WO2025073987A1

WO2025073987A1 - Method for screening a biological fluid sample for an analyte associated with proteinopathy using white light interferometry or atomic force microscopy

Info

Publication number: WO2025073987A1
Application number: PCT/EP2024/078080
Authority: WO
Inventors: Markus Britschgi; Eylul CEYLAN; Tom Moritz KISSLING; Matthias Eckhard LAUER; Philip Michael DETTINGER; Rainer Eugen Martin; Jens André WOLFARD
Original assignee: F Hoffmann La Roche AG; Hoffmann La Roche Inc
Current assignee: F Hoffmann La Roche AG; Hoffmann La Roche Inc
Priority date: 2023-10-06
Filing date: 2024-10-04
Publication date: 2025-04-10
Anticipated expiration: 2026-04-06

Abstract

Disclosed herein is a device and method for measuring spatial mass distribution profiles to weigh the accumulated-aggregated mass of an analyte in a crowded bioorganic material background by employing a high frequency recognition pattern. The recognition pattern allows for the Fourier filtering of mass sensitive image data for background and noise subtraction. The device and method enables the screening of a biological fluid sample for an analyte associated with proteinopathy.

Description

Method for Screening a biological fluid sample for an analyte associated with proteinopathy using White Light Interferometry or Atomic Force Microscopy

Field of disclosure

The present invention lies in the field of analysis of biological fluid samples and in particular in the field of proteinopathy. The invention relates to a method for screening a biological fluid sample for an analyte which is associated with proteinopathy.

Background, prior art

Screening of biological samples for various analytes is crucial in the diagnostic area, as well as in in drug and biomarker discovery, or in monitoring biotechnological processes. 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.

Analytically, it is a challenge to measure the engagement of the recognizing molecules in an environment crowded with many other molecules, in particular because specific and nonspecific interactions cannot be sufficiently well distinguished from each other. 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.

Focal Molography is a known biosensor technology which overcomes this challenge. It is an optical biosensor which enables the detection of an analyte in a complex binding media. It is based on the principle of spatial log-in amplification. A two-dimensional affinity grating is at the heart of this technology, in which recognition elements are arranged alternatively to form a grating resembling in pattern a diffractive lens. A mass modulation on the grating is achieved due to selective binding events with the analyte, and this leads to a cooperative amplification of the resulting signal. 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.

The formation of amyloid in different proteinopathies is accompanied with the structural transition of helically structured or randomly coiled monomers into aggregated oligomers, which have higher beta-sheet structure content. This transition can be measured with Infrared Adsorption Spectroscopy. Biological liquids derived from Alzheimer Disease patients at very early stages of the disease already reveal a amid-band adsorption profile which diverges from healthy control groups. Infrared biosensors which are equipped with molecular recognition elements targeting hallmark proteins in proteinopathies are moving into clinical trials, however they require large sample volumes, and selective and non- selective recognition of analytes are not easily distinguishable from each other, which reduces the value of this approach.

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. Some of the proteins and different proteoforms thereof generated during the pathogenic process can reach body fluids, such as the cerebrospinal fluid or the blood. It is hypothesized that pathogenic seeds are also present in further body fluids. The neuropathological hallmarks are associated with synaptic and overall neuronal dysfunction and ultimately neuronal loss. Neuropathology is also accompanied by abnormally regulated local innate immune cells, a process which in some instances may appear as neuroinflammation. The concomitant and continuous breakdown of brain structure and function can occur over several decades and is reflected by a currently unstoppable worsening of non-motor functions (i.e. , cognitive and overall social behavior) and in some disorders also motor functions. 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. In some disorders, 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). Analytically and therapeutically, such pathology forming proteins are drug targets and diagnostic markers since several decades. Analysis of brain tissue and exploration of disease mechanisms biochemically, in cell culture and in in vivo model systems suggest that disease-associated proteins undergo an abnormal metabolism and post-translational modifications and accumulate into poorly soluble moieties, which can be complex biochemical materials, reach micrometers dimension, and become crowded with poorly degradable material, often with oligomeric, proto-fibrillar, fibrillar or aggregated protein assemblies. Cellular inclusions of these proteins can contain collapsed organelles, lipid vesicles and other unspecified structures and material of cellular origin.

Thus, these 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. Abundant proteins can reach high local or subcellular concentrations and thus have per se a higher chance to accumulate and become part of pathological hallmarks of different proteinopathies. The multifactorial causes, the development of multiple proteinopathies within the same disease spectrum and the inability to derive a biological sample most proximal to the neurodegenerative process (i.e., directly from the brain in a living human being) aggravate proper diagnosis and selection of treatment options on an individual patient level.

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. In this method, 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.

From a technical point of view, a prerequisite of currently known methods to screen for proteinopathies are molecular entities which selectively stain or label abundant hallmark proteins. The detection of aggregated protein forms is a challenge and requires molecules which are selective for such, because competing monomeric forms are abundant. Aggregates as they underlie proteinopathies can be unforeseeably complex and form clumps with other proteins, lipids as well as collapsed cellular organelles as mentioned above. This fact makes it difficult, especially in Parkinson Disease, to reach selectivity for aggregates over monomers in bio fluids, beyond general and technical challenges as mentioned above, such as non-specific particle interactions, molecular crowding and diffusion in bio fluids. The fact, that PET tracer programs known so far have failed and that biomarkers are missing, supports the view that known methods cannot sufficiently well distinguish between aggregated and non-aggregated matter.

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. However, not all antibodies tested in Parkinson’s Disease (PD) targeting aSYN aggregates were capable of discriminating between patients and control groups in biological fluids, at least with the established detection principles. This observation can be related to:

1) unfavorable concentration ratios of aggregates and monomers in the investigated groups,

2) unspecific binding and lack of detection sensitivity in bio fluids by the applied physical detection principle, 3) a generally low abundance of targeted epitopes, and/or 4) the wider spatial structure and constitutional complexity of the targeted aggregate. Published data suggest that the 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.

Ideally, however, aggregates and co-aggregates that are associated with proteinopathy would be detected as analytes in easy to access biological fluids without the need for complex tissue sampling (in particular human brain tissue, which is sampled mostly postmortem) and a method would be applied to investigate and identify the analytes in the undisturbed biological fluid.

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. Multiplexing may be achieved by several grating structures that are spatially separated or are overlapping with different periodicity for providing different diffraction angles. Different grating structures can be functionalized differently to recognize different analytes. Such diffractometric sensing devices have the disadvantage to require coherent light for readout and preferably propagation in one or more waveguides to confine light for increasing detection signals and to obtain dark field illumination for minimizing scattering from any non-patterned volume.

A. et al. Palermo, “Vertical Scanning Interferometry for Label-Free Detection of Peptide- Antibody Interactions”, published on 2019-03-27 in High-Throughput (http://dx.doi.org/10.3390/ht8020007) disclose that vertical scanning interferometry (VSI) can be applied to measure the interaction of peptides with certain antibodies in serum with high-throughput. Peptide arrays on surfaces are incubated with a biological fluid and washed with DPBS to be analyzed with VSI. The immobilized analyte induce a mass dependent change of the interface reflectivity, due to a changed refractive index. The resolution and quantitative value of the method is qualified with a topographic analysis of the immobilized analyte surface with Atomic Force Microscopy (AFM). 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.

J.K. Barrows et al., “Biolayer interferometry for DNA-protein interactions”, PLOS ONE, published on 2022-02-02

disclose that biolayer interferometry (BLI) is a widely used technique for determining macromolecular interaction dynamics in real time. By using changes in the interference pattern of white light reflected off a biosensor tip, BLI can determine binding parameters for protein-protein (e.g. antibody-substrate kinetics) or protein-small molecule (e.g. drug discovery) or DNA-protein interactions. BLI analyzes the difference in interference patterns of white light reflected off a reference layer and a biolayer. The biolayer is conjugated to a molecule of interest and then introduced into a solution containing other molecules of interest. Interactions between the free and stationary molecules alter the interference pattern, leading to a change in optical wavelength that is measured in real-time. These data are then used to ascertain association and dissociation rates using scientific graphing and statistical software (e.g. GraphPad Prism software).

G. Sancho-Fornes et al., “Enhancing the sensitivity in optical biosensing by striped arrays and frequency-domain analysis”, Sensors & Actuators: B. Chemical 281 (2019), p.432-438 discloses an optical biosensor based on patterning strips of biochemical probes and signal processing based on frequency-domain analysis (FDA). The FDA relies on patterning the biorecognition assays on solid substrates as straight and equidistant strips, which generate periodic signals when they are scanned by a laser beam of a DVD drive controlled by custom software. The focused laser beam follows a spiral trajectory at linear velocity and adsorbs and scatters light when it hits the immunoreaction products. 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. In the FDA analysis, 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. To perform the striped patterning of the biorecognition assays required for FDA, 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.

US5398113, published on March 14^th, 1995 in the name of Zygo Corporation, discloses an optical system for measuring the topography of an object. The system includes an interferometer with a multiple-color or white-light source, a mechanical scanning apparatus for varying the optical path difference between the object and a reference surface, a two- dimensional detector array, and digital signal processing apparatus for determining surface height from interference data. Interferograms for each of the detector image points in the field of view are generated simultaneously by scanning the object in a direction approximately perpendicular to the illuminated object surface while recording detector data in digital memory. These interferograms recorded for each image point as a function of scanning height are then transformed into the spatial frequency domain by Fourier analysis, and the surface height for each corresponding object surface point is obtained by examining the complex phase as a function of spatial frequency. A complete three-dimensional image of the object surface is then constructed from the height data and corresponding image plane coordinates. In this system, the fringe contrast is never calculated and the analysis takes place entirely in the spatial frequency domain.

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. In the outer circumferential portion of the droplet, the droplet curvature reduces reflection in the optical axis direction, the modulation contrast falls below a threshold value, and a diameter of the droplet is determined therefrom. Finally, a volume of the droplet is calculated. Instead of the Mirau-type interference objective lens a Michelson-type or Riniku-type interference objective lens may be used. WO2018/102398A1 , published on June 7^th, 2018 in the name of Nanometrics Incorporated, discloses a scanning white-light interferometry system for characterization of patterned semiconductor features. An electric field rather than intensity can be extracted from the interferometric data by performing a Fourier transform of the interferometric data at each pixel. One or more characteristics of the sample, e.g. a height difference at different locations of the sample surface, can be determined based on the electric field and an electric field model as a function of azimuth angle, incidence angle and wavelength for a zero diffraction order.

US5874668, published on February 23^rd, 1999 in the name of Arch Development Corporation, discloses a scanning probe microscope (SPM), in particular atomic force microscope (AFM) or scanning tunneling microscope (STM), for biological specimens. The AFM can include subsystems, such as an electrostatic microscope, an electroprobe microscope or an ultrasoft microscope probe. The cantilever tip incorporates a biospecific molecule which is attached directly to the scanning probe tip or indirectly via an intermediate gold particle. The AFM image results from an interaction between the functionalized tip and the biological specimen or environment. In particular, the biospecific molecule can recognize a specific site e.g. on a receptor or antibody. Dynamic AFM imaging has been used e.g. to observe the formation of antigen-antibody complexes.

DE102010026703 B4, published on January 12^th, 2021 in the name of Technische Universi- tat lllmenau, discloses an atomic force microscope (AFM) having a plurality of cantilevers. Each cantilever has an integrated piezoresistive sensor, a measurement tip, a dynamic actuator electrically coupled to all cantilevers and a separately controllable static actuator. The AFM can be used for scanning biological surfaces. In biological sensing applications, the presence of the biological medium causes a mass change of the functionalized cantilever tip, which is measurable by detecting an amplitude, frequency or phase. Summary of disclosure

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.

The invention 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 pattern elements at least some of 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. obtaining the sensor chip with at least a portion of the analyte of the biological fluid sample having been applied; 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.

In an embodiment, steps b, e, and f. may be omitted. In particular, it may not be necessary to image the recognition pattern without any analyte present. Instead, 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. Subsequently, 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. For peak detection, 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. 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. 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.

In an embodiment, 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.

In an embodiment, 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. In such an embodiment, 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.

Alternatively or additionally, 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. For example, 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.

In an embodiment, the method may be used for drug discovery.

This method and the corresponding device has several advantages. Most importantly, they allow for improved Fourier mass spectrum analysis. The modulated affinity of the recognition pattern enables the discrimination between selective and other binding events, because the mass trapped by the pattern is modulated correspondingly. The physical sensitivity of this approach is theoretically limited by the mass modulation amplitude, and as aggregated matter contributes has a larger mass than monomeric forms, their presence can be detected in a biofluids, even, if they are rare and, also, if the recognition elements have affinity for the monomeric forms. Because the method and device can be operated in a label-free manner, the sensitivity of this method is not limited by the number of exposed epitopes; any randomly or peripheral trapped biological matter, even if only co-aggregated with the targeted species, will contribute to the mass related imaging contrast; and in case of white light interferometry (WLI) with quadratic amplified intensity. In step e. improved Fourier-transformed image data are obtained by Fourier filtering such that relative enhancement of the Fourier-transformed image data in at least a part of the characteristic spatial frequency spectrum of the recognition pattern is achieved. Thus, the Fourier filtering in the spatial frequency space allows for establishing a digital spatial lock-in amplification by enhancing the image data in the spatial frequency spectrum predetermined by the regularity characteristics of the recognition pattern.

In particular, the size, density or the mass of the analyte can be determined using Fourier Amplitude spectrum analysis.

In an embodiment, two sets of image data are recorded. First image data is recorded of the recognition pattern without any analyte present. Second image data are recorded with analyte having been applied to the recognition pattern. By comparing the first image data and the second image data, it is possible to isolate the signal due to the analyte bound to the recognition pattern, in particular to obtain improved image data for quantifying the mass modulation contrast. The comparison may take place in real (direct) space or in Fourier space.

For example, 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.

If the comparison is made in Fourier space, the result may be retransformed back into real space to obtain improved (real space) image data, from which the mass modulation contrast may be quantified. Further, if the comparison is made in Fourier space, the one dimension or two dimensional Fourier spectrum may be truncated, in particular by filtering out one or more parts of the Fourier spectrum which do not correspond to the characteristic spatial frequency spectrum of the recognition pattern. In particular, one or two peaks in the Fourier spectrum may be isolated, the one or two peaks corresponding to the spatial frequency spectrum of the recognition pattern in the one or two pattern vector directions. The pattern vector directions correspond to the lattice vectors of the recognition pattern.

In an embodiment, 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. In other words, 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.

The recognition pattern may be formed at least in part by a periodically repeating DNA origami nanostructure. The DNA origami structure is a two-dimensional structure extending across at least part of the surface of the sensor chip. The DNA origami structure comprises scaffold strands and staple strands. The DNA origami structure provides the pattern elements.

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.

In an embodiment, the DNA origami structure comprises at least two different types of molecular recognition elements. The two different types of molecular recognition elements may have a different spatial periodicity and/or symmetry. The different types of molecular recognition elements may be attached to the same type of staple strand using the same linker element, or may be attached to different types of staple strands using the same or different linker elements.

In an example, a first set of staple strands may form the first pattern elements and have attached thereto, form, or otherwise provide, molecular recognition elements of a first type. Optionally, a second set of staple strands may form the second pattern elements and may optionally have attached thereto, form, or otherwise provide, molecular recognition elements of a second type.

In an embodiment, 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.

In an embodiment, a plurality, or ensembles, of different DNA-origami nanopatterns are present on the same sensor chip. The digitized image data of the sensor chip may then be analyzed, with the image data of each DNA origami structure added to other corresponding structures to improve the signal to noise ratio. This may require identifying the DNA origami structures, rotating, superposing, averaging, etc.

In an embodiment, 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.

In an embodiment, 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.

Periodically functionalized 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.

A plurality of DNA origami structures can be randomly deposited on the sensor chip and analyzed, independently of each other, simultaneously using the methods described herein. In particular, intensity and/or mass modulations due to differential analyte binding at different locations of the recognition pattern may be detected using an imaging microscopy apparatus, such as Atomic Force Microscopy, x-ray or electron microscopy, in particular oligonucleotides and small molecular binders.

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.

Recognition patterns of the mentioned kind can be incubated with biological fluid samples derived from patients, and after a dedicated incubation time may be washed. Loosely or non-specific bound bioorganic matter is likely to be washed off, whereas the analyte which is tightly bound to the recognition pattern is likely to persist and remain attached or adhered. Such incubation-washing experiments are simple to perform, and can be realized everywhere, for example at the home of a patient or user. The incubated sensor can been dried and fixated and then easily be stored or sent for later analysis using the apparatus or method described herein. As the analytical read out is only related to the mass modulation which aligns with this recognition pattern, the denaturation of biological matter on the sensor upon storage and shipping is less of an issue, as the mass stays constant upon drying.

Affinity recognition patterns of the kind mentioned above can be analyzed with Infrared Microscopy, and the adsorption intensity profile which aligns with the periodicity of this pattern can be used to discriminate between selective and non-specific intensity signal, such that smaller sample volumes are required relative to the prior art. For example, sample volumes or masses much lower than in the prior art may be used which depends on the type of analysis. For example, with the highest sensitivity and using WLI, an analyte in the region of two-digit picograms per square millimeter may be detected. Sub-picogram per square millimeter may be used for AFM, which is equivalent to atto-grams per square micrometer, and a single atom per square nanometer.

Compared to confocal coherent molography using evanescent field effects, 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. In particular, 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. Therefore, the wavelength of the recognition pattern can be scaled in a large scalability range depending on needs or ease of producing and/or functionalizing the recognition pattern, and/or depending on at least partially avoiding typical spatial noise component wavelengths. By virtue of the digital spatial lock-in amplification, the sensing device suppresses environmental noise and unspecific binding events are filtered out. Thus, a mass modulation contrast and analyte quantification is achieved in a simplified manner and with an increased signal-to-noise ratio.

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 broader applicability of the method enables a new space of experiments, beyond the real-time detection of analytes related with proteinopathies. A multitude of submillimeter sized sensor chips could be analyzed by use of a sample positioning stage, which is usually part of any commercial optical profiler and many types of microscopes (AFM, EM, etc.). Chips can be incubated with the probing liquid, be washed, sent, to be analyzed later on in its dried form, the mass modulation will stay constant. As the periodicity of the pattern does not require matching the wavelength of the detection principle (such as the wavelength of the laser light), the recognition patterns can be designed to match the analysis at useful magnifications and working distances. Therefore, 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. In contrast to other methods, 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.

Another advantage is that 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. Furthermore, in dynamic imaging applications, one- or two-dimensional films of the recognition pattern can be acquired by repetitive image grabbing using a one- or two- dimensional imaging detector, e.g. optical detector array in white light interferometry; and/or by repetitive image grabbing using 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. In particular, 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 method and device according to the invention preferably represent an approach to measure the mass of analytes, in particular aggregates, which come along with targeted epitopes, and in the presence of monomers, even if the analyte concentration is at the affinity limit of the molecular recognition element. The method and device allow for the detection or monitoring of molecular interaction, such as binding of analytes in the biological fluid sample which contains a large amount and a vast diversity of background binding partners that might interfere with the molecular interactions of the analytes in a wash-free and real-time format, i.e. it allows for wash-free and real-time immunoassays. This is because the non-specific binding of background binding partners is diluted over a large spectrum in Fourier space and is thus not detected by the sensing method and device.

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. In some embodiments, 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. For example, 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.

As mentioned above, the plurality of first molecular recognition elements are configured to bind the analyte associated with proteinopathy. In particular, of the first and second molecular recognition elements only the first molecular recognition elements are configured to bind the analyte associated with proteinopathy. Typically the first molecular recognition elements and eventually the second molecular recognition elements comprise one or more binding sites or affinity sites.

In some embodiments, step b., i.e. providing the biological fluid sample to the one or more recognition patterns of the sensor chip or sensing device is performed such that the biological fluid sample is provided with a flow direction. That is, the biological fluid sample is provided such that it flows across the first surface of the sensor chip and/or then to other recognition patterns. This may for example be achieved by a fluidic system of the sensing device. Such a fluidic system may for example comprise one or more channels, such as micro-channels.

In some embodiments, 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. In particular embodiments, 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”.

The method and device according to the invention allows to directly measure the captured mass (i.e. any molecular interaction between the molecular recognition elements and the analyte) in highly complex biological samples and can to a wide extent suppress environmental noise, because it employs the digital spatial affinity lock-in principle and is inherently self-referencing to the pattern period or spatial frequency or pattern wavelength of the recognition pattern. Thus, the method according to the invention can detect the mass of an analyte associated with proteinopathy or its formed aggregate, and can enable to determine the constitution or in situ formation of a complex aggregate. A formed aggregate may be complex, however as long as it exposes at least one binding site which can interact with the first and optionally second molecular recognition elements it can be detected. This is particularly favorable for certain 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.

In some embodiments, 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.

In some embodiments, 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. In certain embodiments, 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.

In embodiments, 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. Thereby, 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.

In an embodiment, the recognition pattern is implemented as a recognition grating.

In embodiments, 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.

In embodiments, 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.

In embodiments, 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. This allows obtaining recognition patterns with scalable spatial resolution and quality, to obtain pattern wavelengths in the range of 350 to a few micrometer ( .m) and/or in the range of 5 nm to a few micrometers ( .m), in particular with coherent line width in the Fourier space. The recognition pattern can be matched to the resolution and practicability of the selected imaging microscopy apparatus.

In embodiments, 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.

In embodiments, the imaging microscopy apparatus can be selected from: a white light interferometer (WLI), a scanning probe microscope (SPM), in particular atomic force microscope (AFM) or scanning tunneling microscope (STM), a confocal microscope, in particular laser scanning confocal microscope (LSCM), and combinations thereof.

In an embodiment, the imaging microscopy apparatus may include an electron microscope.

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.

In an embodiment, 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.

In an embodiment, a single grating structure can already be multifunctional, meaning that multiple different types of molecular recognition elements are present, which reduces the sensor surface area and allows for smaller sample volumes to be analyzed.

In embodiments, 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.).

In embodiments, one such combination may be to perform an overview imaging step according to steps d. - e. using a first imaging microscopy apparatus, e.g. white light interferometer (WLI), for reading out a first area of the sensor chip or recognition pattern, and to perform a detail imaging step according to steps d. - e. using a second imaging microscopy apparatus, e.g. atomic force microscope (AFM), for reading out a second area, in particular sub-area of the first area, of the sensor chip or recognition pattern. Such a combined use of 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.

In embodiments, 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. Herein, 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.

Alternatively or in addition, 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..

In embodiments, 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.

In embodiments, 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. In the following, 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.

In some embodiments, 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.).

In some embodiments, the measured signal or the measured signals is/are stored after step e. in a memory unit being part of or under control of a control unit, including a circuit and/or a processor.

In some embodiments, the first molecular recognition elements of specific recognition pattern, in particular of each specific recognition pattern, of the plurality of recognition patterns are different from the first molecular recognition elements of at least some of or all of the other recognition patterns. Such embodiments allow multiplexing. In particular, the first molecular recognition elements of each recognition pattern of the plurality of recognition patterns are different from the first molecular recognition elements of each other recognition pattern. In some embodiments, the first molecular recognition elements of one or of a group of recognition patterns are unique as compared to other recognition patterns of the sensing device. In particular, the first 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.

In some embodiments, 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. In particular, 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. In some embodiments, 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. In particular, 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.

One advantage of providing a plurality, i.e. more than one, recognition pattern with different first molecular recognition elements is that it allows to break down the complexity of the analyte of proteinopathy and allows for immunosignaturing. For example, 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. Or, the different first molecular recognition elements bind the analyte associated with proteinopathy with different binding affinities. Since 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. By using different first molecular recognition elements, 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. By employing multiple recognition patterns with different first molecular recognition elements, an immunosignature of biological fluid sample and/or the analyte can be generated. This immunosignature can then be used to identify or monitor the analyte associated with proteinopathy and allows to draw conclusions on the identity, nature and/or clinical or pathologic state of the proteinopathy of a subject, from which the biological fluid sample has been obtained. In some embodiments, the difference of first molecular recognition elements of a given recognition pattern from first molecular recognition elements of another recognition pattern is characterized in that first molecular recognition elements or at least their binding sites, are chemically distinct from each other.

Additionally or alternatively, the difference of 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 analyte associated with proteinopathy.

Additionally or alternatively, the difference of 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.

Additionally or alternatively, the difference of 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 to the same analyte or epitope with a different binding affinity.

In some embodiments, at least some or all of the plurality of recognition patterns, in particular their first and/or second molecular recognition elements, compete for the analyte associated with proteinopathy, and 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. In certain embodiments, also 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.

In some embodiments, in particular in embodiments in which at least some of the plurality of recognition patterns compete for the analyte associated with proteinopathy, 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. In such 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. In a serial competition measurement, it is possible that 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. Preferably in such embodiments, step d. is performed during and optionally already prior to step c. It may also be possible that the biological fluid sample is concomitantly provided to the plurality of recognition patterns, in particular such that it comes essentially simultaneously in contact with all recognition patterns. Preferably also in such embodiments, step d. is performed during and optionally already prior to step c. In preferred embodiments of such serial competition measurements, 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.

In some embodiments, at least some of the recognition patterns are separated from each other, in particular by a wall structure. Such 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. In particular, 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). Preferably, each group may contain one or more recognition patterns. It may further be possible that 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.

In some embodiments, 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. By 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.

In some embodiments, 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.

Additionally or alternatively, 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. Also in this case, 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. In embodiments, 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.

In an embodiment, the imaging microscopy apparatus is a phase contrast microscope.

In embodiments, 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.

In embodiments, 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.

In embodiments, 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. Additionally, 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 (in particular the magnification and working distance) 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.

In embodiments, wherein functionalizing is based on that 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.

In embodiments, the first pattern elements can be configured to bind a first analyte, the second pattern elements can be configured to bind a second analyte, and the sensing device can be configured to quantify a difference between a mass modulation contrast generated by the immobilized first analyte and the immobilized second analyte.

In embodiments, the method further comprises the step of: determining a parameter of the analyte associated with a proteinopathy from the quantified mass modulation contrast and optionally time; and/or 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 quantified mass modulation contrast and optionally time. Also in this case, 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 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.

It is for example possible to observe in real time if the analyte associated with proteinopathy has recruited additional material to form an aggregate or co-aggregate. Furthermore, it is also possible to observe in real time the recruiting of such additional material. The additional material may be any residual biological material, for example cellular components, mitochondria, cell membranes, nucleic acids, proteins and fragments thereof.

In some embodiments, the determined parameter is comprised in the formed immunosignature of the biological fluid sample and/or of the analyte of proteinopathy.

In embodiments, the method further comprises the step of: determining a presence of the aggregate or co-aggregate from the determined parameter, in particular wherein the presence of the aggregate or the co-aggregate is determined if the determined parameter exceeds a predetermined threshold within a predefined incubation time; and preferably determining the presence of the aggregate or co-aggregate comprises a comparison of the determined parameter with a reference parameter, e.g. a reference parameter obtained from different patient populations or from a healthy subject or is a previous parameter of the subject from which the biological fluid sample has been obtained.

In some embodiments, 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. For example 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. In embodiments, 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. In certain embodiments, 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.

In embodiments, 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. For example, it is possible to monitor 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.

In certain embodiments, during the predetermined measurement time, 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.

In some embodiments, step d. comprises one or more single point measurements at a specific point in time, in particular a steady-state or end-point measurement. In some embodiments, the single point measurements, respectively the signals obtained from the single point measurements, is comprised in the immunosignature of the biological fluid sample and/or the analyte associated with proteinopathy. It is understood that a single point measurement, in particular steady-state or end-point measurement, does not exclude the concomitant measurement for a predetermined measurement time. While it may in some embodiments be the case that step d. only comprises one or more single point measurement or only the measurement for a predetermined measurement time, both measurements may in some embodiments also be comprised in step d..

In embodiments, 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.

Alternatively or in addition, a treatment step is performed after step b, the treatment step preferably comprising the application of a treatment agent, such as a detergent selected from: ionic agent, solvent, acid, base, aggregate stabilizer agent, aggregate destabilizer agent, and combinations thereof.

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. For example, 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. In certain embodiments, 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. Such embodiments which comprise the application of aggregate stabilizer agent or an aggregate destabilizer agent are advantageous, because early stage proteinopathies may show a have low level of rigid and stable aggregates whereas later stage proteinopathies or multiple system atrophy may have a higher level of rigid type aggregates with less tendency to decay. Thus, the application of aggregate stabilizer agent or an aggregate destabilizer agent may provide insights on the stage of proteinopathy. Furthermore, the signal measured may in some embodiments also be comprised in the immunosignature of the biological fluid sample and/or the analyte associated with proteinopathy.

In some embodiments step d. is performed for a predetermined measurement time during the rinsing step and/or during the treatment step. This allows to monitor the changes of the measured signal over time and the effect of the rinsing step and/or treatment step. Such a signal measured over time may in some embodiments also be comprised in the immunosignature of the biological fluid sample and/or the analyte associated with proteinopathy.

In some embodiments, 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. In some embodiments, 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. This may in particular be advantageous if 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.

In embodiments, after step b. 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). It is understood that 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. Further, step d. can comprise measuring each signal over a predetermined measurement time. Preferably, the protein sample comprises monomeric proteins. The monomeric proteins may act as substrate for the co-aggregation in the seed amplification assay. Depending on the presence and nature of the analyte of proteinopathy, 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.. In the seed amplification assay the bound analyte associated with proteinopathy (in particular an analyte forming an aggregate) may for example act in situ as a seed that co-aggregates with recombinant or synthesized forms of the analytes associated with proteinopathy or other binding reagents that were provided as a treatment agent. 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).

In particular embodiments a plurality of recognition patterns each with different first molecular recognition elements as described herein are used. In such embodiments, the seed amplification assay produces different signals due to different interactions at the different recognition patterns. The measured signals can in some embodiments be comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy. Such an immunosignature can be highly specific and thus unique for a certain analyte of proteinopathy and/or the clinical stage of the patient from which the biological fluid sample has been obtained.

In embodiments, 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. In preferred embodiments, 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. In some embodiments, the secondary characterization parameter is also comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy.

In some embodiments, the secondary characterization step is performed directly on the biological fluid sample having been provided to the one or more recognition patterns of the sensor chip. Thus, in such embodiments, the secondary characterization step is performed directly when the biological fluid sample is present on the sensing device.

In some embodiments, 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. Optionally also other parameters of the analyte of proteinopathy are comprised in the immunosignature. For example as mentioned herein, in some embodiments, 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 may in some embodiments be regarded as a vector, in particular a time-dependent vector, whose trajectory in the high dimensional vector space is unique for the screened biological fluid sample. The term “high dimensional” refers in this context to at least 3 dimensions or more. Every parameter obtained and/or every signal measured during step d. deconvolves the complexity of the biological fluid sample. The immunosignature can be regarded as a unique fingerprint of the screened biological fluid sample. Therefore, it is in some embodiments possible to generate multiple immunosignatures over time for biological fluid samples having been obtained within defined time intervals, such annually or half annually, from the same subject and compare the immunosignatures with each other or with reference data as outlined further below. The immunosignature is thus a direct measure of the pathological state of the subject from which the biological fluid sample originates from.

In some embodiments, the immunosignature of the biological fluid sample is compared to a database comprising immunosignatures of a plurality of samples. In particular 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. In preferred embodiments, the immunosignatures of the database are each associated with a clinical status of a proteinopathy. Thus, by comparing the immunosignature of the screened biological fluid sample with the immunosignatures comprised in the database, 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. By virtue of such a database comparison it is also possible to perform a pattern recognition analysis during which the immunosignature of the screened biological fluid sample is analyzed for specific patterns which may be found in some of the immunosignatures stored in the database. This allows for detecting a proteinopathy at a very early state, in particular even before the subject from which the biological fluid sample originates, develops recognizable clinical symptoms.

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.

In some embodiments, 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.

In some embodiments, the first molecular recognition elements and the second molecular recognition elements have essentially the same affinity KD to the same background binding partners. In some embodiments, the first molecular recognition elements are bound to the first pattern elements with a surface density of 0.1 to 40 fmol/mm² (femto-mol/square-millimeter) in particular of 1 and 4 fmol/mm², in the case of molecular recognition elements being antibodies or having the size of antibodies (150 kDa). For a smaller affinity element such as nanobodies or aptamers of 15-50 kDa, the first molecular recognition elements are bound to the first pattern elements with a surface density of 1 and 320 fmol/mm², in particular 16 and 64 fmol/mm². Alternatively or additionally, second molecular recognition elements may be bound to the second pattern elements with a surface density of 0.1 to 40 fmol/mm², in particular of 1 and 4 fmol/mm², in the case of molecular recognition elements being antibodies or having the size of antibodies (150 kDa). For a smaller affinity element such as nanobodies or aptamers of 15-50 kDa, the second molecular recognition elements are bound to the second pattern elements with a surface density of 1 and 320 fmol/mm², in particular 16 and 64 fmol/mm². In particular in cases in which the first and eventually second molecular recognition elements are antibodies, such a surface density is advantageous, because on the one hand it avoids interactions of the antibodies with each other and further allows for a relatively dense occupation of the pattern elements, which improves the measurement sensitivity and affinity of the sensing device.

In some embodiments, the biological fluid sample may be pre-treated prior to step c.. For example, the biological fluid sample may be centrifuged and/or filtered prior to step c..

In some embodiments, it is possible to repeat steps c. and d. at least once, at least twice, or even more. Each repetition may be considered as a screening cycle.

It is possible that 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. In another screening cycle, a seed amplification as described herein may be performed on the same biological fluid sample providing a seed amplification signal. In another screening cycle, 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. In another screening cycle, a serial competition measurement according to some of the herein described embodiments is performed providing several competition signals. In preferred embodiments, in another screening cycle a first serial competition measurement is performed by providing the biological fluid sample in a first flow direction to the recognition pattern and in a subsequent screening cycle, a second serial competition measurement is performed by providing the biological fluid sample in a second flow direction to the recognition patterns which is inverse to the first flow direction. In another screening cycle, a parallel measurement according to some of the herein described embodiments is performed providing parallel measurement signals. It is possible to perform one, two, more or all of any of these screening cycles in some embodiments of the method according to the invention.

In certain embodiments the signals and/or parameters obtained in each screening cycle are comprised in the immunosignature of the biological fluid sample and/or of the analyte, in particular the analyte or mixture of analytes associated with proteinopathy.

In embodiments, 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.

In some embodiments after step c. incubation of the one or more recognition patterns with a labelled, in particular mass-labelled or fluorescence-labelled, binding agent could be performed. The mass-labelled binding agent is a binding agent preferably having a known, identifiable and optionally unique mass. In certain embodiments, 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. In some embodiments, the detected fluorescent signal is comprised in the immunosignature of the biological fluid sample and/or of the analyte associated with proteinopathy. In some embodiments, the binding agent may comprise a predefined binding site, which may particularly be configured to bind to the immobilized analyte associated with proteinopathy. In general, 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.

In some embodiments, the signal measured during or after incubation of the one or more recognition patterns with a labelled, in particular mass-labelled or fluorescence-labelled, binding agent, could also be comprised in the immunosignature of the biological fluid sample and/or of the analyte of proteinopathy.

In another aspect, the invention relates to a device for screening a biological fluid sample for an analyte, in particular a device for performing the method according to any one of the preceding claims, the device comprising: a. 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 pattern having a characteristic spatial frequency spectrum in the plane, b. 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 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.

In embodiments, 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.

In embodiments, on the sensor chip 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.

In embodiments, 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.

In embodiments, 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.

In embodiments, 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.

In embodiments, the imaging microscopy apparatus is a scanning atomic force microscope (AFM) comprising a cantilever or cantilever array configured to scan across the plane to obtain AFM image data containing AFM-characteristic information for determining an atomic or molecular height profile of the pattern and therefrom a mass modulation generated by the immobilized analyte.

Throughout this application, the referencing of the steps by letters a., b., c., d., etc. does not imply a specific order of steps, but that these letters serve as reference letters to identify a specific step of the claimed method. Although it may be the case that the method is performed sequentially step by step, starting from step a. until step f., it is also encompassed by the claimed invention that the steps are performed in another order or that at least some of the steps are performed simultaneously. As an example, it is also encompassed by the invention that step a., i.e. providing a sensor chip, can be performed before, during or after step b., i.e. providing the sensing device.

Throughout this application, the term “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. Similarly, 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. In particular, binding or immobilizing may take place at or in sufficiently close neighorhood of the molecular recognition element.

It is generally understood that the biological fluid sample may comprise a single analyte associated with proteinopathy or also multiple analytes associated with proteinopathy. In the latter case, 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.

It is generally understood that the general sensitive of the spatial-digital lock-in principle builds on the affinity-selectivity difference of the recognition elements employed within a pattern. Therefore, the principle can be used to optimize molecules and their binding in complex bio-liquids for the purpose to optimize the binding properties of molecules, as well as to discriminate between selective recognition events and background binding. The principle has applications in drug discovery, monitoring processes as well as in diagnostics.

In addition to the method and the sensing device, the present disclosure also relates to a sensor chip, comprising a recognition grating including a DNA origami structure, the DNA origami structure being functionalized with molecular recognition elements for binding an analyte, the molecular recognition elements forming a substantially periodic pattern.

More specifically, the recognition pattern extends in a two-dimensional plane and has arranged thereon pattern elements. The pattern elements are arranged periodically to form the recognition pattern. At least some of the pattern elements are functionalized with molecular recognition elements for binding an analyte. The sensor chip is characterized in that the recognition pattern is formed at least in part by a periodically repeating DNA origami structure comprising scaffold strands and staple strands. The DNA origami structure provides the pattern elements. The molecular recognition elements are connected to the staple strands by linker elements.

The molecular recognition elements are as described in the present disclosure.

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. In other words, 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. In other words, 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.

In an embodiment, the spatial periodicity, specifically one or more of the unit cell vectors which define the pattern formed by the first type of molecular recognition elements, does not have a common divisor with the unit cells vectors which define the pattern formed by the second type of molecular recognition elements. The same may apply to a third type of molecular recognition element which, if present, would have unit cell vectors which do not have a common divisor with either of the unit cell vectors of the first or second type. This ensures that, in Fourier space, the primary peaks do not overlap and it is possible to distinguish the signal coming from each type of molecular recognition element.

The sensor chip may comprise a plurality of recognition patterns implemented as DNA origami (nano)structures. The DNA origami nanostructures may be grouped into a plurality of sets, each set having different properties, in particular in that they include different molecular recognition elements. For example, a first DNA origami structure may comprise only molecular recognition elements of a first type, while a second DNA origami structure may comprise only molecular recognition elements of a second type. Thereby, a multiplex sensor chip is achieved. In an embodiment, the DNA origami structure comprises two or more different types of molecular recognition element, each molecular recognition element forming a pattern having a distinct and different unit cell (defined by the unit cell vectors) to the one or more other patterns of molecular recognition elements. In an embodiment, the unit cell vectors are not multiples of each other in any dimension. In other words, the unit cell vectors do not have a common divisor other than 1 . This has the benefit that in the Fourier spectrum the principle components are distinct, allowing the assignment of at least some peaks in the Fourier spectrum to a particular type of molecular recognition element and also allowing for each molecular recognition element to have at least one peak in the Fourier spectrum not shared with another molecular recognition element.

In an embodiment, the recognition pattern may not have a significant mass modulation. This is also referred to as a massless recognition pattern. This may be the case with DNA origami structures whose mass modulation may be on the order of only a few nanometers. In such a case, a comparison between the sensor chip without any analyte and the sensor chip with immobilized analyte may not be necessary.

Brief description of the figures

The herein described invention will be more fully understood from the detailed description given herein below and the accompanying drawings which should not be considered limiting to the invention described in the appended claims. The drawings show:

Fig 1 a schematic block diagram of a screening device for screening a biological fluid sample for an analyte;

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. 4 a schematic detailed view of a recognition pattern according to an embodiment of the invention;

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;

RECTIFIED SHEET (RULE 91) ISA/EP Fig. 5b 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 being present as a monomer, and a schematic aggregation curve;

Fig. 6 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention after washing and with an additional mass- labelled binding agent, 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. 12 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention, where a marker protein expressed on the surface of mitochondria is exposed and used to bind to first molecular recognition elements; Fig. 13 a schematic detailed view of a section of a recognition pattern for use in a method according to the invention, where a marker protein expressed on the surface of a lysosome is exposed and used to bind to first molecular recognition elements;

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. 15 shows a flow diagram illustrating a method for screening a biological fluid sample for an analyte;

Fig. 16 shows a representation of image data of a recognition pattern including vertical lines, along with the same image data in Fourier space revealing the amplitude frequency spectrum of the recognition pattern;

Fig. 17 shows a representation of image data of the recognition pattern of Fig. 16, this time with analyte present, represented as small dots which bind selectively to the spaces in the recognition pattern, along with a Fourier transform of the same image, showing how the amplitude of the peaks in the frequency spectrum changes due to the presence of the analyte;

Fig. 18 shows a representation of the image data of the recognition pattern of Figs. 16 and 17, further including a randomly oriented object in white, showing that the signal in the Fourier spectrum is robust against random noise which does not selectively bind to either the ridges or grooves;

Fig. 19 shows a schematic representation of a recognition pattern including a DNA origami structure, with molecular recognition elements ‘a’ linked to staple strands by linker elements, the molecular recognition elements forming a pattern; Fig. 20 shows a schematic representation of a larger section of a recognition pattern formed by the DNA origami structure shown in Fig. 19, including its Fourier spectrum and the corresponding retranslation of the main Fourier components back into real space;

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’; and

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.

Exemplary embodiments

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.

Optionally, the screening device 1 includes a fluid cell which includes the sample to be measured.

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, depending on the embodiment, further comprises a communication interface. The communication interface is configured for data communication with one or more external devices. Preferably, 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. 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.

The skilled person is also aware that least some of the data associated with the program code (application data) or data associated with a particular analyte and described as being stored in the memory of the screening device 1 may be stored on one or more auxiliary storage devices connected to the screening device 1 using the communication interface. Figures 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.

Figure 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 shown arranged between a camera 101 , in particular a CCD camera and the sensor chip 3. The alternating first pattern elements 8 and the second pattern elements 9 are indicated by the alternating lines. A reflective surface 102 such as a mirror is arranged on the opposite side of the sensor chip 3 to the interferometer 100. The WLI 100 provides to the camera 101 an optical measurement signal, the measurement signal being modified according to the analyte which adheres to the surface of the sensor chip 3. In particular, a fringe pattern forms on the surface of the camera 101 which is read out and digitized, the fringe pattern being modulated/modified according to the analyte.

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.

As schematically shown in the detail view of the WLI 100, 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. Once again, a part of the light incident on the beam splitter 104 is reflected off the beam splitter 104 and onto the sensor chip 3, whereupon it is reflected by the reflective surface 102 and again passes through the arms of the WLI 100 A varying arm length between the beam splitter 104 and the reference plane 105 on the one hand and the reflective surface 102 on the other hand causes the intensity of light incident on the camera 101 to vary due to interference.

Depending on the field of view of the WLI 100, the WLI 100 may move in x-y coordinates such that the entire sensor chip 3 may be imaged. For each position (the position being defined using, for example, Cartesian x-y coordinates) on the surface of the sensor chip 3, 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. Alternatively and/or additionally, the distance between the sensor chip 3 and the beam splitter 104 may also be controlled such as to identify a position of maximum and/or minimum intensity of light. However, if the field of view is large enough to image the entire sensor chip 3 at once, such a movement is not necessary.

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.

Figure 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). 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. In the Mirau interferometer, the reference arm is located within a microscope objective assembly.

Similarly to the WLI 100 described with reference to Fig. 2a, 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. In particular, 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.

By scanning at least part of the recognition pattern 5 on the sensor chip 3, image data is obtained 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.

The benefits of the PSWLI and VSWLI 110 include delivering up to 0.1 nm resolution in vertical (PSWLI) dimensions at a moderate throughput and a speed determined by the scanning speed. Mass modulations which have periodicities in the micrometer range and which span over a millimeter can be detected with high accuracy, and even from large working distances. Using phase shift interferometry techniques, the topography (i.e., the height) may be determined from a series of interferograms. In PSWLI the maximal z- scanning range matches the wavelength within the coherence length of the white light. VSWLI is suited to visualized larger analytes and objects and with slightly lower resolution.

Fig. 2c shows a schematic illustration of a sensing device 10 including an imaging microscopy apparatus 2 comprising, or in particular being implemented as, a confocal microscope 120.

The confocal microscope 120 is arranged between the camera 121 and the sensor chip 3 in a similar manner as the WLI 100 and the VSWLI 110 described above with reference to Figs. 2a and 2b. The confocal microscope 120 includes a laser source 123. Alternatively, one or more one color LEDs and/or a white light source (e.g. implemented using a 3 colour LED) may also be used.

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. In particular, the z-scanner 127 is configured to focus the laser light at a height which takes into account any immobilized analyte. Whether the focal position coincides with the sensor chip 3 or in particular the analyte may be determined using the camera 121. Specifically, 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 height position at which the focal point coincides with the recognition pattern, in particular the analyte, is recorded at each position in the plane. The confocal microscope 120 is scanned across at least part of the recognition pattern 5 and at each position the height is determined and used to generate the image data as a function of one, two or more measurement coordinates in the x-y plane.

A benefit of the confocal microscope 120 is that it can deliver a vertical resolution of up to 1 nm. However the lateral dimension is diffraction limited. The throughput and dynamic range is also lower. The confocal microscope 120 is beneficial in particular once the size or diameter of the analyte has dimensions corresponding to the wavelength of the laser source 123.

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. In particular, 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. Using a feedback controller 136, 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 height of the piezo stage 135 and/or the deflection of the laser beam as recorded by the sensor 134 may be used to determine a height at a particular position in the plane. By scanning across the sensor chip, a height profile in one or two spatial dimensions may be obtained.

The height profile is used to generate the image data as a function of one or two measurement coordinates in the x-y plane.

The AFM 130 may be operated in one of a plurality of modes, for example including a tapping mode, or a contact mode. For operating the AFM in tapping 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.

Figs. 3 show example images and data obtained. Fig. 3a shows an example of image data including an AFM height map recorded on a blank recognition pattern 5 which include, as first recognition elements 8, antibodies and as second recognition elements 9, short chains of Poly-Ethylene-Glycol (PEG). The AFM height map was recorded in tapping mode in air. The mass modulation caused by the size and decoration difference of the different recognition elements 8, 9 is visible in the parallel stripes which modulate the topography along the pattern, i.e. along the recognition pattern 5. The topography modulation (i.e. the height profile) is, along with a frequency of the pattern, mapped in real space and may be used for calibration of optical methods, in particular for calibration of the imaging microscopy apparatuses described with reference to Figs. 2a - 2c.

Fig. 3b shows an example of Fourier-transformed image data. The periodic nature of the image data shown in Fig. 3a results in two frequency peaks after Fourier-transformation. The frequency peaks are visible as series of horizontally spread dots in the center of Fig. 3b.

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. When retransforming the two peak frequencies into real space, as indicated by the two inserted images showing real space representations of the two peaks, respectively, the filtered-striped modulations of the recognition pattern are reobtained.

While in some embodiments the sensing device 10, respectively its sensor chip 3, comprises only one recognition pattern 5, it may well be possible that the sensing device, respectively its sensor chip 3, comprises a plurality of such sensing spots in the form of a plurality of recognition patterns 5. This is for example shown in Figs. 10 to 11. Fig. 4 shows a detailed view of a recognition pattern 5. As can be seen, the sensor chip 3 comprises a plurality of first pattern elements 8, in particular ridges 8, and a plurality of second pattern elements 9. The first and second pattern elements 8, 9 are interdigitated with each other such that the plurality of first pattern elements 8 are arranged in an alternating manner with second pattern elements 9. All first pattern elements 8, which in this embodiment are in the form of curved elongated lines or bars or ridges, and all second pattern elements, which in this embodiment are also in the form of curved elongated lines or bars or grooves, form together the recognition pattern 5. 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. In addition, 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.

To use available lithography systems in combination with white light interferometry, a recognition pattern or recognition pattern e.g. with a periodicity of 1 .8 .m and a square area of 600 x 600 .m² can be suitable.

In an embodiment, 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 mm² of 500 nm (3o interval) and over 50 x 50 mm² 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). As can be seen, a plurality of first molecular recognition elements 10 is bound to first pattern elements 8, and 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. In general as used herein, 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. As can be seen, first molecular recognition elements 10 comprise a binding site A, while second molecular recognition elements 11 are devoid of such a binding site A. In general as used herein, 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. In the graph below, 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. Since 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.

Similarly to Fig. 5a, Fig. 6 also shows an exemplary screening for an analyte 13 associated with proteinopathy, which forms an aggregate. Again, as it can be seen from the measured signal, measurement commences during time interval 1. In time interval 2, the biological fluid sample comprising amongst others the aggregated analyte 13 associated with proteinopathy is added. Thereafter, in time interval 5, a mass-labelled binding agent 14 is added. For example, such a mass-labelled binding agent may comprise a gold nanoparticle as mass label.

Furthermore, the mass-labelled binding agent may in this or any other embodiment as described herein, comprise a predefined binding site, which may particularly be configured to bind to the analyte associated with proteinopathy. In general, the predefined binding site may for example be configured such that it does not bind to the first and/or second molecular recognition elements. As can be seen in Fig. 6, upon addition of the mass-labelled binding agent, the signal increases in time interval 5, because the binding agent binds to the analyte 13, which is already bound to first molecular recognition elements 10. Such embodiments are advantageous as these further can help to deconvolve and thus characterize the proteinopathy from which the patient as the source of the biological fluid sample being screened suffers. For example, if one knows that specific types of diseases show an analyte which exposes binding site b, the mass increase shows the presence of such a binding site b and may thus help to exclude other proteinopathies. It is understood that the mass label has a defined and known mass.

Fig. 8 shows a method according to an embodiment of the invention, in which a treatment step is performed. In this embodiment, the treatment step comprises the application of a detergent. As can be seen in the upper part, an aggregated analyte is bound to the first molecular recognition element 10. The corresponding signal increase is seen in time intervals 2 to 4. Then, in time interval 5 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.

Fig. 8 depicts an embodiment of the method according to the invention in which after step b. a seed amplification assay is performed. As can be seen in the upper part of Fig. 8, the biological fluid sample contains analytes 13 associated with proteinopathy. After providing the biological fluid sample, a protein sample comprising monomeric proteins 15 is added to the corresponding recognition pattern comprising first pattern elements 8 and second pattern elements 9. Since the analyte 13 associated with proteinopathy shows for example prion-like behavior, it effects misfolding and aggregation of added monomeric proteins. Therefore, as seen in the middle part of Fig. 8, the aggregation increases and additional aggregates form. Since the mass increase due to advancing aggregation can be directly measured, the seed amplification can be observed in real time. This is shown in the lower part of Fig. 8 showing the measured signal over time. As can be seen, the measured signal continuously increases during time interval 2, which directly indicates that seed amplification occurs.

Fig. 9 shows an embodiment of the method according to the invention in which the sensing device (not shown) comprises a plurality of recognition patterns 5’a, 5”a, 5’b, 5”b, 5’c, 5”c, 5’d and 5”d. Each recognition pattern comprises first pattern elements with first molecular recognition elements bound thereto and second pattern elements with second molecular recognition elements bound thereto. Preferably, at least some or even all of the recognition patterns can in this or any other embodiment described herein comprise unique first molecular recognition elements and/or unique second molecular recognition elements. 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. As can be seen from 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. If for example the first two recognition patterns 5’a, 5”a comprise first molecular recognition elements having binding site A, the central two recognition patterns 5’b, 5”b comprise different first molecular recognition elements having binding site B, and the last recognition patterns 5’c, 5”c comprise yet other, different first molecular recognition elements having binding site C, the behavior of the analyte 13 associated with proteinopathy to these different first molecular recognition elements 10 can provide valuable information on the analyte 13 and the proteinopathy. 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. As can be seen 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. As can be seen, 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. As can be seen, 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. 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. It is known for certain proteinopathies that cell residues, such as mitochondria or parts thereof, are recruited and form an aggregate. Further, it is known that such mitochondria or their parts may comprise a specific marker protein, such as protein b, e.g. VDAC. Thus, exploiting this knowledge by equipping the first molecular recognition elements 10 with binding sites A binding to a certain binding site and/or epitope a on the marker and also with binding sites B binding specifically to the mitochondrial protein b, 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. In this case, a protein co-aggregate is screened as analyte 13 associated with proteinopathy. In this variant one may for example assume that the patient suffers from a specific proteinopathy, which involves a protein aggregate that has a specific post-translational modification d. By employing 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.

In step S1 , a sensor chip is provided. The sensor chip comprises a recognition pattern having molecular recognition elements as described herein. Depending on the implementation of the method, 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. In step S2 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.

In 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. Alternatively, 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.

In 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.

In 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. In particular, 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.

It is clearly evident that by comparing (filtering) the obtained Fourier spectrum using the Fourier spectrum obtained without any analyte (black dots) present, the effect or signal of the black dots can be isolated. Thereby, the mass modulation of the black dots can be quantized.

Fig. 18. shows the same recognition pattern as shown in Figs. 16 and 17, however in addition to the black dots which represent the analyte, a further white line has been drawn randomly over the recognition pattern. The while line is indicative of further parts of a biological fluid sample which adheres to the recognition pattern. The further parts adhere non-selectively, i.e. they cover both grooves and ridges randomly and therefore roughly equally, because the further parts do not bind to the molecular recognition elements. 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. As can be seen in the Fourier spectrum (bottom), 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.

These DNA origami microstructures can be imaged in a label free manner with atomic force and electron microscopy. Invented about 30 years the concepts of DNA-origami today enable to spatially organize DNA in plane and space in a programmed manner. At the bottom is a DNA scaffold strand (continuous lines) which is incubated with a series of complementary staple strands (dotted lines), to give a well-defined pattern.

The staple strands can be equipped with extensions or linker elements. Molecular recognition elements a as described herein can be attached to the linker elements, thereby yielding a spatially organized recognition pattern.

These extensions or linker elements may be implemented as DNA single strands with a specific sequence. Further, it is possible to directly conjugate a recognition element to a specific staple strand. The latter has the advantage that the spatially uncertainty is less, because the linker element is shorter and therefore the molecular recognition element can be arranged closer to the staple strands.

Patterns of the kind shown are well-suited to be analyzed in Fourier Space (see Fig. 20).

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.

Fig. 20 shows an illustration of a recognition pattern (top left) including the DNA origami structure described above with reference to Fig .19. The Fourier analysis (top right) shows as bright spots the frequency components of the recognition pattern. The circled primary frequency components, when retransformed into real space, reveal the unit cell vectors in the form of two-dimensional patterns, thereby showing that the recognition pattern is suitable for use in the described invention. In particular, the 2D spectrum enables the monitoring of multiple amplitude modulations associated with selective changes associated with the in-plane grating symmetry. Modulations affecting different symmetry vectors of the unit cell can independently be analyzed, and therefore be used to filter modulation related with independent frequencies, symmetry and orientation.

Fig. 21 shows a recognition pattern, similar to the one described above with reference to Figs. 19, 20, comprising multiple different types of molecular recognition elements a, b, c. In such a manner, the DNA origami structure is functionalized to establish multiplexed biosensors with a nanometer resolution. Employing the digital look-in amplification principle to micrographs recorded with atomic force or electron microscopy allows to filter and distinguish between amplitude modulations at different recognition sites along the grating. In other words, the different types of molecular recognition elements a, b, c may bind different analytes. Thereby, the same recognition pattern can be used to detect multiple different analytes.

Preferably, 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.

For example, single frequency components of the unit cell are spatially organized to follow in respect to symmetry and dimension the series v = 1/n ; n = 1 , 2, 3, 5, 7, 11 , 13, 17 (such as prime numbers and as shown at the right) the Fourier components of the resulting unit cell are not likely to superpose or to be multiple of each other; consequently, different recognition sites can be monitored/filtered widely independent. The concept applies to micro-structures as well.

More generally, the unit cell vectors of the patterns formed by each of the different types of molecular recognition elements a, b, c in the recognition pattern do not have common denominators. In other words, the prime decomposition do not share any prime numbers. Thereby, the principle Fourier components also do not coincide.

Fig. 22 illustrates exemplary relative spatial frequencies between the different types of molecular recognition elements a, b, c, with molecular recognition element a having a relative spatial frequency of 1/5, molecular recognition element b having a relative spatial frequency of 1/3, and molecular recognition element a having a relative spatial frequency of 1/7.

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. As can be seen, each origami structure has dimensions on the order of 100 nm. By pattern matching or other image analysis tools, 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. In particular, 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.

In an embodiment, each recognition pattern has a fiducial marker attached at a particular point (as illustrated by a white dot in one of the zoomed-in insert images). The fiducial marker may be a metallic nanoparticle, for example. The fiducial marker allows for detection of the position and orientation of the recognition pattern even if the electron microscopy image has an overall low contrast level.

The inset image with the arrow pointing to the fiducial marker is the superposed image of the other recognition patterns rotated and added to each other. In an embodiment, 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. In another example, 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).

List of designations

1 screening device

10 sensing device

100 white light interferometer (WLI)

101 camera

102 reflective surface

103 three color LED

104 beam splitter

105 reference plane

110 vertical scanning white light interferometer (VSWLI)

2 imaging microscopy apparatus, imaging means

3 sensor chip

4 control unit

41 processor

42 memory unit

5 recognition pattern

5’a, 5”a recognition pattern

5’b, 5”b recognition pattern

5’c, 5”c recognition pattern

5’d, 5”d recognition pattern

6 light source

7 detector, optical image detector

8 first pattern elements

9 second pattern elements

10 first molecular recognition elements

11 second molecular recognition elements

12 wall structure

13 analyte of proteinopathy

14 mass labelled binding agent

15 monomeric proteins

Claims

1. 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 (3) comprising a recognition pattern (5) extended in a two-dimensional plane and having arranged thereon pattern elements (8), at least some of the pattern elements (8) being functionalized with molecular recognition elements (10) for binding an analyte (13), the recognition pattern (5) having a characteristic spatial frequency spectrum in the plane; b. providing a sensing device (10), which is or comprises an imaging microscopy apparatus (2) being configured for detecting a measurement signal indicative of the recognition pattern (5); c. obtaining the sensor chip (3) with at least a portion of the analyte (13) of the biological fluid sample having been applied; d. imaging at least a part of the recognition pattern (5) 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 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 (5); 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 (13).

2. The method according to claim 1 , wherein obtaining the sensor chip (3) with at least a portion of the analyte (13) applied comprises providing the biological fluid sample to the sensor chip (3) such that at least a portion of the analyte (13) is immobilized at at least some of the molecular recognition elements (10, 11), thereby modifying the measurement signal.

3. The method according to any one of the preceding claims, wherein the pattern elements (8) comprise a first pattern element (8) and a second pattern element (9), in particular ridges and grooves, at least one of the first and second pattern elements (8, 9) being functionalized with the molecular recognition elements (10, 11).

4. The method according to any one of the preceding claims, wherein the recognition pattern (5) comprises a plurality of different types of pattern elements (8), each type of pattern element (8) having a different spatial periodicity and/or symmetry such that the recognition pattern (5) has a corresponding plurality of characteristic spatial frequency spectrums in the plane.

5. The method according to any one of the preceding claims, wherein the characteristic spatial frequency spectrum of the recognition pattern (5) comprises at least one peak region, which represents a pattern wavelength and/or a pattern shape of the recognition pattern (5).

6. The method according to any one of the preceding claims, wherein a pattern wavelength of the recognition pattern (5) 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.

7. The method according to any of the preceding claims, wherein the recognition pattern (5) is formed at least in part by a periodically repeating DNA origami structure comprising scaffold strands and staple strands, the DNA origami structure providing the pattern elements (8).

8. The method according to claim 7, wherein the molecular recognition elements are connected to the staple strands by linker elements or oligonucleotides.

9. The method according to one of claims 7 or 8, wherein the DNA origami structure comprises at least two different types of molecular recognition elements, in particular where the at least two different types of molecular recognition elements have a different spatial periodicity and/or symmetry.

10. The method according to any one of the preceding claims, wherein 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 (5).

11. The method according to any one of the preceding claims, the recognition pattern (5) having the first pattern elements (8) 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 having the alternating second pattern elements (9) selected from: lines of interstitial sites; straight lines or curved lines of interstitial sites; array of dot-like interstitial sites; and combinations thereof.

12. The method according to any one of the preceding claims, wherein the analyte when immobilized modifies a height and/or refractive index of the recognition pattern (5).

13. The method according to any one of the preceding claims, wherein the imaging microscopy apparatus (2) is selected from: a white light interferometer (WLI), a scanning probe microscope (SPM), in particular atomic force microscope (AFM) or scanning tunneling microscope (STM), an electron microscope, a confocal microscope, in particular laser scanning confocal microscope (LSCM), an infrared microscope, a Raman spectrometer, a phase contrast microscope, and combinations thereof.

14. The method according to any one of the preceding claims, providing in step a. a multiplexed sensor chip (3) that includes a plurality of recognition patterns (5), preferably arranged at different locations on the sensor chip (3), in particular specific recognition patterns (5) being characterized by at least one of: a specific functionalization of the at least one first or second pattern element (8, 9) for binding one or more specific analytes (13); a specific characteristic spatial frequency spectrum in the plane; a specific localization of the recognition pattern(s) (5) on the sensor chip (3); a specific arrangement of the recognition patterns, e.g. in a rectangular or quadratic array, on the sensor chip (3).

15. The method according to any one of the preceding claims, wherein the imaging microscopy apparatus (2) 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 (5) and therefrom a mass modulation generated by the immobilized analyte (13).

16. The method according to any one of the preceding claims, wherein the imaging microscopy apparatus (2) is a scanning probe microscope, such as a scanning force microscope, which comprises a cantilever or cantilever array configured to profile the plane to obtain AFM image data containing AFM-characteristic information for determining an atomic or molecular height profile of the pattern and therefrom a mass modulation generated by the immobilized analyte (13).

17. The method according to any one of the preceding claims, wherein functionalizing is based on that the molecular recognition elements, in particular antibodies, provide chemical specificity to bind at least one specific analyte (13) associated with proteino- pathy and/or are configured to bind a specific epitope of the analyte (13) associated with proteinopathy and/or are configured to bind the analyte or epitope of the analyte associated with proteinopathy with a specific binding affinity.

18. The method according to any one of the preceding claims, wherein the first pattern elements (8) are configured to bind a first analyte, the second pattern elements (9) are configured to bind a second analyte, and the sensing device (10) is configured to quantify a difference between a mass modulation contrast generated by the immobilized first analyte and the immobilized second analyte.

19. The method according to any one of the preceding claims, the method further comprising the step of: determining a parameter of the analyte (13) associated with a proteinopathy from the quantified mass modulation contrast and optionally time; and/or determining a parameter of an aggregate of the analyte (13) associated with a proteinopathy or co-aggregate with the analyte (13) associated with a proteinopathy from the quantified mass modulation contrast and optionally time.

20. The method according to claim 19, the method further comprising the step of: determining a presence of the aggregate or co-aggregate from the determined parameter, in particular wherein the presence of the aggregate or the co-aggregate is determined, if the determined parameter exceeds a predetermined threshold within a predefined incubation time; and preferably determining the presence of the aggregate or co-aggregate comprises a comparison of the determined parameter with a reference parameter, e.g. a reference parameter obtained from different patient populations or from a healthy subject or is a previous parameter of the subject from which the biological fluid sample has been obtained.

21. The method according to any one of the preceding claims, wherein 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.

22. The method according to any one of the preceding claims, wherein 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.

23. The method according to any one of the preceding claims, wherein a rinsing step is performed after step c. and in particular before or during step d; and/or wherein a treatment step is performed after step b, the treatment step preferably comprising the application of a treatment agent, such as a detergent selected from: ionic agent, solvent, acid, base, aggregate stabilizer agent, aggregate destabilizer agent, and combinations thereof.

24. The method according to any one of the preceding claims, wherein after step b. a seed amplification assay is performed by adding a binding reagent sample, in particular a protein sample, to the recognition pattern (5).

25. The method according to any one of the preceding claims, wherein the method further comprises a secondary characterization step for characterizing the analyte (13) associated with proteinopathy or a state of an aggregate or co-aggregate formed by the analyte (13) associated with proteinopathy, wherein the secondary characterization step preferably includes ELISA, FT-IR, Raman or fluorescence spectroscopy; in particular wherein the secondary characterization step is performed directly on the biological fluid sample having been provided to the recognition pattern

26. The method according to any one of the preceding claims, wherein the signals measured in step c. and d. form an immunosignature of the biological fluid sample; in particular wherein the immunosignature of the biological fluid sample is compared to a database comprising immunosignatures of a plurality of samples, e.g. patient samples, autologous patient samples and/or reference samples, wherein the immunosignatures of the database are preferably each associated with a clinical status of a proteinopathy.

27. A screening device (1) for screening a biological fluid sample for an analyte, in particular a device (1) for performing the method according to any one of the preceding claims, the device (1) comprising: a. a sensor chip (3) comprising a recognition pattern (5) extended in a two- dimensional plane and having arranged thereon pattern elements, at least some of the pattern elements (8) being functionalized with molecular recognition elements (10) for binding an analyte (13), the recognition pattern (5) having a characteristic spatial frequency spectrum in the plane; b. a sensing device (10), which is or comprises an imaging microscopy apparatus (2) being configured for detecting a measurement signal indicative of the recognition pattern (5) as well as detecting a modified measurement signal, the measurement signal being modified due to the sensor chip having had applied thereon at least a portion of the analyte (13) of the biological fluid sample; c. the sensing device (10) comprising: imaging means (2) for imaging at least a part of the recognition pattern (5) to obtain image data as a function of one or two spatial measurement coordinates in the plane, and processor (41) configured for digitalizing the image data and for determining Fourier-transformed image data as a function of one or two corresponding spatial frequency coordinates; d. the sensing device (10) further comprising: a processor (41) 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 (5); and e. the processor (41) configured to retransform the improved Fourier- transformed image data into direct space to obtain improved image data and for quantifying therefrom a mass modulation contrast generated by the immobilized analyte.

28. The screening device (1) according to claim 27, wherein the sensing device (10) further comprises sampling means for providing the biological fluid sample to the sensor chip (3) such that the portion of the analyte (13) is immobilized at at least some of the molecular recognition elements (8, 9), thereby modifying the measurement signal;

29. The screening device (1) according to one of claims 27 or 28, wherein the pattern elements (8) comprise a first pattern element (8) and a second pattern element (9), in particular ridges and grooves, at least one of the first and second pattern elements (8, 9) being functionalized with the molecular recognition elements (10, 11).

30. The screening device (1) according to one of claims 27 to 29, wherein the recognition pattern (5) comprises a plurality of different types of pattern elements (8), each type of pattern element (8) having a different spatial periodicity such that the recognition pattern (5) has a corresponding plurality of characteristic spatial frequency spectrums in the plane.

31. The screening device (1) according to one of claims27 to 30, wherein the processor (41) is configured for filtering the Fourier-transformed image data for relative enhancement in a selected area, in particular in a narrow spatial frequency range, in which the pattern of the recognition pattern (5) has a characteristic spatial frequency peak representing a pattern period and/or pattern shape of the recognition pattern (5).

32. The screening device (1) according to any one of the claims 27 to 31 , wherein on the sensor chip (3) a pattern wavelength of at least one recognition pattern (5) 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 (5).

33. The screening device (1) according to any one of the claims 27 to 31 , wherein the recognition pattern (5) is formed at least in part by a periodically repeating DNA origami structure comprising scaffold strands and staple strands, the DNA origami structure providing the pattern elements (8).

34. The screening device (1) according to claim 33, wherein the molecular recognition elements are connected to the staple strands by linker elements.

35. The screening device (1) according to one of claims 33 or 34, wherein the DNA origami structure comprises at least two different types of molecular recognition elements, in particular where the at least two different types of molecular recognition elements have a different spatial periodicity.

36. The screening device (1) according to any one of the claims 27 to 35, wherein the recognition pattern (5) has the first pattern elements (8) 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 having the alternating second pattern elements (9) selected from: lines of interstitial sites; straight lines or curved lines of interstitial sites sites; array of dot-like interstitial sites; and combinations thereof.

37. The screening device (1) according to any one of the claims 27 to 36, wherein the sensor chip (3) is multiplexed to include a plurality of recognition patterns (3), preferably arranged at different locations on the sensor chip (3), in particular specific recognition patterns (5) being characterized by at least one of: a specific functionalization of the at least one first or second pattern element (8, 9) for binding one or more specific analytes (13); a specific characteristic spatial frequency spectrum in the plane; a specific localization of the recognition pattern(s) (5) on the sensor chip (3); a specific arrangement of the recognition patterns (5), e.g. in a rectangular or quadratic array, on the sensor chip (3).

38. The screening device (1) according to any one of the claims 27 to 37, wherein the imaging microscopy apparatus (2) 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 and/or refractive index distribution of the recognition pattern (5) and therefrom a mass modulation generated by the immobilized analyte (13).

39. The screening device (1) according to any one of the claims 27 to 38, wherein the imaging microscopy apparatus (2) is a scanning atomic force microscope (AFM) comprising a cantilever or cantilever array configured to scan across the plane to obtain AFM image data containing AFM-characteristic information for determining an atomic or molecular height profile of the recognition pattern (5) and therefrom a mass modulation generated by the immobilized analyte (13).

40. A sensor chip (10), in particular for use with the screening device (1) according to one of claims 27 to 39 or for use in the method according to one of claims 1 to 26, comprising a recognition grating including a DNA origami structure, the DNA origami structure being functionalized with molecular recognition elements for binding an analyte, the molecular recognition elements forming a substantially periodic pattern.