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WO2025073987A1 - Procédé de criblage d'un échantillon de fluide biologique pour un analyte associé à une protéinopathie à l'aide d'une interférométrie en lumière blanche ou d'une microscopie à force atomique - Google Patents

Procédé de criblage d'un échantillon de fluide biologique pour un analyte associé à une protéinopathie à l'aide d'une interférométrie en lumière blanche ou d'une microscopie à force atomique Download PDF

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Publication number
WO2025073987A1
WO2025073987A1 PCT/EP2024/078080 EP2024078080W WO2025073987A1 WO 2025073987 A1 WO2025073987 A1 WO 2025073987A1 EP 2024078080 W EP2024078080 W EP 2024078080W WO 2025073987 A1 WO2025073987 A1 WO 2025073987A1
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WIPO (PCT)
Prior art keywords
pattern
recognition
analyte
elements
image data
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Inventor
Markus Britschgi
Eylul CEYLAN
Tom Moritz KISSLING
Matthias Eckhard LAUER
Philip Michael DETTINGER
Rainer Eugen Martin
Jens André WOLFARD
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F Hoffmann La Roche AG
Hoffmann La Roche Inc
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F Hoffmann La Roche AG
Hoffmann La Roche Inc
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Publication of WO2025073987A1 publication Critical patent/WO2025073987A1/fr
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G01N21/45—Refractivity; Phase-affecting properties, e.g. optical path length using interferometric methods; using Schlieren methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B9/00—Measuring instruments characterised by the use of optical techniques
    • G01B9/02—Interferometers
    • G01B9/0209—Low-coherence interferometers
    • G01B9/02091—Tomographic interferometers, e.g. based on optical coherence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N21/7703—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator using reagent-clad optical fibres or optical waveguides
    • G01N21/774—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator using reagent-clad optical fibres or optical waveguides the reagent being on a grating or periodic structure
    • G01N21/7743—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator using reagent-clad optical fibres or optical waveguides the reagent being on a grating or periodic structure the reagent-coated grating coupling light in or out of the waveguide
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing
    • G01N33/54373—Apparatus specially adapted for solid-phase testing involving physiochemical end-point determination, e.g. wave-guides, FETS, gratings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q60/00—Particular types of SPM [Scanning Probe Microscopy] or microscopes; Essential components thereof
    • G01Q60/24—AFM [Atomic Force Microscopy] or apparatus therefor, e.g. AFM probes
    • G01Q60/38—Probes, their manufacture, or their related instrumentation, e.g. holders
    • G01Q60/42—Functionalisation

Definitions

  • Molecular biological processes build on the selective recognition of molecules. Recognition processes are often associated with a spatial change of mass, because the recognizing molecules form a novel and differently sized complex. Evolution has tuned molecular recognition in cells and organisms to be selective, because they compete with random particles in a complex and cluttered environment. A biological sample is crowded with non- selective and competitively binding molecules, but the recognizing entities can be rare.
  • the recognizing molecules can be proteins, small molecules, oligonucleotides, lipids, metabolites in and between many other proteins, small molecules, oligonucleotides, salts and water.
  • a prerequisite or requirement for performing such a measurement is a selective recognition element, which is a recognizing or “capture” molecule.
  • the capture molecule must have a high affinity for the targeted analyte.
  • the formation of a binding complex due to the interaction of this capture molecule with the recognition of the corresponding analyte generates a novel particle or compound with has different physical properties from the capture molecule or the analyte alone. For example, its volume, density, mass, and/or optical properties may be different. However, these differences may be small and therefore difficult to measure in situations where the environmental background is crowded with comparably constituted particles, because these comparable particles or compounds introduce binding noise due to non-specific interactions and random diffusion. Such a noisy environmental background is found in typical biological samples.
  • Coherent light e.g, from a laser source having a defined wavelength, which is diffracted at different locations of this affinity grating (diffractive recognition lens) is focused or gathered in a single focal spot, and the intensity of back- scattered light measured at this spot overcomes random scattering due to binding events which affect the entire sensor surface equally.
  • a physical constraint of this detection principle is that the pattern structure and periodicity of the affinity grating has to match coherently the wavelength of the laser light, thus the unit cell dimensions of the affinity grating may not be freely varied. Consequently, the size and mass dimensions of the analyte cannot be larger than a quarter of the wavelength of the light. The sensitivity of this detection principle for very small analytes would on the other end be increased if the grating would be finer.
  • Proteinopathies are a group of disorders which can affect the entire body. In the brain or in the peripheral nervous system, they are characterized by the appearance of pathological extracellular or intracellular protein accumulations. The pathology can be analyzed postmortem in the brain or in biopsies in peripheral tissues. The temporospatial distribution and abundance of the proteinopathy is used for disease staging and postmortem confirmation of the clinical diagnosis. In some neurodegenerative disorders, the sequence of appearance of proteinopathy follows neuroanatomical connections throughout the entire nervous system. This observation gave rise to the hypothesis, that certain pathogenic species of the involved proteins (in some instances called ‘pathogenic seeds’) may spread in a time-dependent and region-specific manner between neurons leading to a neuron-to- neuron propagation and development of pathology.
  • pathogenic seeds may spread in a time-dependent and region-specific manner between neurons leading to a neuron-to- neuron propagation and development of pathology.
  • the early stages of neurodegeneration and the development of a proteinopathy often develop unnoticed for several years. Because often the early symptoms are subtle or not recognized, initially not specific to one disorder, and no so-called ‘cardinal symptoms’ can be observed, the disease stage before being diagnosed is considered ‘pre-clinical’ or prodromal, if a certain higher likelihood to develop a disorder is achieved based on preclinical symptoms. Clear detection of the preclinical stage and distinction of potential disease trajectories early on would help to identify patients and improve diagnosis towards upcoming novel therapies for neurodegenerative disorders. In addition, it would be desirable to allow for better categorizing and stratification of the patients into clinical subtypes, and for monitoring disease progression and treatment efficacy.
  • the brains of a patient suffering from Alzheimer's disease, Parkinson's disease, Huntington's disease, or other related proteinopathies of the nervous system is interspersed with abnormal proteinaceous cellular inclusion bodies.
  • similar inclusions can be found in the peripheral nervous system as well, and some proteins or peptide fragments thereof accumulate in the extracellular space of the brain into so called plaques (for instance, in Alzheimer’s disease they are called senile or amyloid plaques).
  • plaques for instance, in Alzheimer’s disease they are called senile or amyloid plaques.
  • pathology forming proteins are drug targets and diagnostic markers since several decades.
  • pathological features which may be analytes associated with proteinopathy, are highly diverse and heterogeneous.
  • the analysis of the composition of cellular inclusions and plaques of the brain is only possible postmortem.
  • the totality of data from human tissue and model systems suggests multifactorial contributions to the development of a proteinopathy, including maturation over a longer period of time based on certain intrinsic factors (i.e. , genetic susceptibility, aging processes) or due to extrinsic triggers (i.e. viral or bacterial infection, toxins), and accumulation of specific proteins due to overproduction, or due to dysfunctional clearance mechanisms or lipid turn-over.
  • Brain imaging with specific radioactive tracers i.e., by positron emission tomography, PET) during the disease course allows so far for the detection of only one type of proteinacious inclusion (i.e., Tau tangles) and of extracellular amyloid-beta plaques.
  • Some proteinopthay causing proteins or proteoforms thereof can be specifically detected and quantified by immunoassays or mass spectrometry in body fluids such as blood or cerebrospinal fluid. Only for very few and only for certain monomeric p-amyloid peptides and Tau proteoforms this has turned into a biomarker-based support of the clinical diagnosis so far.
  • Detection by immunoassays of individual aggregated forms of proteins are so far not robust enough for proper validation and qualification or lack sufficient sensitivity and specificity to serve as diagnostic markers.
  • Other methods propose to detect aggregated proteins in biological fluids by employing a so-called seed amplification assay.
  • a biological fluid is suspected to contain protein aggregates that are associated with a particular proteinopathy and that can act as a seed to trigger aggregation of the monomeric recombinant form of the same protein, which is added as substrate to the amplification process.
  • the in vitro amplified aggregates become typically detectable by an amyloidspecific fluorescent dye. So far, this assay shows low reproducibility between labs, assay performance allows only for a qualitative readout and the assay requires several days of handling and incubation. Together, this makes the current seed amplification difficult to develop into a diagnostic assay.
  • the dissociation constants of recognition elements as antibodies and other molecular entities targeting intrinsically disordered proteins typically have nM (nano-molar) affinity. Structured epitopes and/or targets with multiple binding sites enable to reach pM (picomolar) affinity. Well-structured oligomers and coherently assembled fibrils are such examples.
  • PD Parkinson’s Disease
  • concentration range which here means the concentration or amount of targeted epitopes, matches the affinity-selectivity limit of all so far established recognition elements (such as antibodies).
  • the seed amplification assay is the only approach, which enables to distinguish between PD patients and healthy control groups. Interestingly, this assay does not require antibodies or other recognition elements in the first place, because it measures the nucleation potential of a large fluid volume, and it is known that traces of a seed can be sufficient to start the amplification processes.
  • Biomarkers beyond aggregated proteins and which are based upon pathologic metabolite and/or RNA concentration profiles are reaching the attention of the field. Their detection in bio liquids is a challenge, because they are rare and/or the analytes are small molecules, which makes it difficult to localize them based upon their physical property contrast, in a label free manner. Only methods which reach single molecule sensitivity, and experiments which sufficiently suppress noise such as due to non-specific binding have a chance to achieve this, such as atomic force microscopy or electron microscopy.
  • Bacteria, cellular organelles, supramolecular assemblies or aggregated proteins in proteinopathies are large particles and do not necessarily match the size dimension suitable for diffractive sensors based upon laser light.
  • Suitable and scalable recognition patterns of high quality can be generated with photolithography in all frequencies, and mass modulations due to binding of the analyte can also be read out with optical interferometers.
  • the lateral resolution is still limited by the wavelength applied, the vertical resolution of interferometers can, however, reveal atomistic mass mass changes. Therefore, light interferometry is suitable for large patterns and larger analytes.
  • Commercial interferometers already have a high degree of acquisition automation, and a multitude of different gratings can be measured in short time. Mass modulations related with the affinity of the selected or probed recognition elements, capture molecules, can be verified with high throughput; to support drug and biomarker discovery, or to identify most potent recognition elements in diagnostics.
  • EP2872478 A1 published on September 1st, 2021 in the name of ETH Zurich, discloses a diffractometric biosensing device for analyzing molecular interactions by using coherent laser light.
  • the device comprises a 3-dimensional transparent carrier medium having a grating structure with a plurality of consecutive curved surfaces or lines and a plurality of binding sites arranged thereon.
  • the binding sites are configured to interact with one or more target molecules.
  • the grating structure is configured to diffract a portion of coherent light propagating in the carrier medium so as to produce a constructive interference signal at a light detector, the signal being dependent on molecular interactions at or in the vicinity of the binding sites.
  • the curved surfaces can be paraboloids focusing a plane wave of incoming coherent light, or spheroids or ellipsoids configured to focus a spherical wave of the incoming coherent light, or planar surfaces in 3D arranged to diffract an incoming plane wave of the coherent light into a predetermined direction.
  • the surfaces of the grating structure are lithographically patterned into the carrier medium by interference lithography or multiphoton lithography.
  • the grating structure with the plurality of 2-dimensional or 1 -dimensional surfaces or lines can be arranged within a waveguide or its evanescent field region and functions as a distributed Bragg reflector.
  • the resolution and quantitative value of the method is qualified with a topographic analysis of the immobilized analyte surface with Atomic Force Microscopy (AFM).
  • AFM Atomic Force Microscopy
  • the VSI technique enables to analyze an array area of 5.1 square millimeters within 3 - 4 min at a resolution of 1.4 urn lateral and 0.1 nm vertical, in the full automation mode.
  • the drive converts the analog signals into binary signals, that are arranged as images in a computer.
  • the interaction with the immunoreaction products causes intensity variations in the reflected laser beam, and the light attenuations correlate with the analyte concentration.
  • the periodic signals become unified in a single frequency peak by converting them into their frequency counterpart using the Fourier transform, averaging in the frequency domain, using the height of the resulting peak as analytical signal to suppress undesired contributions spread along the spectrum, and retransforming the analytical signal into direct space to visualize the resulting FDA peak centered at the period value of the striped biorecognition assay pattern.
  • incubation masks are made from silicon-free adhesive plastic film and contain relatively broad striped chambers to be attached on the assay substrate.
  • the signal- to-noise ratio is higher than in raw or digitally filtered microarray methods.
  • EP3404356A1 published on November 21 st , 2018 in the name of NTN Corporation, discloses a method of measuring a volume of a micro projection, such as a liquid droplet, using white-light interferometry.
  • a Mirau-type interference objective lens includes a lens, reference mirror and beam splitter for separating the white light emitted from the light source into two beams, of which one irradiates a surface of an object and the other irradiates a reference mirror plane, and for recombining the light beams reflected from the object surface and reference mirror plane to interfere with each other.
  • a white light source is used, for which the interference light intensity is maximized only at a focal position of the Mirau- type interference objective lens, unlike the case of using a single wavelength light source such as a laser.
  • This allows to measure a three-dimensional shape of the liquid droplet.
  • the Mirau-type interference objective lens and optionally the substrate itself is or are moved up and down for generating a plurality of images with a variable interference modulation contrast for each pixel position x, y in the droplet plane. For each pixel, a height position where an envelope of the interference light intensity peaks is determined from the plurality of images, and a height of the liquid droplet is detected by comparing with reference plane areas outside the liquid droplet area.
  • the object of the invention is to provide an improved device and method for screening biological fluid analytes, such as analytes associated with proteinopathy. This object is achieved by the subject-matter set forth in the independent claims. Some embodiments are given in the dependent claims and claim combinations and in the description and can provide further improvements.
  • steps b, e, and f. may be omitted.
  • it may only be necessary to perform step d namely imaging at least a part of the recognition pattern to obtain image data, digitalizing the image data, and determining Fourier-transformed image data as a function of one or two corresponding spatial frequency and/or spatial wave vector coordinates.
  • the Fourier-transformed image data may be analyzed to quantify therefrom the mass modulation generated by the immobilized analyte. For example, one or more peaks may be detected in the Fourier-transformed image data, the amplitude of the peaks being indicative of the mass modulation.
  • recognition pattern properties may be used.
  • the invention also relates to a method for screening a biological fluid sample for an analyte, preferably a single analyte or mixture of analytes associated with proteinopathy, the method comprising the steps of: a. providing a sensor chip comprising a recognition pattern extended in a two- dimensional plane and having arranged thereon in an alternating manner first pattern elements and second pattern elements, in particular ridges and grooves, at least one of the first and second pattern elements being functionalized with molecular recognition elements for binding an analyte, the recognition pattern having a characteristic spatial frequency spectrum in the plane, b. providing a sensing device, which is or comprises an imaging microscopy apparatus being configured for detecting a measurement signal indicative of the recognition pattern, c.
  • the biological fluid sample to the sensor chip such that at least a portion of the analyte is immobilized at at least some of the molecular recognition elements, thereby modifying the measurement signal; d. imaging at least a part of the recognition pattern to obtain image data as a function of one or two spatial measurement coordinates in the plane, digitalizing the image data, and determining Fourier-transformed image data as a function of one or two corresponding spatial frequency and/or spatial wave vector coordinates, e. determining improved Fourier-transformed image data by filtering the Fourier- transformed image data using the characteristic spatial frequency spectrum of the recognition pattern, and f. retransforming the improved Fourier-transformed image data into direct space to obtain improved image data, and quantifying therefrom a mass modulation contrast generated by the immobilized analyte.
  • the pattern elements comprise a plurality of types of pattern elements, for example a first pattern element of a first type and a second pattern element of a second type.
  • the first pattern element may be a ridge of a recognition grating
  • the second pattern element may be a groove of a recognition grating.
  • At least one of the types of pattern elements may be functionalized with a molecular recognition element.
  • the recognition pattern comprises a plurality of different types of pattern elements.
  • Each type of pattern element has a different spatial periodicity and/or symmetry.
  • the recognition pattern therefore has a corresponding plurality of characteristic spatial frequency spectrums in the plane.
  • the plurality of different types of pattern elements may each be functionalized with a different type of molecular recognition element. Thereby, different types of analytes with similar or different masses may be efficiently recognized.
  • the Fourier-transformed image data may be determined by filtering the Fourier-transformed image data in a spatial frequency filtering range lying outside the characteristic spatial frequency spectrum of the recognition pattern and/or by amplifying the Fourier-transformed image data in a spatial frequency amplification range lying inside the characteristic spatial frequency spectrum of the recognition pattern.
  • Providing the biological fluid sample to the sensor chip causes, through analyte immobilization, an induced mass modulation distribution which has the frequency or periodicity of the recognition pattern. This causes a modification of the measurement signal.
  • the biological fluid sample may be provided to the sensor chip by a third party, at a remote location, or in a context separate from at least some of the methods disclosed herein.
  • a user may receive the sensor chip, apply the biological fluid sample, and then provide the sensor chip together with the applied biological fluid sample for analysis.
  • the size, density or the mass of the analyte can be determined using Fourier Amplitude spectrum analysis.
  • the first image data may be subtracted from the second image data, the resulting difference being indicative of the analyte bound to the recognition pattern.
  • the subtraction may be in real space or in Fourier space.
  • the first image data is pre-defined and may be generated by a computer and pre-stored.
  • the first image data is associated with the recognition pattern.
  • idealized or synthetic first image data corresponding generated using a model of the recognition pattern may be used.
  • the first image data may be generated further depending on a type or model of an imaging microscopy apparatus used for generating the second image data. Thereby, only the second image data actually needs to be recorded by the imaging microscopy apparatus.
  • the recognition pattern can be in the form of a recognition grating or any other spatially repetitive arrangement of functionalized first pattern elements and non-functionalized or differently functionalized second pattern elements.
  • the recognition pattern typically has some kind of spatial periodicity and symmetry.
  • Single DNA origami structures do not need to be coherently assembled next to each other. An ensemble of similar or different DNA origami nanostructures is possible.
  • the pattern elements may be functionalized with molecular recognition elements.
  • the pattern elements may form part of, or be attached to, the staple strands.
  • the molecular recognition elements may be connected to the staple strands by linker elements.
  • the unit cell of the DNA origami structure has a multitude of symmetrically independent recognition elements, and which in respect to their frequency and orientation are not multiple of the other.
  • a DNA-origami nanopattern has a fiducial marker which breaks the symmetry of the pattern, thus that the relative position and/or orientation of each unit cell may be defined and easily detected in the image data.
  • a DNA-origami nanopattern has fixed fiducial marker which is a solid nanoparticle providing orders of magnitude larger scattering contrast.
  • the fiducial marker is used to find and select meaningful areas in the electron micrograph image data, even if the DNA origami patterns are not immediately visible because it is hidden by randomly deposited material.
  • the selected areas may be used for multivariate statistical analysis, for example as established in 3D reconstruction of single proteins with high-resolution cryogenic transmission electron microscopy.
  • DNA-origami structures on glass slides are known, They are region-selectively decorated or functionalized with a multitude of different capture elements or molecular recognition elements, and each element can be placed with a spatial accuracy of below 1 nm. These highly coherent nanostructures may further be equipped with a series of different molecular recognition elements, thereby periodic and multiplexed recognition patterns with a very high spatial frequency may be realized on a relatively small sensor area.
  • Binding events which are related with this recognition pattern can be distinguished from binding events which are random, especially if the image data are analyzed in the Fourier Space. Random binding events are pattern independent and are expected at low spatial frequencies, because they appear independent of each other, here and there.
  • the recognition pattern can be read out with simplified means and with high throughput.
  • the detection device can include optical profiling such as low-coherence white light interferometry (WLI), confocal scanning microscopy, IR and Raman microscopy, phase contrast microscopy, scanning probe microscopy and/or electron microscopy. Background speckle patterns are avoided, and coherent light diffraction is not needed, which simplifies the arrangement of the sensor chip for read-out by the sensing device and the broader applicability of the spatial lock-in principle for large objects in general.
  • a wavelength of coherent light and a spatial wavelength (or spatial carrier frequency, resp.) of the recognition pattern need not be matched to one another.
  • the size, symmetry and alignment of the recognition pattern is less constrained and may be adapted for a particular purpose.
  • Coherent and multiplexed nanopatterns may be established using known techniques in DNA nanotechnology. The high spatial frequency of these nano patterns enables the application of the digital log in amplification principle on the single molecule level, which results in an extreme reduction of the sensor surface area.
  • a sensor surface decorated or functionalized with many similar or even different DNA origami nanostructures is expected to have a higher sensitivity than a similarly sized sensor area with a microstructure, and even rare analytes can become detectable.
  • the detection principle can be conducted within well-plates, and enable interaction analysis of reporter molecules, biomarker monitoring in drug discovery and many other applications, with high throughput.
  • the method according to the invention is not only suitable on a molecular or level, but it can also be used reliably for larger aggregates, such as aggregates of cells and cellular components, being at least 10x or even 50x larger than a single human cell. Additionally, the present method is not limited in terms of the employed wavelength.
  • one- or two-dimensional images of the recognition pattern can be acquired simultaneously by using a one- or two-dimensional imaging detector, e.g. optical detector array in white light interferometry, or by a one- or two-dimensional scanning procedure across the recognition pattern, e.g. in atomic force microscopy or confocal microscopy.
  • a one- or two-dimensional imaging detector e.g. optical detector array in white light interferometry
  • a one- or two-dimensional scanning procedure across the recognition pattern e.g. in atomic force microscopy or confocal microscopy.
  • the one- or preferably two-dimensional image acquisition also has the advantage that a plurality of recognition patterns can be read out and their Fourier mass distribution can be analyzed in parallel and preferably simultaneously.
  • the method according to the invention therefore may be performed with a high throughput.
  • the method according to the invention can be performed with a high throughput and in an operationally simple manner, because the results can be readily read out and interpreted, for example by comparison with standard analyte measurements.
  • the biological fluid sample may typically be a sample having been obtained from a subject.
  • the biological fluid is preferably any solution or suspension derived from a human or an animal body (i.e., body fluids such as but not restricted to blood, serum, plasma, cerebrospinal fluid, interstitial fluid, saliva, lacrimal fluid, urine) or is derived from in vitro cellular or biochemical systems. It is clear however that the method according to the invention as such is typically performed in-vitro.
  • the biological fluid sample has been obtained from a subject (e.g. a human subject), which has not yet been diagnosed with a proteinopathy.
  • the biological fluid sample may also comprise all biological fluids and biochemically processed forms of tissue, cells, or excrements derived from a human or an animal body or from in vitro cell culture systems (i.e., tissue or cell extracts or homogenates, stool samples).
  • a biological fluid sample may also comprise intact cells or subcellular structures (for instance but not restricted to cellular nuclei, lysosomes, exosomes, vesicles, nucleic acids containing molecules such a DNA and RNA, or lipid aggregates) isolated from a human or an animal body or derived from in vitro cell culture systems.
  • the biological fluid sample typically contains the analyte associated with proteinopathy.
  • Such an analyte exposes a moiety on its surface, in particular a molecular moiety, which is known to play a role in proteinopathy.
  • a molecular moiety which is known to play a role in proteinopathy.
  • it may be a moiety which is in a certain form or three dimensional structure or which occurs at different levels in a biological fluid sample obtained from a patient suffering from a proteinopathy as compared to a healthy subject or a patient with a different condition.
  • a biological fluid is preferably any aqueous fluid derived from a human or an animal body (i.e. , body fluids such as but not restricted to blood, serum, plasma, cerebrospinal fluid, interstitial fluid, saliva, lacrimal fluid, urine) or derived from in vitro cellular or biochemical systems.
  • the plurality of first molecular recognition elements are configured to bind the analyte associated with proteinopathy.
  • the first and second molecular recognition elements only the first molecular recognition elements are configured to bind the analyte associated with proteinopathy.
  • the first molecular recognition elements and eventually the second molecular recognition elements comprise one or more binding sites or affinity sites.
  • the first molecular recognition elements and eventually the second molecular recognition elements can be antibodies, particularly nanobodies, proteins, peptides, engineered sequences of natural L- or artificial D-type amino acids, peptidic polymers derived from amino acid-like molecules, or single or double stranded sense or antisense oligonucleotide sequences or structures, or small molecules.
  • the first molecular recognition elements and eventually the second molecular recognition elements are antibodies. It may also be possible that the first and eventually second molecular recognition elements are chemical and/or physical binders being configured to bind the analyte associated with proteinopathy in any of the chemical bonding or physical force of attraction event as mentioned further below under the definition of “binding”.
  • analytes associated with a proteinopathy because they have been found to recruit additional material, such as any residual biological material, for example cellular components and subcellular components, such as mitochondria, cell membranes, vesicular membranes, nucleic acids, proteins, small molecules, peptides and poorly soluble-digested fragments thereof, which results in growing aggregates of highly heterogeneous and complex nature.
  • additional material such as any residual biological material, for example cellular components and subcellular components, such as mitochondria, cell membranes, vesicular membranes, nucleic acids, proteins, small molecules, peptides and poorly soluble-digested fragments thereof, which results in growing aggregates of highly heterogeneous and complex nature.
  • the sensor chip is incubated for a certain time with a biological fluid, washed, sent and analyzed later on.
  • Optical apparatus are equipped with micrometer positioning tables and able to adjust the sensor chip surface focus automatically.
  • a multitude of recognition patterns on one sensor chip, or an array of different sensor chips, can be analyzed automatically and at high-throughput. This enables the gathering and analyzing of data of large sample populations efficiently; otherwise, large data libraries related to patients remain poorly accessible.
  • the first molecular recognition elements and eventually the second molecular recognition elements may each comprise only a single binding site per molecular recognition element or they may each comprise multiple binding sites per molecular recognition element, in particular multiple different binding sites per molecular recognition element.
  • the first molecular recognition elements may comprise only a single binding site being configured to bind the analyte associated with proteinopathy per first molecular recognition element or they may each comprise multiple binding sites being configured to bind the analyte associated with proteinopathy per first molecular recognition element, in particular multiple different binding sites being configured to bind the analyte associated with proteinopathy per first molecular recognition element.
  • the recognition pattern is configured such that its characteristic spatial frequency spectrum comprises at least one peak region, which represents a pattern period, or equivalently pattern wavelength, and/or a pattern shape of the recognition pattern.
  • the recognition pattern provides a spatial carrier frequency, which allows to separate the relevant image data relating to the recognition pattern from background noise which is largely uncorrelated with the recognition pattern.
  • a pattern wavelength of the recognition pattern is selected to be shorter than typical distances between spatial noise components stemming from species present in the biological fluid sample or arbitrarily binding to the recognition pattern. Designing the recognition pattern with such a short wavelength, or equivalently with such a high periodicity or spatial frequency of the alternating first and second pattern elements, allows to intrinsically separate the noise spectrum from the image data spectrum largely contained on the spatial carrier frequency of the recognition pattern, and thereby to improve in step e. the efficiency of filtering the Fourier-transformed image data. In embodiments, in step e. the improved Fourier-transformed image data are determined by low-pass filtering or high-pass filtering or band-pass filtering to discard spatial noise components outside at least one peak region of the characteristic spatial frequency spectrum of the recognition pattern.
  • the pattern can have the first pattern elements selected from: lines of functionalization sites repetitively shifted in a transverse direction; straight or curved lines of functionalization sites repetitively shifted in a transverse direction, array of dot-like functionalization sites; and combinations thereof; and further can have the alternating second pattern elements selected from: lines of interstitial sites; straight lines or curved lines of interstitial sites sites; array of dot-like interstitial sites; and combinations thereof.
  • the sensor chip can be produced by photolithography, spotting or contact printing, in particular can be functionalized by reactive immersion lithography (RIL) or nanostructures based on DNA origami.
  • RIL reactive immersion lithography
  • the recognition pattern can be matched to the resolution and practicability of the selected imaging microscopy apparatus.
  • the analyte when immobilized modifies the topography (in particular, the height or height profile) and/or refractive index of the pattern. Such modifications can be read-out by the sensing device.
  • the electron microscope comprises an electron beam configured to profile the phase contrast and spatial mass distribution of the recognition pattern and therefrom quantify a mass modulation generated by the immobilized analyte.
  • the imaging microscopy apparatus may include an X-ray diffractometer.
  • White Light Interferometers, Atomic Force Microscopes and optical imaging apparatus have the benefit that they are capable of detecting binding induced mass or size modulations on sensor chips at all dimensions, and therefore improve the flexibility in designing and optimizing such experiments. Further, digital images may be recorded on periodic arrangements of pattern elements which can be analyzed in Fourier space.
  • One advantage of this technique over conventional focal Molography is that the periodicity of the sensor chip is scalable, and further that the grating structure can be multifunctional.
  • the imaging microscopy apparatus is an infrared (IR) or Raman spectroscope and the spatial-digital lock-in principle is applied to image the modulation intensity difference of absorbed IR light due to modulated beta-sheet content caused by recognized-structured aggregates with high beta-sheet content (aBeta, tau, aSYN, etc.).
  • IR infrared
  • Raman spectroscope Raman spectroscope
  • the spatial-digital lock-in principle is applied to image the modulation intensity difference of absorbed IR light due to modulated beta-sheet content caused by recognized-structured aggregates with high beta-sheet content (aBeta, tau, aSYN, etc.).
  • imaging microscopy apparatuses can allow for a first relatively quick quantification of mass modulation contrast generated by one of the immobilized analytes and a second more detailed and slower quantification of mass modulation contrast generated by same or different immobilized analytes.
  • the latter can be used to calibrate the read out of optical profilers.
  • multiplexed sensor chips can be provided that include a plurality of recognition patterns, preferably arranged at different locations on the sensor chip.
  • the plurality of recognition patterns can comprise specific recognition patterns characterized by at least one of: specific functionalization of the at least one of the first and second pattern elements for binding one or more specific analytes; selecting a specific recognition pattern having a specific characteristic spatial frequency spectrum in the plane; specific localization of the recognition patterns on the sensor chip; specific arrangement of the recognition patterns, e.g. in a rectangular or quadratic array, on the sensor chip.
  • specific means at least partially different, in particular completely different, and being distinguishable during read-out.
  • the plurality of recognition patterns may be used to determine at least partly a immunsignature, as further discussed herein, from one measurement.
  • multiplexed sensor chips may also comprise overlapping recognition patterns that have sufficiently different characteristic spatial frequency spectra, in particular different carrier frequencies, in the plane. This allows for separate read-out of the overlapping recognition patterns by using specifically adapted spatial filtering ranges and/or specifically adapted spatial frequency amplification ranges for each overlapping recognition pattern in step e..
  • the imaging microscopy can preferably be a dynamic imaging device for producing a one- or two-dimensional film of analyte binding or aggregation behavior at the molecular recognition elements as a function of time.
  • the dynamic imaging device can be combined with providing multiplexed sensor chips that include a plurality of recognition patterns arranged at different locations and/or being overlapping on the sensor chip, in particular as disclosed herein.
  • embodiments relating to a plurality, in particular array, of recognition patterns and in particular their use for determining a partial or complete immunosignature are disclosed.
  • each recognition pattern contains specific first and second molecular recognition elements and can be read-out distinguishably from one another, e. g. by a specific localization and/or spatial frequency spectrum, in particular carrier frequency, on the sensor chip. Therefore, each recognition pattern may generate its own modified measurement signal (step c.) and its own image data (steps d. - e.).
  • the second molecular recognition elements of a recognition pattern, in particular of each recognition pattern, of the plurality of recognition patterns are different from the second molecular recognition elements of at least some of or all of the other recognition patterns.
  • the second molecular recognition elements of each recognition pattern of the plurality of recognition patterns are different from the second molecular recognition elements of each other recognition pattern.
  • the second molecular recognition elements of one recognition pattern or of a group of recognition patterns are unique as compared to other recognition patterns of the sensing device.
  • the second molecular recognition elements of each recognition pattern or of each group of recognition patterns are unique as compared to other recognition patterns of the sensing device.
  • a first recognition pattern may comprise first molecular recognition elements which are configured to bind a first binding site of the analyte associated with proteinopathy, while another recognition pattern may be configured to bind a second binding site of the analyte associated with proteinopathy.
  • the different first molecular recognition elements bind the analyte associated with proteinopathy with different binding affinities.
  • analytes associated with proteinopathy can in some embodiments be not only single molecules, but be aggregates of multiple moieties, such as cellular components or fragments thereof, proteins, nucleic acids and the like, using such multiple recognition patterns can provide detailed information on the analyte.
  • the complexity of the analyte can be broken down.
  • Each signal generated at the detector of the sensing device, respectively at specific detector pixels or pixel arrays or (partial) imaging areas of the detector is dependent on the mass increase effect of a molecular interaction between the corresponding first molecular recognition elements and the analyte. Therefore, each signal may be used as a quantity or parameter of the analyte in a high dimensional vector space.
  • first molecular recognition elements of a given recognition pattern from first molecular recognition elements of another recognition pattern is in some embodiments characterized in that they are configured to bind a different epitope of the analyte associated with proteinopathy.
  • step d. is performed for a predetermined measurement time to monitor competing of the modified measurement signals, or image data, of the recognition patterns for the analyte associated with proteinopathy.
  • the signal measured for the predetermined measurement time is considered a time dependent signal and is comprised in an immunosignature of the biological fluid sample.
  • step c. is performed such that the biological fluid sample is provided to the plurality of recognition patterns such that it comes in contact with one recognition pattern after the other.
  • These embodiments may be considered as a serial competition measurement.
  • a serial competition measurement at least some or even all recognition patterns are in fluidic communication with each other. That is, the recognition patterns are not separated from each other by a wall structure or similar.
  • the biological fluid sample is provided with a flow direction, i.e. it flows in a particular direction and may thus sequentially contact the recognition patterns.
  • step d is performed such that the biological fluid sample is provided to the plurality of recognition patterns such that it comes in contact with one recognition pattern after the other.
  • the sensing device comprises recognition patterns which comprise different first and/or different second molecular recognition elements. That is, the first pattern element of a given recognition pattern may bind first molecular recognition elements which are different and preferably unique, as compared to the first molecular sensing elements being bound to the first pattern elements of another recognition pattern of the sensing device.
  • the recognition patterns are separated from each other, in particular by a wall structure.
  • a wall structure may be configured to prevent that the biological fluid sample flows between the separated recognition patterns, im particular during or after step c..
  • Such a measurement may be considered as a parallel measurement.
  • the recognition patterns may be divided into multiple groups of recognition patterns, wherein the different groups of recognition patterns are separated from each other, for example by the wall structure(s).
  • each group may contain one or more recognition patterns.
  • each group is characterized by different first and/or second molecular recognition elements as compared to the first and/or second molecular recognition elements of another group of recognition patterns. That is for example, the first group may comprise two recognition patterns which each have the same first molecular recognition elements. However, the second group may also comprise two recognition patterns which each have different first molecular recognition elements compared to the two recognition patterns of the first group.
  • the method further comprises the step of determining the origin of at least one or all of the measurment signals or modified measurement signals which are measured at the detector.
  • determining the origin of the signal measured at the detector it may for example be possible to identify the particular recgnition pattern from which the (modified) measurement signals originated. Since it is known what first and/or second molecular recognition elements are present at each recognition pattern, further information on the biological fluid sample and/or the analyte associated with proteinopathy can be obtained.
  • the method further comprises the step of determining a parameter of the analyte associated with a proteinopathy from the mass difference dependent and optionally time dependent modified measurement signal or image data measured in step d.
  • the determined parameter may for example be single point, such as a mass or a mass per surface area, or the mass increase or decrease at the one or more recognition patterns, in particular at its first molecular recognition elements, over time, or the slope or any higher derivative or its change of the mass increase or decrease at one or more recognition patterns, in particular at its first molecular recognition elements, over time.
  • the method further comprises the step of determining a parameter of an aggregate of the analyte associated with a proteinopathy or co-aggregate with the analyte associated with a proteinopathy from the mass difference dependent and optionally time dependent modified measurement signal(s) or image data.
  • the determined parameter may for example be single point, such as a mass or a mass per surface area, or the mass increase at the one or more recognition patterns, in particular at its first molecular recognition elements, over time, or the slope or any higher derivative or its change of the mass increase or decrease at one or more recognition patterns, in particular at its first molecular recognition elements, over time.
  • the imaging microscopy apparatus is a white light interferometer (WLI) operating in reflection and comprising an imaging detector having a linear or planar pixel array configured to obtain interferometric image data containing phase-sensitive information for determining a height and/or refractive index distribution of the recognition pattern and therefrom a mass modulation generated by the immobilized analyte.
  • WLI white light interferometer
  • the imaging microscopy apparatus is a phase contrast microscope.
  • the imaging microscopy apparatus is a scanning probe microscope, such as an atomic force microscope (AFM) comprising a mechanical or other sensor, or an array of such, to profile mass-related property differences along the pattern in real space.
  • AFM height data containing surface topography variations caused by recognized biological matter are the analyzed and used to calibrate optical property modulations.
  • the periodicity and spatial dimension of the recognition pattern is optimized to measure recognition processes of recognition elements at a large field of view and at large working distances.
  • the recognition pattern may be scaled and integrated into a well-plate.
  • the pattern frequency may be optimized such that it is accessible by an imaging microscopy apparatus from the top and/or from below.
  • the well-plate includes a number of well-shaped receptacles arranged in an array on a plate.
  • a recognition pattern may be arranged in a bottom area of each well.
  • an optically reflective surface such as provided by a mirror (e.g., implemented using a silicon wafer surface) would be arranged above or below the recognition pattern.
  • the dimensions and parameters of the recognition pattern and the specifications of the imaging microscopy apparatus are suitably matched such that mass modulation events are observable with a high throughput.
  • the concentration of reporter molecules, the expression level of a target protein, and the aggregation rate of bioorganic matter can then be monitored in real-time, preferably for a plurality of wells simultaneously. This enables the weighing of extracellular bioorganic and chemical matter at high-throughput. Such approaches are of importance for screening experiments; such as to search aggregation inhibitors in a quantitative way. Such a set-up could also be used to monitor organoid farms, or toxins or inflammation markers in realtime.
  • the molecular recognition elements in particular antibodies, provide chemical specificity to bind at least one specific analyte associated with proteinopathy and/or are configured to bind a specific epitope of the analyte associated with proteinopathy and/or are configured to bind the analyte or epitope of the analyte associated with proteinopathy with a specific binding affinity.
  • the determined parameter may for example be single point, such as a mass or a mass per surface area, or the mass increase or decrease at the one or more recognition patterns, in particular at first molecular recognition elements, over time, or the slope or any higher derivative or its change of the mass increase or decrease at one or more recognition patterns, in particular at its first molecular recognition elements, over time.
  • the additional material may be any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof.
  • the determined parameter is comprised in the formed immunosignature of the biological fluid sample and/or of the analyte of proteinopathy.
  • the determined parameter is compared with a database comprising a plurality of reference parameters, in particular a plurality of reference parameters of different patient populations, healthy subjects and/or previous parameter of the subject from which the biological fluid sample has been obtained.
  • the database may comprise reference parameters for multiple patient populations which suffer from different proteinopathies (as compared to other patient groups in the database). It may also be possible that the database comprises multiple patient groups, which suffer from the same proteinopathy but at different clinical stages (as compared to other patient groups in the database). Such embodiments provide valuable data for physicians to assess the presence, nature, stage and progression of a proteinopathy.
  • the analyte associated with proteinopathy comprises one or more of p- amyloid, tau, a-synuclein, prion proteins, fused in sarcoma, wild type or mutant poly-Q huntingtin, Ubiquitin, Ataxin-3, Optineurin, TAR DNA-binding protein 43, neurofibrilary light chain light (NfL), soluble or shed Triggering Receptor expressed on myeloid cells 2 (sTREM2), Chitinase-3-like protein 1 , Glial Fibrillary Acidic Protein and truncated or otherwise post-translationally modified forms of these.
  • Post-translational modifications can comprise but are not restricted to phosphorylation, nitration, ubiquitination, glycation and glycosylation, oxidation and dityrosine bonds due to oxidation, and methylation.
  • the analyte associated with proteinopathy may also be an aggregate or coaggregate which comprises one or more of the above mentioned moieties (including itself or post-translationally modified forms of itself), and additional material, such as any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof.
  • step d. is performed during a measurement time and the image data are measured as a function of the measurement time to determine an aggregation behavior, in particular aggregation of additional material in the biological fluid sample such as any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof; or step d. comprises a single point measurement at a specific point in time, in particular a steady-state measurement.
  • a signal having been measured for a predetermined measurement time may in some embodiments be further comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy. Upon measuring the signal for a predetermined measurement time, additional insight about the nature of the analyte and the proteinopathy can be gained.
  • the behavior towards the molecular recognition elements over time in particular towards different molecular recognition elements over time, or to monitor changes over time upon exposing the provided biological fluid sample to changed conditions, such as pH, differently composed media, soluble binding partners (natural ligands) or synthesized or engineered molecules that act as artificial ligands or interfere with the stability of the analyte, such as for example aggregate stabilizer agents or aggregate destabilizer agents as mentioned herein.
  • changed conditions such as pH, differently composed media, soluble binding partners (natural ligands) or synthesized or engineered molecules that act as artificial ligands or interfere with the stability of the analyte, such as for example aggregate stabilizer agents or aggregate destabilizer agents as mentioned herein.
  • changes of each signal are measured, wherein the changes are effected by aggregation, in particular by aggregation of additional material in the biological sample, such as any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof, in particular cellular components and/or proteins.
  • a rinsing step is performed after step c. and in particular before or during step d..
  • the rinsing step may be performed with a rinsing solution.
  • An aggregate stabilizer agent is an agent being configured for stabilizing aggregates and an aggregate destabilizer agent is an agent being configured to destabilize, e.g. disassemble, aggregates.
  • the aggregate stabilizer agent or the aggregate destabilizer agent may be chemical molecules, such as small molecules (which have generally a molecular mass of ⁇ 1000 Da) or large molecules (which have generally a molecular mass of >1000 Da), such as antibodies, Fab fragments or nanobodies.
  • the aggregate stabilizer agent or aggregate destabilizer agent may be recombinant or synthesized forms of the analyte associated with proteinopathy, natural ligands, synthesized or engineered molecules that act as artificial ligands or interfere with the stability of the aggregated analyte.
  • step d. comprises the single point measurement at a specific point in time before the rinsing step and/or treatment step. In some embodiments, step d. comprises the single point measurement at a specific point in time after the rinsing step and/or treatment step. In some embodiments, such measurement, respectively the signal obtained from such a measurement may also be comprised in the immunosignature of the biological fluid sample and/or the analyte associated with proteinopathy.
  • a binding agent is configured to bind to the analyte associated with proteinopathy, in particular to bind to lysosomal markers, mitochondrial markers, nucleotides, sugars, post-translational modifications or mitochondrial DNA of the analyte associated with a proteinopathy.
  • the analyte is an aggregate or co-aggregate of additional material, such as any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof, in particular cellular components and/or proteins.
  • a seed amplification assay is performed by adding a binding reagent sample, in particular a protein sample, to the recognition pattern.
  • Performing the seed amplification assay comprises adding a binding reagent sample, in particular a protein sample to the one or more recognition pattern(s).
  • the binding reagent sample is typically added after step c. since the analyte associate with proteinopathy being present in the biological fluid sample acts as a seed to trigger aggregation, for example aggregation of other monomeric proteins, in particular recombinant or synthetic forms of the monomeric analyte.
  • step d. can comprise measuring each signal over a predetermined measurement time.
  • the protein sample comprises monomeric proteins.
  • the monomeric proteins may act as substrate for the co-aggregation in the seed amplification assay.
  • the added proteins of the protein sample misfold and aggregate with the analyte during the predetermined measurement time. Since the aggregation causes a mass increase, it can be readily measured during step d..
  • the bound analyte associated with proteinopathy in particular an analyte forming an aggregate
  • the seed amplification assay can in some embodiments further comprise adding other binding reagents in addition to the protein sample or as an alternative to the protein sample.
  • Binding reagents which may be comprised in the binding reagent sample may comprise but are not restricted to small molecules (i.e. having a mass of ⁇ 1000 Da, such as PET tracer like molecules or amyloid structure binders, peptides or oligonulceotides as described above) or large molecules (i.e., having a mass of >1000 Da, such as antibodies, nanobodies, other polypetides).
  • the method further comprises a secondary characterization step for characterizing the analyte associated with proteinopathy or a state of an aggregate or coaggregate formed by the analyte associated with proteinopathy.
  • the secondary characterization step preferably includes ELISA, FT-IR, Raman or fluorescence spectroscopy.
  • the secondary characterization can be performed at any time, in particular during or after step c..
  • the secondary characterization step provides a secondary characterization parameter.
  • the secondary characterization parameter is also comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy.
  • the signal or signals measured for each recognition pattern during step d. form an immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy.
  • other parameters of the analyte of proteinopathy are comprised in the immunosignature.
  • the parameter of the analyte associated with a proteinopathy determined from the mass dependent and optionally time dependent signal measured in step d., signals measured during a seed amplification assay, the determined origin of at least one or all of the signals measured at the detector, the signal obtained from application of the treatment agent and/or the secondary characterization parameter can also be comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy.
  • the immunosignature of the biological fluid sample is compared to a database comprising immunosignatures of a plurality of samples.
  • the samples may be patient samples, autologous patient samples (i.e. previously screened samples from the patient from which the presently screened biological fluid sample has been obtained) and/or reference samples.
  • the immunosignatures of the database are each associated with a clinical status of a proteinopathy.
  • Comparison with a database can in general be computer-implemented.
  • the database may for example be stored in a memory module, such as included in a control unit, computer, a server, a hard drive or a cloud-based server.
  • the comparison with the database may for example be conducted by a control unit, including a circuit or a processor.
  • the highdimensional and multimodal data type stored in relevant databases or generated in the afore described analyses may additionally be analyzed and/or processed by machine learning and artificial intelligence-type analytical methods to identify immunosignatures.
  • the first molecular recognition elements and the second molecular recognition elements of each recognition pattern are both configured to interact with the same background binding partners.
  • Background binding partners may be any chemical or biological moieties which can be bound by the first and/or second molecular recognition elements. These may be molecules, such as small molecules or proteins, receptors on cells or cellular components being present in the sample or binders having epitopes being able to bind to the first and/or second molecular recognition elements.
  • Such embodiments are advantageous, because interactions of background binding partners being present in the sample and not being associated with the particular analyte associated with proteinopathy are inherently not or essentially not detected.
  • the first molecular recognition elements and the second molecular recognition elements have essentially the same affinity KD to the same background binding partners.
  • the first molecular recognition elements are bound to the first pattern elements with a surface density of 0.1 to 40 fmol/mm 2 (femto-mol/square-millimeter) in particular of 1 and 4 fmol/mm 2 , in the case of molecular recognition elements being antibodies or having the size of antibodies (150 kDa).
  • the first molecular recognition elements are bound to the first pattern elements with a surface density of 1 and 320 fmol/mm 2 , in particular 16 and 64 fmol/mm 2 .
  • second molecular recognition elements may be bound to the second pattern elements with a surface density of 0.1 to 40 fmol/mm 2 , in particular of 1 and 4 fmol/mm 2 , in the case of molecular recognition elements being antibodies or having the size of antibodies (150 kDa).
  • the second molecular recognition elements are bound to the second pattern elements with a surface density of 1 and 320 fmol/mm 2 , in particular 16 and 64 fmol/mm 2 .
  • steps c. and d. at least once, at least twice, or even more. Each repetition may be considered as a screening cycle.
  • each screening cycle is different. For example, it may be possible that in a screening cycle, only the blank biological fluid sample is screened, providing a blank signal.
  • a seed amplification as described herein may be performed on the same biological fluid sample providing a seed amplification signal.
  • a treatment step and/or a rinsing step is performed as described in some of the embodiments herein providing a treatment signal and/or a rinsing signal.
  • a serial competition measurement according to some of the herein described embodiments is performed providing several competition signals.
  • the method and device disclosed herein may be combined with second or further characterization steps e.g involving labelling techniques, e.g. by incubation with a labelled binding agent and reading out a labelled binding agent signal.
  • the mass-labelled binding agent is a binding agent preferably having a known, identifiable and optionally unique mass.
  • the mass labelled binding agent may for example comprise nanoparticles.
  • the nanoparticles preferably comprise a transition metal or a transition metal oxide, such as gold, TiC>2, Ta2Os and silver. If a fluorescent-labelled binding agent is used, the fluorescent signal can in some embodiments be measured with a fluorescent detector.
  • the fluorescent detector is typically an additional and thus separate detector.
  • the detected fluorescent signal is comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy.
  • the binding agent may comprise a predefined binding site, which may particularly be configured to bind to the immobilized analyte associated with proteinopathy.
  • the predefined binding site of the binding agent may in some embodiments be configured such that it does not bind to the first and/or second molecular recognition elements, meaning the targeted species is different.
  • the sensing device comprising sampling means for providing the biological fluid sample to the sensor chip such that at least a portion of the analyte is immobilized at at least some of the molecular recognition elements, thereby modifying the measurement signal, d.
  • the sensing device comprising: imaging means for imaging at least a part of the recognition pattern to obtain image data as a function of one or two spatial measurement coordinates in the plane, and a processor configured to digitalize the image data and for determining Fourier-transformed image data as a function of one or two corresponding spatial frequency coordinates, e. the processor configured to improve the Fourier-transformed image data by filtering the Fourier-transformed image data using the characteristic spatial frequency spectrum of the recognition pattern, and f. the processor configured to retransform the improved Fourier-transformed image data into direct space to obtain improved image data and quantify therefrom a mass modulation contrast generated by the immobilized analyte.
  • the processor is configured to band-pass filter the Fourier-transformed image data in a selected area, in particular in a narrow spatial frequency range, in which the pattern of the recognition pattern has a characteristic spatial frequency peak representing a pattern period and/or pattern shape of the recognition pattern.
  • a pattern wavelength of at least one recognition pattern is selected to be shorter than typical distances between spatial noise components stemming from species present in the biological fluid sample or arbitrarily binding to the recognition pattern.
  • the recognition pattern can have the first pattern elements selected from: lines of functionalization sites repetitively shifted in a transverse direction; straight or curved lines of functionalization sites repetitively shifted in a transverse direction, array of dot-like functionalization sites; and combinations thereof; and can further have the alternating second pattern elements selected from: lines of interstitial sites; straight lines or curved lines of interstitial sites sites; array of dot-like interstitial sites; and combinations thereof.
  • the sensor chip is multiplexed to include a plurality of recognition patterns, preferably arranged at different locations on the sensor chip, in particular specific recognition patterns being characterized by at least one of: a specific functionalization of the at least one first or second pattern element for binding one or more specific analytes; a specific characteristic spatial frequency spectrum in the plane; a specific localization of the recognition pattern(s) on the sensor chip; a specific arrangement of the recognition patterns, e.g. in a rectangular or quadratic array, on the sensor chip.
  • the imaging microscopy apparatus is a white light interferometer (WLI) operating in reflection and comprising an optical imaging detector having a linear or planar pixel array configured to obtain interferometric image data containing phase-sensitive information for determining a height (i.e. , a height profile or a topography) and/or refractive index distribution of the recognition pattern and therefrom a mass modulation generated by the immobilized analyte.
  • WLI white light interferometer
  • step a. i.e. providing a sensor chip
  • step b. i.e. providing the sensing device.
  • binding or immobilizing is to be understood broadly. It can relate to any chemical bonding or physical force of attraction event in particular on molecular level, such as but not limited to one or more of covalent bonding, hydrogen bonding, ionic binding, Van-der-Waals forces, hydrophobic effect and phase separations, and the like.
  • a molecular interaction of the first molecular recognition elements or second molecular recognition elements as used herein typically comprises a chemical bonding or physical force of attraction event between the corresponding molecular recognition element and an interaction partner, such as the analyte associated with proteinopathy or a background binding partner.
  • binding or immobilizing may take place at or in sufficiently close neighorhood of the molecular recognition element.
  • the biological fluid sample may comprise a single analyte associated with proteinopathy or also multiple analytes associated with proteinopathy.
  • the method may be performed for only one, a portion or all of the analytes associated with proteinopathy being present in the biological fluid sample.
  • the DNA origami structure may comprise two or more different types of molecular recognition elements.
  • the two or more different types of molecular recognition elements may have a different spatial periodicity.
  • the molecular recognition elements of a first type form a distinct pattern to the pattern formed by the second type, distinct in that the periodicity of both patterns is different.
  • the unit cells of both patterns do not overlap, because the units cells do not have identical unit cell vectors which define them. The consequence is that the unit cells of both patterns may have the same shape, but a different orientation relative to each other, or that the unit cells have a different shape.
  • Fig. 2a, 2b a schematic representation of a sensing device being a white light interferometer (WLI) according to embodiments of the invention
  • Fig. 2c a schematic representation of a sensing device being a confocal microscope microscope according to embodiments of the invention
  • Fig. 2d a schematic representation of a sensing device being an atomic force microscope (AFM) according to embodiments of the invention
  • Fig. 3a, 3b, 3c shows (real space) image data of a recognition pattern obtained using an AFM and Fourier-transformed image data generated using the real space image data;
  • Fig. 5a a schematic detailed view of a section of a recognition pattern for use in a method according to the invention screening for an analyte associated with proteinopathy forming an aggregate, and a schematic aggregation curve;
  • Fig. 7 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention where after a predetermined time interval, a treatment step including the application of a detergent is performed, and a schematic aggregation curve;
  • Fig. 8 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention where a seed amplification assay is performed, and a schematic aggregation curve;
  • Fig. 9 a schematic representation of a method according to an embodiment of the invention in which the measured signals form an immunosignature
  • FIG. 10a, 10b schematic representations of a portion of a sensing device where a serial competition measurement is performed
  • Fig. 11 a schematic representation of a portion of a sensing device where a parallel measurement is performed
  • Fig. 14 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention, where a protein co-aggregates are employed as analyte associates with proteinopathy;
  • Fig. 21 shows a schematic representation of a recognition pattern including a DNA origami structure, with a plurality of different types of molecular recognition elements ‘a’, ‘b’, ‘c’ linked to the staple strands, each of the different types of molecular recognition elements forming recognition patterns of different spatial frequency;
  • Fig. 22 shows schematically the different spatial periodicity of the molecular recognition elements ‘a’, ‘b’, ‘c’;
  • Fig. 23 shows an electron microscopy image of a sensor chip comprising a plurality of recognition patterns, each recognition pattern being a rectangular two dimensional DNA origami structure.
  • Fig. 1 shows a block diagram illustrating schematically a screening device 1.
  • the screening device comprises a sensor chip 3, a sensing device 10, and a control unit 4.
  • the sensing device comprises an imaging microscopy apparatus 2.
  • the control unit 4 is connected to the imaging microscopy apparatus 2.
  • the control unit 4 embodies a programmable device and comprises, for example, one or more processors 41 , and one or more memory modules 42 having stored thereon program code, data, as well as programmed software modules for controlling the processors 41 , and/or other programmable circuits or logic units included in the control module 4, such as ASICs (Application-Specific Integrated Circuits) and/or GPUs (graphics processing units).
  • the memory modules 42 comprise volatile and/or non-volatile storage media, for example random access memory and/or flash memory, respectively.
  • the control module 4 is connected to other components and modules of the screening device 1 as disclosed herein, in particular the sensing device 10.
  • the connection is a wired and/or wireless connection configured to exchange control signals and/or sensor signals.
  • the control module 4 further comprises a communication interface.
  • the communication interface is configured for data communication with one or more external devices.
  • the communication interface comprises a network communications interface, for example an Ethernet interface, a WLAN inter-face, and/or a wireless radio network interface for wireless and/or wired data communication using one or more networks, comprising, for example, a local network such as a LAN (local area network), and/or the Internet.
  • the control module 4 performs one or more steps and/or functions as described herein, for example according to the program code stored in the one or more memory modules 42. Additionally, or alternatively, the program code can be wholly or partially stored in one or more auxiliary processing devices, for example a computer.
  • auxiliary processing devices for example a computer.
  • the skilled person is aware that at least some of the steps and/or functions described herein as being performed on the processor 41 of the screening device 1 may be performed on one or more auxiliary processing devices connected to the screening device 1 using the communication interface.
  • the auxiliary processing devices can be co-located with the screening device 1 or located remotely, for example on a remote server computer.
  • FIG. 2a - 2c show three different embodiments of a sensing device 10 implemented as phase contrast microscopes. It is understood that other types or arrangements of phase contrast microscopes are known which may be used to implement the invention disclosed herein.
  • FIG 2a shows schematically an embodiment of a sensing device 10 including, in particular being, a white light interferometer (WLI) 100.
  • the WLI 100 is a special type of Michelson interferometer.
  • the WLI 100 is configured to scan the height and/or refractive index of the sensor chip 3, in particular the recognition pattern 3 on the sensor chip 3.
  • the WLI 100 is depicted, for illustrative purposes, with a plurality of rays extending between the WLI 100 and the sensor chip 3 and also between the WLI 100 and the camera 101.
  • the WLI 100 includes a three colour LED 103 configured to emit white light.
  • the white light is incident on a beam splitter 104 which diverts a portion of the light towards the camera 101 , and allows a portion of the light to pass.
  • the portion of light which passes through the beam splitter 104 reflects off a reference plane 105 (which is reflective) which can be adjusted so as to increase and or decrease the distance between the reference plane 105 and the beam splitter 104.
  • the WLI 100 may move in x-y coordinates such that the entire sensor chip 3 may be imaged.
  • the WLI 100 moves the reference plane 105 (e.g. moves the reference plane 105 back and forth) to determine a position of maximum and/or minimum intensity of light as measured by the camera 101.
  • the position of the reference plane 105 when the maximum light intensity is measured is, for example, indicative of a height and/or refractive index of the sensor chip 3 at the particular position of sensor chip 3 which the WLI 100 is currently measuring.
  • the WLI 100 may be configured, for example in conjunction with the control unit 4, to scan at least part of the surface of the sensor chip 3, in particular at least part of the surface which includes the recognition pattern 5, thereby obtaining image data as a function of one, two, or more measurement coordinates in the x-y plane.
  • the image data is then provided to the control unit 4.
  • the control unit 4 then Fourier transforms the image data to determine Fourier-transformed image data as a function of one or two spatial frequency coordinates which correspond with the one or two measurement coordinates.
  • FIG. 2b shows a schematic illustration of an embodiment of a sensing device 10 including an imaging microscopy apparatus 2 comprising, or in particular being implemented as, a phase shift white light interferometer (PSWLI) or a vertical scanning white light interferometer 110 (VSWLI).
  • PSDLI phase shift white light interferometer
  • VSWLI vertical scanning white light interferometer
  • the VSWLI may also be referred to as a Mirau interferometer which works on the same principle as the Michelson interferometer, the difference being the physical arrangement of the reference arm.
  • the reference arm is located within a microscope objective assembly.
  • the PS--/VSWLI 110 is arranged between a camera 111 and the sensor chip 3, which sensor chip 3 includes the recognition pattern 5. Rays of light are shown, for illustrative purposes, extending from the PS-/VSWLI 110 to the sensor chip 3 and from the PS-/VSWLI 110 to the camera 110 at a plurality of positions along the x-axis.
  • the PS-/VSWLI 110 is configured, however, to measure only a single position in the plane, i.e. in the x-y plane, at a time, and to scan across the plane, i.e. across the x-y plane, therefore measuring a plurality of x-y positions in sequence.
  • the PS-/VSWLI 110 includes a 3 color LED 113. Downstream from the 3 color LED 113, an aperture stop 114 is arranged. The light from the 3 color LED 113 is incident on the beam splitter 116 which reflects a part of the light down, through an objective housing and through a reference plane 118 and further beam splitter 119 onto the sensor chip 3, behind which a reflective surface 112, in particular an atomically smooth mirror plane is arranged.
  • the objective housing is arranged on piezo stage 117 which is configured to move the PS- /VSWLI, in particular the focal point on the sensor chip 3, in the x-y plane such as to scan the sensor chip 3.
  • the piezo stage 117 may further alter a distance between the PS-/VSWLI 110 and the sensor chip 3.
  • the beam splitter 119 and the reference plane 118 are configured to enable the formation of a reference beam which interferes with the measurement beam incident on the sensor surface 3.
  • the distance between the beam splitter 119 and the reference plane 118 may be configured to be the same a distance between the beam splitter 119 and the surface of the recognition pattern 5 such that constructive interference between a reference beam and a measurement beam occurs and the light intensity incident on the camera 111 is increased.
  • a downstream beam splitter 125 is arranged to divert a part of the laser light onto the sensor chip 3.
  • This part of the laser light passes through a lens system 126, arranged in an objective housing 128, which lens system 126 is configured to focus the laser light onto the surface of the sensor chip 3, in particular onto the recognition pattern 5.
  • the vertical (height) position of the focal point may be adjusted by control of the lens system 126 resp. the objective housing 128 in the vertical direction, in particular using a z-scanner 127, attached to the objective housing 128, which may be implemented using a piezo-stack.
  • the z-scanner 127 is configured to focus the laser light at a height which takes into account any immobilized analyte.
  • the focal position coincides with the sensor chip 3 or in particular the analyte may be determined using the camera 121.
  • the light reflected off the sensor chip 3 and/or the reflective surface 122, in particular an atomically smooth and reflective mirror plane passes back through the lens system 126, through the beam splitter 125, through a pin-hole 124 and is incident on the camera 121 , in particular a CCD camera.
  • the image data is then provided to the control unit 4.
  • the control unit 4 then Fourier transforms the image data to determine Fourier-transformed image data as a function of one or two spatial frequency coordinates which correspond with the one or two measurement coordinates.
  • Fig. 2d shows a schematic illustration of an embodiment in which the sensing device 10 includes an imaging microscopy apparatus 2 comprising, or being implemented as, an atomic force microscope (AFM) 130.
  • the AFM 130 includes a cantilever arm 131 underneath which a tip 132 is arranged.
  • a piezo stage 135 is configured to scan the surface of the sensor chip 3, in particular the recognition pattern 5 or part thereof, in order to determine a height profile in one or two spatial directions (e.g., a topography). The height profile is determined by measuring a deflection of the arm 131 through reflection of a laser beam on a top side of the cantilever arm 131.
  • a laser source 133 is configured to direct a laser beam onto the top side of the cantilever arm 131.
  • the laser beam is reflected off the top side of the arm 131 and is incident on a sensor 134.
  • the sensor 134 which may be implemented as a position sensitive device (PSD), a photodetector array, four quadrant photodiode, or the like, is configured to determine a position of the laser beam.
  • the position of the laser beam is indicative of a deflection of the cantilever arm 131.
  • the piezo stage 135 is controlled such that the cantilever arm 131 of the AFM 130 is in contact with the recognition pattern 5 but does not apply a force which may unduly affect the analyte and/or the arm 131 .
  • the image data is then provided to the control unit 4.
  • the control unit 4 then Fourier transforms the image data to determine Fourier-transformed image data as a function of one or two spatial frequency coordinates which correspond with the one or two measurement coordinates.
  • the AFM 130 may be operated in one of a plurality of modes, for example including a tapping mode, or a contact mode.
  • the cantilever arm 131 may be connected to a further piezo-electric element configured to drive the cantilever arm such that it oscillates, for example at its resonant frequency.
  • Fig. 3c shows a plot of the amplitude (pm) as a function of the frequency (1/pm) of the Fourier transformed image data. Due to the lithographic process, two carrier frequencies are found at 397 nm and 367 nm, as indicated by the labeled peaks in the plot. Both of these modulations are seen in the frequency (i.e., Fourier) domain and also relate to the amplitude of the topography modulation.
  • the analytical signal is independent of random contributions which are spread along the real-space image and the frequency spectrum, respectively.
  • a plurality of first molecular recognition elements (not shown, see for example Fig. 5a), which are configured to bind an analyte associated with proteinopathy, is bound to first pattern elements 8.
  • a plurality of second molecular recognition elements (not shown, see for example Fig. 5a) being different from the first molecular recognition elements may be bound to second pattern elements 9.
  • the recognition pattern is not a diffractive grating. Specifically, the recognition pattern does not act as a diffractive lens, particularly for a specific wavelength or range of wavelengths. For example, the recognition pattern is not a Fresnel zone plate.
  • An embodiment of a high-resolution recognition pattern 5 produced by photolithography, in particular reactive immersion lithography (RIL), can have the following parameters: minimum feature size of 1 .m; minimum line spacing of 1.5 .m; address grid of 50 nm; edge roughness of 150 nm (3o interval); CD uniformity of 300 nm (3o interval); 2 nd layer alignment over 5 x 5 mm 2 of 500 nm (3o interval) and over 50 x 50 mm 2 of 1000 nm (3o interval).
  • Fig. 5a and 5b show a comparison of the method performed on an analyte associated with proteinopathy forming an aggregate (Fig. 6a) and on an analyte being a monomer (Fig. 6b).
  • Both figures show a detailed cross-sectional view of a first pattern elements 8 and second pattern elements 9 of a recognition pattern (such as the recognition pattern 5 shown in Figs. 4 and 5).
  • a plurality of first molecular recognition elements 10 is bound to first pattern elements 8
  • a plurality of different second molecular recognition elements 11 is bound to second pattern elements 9.
  • the analyte of proteinopathy 13 forms an aggregate which exposes several binding sites denoted by a, b, c, d and e.
  • different letters refer to different binding sites. That is, in this example, analyte 13 associated with proteinopathy has five different types of binding sites a, b, c, d and e.
  • first molecular recognition elements 10 comprise a binding site A, while second molecular recognition elements 11 are devoid of such a binding site A.
  • a recognition-pattern-specific binding site denoted by a capital letter, such as binding site “A” is generally configured to bind an analyte-specific binding site denoted with the same lower case letter, such as binding site “a”, but preferably not a binding site with another lower case letter. It can be seen in Fig. 5a that after providing a biological fluid sample to sensing the recognition pattern 5 and thus to its first and second pattern elements 8, 9, the analyte 13 associated with proteinopathy binds with its binding site a to a binding site A of a first molecular recognition element 10.
  • the signal measured at the detector is shown as a function of time. Since non-binding background material being present in the biological fluid sample does not interact with first or second molecular recognition elements 10, 11 , or since these do so with essentially equal probability, the obtained signal is inherently self-referencing and it is not necessary to wash away background material to obtain a reliable measurement signal. It can be seen that the measurement signal obtained from the recognition pattern 5 commences during time interval 1. Binding of the analyte 13 associated with proteinopathy occurs during time interval 2, which leads to a mass increase being clearly visible in the obtained time dependent signal.
  • the first molecular recognition elements 10 can be selected such that they are highly selective binders for binding sites a of analyte 13, already such a measurement can provide detailed information on the nature or clinical picture of a proteinopathy. Further, because the system is essentially only dependent on the mass of the binding partner with which the molecular recognition elements interact, it is irrelevant how complex and how heterogeneous the aggregate formed by analyte 13 is. Fig. 3b shows the signal obtained, if only monomers are detected. As the measured signal is dependent on the mass interacting with the molecular recognition elements 10, aggregates formed by the analyte 13 associated with proteinopathy have a significantly intense signal.
  • Fig. 8 shows a method according to an embodiment of the invention, in which a treatment step is performed.
  • the treatment step comprises the application of a detergent.
  • an aggregated analyte is bound to the first molecular recognition element 10.
  • the corresponding signal increase is seen in time intervals 2 to 4.
  • a detergent is applied, which results in removal of parts of or the complete analyte (see middle part). Due to the loss of mass interacting with the first molecular recognition elements 10, the signal decreases.
  • the measured signal can generally further be comprised in the immunosignature of the analyte of proteinopathy and/or the biological fluid sample. If it is known for example that certain analytes are sensitive towards different treatments, such as detergents, pH, etc., then such treatment steps can be used to provide further information on the analyte associated with proteinopathy.
  • the term “unique” or “specific” means in this context that the first (or second) molecular recognition elements of a particular recognition pattern are different from the first (or second) molecular recognition elements of the other recognition patterns of the sensing device, however typically the first molecular recognition elements within each recognition pattern are typically the same.
  • the graph depicting the time dependent measured signal one signal per reognition pattern is obtained, once the biological fluid sample S is provided to the recognition patterns. These signals can then all represent a parameter in a high dimensional vector space as indicated by the matrix a11 - a43.
  • This matrix of parameters may in this or any other embodiments described herein be an immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy having been screened with the method according to the invention.
  • This immunosignature can then by a control unit 4 comprising a processor 41 , be compared with a database being stored in a memory 42 of the control unit 4. If this database then contains data associated with a clinical status of a proteinopathy of patient samples, autologous patient samples (i.e. previously screened samples from the patient from which the presently screened biological fluid sample has been obtained) and/or reference samples, it is possible to characterize the nature and/or clinical status of the proteinopathy with which the analyte is associated. Furthermore, it is possible to forecast the progression of the proteinopathy. Additionally, also other parameters can be included into the immunosignature, such as the signal(s) obtained from an seed amplification assay as described herein or the signal obtained from the addition of a mass-labelled binding agent as described herein.
  • Figs. 10a and 10b show embodiments of the method according to the invention, in which the plurality of recognition patterns of the sensing device compete for the analyte associated with proteinopathy.
  • Both Fig. 10a and Fig. 10b show serial competition measurements and the arrow indicates the direction of flow with which the biological fluid sample is provided. That is, in Fig. 10a, the biological fluid sample is provided such that it first contacts the two recognition patterns 5’a, 5”a, then the two recognition patterns 5’b, 5”b, and then the two recognition patterns 5’c, 5”c.
  • Figure 810 shows a serial competition measurement with the same sensing device as shown in Fig. 10a, however, the direction of flow is inversed. Performing such serial competition measurements, i.e. a first one with a first direction of flow and a second one with the inversed direction of flow provides additional information, which allows to further characterize the analyte 13 and the proteinopathy.
  • Fig. 11 shows a parallel measurement.
  • the sensing device comprises three groups of recognition patterns, each group comprising two recognition patterns.
  • the first group comprises recognition patterns 5’a, 5”a with first molecular recognition elements having binding site A.
  • the second group comprises recognition patterns 5’b, 5”b with first molecular recognition elements having binding site B.
  • the third group comprises recognition patterns 5’c, 5”c with first molecular recognition elements having binding site C.
  • the sensing device comprises wall structures 12, which separate the groups of recognition patterns from each other.
  • the wall structures 12 may in this or any other embodiment form channels, such as microfluidic channels, which are part of a fluidic system of the sensor chip.
  • the biological fluid sample can be provided to the sensing device upstream of the recognition patterns. It then flows in parallel over the recognition patterns. Due to the presence of wall structures 12, the biological fluid sample cannot flow from one channel into another.
  • Fig. 12 shows another embodiment of the method according to the invention.
  • the recognition patterns of the employed sensing device comprises first molecular recognition elements 10 bound to first pattern elements 8, which each comprise two different binding sites A and B.
  • first pattern elements 8 which each comprise two different binding sites A and B.
  • This is beneficial, as it allows for a more accurate characterization of analyte 13, which expresses on its surface complementary binding sites and/or epitopes a and b.
  • cell residues such as mitochondria or parts thereof, are recruited and form an aggregate.
  • mitochondria or their parts may comprise a specific marker protein, such as protein b, e.g. VDAC.
  • the first molecular recognition elements 10 can further characterize the nature or composition of the analyte 13.
  • Fig. 13 shows a similar principle as Fig. 12, but in this case a lysosome is part of the analyte 13, which specifically exposes surface protein c, such as LAMP1. Therefore, if the first molecular recognition elements 10 comprise binding site C being configured to bind to protein c, further details on the composition of the analyte 13 and the nature of the associated proteinopathy can be provided.
  • Fig. 14 shows another variant being similar to the examples shown in Figs. 12 and 13.
  • a protein co-aggregate is screened as analyte 13 associated with proteinopathy.
  • a sensing device having at least one recognition pattern with first recognition elements which comprise each two binding sites A and D, wherein the latter is specific for the post-translational modification d, it is possible to generate information on the nature of the proteinopathy, respectively the analyte 13 associated therewith.
  • Fig. 15 shows a method 100 for screening a biological fluid sample for an analyte according to an embodiment of the invention.
  • the method 100 includes a number of steps S1-S5.
  • the method 100 is performed using a sensing device 10 as described herein. Specifically, at least one of the steps is performed using the sensing device.
  • the method 100 may be performed at one location, for example a laboratory or analysis facility.
  • the method 100, or in particular one or more steps of the method 100 may alternatively be performed at a separate location, as detailed below.
  • a sensor chip is provided.
  • the sensor chip comprises a recognition pattern having molecular recognition elements as described herein.
  • the sensor chip may be sent or otherwise provided to a third party such that the third party may apply the biological fluid sample as described below.
  • a sensing device is provided.
  • the sensing device comprises an imaging microscopy apparatus.
  • the imaging microscopy apparatus is configured to image the sensor chip, recording image data.
  • the sensor chip may be imaged in one or more states. For example, the sensor chip may be imaged without any biological fluid sample having been applied.
  • the sensor chip may be imaged at one or more time-points after the biological fluid sample has been applied, thereby recording image data which includes the analyte.
  • step S3 the sensor chip with the bound analyte is obtained.
  • the biological fluid sample may be applied to the sensor chip as part of the method in an optional step S31.
  • the application of the biological fluid sample to the sensor chip may take place with the sensor chip being actively imaged/recorded by the sensing device. Thereby, time-series image data of a plurality of time points may be obtained, allowing for the dynamic adsorption of the analyte by the sensor chip being recorded.
  • the biological fluid sample may have been previously applied, the sensing device recording the sensor chip once the analyte has been maximally adsorbed onto the sensor chip.
  • step S4 the sensor chip is imaged by the imaging microscopy apparatus.
  • the image data obtained may be stored, transmitted or further processed by the sensing device itself.
  • step S5 the mass modulation contrast is quantified using the recorded image data.
  • Step S5 may be performed by the sensing device itself, or a further device which receives the imaging data from the sensing device.
  • the quantification of the image data may comprise filtering the image data to obtain improved image data.
  • the image data may be filtered by comparing the recording of the sensor chip with the analyte with image data related to the sensor chip without analyte present. Thereby, the sensor chip, in particular the mass modulation already present on the sensor chip due to the recognition pattern, may be filtered out.
  • Fig. 16 shows a flow diagram illustrating a method for screening a biological fluid sample for an analyte.
  • the figure illustrates the digital look-in amplification principle.
  • a binary model grating of vertical lines (top) was generated.
  • the Fourier analysis (bottom) reveals the amplitude frequency spectrum in the reciprocal space (inlay at the bottom).
  • the grating periodicity, symmetry, orientation and amplitude reveals the Fourier components.
  • the line section plot at the bottom illustrates the Fourier components of the single dimensional grating (i.e. the grating has a periodicity only in a single dimension).
  • the primary peak in the Fourier spectrum corresponds to the grating spacing. Higher order peaks appear at multiples of the grating spacing.
  • the figure is illustrative of image data in real space and in reciprocal space of a simple recognition pattern.
  • the recognition pattern includes ridges and grooves.
  • the ridges and grooves may be mass modulations (i.e. physical ridges and grooves), or optical ridges and grooves, i.e. mere changes in the optical properties of the surface, such as the refractive index.
  • Fig. 17 shows the same recognition pattern shown in Fig. 16, however with black dots illustrating analyte which has selectively attached or adsorbed to sites in the grooves (white lines).
  • the analyte has bound only to the grooves due to the molecular recognition elements being present only in the grooves.
  • the black dots thereby exclusively-selectively hide white pattern elements, reduce the overall white grating area.
  • the amplitude-frequency spectrum (bottom) still reveals the same Fourier components as in Fig. 16, however with a decreased amplitude, particularly evident in the amplitude of the first peak being reduced.
  • the non-selective reduction of the grating area due to the further parts results in a random decrease of white and black pattern elements affect the amplitudes of the grating Fourier components much less, even if the reduced grating area is an order of magnitude larger (top), than the selective reduction of white grating elements, as shown in Fig. 17.
  • the peak amplitude in particular the peak amplitude of the first Fourier component, is not significantly changed due to the presence of the further parts on the recognition pattern. This demonstrates that the technique is robust to the presence of randomly adhered particles.
  • Fig. 19 shows an illustration of a DNA-Origami structure functionalized with molecular recognition elements a as described herein.
  • the recognition grating has a scale on the order of nanometers.
  • the basic working principle of DNA-Origami biosensors was described in Wang S, Zhou Z, Ma N, Yang S, Li K, Teng C, Ke Y, Tian Y. DNA Origami- Enabled Biosensors. Sensors (Basel). 2020 Dec 3;20(23):6899. doi: 10.3390/s20236899. PMID: 33287133; PMCID: PMC7731452.
  • DNA origami microstructures can be imaged in a label free manner with atomic force and electron microscopy.
  • the concepts of DNA-origami today enable to spatially organize DNA in plane and space in a programmed manner.
  • a DNA scaffold strand continuously lines
  • dotted lines a series of complementary staple strands
  • the vectors e(y) and e(x) represent the unit cell vectors of the pattern formed by the molecular recognition elements a. Although there is some variation in the exact position of the elements a with respect to the unit cell vectors, the Fourier analysis reveals the fundamental Fourier components to be large enough to use such a grating in the invention described herein.
  • the spatial frequency of the patterns formed by the molecular recognition elements a, b, c are different such that the Fourier components, in particular the primary Fourier components, do not overlap.
  • Fig. 23 shows an electron microscopy image of a sensor chip.
  • a plurality of recognition patterns are present, each implemented as a DNA origami structure of rectangular shape.
  • the overall size of the sensor chip is approximately 5x5 micrometers.
  • each origami structure has dimensions on the order of 100 nm.
  • the electron microscopy image can be analyzed to identify the recognition patterns.
  • the recognition patterns can then be overlaid to improve the signal to noise ratio.
  • the recognition patterns are identified, then rotated if needed such that they align with each other.
  • the images can then be added/superposed to each other to improve the signal to noise ratio for further analysis in Fourier space.
  • the sensor chip may comprise a plurality of sets of recognition patterns, each set having different molecular recognition elements. Thereby, a single sensor chip may be functionalized such as to detect a plurality of different analytes.
  • Each set of recognition pattern may have a different characteristic, allowing them to be distinguished from each other. For example, each set may have a different shape or dimension of recognition pattern.
  • each set may have a different fiducial marker, or have a fiducial marker attached at a different point (e.g., along an edge or at a corner).
  • imaging means 2 imaging microscopy apparatus, imaging means

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Abstract

L'invention concerne un dispositif et un procédé de mesure de profils de distribution de masse spatiale pour peser la masse agrégée accumulée d'un analyte dans un arrière-plan de matériau bioorganique encombré à l'aide d'un motif de reconnaissance haute fréquence. Le motif de reconnaissance permet le filtrage de Fourier de données d'image sensibles à la masse pour une soustraction d'arrière-plan et de bruit. Le dispositif et le procédé permettent le criblage d'un échantillon de fluide biologique à la recherche d'un analyte associé à une protéinopathie.
PCT/EP2024/078080 2023-10-06 2024-10-04 Procédé de criblage d'un échantillon de fluide biologique pour un analyte associé à une protéinopathie à l'aide d'une interférométrie en lumière blanche ou d'une microscopie à force atomique Pending WO2025073987A1 (fr)

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