12 and with respect to their target complementary nucleic acid sequence 10 in order to allow for specific detection of the two or more target molecules 1. Contacting the sample with the two or more orthogonal primary binders 14 may be a single step in which all orthogonal primary binders 14 are added to the sample at once, preferably in a step upstream the cycle of detection rounds. This means that the sample may first be contacted with all primary binders 14, and then the cycle of M detection rounds starts with the first detection round by contacting the sample with the first single-stranded nucleic acid molecule as described above. Alternatively, adding one or more but not all orthogonal primary binders 14 may be a step upstream of the detection cycle, and the rest of the orthogonal primary binders 14 may be added to the sample as part of one or more detection rounds as described above. The orthogonal primary binders 14 may be grouped for addition to the sample according to reaction condition requirements and/or constraints that are given by the way of coding the labeling of the different target molecules 1 (see also explanations relating to codebook below). Primary binders 14 may not be required for target molecules 1 that may be directly bound by single-stranded nucleic acid molecules 2, for example for target molecules comprising the same type of nucleic acid as the single-stranded nucleic acid molecule 2. In this case, the target complementary nucleic acid sequence 10 is a domain of the target molecule 2. Accordingly, the step of contacting such target molecules 1 with primary binders 14 may be omitted. Alternatively, it is also possible to have one or more additional intermediate binding molecules (not shown) between the primary binder 14 and the single-stranded nucleic acid molecule 2. In this respect reference is made to the above provided example of biotin and streptavidin-labelled antibodies. The step of adding a single-stranded nucleic acid molecule 2 to the sample may be performed at the same time as adding the target complementary nucleic acid sequence 14. Particularly, the nucleic acid molecule 2 may be contacted with the target complementary nucleic acid sequence 14 before adding both to the sample. Thus, the nucleic acid molecule 2 may be hybridized to the target complementary nucleic acid sequence 14 when it is added to the sample. The complementary nucleic acid sequences being labeled by an imaging molecule 20 may be added to the sample together with the corresponding nucleic acid molecules 2 and/or primary binders. However, it is preferred to add the complementary nucleic acid sequences being labeled by an imaging molecule 20 after the optional addition of the corresponding primary binders 14 and the corresponding nucleic acid molecules 2, particularly after some incubation time in order to allow for the primary binders 14 and the nucleic acid molecules 2 to stably bind. The first nucleic acid sequence 4 of the single-stranded nucleic acid molecule 2 may have a length that is appropriate for the respective application. In general, the length may be as short as possible to avoid unnescessary long reaction time, and as long as necessary to provide for the required specificity and number of orthogonal sequences. For example, the first nucleic acid sequence 4 may have a length of 4 to 30 nucleotides, preferably 16 to 24 nucleotides. Additionally or alternatively, the first nucleic acid sequence may have a GC-content of 45%- 55%, preferably 50%. The GC-content may be used to set the binding kinetics as appropriate. The second nucleic acid sequence 6 of the single-stranded nucleic acid molecule 2 may have a length of The target nucleic acid sequence in a primary binder may have a length of 6nt to 150nt, preferably 10nt to 50 nt, more preferably 12 to 20 nt, for example 21 nt. The nucleic acid sequence being labeled by an imaging molecule may have any suitable length. The nucleic acid sequence being labeled by an imaging molecule may have a length of 4 to 10 nucleotides. Generally, as already explained above, the length of any of the above mentioned nucleic acid sequences may also be used, within the constraints of specificity and orthogonality, for setting the binding kinetics to the complementary nucleic acid sequence. The present invention provides for several advantages. The invention uses two hybridizations, HybA and HybB. HybA of the first nucleic acid sequence 4 of the single stranded nucleic acid molecule 2 to the target complementary nucleic acid sequence 10 of or bound to the target molecule 1 provides for a high degree of multiplexing. In other words, the number of available orthogonal first sequences 4 is high enough to enable detection of a large number of different target molecules 1 in one sample, e.g. proteins in a single cell. The imaging molecule 22 is indirectly bound to the target molecule 1 via the complementary nucleic acid sequence being labeled by an imaging molecule 20 and the second nucleic acid sequence 6 of the single-stranded nucleic acid molecule 2, i.e. HybB. Thus, the degree of multiplexing is decoupled form the binding kinetics of the complementary nucleic acid sequence being labeled by an imaging molecule 20 via HybB. This is particularly useful for those embodiments in which the second nucleic acid sequences 6 comprise or consist of sequences being selected from (TCC)n, (ACC)n, (CTT)n, (AAC)n, (CT)n, and (AC)n, wherein n is 4 to 12. For example the sequences known from the SPEED- PAINT technique. The complementary nucleic acid sequences being labeled by an imaging molecule 20 have complementary sequences. This enables speed-optimized acquisition of images in the detection rounds. Nevertheless, the multiplexing may be 20 to 30, i.e. such numbers of different target molecules 1 may be imaged in one sample. In order to increase the multiplexing capabilities further, the single-stranded nucleic acid molecule 2 may comprise more than 1 second nucleic acid sequences 6. Particularly, the single-stranded nucleic acid molecule 2 may comprise 2 or more,3 or more, 4 or more, 5 or more, or 6 or more second nucleic acid sequences 6. This may enable binding of a corresponding number of complementary nucleic acid sequences being labeled by an imaging molecule 20. At least some of the second nucleic acid sequences of one single-stranded nucleic acid molecule 2 may be orthogonal. This may enable binding of orthogonal complementary nucleic acid sequences being labeled by an imaging molecule 20 and thus different imaging molecules 22. The same type of imaging molecules 22 may be bound to one nucleic acid molecule 2 via the same type of second nucleic acid sequence 6 or via different types of second nucleic acid sequence 6. Additionally or alternatively, multiplexing may be increased by binding more than one primary binder 14 per target molecule 1. Particularly, 2 or more, 3 or more, 4 or more, 5 or more, or 6 or more primary binders 14 and thus corresponding single stranded nucleic acid molecules 2 may be used per target molecule 1. However, for both cases multiple second nucleic acid sequences 6 per single-stranded nucleic acid molecule and multiple primary binder 14 per target molecule 1 care needs to be taken not to corrupt the binding kinetics of HybA and/or HybB. The singe-stranded nucleic acid molecules 2 according to the invention provide for another advantage: Using them as imager targets and washing them off after a detection round stabilizes imager accessibility of the target molecule 1. Nucleic acid strands that hybridize to complementary nucleic acid sequences being labeled by an imaging molecule 20 tend to get corrupted by photodamage in prolonged DNA-PAINT experiments [doi:10.3390/molecules23123165]. Therefore, replacing the single-stranded nucleic acid molecules in between detection rounds provides for fresh single-stranded nucleic acid molecules in the subsequent detection round, thus increasing the reliability. Figure 3 illustrates a proof of principle experiment. Insert A shows the design of an experiment according to the present invention. Insert B shows an exemplary image of the respective experiment. On the left of insert A, artificial samples 40 based on DNA origami are schematically shown. DNA origami is a technique generally known in the art. The term is used for DNA that is artificially designed to fold into a specific structure or shape. In short, a long scaffold strand and multiple small so called staple strands are designed. When the staple strands bind to the scaffold strand the scaffold is forced into a desired shape. It is useful to know that DNA origami may provide for very well defined structures with defined lengths and distances in between certain points, which can be simply addressed with DNA strands by extending staple strands, such that single-stranded nucleic acid sequences are present at the respective positions. Therefore, DNA origami is often used as a microscopy standard or ruler. Further information may be found in (Steinhauer et al. (2009), Angewandte Chemie Int. Ed., 48(47): 8870-8873; Schnitzbauer et al. (2017), Nature Protocols, 12:1198–1228), the content of which is incorporated herein by reference in its entirety. The structure of the DNA-origami was chosen to have an overall shape approximating a cuboid, with a significant number of helices lying essentially parallel, and a thickness of only one layer of helices. The comic in figure 3 shows a top view of a DNA-origami cuboid 40 including the addressable staple strand positions 42 of the structure, represented by hexagons (only one of them is referenced with reference sign 42). Some of the positions 42 were designed to be a first type of target molecules 1. Some of the positions 42, represented by dotted hexagons, were designed to be regular DNA-PAINT targets as known in the art. A pattern was created by providing the target molecules 1 of the first type at 12 positions 42 with a spacing of 20 nm, indicated by cross hatch, and the regular DNA-PAINT targets at clusters of positions 42 in the four corners of the shown top of the cuboid 40. A second type of these DNA origami were created, using different extension sequences at the cross hatch positions (second type of target molecules 1), acting as target complementary nucleic acid sequences as described herein below. A first DNA origami structure was designed to comprise a first target complementary nucleic acid sequence 10 at the cross hatched positions and a second DNA origami structure was designed to comprise a second target complementary nucleic acid sequence 10 at the cross hatched positions. In all cases, the DNA-origami were immobilized on a surface of a cover slip of a reaction chamber. Labelling of the target molecules 1 (in this case the respective positions of the scaffold) at the cross-hatched positions 42 with the target complementary nucleic acids 10 occurred during DNA origami folding by adding first primary binders 14 (extended staples) with the first binding molecule 12 (staple sequence) and the first target complementary nucleic acid sequence 10 to assemble the first DNA origami structure, and the second primary binders 14 to assemble the second DNA origami structures. Labelling (i.e. in this case attaching a docking strand) of the regular DNA-PAINT targets (in this case the respective positions of the scaffold) at the dotted positions 42 for standard DNA-PAINT was done during DNA origami folding by extending the respective staple strand sequences with a docking strand sequence. During the experiment, the first and second DNA origami comprising the first and second target complementary nucleic acid sequences 10 were mixed and immobilized on a surface of a cover slip of a reaction chamber. Detection occurred in three detection rounds, a first detection round using standard DNA-PAINT to localize the DNA origami using the regular DNA-PAINT targets (dotted positions in Fig.3), and a second and third detection round according to the present invention for the first and second type target molecule 1 represented by cross hatched positions. The sample, i.e. the first and second DNA origami which already incorporated standard DNA PAINT docking strands at the dotted positions and a first and second primary binder 14, respectively, at the cross hatched positions, was contacted in the first imaging round with an imager capable of transiently binding the standard DNA PAINT docking strands and a standard DNA PAINT dataset was recorded. In the second detection round, a first single-stranded nucleic acid molecule, comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to the first target complementary nucleic acid sequence 10 and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence, was added to the sample. Then, an imaging buffer comprising the complementary nucleic acid sequence being labeled by an imaging molecule 20 was added. Then, detection of the imaging molecule 22 was performed according to PAINT. Thereafter, toehold mediated removal of the first nucleic acid molecule 2 from the first primary binder 14 was conducted. A third detection round was performed for the second target complementary nucleic acid sequence 10 with the second nucleic acid molecule 2 but with the same type of complementary nucleic acid sequence being labeled by an imaging molecule 20. The two DNA origami structures and their different target molecule types 1 were distinguished via their imaging time (second detection round or third detection round). The result of the experiment is shown in the false color picture in panel B of Fig.3. The top row shows signal from a first DNA origami structure and the bottom row shows signal from a second DNA origami structure. The left, second to left and second to right columns show the signal for the first, second and third imaging round, respectively, and the right column shows the overlayed signal. The duration of an experiment with the technique described so far still increases for every additional target molecule 1, i.e. the more target molecules to be detected the longer the overall detection time. In order to further increase the overall detection time and/or increase the multiplexing capabilities another aspect of the present invention is related to a specific way of barcoding the target molecules 1, which will be described in the following with reference to Fig.4. Figure 4 shows a scheme for up to 1820-plex single protein imaging in 16 sequential detection rounds. The numbers are only exemplary and depend on the length of the used barcodes and whether a single type of imager and primary binders with exactly one target complementary nucleic acid sequence 10 are used (as in the scheme), or more, which would of course further increase the plexing-number (see above). The concept is as follows: To every type of target molecule 1 a unique barcode (i.e. identification sequence) having M ordered positions is assigned. Every position represents one detection round and is assigned with either “to be imaged” or “not to be imaged”. Thus, the identification sequence defines for every one of the M detection rounds, whether the corresponding type of target molecule 1 is to be labeled and detected according to the invention described above or not. The identification sequences assigned to the target molecules 1 are referred to as a codebook. Such a codebook, also termed encoding scheme, is represented in Fig.4D for M=16 detection rounds and N types of target molecules Seg1, Seg2, …, SegN. In the shown codebook, “to be imaged” is represented by “1” and “not to be imaged” is represented by “0”. For every type of target molecule 1 a unique single-stranded nucleic acid molecule 2 is used, as represented by the library in Fig.4B. The types of single-stranded nucleic acid molecules 2 have orthogonal first nucleic acid sequences 4 but the same second nucleic acid sequence and are consequently configured to bind the same nucleic acid sequence labeled by an imaging molecule 20, i.e. the same type of imaging molecule 22. (Note that in other embodiments different second nucleic acid sequences 2 and different types of complementary nucleic acid sequences being labeled by an imaging molecule and different types of imaging molecules may be used.) In Fig.4C, the pictures schematically illustrate a portion of a cell is schematically illustrated by the thick black curved lines (note that this is an exemplary environment). Target molecules 1 of different types are represented by hexagons with different numbers (T1, T4, T5 and T8). Figure 4C depicts three of 16 detection rounds, namely round 1 in the left column, round 2 in the middle column and round 16 in the right column. The top row of Fig.4C schematically depicts the nucleic acid strands that are present/bound. The bottom row shows schematic illustrations of what is detected in imaging. In the shown detection round M, all target molecules 1 that have been assigned with “to be imaged” in that detection round M as set out in their identification sequence are detected as described herein. For example, in detection round 1 (left column of fig.4C) target molecules T5 and T8 are labelled, as indicated by the black stars representing an imaging molecule 22. Target molecules of type T5 and T8 are bound by corresponding primary binders (represented by the short black straight line extending from the hexagons; note the different degrees of abstraction of the drawn molecules in different figures as explained in Fig.4A), which present target specific target complementary nucleic acid sequences. Secondary binders (long black line) with first nucleic acid sequences corresponding to the respective target complementary nucleic acid sequences have been added to the sample and are hybridized to the target complementary nucleic acid sequence. The secondary probes present the same second nucleic acid sequences capable of transiently binding to the complementary nucleic acid sequence being labelled by an imaging molecule (short black line with black star). It must be noted that, due to this transient binding, the “labelling” is still of the stochastic nature that is the basic principle of the PAINT technique. After imaging, the result of which is represented in the bottom left comic of Fig. 4C (black stars indicate a signal, white/empty stars indicate “no signal”), the secondary probes, which are shown to include a toehold seed (Fig.4B), are removed via addition of corresponding invader strands to the sample and subsequent washing of the sample. In the middle column of Fig.4C the same cell with the same target molecules 1 as in the left column is depicted. However, the next detection round M=2 is visualized: In accordance with the codebook, target molecule type T5 is labelled and imaged, while T1, T4 and T8 are not labelled. In the right column, all target molecules 1 that are to be labeled in detection round M=16 according to their identification sequence are indicated by a black star as being labelled and detected. In between two detection rounds, the secondary probes and the nucleic acid sequences being labelled by an imaging molecule of the previous round are removed via toehold mediated replacement and washing. In the embodiment shown in Fig.4C, primary binders are added such that all target molecule types are bound to corresponding primary binders before starting the imaging rounds and adding the first secondary probes. As already explained, this is optional; the primary binders may also be added individually in the individual detection rounds, e.g. together with the secondary probes. Finally, the imaging data collected in the different imaging rounds is processed, particularly aligned. The imaging data in combination with the knowledge, which target types are labelled in which imaging round (i.e. the data from the codebook), the individual target types may not only imaged in high resolution but also be identified. Properties of the identification sequences may be set according to generally known coding theory. For example, the maximum ratio of identification sequence positions being “1” over the total number of identification sequence positions may be chosen to allow for error correction. For example, the ratio may be from 1/16 to 10/16, or from 1/10 to 7/10, or from ¼ to 3/4. Additionally or alternatively, specific rules may be set, defining the minimum number of “0”-positions between neighboring “1”-positions, for example at least 1, at least 2, or at least 3 “0”-postions between neighboring “1”-positions or vice versa at least 1, at least 2, or at least 3 “1”-postions between neighboring “0”-positions. This allows for certain error correction because false negative or false positive detection events may be excluded. According to another aspect the present invention relates to a method of mapping the localization of different target molecules within a sample, preferably within cells of the sample. Optionally, an interaction pattern of the different target molecules based on their localization may be performed, preferably by nearest neighbor-based analysis. It is emphasized that this aspect of the present invention is an additional and independent invention. It is independent of the specific imaging technique and tools, particularly of the imaging techniques and tools mentioned above. Any imaging technique that provides for the required imaging quality may be used. Nevertheless, the imaging techniques and tools mentioned above are among the preferred ones. Molecularly resolved, multiplexed image data may be used to rationally inform the choice and further development of drugs. The image data may not only be used for improving quantification of the target molecules by counting instead of using fluorescence intensity as a metric, but also the key information of target molecule maps such as distances, angles, molecular orientations, oligomerisation, cluster contributions (cluster size, cluster shape, protein ratios, protein motifs) may be used for characterizing differences between samples for better patient stratification as well as developing multivalent drugs that specifically target one type of molecular key motif, thereby increasing specificity and sensitivity in treated patients while reducing/inhibiting undesired side effects as well as significantly reducing development time and costs. The present invention provides to-date unprecedented biomedical understanding and insight into basic principles of cell-cell communication and thus not only in the development of novel multi-specific drugs but also in the characterization of the mechanism of action of already existing as well as novel drugs by being able to characterize the spatial organization of key target molecules (e.g. proteins) at molecular resolution, determining specificity and efficiency of target drug, and molecular reorganization at the nanoscale in response to different drug doses. Thus, it may guide the design of dosing, toxicity and drug combination studies, as well as for patient stratification. This aspect of the present invention is most relevant to drugs that specifically bind to target molecules, e.g. therapeutic and diagnostic antibodies or other binders (e.g. nanobody, aptamer, scFV). For small-molecule drugs (supporting no DNA-modification either due to molecular size and architecture or lack of functional groups feasible for conjugation), the present invention may enable study potential reorganization of the molecular architecture, thus enabling mode of action or biomarker investigations. Indications most relevant to the present invention for drug development are those targetable with therapeutic antibodies, such as cancer, autoimmune diseases, migraine and various infectious diseases (e.g. HIV, HPV, Ebola, Alzheimer’s disease, Crohn’s disease, multiple sclerosis, rheumatic arthritis, as well as cancers like melanoma, non-small cell lung cancer, breast cancer, and others). The following description is given in relation to DNA-Origami and their 42-plex detection using a codebook with only 1 on-bit and 41 off-bits (SUM-PAINT). Some representative results are shown in Fig.5. 42 different DNA origami structures were designed, each with a pattern as shown in Figure 3A, comprising of four corners that can be directly imaged using standard DNA-PAINT, and 12 20nm spaced positions, at which staples were extended by target complementary nucleic acid sequences, which was the same on one type of DNA origami structure but differed between the 42 types of DNA origami structures. See Table 1 for the sequences used. DNA origami sample preparation was done in a 6-channel u-slide (Ibidi Cat.: 80607). First, 50 µl of biotin-labeled bovine albumin (1 mg/ml, Sigma-Aldrich, Catalog number 1002110085, dissolved in buffer A+) was flushed into the chamber and incubated for 3 min. The chamber was subsequently washed with 1 ml of buffer A+ followed by incubation with 200 µl of neutravidin (0.5 mg/ml, ThermoFisher Catalog number 31000, dissolved in buffer A+) for 3 min. Afterwards, the chamber was washed again with 1 ml of A+ and subsequently with 1 ml of B+ buffer. Afterwards, it was incubated with biotin-labeled DNA origami (100pM of each type of DNA origami in buffer B+, totalling to 4.2nM for all 42 DNA origami – and in case of a sample with a single type of DNA origami: 4.2nM of the respective DNA origami) for 3 min. This step was followed by washing with 1 ml B+ buffer and 1 ml 2xSSC buffer. Secondary label (single stranded nucleic acid molecule) incubation was done at a concentration of 100 nM per secondary label used in one round (totalling to 600 nM of secondary labels) for 15 min in hybridization buffer. Finally, the chamber was washed with 5 ml of 2xSSC buffer and 1 ml of B+ buffer until 1 ml of imager solution was applied for and between imaging rounds. After all imaging rounds for one set of secondary labels, the chamber was incubated with dehybridization buffer for 15 min. More explicitly, after immobilization of the 42 DNA origami in one chamber and one DNA origami each in 42 chambers and washing all chambers with buffers B+ and 2xSSC, secondary labels 1, 2, 3, 4, 5, and 6 were incubated at a concentration of 100 nM each in hybridization buffer for 15 min. Subsequently, the chamber was washed with 5ml of 2xSSC and 1ml B+ buffer, and flushed with 1ml R1 imager solution (500 pM R1 in Buffer B+ with 7 µg/ml PCD (Sigma-Aldrich cat. P8279-25UN), 0,4 mg/ml PCA (Sigma-Aldrich 37580-25g-f), and 0,3 mg/ml Trolox (Sigma-Aldrich 238813-1G)). After DNA-PAINT imaging (10000 frames at exposure of 100 ms with an illumination of 25 mW at 561nm) of this imager, the chamber was washed with 3ml B+ buffer, and 1ml R2 imager solution was applied. After sequentially imaging the imagers R1-R6, the secondary labels (single stranded nucleic acid molecules) were stripped off by flushing 1ml of 100nm secondary label-specific toehold strands (as listed in Table 1, 600nm in total) in dehybridization buffer into the chamber and incubating for 15 min, followed by injecting 1ml of new secondary labels at 100 nM each in hybridization buffer and incubating for 15 min. Subsequently, the chamber was washed with 5ml of 2xSSC and 1ml B+ buffer, and flushed with 1ml R1 imager solution, repeating the rounds of imagers. Tables 1 and 2 list the sequences and codes used, and Figure 5 shows representative results, where the single- DNA origami samples could be used to confirm the correct identification of DNA origami structures based on their codes. A code for a given location is created by a binary number with as many digits as DNA-PAINT imaging rounds are performed (in this case 42), where a ‘0’ denotes no imager binding in the respective location in the respective imaging round, while a ‘1’ denotes registered imager binding at that location. For that purpose, DNA-PAINT imaging rounds are taken in chronological order, so for example the third DNA-PAINT imaging round (of imager R3 in the present case) of the second secondary labeling round (II) is the 9th DNA- PAINT imaging round in total, so it is represented in the 9th digit of the code. SeqID No of target Target Barcode complementary SeqID No of SeqID No of No ID sequence Secondary Label toehold 1 Sec1 2727 2685 2769 2 Sec3 2728 2686 2770 3 Sec5 2729 2687 2771 4 Sec7 2730 2688 2772 5 Sec9 2731 2689 2773 6 Sec11 2732 2690 2774 7 Sec2 2733 2691 2775 8 Sec4 2734 2692 2776 9 Sec6 2735 2693 2777 10 Sec8 2736 2694 2778 11 Sec10 2737 2695 2779 12 Sec12 2738 2696 2780 13 Sec74 2739 2697 2781 14 Sec109 2740 2698 2782 15 Sec127 2741 2699 2783 16 Sec131 2742 2700 2784 17 Sec166 2743 2701 2785 18 Sec188 2744 2702 2786 19 Sec76 2745 2703 2787 20 Sec110 2746 2704 2788 21 Sec128 2747 2705 2789 22 Sec152 2748 2706 2790 23 Sec175 2749 2707 2791 24 Sec190 2750 2708 2792 25 Sec101 2751 2709 2793 26 Sec114 2752 2710 2794 27 Sec136 2753 2711 2795 28 Sec153 2754 2712 2796 29 Sec176 2755 2713 2797 30 Sec192 2756 2714 2798 31 Sec108 2757 2715 2799 32 Sec117 2758 2716 2800 33 Sec144 2759 2717 2801 34 Sec155 2760 2718 2802 35 Sec183 2761 2719 2803 36 Sec204 2762 2720 2804 37 Sec103 2763 2721 2805 38 Sec126 2764 2722 2806 39 Sec143 2765 2723 2807 40 Sec161 2766 2724 2808 41 Sec186 2767 2725 2809 42 Sec196 2768 2726 2810 Table 1. Sequences used DNA Imaged in SeqID No SeqID No of Sequence of imager Codebook entry
Figure imgf000046_0002
. g g g y , g , .
Figure imgf000046_0001
The following description is given in relation to DNA-Origami and their 210-plex detection using codebook codes with four on-bits and six off-bits (MER-PAINT), and a Hamming distance of four. For an example of more efficient multiplexing, 210 different DNA origamis were designed using 1220nm spaced positions of target complementary sequences, as described herein above. Secondary labels (single-stranded nucleic acid molecules) for a target complementary sequence were used in four secondary label hybridization rounds each, while they were absent in six secondary label hybridization rounds, leading to codebook entries as described in Table 3. All Second nucleic acid sequences on the secondary labels were SeqID No 3527, and only one imaging round (namely using imager of SeqID No 3533) was performed per secondary label hybridization round. The sequences used are shown in Table 3. Everything else was performed analogous to the example above for 42plex DNA origami detection. SeqID No of SeqID No Codebook entry target of Barcode complementary Secondary SeqID No Target No ID sequence Label of toehold 1 Sec1 3021 2811 3231 0000001111 2 Sec2 3022 2812 3232 0000010111 3 Sec3 3023 2813 3233 0000011011 4 Sec4 3024 2814 3234 0000011101 5 Sec5 3025 2815 3235 0000011110 6 Sec6 3026 2816 3236 0000100111 7 Sec7 3027 2817 3237 0000101011 8 Sec8 3028 2818 3238 0000101101 9 Sec9 3029 2819 3239 0000101110 10 Sec10 3030 2820 3240 0000110011 11 Sec11 3031 2821 3241 0000110101 12 Sec12 3032 2822 3242 0000110110 13 Sec13 3033 2823 3243 0000111001 14 Sec14 3034 2824 3244 0000111010 15 Sec15 3035 2825 3245 0000111100 16 Sec16 3036 2826 3246 0001000111 17 Sec17 3037 2827 3247 0001001011 18 Sec18 3038 2828 3248 0001001101 19 Sec19 3039 2829 3249 0001001110 20 Sec20 3040 2830 3250 0001010011 21 Sec21 3041 2831 3251 0001010101 22 Sec22 3042 2832 3252 0001010110 23 Sec23 3043 2833 3253 0001011001 24 Sec24 3044 2834 3254 0001011010 25 Sec25 3045 2835 3255 0001011100 26 Sec26 3046 2836 3256 0001100011 27 Sec27 3047 2837 3257 0001100101 28 Sec28 3048 2838 3258 0001100110 29 Sec29 3049 2839 3259 0001101001 30 Sec30 3050 2840 3260 0001101010 31 Sec31 3051 2841 3261 0001101100 32 Sec32 3052 2842 3262 0001110001 33 Sec33 3053 2843 3263 0001110010 34 Sec34 3054 2844 3264 0001110100 Sec35 3055 2845 3265 0001111000 Sec36 3056 2846 3266 0010000111 Sec37 3057 2847 3267 0010001011 Sec38 3058 2848 3268 0010001101 Sec39 3059 2849 3269 0010001110 Sec40 3060 2850 3270 0010010011 Sec41 3061 2851 3271 0010010101 Sec42 3062 2852 3272 0010010110 Sec43 3063 2853 3273 0010011001 Sec44 3064 2854 3274 0010011010 Sec45 3065 2855 3275 0010011100 Sec46 3066 2856 3276 0010100011 Sec47 3067 2857 3277 0010100101 Sec48 3068 2858 3278 0010100110 Sec49 3069 2859 3279 0010101001 Sec50 3070 2860 3280 0010101010 Sec51 3071 2861 3281 0010101100 Sec52 3072 2862 3282 0010110001 Sec53 3073 2863 3283 0010110010 Sec54 3074 2864 3284 0010110100 Sec55 3075 2865 3285 0010111000 Sec56 3076 2866 3286 0011000011 Sec57 3077 2867 3287 0011000101 Sec58 3078 2868 3288 0011000110 Sec59 3079 2869 3289 0011001001 Sec60 3080 2870 3290 0011001010 Sec61 3081 2871 3291 0011001100 Sec62 3082 2872 3292 0011010001 Sec63 3083 2873 3293 0011010010 Sec64 3084 2874 3294 0011010100 Sec65 3085 2875 3295 0011011000 Sec66 3086 2876 3296 0011100001 Sec67 3087 2877 3297 0011100010 Sec68 3088 2878 3298 0011100100 Sec69 3089 2879 3299 0011101000 Sec70 3090 2880 3300 0011110000 Sec71 3091 2881 3301 0100000111 Sec72 3092 2882 3302 0100001011 Sec73 3093 2883 3303 0100001101 Sec74 3094 2884 3304 0100001110 Sec75 3095 2885 3305 0100010011 Sec76 3096 2886 3306 0100010101 Sec77 3097 2887 3307 0100010110 Sec78 3098 2888 3308 0100011001 Sec79 3099 2889 3309 0100011010 Sec80 3100 2890 3310 0100011100 Sec81 3101 2891 3311 0100100011 Sec82 3102 2892 3312 0100100101 Sec83 3103 2893 3313 0100100110 Sec84 3104 2894 3314 0100101001 Sec85 3105 2895 3315 0100101010 Sec86 3106 2896 3316 0100101100 Sec87 3107 2897 3317 0100110001 Sec88 3108 2898 3318 0100110010 Sec89 3109 2899 3319 0100110100 Sec90 3110 2900 3320 0100111000 Sec91 3111 2901 3321 0101000011 Sec92 3112 2902 3322 0101000101 Sec93 3113 2903 3323 0101000110 Sec94 3114 2904 3324 0101001001 Sec95 3115 2905 3325 0101001010 Sec96 3116 2906 3326 0101001100 Sec97 3117 2907 3327 0101010001 Sec98 3118 2908 3328 0101010010 Sec99 3119 2909 3329 0101010100 Sec100 3120 2910 3330 0101011000 Sec101 3121 2911 3331 0101100001 Sec102 3122 2912 3332 0101100010 Sec103 3123 2913 3333 0101100100 Sec104 3124 2914 3334 0101101000 Sec105 3125 2915 3335 0101110000 Sec106 3126 2916 3336 0110000011 Sec107 3127 2917 3337 0110000101 Sec108 3128 2918 3338 0110000110 Sec109 3129 2919 3339 0110001001 Sec110 3130 2920 3340 0110001010 Sec111 3131 2921 3341 0110001100 Sec112 3132 2922 3342 0110010001 Sec113 3133 2923 3343 0110010010 Sec114 3134 2924 3344 0110010100 Sec115 3135 2925 3345 0110011000 Sec116 3136 2926 3346 0110100001 Sec117 3137 2927 3347 0110100010 Sec118 3138 2928 3348 0110100100 Sec119 3139 2929 3349 0110101000 Sec120 3140 2930 3350 0110110000 Sec121 3141 2931 3351 0111000001 Sec122 3142 2932 3352 0111000010 Sec123 3143 2933 3353 0111000100 Sec124 3144 2934 3354 0111001000 Sec125 3145 2935 3355 0111010000 Sec126 3146 2936 3356 0111100000 Sec127 3147 2937 3357 1000000111 Sec128 3148 2938 3358 1000001011 Sec129 3149 2939 3359 1000001101 Sec130 3150 2940 3360 1000001110 Sec131 3151 2941 3361 1000010011 Sec132 3152 2942 3362 1000010101 Sec133 3153 2943 3363 1000010110 Sec134 3154 2944 3364 1000011001 Sec135 3155 2945 3365 1000011010 Sec136 3156 2946 3366 1000011100 Sec137 3157 2947 3367 1000100011 Sec138 3158 2948 3368 1000100101 Sec139 3159 2949 3369 1000100110 Sec140 3160 2950 3370 1000101001 Sec141 3161 2951 3371 1000101010 Sec142 3162 2952 3372 1000101100 Sec143 3163 2953 3373 1000110001 Sec144 3164 2954 3374 1000110010 Sec145 3165 2955 3375 1000110100 Sec146 3166 2956 3376 1000111000 Sec147 3167 2957 3377 1001000011 Sec148 3168 2958 3378 1001000101 Sec149 3169 2959 3379 1001000110 Sec150 3170 2960 3380 1001001001 Sec151 3171 2961 3381 1001001010 Sec152 3172 2962 3382 1001001100 Sec153 3173 2963 3383 1001010001 Sec154 3174 2964 3384 1001010010 Sec155 3175 2965 3385 1001010100 Sec156 3176 2966 3386 1001011000 Sec157 3177 2967 3387 1001100001 Sec158 3178 2968 3388 1001100010 Sec159 3179 2969 3389 1001100100 Sec160 3180 2970 3390 1001101000 Sec161 3181 2971 3391 1001110000 Sec162 3182 2972 3392 1010000011 Sec163 3183 2973 3393 1010000101 Sec164 3184 2974 3394 1010000110 Sec165 3185 2975 3395 1010001001 Sec166 3186 2976 3396 1010001010 Sec167 3187 2977 3397 1010001100 Sec168 3188 2978 3398 1010010001 Sec169 3189 2979 3399 1010010010 Sec170 3190 2980 3400 1010010100 Sec171 3191 2981 3401 1010011000 Sec172 3192 2982 3402 1010100001 Sec173 3193 2983 3403 1010100010 Sec174 3194 2984 3404 1010100100 Sec175 3195 2985 3405 1010101000 Sec176 3196 2986 3406 1010110000 Sec177 3197 2987 3407 1011000001 Sec178 3198 2988 3408 1011000010 Sec179 3199 2989 3409 1011000100 Sec180 3200 2990 3410 1011001000 Sec181 3201 2991 3411 1011010000 Sec182 3202 2992 3412 1011100000 Sec183 3203 2993 3413 1100000011 Sec184 3204 2994 3414 1100000101 Sec185 3205 2995 3415 1100000110 Sec186 3206 2996 3416 1100001001 Sec187 3207 2997 3417 1100001010 Sec188 3208 2998 3418 1100001100 Sec189 3209 2999 3419 1100010001 Sec190 3210 3000 3420 1100010010 Sec191 3211 3001 3421 1100010100 Sec192 3212 3002 3422 1100011000 Sec193 3213 3003 3423 1100100001 Sec194 3214 3004 3424 1100100010 Sec195 3215 3005 3425 1100100100 Sec196 3216 3006 3426 1100101000 Sec197 3217 3007 3427 1100110000 Sec198 3218 3008 3428 1101000001 Sec199 3219 3009 3429 1101000010 Sec200 3220 3010 3430 1101000100 Sec201 3221 3011 3431 1101001000 Sec202 3222 3012 3432 1101010000 Sec203 3223 3013 3433 1101100000 Sec204 3224 3014 3434 1110000001 Sec205 3225 3015 3435 1110000010 Sec206 3226 3016 3436 1110000100 Sec207 3227 3017 3437 1110001000 Sec208 3228 3018 3438 1110010000 Sec209 3229 3019 3439 1110100000 Sec210 3230 3020 3440 1111000000 Table 3. Sequences used for DNA origami MERPAINT The following description is given in relation to the multiplexed analysis of neuronal proteins in neurons using the invention described herein. Rat primary hippocampal neurons were fixed using 4% paraformaldehyde for 30 min at room temperature, washed four times with 1ml PBS and submerged in PBS. For imaging, the neurons were washed three times with 1ml PBS again and incubated in blocking buffer 2 for blocking and permeabilization for 45 min. Afterwards, the sample was washed three times with 1 ml PBS, and a 1:3 dilution of Gold nanoparticles (90nm Standard Gold Nanoparticles, Cytodiagnostics, Inc., cat.: G-90-100) were incubated for 5 min as fiducial markers for drift correction. Target complementary nucleic acids were conjugated to the C-terminus of their respective secondary binding molecules (in this case secondary nanobodies) according to table 4 and 5 (Mouse: FluoTag- X2 anti-Mouse Ig kappa light chain, NanoTag Biotechnologies; Rabbit: FluoTag-X2 anti- Rabbit IgG, NanoTag Biotechnologies; Synaptotagmin: FluoTag-X2 anti-Synaptotagmin 1, NanoTag Biotechnologies, PSD95: sdAb anti-PSD95, NanoTag Biotechnologies) to form secondary binders: First, buffer was exchanged to 1× PBS + 5 mM EDTA, pH 7.0 using Amicon centrifugal filters (10k Da molecular weight cut-off) and free cysteines were reacted with 20-fold molar excess of bifunctional maleimide-DBCO linker (Sigma Aldrich, cat: 760668) for 2-3 hours on ice. Unreacted linker was removed by buffer exchange to PBS using Amicon centrifugal filters. Azide-functionalized DNA was added with 3-5 molar excess to the DBCO-nanobody and reacted overnight at 4°C. Unconjugated nanobody and free azide-DNA was removed by anion exchange using an ÄKTA Pure liquid chromatography system equipped with a Resource Q 1 ml column. Nanobody-DNA concentration was adjusted to 5 µM (in 1xPBS, 50% glycerol, 0.05% NaN3) and stored at -20°C. Target molecule labeling with these secondary binders was done by performing a preincubation of the antibody (binding molecule) with their respective secondary binder in 10 ul antibody incubation buffer at room temperature for 2 h, for each target molecule separately. After the preincubation time, a large excess (molar ratio of 1:2) of unlabeled secondary nanobody was introduced and incubated for 5 min. Subsequently, the six antibody-to-secondary-binder complexes corresponding to the first round of secondary label (single-stranded nucleic acid molecule) hybridization were pooled in 300 ul antibody incubation buffer and incubated in the neuron sample for 90 min. Then the sample was washed five times with 1ml PBS and once with 1 ml Buffer C followed by a postfixation with 2.4% paraformaldehyde in PBS for 7 min. Afterwards, the sample was washed three times with 1ml PBS and once with 1ml 2xSSC buffer and the secondary label (nucleic acid molecule) hybridization for barcoding round 1 was carried out according to table 4 with 100 nM of each secondary label for 20 min (totalling 600 nM). Finally, the sample was washed five times with 1ml 2xSSC buffer and once with 1ml buffer C. The sample was then flushed with 1ml R1 imager solution (concentration as described in Table 4, in buffer C with 7 µg/ml PCD (Sigma-Aldrich cat. P8279-25UN), 0,4 mg/ml PCA (Sigma-Aldrich 37580-25g-f), and 0,3 mg/ml Trolox (Sigma-Aldrich 238813-1G)). After DNA-PAINT imaging (15 mW at 561nm HILO illumination) of this imager, the chamber was washed with 1ml buffer C, and 1ml R2 imager solution was applied. After sequentially imaging the imagers R1-R6 (SeqID No 3533- 3538), the secondary labels (single-stranded nucleic acid molecules) were blocked by flushing 1ml 100 nM blocking strands per second nucleic acid sequence to block binding of the complementary nucleic acid sequences being labeled by an imager (corresponding to the six imagers previously imaged, totalling 600 nM) in hybridization buffer into the chamber and incubating for 15 min, followed by injecting 1ml of new secondary labels at 100 nM each in hybridization buffer and incubating for 15 min. Subsequently, the chamber was washed with 5ml of 2xSSC and 1ml C buffer, and flushed with 1ml R1 imager solution (concentration as described in Table 4, in C buffer), repeating the rounds of imagers. Fig 6 shows the results of the described 29-plex SUM-PAINT experiment mapping the 3D protein distribution of a single neuron with all the 29 channels color-coded and overlayed. By restricting the channel display for either only cytoskeleton or synaptic protein, the high specificity of the imaging method can be appreciated, showing distinct signals in both compositions. An individual imaging round (one channel) can be completed in 17 min and yields an average localization precision of 6.6 nm. The gallery with the respective protein distribution and localization precision can be seen in Fig 6 B. Summing up the acquisition time of single channels and adding the transition time between barcoding rounds the whole 29-plex single molecule atlas can be acquired in only 30h of total experimental time. Vendor Cat. seconda
Figure imgf000053_0001
GAAGAA 171
Figure imgf000054_0001
Tabel 4: Target labeling parameters SeqID No of SeqID No SeqID No target of of Barcode complementary Secondary blocking Target No ID sequence Label strand 1 Sec1 3470 3441 3498 2 Sec126 3471 3442 3499 3 Sec127 3472 3443 3500 4 Sec161 3473 3444 3501 5 Sec166 3474 3445 3502 6 Sec192 3475 3446 3503 7 Sec2 3476 3447 3504 8 Sec110 3477 3448 3505 9 Sec136 3478 3449 3506 10 Sec8 3479 3450 3507 11 Sec186 3480 3451 3508 12 Sec65 3481 3452 3509 13 Sec101 3482 3453 3510 14 Sec4 3483 3454 3511 15 Sec144 3484 3455 3512 16 Sec155 3485 3456 3513 17 Sec10 3486 3457 3514 18 Sec12 3487 3458 3515 19 Sec108 3488 3459 3516 20 Sec117 3489 3460 3517 21 Sec67 3490 3461 3518 22 Sec176 3491 3462 3519 23 Sec204 3492 3463 3520 24 Sec76 3493 3464 3521 25 Sec43 3494 3465 3522 26 Sec152 3495 3466 3523 27 Sec9 3496 3467 3524 28 Sec196 3497 3468 3525 29 Sec72 3541 3469 3526 Table 5: Nucleic acid sequences The following description is given in relation to membrane proteins, more specifically immune receptors, but creating direct interaction patterns for intracellular proteins is also contemplated. These may be very relevant in other applications, especially for characterization of the mode of action of drugs, or for biomarker discovery and validation. Other target molecules than proteins are also contemplated. One of the most important interfaces for cell-cell communication is the cell surface. The receptor-ligand interactions engaged during a cell-cell encounter can trigger signalling cascades that have a profound downstream impact upon cell behaviour and differentiation. This is particularly important in the immune system, where the outcome of cellular interactions can dictate whether an immune response is initiated, the nature of the ensuing response, and whether an existing response is sustained or extinguished. In the case of T cells, the surface context in which specific antigen is first encountered on dendritic cells (DCs) determines whether or not T cells are productively activated. The ligands subsequently encountered by activated, effector T cells on the surface of both antigen-presenting cells (such as DCs) and target cells, such as tumour cells, can then either amplify or blunt an established T cell response. Specific classes of activatory and inhibitory surface ligands regulate the outcome of these cell-cell encounters. Surface encounter of specific peptide-MHC antigen complexes alongside co-stimulatory molecules, such as CD80 and CD86, can drive both initial T cell differentiation into effector cells, and subsequent expansion and differentiation of an established effector T cell response. Conversely, antigen encounter in the context of excessive immune checkpoint molecules, such as PD-L1 and PD-L2, can both block initial T cell priming, and restrain effector T cell function and differentiation. Manipulating the activity of co-stimulatory and checkpoint molecules through blocking or agonistic antibodies underpins many current immunotherapy approaches that either amplify anti-tumour T cell immunity, or blunt T cell responses during autoimmune disease. The T cell stimulatory capacity of a given cell type has classically been defined by the absolute cell surface levels of key immunomodulatory ligands. In contrast, the native, single protein spatial organisation of immune regulatory molecules on the cell surface, and how this spatial organisation contributes to T cell control, remains poorly understood. This represents a major knowledge gap, particularly given that protein clustering is a well-defined determinant of surface protein signalling and function. Progress has been limited in this area due to a lack of technologies capable of concurrent spatial mapping of multiple proteins at the single molecule level. Prior super-resolution imaging studies of spatial protein organization on immune cells were typically unable to simultaneously image more than three proteins at a time and/or not of sufficient resolution to resolve individual proteins. For this reason, the spatial organization of the key molecules involved in T cell activation, namely MHC molecules, CD80, CD86, PD-L1 and PD-L2, is poorly characterized. Surface clustering of MHC molecules has been observed by super-resolution imaging, while biochemical and structural studies have suggested that CD80 and CD86 exist as homodimers. In contrast, for PD-L1 and PD-L2 there is conflicting data on whether they homodimerise on the cell surface. Even less is known about how these molecules interact with each other apart from a well characterised interaction between CD80 and PD-L1 that blocks PD-L1 function and promotes immune activation. Furthermore, the higher order structures that these proteins form on the cell surface, and how their surface organisation evolves between immune suppressive and activatory cell states, is unknown. To address this knowledge gap and thus insights into cell response modulation, we have imaged the single protein organisation of the key molecules involved in T cell activation on both activated DCs that promote T cell activation, and tumour cells that evade T cell immunity. To overcome the technical limitations of previous studies, we employed multiplexed Exchange- PAINT (Exchange - Point Accumulation for Imaging in Nanoscale Topography) imaging, a super-resolution imaging approach that enables both highly multiplexed imaging of multiple targets, and sufficient resolution to image individual proteins. Collectively, this has allowed us to simultaneously image 6 proteins at single protein resolution (MHC Class I (MHC-I), MHC Class II (MHC-II), CD80, CD86, PD-L1, PD-L2), which represents the most highly multiplexed single protein resolution study conducted on immune cells. In addition to confirming known protein-protein interactions, we identify a range of previously unreported protein-protein associations with important functional implications. Strikingly, we see fundamental differences in the surface architecture of MHC-I and PD-L1 on the surface of tumour cells versus DCs, with MHC-I and PD-L1 aggregation specifically seen on tumour cells. This can be a starting point for the development of more specific drugs, with less side effects. Through investigating differences in the protein-protein interaction network of tumour cells versus DCs, we identify CD80 as a key hub molecule involved in orchestrating these differences in cell surface organisation. Deletion of CD80 caused PD-L1 and MHC-I aggregation on DCs, while CD80 over-expression on tumour cells prevented aggregation. Critically, Abatacept, a clinically approved immunosuppressive fusion protein that disrupts CD80 and PD-L1 interactions, can phenocopy these effects, implying surface remodelling as a contributing factor to clinical efficacy. While the close spacing (14nm) seen in CD80 knock-out cells potently inhibited T cell responses, the increased spacing enforced by CD80 (28nm) rendered PD-L1 unable to restrain T cell activation. Collectively, these data identify a new function for the molecule CD80, namely as a cell surface organiser that rewires surface protein clustering into a state that favours T cell activation. More broadly, our data provide the first highly multiplexed, single protein view of the cells that influence T cell immunity. This and similar studies leading to comparable insights can be performed by performing the following steps: Detailed step descriptions and specific example: 0. TERMINOLOGY: Target molecule (of interest) – the target to be labeled Binding molecule – the affinity molecule/antibody/antibody mimetic/…. to tag the target molecule Secondary binder – an affinity molecule tagging the binding molecule, either directly fluorescently labeled, or labeled with a DNA-PAINT docking sequence Primary binder – Binding molecule, conjugated with DNA oligo comprising an imager docking strand Reference target – a fusion to the Target molecule (in test systems), which itself can be tagged (e.g. Fluorescent protein ,ALFA-tag, Halo-tag, etc Reference binder– affinity molecule tagging the Reference target 1. Binding molecule evaluation and development Binding molecule evaluation and development is an optional aspect of the present invention. This also applies to all of its sub-aspects. As mentioned above, for optimal sample analysis, it may be beneficial to first characterize binding molecules and/or primary binders that are later used for labelling target molecules. This characterization can then be integrated into the data evaluation as a calibration. 1.1 Bare binding molecule preselection 1.1.1 Sample preparation for bare binding molecule preselection: Required steps: 1. Seed cells that preferably do not endogenously express one or more target molecules of interest on a microscopy slide. 2. Transfect cells with a target molecule construct which leads them to express a protein fusion of the target molecule of interest (e.g. MHC-I, MHC-II, CD86, CD80, PD-L1, PDL2), and one or more reference targets for reference labelling (e.g. epitope tags like ALFA-tag, Halo- tag, SNAP tag, SPOT tag, FLAG-tag, His-Tag, sortase tag or fluorescent proteins), unnatural amino acid labeling or gene editing (e.g. CRISPR). 3. Incubate cells until target molecule construct will be expressed. 4. Cell fixation and permeabilization. 5. binding molecule and secondary binder binding according to version 1 (preferably if the cells express a single target molecule): o Incubate a binding molecule with the cell sample to let it attach to the target molecule; o Incubate a secondary binder against the binding molecule in the cell sample, where the secondary binder comprises a modification for stable or transient fluorescence (e.g. fluorophore modification, or DNA oligomer conjugation comprising a docking sequence for a complementary nucleic acid sequence being labelled by an imaging molecule, preferably a DNA-PAINT imager). 6. Binding molecule and secondary binder binding according to version 2 (preferably if the cells express multiple target molecules each with a unique reference target) – as an alternative to step 5, version 1: o Incubate a secondary binder against the binding molecule in bulk solution, where the secondary binder comprises a modification for stable or transient fluorescence (e.g. fluorophore modification, or DNA oligomer conjugation comprising a docking sequence for a complementary nucleic acid sequence being labelled by an imaging molecule, preferably a DNA-PAINT imager). O Incubate the complex of secondary binder and binding molecule against the sample, to let it attach to the target molecule. Optional steps: 1. Add fiducial markers, e.g. gold nanoparticles. 2. Perform post-fixation. 3. Create test samples for each target protein and/or binding molecule separately, or test for two or more target molecules of interest in one sample. Use different DNA-PAINT imagers and second nucleic acid sequences that are capable of transiently binding to a complementary nucleic acid sequence being labeled by the DNA-PAINT imagers if combining. One DNA-PAINT imager per second nucleic acid sequence is used. 4. Label the reference targets, i.e. e.g. epitope tags like ALFA-tag, Halo-tag, SNAP tag, SPOT tag, FLAG-tag, His-Tag, sortase tag or fluorescent proteins, as chosen in required step 2. This can be done either via reference binders that are directly fluorescently labeled, or reference binders that are DNA-conjugaed for e.g. conventional DNA-PAINT imaging. This step can, e.g., be left out if the fluorescence of fused fluorescent proteins is used as a reference. Specific Example: CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers the day prior to transfection at a density of 15000 cells per cm2. CHO cells were transfected with a single receptor construct (one of the following set in each sample: mEGFP-ALFA-MHC-I, mEGFPALFA-MHC-II, mEGFP-ALFA-CD86, mEGFP-ALFA-CD80, meGFP-ALFA-PD-L1, meGFP- ALFA-PDL2) for binding molecule characterization using Lipofectamine LTX as specified by the manufacturer. CHO cells were allowed to express mEGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100U/ml Penicilin and 100μg/ml Streptomycin followed by fixation.4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% Triton-X-100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C. Non-transfected CHO cells served as a reference. Binding molecules (one each for the corresponding samples: CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2) and ALFA-tag nanobody (as reference binders) were diluted in blocking buffer and added at a final concentration of 50nM each for 90min at 24°C. Unbound binding molecules and reference binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Fluorescently labelled secondary antibodies targeting binding molecules (Secondary binders) were dissolved in blocking buffer and added at a final concentration of 100nM each for 60min at 24°C (to the corresponding sample: CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2). Unbound secondary antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post- fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS. Subsequently, 250 μl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. 1.1.2 Binding molecule preselection Imaging: Required steps: 1. Perform at least two-plex fluorescence imaging of the sample cells, by using two-plex DNA-PAINT or optionally by chromatic splitting using dichroic mirrors or filters, preferentially using light sheet, confocal, epifluorescence or TIRF microscopy. Continuation of the Specific Example: Prior to image acquisition, all fluorophores (e.g. CHO-mEGFP-ALFA-MHC-I, CHO-mCherry- CD80) were deactivated by a high intensity bleach pulse (488 nm, 150 mW at the sample plane, for 1 minute). Cellular imaging was conducted via two subsequent conventional DNA- PAINT imaging rounds (i.e. detection rounds) using distinct imagers for each binding molecule with only one of the imagers present at a time. Using Cy3B as the imaging molecule, Cy3B-conjugated imagers were dissolved in Buffer Z and imager solution was added to the sample to perform conventional DNA-PAINT measurements. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected, followed by incubation of Buffer X for 5min. Then, the next imager solution was introduced. 1.1.3 Binding molecule preselection determination: Required steps: 1. Quantify the number of secondary binder molecules (e.g. secondary nanobody) bound to binding molecules, which in turn are bound to their target molecules. This can be done with various accuracies and precisions, e.g. using one of the following methods: o Using diffraction-limited fluorescence intensity of secondary binder in the test sample as a proxy for secondary binder molecule numbers, optionally in the image region confirmed to be covered by a cell; o Using diffraction-limited fluorescence intensity of the corresponding imager of secondary binder in the test sample, colocalizing with fluorescent protein signal as a proxy for secondary binder molecule numbers; o Performing conventional DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to secondary binder molecule localizations. Using secondary binder molecule counts as a proxy for secondary binder molecule numbers, optionally only those that are in an image region confirmed to be covered by a cell. O Same as the previous method above, but only taking secondary binder molecule localizations into account which are close to a reference binder localization; 2. Quantify the number of secondary binder molecules (e.g. secondary nanobody) bound to binding molecules, which in turn are not bound to their respective target molecules. This can be done with various accuracies and precisions; e.g. using one of the following methods: o Using diffraction-limited fluorescence intensity of secondary binder in the control sample as a metric, optionally in the image region confirmed to be covered by a cell; o Using diffraction-limited fluorescence intensity of the corresponding imager of secondary binder in the test sample as a proxy, optionally in the image region confirmed to be covered by a cell; o Performing DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to secondary binder molecule localizations. Using secondary binder molecule counts as a proxy, optionally only those that are in an image region confirmed to be covered by a cell. 3. Calculate a metric for bare binding molecule selection, e.g. the quotient of the number of secondary binders bound to the binding molecule and further target molecules and the sum of the number of secondary binders bound and not bound to the targets. Continuation of the Specific Example: For Diffraction-limited based determination of binder selection, background-corrected integrated fluorescence intensity values were determined from raw fluorescence data for cellular experiments. Prior to integration, fiducial markers (e.g. gold particles) were used to align channels of target secondary binder and reference in the test sample of 2-plex experiments. Underlying binding molecule density was evaluated by correlating respective integrated bulk intensity values to the background-corrected intensity value of a clearly defined monomeric single fluorophore and further calculating corresponding ratios between control and test sample as well as ratios between secondary binder and reference binder density in test sample only. As an alternative to diffraction-limited based determination of binder selection binding molecule selection may be done via conventional DNA-PAINT. For conventional DNA-PAINT based determination of binding molecule selection, raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Optionally, gold particles were also used to align all rounds for conventional 2-plex Exchange-PAINT experiments. After channel alignment, DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Binding molecule selection was evaluated by counting the number of secondary binder signals in control and test sample within the cell or optionally, in the image region confirmed to be covered by a cell, determining underlying secondary binder density and further corresponding ratios between control and test sample as well as ratios between secondary binder and reference binder density in test sample only. 1.2. Primary binder specificity testing After successful preselection, a short DNA oligo (target complementary nucleic acid) can be conjugated onto the binding molecule to make it a primary binder. For a good sample analysis, it is advantageous that the primary binder shows little off-target binding. This can be characterized by primary binder specificity testing. 1.2.1 Sample preparation for primary binder specificity imaging (Version 1). Required steps: 1. Seed cells that do not endogenously express proteins of interest on a microscopy slide. Split into control and test sample. 2. Transfect cells in test sample with a receptor construct, leading them to express a protein fusion of the target molecule (e.g. MHI-I, MHC-II, CD86, CD80, PDL1, PD-L2), optionally an epitope tag (e.g. Alfa-tag, Halo-tag, FLAG-tag, His-Tag, sortase), and a fluorescent protein – or CRISPR edit. 3. Incubate cells until target receptor construct will be expressed on cells of the test sample. 4. Cell fixation and permeabilization. 5. Incubate the primary binder against the target molecule of interest. Optional steps: 1. Add fiducial markers, e.g. gold nanoparticles. 2. Perform post-fixation. 3. Incubate reference binders against the reference target(s), where the reference binders comprise a docking sequence, where this docking sequence is orthogonal to the docking sequence on the primary binder (binding molecule with docking strand). 4. Create test samples for each target protein separately, or test for two or more target molecules in one sample. Use different DNA-PAINT imagers and docking sequences if combining. Specific Example: CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers the day prior to transfection at a density of 5000 cells per cm2. CHO cells were transfected with a single receptor construct (e.g. meGFP-ALFA-MHC-I, meGFP-ALFA-MHC-II, meGFP-ALFA-CD86, meGFP-ALFA-CD80, meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2) for primary binder characterization using Lipofectamine LTX as specified by the manufacturer. CHO cells were allowed to express meGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100U/ml Penicilin and 100μg/ml Streptomycin followed by fixation.4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX- 100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C. Non-transfected CHO cells served as a reference. DNA-conjugated antibodies (CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2) – primary binders - were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound primary binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to addition of gold fiducials, samples were washed with PBS. Subsequently, 250 μl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. 1.2.2 Sample preparation for primary binder specificity imaging (Version 2). Required steps: 1. Seed cells that express the target protein of interest on a microscopy slide. 2. Seed cells from step 1 on a microscopy slide, from which the target protein was knocked out. 3. Cell fixation and permeabilization. 4. Incubate primary binders. Optional steps/aspects: 1. Add fiducial markers, e.g. gold nanoparticles. 2. Create test samples for each target protein separately, or test for two or more target molecules of interest in one sample. Use different DNA-PAINT imagers and docking sequences if combining. Specific Example: MutuDC 1940 wt were seeded on ibidi 8 Well high Glass Bottom chambers at a density of 20000 cells per cm2 several hours prior fixation. For determination of binder specificity for each receptor distinct MutuDC1940 KO (MutuDC1940 MHC-I KO, MutuDC1940 MHC-II KO, MutuDC1940 CD86 KO, MutuDC1940 CD80 KO, MutuDC1940 PD-L1 KO, MutuDC1940 PD-L2 KO) cells served as a reference. Two sets of cells were stimulated using 500nM CpG1826 + 100U/ml IFN ^^ + ovalbumin, and 500nM CpG1826 + 100U/ml IFN ^^, respectively; each for 6 hours at 37°C. 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX-100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C. DNA-conjugated antibodies (CD80, CD86, MHC-I/MHC-I OVA, MHC-II, PD-L1, PD-L2) were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS. Subsequently, 250 μl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. 1.2.3 DNA-PAINT imaging for primary binder specificity determination (Version 1). This step may be performed after Version 1 of sample preparation for primary binder specificity imaging. Required steps: 1. Image test and control samples on a microscope. If required by the options chosen in sample prep (1.2.1), generate one- to higher plex DNA-PAINT data. Optional steps: 1. Use high power illumination to bleach fluorescent proteins if present in the sample. Specific Example: Cellular imaging of non-transfected CHO cells and transiently transfected CHO cells expressing a single target receptor (e.g. meGFP-ALFA-MHC-I, meGFP-ALFA-MHC-II, meGFP- ALFA-CD86, meGFP-ALFA-CD80, meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2) was conducted via imaging single target receptors using distinct imagers for each primary binder (see table 6). Prior to imaging a high intensity bleach pulse was applied until no residual signal from meGFP was observable. Cy3b-conjugated imager strands were dissolved in Buffer Z and imager solution was added to the sample to perform DNA-PAINT measurements. 1.2.4 DNA-PAINT imaging for primary binder specificity determination (Version 2). This step may be performed after Version 2 of sample preparation for primary binder specificity imaging. Required steps: 1. Image test and control samples on a microscope, creating an imager transient binding movie. Imager Imager Cluster
Figure imgf000064_0001
Table 6: Imaging parameters for primary binder specificity imaging. Optional steps: 1. If using one sample for multiple target molecules of interest, wash and add corresponding imager between imaging rounds. Specific Example: Cellular imaging of MutuDC1940 wt and MutuDC1940 KO cells was conducted via imaging single target receptors using distinct imagers for each binder (see Table 6). The imagers were dissolved in Buffer Z and imager solution was added to the sample to perform DNA-PAINT measurements. Imaging parameters are listed in detail in Table 6. 1.2.5 Primary Binder specificity determination Required steps: 1. Quantify the number of primary binders, which are bound to their respective target molecules. This can be done with various accuracies and precisions e.g. using one or a combination of the following methods: o Using diffraction-limited fluorescence intensity in the test sample as a metric, optionally in the image region confirmed to be covered by a cell; o Using diffraction-limited fluorescence intensity of the corresponding imager in the test sample, colocalizing with fluorescent protein signal as a metric. O Performing DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to primary binder molecule localizations. Using primary binder molecule counts as a metric, optionally only those that are in an image region confirmed to be covered by a cell. O Same as the method just above, but only taking primary binder molecule localizations into account which are close to a reference binder localization. 2. Quantify the number of primary binders, which are not bound to their respective target molecules. This can be done with various accuracies and precisions e.g. using one or a combination of the following methods: o Using diffraction-limited fluorescence intensity in the control sample as a metric, optionally in the image region confirmed to be covered by a cell. O Using diffraction-limited fluorescence intensity of the corresponding imager in the test sample as a metric, optionally in the image region confirmed to be covered by a cell. O Performing DNA-PAINT image analysis by identifying and localizing imager binding events and aggregating to primary binder localizations. Using primary binder counts as a metric, optionally only those that are in an image region confirmed to be covered by a cell. 3. Calculate a metric specifying primary binder specificity, e.g. the quotient of the number of primary binders bound to target molecules and the sum of the number of primary binders bound and not bound to target molecules. Optional steps: 1. Optionally, the number of primary binders found to be bound to target molecules can be corrected for the labelling efficiency of the reference binders (e.g. epitope tags like ALFA-tag, Halo-tag, SNAP tag, SPOT tag, FLAG-tag, His-Tag, sortase tag or fluorescent proteins, as chosen in 1.1.1; this is more beneficial if only one reference label is used; with more reference labels, the overall labelling efficiency gets closer to 100%, which renders the correction less useful). Specific Example: For Diffraction-limited based determination of primary binder specificity, background- corrected integrated fluorescence intensity values were determined from raw fluorescence data for cellular experiments. Optionally, prior to integrating, fiducial markers (e.g. gold particles) were used to align channels of primary binder and reference target or binder in the test sample of 2-plex experiments. Underlying primary binder density was evaluated by correlating respective integrated and background corrected bulk intensity values to the background-corrected intensity value of a clearly defined monomeric single fluorophore and further calculating corresponding ratios between control and test sample. For DNA-PAINT based determination of primary binder specificity, raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Optionally, gold particles were also used to align all rounds for 2-plex Exchange-PAINT experiments. After channel alignment, DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centred around local maxima were identified and grouped (assigned a unique identification number). Primary binder specificity was evaluated by counting the number of binder signals in control and test sample within the cell or alternatively, in the image region confirmed to be covered by a cell, determining underlying binder density and further corresponding ratios between control and test sample. 1.3. Primary binder labelling efficiency testing For sample analysis, it may be beneficial to correct the observed data for incomplete binding of primary binders to target molecules. For example, primary binder labelling efficiency may be tested. 1.3.1 Sample preparation for primary binder labelling efficiency imaging: Required steps: 1. Seed cells that do not endogenously express proteins of interest (i.e. target molecules) on a microscopy slide. 2. Transfect cells with a receptor construct which leads them to express a protein fusion of the target molecule of interest (e.g. MHI-I, MHC-II, CD86, CD80, PD-L1, PD-L2), and one or more targets for reference labeling (e.g. epitope tags like Alfa-tag, Halo-tag, FLAG-tag, His- Tag, sortase or fluorescent proteins) – or CRISPR edit. 3. Incubate cells until target receptor construct will be expressed. 4. Cell fixation and permeabilization. 5. Incubate primary binders. 6. Incubate reference binder against the one or more reference targets (e.g. epitope tags and/or the one or more fluorescent proteins as chosen in 1.1.1), the one or more reference targets in either case being conjugated to a DNA-oligo comprising a docking sequence for a complementary nucleic acid sequence being labeled by an imaging molecule orthogonal to other complementary nucleic acid sequence being labeled by imaging molecules used, where the different epitope tags and or fluorescent proteins may be tagged with the same or different docking sequences; those must be orthogonal to the docking sequence used in 5. Optional steps/aspects: 1. Add fiducial markers, e.g. gold nanoparticles. 2. Perform post-fixation. 3. Create test samples for each target protein of interest separately, or test for two or more target molecules of interest in one sample. Use different DNA-PAINT imagers and docking sequences/complementary sequences if combining. Specific Example: CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers the day prior to transfection at a density of 15000 cells per cm2. CHO cells were transfected with a single receptor construct (meGFP-ALFA-MHC-I, meGFP-ALFA-MHC-II, meGFP-ALFA-CD86, meGFPALFA-CD80, meGFP-ALFA-PD-L1, meGFP-ALFA-PD-L2) for primary binder characterization using Lipofectamine LTX as specified by the manufacturer. CHO cells were allowed to express eGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100U/ml Penicilin and 100μg/ml Streptomycin followed by fixation.4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX- 100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C. DNA-conjugated antibodies (CD80, CD86, MHC-I, MHC-II, PD-L1, PD-L2) – primary binders – and ALFA-tag nanobody – reference binders – were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound primary and reference binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior addition of gold fiducials, samples were washed with PBS. Subsequently, 250 μl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. 1.3.22-plex Exchange-PAINT imaging for primary binder labelling efficiency determination Required steps: 1. Use one of the known DNA-PAINT techniques or of the DNA-PAINT techniques presented herein to perform at least 2-plex imaging of target molecule and one or more references, where the one or more references can be labelled with the same or different docking sequences. Multiplexing may be performed e.g. with Exchange-PAINT, spectral separation, or kinetic barcoding. Specific Example: Prior to image acquisition, all fluorophores (e.g. CHO-meGFP-ALFA-MHC-I) were deactivated by a high intensity bleach pulse (wavelength = 488 nm, Power at objective = 150mW, 1 minute). Cellular imaging was conducted via two subsequent imaging rounds using distinct imagers for each primary binder (Table 7) with only one of the imagers present at a time. Cyb3-labeled imagers were dissolved in Buffer Z and imager solution was added to the sample to perform DNA-PAINT measurements. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5min. Then, the next imager solution was introduced. Imaging parameters are listed in detail in Table 7.
Image Clust Clust
Figure imgf000068_0001
Table 7: Imaging parameters for binder labelling efficiency imaging. For imaging, primary binders will be selected which specifically tag target molecules. 1.3.3 Primary Binder labelling efficiency determination Required steps: 1. Quantify the number of primary binder to test, which are bound to their respective molecule. This can be done with various accuracies and precisions e.g. using one of the following methods: o Using diffraction-limited fluorescence intensity of the primary binder-imager and reference-imager binding events in the sample or diffraction-limited fluorescence intensity of fluorescently tagged primary binder and reference signals as a metric, optionally in the image region confirmed to be covered by a cell and/or corrected by binder specificity. O Performing DNA-PAINT image analysis of the primary binder-imager and reference-imager by identifying and localizing imager binding events and aggregating to primary binder/reference molecule localizations. Using primary binder/reference binder counts as a metric, optionally only those that are in an image region confirmed to be covered by a cell, and/or optionally corrected by primary binder specificity. O Same as previous method above, but only taking binding molecule localizations into account which are close to a reference target localization. 2. Calculate a metric specifying primary binder labelling efficiency, e.g. the quotient of the number of target molecules bound to their respective primary binders and the total number of target molecules. Optional steps: 1. Optionally, the number of primary binders found to be bound to target molecules can be corrected for the labelling efficiency of the reference labels (this is more beneficial if only one reference label is used; with more reference labels, the overall labelling efficiency gets closer to 100%, which renders the correction less beneficial). Specific Example: Diffraction-limited analysis option: For Diffraction-limited based determination of binder labelling efficiency, background corrected integrated fluorescence intensity values were determined from raw fluorescence data for cellular experiments. Prior to integration, fiducial markers (e.g. gold particles) were used to align channels of target binder and reference in the test sample of 2-plex experiments. Underlying stoichiometry was determined by a maximum-likelihood estimator to determine position, integrated brightness B, full width at half- maximum (FWHM), and local background of individual signals in the images as disclosed in Moertelmaier, M.; Brameshuber, M.; Linimeier, M.; Schutz, G. J.; Stockinger, H. Thinning out Clusters While Conserving Stoichiometry of Labeling. Appl. Phys. Lett.2005, 87 (26), 1−3. ; and Schmidt, T.; Schutz, G. J.; Gruber, H. J.; Schindler, H. Local Stoichiometries Determined by Counting Individual Molecules. Anal. Chem.1996, 68 (24), 4397−4401, which are incorporated herein by reference in their entirety. Detected signal positions were counted as colocalized if signals colocalized within resolution limits (>200nm). Specific Example: DNA-PAINT analysis alternative: For DNA-PAINT based determination of primary binder labelling efficiency, raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Further gold particles were used to align all subsequent imaging rounds for 2- plex Exchange-PAINT experiments. After channel alignment, DNA-PAINT data were analysed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Primary binder labelling efficiency was determined as follows: 1. Cross-nearest neighbour distances were determined for primary binder and reference binder, corresponding histograms were plotted and colocalizing contributions of primary binder and reference binder were determined based on comparison of experimental and simulated data. Underlying labelling efficiency was extracted by simulating different contributions of randomly distributed monomers (reference) and dimers (target + reference) and fitting respective contributions to optimally fit distribution of experimental data. In the simulation process, incomplete labelling of the reference was taken into account for a better accuracy of the results. Optional steps for primary binder labelling efficiency determination based on DNA-PAINT data with various accuracies and precisions are: 1. Individual primary binder and reference signals were counted as colocalized if signals were found to be within primary binder offset distance. Corresponding primary binder offset distance was determined based on the molecular size of used primary binder and reference binder accounting for the underlying binding epitope of both, primary and reference binder. 2. Cross-nearest neighbour distances were determined for primary and reference binders and a distinct cut-off distance accounting for identical receptor position identity was chosen. Respective cut-off distance was based on the molecular size of used target primary binder and reference primary binder accounting for the underlying binding epitope of both, target and reference primary binder. 3. Cross-nearest neighbour distances were determined for primary and reference binders, corresponding histograms were plotted and colocalizing contributions of primary and reference binders were determined based on comparison of experimental and simulated data. Underlying labelling efficiency was extracted by a multi-gaussian fit of simulated CSR distribution of dimers only (target + reference) or monomers only (reference) to optimally fit experimental data. 4. Off-reference and Off-target binding was accounted for by randomly removing signals according to percentage of non-specific binding from respective region of interest with the labeling efficiencies determined beforehand. 2. Sample preparation for multiplexed immune receptor DNA-PAINT For the analysis of target molecule patterns, samples have to be prepared for imaging. This can be done in multiple ways and also depends on the sample type. 2.1 Preparation of functionalized planar glass-supported lipid bilayers (SLBs): This is generally an optional step. Especially for live-cell analysis, which may be useful for the elucidation of mode of action, it is beneficial to use biomimetic environments, and SLBs are one option for this. This step describes the use of supported lipid bilayers to that end. Other approaches could include embedding cells in a 3D matrix, such as in agarose or PDMS. Required steps: 1. Follow published steps for creating an SLB (e.g. doi: 10.1016/j.surfrep.2006.06.001, doi: 10.1021/acs.langmuir.9b03706, which are incorporated herein by reference in their entirety). Specific Example: Vesicles containing 98% 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 2% 1,2-dioleoyl-sn-glycero-3-[N(5-amino-1-carboxypentyl)iminodiaceticacid]succinyl[nickel salt] (Ni-DOGS NTA) were prepared at a total lipid concentration of 0.5mg ml-1 in 10x Dulbecco’s phosphate-buffered saline (PBS). Vesicle refers to a spheroid compartment of aqueous solution with a lipid bilayer as a boundary and a diameter smaller than 100 µm. Glass coverslips were plasma cleaned for 3 min and attached to the bottom side of an 8-well chamber slide. Coverslips were incubated with a fivefold diluted vesicle solution for 10 min at 24°C, before they were extensively rinsed with PBS. For specific and efficient cell attachment sequential two-step incubation procedure is required. First, SLBs were incubated with DNA-modified lipids (e.g. cholesterol) at a final concentration of 100nM for 60min at 30°C followed by washing excessive lipids off with PBS. Second, SLBs were incubated with histidine-tagged adhesion proteins (e.g. His10-tag ICAM-1) at a final concentration of 2.5 μg mL-1 for 90 min at 37°C and then rinsed off with PBS. PBS was replaced with HBSS supplemented with 2% FBS, 2mM CaCl2 and 2mM MgCl2 prior cell seeding. Figure 10 shows the general concept of the preparation steps according to a preparation method, exemplarily performed in the Specific Example: 1. Contacting a coverslip, preferentially a glass coverslip, with an aqueous solution comprising SUVs. 2. This results in a supported lipid bilayer (SLB) supported by the coverslip. 3. Contacting the SLB with a DNA-modified lipid and contacting the SLB with adhesion proteins. The result is that the SLB presents adhesion proteins and the DNA of the DNA- modified lipids. 4. Contacting the SLB with stimulatory proteins (for example cRGD), or inhibitory proteins, coupled to the reverse complement of the DNA-modified lipids. The result is that the SLB presents adhesion and stimulatory proteins. 2.2 Cell preparation for multiplexed immune receptor DNA-PAINT imaging: In this step, the sample preparation for imaging of single cells is described. For other sample types, such as FFPE or fresh frozen tissue, or extracellular vesicles, purification and/or sample preparation steps are described below. Required steps: 1. Prepare sample on microscopy slide, e.g.: o Allow single-cell suspension to adhere on SLB; o Seed cells from single-cell suspension on a coverslip (optionally treated with Poly-L-lysine, ibitreatR, agarose, ...); o Immobilize fresh frozen tissue section onto a coverslip (e.g. as described in doi: 10.1002/cpns.79) o Reactivate FFPE tissue section and immobilize on a coverslip (e.g. as described in doi: 10.1038/srep40766) o Purify extracellular vesicles and immobilize on a coverslip (e.g. as described in doi: 10.1186/s12929-018-0494-5) 2. Fix and permeabilize sample. 3. Incubate sample with primary binders against multiple target molecules. Optional steps: 1. Treat sample with stimulatory reagents (e.g. CpG1826, IFN ^^, ovalbumin), optionally at different time points. 2. Postfixation. 3. Add fiducial markers (e.g. gold nanoparticles). Specific Example: 6 x 104 cells/cm2 were allowed to adhere onto the SLB for 45 minutes at 37°C. Cells were treated using different stimulatory reagents (e.g. CpG1826, IFN ^^, ovalbumin) for multiple different durations to allow visualization of molecular dynamics and nanoscale protein reorganisation over the course of time. Cells were exclusively treated at physiological relevant temperatures. Unstimulated cells served as a reference. 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX-100 dissolved in PBS for 1 minute, washed with PBS followed by surface passivation with blocking buffer for 60min at 24°C. Primary binders for multiple different target proteins (e.g. CD80, CD86, MHC-I/MHC-I OVA , MHC-II, PD-L1, PD-L2) were dissolved in blocking buffer and added at a final concentration of 100nM each for 90min at 24°C. Unbound primary binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed for 5min at ambient temperatures using distinct post-fixation buffers for different kinds of binding molecules (e.g.2% paraformaldehyde in PBS for antibody-based imaging). Prior to addition of gold fiducials, samples were washed with PBS. Subsequently, 250 μl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. 3 Data acquisition This step describes the imaging procedure for multiplexed molecular imaging of targets. 3.1 Multiplexed cellular imaging of immune receptors: Required steps: 1. Flush imager solution for the current target molecule into the sample. Imager concentration 1pM to 10nM, more preferably 50pM to 500pM; in Buffer, preferably buffer Z, B+ or C+, most preferably buffer Z. 2. Acquire movie of transient imager binding events; exposure time 1ms to 1s, more preferably 50ms-200ms; number of frames 5.000-100.000, more preferably 10.000-30.000. 3. Repeat steps 1&2 for all target molecules. Optional steps: 1. Deactivation of remaining fluorophores, e.g. GFP, for example via high-energy epifluorescent illumination, such as 150 mW at 488 nm for 1 min. 2. Wash sample between imaging rounds, e.g. to deplete sample of imagers of preceding imaging round. 3. Incubation with buffer between imaging rounds to deplete sample of imagers of preceding imaging round. Specific Example: Prior image acquisition, all fluorophores (e.g. endogenous GFP) were deactivated by a high intensity bleach pulse, in this case 150 mW 488 nm epifluorescently illuminating light for one minute. Multiplexed cellular imaging was conducted via multiple subsequent imaging rounds using the six speed-optimized imagers R1-R6 (see, e.g. doi: 10.1038/s41592-020-0869-x) with only one of the imagers present at a time. Fluorophore-conjugated (e.g. Cy3b) imager strands (also called complementary nucleic acid sequence being labeled by an imager) were dissolved in Buffer Z and the imager solution was added to the sample to perform DNA- PAINT measurements. The result of DNA-PAINT measurements was raw imaging data in the 5 form of transient binding movies. In between imaging rounds, the sample was washed with PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5min. Then, the next imager solution was introduced. Imaging parameters for DNA-PAINT cell experiments are listed in detail in Table 3. 10 Power (at Ima er Ima er Ima er Cluster
Figure imgf000073_0001
AGAGAGA 20 20
Figure imgf000074_0001
a e : mmune ecep o magng pa ame e s. 4. Data Evaluation To get an understanding of molecular target patterns, the raw imaging data needs to be processed. This is described here. 4.1 Image analysis: Here, the raw super-resolution imaging data is postprocessed. The step describes getting from multiple single channel transient binding movies to one multiplexed molecular map, which specifies the localizations of all target molecules detected in the sample. Required steps: 1. Identification of images of bound imagers in each frame of the transient binding movies. 2. Localization of the identified bound imagers. 3. Aggregation from imager localizations to target molecule localizations (multiplexed molecular map). Optional steps: 1. Inter-frame drift correction of transient binding movie. 2. Alignment of localizations across transient binding datasets, to compensate for inter- dataset drift. 3. Quantification of target molecules per molecule localization. Specific Example: Raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Gold particles were also used to align all 6 rounds for multiplexed Exchange-PAINT experiments. After channel alignment, DNA-PAINT data were analyzed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picassohttps://github.com/jungmannlab/picasso) for each immune receptor (i.e. primary binder, and thus target molecule) individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Subsequently, the centers of the localization groups were calculated as weighted mean by employing the squared inverse localization precisions as weights. These centers represent the single-protein positions of the respective imaging round. Merging localizations of all rounds yields the final multiplexed DNA-PAINT data/image (multiplexed molecular map). 4.2 Data analysis: The data needs to be aggregated to elucidate the direct interaction patterns present in the sample. This step describes getting from the multiplexed molecular map to one or more direct interaction patterns present in the sample. A direct interaction pattern describes a set of target molecules commonly found in close proximity, and optionally probability distributions of their relative distances. Required steps: 1. Analysis of the multiplexed molecular map and separation into clustered, random and dispersed target molecules, preferentially using Ripley’s K function or a derivative of it. 2. Classification of clustered target molecules into distinct direct interaction patterns. Optional steps: 1. Correction of clustered target molecule groups by labelling efficiency. 2. Correlation of direct interaction patterns to complete spatially random (CSR) distribution to confirm pattern existence. 3. Direct interaction pattern recognition using dBscan. DBscan is short for Density-Based Spatial Clustering of Applications with Noise as generally known in the art. Specific Example: Data analysis – receptor correlation matrix. The interaction of receptor molecules was assessed via modified version of Ripley’s K function [as disclosed, e.g., at https://rss.onlinelibrary.wiley.com/doi/10.1111/j.2517- 6161.1977.tb01615.x]. For interaction between receptor molecules of the same type, we calculated Ripley’s K function as ^^ ^^ ^^ where ^^ is the density of points, ^^ ^^
Figure imgf000075_0001
^^ points within radius ^^ of the i-th point ^^ ^^, and the sum is taken over ^^ points. For quantifying the interaction between receptors of type A with receptors of type B, we used cross-Ripley's K function given by ^^ ^^ where ^^ ^^ ^^ is the number of type B
Figure imgf000075_0002
of a point of type A, and ^^ ^^ is the density of points of type B. In order to assess the amount of clustering or dispersion, we compared the results with a scenario in which the points were present at complete spatial randomness. For this, we simulated 100 realizations of points distributed randomly over the cell area. For each cell, a cell mask was created by applying a Gaussian filter with ^^ = 0.3 to the 2D histogram of all localization data on a 512x512 grid of bins and setting a threshold of 1 for a bin to lie within the cell. The cell area was determined via the total area of bins constituting the cell, and the density by the total number of points divided by the cell area. Next, we calculated the mean and the 2.5 and 97.5 percentiles of all Ripley’s K curves obtained for the simulated random controls. For normalization of Ripley’s K curves, we subtracted the obtained mean from each curve, and normalized the data such that the 2.5 and 97.5 percentiles corresponded to values of –1 and 1, respectively. We calculated the integral of the normalized Ripley’s curve for each receptor and all receptor pairs over a range from 4nm to 200nm. Finally, we averaged the integral values obtained from all individual cells of a single cell type and corresponding condition. The 95% confidence interval of the integral for complete spatial randomness is given by [− ^^, ^^], where ^^ is the length of the integration interval. The patternbar in the corresponding Figures (Fig.12C, Fig.13D, Fig.16B-27B, Fig.29B-33B, Fig.35B-38B) indicates integral values scaling from 2000 (max. clustering) to -2000 (max. dispersion). Data analysis – receptor interactions. In order to quantify the spatial interactions of the receptors, Nearest-Neighbor Distance (NND) based analysis was performed for 6-plex datasets of individual cell types and corresponding conditions. First, the NND of each single-protein were computed for single channel (e.g. CD80-CD80) and for cross channel (e.g. CD80-PD-L1). Then, the experimental histogram of NND was compared to numerical simulations of combinations of populations of oligomers as described below. The most likely proportions of populations of oligomers were obtained through a least-squares optimization procedure. Single channel Completely spatially random (CSR) distributions of monomer and dimer populations were simulated and corresponding NNDs calculated. The algorithm consists of the following steps: 1. The following parameters are used: Density of monomers: number of monomers per unit area. Density of dimers: number of dimers per unit area. Dimer distance: expected distance between the two molecules including the labeling construct. Uncertainty: variability in the position of each molecule due to labeling and localization errors. Labeling efficiency: fraction of ground-truth molecules that will actually be labeled and measured. The Observed density, which has to match the experimental parameter, then becomes Observed density = (Density of monomers + Density of dimers) x Labeling Efficiency. Where not specified differently, the initial parameters were: Density of monomers: 55/µm2, Density of dimers: 45/µm2, Dimer distance: 12 nm, Uncertainty: 5 nm, Labeling efficiency: 50%. 2. Simulation of monomers: a set of spatial coordinates with CSR distribution and given density is generated. Simulation of dimers: a set of spatial coordinates with CSR distribution is generated, representing the center of each dimer. For each dimer center two positions are generated with a random orientation and a distance with expected value Dimer distance. The positions of each pair of molecules are calculated taking into account the Uncertainty parameter (drawn from a gaussian distribution). 3. A random subset of “detectable” molecules is taken from the ground-truth set generated in step 2 (the fraction of molecules remaining equals the Labeling Efficiency) to simulate the labeling process. 4. Calculate nearest neighbor distance distributions (NND) on the subset of detectable molecules. 5. Simulations (steps 1 to 3) are performed for each set of parameters spanning the whole parameter space to be sampled and the set of parameters that optimizes the least-square fit is obtained. In this case the explored parameter were the proportions of monomers and dimers. Cross channel Briefly, six populations were simulated containing proteins of species A and B (e.g. CD80, PDL1): monomers of species A (A), monomers of species B (B), heterodimers (AB), homodimers of species A (AA), homodimers of species B (BB) and heterotetramers (AABB). The algorithm to obtain the optimal proportions is analogous to the single channel case, considering the constraints described below. Constraints on oligomers populations Based on 2nd NND comparison we assumed that possible oligomers formed by two species A and B are either monomers, homodimers, heterodimers or heterotetramers. Under this working hypothesis a set of equations relating the proportions of oligomer populations for single (monomers and homodimers) and cross channel (monomers, homodimers, heterodimers, heterotetramers) can be derived as follows:
Figure imgf000077_0001
^^ ^^ = ^^ ^^0 − 1 2 ^^ ^^ ^^ ^^ = ^^ ^^1 − 1 2 ^^ ^^ , where ^^ ^^ : density of molecules in monomeric state of type A ^^ ^^ : density of molecules in monomeric state of type B ^^ ^^ : density of molecules in homodimeric state of type A (i.e. AA) ^^ ^^ : density of molecules in homodimeric state of type B (i.e. BB) ^^ ^^ : density of molecules in heterodimeric state (i.e AB) ^^ ^^ : density of molecules in heterotetrameric state (i.e. AABB) X : fraction of dimers of species A obtained from the single channel analysis Y : fraction of dimers of species B obtained from the single channel analysis Furthermore, the fact that the number of molecules is always equal or larger than 0 imposes the following constraints ^^ ^^ ^^ ^^ ^^ ^^ ^^1
Figure imgf000077_0002
Data analysis – receptor motifs. Receptor motifs are identified by applying DBSCAN on the localization cluster centers in the multiplexed DNA-PAINT dataset independent of the receptor identity. DBSCAN parameters were chosen based on receptor interaction distances (ε=35nm) and counted as clusters at a minimal number of 3 receptors per cluster (minpts = 3): ε represents the upper limit for direct molecular interactions based on NND distributions and minpts sets the lower limit for the motif size to three receptors, thus excluding isolated dimers. Subsequently, the following metrics are determined for each receptor cluster identified by DBSCAN: 1. The total number of proteins, 2. The number of copies of each receptor species in the cluster, 3. The area of the cluster was defined as the sum of the area in the convex hull and the area of the rim region. The area of the rim is approximated by the perimeter of the convex hull multiplied by the linker uncertainty. Taking the area inside the convex hull alone would lead to unreasonably small areas for clusters of only three or four centers: In the limit case where the centers form nearly a line, the area in the convex hull converges towards zero. To counteract this underestimation of the area covered by the receptors, a rim region around the convex hull was defined based on the uncertainty in position imposed by the binder (d=10nm). Moreover, the number of centers that are part of a DBSCAN cluster is determined for each protein species and compared to the ones that are not assigned to a cluster. The amount of detected motifs that is expected solely from random colocalization of proteins in the absence of any specific direct molecular interaction is estimated via a simulated 6-plex CSR dataset. To generate a comparable CSR dataset for a given cell, the surface density of each receptor species is calculated from the number of receptors found on the cell surface area. The latter is measured via masking the multiplexed DNA-PAINT image. Then, a completely spacially randomly (CSR) distributed dataset is generated for each receptor species at the respective experimental protein density. The data points of the CSR distribution are placed within an area defined by the cell outline. Subsequently, the DBSCAN analysis is performed on the “in silico” dataset in the same way as for the experimental data. Finally, the properties of protein clusters in the cell and in the CSR scenario can be compared. Dominating receptor motifs were identified by grouping individual clusters based on their receptor species, weighted by underlying area and plotted as normalized histograms (mean ± 95% CI). Frequencies of all 63 receptor motifs were then compared to respective CSR distribution and tested for significance. In analogy, receptor contributions for all significant receptor motifs were quantified, compared to respective CSR distributions and tested for significance. Outputs of the data analysis that may be used for diagnostic or drug development: • Cell area • Global receptor density and corresponding ratio for each receptor • Percentage of directly interacting receptor pairs • Degree of oligomerization of directly interacting receptor pairs • Nano- and μ-cluster sizes • Circularity of clusters • Number of clusters per cell • Receptor contribution per cluster • Percentage of each receptor found within or outside of clustered areas • Identification of distinct molecular configurations and respective average architecture (inter/intramolecular distances, angles, stoichiometry) • Dominating molecular key motifs via model-free-averaging or any other structural modelling tool • Pre-clustered/ organelle associated/ intracellular targets analyzed applying masking algorithm and further analysis within masked area 5. Applications/Results In the following, some specific applications and results are presented that have been obtained using aspects of the present invention. 5.1. Application 1| Single protein imaging of immune checkpoint molecules enables spatial surface mapping Figure 11 (split into Figures 11.1 and 11.2 on two pages) shows multiplexed molecular immune receptor imaging and quantification. We examined the molecular reorganization of key immune checkpoint receptors (target molecules) (Fig.11A). Figure 11A schematically shows dendritic cells (DCs) tuning T cell activity by presenting tumor-associated antigens on MHC class I and MHC class II molecules (Signal 1). For effective antitumor immunity, costimulatory molecules are required to support prolonged signal propagation (CD86, CD80 ; white (empty) arrows). Conversely, negative signaling via inhibitory receptors (programmed cell death 1 ligand (PD-L1), PD-L2; black (filled) arrows) limits T cell activation. For examination, we employed multiplexed Exchange-PAINT imaging (Fig.11B). Figure 11B shows an Exchange-PAINT schematic. Exchange-PAINT uses orthogonal strands linked to target molecules 1 (here: receptors) and sequential imaging and washing of complementary imagers for multiplexed super-resolution; see Jungmann et al., Nature Methods, 11:313–318 (2014). A legend of the symbols indicating the different types of receptors (e.g. the x and the +) is provided in Figs.11D and 11E. The multiplexed Exchange-PAINT imaging was used to simultaneously map the MHC class I (H2-Kb protein) and MHC class II molecules, the co-stimulatory receptors CD86 and CD80 and the inhibitory checkpoint receptors PD-L1 and PD-L2 on the surface of individual mouse conventional DCs (MutuDC cDC1 cell line) and melanoma cells (B16-F10 cells) at the single protein level. Here, sequential imaging with different imager strands labelled with the same fluorophore (Cy3b) was utilized to visualize respective immune checkpoint receptors simultaneously on target cells, overcoming the classical diffraction limit using DNA-PAINT as a single-molecule localization technique. Site-specific DNA-labeled binders for each target molecule were validated (Fig.7, 8, 9) prior to unbiased quantitative analysis and the resulting super-resolved 6-plex DNA-PAINT image was generated by aligning and overlaying pseudo- colored images from all rounds (Fig.11C). Figure 11C shows the 6-plex Exchange-PAINT image of a MutuDC stimulated for 6 h with CpG and IFN ^^. The zoom-ins in the right column depict diffraction- limited vs. super-resolution representation (top vs. middle). Subsequent spot analysis allows digitization of receptor molecules and reveals their molecular arrangement (bottom). In stark contrast to the diffraction-limited image, individual receptors could be clearly identified in the respective DNA-PAINT image. Subsequent image analysis to identify individual fluorescent molecular spots enabled digitization of receptor positions and revealed spatial molecular patterns on single cells in unprecedented detail. 5 To determine how surface patterning evolves as DCs are activated by stimuli that promote immunogenicity, we mapped nanoscale molecular reorganization on the surface of dendritic cells stimulated with CpG and IFN ^^ over 24 hours (Fig.11D). This revealed dynamic changes in receptor organization that were seen alongside the expected altered cell morphology and increased absolute surface expression of these markers known to occur during DC activation10 (see https://www.annualreviews.org/doi/full/10.1146/annurev-immunol-061020-053707 ) (Fig.11D). Figure 11D shows morphological changes of MutuDC stimulated with CpG and IFN ^^ over 24 h at molecular resolution. Zoom-ins (bottom row) at multiple different time-points (0 h, 3 h, 6 h, 12 h, 24 h) reveal the spatial details of key receptor interactions. Quantitative analysis of morphology and the underlying receptors enabled measurement of absolute cell surface area15 (Fig.11E, table 9) and absolute receptor density (Fig.11F, table 9) over time (24 h stimulation), with a peak overall receptor density observed at 6 hours after beginning of stimulation in MutuDC (for the mean over multiple experiments). As expected, all markers increased significantly after the beginning of stimulation, particularly CD80 and PD-L1, which varied by a factor of almost 20 over the stimulation time course (table 9). As can be20 seen in Fig. 12E, MutuDC’s maximize their surface area upon stimulation (Data represents median ± 95% CI of 10 cells). MHC
Figure imgf000080_0001
Figure imgf000081_0001
a e : e paa ees a esu s o ppca o 5.2. Application 2 | Multiplexed spatial pattern analysis reveals novel receptor motifs on 5 dendritic cells Figure 12 shows that multiplexed spatial receptor pattern analysis reveals novel key interaction motifs in dendritic cells. (G) Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. A dominating exemplary motif is shown in the zoom-in. (H) Quantitative analysis of the key receptor motif from (G) reveals CD86/CD80/PD-L1 heterotrimers. The motif represents 5.9% ± 2.1% of all clusters. (Data is shown as mean ± 95% confidence interval, *** p < 0.001; n.s., not significant) Receptors can be grouped into either “clustered” or “non-clustered” areas on the cell surface of DCs (Fig.12A). Figure 12A shows a DNA-PAINT image of stimulated MutuDC (6 hours after the beginning of stimulation) showing receptor positions of investigated immune checkpoint receptors. Due to reorganization processes, receptors are either found in groups (= clustered) or are more evenly distributed (= non-clustered) to effectively perform their function within the cell. Statistical analysis allowed classification of all 36 possible receptor interactions into either of three distinct classes: clustered, random, dispersed (Fig.12B,C). Figure 12B shows a schematic sketch of possible spatial distributions of receptors on the cell membrane (top row) together with exemplary zoom-ins of different receptor distributions (clustered = CD80 & PD-L1, random = PD-L2, dispersed = MHC-II). Figure 12C shows a correlation matrix for all 36 possible receptor combinations, which allows classification into “clustered”, “random” or “dispersed” receptor distributions. Thus, we were able for the first time to identify multiple different key receptor interactions in a single cell at true molecular resolution (see Fig.12D, which shows 10 different interactions). However, to better understand the clustering attributes of the receptors, we applied nearest neighbor distance analysis (see Fig.12E: Representative whole-cell analysis of first nearest neighbor distances (short: NND) of MHC-I to CD80 receptors (black) and PD-L2 (white) are shown in histograms with a total area of 1. The fit of simulations to these data is shown as solid and dashed lines, respectively. This allowed us to determine the percentage of molecular interactions for each receptor pair (Fig.12F), accounting for the underlying labeling efficiency of the target binders (fig.9). The numbers represent the fraction of receptors in the group that interact with the receptors on the x-axis. For example, 49% of the MHC-I molecules (left-most cluster in Fig. 12F) are in an interaction cluster with CD80. Prior to stimulation most molecules were not clustered and only minor CD86/CD86 and CD80/CD80 interactions were observed (Fig.16). Starting at 3 h and becoming most prominent at 6h stimulation, there was a substantial remodelling of receptor interactions, particularly interactions involving CD80 (Fig.12F, Fig.16 - 20). At this stimulation duration of 6h, known interactions between CD80 and PD-L1 (20%± 6% of PD-L1 complexed with CD80), CD80 with itself (25% ± 6% of CD80), CD86 with itself (48% ± 7%), and MHC-I with itself (8% ± 2%) were observed, validating that our imaging approach can recapitulate known interactions. It has previously been debated whether PD-L1 is a monomer or homodimer based on structural data, and interestingly we did observe a sub-population of homodimeric PD-L1 on the cell surface (9% ± 5%). Strikingly, though, we also observed many previously unreported interactions, including between CD80 and MHC-I (49%± 15% of MHC-I), CD80 and CD86 (48% ± 7% of CD86), CD86 and MHC-I (17% ± 7% of MHC-I), PD-L1 and MHC-I (28% ± 11% of MHC-I) and CD86 and PD-L1 (30% ± 13% of PD-L1). The clustering between MHC- I and co-stimulatory molecules was of particular interest as it implies that a sub-population of MHC-I is pre-configured to recruit co-stimulatory molecules to the immune synapse upon antigen recognition. Of note, clustering was not observed for all molecules imaged. PD-L2 was either randomly distributed or mildly dispersed from other molecules, while MHC-II was strongly dispersed away from all other imaged molecules. This may imply that MHC-II and PD-L2 are genuinely dispersed away from protein-protein interactions, or that they are recruited into other complexes containing factors that are not being imaged. Nevertheless, small MHC- II clusters were observed at 12h after stimulation (Fig. 19), although they dispersed again at 24h (Fig.20). We next wished to determine if clusters containing 3 or more proteins could be identified. By applying DBSCAN to the receptor centers in the multiplexed DNA-PAINT data independent of the receptor identity, key receptor motifs could be identified (Fig.12G, Fig.18). Global DBSCAN analysis is used to identify receptor motifs within a 35 nm search radius in clustered regions containing at least three receptors. A dominating exemplary motif is shown in the zoom-in of fig.12G. Quantification of receptor motifs (as shown in Fig.12G) identified CD86/CD80/PD-L1 hetero-trimers and MHC-I/CD86/CD80/PD-L1 hetero-tetramers as dominating interaction motifs on dendritic cells stimulated for 6 hours (see Figure 12H, the motif represents 5.9% ± 2.1% of all clusters; black filled bars represent the number of respective receptors expected to occur in the area of the motive due to a spatially random distribution based on their observed density, without motif interaction; white (empty) bars represent the observed receptors in the cluster; only contributors that have statistically significant values are included in the motif. Data is shown as mean ± 95% confidence interval, *** p < 0.001; n.s., not significant) This again aligns with the idea that pre-configured structures that favour T cell activation are present on the DC surface; CD80 and CD86 clustering with MHC-I favours recruitment to the synapse, and CD80 binding to PD-L1 will block PD-L1 suppressive function. Finally, we investigated whether antigen phagocytosed by DCs during activation and cross- presented on MHC-I as specific peptide antigens would similarly incorporate into these surface structures. MutuDCs were stimulated for 6h with CpG and IFN ^^ in the presence of the model protein antigen ovalbumin, and instead of imaging total MHC-I, we specifically measured MHC-I (H-2Kb protein) bound to the SIINFEKL ovalbumin peptide antigen using a specific detection antibody. Similar receptor motifs were identified containing peptide antigen-bound MHC-I molecules, indicating that specific, cross-presented antigens acquired by DCs can be incorporated into these surface motifs (Fig. 21). Collectively, these data support a surface architecture on DCs that favours T cell activation, and identify a large number of previously unknown surface protein interactions of key immune molecules. 5.3. Application 3 | Pronounced PD-L1 and MHC-I clustering on melanoma cells Figure 13 shows that the absence of costimulatory receptors drives formation of PD- L1/MHC-I clusters in B16-F10. Most molecules imaged in this study are expressed not only in dendritic cells but also in tumor cells. As cancer cells have evolved to efficiently evade recognition by surrounding T cells, we speculated that unlike DCs, tumour cells may harbour a surface architecture that favours immune suppression. To address this, we similarly performed 6-plex Exchange-PAINT on the B16-F10 melanoma cell line. As B16-F10 cells minimally express many of the molecules being imaged without stimulation, cells were stimulated for 24 hours with IFN ^ and surface organisation was quantified over time. Consistent with the known effects of IFN ^ on B16-F10 cells, we observed strong induction of MHC-I, PD-L1 and MHC-II alongside a change in cell morphology (Fig.13A). Figure 13A shows morphological changes of stimulated B16-F10 cells (see table 9 for the number of cells analyzed) over 24 h. Zoom-ins (bottom row) at multiple different time-points (0 h, 3 h, 6 h, 12 h, 24 h) reveal key receptor interaction patterns. Quantitative analysis revealed increased surface area (Fig.13B, table 9) and overall receptor densities (Fig.13C, table 9) that were similar to those of activated MutuDCs, although the absolute densities of specific molecules (particularly CD80 and PD-L1) differed between the cell types. Strikingly, though, and unlike DCs, the melanoma cells displayed visible, micron-scale clusters of MHC-I and PD-L1 on their cell surface from 6h onwards (Fig.13A). As shown in Figure 13B, the change in surface area of stimulated B16-F10 (dashed curve, thin line hatching) compared to MutuDC (continuous line, thick line hatching) (median ± 95% CI) shows no significant difference. Figure 13C shows the dynamics of overall receptor density and respective species contributions in B16-F10s (solid outline) vs. MutuDCs (dashed outline) over 24 h stimulation. Overall receptor density is almost identical for dendritic and cancer cells, significant differences can be observed for individual receptor densities. Consistent with this visible difference in surface architecture, the pairwise clustering patterns between proteins on melanoma cells (Fig. 13D: Correlation matrix of all 36 possible receptor combinations allows identification of key receptor interactions in B16-F10 cells.) was noticeably different from that of DCs (Fig.12C), with a strong enrichment of PD-L1 and MHC- I clustering and a paucity of CD80 interactions (likely due to its low surface abundance) (Fig.13D). This was confirmed by directly comparing protein interaction networks between the two cell types, which revealed a CD80-centric interaction map on DCs compared to a MHC- I/PD-L1 dominated interaction network on melanoma cells (Fig.13E). Figure 13E is a visualization of corresponding receptor interactions in B16-F10 cells (top) using a river plot, which reveals homo-interactions of MHC-I, MHC-II and PD-L1. Most significant however are MHC-I/PD-L1 hetero-interactions. A comparison to MutuDCs (bottom) reveals downregulation of costimulatory receptors (mainly CD80) as driving force for formation of MHC-I/PD-L1 clusters. Quantitative NND analysis confirmed this pattern, with a 4-fold increase in the percentage of MHC-I/PD-L1 heterodimers on melanoma cells relative to DCs (Fig.13F,G). Figure 13F shows the quantification of dominating receptor interactions in B16- F10 and a comparison to MutuDC (if possible) shows significant differences in MHC-I, MHC-II and PD-L1 interactions. Figure 13G shows exemplary zoom-ins of key receptor interactions in B16-F10. Data are shown as the mean ± 95% confidence interval. *** p < 0.001. Additionally, a 3-fold increase in the percentage of MHC-I and PD-L1 homodimers was seen on melanoma cells, and unlike on DCs where MHC-II was exclusively non-clustered, MHC-II showed pronounced self-association on melanoma cells. Quantification of key receptor motifs similarly identified MHC-I/PD-L1 clusters as the dominant receptor motif on melanoma cells. Strikingly, even on non-stimulated B16-F10 cells, MHC-I/PD-L1 heteroclusters accounted for 26.4% of all detected clusters, and at 6h this increased to almost 60% of all identified receptor motifs detected on the cell surface (Fig.22-24). Interestingly, from 12 hours post-stimulation onwards, MHC-II was recruited to MHC-I/PD-L1 clusters (Fig. 25-26). As the B16-F10 line used for imaging transgenically expressed ovalbumin, we could again determine if specific peptide antigen-MHC-I complexes similarly incorporate into these structures, and we found comparable PD-L1 clustering with antigen-bound MHC-I as for total MHC-I (Fig. 27). Thus, there are profound differences in surface organisation of immune checkpoint molecules on melanoma cells relative to DCs, with structures that favour PD-L1 recruitment into the synapse upon antigen recognition enriched on cancer cells. These results exemplify the approach of the present invention for precision- development of drugs and the identification of drug targets. 5.4. Application 4 | CD80 is an antagonist of higher order MHC-I/PD-L1 clustering We next sought to identify the mechanism underlying the profound differences in surface architecture between DCs and melanoma cells. Our previous protein-protein network analysis (Fig.13E) identified a CD80-dominant interaction map on DCs but not tumour cells. We thus speculated that the presence or absence of CD80 could be the driving force underpinning the differences in surface organisation between the two cell types. To test this hypothesis, we first generated primary mouse cDC1s with the CD80 gene deleted by CRISPR/Cas9 gene editing alongside control wild-type cells (Fig.28). In Figure 14A, top row, schematic sketches of our understanding of relevant receptor interactions (MHC-I - CD80 - PD- L1; with the receptor identified by signs according to the legend) as revealed by the analysis presented herein are illustrated for cDC1, cDC1 CD80 knock out, B16-F10 and CD80 overexpressing B16-F10 either expressing the physiological costimulatory receptor CD80 or the L107E mutant CD80, lacking its PD-L1 binding domain. Crossed-out receptors indicate knock-out, upward-facing arrows annotated with ‘High’ indicate overexpression, downward- facing arrows annotated with ‘Low’ indicate a low expression level, an encircled minus indicates a noninteracting mutant. Horizontal arrows indicate interaction where one-sided arrows indicate that the interaction is driven by one receptor (e.g. in the case of CD80 overexpression, the interaction between CD80 and PD-L1 is driven by CD80). In the middle and bottom row of Fig.14A, morphological differences are shown for all cells together with corresponding zoom-ins to illustrate key receptor interactions at molecular resolution at 6 h stimulation. Strikingly, CD80 deletion was sufficient to precipitate MHC-I/PD-L1 clustering on DCs at levels that were comparable to those observed on melanoma cells (Fig.14B, Fig.29, 30). In Figure 14B receptor interactions are visualized via a circle plot; Receptor species are positioned at the corners of a hexagon, with the circle size proportional to the average receptor density and connections between interacting receptors marked by lines with the line thickness being proportional to the average interaction score derived from pairwise correlation analysis), with this pattern already detectable prior to DC stimulation (Fig.31-32). Similar patterns were seen in wild-type (Fig.16, 18, 21) and CD80 knock-out MutuDCs (Fig. 33). Conversely, retroviral over-expression of CD80 on melanoma cells (Fig. 34) completely disrupted MHC-I/PD-L1 clustering alongside reinstating PD-L1/CD80 interactions (Fig.14B). Interestingly, it also enforced other key receptor motifs that were almost identical to those seen on DCs (Fig.14B, Fig. 37, 38, 30). To test whether CD80 binding to PD-L1 was required for this surface reorganization effect, B16-F10 cells were engineered to over-express a mutant form of CD80 (CD80 L107E) unable to bind PD-L1. Notably, over-expression of this mutant form of CD80 was unable to block MHC-I/PD-L1 clustering on melanoma cells (Fig.14B, Fig.36). Collectively, this demonstrates that CD80 binding to PD-L1 prevents the aggregation of PD- L1 and MHC-I, and more broadly enforces cell surface changes that favour T cell activation. Soluble CTLA4-Ig fusion reagents, such as Abatacept, are CD80-blocking immunosuppressive reagents that are used in the clinic to treat autoinflammatory conditions, such as rheumatoid arthritis, juvenile idiopathic arthritis, and psoriatic arthritis. While the predominant mode of action is presumed to be due to CD80 blockade, CTLA4-Ig fusion proteins are also known to disrupt the interaction between CD80 and PD-L1, suggesting that PD-L1 liberation may contribute to clinical efficacy. Given that PD-L1 liberation from CD80 also causes cell surface remodelling, we speculated that Abatacept treatment may be able to phenocopy the cell surface reorganisation seen on DCs upon CD80 loss. Consistent with this idea, brief (30 min) Abatacept treatment of activated MutuDCs was sufficient to precipitate MHC-I/PD-L1 clustering (47 ± 1 % MHC-I/PD-L1 heterodimers in Abatacept-treated cells compared to 17 ± 1 % in control cells) (Fig.14C, fig.37; Figure 14C shows information relating to untreated dendritic cells on the left and information relating to Abatacept-treated cells on the right, analogously to the schemes/images/graphs in Figures 14A and 14B. CD80 strongly interacts with MHC-I and PD-L1 in untreated dendritic cells (as shown on the left side in a cartoon, as a representative cluster, and in the interaction network, where the interaction arrows between PD-L1 and CD80 as well as MHC-I and CD80 are strong). Upon Abatacept- treatment, PD-L1 is released from CD80 and clusters with MHC-I (as shown on the rights side in a cartoon, as a representative cluster, and in the interaction network, where the interaction of PD-L1 with CD80 is much lower than on the left, but a strong interaction of PD-L1 with MHC- I can be observed). Therefore, the presence of CD80 alone is not sufficient to inhibit MHC- I/PD-L1 clustering). This effect was CD80 dependent, as Abatacept treatment of CD80 knock- out cells did not alter MHC-I/PD-L1 clustering (Fig. 38). Thus, cell surface remodelling into an immunosuppressive state may contribute to the clinical efficacy of Abatacept. This result exemplifies testing the mode of action of a drug according to the presented invention. Finally, we elucidated if the CD80-enforced nanoscale differences in MHC-I and PD-L1 spacing on the cell surface have functional consequences for T cell activation. To explore the biological impact of receptor spacing, we retro-engineered the identified MHC-I/PD-L1 consensus distances observed in cDC1 versus B16-F10 cells (Fig.39) through nano-controlled display of these proteins on a previously validated, immobilised DNA origami platform (DOI: 10.1021/acsnano.1c05540) (Fig.14D, Fig.40; Fig.14D shows that differences in MHC-I/PD- L1 clustering on DCs versus cancer cells can have profound effects in the functional capacity to stimulate T cells. While DCs showed relatively low levels of MHC-I/PD-L1 clustering (typical distance between MHC-I and PD-L1 of about 30 nm), B16-F10 cells displayed a significant increase in MHC/PD-L1 heterodimers (typical distance between MHC-I and PD-L1 of about 15 nm) which could limit T cell activation (left). The impact of these clustering differences on T cell proliferation was explored using DNA origami structures. MHC-I and PD- L1 were arranged into clusters separated by either a small (close, 10-15 nm) or larger (far, 25- 30 nm) distance, as observed on B16-F10 and DCs, respectively (middle). Additionally, DNA origami structures presenting no (empty) or only stimulatory ligands (pMHCs) were included for comparison (right).), and interrogated the primary T cell response. After three days of co- culture, the close cluster pattern – matching the immuno-suppressive cancer-cell state – indeed blocked T cell proliferation, whereas non-clustered MHC-I/PD-L1 failed to prevent T cell expansion, despite there only being a ~15nm difference in spacing between the two configurations (Fig 14E; Fig.40; Figure 14E shows that spacing between MHC-I and PD-L1 clusters have profound effect on T cell proliferation as shown by the relative cell count. While a close configuration – replicating the immune suppressive state of cancer cells - inhibits T cell proliferation, a larger pattern did not affect T cell stimulation. Data are represented as the mean of three independent experiments, *** p < 0.001). Taken together, our data suggest a clear functional relevance of the observed interaction patterns, underlining the profound regulatory effect of nanometer-scale pattern differences. 6. Drug development The results from the data analysis may be further used for drug development. Specific Example: Using the result above, several strategies for drug development may be conceived: The comparison of the direct interaction patterns of dendritic and cancer cells in the example shows that MHC-II does not interact with any of the other investigated receptor molecules, nor with itself, in the dendritic cell, but on the contrary covers the maximal surface area on the cell avoiding close proximity to itself as well as to other receptors. However, MHC-II shows homo-clustering in the cancer cell, which might be counterintuitive regarding the predominant opinion that clustering of molecules is a prerequisite for signaling. It could be demonstrated for MHC-II that close proximity is disadvantageous for initiating a potent immune response in CD4+ T-cells in stark contrast to it’s molecular family member MHC-I, where clustering supports prolonged signal propagation in CD8+ T-cells. Similarly, the direct interaction map shows strong interactions between PD-L1 and MHC-I in the cancer cell, while this interaction is weak in the dendritic cell due to the presence of CD80. In contrast PD-L1 and CD86 and CD80 interact strongly in the dendritic cell and hardly in the cancer cell, even though all molecules are present on the membrane of both cells. Thus, cytotoxic bi- or multispecific compounds against PD-L1 and MHC-I together with MHC-II could be developed to specifically kill cancer cells by recruiting not only CD4+ Helper T-cells but also CD8+ Killer T-cells followed by further recruitment of multiple different immune cells. Discussion The higher order clustering of immune-regulatory ligands on the cell surface likely plays a critically important role in guiding T cell immunity, but our understanding of this aspect of immune cell biology is very limited. We have applied multiplexed Exchange-PAINT super- resolution imaging to tackle this knowledge gap, and in doing so have conducted the first highly multiplexed (>3 targets) single protein resolution assessment of cell surface organisation. We define a range of protein-protein associations that have not been previously reported, including complex surface motifs (e.g. hetero-tetramers) that are challenging to identify and resolve in a cellular environment by current state-of-the-art super-resolution methods. Importantly, we observe profound differences in surface architecture between a cell type adapted to stimulate T cell immunity (activated DC) and a cell type that evades the T cell response (melanoma cell). Moreover, these differences in surface organisation have functional implications. The DC surface was enriched for complexes which suggested a pre-configured architecture that favours co-stimulatory protein recruitment to the synapse, and T cell activation upon antigen recognition. In contrast, melanoma cells were enriched for higher order immunosuppressive MHC-I/PD-L1 aggregates, and other motifs that were not present at significant levels on DCs. CD80 was the key determinant of this surface architecture switch, as it was both necessary and sufficient to prevent MHC-I/PD-L1 aggregation. Importantly, similar surface restructuring could be precipitated by a clinically approved immunosuppressive agent that disrupts the CD80/PD-L1 interaction (Abatacept), implicating surface remodelling as a contributing factor to clinical efficacy. Overall, this implicates changes in surface architecture as an important regulator of T cell immunity. Nanoscale surface organisation has typically been inferred through biochemical approaches, like co-immunoprecipitation, indirect methods such as Fluorescence Energy Resonance Transfer (FRET), or through structural studies. While these existing methodologies are powerful tools for probing protein-protein association, they are unable to spatially resolve complex clustering at the single protein level on the cell surface, particularly those clusters involving 3 or more species. Super-resolution imaging has the potential to address this knowledge gap, however prior methodologies were not sufficiently resolved or multiplexed. While current state-of-the -art super-resolution techniques have made major effort to break the molecular resolution barrier, the present invention overcame these limitations, and the value of higher-plex single protein imaging was illustrated. We identified multiple novel protein-protein associations, complex clustering that would be challenging to identify by other means, and functionally important global differences in surface organisation between cell types. These findings involved some of the most heavily studied proteins in immunology, indicating that significant new biology can be revealed by spatial studies of protein organisation. The spatial patterns found on each cell type correlated with their functional capacity to stimulate T cell immunity. DCs had relatively low levels of MHC-I/PD-L1 clustering, and high levels of clustering between costimulatory molecules and MHC-I. Moreover, the MHC- I/PD-L1 clusters on DCs were often within complex hetero-tetramers that also contained a CD80 molecule. As CD80 can sterically obstruct PD-L1 engagement of PD-1 (https://pubmed.ncbi.nlm.nih.gov/31000591/), this suggests that most MHC-I associated PD- L1 on DCs is likely non-functional. Thus, the “ground state” of an activated DC appears wired to promote T cell activation. In contrast, melanoma cells exhibited MHC-I/PD-L1 aggregation into a configuration that our subsequent functional work demonstrated is potently inhibitory to T cells. PD-1 engagement inhibits T cells through recruitment of the phosphatase SHP2, which can inhibit signalling downstream of both CD28 and TCR engagement (see https://pubmed.ncbi.nlm.nih.gov/16227604/; https://pubmed.ncbi.nlm.nih.gov/22641383/; https://pubmed.ncbi.nlm.nih.gov/28280247/; https://pubmed.ncbi.nlm.nih.gov/28280249/). Clustering between MHC-I and PD-L1 would thus favour PD-1-dependent inhibition of TCR signalling by bringing PD-1 into close proximity with the antigen-engaged TCR complex, although increased PD-L1 recruitment into the immune synapse may also inhibit CD28. Importantly, we identify CD80 as a central regulator of MHC-I/PD-L1 interactions that disrupts clustering by binding to PD-L1. Our data imply that CD80-bound PD-L1 is sequestered away from other proteins, which in turn has significant downstream effects on surface organisation. This remodelling effect could be mimicked by Abatacept treatment, consistent with the known capacity of CTLA4-Ig fusion proteins to disrupt CD80/PD-L1 interactions (https://www.pnas.org/doi/full/10.1073/pnas.2023739118). However, regulatory T cells are also known to strip CD80 from the DC surface in a CTLA4- dependent manner (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198051/), which in turn liberates PD-L1 (https://www.pnas.org/doi/full/10.1073/pnas.2023739118). Thus, surface remodelling of DCs by CD80 stripping may also contribute to regulatory T cell-mediated immunosuppression. Overall, these data identify a third major function of CD80: in addition to co-stimulation through CD28, and inhibition of PD-L1, CD80 also remodels the cell surface into a configuration that favours T cell activation. More broadly, our results highlight the importance of considering spatial organisation in drug design. We demonstrate that the cell surface is in a finely tuned equilibrium, where small perturbations can drastically remodel surface architecture. We find that blocking reagents, such as those used for immunotherapy in autoimmunity and cancer, can mimick these effects. In the case of Abatacept, the surface changes precipitated by target binding will likely augment the intended immunosuppressive activity of this clinical agent. 7. Diagnostics development The results from the data analysis may be further used for drug development. Specific Example: Using the results above, several strategies for diagnostics development can be conceived: The comparison of the direct interaction patterns of dendritic and cancer cell in the example shows that not homo-distributions of individual molecules can be exclusively used for classification and identification of pathogenic cells within tissue but molecular motifs consisting of multiple different key molecules. Specificity as well as sensitivity of cell classification can be significantly increased by mimicking molecular motifs via either a multispecific binder or using a scaffold such as DNA-origami as molecular pegboard to specifically decorate them with respective molecules. Transient binding of monovalent entities (e.g.3 different Fab fragments against target molecules binding with >1μM affinity compared to <10nM affinity of related monoclonal antibodies against same target molecules) against target molecules will inhibit unspecific binding to individual target molecules but will support prolonged, stable binding to respective key motifs due to multivalent stabilization. High-through put analysis for clinical applications can be realized by using confocal or widefield microscopy to detect fluorescently labeled multivalent binders against target key motifs on cells. Here, intensity of fluorescence may serve as a read-out parameter. 8. Materials and Setups For the Examples explained above, several materials, buffers, preparation protocols and setups were used, which are explained in more detail in this section. Materials: DNA oligonucleotides modified with DBCO-PEG4 and Cy3B were ordered from IDT and MWG Eurofins. Magnesium chloride (1 M; AM9530G), sodium chloride (5 M; AM9759), ultrapure water (10977-035), EDTA (0.5 M, pH 8.0; AM9260G), 1x PBS (pH 7.2; 20012-019), 10x PBS (70011051) Iscove’s Modified Dulbecco’s Medium (IMDM) (12440053), SiteClickTM antibody azido modification kit (S20026), Salmon Sperm DNA (15632011), mouse PD-L1 (A42615), Pluronic F127 (P6867), SYBR safe (S33102), Streptavidin (21122), Roswell Park Memorial Institute 1640 Medium supplemented with GlutaMAXTM (RPMI-GlutaMAXTM) (61870010), Penicillin-Streptomycin (15140122), HEPES (15630-056), β-mercaptoethanol (31350-010), Fixable Viability Dye eFluorTM 780 (65-0865-14) and Live/dead fixable blue dead cell stain (L34962) were purchased from Thermo Fisher Scientific.1-palmitoyl-2-oleoyl-sn-glycero-3- phosphocholine (POPC, 850457C) and 2% 1,2-dioleoyl-sn-glycero-3-[N(5-amino-1- carboxypentyl)iminodiaceticacid]succinyl[nickel salt] (Ni-DOGS NTA, 790404C) were ordered from Avanti Polar Lipids. Sodium hydroxide (31627.290), ), PEG8000 (0159-500G) and 40 μm cell strainers (734-2760) were purchased from VWR. Triton X-100 (6683.1) was purchased from Carl Roth. Paraformaldehyde (15710) was obtained from Electron Microscopy Sciences. Calcium chloride (1 M; 21115), BSA (A4503-10G), Accutase solution (A6964-100ml), sodium azide (769320), Tween-20 (P9416-50ML), methanol (32213-2.5L) and (±)-6-hydroxy-2,5,7,8- tetra-methylchromane-2-carboxylic acid (Trolox; 238813-5G) were obtained from Sigma- Aldrich. Sticky-slide 8 well chambers (80808) were purchased from Ibidi and glass slides (10756991) were purchased from Marienfeld. Double-sided tape (665D) was ordered from Scotch. FBS Good (P40-37500) was purchased from PAN Biotech. FBS Advanced (FBS-11A, heat inactivated) was ordered from Capricorn Scientific. His10-tag ICAM-1 (50440-M08H) was purchased from Biozol. Anti-mouse CD80 antibody (104702), anti-mouse CD86 antibody (105001), anti-mouse H-2Kb (MHC-I) antibody (116501), APC anti-mouse H-2Kb (MHC-I OVA) bound to SIINFEKL antibody (141605), anti-mouse I-A/I-E (MHC-II) antibody (107601), anti- mouse CD274 (PD-L1) antibody (124301), anti-mouse CD273 (PD-L2) antibody (107202), PE- Cy5.5 anti-mouse CD8a antibody (100720), PE anti-mouse CD3 (100308), anti-mouse CD28 (102116), mouse IL-7 (BLG-577802-10ug), mouse IL-2 (BLG-575402-10uG) and Precision Counting Beads (BLG-424902-100TST) were purchased from BioLegend. EndoFitTM ovalbumin (vac-pova) and Primocin® (ant-pm-05) were obtained from InvivoGen. CpG1826 was ordered from Pfizer. Recombinant murine IFNγ (315-05) was purchased from PeproTech. Ninety- nanometer gold nanoparticles (G-90-100) were ordered from Cytodiagnostics. High-binding 96-well plates (3361) were obtained from Corning. EasySepTM Mouse CD8+ T Cell Isolation Kit (19853) was purchased from STEMCELL Technologies. Buffer Recipes: • Buffer X: 1x PBS, 500mM NaCl. • Buffer Y: 1x PBS, 1mM EDTA, 0.01% Tween-20. • Buffer Z: 1x PBS, 1 mM EDTA, 500 mM NaCl (pH 7.4), 0.01%Tween-20 supplemented with 1x Trolox. • Blocking buffer: 1x PBS, 1mM EDTA, 0.02% Tween-20, 0.05% NaN3, 2% BSA, 0.05 mg/ml sheared salmon sperm DNA. • Trolox: 100x Trolox was made by adding 100 mg Trolox to 430 μl of 100% methanol and 345 μl of 1 M NaOH in 3.2 ml water. ^ Hybridization buffer: 4xSSC 10% Dextrane sulfate 10% ethylanecarbonate 0.04% Tween20 ^ Dehybridization buffer: 2xSSC 10% Dextrane sulfate 20% ethylanecarbonate 0.04% Tween20 ^ Blocking Buffer 2: 1x PBS, 3% BSA 0.25% Triton 0.05 mg/ml sheared salmon sperm DNA (DNA-PAINT) microscope setup: Fluorescence imaging was carried out on an inverted microscope (Nikon Instruments, Eclipse Ti2) with the Perfect Focus System, applying an objective-type TIRF configuration equipped with an oil-immersion objective (Nikon Instruments, Apo SR TIRF 100x, NA 1.49, Oil). A 560- nm laser (MPB Communications, 1 W) was used for excitation. The laser beam was passed through a cleanup filter (Chroma Technology, ZET561/10) and coupled into the microscope objective using a beam splitter (Chroma Technology, ZT561rdc). Fluorescence was spectrally filtered with an emission filter (Chroma Technology, ET600/50m and ET575lp) and imaged on an sCMOS camera (Andor, Zyla 4.2 Plus) without further magnification, resulting in an effective pixel size of 130 nm (after 2 x 2 binning). The readout rate was set to 540 MHz. Images were acquired by choosing a region of interest with a size of 512 x 512 pixels. SiteClickTM antibody-DNA conjugation: Prior functionalization unconjugated antibodies (MHC-I, MHC-I OVA, MHC-II, PD-L2) were concentrated to 1mg/ml in Tris, pH 7.0, by using Amicon centrifugal filters (50,000 MWCO). For each conjugation 200µg of respective antibody were used. Azide-modified antibodies were produced following manufacturer’s protocol. Azido-modified antibodies were reacted with 10x molar excess of DBCO- functionalized DNA (R2 - MHC-II, R5 - MHC-I/MHC-I OVA and R6 - PD-L2) in Tris, pH 7.0, overnight at 25°C, 300rpm. Unreacted DNA was removed by buffer exchange to PBS using Amicon centrifugal filters (50,000 MWCO). Unconjugated antibody were removed by anion exchange chromatography using an ÄKTA pure system equipped with a Resource Q 1-ml column and antibody concentration was adjusted to 5µM. DNA-conjugated antibodies were stored at 4°C. Enzymatic antibody-DNA conjugation:Prior functionalization unconjugated antibodies (CD80, CD86, PD-L1) were concentrated to 1mg/ml in TBS + 0.05% Tween20 by using Amicon centrifugal filters (50,000 MWCO). For each conjugation 100µg of respective antibody were used. PNGase (0.6 U), mTG (1.2 U) and an 80-fold molar excess of bifunctional azide-PEG3- amine linker were added to the antibody and reacted for 16h at 37°C, 300rpm. Enzymes and excessive linker were removed by buffer exchange to PBS using Amicon centrifugal filters (50,000 MWCO). Azido-modified antibodies were reacted with 10x molar excess of DBCO- functionalized DNA (R1 – CD86, R3 – CD80 and R4 - PD-L1) overnight at 25°C, 300rpm. Unreacted DNA and unconjugated antibody were removed by anion exchange chromatography using an ÄKTA pure system equipped with a Resource Q 1-ml column and antibody concentration was adjusted to 5µM. DNA-conjugated antibodies were stored at 4°C. ALFA-tag nanobody-DNA conjugation. ALFA-tag nanobodies were conjugated as described previously1. Unconjugated nanobodies were thawed on ice, then 20-fold molar excess of bifunctional DBCO-PEG4-Maleimide linker was added and reacted for 2 h on ice. Unreacted linker was removed by buffer exchange to PBS using Amicon centrifugal filters (10,000 MWCO). The DBCO-modified nanobodies were reacted with 5xmolar excess of azide- functionalized DNA (R3, R4) overnight at 4°C. Unconjugated protein and free DNA were removed by anion exchange chromatography using an ÄKTA pure system equipped with a Resource Q 1-ml column. Preparation of functionalized planar SLBs. Vesicles containing 98% 1-palmitoyl-2-oleoyl-sn- glycero-3-phosphocholine (POPC) and 2% 1,2-dioleoyl-sn-glycero-3-[N(5-amino-1- carboxypentyl)iminodiaceticacid]succinyl[nickel salt] (Ni-DOGS NTA) were prepared at a total lipid concentration of 0.5mg ml-1 as described ( J. B. Huppa, et al., TCR-peptide-MHC interactions in situ show accelerated kinetics and increased affinity. Nature 463, 963–967 (2010)) in 10x Dulbecco’s phosphate-buffered saline (PBS). Glass coverslips were plasma cleaned for 3 min and attached to the bottom side of 8-well chamber slide. Coverslips were incubated with a fivefold diluted vesicle solution for 10 min at 24°C, before they were extensively rinsed with PBS. For functionalization, SLBs were incubated for 90 min with His10-tag ICAM-1 (2.5 µg mL-1) at 37°C and then rinsed off with PBS. PBS was replaced with HBSS supplemented with 2% FBS, 2mM CaCl2 and 2mM MgCl2 prior cell seeding. Sample preparation for binding molecule specificity imaging.6×104 cells/well were allowed to adhere onto the SLB for 45 minutes at 37°C. For determination of binder specificity for each receptor distinct MutuDC1940 KO (MHC-I KO, MHC-II KO, CD86 KO, CD80 KO, PD-L1 KO, PD-L2 KO) cells served as a reference. Cells were stimulated using 500 nM CpG1826 + 100 U/ml IFN ^ ± ovalbumin for 6 hours at 37°C. The 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX-100 dissolved in PBS for 1 minute, washed with PBS followed by surface passivation with blocking buffer for 60 min at 24°C. Target binding molecules (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti- CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C. Unbound antibodies (binding molecules) were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to the addition of gold fiducials, samples were washed with PBS. Subsequently, 250 µl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. DNA origami preparation. For folding DNA origami, 10 nM single-stranded DNA scaffold from M13p18 bacteriophages (SeqID No 3541, Tilibit GmbH cat. M1-10), 200 nM of each core staple (SeqID No 1 – 164), 500 nM of staples with Target complementary nucleic acid extension (see Table 10, for the respective barcode), and 20 nM of biotin-conjugated staples (SeqID No 3540) were pooled into 50 µl of 10 mM Tris-HCl, pH 8.0, 0.2 mM EDTA, 150 mM NaCl, 10 mM MgCl2 buffer. Structures were thermally annealed in a Thermocycler (Eppendorf Inc.) by gradual cooling the mixture at a rate of 1 °C per 3 minutes from 60 °C to 4 °C. The folded origami structures were then purified from excess staples using 100 kDa MWCO centrifugal filters. Purified origami structures were stored in buffer C (PBS, 500 mM NaCl) at -20 °C until usage. B ID ID f l
Figure imgf000094_0001
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Figure imgf000095_0001
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Figure imgf000098_0001
. Sample preparation for binding molecule labeling efficiency imaging. CHO cells were seeded on ibidi 8 Well high Glass Bottom chambers (Cat.No: 80807) the day prior to transfection at a density of 15×104 cells/well. CHO cells were transfected with a single receptor construct (mEGFP-ALFA-MHC-I, mEGFP-ALFA-MHC-II, mEGFP- ALFA-CD86, mEGFP-ALFA-CD80, mEGFP-ALFA-PD-L1, mEGFP-ALFA-PD-L2) at a time for binding molecule characterization using Lipofectamine LTX as specified by the manufacturer. CHO cells were allowed to express mEGFP-ALFA-receptors for 16–24 h. Then, the medium was replaced with fresh F-12K Medium + 10% FBS + 100 U/ml Penicillin + 100 µg/ml Streptomycin followed by fixation. 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX-100 dissolved in PBS for 5 minutes, washed with PBS followed by surface passivation with blocking buffer for 60 min at 24°C. Target binding molecules (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti-CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C followed by addition of ALFA-tag nanobody, dissolved in blocking buffer and added at a final concentration of 500 pM for 60 min at 24°C.. Unbound binders were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to the addition of gold fiducials, samples were washed with PBS. Subsequently, 250 µl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. Sample preparation for multiplexed immune receptor DNA-PAINT imaging.6×104 cells/well were allowed to adhere onto the SLB for 45 minutes at 37°C. Cells were stimulated using 500 nM CpG1826 + 100 U/ml IFN ^ for either 3, 6, 12 or 24 hours at 37°C. For ovalbumin treatment, cells were stimulated for 6 hours using 500 nM CpG1826 + 100 U/ml IFN + 2µM ovalbumin. For Abatacept treatment, cells were stimulated for 6 hours using 500 nM CpG1826 + 100 U/ml IFN and during the last 30 minutes Abatacept was added at a final concentration of 150nM. Unstimulated cells served as a reference. The 4% PFA solution was preheated to 37°C before addition to the cells. Cells were fixed in 4% PFA for 15 minutes and washed with PBS. Cells were permeabilized in 0.125% TritonX-100 dissolved in PBS for 1 minute, washed with PBS followed by surface passivation with blocking buffer for 60 min at 24°C. DNA-conjugated antibodies (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti-CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) were dissolved in blocking buffer and added at a final concentration of 100 nM each overnight at 4°C. Unbound antibodies were removed by washing with Buffer Y, followed by washing once with Buffer X for 5 min. Post-fixation was performed with 2% paraformaldehyde in PBS for 5 min. Prior to the addition of gold fiducials, samples were washed with PBS. Subsequently, 250 µl of 90 nm standard gold nanoparticles, diluted 1:3 in PBS, were added and incubated for 5 min before washing with PBS. Preparation of ligand-decorated DNA origami discs. DNA origami discs (Fig. S32) were assembled as previously [https://pubmed.ncbi.nlm.nih.gov/34613711/]. For ligand attachment, the staples corresponding to the functionalization points were extended at their 3′ end with a 21-nucleotide overhang of docking strands (SeqID No 3527-3532). Purification from excess staples was performed using PEG precipitation protocol [https://pubs.acs.org/doi/10.1021/acs.nanolett.2c00275]. For functionalization, purified DNA origami structures were incubated with 10x molar excess of DNA-labeled pMHC and PD-L1 proteins overnight at 22°C. Excess ligand was removed using 100 kDa Amicon Ultra centrifugal filters pre-treated with 5% (w/v) Pluronic F127. The concentration of purified DNA origami discs was quantified by absorption at 260 nm using a Nanodrop (Quawell Technology). Structure folding and ligand attachment were verified by agarose gel electrophoresis (2% agarose, 0.5x TBE buffer, 8 mM MgCl2 and 1x SYBR Safe) (Fig. S32). The gel was run at 70 V for 150 min in an ice bath and visualized with ChemiDoc MP imaging system (Bio-Rad). Purified DNA structures were stored at 4 °C and used within 3 days. DNA-PAINT imaging for binding molecule specificity determination. Cellular imaging of wild-type MutuDC1940 and MutuDC1940 KO cells was conducted by imaging single target receptors using distinct imagers for each binding molecule (table 12). Cy3B-conjugated imager strands were dissolved in Buffer Z and 600 µl of the imager solution was added to the sample to perform DNA-PAINT measurements. Imaging parameters are listed in detail in table 6. 2-plex Exchange-PAINT imaging for binding molecule labeling efficiency determination. Prior image acquisition, all fluorophores (e.g. CHO-mEGFP-ALFA-MHC-I) were deactivated by a high intensity bleach pulse. Cellular imaging was conducted via two subsequent imaging rounds using distinct imagers for each binding molecule (table 12) with only one of the imagers present at a time. Cy3B-conjugated imager strands were dissolved in Buffer Z and 600 µl of the imager solution was added to the sample to perform DNA-PAINT measurements. In between imaging rounds, the sample was washed with 2 ml PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5 min. Then, the next imager solution was introduced. Imaging parameters are listed in detail in table 7. Multiplexed cellular imaging of immune receptors. Prior image acquisition, all fluorophores (B16-F10 CD80- mCherry, B16-F10 CD80 (L107E)-mCherry) were deactivated by a high intensity bleach pulse. Multiplexed cellular imaging was conducted via six subsequent imaging rounds using the six imagers R1-R6 (as published in https://pubmed.ncbi.nlm.nih.gov/32601424/ ) with only one of the imagers present at a time. Cy3B- conjugated imager strands were dissolved in Buffer Z and 600 µl of the imager solution was added to the sample to perform DNA-PAINT measurements. In between imaging rounds, the sample was washed with 2 ml PBS until no residual signal from the previous imager solution was detected followed by incubation of Buffer X for 5 min. Then, the next imager solution was introduced. Imaging parameters for DNA-PAINT cell experiments are listed in detail in table 8. Microscope setup. Fluorescence imaging was carried out on an inverted microscope (Nikon Instruments, Eclipse Ti2) with the Perfect Focus System, applying an objective-type TIRF configuration equipped with an oil- immersion objective (Nikon Instruments, Apo SR TIRF×100, NA 1.49, Oil). A 560-nm laser (MPB Communications, 1 W) was used for excitation. The laser beam was passed through a cleanup filter (Chroma Technology, ZET561/10) and coupled into the microscope objective using a beam splitter (Chroma Technology, ZT561rdc). Fluorescence was spectrally filtered with an emission filter (Chroma Technology, ET600/50m and ET575lp) and imaged on an sCMOS camera (Andor, Zyla 4.2 Plus) without further magnification, resulting in an effective pixel size of 130 nm (after 2×2 binning). The readout rate was set to 540 MHz. Images were acquired by choosing a region of interest with a size of 512×512 pixels. Detailed imaging conditions for the respective experiments are shown in table 6-8. Image analysis. Raw fluorescence data were subjected to super-resolution reconstruction using the Picasso software package (latest version available at https://github.com/jungmannlab/picasso). Drift correction was performed with a redundant cross-correlation and gold particles as fiducials for cellular experiments. Gold particles were also used to align all rounds for multiplexed Exchange-PAINT experiments. After channel alignment, DNA- PAINT data were analyzed using the Picasso clustering algorithm (latest version available at https://github.com/jungmannlab/picasso) for each target individually. Circular clusters of localizations centered around local maxima were identified and grouped (assigned a unique identification number). Subsequently, the centers of the localization groups were calculated as weighted mean by employing the squared inverse localization precisions as weights. Merging localizations of all rounds yields the final multiplexed DNA-PAINT image. Data analysis – Binding molecule specificity. Binding molecule specificity was evaluated by counting the number of binding molecule signals (circular clusters of localizations centered around local maxima) in stimulated wild-type MutuDC1940 and MutuDC1940 KO samples within the cell area, determining underlying binding molecule density and further corresponding ratios between MutuDC1940 and MutuDC1940 KO samples. Data analysis - Labeling efficiency. Labeling efficiency of target binding molecules (anti-MHC-I/MHC-I OVA antibody, anti-MHC-II antibody, anti-CD86 antibody, anti-CD80 antibody, anti-PD-L1 antibody, anti-PD-L2 antibody) was determined as described in the following: In order to quantify the labeling efficiency of a given target binding molecule the nearest neighbor distance (NND) distribution extracted from the data is compared to a simulation. Briefly, the simulation consists of simulating monomers of the reference protein, monomers of the target protein and dimers of reference-target protein at different proportions. Subsequently, the NND distribution of a given simulation is calculated and compared to the experimental NND distribution. The most likely proportions of populations of monomers (pRef) and dimers (pRef-Target) were obtained through a least- squares optimization procedure. The labeling efficiency is then calculated as LE (%)= pRef-Target / (pRef+ pRef-Target) * 100 The algorithm of the simulation can be summarized as follows: 1. Parameters. Density of target monomers: number of target monomers per unit area. Density of reference monomers: number of reference monomers per unit area. Density of target-reference dimers: number of dimers per unit area. Dimer distance: expected distance between reference and target molecule including the labeling construct. Uncertainty: variability in the position of each molecule due to labeling and localization errors. The total density for target and reference is set to match the respective experimentally observed densities in each channel. 2. Simulation of monomers: a set of spatial coordinates with CSR distribution and given density are drawn. Simulation of dimers: a set of spatial coordinates with CSR distribution are drawn, representing the center of each dimer. For each dimer center two positions are generated with a random orientation and a distance with expected value Dimer distance. The position of each pair of molecules are drawn taking into account the Uncertainty parameter (drawn from a gaussian distribution). 3. NND are calculated on the subset of detectable molecules. Cell culture. Mice. C57BL/6 mice were purchased from the Walter and Eliza Hall Institute animal facility and maintained at the animal facility in Peter MacCallum Cancer Centre (Melbourne, Australia). All care and use of animals were conducted in compliance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purpose and ethical approval was obtained from the Animal Experimentation Ethics Committee at Peter MacCallum Cancer Centre Cell lines. CHO cells (CCL-61, ATCC) were cultured in Gibco™ Ham's F-12K (Kaighn's) Medium, supplemented with 10% Fetal Bovine Serum (FBS) (11573397, Gibco), 100 U/ml Penicillin, 100µg/ml Streptomycin. Murine dendritic cell line (MutuDC 1940) was cultured in IMDM supplemented with 10% FBS, 100µM β- Mercaptoethanol (Gibco), 100U/ml Penicilin, 100µg/ml Streptomycin and 1.32 mM Glutamax (Gibco). Murine melanoma B16-F10melanoma cell line was cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% FBS, 100 U/ml Penicillin and 100µg/ml Streptomycin. Cells were maintained and passaged using Accutase solution. Cloning. Receptor c-terminally tagged with ALFA-tag and mEGFP (MHC-I-ALFA-mEGFP, MHC-II-ALFA- mEGFP, CD86-ALFA-mEGFP, CD80-ALFA-mEGFP, PD-L1-ALFA-mEGFP, PD-L2-ALFA-mEGFP) were individually cloned into pcDNA™3.1 (+) Mammalian Expression Vector (Invitrogen Cat. No. V79020). Retroviral Transduction of B16-F10. The retroviral transduction involved the production of murine stem cell virus (MSCV) followed by the transduction of the B16-F10 cell line. MSCV vectors were produced in HEK293GP cells by co-transfection of packaging plasmids containing mCD80 L107E (IRES-mCherry), mCD80 (IRES- mCherry) and empty vector with the VSV-G envelope glycoprotein, using Lipofectamine 2000 (Invitrogen) for 6 hours. Following five days of culture in DMEM supplemented with 10% FBS, 100 U/ml Penicillin, 100 µg/ml Streptomycin and 1.32 mM Glutamax (Gibco), virus-containing supernatant was collected, filtered and frozen at -20°C.5×104 B16-F10 cells were seeded in each well of 6-well plate. Once adhered, the cells were transduced with 2ml of viral supernatant and 8 ug/ml polybrene (Sigma Aldrich) followed by 1-hour centrifugation at 500G. This was done twice a day for three days for a total of five viral hits. CD80-mCherry expression was validated by FACS and mCherry positive B16-F10 cells were sorted using FACSAria Fusion Flow Cytometer (BD Biosciences). Flow cytometry data was analyzed using FlowJo (v10.8.1, Treestar). CRISPR/Cas9 gene editing. The CRISPR editing protocol for the MutuDC1940, B16-F10 and primary bone marrow dendritic cells was adapted from https://pubmed.ncbi.nlm.nih.gov/32152070/. For KO experiments, sgRNAs targeting the murine MHC-I, MHC-II, CD86, CD80, PD-L1, PD-L2 and non-targeting Ctrl genes were obtained from Integrated DNA Technology (sequences of each target sgRNA is described in table 11). 5×106 cells were electroporated for the MutuDC1940 cell lines (MHC-I, MHC-II, CD86, CD80, PD-L1, PD-L2 and non-targeting Ctrl), for the B16-F10 cell lines (CD80 and non-targeting Ctrl) and for the primary bone marrow cells (CD80 and non-targeting Ctrl), and 100×106 cells were electroporated for the primary bone marrow cells. For sgRNA/Cas9 RNP complex formation, Cas9 protein and sgRNA were combined and incubated at room temperature for 10 minutes. Cells were resuspended in P3 buffer per electroporation reaction, which consisted of P3 Primary Cell Solution and Supplement 1 (Lonza 4D Nucleofector kit). sgRNA/Cas9 RNP complex were added to the cells and cells/RNP mix was transferred into Lonza nucleofector cuvette (Lonza) and inserted into the Amaxa Nucleofector and cells were electroporated using the program ‘DC mouse mature’ for MutuDC1940, ‘HEK293’ for B16-F10 and ‘Primary cells P3’ for the primary bone marrow cells. Cells were rested for 10 minutes in media after electroporation at 37°C and 5% CO2.
Figure imgf000101_0001
. . In vitro generation of cDC1. Bone marrow cells were collected from the femurs and tibia of C57BL/6 mice. Red blood cells were lysed using RBC lysis buffer (Ammonium Chloride) for 2 minutes and washed in media. Cells were filtered and spun down at 200G for 7 minutes. Cells were counted and divided for KO experiments using sgRNAs targeting the murine CD80 and nontargeting Ctrl sgRNA (Integrated DNA Technologies). Following electroporation, primary bone marrow cells were cultured at 1.5×106 cells/ml for 8 days in RPMI 1640 media (ThermoFisher Scientific) supplemented with 1.32 mM Glutamax, 10% FBS, 90 µM β-mercaptoethanol, 100 U/ml Penicillin, 100µg/ml Streptomycin and 150 ng/ml Flt3L (BioXCell). Following 8 days of culture, cells were stimulated using 500 nM CpG1826 (Pfizer) + 100 U/ml IFN (Peprotech) for 6 hours in suspension. After stimulation, both unstimulated and stimulated cells were incubated in Fc Block (BD Biosciences) for 10 minutes on ice, stained for 30 minutes using B220 (PE, Clone R836B2, BD #553090), SIRPα (BV510, Clone P84, BD #740159), CD11c (BV785, Clone N418, BioLegend #117336), CD24 (BUV395, Clone M1/69, BD #744471) antibodies. B220- CD11chigh CD24high SIRPαlow cDC1s were then sorted using FACSAria Fusion Flow Cytometer (BD Biosciences). CD80 deletion was validated on a separate sample using CD80 (APC, Clone 16-10A1, BioLegend #104714) antibody. Following the sort, the cells were washed in media and resuspended in Hank’s Balanced Salt Solution (HBSS) ((H8264-500ML, Sigma Aldrich).) supplemented with 2% FBS, 2 mM CaCl2 and 2 mM MgCl2 and seeded onto the SLB. Im r S Imager name Imager
Figure imgf000102_0001
Table 12. Imager sequences used for binder characterization. It is emphasized that in the examples for a method of mapping the localization of different target molecules within a sample DNA-PAINT was used as the imaging method. DNA-PAINT shows advantageous characteristics for the analyzing of direct interaction patterns, due the combination of molecular resolution and high degree of multiplexing. Nonetheless, other super-resolution fluorescence imaging techniques, such as dSTORM, may be used as well. It is also emphasized that not only the conventional DNA-PAINT techniques may be used but also the inventive concepts presented in this disclosure. With respect to molecular resolution it is stressed that the analysis in the methods of mapping the localization of different target molecules within a sample according to the present invention analysis is preferably based on data with such a high resolution that proteins that touch each other can be spatially separated, i.e. the resolution is about 5 nm. This means that imaging data can be interpreted as and transformed into primary binder localizations or target molecule positions. Nonetheless, cluster analysis of lower-resolution imaging data and/or analysis of imager localizations may also be used and yield usable results.

Claims

New PCT- Patent Application Max-Planck-Gesellschaft; Ludwig-Maximilians-Universität München; Vossius Ref.: AG2105 PCT S5 Claims 1. A single-stranded nucleic acid molecule, comprising (a) a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence, and (b) a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, wherein the first nucleic acid sequence is capable of stronger associating with its complementary nucleic acid sequence than the second nucleic acid sequence.
2. The nucleic acid molecule of claim 1, wherein the first nucleic acid sequence is capable of stably hybridizing to its target complementary nucleic sequence.
3. A hybridization complex, wherein the nucleic acid molecule of claim 1 or 2 is specifically hybridized to a target complementary nucleic acid sequence.
4. The hybridization complex of claim 3, wherein the target complementary nucleic acid sequence is conjugated to a binding molecule, wherein the binding molecule is preferably a nucleic acid sequence, a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer.
5. The hybridization complex of claim 4, wherein i) the target complementary nucleic acid sequence is conjugated to the binding molecule via a linker, wherein the linker preferably comprises biotin and one of avidin or streptavidin, or ii) the target complementary nucleic acid sequence is covalently coupled to the binding molecule via NHS-chemistry or site- specific labeling via click chemistry.
6. The nucleic acid molecule of claim 1 or 2, or the hybridization complex of any one of claims 3 to 5, wherein the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence is based on the formation of more hydrogen bonds than the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule.
7. The nucleic acid molecule or the hybridization complex of any preceding claim, wherein the specific hybridization between the first nucleic acid sequence and its target complementary nucleic acid sequence has a melting temperature of between 25°C and 90°C, preferably between 45°C and 85°C and most preferably between 62°C and 78°C and/or the transient binding between the second nucleic acid sequence and the complementary nucleic acid sequence being labeled by an imaging molecule has a melting temperature of between 8°C and 22°C, preferably 12°C to 18°C and most preferably 14°C and 16° C.
8. The nucleic acid molecule or the hybridization complex of any preceding claim, wherein the imaging molecule is a fluorescent molecule. 1
9. The nucleic acid molecule or the hybridization complex of any preceding claim, wherein the nucleic acid molecule further comprises a toehold seed, whereby the specific hybridization between the first nucleic acid sequence and its complementary target nucleic acid sequence can be disconnected via toehold mediated strand displacement.
10. A plurality of nucleic acid molecules or hybridization complexes of any one of the preceding claims, wherein the nucleic acid molecules comprise different first nucleic acid sequences that differ from each other in that they are capable of specifically and stably hybridizing to different target complementary nucleic acid sequences; and/or different second nucleic acid sequences that differ from each other in that they are capable of transiently binding to different complementary nucleic acid sequences optionally being labeled by at least two, at least three, at least four, or at least five different imaging molecules, wherein preferably each of the different target complementary nucleic acid sequences forms a cognate pair with a different imaging molecule.
11. A kit or composition comprising (a) the nucleic acid molecule or the hybridization complex or the plurality of nucleic acid molecules or hybridization complexes of any preceding claim and at least one complementary nucleic acid sequence being labeled by an imaging molecule; or (b) one or more single-stranded nucleic acid molecules comprising a first nucleic acid sequence being capable of specifically hybridizing to a target complementary nucleic acid sequence and a second nucleic acid sequence that differs from the first nucleic acid sequence and is capable of transiently binding to a complementary nucleic acid sequence being labeled by an imaging molecule, one or more target complementary nucleic acid sequences capable of specifically hybridizing to the first nucleic acid sequence of the one or more single-stranded nucleic acid molecules, and one or more complementary nucleic acid sequences being labeled by an imaging molecule capable of transiently binding to the second nucleic acid sequence of the one or more single- stranded nucleic acid molecules, wherein in the kit or composition the amount of the single-stranded nucleic acid molecules is at least equal to, preferably at least 10-times or100-times or 1000-times the amount of the complementary nucleic acid sequences being labeled by an imaging molecule.
12. A method of detecting a target molecule in a sample, comprising: a. contacting the sample with the nucleic acid molecule or the kit or composition of any preceding claim; b. optionally contacting the sample with a target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the 2 nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds the target molecule in the sample; c. contacting the sample with a complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the nucleic acid molecule or composition of (a); and d. detecting the imaging molecule in the sample, thereby detecting the biological target molecule in the sample.
13. The method of claim 12, wherein (a) the target molecule in the sample is a nucleic acid sequence in the sample and the binding molecule is a nucleic acid being complementary to the nucleic acid sequence in the sample, or (b) the target molecule in the sample is a protein or peptide or polysaccharide in the sample and the binding molecule is a small molecule or a protein, wherein the protein is preferably an antibody, antibody mimetic, or aptamer, specifically binding to the protein or peptide or polysaccharide in the sample, or (c) the target molecule in the sample is a nucleic acid sequence comprising a/the target complementary nucleic acid sequence .
14. A method of detecting two or more target molecules in a sample, comprising a. contacting the sample with a first nucleic acid molecule or composition of any preceding claim and a second nucleic acid molecule or composition of any preceding claim; b. contacting the sample with a first target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the first nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds a first target molecule in the sample, and a second target complementary nucleic acid sequence under condition wherein it specifically hybridizes to the first nucleic acid sequence of the second nucleic acid molecule or composition of (a), wherein the target complementary nucleic acid sequence is conjugated to a binding molecule that specifically binds a second target molecule in the sample, c. contacting the sample with a first complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the first nucleic acid molecule or the composition of (a) and contacting the sample with a second complementary nucleic acid sequence being labeled by an imaging molecule under conditions wherein it transiently binds to the second nucleic acid sequence of the second nucleic acid molecule or the composition of (a); and d. detecting the imaging molecules, thereby detecting the first and second biological target molecules in the sample, wherein optionally (i) the method is carried out sequentially, wherein the sample is first contacted with the first nucleic acid molecule or the composition, the first target complementary nucleic acid sequence, and the first complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the first target molecule in the sample, and then the sample is contacted with the second nucleic acid molecule or the composition, the second target complementary nucleic acid sequence, and the second complementary nucleic acid sequence being labeled by an imaging molecule and the imaging molecule is detected, thereby detecting the second biological target molecule in the sample, wherein preferably the first complementary nucleic acid sequence being labeled by an 3 imaging molecule is removed before the second complementary nucleic acid sequence being labeled by an imaging molecule is added; (ii) the imaging molecule of the first complementary nucleic acid sequence being labeled by an imaging molecule is different from the imaging molecule of the second complementary nucleic acid sequence being labeled by an imaging molecule, wherein the imaging molecules are preferably spectrally distinct fluorescent molecules; and/or (iii) the method additionally comprises the provision of a codebook that unambiguously maps a unique identification sequence to the first target molecule in the sample and another unique identification sequence to the second target molecule in the sample, wherein preferably each identification sequence has N ordered positions, and each position is assigned with either a gap or one or more second nucleic acid sequences; whereby by options (i) to (iii) the first and the second target molecule in the sample can be distinguished in step (d); and/or wherein optionally (iv) the hybridization complex between the first target complementary nucleic acid sequence and the first nucleic acid sequence of the first nucleic acid molecule or composition is dissociated before the formation of the hybridization complex between the second target complementary nucleic acid sequence and the first nucleic acid sequence of the second nucleic acid molecule or composition, wherein the dissociation is preferably achieved by stringent buffer conditions, toehold-mediated strand displacement, or heat; and/or (iv.b) the second nucleic acid sequence of the first nucleic acid molecule or composition is blocked with a complementary blocking strand.
15. The method of claim 14, wherein the two or more target molecules in a sample are at least 10 target molecules and the method comprises in step (a) at least 10 different nucleic acid molecules or compositions, in step (b) at least 10 different target complementary nucleic acid sequences, and optionally in step (c) at least 10 different first complementary nucleic acid sequences being labeled by an imaging molecule; preferably at least 30 target molecules and the method comprises in step (a) at least 30 different nucleic acid molecules or compositions, in step (b) at least 30 different target complementary nucleic acid sequences, and optionally in step (c) at least 30 different first complementary nucleic acid sequences being labeled by an imaging molecule; more preferably at least 60 target molecules and the method comprises in step (a) at least 60 different nucleic acid molecules or compositions, in step (b) at least 60 different target complementary nucleic acid sequences, and optionally in step (c) at least 60 different first complementary nucleic acid sequences being labeled by an imaging molecule; and most preferably at 100 least target molecules target molecules and the method comprises in step (a) at least 100 different nucleic acid molecules or compositions, in step (b) at least 100 different target complementary nucleic acid sequences, and optionally in step (c) at least 100 different first complementary nucleic acid sequences being labeled by an imaging molecule; wherein the at least 10, at least 30, at least 60 or at least 100 target molecules in the sample are distinguished from each other in step (d) by one or more of options (i) to (iii), preferably option (iii), optionally option (iv) and/or (v), as set forth in claim 14 and further comprising 4 e. mapping the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of the at least 10, at least 30, at least 60 or at least 100 target molecules within the sample, preferably within cells of the sample; and f. optionally determining the interaction pattern of the at least 10, at least 30, at least 60 or at least 100 target molecules based on the localization of (e), preferably by nearest neighbor- based analysis. 5
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