An ultra-sensitive biomolecular sensing detection method based on dynamic single binding event analysis

Abstract

The application provides a kind of based on dynamic single binding event analysis ultra-sensitive biomolecule sensing detection method.The method comprises the following steps: capturing target biological sample on functionalized detection substrate, forming complex by using signal probe labeled second specific binding agent;The kinetic parameters of single binding event are extracted by high space-time resolution imaging, and specific binding and non-specific interference event are accurately distinguished;Combined with standard curve, the target biological sample to be measured is digitally counted and quantified.The application does not need complex signal amplification and washing steps, and significantly improves the detection sensitivity, specificity and speed by single binding event kinetic analysis, is suitable for low-abundance protein, nucleic acid and other trace substance detection, and is compatible with conventional micro equipment, easy to miniaturization and POCT application, has wide biomedical detection potential.

Description

Technical Field

[0001] This invention relates to the field of biomolecular detection, specifically to an ultrasensitive biomolecular sensing and detection method based on dynamic single binding event analysis. Background Technology

[0002] In life science research, clinical diagnosis, and environmental monitoring, the ultrasensitive and highly specific detection of biomolecules (including but not limited to proteins, nucleic acids, exosomes, pathogen antigens, and small molecule compounds) is of great significance. For example, in clinical diagnosis, trace changes in biomolecules such as tumor markers, cardiovascular disease markers, and neurodegenerative disease markers are key evidence for early disease screening, disease monitoring, and prognostic assessment. In environmental monitoring, the accurate detection of trace pathogens and pollutants in water or soil is also necessary to ensure public safety. However, current biomolecule detection technologies still face several technical bottlenecks, making it difficult to meet the comprehensive requirements of detection performance, ease of operation, and cost control in practical applications.

[0003] Currently, commonly used biomolecular detection methods in clinical and research settings include enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay (CLIA), fluorescence immunoassay, and colloidal gold chromatography. These methods are mostly based on the principle of antigen-antibody specific binding, achieving detection through label signal amplification. However, they generally suffer from the following drawbacks: First, limited sensitivity. Most methods rely on population average signals, requiring a certain concentration of the target molecule to produce an effective response. The limit of detection (LoD) is often in the pg / mL to ng / mL range, making it difficult to capture trace target molecules in early disease stages or in environments with minimal contamination, easily leading to false negatives. Second, insufficient specificity. The complex matrix components of blood, body fluids, and environmental samples, along with factors such as non-specific binding, probe aggregation, and substrate drift, generate strong background noise. Existing methods struggle to effectively distinguish between specific binding signals and interfering signals, leading to biased results or false positives. Third, cumbersome and time-consuming operation. Most methods involve multiple steps of incubation, washing, enzymatic reactions, or fluorescence signal amplification. To improve sensitivity, some methods require extended incubation times or increased sample volumes, often resulting in detection cycles exceeding 30 minutes. The low throughput makes it difficult to adapt to scenarios such as point-of-care testing (POCT) and on-site emergency testing; fourth, it is highly dependent on instruments. In order to achieve ultra-sensitive detection, some methods require high-end equipment such as total internal reflection fluorescence microscopy (TIRF), confocal microscopy, surface plasmon resonance imaging (SPR imaging) or mass spectrometer. These devices are complex in structure, expensive (the cost of a single device often reaches hundreds of thousands to millions of yuan), and bulky, making it difficult to miniaturize and popularize them, which limits their application in primary medical institutions, field monitoring and other scenarios.

[0004] In recent years, single-molecule detection technology, with its core advantage of "counting immune complexes one by one", has avoided the information loss caused by the averaging of population signals. It can capture and quantify targets at the molecular level, significantly improving detection sensitivity and providing a new direction for the detection of trace biomolecules.

[0005] To address the aforementioned issues, the inventors of this invention proposed a detection scheme based on a modified microlens array in their prior research, CN118169787A. This scheme effectively enhances the optical signal of signal groups through microlenses, enabling the counting of signal groups and simplifying the optical system and image processing to some extent. Combined with a specific binding system, it achieves biomolecule detection, improving sensitivity and specificity while also simplifying the detection method. However, this scheme lacks the ability to extract and dynamically analyze information about individual molecule binding events over time, which limits its detection limits, speed, and accuracy. For example, this scheme only counts signal groups in images by tracking their presence or absence in continuous images, excluding free / adsorbed particles, without clearly defining the dynamics of molecular interactions.

[0006] Besides the aforementioned methods, other mainstream single-molecule detection technologies mostly employ an "endpoint method" analysis mode—that is, the reaction is terminated after incubation for a period of time, and the number of binding events is read through methods such as enzyme-catalyzed amplification and fluorescence signal amplification. Although these technologies achieve single-molecule-level detection sensitivity, they still face common challenges: low mass transfer efficiency of trace targets in the reaction system; large differences in probe binding efficiency; non-specific binding interference has not been fundamentally resolved; and most technologies still rely on complex optical components such as high numerical aperture objectives and laser light sources, resulting in high equipment costs and operational barriers, making large-scale promotion difficult.

[0007] In summary, existing biomolecular detection technologies (including the scheme proposed in published patent CN 118169787 A) have significant shortcomings in terms of sensitivity, specificity, ease of operation, equipment cost, and sample compatibility. There is an urgent need to develop a dynamic detection technology that is universally applicable, ultrasensitive, highly specific, low-cost, and adaptable to complex samples to meet the detection needs of various biomolecules such as proteins, nucleic acids, exosomes, and pathogens in different scenarios. Summary of the Invention

[0008] This invention aims to overcome the aforementioned shortcomings of existing technologies and provides a detection method based on dynamic single binding event analysis. This method enables rapid, specific, digital counting, and highly sensitive detection of biomolecules to meet the clinical needs for early diagnosis and dynamic monitoring of acute myocardial injury. Specifically, by constructing a detection substrate, this invention achieves real-time imaging and tracking of the binding events between immune probes and target protein molecules without the need for complex imaging systems, acquiring rich kinetic information. Furthermore, by analyzing the kinetic information of individual binding events (such as trajectory, residence time, and binding / dissociation rate), it effectively distinguishes between specific binding events and non-specific background noise, significantly reducing or eliminating washing steps, and significantly improving the specific recognition ability of target protein molecules in complex matrices, thereby greatly enhancing the specificity and accuracy of detection.

[0009] The first aspect of this invention provides a dynamic single-binding event detection method based on a detection basis, which includes the following steps: S1) A detection substrate is provided, the detection substrate comprising a functionalized capture interface, the capture interface comprising a first specific binder capable of specifically binding to a target biological sample; S2) Obtain a second specific binder for the target biological sample labeled with the signal probe, bind it to the target biological standard sample, and then contact it with the detection substrate; S3) The detection substrate surface is dynamically and continuously imaged using a detection device; S4) Analyze the image sequence obtained by imaging one by one to realize trajectory tracking of single-combination events, and extract the dynamic parameters of the trajectory of each single-combination event; according to the motion characteristics of different events, the events are divided into specific combination events and non-specific interference events; and the range of dynamic parameters corresponding to specific combination events and non-specific interference events is determined. S5) Digitally count the target biological sample: The target biological sample binds to the second specific binder labeled with the signal probe, and then reacts with the detection substrate immobilized with the first specific binder; the surface of the detection substrate is dynamically and continuously imaged using an optical detection device; the image sequence obtained by imaging is analyzed one by one to realize the trajectory tracking of single binding events, and a unique number is assigned to each single binding event; the kinetic parameters of each single binding event are extracted, and specific binding events and non-specific interference events are divided according to the standard in step S4), non-specific interference events are excluded, and specific binding events are counted; S6) Substitute the specific binding event counts obtained in step S5) into the pre-established standard curve to calculate the concentration of the target biological sample. The method for establishing the standard curve of the target biological is as follows: Prepare a series of target biological standard solutions with known concentrations; for each concentration of standard, count the specific binding events according to step S5), and use the count results as the vertical axis and the corresponding known concentration of the target biological standard as the horizontal axis, and perform linear regression fitting to obtain the standard curve.

[0010] Furthermore, the dynamic continuous imaging described in step S3) adopts a time-resolved acquisition mode, and continuously acquires image sequences through a high-speed imaging device.

[0011] Furthermore, in step S3), the optical detection device comprises a detection substrate, an imaging device, a recording device, and a light source.

[0012] Furthermore, the light source in step S3) is selected from broadband LEDs, multi-wavelength lasers, or supercontinuum light sources.

[0013] Furthermore, the imaging device in step S3) is selected from a transmission or reflection inverted microscopy device equipped with a low numerical aperture objective lens (NA value of 0.3-0.65).

[0014] Furthermore, the recording device in step S3) is selected from optical image recording devices capable of achieving a single frame acquisition time of ≤10ms and a frame rate of ≥100 frames per second, such as a high-speed CMOS camera or a high-speed CCD camera.

[0015] Furthermore, in step S3), the imaging device is configured to clearly present the depth of focus range of the detection substrate surface.

[0016] Furthermore, the optical image recording device described in step S3) adopts a bright field imaging mode, continuously acquires image sequences at a frame rate of 100-1000 frames per second (fps), a single frame image resolution of 1920×1080 pixels or higher, and an exposure time of 1 ms-10 ms.

[0017] Furthermore, in step S4), the trajectory tracking uses a particle tracking algorithm to track the signal probes in the image sequence.

[0018] Furthermore, particle tracking algorithms employ ImageJ's Trackmate plugin, TrackPy algorithm, or custom deep learning tracking models.

[0019] Furthermore, the method for tracking the trajectory of a single-linked event by analyzing the image sequences obtained from imaging is as follows: the Trackmate plugin is used to identify single-linked events in different image sequences and match single-linked events in adjacent image sequences. The maximum inter-frame displacement rule is set to determine whether particles in different image sequences belong to the same single-linked event. By associating the same single-linked event in different image sequences, the motion trajectory of the single-linked event in the time dimension is obtained.

[0020] Furthermore, the maximum inter-frame displacement rule refers to the rule that when the displacement of a single-linked event in two consecutive image sequences is lower than the maximum inter-frame displacement, the single-linked events in the two adjacent image sequences are considered to be the same single-linked event.

[0021] Furthermore, the maximum inter-frame displacement is set to be 0.1-1 times the diameter of the signal probe.

[0022] Furthermore, based on the single-combined event trajectory data obtained above, the dynamic parameters of each single-combined event trajectory are extracted.

[0023] The kinetic parameters include kinematic parameters, diffusion characteristic parameters, and binding / dissociation kinetic parameters; the kinetic parameters are selected from displacement, velocity, and mean square displacement, and the velocity further includes instantaneous velocity and average velocity; the diffusion characteristic parameters are selected from diffusion coefficient and diffusion exponent; the binding / dissociation kinetic parameters are selected from residence time, binding initiation time, dissociation rate constant, and binding rate constant.

[0024] Furthermore, the single-binding event is classified into a specific binding event or a non-specific interference event based on the properties and state of the physical entity that generates the signal.

[0025] Furthermore, the different events in step S4) include: i) A free-moving signal probe, wherein the free-moving signal probe is a second specific binding agent labeled with a signal probe that has not bound to a target molecule; ii) Adsorption signal probe, wherein the adsorption signal probe is a signal probe that is adsorbed onto the surface of the detection substrate due to non-specific effects; iii) Detection signal probe, wherein the adsorption signal probe is a ternary complex formed on the surface of the substrate by contacting and reacting the target molecule with a second specific binder carrying the signal probe and a first specific binder fixed on the substrate respectively; Among them, the free-movement signal probe and the adsorption signal probe are non-specific interference events, while the adsorption signal probe is a specific binding event.

[0026] Furthermore, the motion characteristics of different events in step S4) are as follows: i) Free-moving signal probe: Brownian motion, and the trajectory coverage is not limited; ii) Adsorption signal probe: stationary; iii) Detection signal probe: Brownian motion, and the trajectory coverage is limited.

[0027] Furthermore, the method for determining the range of kinetic parameters corresponding to specific binding events and non-specific interference events in step S4) is as follows: A control experiment without biological target samples was conducted. Based on the observed motion characteristics, different events were classified into free motion signal probes and adsorption signal probes. Based on the extracted kinetic parameters of different events, the kinetic parameters of different events were analyzed to obtain the kinetic parameter characteristics and thresholds of non-specific interference events. In experiments involving biological target samples, kinetic parameters of different events are obtained. Based on the characteristics and thresholds of the kinetic parameters of non-specific interference events, non-specific interference events are excluded, and the remaining events are specific binding events. The kinetic parameters of the events identified as specific binding events are extracted and analyzed to obtain the characteristics and thresholds of the kinetic parameters of specific binding events.

[0028] The control test without biological target sample mentioned above refers to the test in which the second specific binder of the target biological sample labeled with the signal probe is directly contacted with the detection substrate.

[0029] The experiment involving biological target samples involves binding the target biological sample with a second specific binder of the target biological sample labeled with a signal probe to form a complex, which is then reacted with the detection substrate, and the experiment is conducted.

[0030] Furthermore, when using metal nanoparticles as signal probes, the three types of signal probes are classified based on motion characteristics: events whose motion trajectories exhibit unrestricted Brownian motion and whose dwell time is shorter than a first threshold are classified as free motion signal probes; events whose motion trajectories exhibit stationary motion and whose dwell time is longer than a second threshold are classified as adsorption signal probes; and events whose motion trajectories exhibit restricted Brownian motion and whose dwell time is between the first and second thresholds are classified as detection signal probes.

[0031] The term "restricted" refers to Brownian motion that has a range of motion but is confined to a local area and is subject to regular movement.

[0032] Furthermore, the first threshold and the second threshold are determined by: performing detection under conditions where no target molecules are present, obtaining the statistical distribution of the residence time of the freely moving signal probe, the residence time of the adsorbed signal probe, and the tracking start time; setting the first threshold as the high percentile of the distribution of the residence time of the freely moving signal probe and the tracking start time of the adsorbed signal probe; and setting the second threshold as the low percentile of the distribution of the residence time of the adsorbed signal probe.

[0033] Furthermore, the high percentile is a percentile between 90% and 99%, and the low percentile is a percentile between 1% and 10%.

[0034] Furthermore, when determining the first and second thresholds, multidimensional dynamic parameters extracted from each single-linked event trajectory are used as features. Through machine learning or deep learning models, automatic and accurate classification of event types and identification of background noise are achieved. The deep learning models include Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).

[0035] Further, in step S5), the observed single binding events are compared with the aforementioned threshold range. Events that meet the characteristics of the detection signal probe parameters are determined to be specific binding events; while events that meet the characteristics of the free-moving signal probe or the adsorption signal probe parameters are determined to be non-specific interference events and are excluded. This allows for the digital counting of specific binding events without the need for a washing step.

[0036] Furthermore, in step S6), events identified as specific binding events are uniquely numbered and digitally counted, their quantity is statistically analyzed, and a quantitative correspondence is established with the target concentration.

[0037] Furthermore, in step S6), the concentration of the standard used to establish the standard curve covers a range from 0.1 pg / mL to 10 ng / mL.

[0038] Furthermore, in step S1), the functionalized capture interface of the detection substrate is disposed on the surface of the carrier, and the carrier is selected from inorganic material carriers, polymer carriers, metal carriers, porous membrane carriers, and optical enhancement carriers.

[0039] Furthermore, in step S1), the inorganic material carrier is selected from glass, quartz, and silicon wafer, and the surface is modified by silanization to fix the captured molecules; The polymer substrate is selected from polystyrene, polymethyl methacrylate, and polydimethylsiloxane, and its surface is modified by physical adsorption or chemical coupling. The metal substrate is selected from gold film and silver film; The porous membrane material is selected from nitrocellulose membranes; The optical enhancement carrier is a carrier with a microlens array.

[0040] Further, in step S1), the material of the microlens array in the optical enhancement carrier is selected from one or more combinations of barium titanate glass (BTG), titanium dioxide, polystyrene, melamine, polymethyl methacrylate, silicon, quartz, zirconium oxide, metal core-shell structure, liquid crystal elastomer, or other micro / nano structures with light field focusing and enhancement capabilities, such as photonic crystals, plasma nanostructure arrays, optical microcavity arrays, etc.

[0041] Furthermore, in step S1), the microlens array in the optical enhancement carrier consists of microlenses closely arranged on the substrate.

[0042] Furthermore, in step S1), a first specific binder is fixed on the surface of the microlens. The first specific binder can specifically bind to the target biological sample, and the binding site is different from the binding site of the second specific binder.

[0043] Further, in step S2), the signal probe is a nanoparticle or microparticle with optical signal characteristics that can be significantly enhanced (such as strong scattering or fluorescence), wherein the optical signal characteristics are strong scattering or fluorescence. The signal probe is selected from gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), copper nanoparticles (CuNPs), aluminum nanoparticles (AlNPs), fluorescent microspheres (such as polystyrene fluorescent microspheres), quantum dots (such as CdSe / ZnS), upconversion nanoparticles, Raman-active nanoparticles, and alloys or composite nanoparticles of the above materials (such as Au-Ag alloys, Au-Cu alloys, etc.).

[0044] Furthermore, the first and second specific binders are reagents capable of specifically binding to the target biological sample, and the first and second specific binders can bind to the target organism simultaneously. The first and second specific binders are selected from one or more of antibodies, nucleic acid probes, receptor-ligand pairs, enzyme-substrate pairs, or multivalent assembly binders; preferably, the antibody is a monoclonal or polyclonal antibody, the nucleic acid probe is a DNA or RNA probe, and the receptor-ligand pair includes antigen-antibody, hormone-receptor, or glycosyl-lectin.

[0045] Furthermore, in step S2), the target biological sample is selected from proteins, nucleic acids (DNA, RNA), exosomes, viral particles, bacteria, small molecule compounds, etc.

[0046] Another aspect of the present invention provides a detection and analysis apparatus for the above-described detection method, comprising: A detection substrate, the detection substrate including a functionalized capture interface; An imaging module is used to dynamically image the surface of the detection substrate and acquire image sequences; The analysis and processing module is used to perform trajectory tracking of single binding events, classification and discrimination based on dynamic parameters, digital counting of specific binding events, and quantitative analysis of the biological targets to be tested on the image sequence.

[0047] Furthermore, the detection substrate includes a microlens array structure, and a functionalized capture interface is present on one side surface of the microlens array, wherein the capture interface contains a first specific binder capable of specifically binding to the target biological sample. Furthermore, the detection substrate is the same as the detection substrate obtained in S1) of the above detection method; Furthermore, the imaging module is capable of performing continuous imaging in step S3) of the above detection method.

[0048] Furthermore, the analysis and processing module can perform the analysis and calculation processing in steps S4)-S6) of the above detection method.

[0049] Beneficial effects: This invention enables the description of single binding events in the time dimension, capturing the trajectory of single binding events (such as residence time, displacement, diffusion coefficient, and other kinetic parameters) at millisecond-level temporal resolution. Based on these parameters, it accurately distinguishes specific binding events from non-specific interference events (such as random diffusion of free particles and retention of adsorbed particles), avoiding the misjudgment problem caused by traditional methods that rely solely on signal intensity or presence. This improvement significantly enhances the sensitivity (effectively eliminating background noise and enabling the detection of lower concentrations of target molecules), speed (real-time dynamic monitoring without waiting for reaction equilibrium), and accuracy (multi-dimensional discrimination based on kinetics). At the same time, it expands the application potential of this method in complex biological systems (such as low-abundance proteins and dynamic interaction processes).

[0050] This invention significantly improves the signal-to-noise ratio by using a substrate with an optical enhancement carrier to greatly focus and efficiently collect weak optical signals from a single signal probe. It can directly perform real-time tracking, dynamic analysis, and digital counting of single binding events, thereby fundamentally improving detection sensitivity. This invention effectively distinguishes between specific binding events and non-specific background noise by dynamically analyzing the kinetic characteristics of single binding events and establishing criteria, significantly improving the specificity and accuracy of identifying trace target molecules in complex matrices. By utilizing the output digital signal, it avoids the errors caused by traditional population signal averaging, thus improving detection sensitivity and accuracy.

[0051] This invention uses single binding events as the statistical unit, and the concentration of the target molecule is directly proportional to the number of observed effective binding events. Through real-time monitoring, rapid and ultrasensitive detection is achieved.

[0052] This invention uses a signal probe as a labeling probe, eliminating the need for complex chemical signal amplification, multi-step incubation, and cleaning steps. The entire detection process can be simplified into a one-step operation, making it suitable for rapid judgment and dynamic monitoring of target analyte levels in acute events.

[0053] Optical inspection platforms built on optically enhanced substrates can be directly integrated with conventional inverted microscopy imaging equipment without the need for high-cost optical systems. They are easy to miniaturize and integrate, making them suitable for developing portable, low-cost POCT devices. The principle and platform of this invention based on single binding event dynamics analysis are universal and suitable for the detection of various biological or chemical targets such as proteins, protein biomarkers, nucleic acids, exosomes, pathogens, and small molecules, and have broad application expansion capabilities.

[0054] This invention can also optimize the model structure to achieve real-time or near real-time data processing and result output, greatly improving analysis efficiency and better adapting to point-of-care testing (POCT) and other instant detection application scenarios that require rapid results. Attached Figure Description

[0055] Figure 1 This is a flowchart of the detection method of the present invention; Figure 2 This is a schematic top view of the optical enhancement substrate of the present invention; Figure 3 This is a schematic front view of the optical enhancement substrate of the present invention; Figure 4 This is a schematic diagram of a ternary complex consisting of a "first specific binder - target biological standard sample - signal probe-labeled second specific binder" formed on the surface of an optical substrate in some embodiments of the present invention. Figure 5 This is a schematic diagram of an optical detection device in some embodiments of the present invention; Figure 6 This is a schematic diagram illustrating the association and tracking of event trajectories in some embodiments of the present invention. Figure 7 These are typical schematic diagrams of three signal probes in some embodiments of the present invention. Wherein, Figure 7 (a) is a freely moving signal probe; Figure 7 (b) is an adsorption signal probe; Figure 7 (c) is a probe for detecting signals; Figure 8 This is a statistical analysis of the dwell time of a freely moving signal probe in some embodiments of the present invention; Figure 9 In some embodiments of the present invention, the tracking start time of the adsorption signal probe is statistically analyzed; Figure 10 This is a statistical analysis of the dwell time of three signal probes in some embodiments of the present invention; Figure 11 This is a schematic diagram of the steps involved in the fabrication of the microlens array in some embodiments of the present invention; Figure 12 In some embodiments of the present invention, dynamic digital counting (a) and standard curve (b) at different cTnI concentrations are shown. The annotations in the attached figures are explained as follows: 1: Microlens; 2: Fixation layer; 3: Microlens array region; 4: First specific binder; 5: Substrate; 6: Second specific binder; 7: Signal probe; 8: Target biological standard sample; 9: Ternary complex; 10: Optical enhancement substrate; 11: Imaging system; 12: Objective lens; 13: Beam splitter; 14: Imaging optical path; 15: Detection unit; 16: Illumination system; 17: Free-moving signal probe; 18: Adsorption signal probe; 19: Detection signal probe. Detailed Implementation

[0056] The following detailed description of the present invention through specific embodiments illustrates the preferred aspects of the invention. It should be noted that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are followed. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially. The following detailed description of the preferred features and performance of the present invention, in conjunction with the embodiments, provides further details.

[0057] Currently, driven by various needs such as disease diagnosis and scientific research, the demand for concentration detection of biological samples, especially trace concentration detection, is increasing daily. Existing biomolecular detection technologies (such as commonly used methods like ELISA and CLIA) generally suffer from insufficient sensitivity, with most detection limits only reaching the ng / mL level, making it difficult to capture trace target molecules. Secondly, existing protocols lack specificity; complex sample matrices can easily lead to non-specific binding, resulting in background noise, causing result bias or false positives. Furthermore, they are cumbersome, time-consuming, and instrument-dependent, often requiring multiple incubation and washing steps and high-end equipment, making them unsuitable for POCT and primary healthcare scenarios. Although some detection methods based on single-molecule detection technologies have emerged, current single-molecule detection technologies mostly employ "endpoint methods," lacking dynamic analysis capabilities, exhibiting low quality transfer efficiency, large differences in probe binding efficiency, and high equipment costs and operational barriers, hindering large-scale promotion.

[0058] To address the aforementioned issues, this invention provides a dynamic single-binding event detection method based on an optically enhanced substrate, which significantly improves the detection limit and accuracy, while also extending the method to achieve good results in complex biological systems (such as low-abundance proteins and dynamic interaction processes).

[0059] The detection method of this invention is universal and can detect various types of biological samples, such as proteins, nucleic acids (DNA, RNA), exosomes, viral particles, bacteria, and small molecule compounds. Based on different target molecules, it can be applied in multiple fields such as medicine, testing, environmental monitoring, food safety, and basic research.

[0060] like Figure 1 The flowchart shown illustrates that the detection method provided by this invention mainly includes the following specific steps: S1) Provides an optical enhancement substrate, the structural schematic of which is shown in the figure. Figure 2 and Figure 3 As shown. The optical enhancement substrate includes a microlens array region 3 composed of multiple microlenses 1, which is placed on the substrate 5; and a fixing layer 2, which covers the surface of the substrate 5 and isolates and fixes the microlenses 1 in the array from each other. One side surface of the microlens array region 3 has a functionalized capture interface, which contains a first specific binder 4 capable of specifically binding to the target biological sample; The technical solution of this invention can only be realized based on the optical enhancement substrate described in this invention. The optical enhancement substrate effectively enhances the optical signal (such as backscattering) from a single signal probe, generating a sufficient number of detectable photons and a high signal-to-noise ratio (SNR), enabling imaging resolution exceeding the diffraction limit and high time-resolved single-particle tracking under conventional bright-field microscope optical paths.

[0061] In some specific implementations, the microlens material may be selected from barium titanate, polystyrene, arsenic trisulfide, arsenic triselenide, lanthanide optical glass, barium oxide, silicon oxide, titanium oxide, zinc oxide, or a mixture of two or more of barium oxide, silicon oxide, titanium oxide, and zinc oxide, or composed of such materials.

[0062] In some specific implementations, the microlenses are transparent.

[0063] In some specific implementations, the microlens is a microsphere lens, a spherical microlens, a spherical microlens, a hemispherical microlens, a cylindrical lens, or an aspherical microlens, such as an ellipsoidal microlens, a parabolic microlens, or a hyperboloidal microlens.

[0064] In some specific embodiments, the diameter of the microlens is 3 to 400 micrometers, preferably 20 to 100 micrometers, more preferably 40 to 70 micrometers, even more preferably 45 to 60 micrometers, even more preferably 50 to 55 micrometers, and even more preferably 52 micrometers.

[0065] In some specific embodiments, the refractive index of the microlens is 1.65 to 2.20, preferably 1.80 to 2.10, more preferably 1.90 to 2.00, even more preferably 1.91 to 1.93, and even more preferably 1.92.

[0066] In some specific embodiments, the number of microlenses in the microlens array is greater than or equal to 1, preferably greater than or equal to 10, more preferably greater than or equal to 100, and even more preferably 10. 2 Up to 10 8 Between, more preferably 10 3 Up to 10 6 between.

[0067] In some specific implementations, the number of microlens array regions on the optical enhancement substrate is greater than or equal to 1, preferably 4.

[0068] In some specific implementation schemes, the modified microlens array or chip provided by Chinese Patent CN 118169787 A is used, and the entire contents of this patent are incorporated into the content of this invention.

[0069] In some specific implementations, the first specific binder is one or more of an antibody, a nucleic acid probe, a receptor-ligand pair, an enzyme-substrate pair, or a multivalent assembly binder.

[0070] S2) Obtain the second specific binder 6 of the target biological sample labeled with the signal probe 7, and bind it to the target biological standard sample 8. Then, react with the optical enhancement substrate immobilized with the first specific binder 4 to form a ternary complex 9 on the surface of the optical enhancement substrate, consisting of the first specific binder, the target biological standard sample, and the second specific binder labeled with the signal probe. Figure 4 As shown.

[0071] The number of binding events generated by the target-specific binding of the ternary complex formed on the substrate surface corresponds to the number of signal probe particles fixed on the substrate surface.

[0072] In some specific implementations, the second specific binder is one or more of an antibody, nucleic acid probe, receptor-ligand pair, enzyme-substrate pair, or multivalent assembly binder. The first and second specific binders can simultaneously bind specifically to the target biological sample.

[0073] In some specific implementations, the first specific binder and the second specific binder may be the same or different.

[0074] In some specific implementations, the signal probe is selected from micron or nanoparticles that have optical signal properties that can be significantly enhanced.

[0075] In some specific implementations, the signal probe is selected from gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), copper nanoparticles (CuNPs), aluminum nanoparticles (AlNPs), fluorescent microspheres (such as polystyrene fluorescent microspheres), quantum dots (such as CdSe / ZnS), upconversion nanoparticles, Raman-active nanoparticles, and alloys or composite nanoparticles of the above materials (such as Au-Ag alloys, Au-Cu alloys).

[0076] In some specific implementations, the signal probe can be connected to the second specific binder in a specific covalent or non-covalent manner, and the two can be directly connected or connected through a linker.

[0077] In some specific implementations, the second specific binder of the target biological sample labeled by the signal probe binds to the sample on the target biological surface by co-incubating the two and forming a specifically bound complex.

[0078] S3) Dynamic continuous imaging of the optically enhanced substrate surface is performed using an optical detection device, and the optical signal of a single signal probe is enhanced by the microlens array structure to achieve clear detection of a single binding event.

[0079] The optical detection device mainly consists of the aforementioned optical enhancement substrate 10, imaging system 11, detection unit 15, and illumination system 16, as shown below. Figure 5 As shown.

[0080] The optically enhanced substrate plays a dual role in the imaging process: First, light field focusing and enhancement: the microlens structure can effectively focus the incident light onto the sample surface, thereby significantly enhancing the scattering signal of the plasmonic nanoparticles (such as AuNPs, AgNPs, etc.) bound to the chip surface.

[0081] Second, backscattered signal amplification: The microlens geometry and refractive index design optimize the collection efficiency of backscattered light, generating a sufficient number of detectable photons, so that even under low NA objective conditions, a high signal-to-noise ratio (SNR) image signal can still be obtained.

[0082] Thanks to the effective enhancement of local optical signals by the optically enhanced substrate, this invention can break through the diffraction limit of conventional imaging under the conventional bright-field microscope optical path conditions, achieve high spatial resolution (e.g., better than 200 nm) single-particle positioning accuracy, and achieve high temporal resolution (e.g., millisecond-level) single-particle dynamic tracking by combining high-speed imaging.

[0083] The imaging system 11 includes a low numerical aperture objective lens 12 and associated optical path elements (such as a beam splitter 13). In some specific embodiments, the imaging system may be a transmission or reflection inverted microscopy imaging device configured with a low numerical aperture objective lens.

[0084] The detection unit 15 is a recording device used to record the image displayed by the imaging device and fix the image in the time dimension, that is, it can record continuous images, such as a high-speed CMOS camera, a high-speed CCD camera, or an optical image recording device with a frame rate of ≥100 frames per second, etc.

[0085] The lighting system 16 consists of a high-performance light source, which may be a broadband LED or a laser.

[0086] The optical detection device does not require complex optical paths, high numerical aperture objectives, or cumbersome image processing, thus greatly simplifying the optical device and image processing process. In some specific implementations, the NA value of low numerical aperture objectives is 0.3-0.65.

[0087] In some specific implementations, the recording device adopts a bright-field imaging mode, continuously acquires image sequences at a frame rate of 100-1000 frames per second (fps), a single frame image resolution of 1920×1080 pixels or higher, and an exposure time of 1ms-10ms.

[0088] S4) Analyze the image sequence obtained by imaging one by one to realize trajectory tracking of single-combination events, and extract the dynamic parameters of the trajectory of each single-combination event; according to the motion characteristics reflected by the dynamic parameters, the events are divided into specific combination events and non-specific interference events. S41) Trajectory Association and Tracking: This involves cross-frame association of single-linked events identified in an image sequence to obtain a complete motion trajectory and match it with a unique identifier, such as... Figure 6As shown. This process can be implemented using known models or target tracking algorithms in existing technologies. For example, the Trackmate plugin can be used to identify single-linked events in different image sequences and match single-linked events in adjacent image sequences. By setting a maximum inter-frame displacement rule, it can be determined whether particles with similar spatial positions in adjacent images belong to the same single-linked event. By associating the spatial positions of the same single-linked event in different image sequences, the complete motion trajectory of the single-linked event can be obtained, and a unique number can be assigned to it. The maximum inter-frame displacement rule means that if the displacement of a single-linked event in two adjacent image sequences is lower than the maximum inter-frame displacement, the single-linked events in the two adjacent image sequences are considered to be the same single-linked event.

[0089] In some specific implementations, the maximum inter-frame displacement can be set to a physical scale equivalent to the diameter of the signal probe to ensure the accuracy of the correlation. In some specific implementations, the physical scale equivalent to the diameter of the signal probe is 0.1 to 1 times the probe diameter.

[0090] S42) Dynamic Parameter Extraction: Through trajectory tracking in step S41 above, the motion trajectory of each individual combined event in the time dimension can be obtained. Based on this trajectory data, dynamic parameters that quantify its dynamic behavior can be further extracted.

[0091] The kinetic parameters include, but are not limited to: kinematic parameters such as velocity (instantaneous velocity / average velocity), displacement, and mean square displacement; diffusion characteristic parameters such as diffusion coefficient and diffusion index; and binding / dissociation kinetic parameters such as residence time, binding initiation time, dissociation rate constant, and binding rate constant.

[0092] S43) Event classification based on motion features: Based on the dynamic parameters extracted in step S42, each single-association event observed in the image sequence is classified, such as... Figure 7 As shown, they can be divided into the following three categories: Free-moving signal probe 17: a second specific binder labeled with a signal probe that has not bound to the target molecule; Adsorption signal probe 18: that is, the signal probe is adsorbed on the surface of the optically enhanced substrate due to non-specific effects.

[0093] Detection signal probe 19: a ternary complex formed on the surface of the substrate by the target molecule reacting with a second specific binder carrying the signal probe and a first specific binder fixed on the substrate. The detection signal probe 19 is a specific binding event, while the free motion signal probe 17 and the adsorption signal probe 18 are non-specific interference events.

[0094] Different types of signal probes exhibit significant differences in motion characteristics due to the different properties and states of their physical entities.

[0095] like Figure 7 As shown in (a), the free-moving probe 17, being unfixed, exhibits typical Brownian motion in the solution, with a large trajectory coverage, spanning approximately 2 μm in the two-dimensional plane (x and y directions). Its trajectory crosses the focal plane of the imaging, resulting in the shortest dwell time for a single trajectory in the image sequence, typically appearing only in a small number of consecutive frames. This characteristic allows it to be clearly distinguished from specific binding and non-specific adsorption events with longer dwell times.

[0096] like Figure 7 As shown in (b), the adsorption signal probe 18 is fixed on the optical enhancement substrate due to non-specific adsorption (such as strong electrostatic interaction), its movement is extremely restricted, its trajectory is close to stationary, its displacement or velocity is lower than expected by Brownian motion, and its residence time is relatively long.

[0097] like Figure 7 As shown in (c), the detection signal probe 19 is specifically bound to the surface of the optically enhanced substrate, and the motion characteristics of its trajectory are between the two mentioned above, usually exhibiting restricted Brownian motion, while the dwell time is consistent with the expected specific binding.

[0098] Based on the motion characteristics of the three types of signal probes mentioned above, motion characteristic analysis was first performed on all uniquely numbered single binding events, and each single binding event was then classified into one of the three types of signal probes. Then, the dynamic parameters of different categories of probes were summarized and statistically analyzed to effectively distinguish between specific binding events and non-specific interference events.

[0099] In a specific implementation scheme using gold nanoparticles as signal probes, in a control experiment without a biological target sample, all observed events can be classified into freely moving signal probes and adsorbed signal probes based on their motion characteristics. For example... Figure 8 As shown, statistical analysis of 520 particles identified as freely moving signal probes revealed that their dwell time (or trajectory duration) was mainly within 250 milliseconds; their motion trajectories covered an area of ​​approximately 2 μm in the two-dimensional plane (x and y directions), and their mean square displacement-time curves conformed to the characteristics of free Brownian motion. Figure 9 As shown, analysis of 100 particles identified as adsorption signal probes revealed that approximately 96% of the particles adsorbed onto the surface of the optically reinforced substrate within 2 seconds of the start of tracking; and their mobility was extremely restricted, exhibiting an apparent diffusion coefficient close to zero and a near-stationary trajectory.

[0100] By quantitatively extracting the dynamic parameters (such as residence time) of the above three signal probes, Figure 10 This allows for the establishment of discrimination thresholds to distinguish between specific binding events and non-specific events. Subsequent experiments involving the target will exclude events that meet these non-specific characteristics, thereby significantly improving the specificity and accuracy of the detection.

[0101] In a preferred embodiment, the first threshold for the dwell time is set to 2 seconds, and the second threshold is set to 20 minutes.

[0102] In some specific implementation schemes, when determining the first and second thresholds, multidimensional dynamic parameters extracted from each single-event trajectory are further used as features. Machine learning or deep learning models are then used to achieve automatic and accurate classification of event types and identification of background noise. These deep learning models include Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Using machine learning or deep learning models avoids dependence on fixed thresholds.

[0103] S5) Digitally count the target biological sample: The target biological sample binds to the second specific binder labeled with the signal probe, and then reacts with the optically enhanced substrate immobilized with the first specific binder, thereby forming a complex on the surface of the substrate; the surface of the optically enhanced substrate is dynamically and continuously imaged using an optical detection device; the image sequence obtained by imaging is analyzed one by one to realize the trajectory tracking of single binding events, and a unique number is assigned to each single binding event; the kinetic parameters of each single binding event are extracted, and specific binding events and non-specific interference events are divided according to the standard in step S4), non-specific interference events are excluded, and specific binding events are digitally counted; S6) Substitute the specific binding event counts obtained in step S5) into the pre-established standard curve to calculate the concentration of the target biological sample. The method for establishing the target biological standard curve is as follows: Prepare a series of target biological standard solutions with known concentrations, covering the expected detection range. For each concentration of standard, count the specific binding events according to step S5), and use the count results as the ordinate and the corresponding known concentration of the target biological standard as the abscissa. Perform linear regression fitting to obtain the standard curve.

[0104] The above detection method uses an optical detection and analysis device, which has a simple structure and is easy to integrate. It includes an optical enhancement substrate, an imaging recording module, and an analysis and processing module. i) An optically enhanced substrate comprising a microlens array structure, wherein a functionalized capture interface is present on one side surface of the microlens array, the capture interface containing a first specific binder capable of specifically binding to the target biological sample. Its core function is to significantly enhance the optical signal of a single signal probe.

[0105] ii) An imaging recording module, used to dynamically image the surface of the optically enhanced substrate and acquire image sequences; The imaging recording module consists of an imaging system, a detection unit, and an illumination system. It can perform the dynamic continuous imaging function described in step S3) to acquire image sequences with high temporal resolution and high signal-to-noise ratio.

[0106] iii) An analysis and processing module, used to perform trajectory tracking of single binding events, classification and discrimination based on kinetic parameters, digital counting of specific binding events, and quantitative analysis of the target biological markers in the image sequence. This module can realize the trajectory analysis, event discrimination, digital counting, and quantitative calculation functions described in steps S4-S6.

[0107] Example 1. Construction of an optically enhanced substrate for cardiac troponin I (cTnI) detection based on single binding event analysis. 1.1 Fabrication of Microlens Array like Figure 11 The diagram shows the steps involved in fabricating a microlens array. The main steps include the following detailed steps: A regularly arranged array of micropillars, whose size matches that of the microlenses, is fabricated on a silicon substrate using photolithography. Subsequently, an uncured flexible material (preferably polydimethylsiloxane, PDMS) is spin-coated onto its surface, and a flexible layer is formed after heating and curing. The solidified flexible layer is peeled off from the surface of the micropillar array and transferred to a glass substrate to obtain a flexible positioning layer with a regularly arranged micro-pit structure. Microlenses with a particle size range of 5 μm-100 μm are filled into the above-mentioned pits. With the help of the size limitation effect of the pits and the gravity settling of the microlenses themselves, the bottom edge of the microlenses is made to fit the bottom surface of the pits, forming an initial coplanar microlens array. Another layer of uncured flexible coating material is spin-coated onto the surface of the array of microlenses. After heating and curing, a fixing layer is formed to wrap and stabilize the arrangement of the microlenses. Subsequently, the flexible composite layer containing the microlens array (consisting of a flexible positioning layer and a fixing layer) was peeled off from the glass substrate, and the exposed surface of the fixing layer was subjected to plasma treatment. Its structure was then flipped to bond the treated fixing layer to a new glass substrate. Finally, by removing the flexible positioning layer that serves as a temporary support, a structurally stable microlens array can be obtained.

[0108] A thin layer of flexible or shaped material is spin-coated onto the surface of the microlens array to form a semi-encapsulated state, thereby eliminating optical interference at the air interface and improving the imaging signal-to-noise ratio.

[0109] The method described in this invention, through the coplanarity maintenance technology during the micro-dimple-guided positioning and flipping transfer process, combined with the flexible material encapsulation and fixation strategy, successfully fabricated a microlens array chip with regular array arrangement, consistent focal plane height, structural stability, and good reproducibility.

[0110] 1.2 Surface Functionalization of Microlenses The surface of the fabricated microlens array chip was cleaned with a plasma cleaner for several minutes.

[0111] The microlens chip was immersed in a solution of 3-aminopropyltriethoxysilane (APTES), reacted at room temperature for several minutes, rinsed with anhydrous ethanol, and dried under nitrogen; then incubated at a certain temperature for several hours. The microlens chip was cooled to room temperature and immersed in glutaraldehyde (GA) solution, reacting at room temperature for several hours. It was then rinsed with phosphate-buffered saline (PBS) and dried under nitrogen. A capture antibody specific to cardiac troponin I was dropped onto the microlens array chip described above. After reacting at room temperature for several hours, the chip was rinsed with deionized water and dried with nitrogen. Unreacted active sites were blocked with casein solution, incubated at room temperature, and then rinsed with deionized water to obtain the chip sensing interface.

[0112] The optical enhancement substrate constructed in this invention will play a dual role in the subsequent imaging process: First, light field focusing and enhancement: the microlens structure can effectively focus the incident light onto the sample surface, thereby significantly enhancing the scattering signal of the plasmonic nanoparticles (such as AuNPs, AgNPs, etc.) bound to the chip surface.

[0113] Second, backscattered signal amplification: The microlens geometry and refractive index design optimize the collection efficiency of backscattered light, generating a sufficient number of detectable photons, so that even under low NA objective conditions, a high signal-to-noise ratio (SNR) image signal can still be obtained.

[0114] Example 2. A method for detecting cardiac troponin I (cTnI) based on dynamic single binding event analysis. 1. Preparation of antibodies labeled with gold nanoparticles (AuNPs) An aqueous solution of gold nanoparticles (AuNPs) was mixed with a 100-5000 Da polyethylene glycol binder (PEG-1) and reacted at room temperature for several minutes. Add the specific detection antibody for cardiac troponin I to the above solution and incubate. Add PEG blocking agent (PEG-2), which has a smaller molecular weight than polyethylene glycol linker PEG-1, and react at room temperature for several minutes.

[0115] The solution after reaction was centrifuged and washed three times, then resuspended in an aqueous solution to obtain the AuNPs-labeled antibody.

[0116] 2. Specific sandwich immune recognition response The test sample solution containing cTnI was mixed with the AuNPs-labeled antibody solution and reacted at room temperature for several minutes to form a cTnI-AuNPs-labeled antibody immune complex. The above mixed solution was dropped onto the microlens chip sensing interface obtained in Example 1. As the reaction proceeded, the cTnI-AuNPs-labeled antibody immune complex in the solution specifically bound to the capture antibody immobilized on the chip sensing interface, forming an "AuNPs-labeled antibody-cTnI-capture antibody" sandwich immunoconjugate.

[0117] In the aforementioned immune response, plasma nanoparticles (AuNPs) serve as signal tags, and the dynamic binding of AuNPs to the chip sensing interface is directly related to the sandwich immune binding event formed by the target.

[0118] 3. Optical Detection and Imaging Turn on the LED white light source and adjust the light source power to 10mW so that the light shines perpendicularly on the chip surface; Adjust the microscope focus and observe the chip surface through a 40× / 0.55NA air objective until the focused area of ​​the microlens array is clearly visible in the field of view; Start the high-speed CMOS camera and set the imaging parameters: exposure time 2ms, frame rate 500fps. Continuously acquire a 20-minute bright-field image sequence (each frame image resolution 2560×2048 pixels), and save the image data to the computer in real time.

[0119] Thanks to the effective enhancement of local optical signals by the optically enhanced substrate, this invention can break through the diffraction limit of conventional imaging under the traditional bright-field microscope optical path conditions, achieve single-particle positioning accuracy with high spatial resolution better than 200 nm, and achieve high temporal resolution (e.g., millisecond level) single-particle dynamic tracking by combining high-speed imaging.

[0120] 4. Single-event analysis and judgment In the aforementioned microlens chip-assisted optical detection system, AuNPs are only captured when they diffuse or are incorporated into the depth of field of the microlens chip surface. The dynamic process of the captured AuNPs is recorded in real time by a CMOS camera.

[0121] The particle tracking algorithm is used to extract the two-dimensional trajectory coordinates of AuNPs captured in the image sequence, and to calculate parameters such as displacement, velocity, trajectory start and end time, and dwell time.

[0122] In particular, this invention uses Trackmate to track the location information of individual AuNPs in several image sequences for the analysis of binding and non-binding events.

[0123] Based on the motion trajectory and dynamic characteristics, AuNPs exhibit three representative motion modes: Free-moving signal probe: It undergoes high-speed Brownian motion in the solution and is briefly captured when it randomly diffuses near the chip surface, and then quickly dissociates, with a short residence time; Adsorption signal probe: Due to non-specific adsorption (mainly due to strong electrostatic interaction), the adsorption signal probe will quickly attach to the chip surface and is difficult to dissociate. It stays for a long time and remains almost stationary. Detection signal probe: The detection signal probe, which is considered to be a target positive, exhibits restricted Brownian motion, with small positional changes and a long dwell time.

[0124] Statistical analysis of 520 free particles revealed that their duration was primarily within 250 milliseconds. The free-motion signal probe exhibited random Brownian motion within a range of approximately 2 μm in both the x and y directions.

[0125] Then, without a target, 100 adsorption signal probes were tracked, and approximately 96% of the particles attached to the chip surface within 2 seconds.

[0126] Meanwhile, observations revealed that the slight distance fluctuations of the detection signal probe along the x and y directions were attributed to the tethering effect between the particles and the surface.

[0127] Three types of signal probe dynamics criteria were established based on the above detection results: Free particles: residence time < 250 ms, displacement in x / y direction > 2 μm, velocity > 1 μm / s; Adsorbed particles: residence time > 2s, x / y displacement < 0.1μm, velocity < 0.01μm / s; Particle detection: residence time 2s – T (detection time), displacement in the x / y direction 0.1-2μm, velocity 0.01-1μm / s.

[0128] In this invention, the free-moving signal probe and the adsorption signal probe are the main sources of non-specific background noise. Only the detection signal probe is considered as a single binding event of target-specific binding, which is used for subsequent counting and quantification.

[0129] 5. Digital counting and quantitative analysis A sequence of bright-field images captured over 20 minutes on the chip surface by AuNPs under a 40× low numerical aperture objective lens was continuously recorded and used for subsequent dynamic tracking and feature analysis of single particles.

[0130] Precise localization is achieved by comparing the positions of particles in adjacent frames: particles in adjacent frames mainly exist in three states: combined, displaced, and dissociated. Particles within a certain displacement range (a physical scale equivalent to the diameter of the signal probe, for example, equal to 0.1-1 times the probe diameter) are considered to be the same particle and assigned a consistent Track ID.

[0131] Single-binding event analysis and discrimination are performed on the same signal probe in an image sequence. Based on the differences in the motion trajectory and dynamic characteristics of the single probe, free particles, adsorbed particles, and detection particles in the image sequence can be effectively distinguished. Due to the short residence time of free particles (<250 ms), free, adsorbed, and detection signal probes in the image sequence can be effectively distinguished.

[0132] Specifically, the probe signals are classified using the criteria obtained in step 4 above.

[0133] Free particles: residence time < 250 ms, displacement in x / y direction > 2 μm, velocity > 1 μm / s; Adsorbed particles: residence time > 2s, x / y displacement < 0.1μm, velocity < 0.01μm / s; Particle detection: residence time 2s-20min, displacement in x / y direction 0.1-2μm, velocity 0.01-1μm / s.

[0134] After classifying the particles based on the above criteria, the identified free particles and adsorbed particles (non-specific background noise) are removed. Detected particles represent positive events that specifically bind to the target, and accurate counting of specific binding events is achieved by statistically analyzing all positive events.

[0135] like Figure 12 As shown in (a), tests were conducted at cTnI concentrations of 0 pg / mL (Blank), 5, 10, 100, 1000, and 10000 pg / mL. The specific binding events at different concentrations could be digitally counted in real time. A standard curve was plotted with the specific binding event count as the ordinate and the known concentration as the abscissa, as shown in (a). Figure 12As shown in (b). The results showed that, in the concentration range of 5 pg / mL to 10 ng / mL, the count and concentration exhibited a good linear relationship, with a linear correlation coefficient R² > 0.99.

[0136] Experiments have demonstrated that this method has an extremely low limit of detection (LOD) for cTnI, as low as 0.061 pg / mL, which is far below the clinical threshold for healthy individuals, indicating the potential of this invention in the early diagnosis of acute myocardial infarction (AMI).

[0137] The limit of detection (LOD) of this method is determined as follows: At least 10 independent assays are performed on a blank sample (excluding the target), and the number of events identified as specific binding events in each assay is recorded. The mean (μ_blank) and standard deviation (σ_blank) of these counts are calculated. The LOD is defined as μ_blank + 3 × σ_blank. Substituting this LOD count value into the standard curve yields the corresponding concentration detection limit.

[0138] The digital analysis method based on dynamic characteristics developed above not only improves the accuracy of distinguishing specific binding from non-specific background noise, but also significantly simplifies the experimental procedure.

Claims

1. A dynamic single-association event detection method based on a detection basis, characterized in that, It includes the following steps: S1) A detection substrate is provided, the detection substrate comprising a functionalized capture interface, the capture interface comprising a first specific binder capable of specifically binding to a target biological sample; S2) Obtain a second specific binder for the target biological sample labeled with the signal probe, bind it to the target biological standard sample, and then contact it with the detection substrate; S3) The detection substrate surface is dynamically and continuously imaged using a detection device; S4) Analyze the image sequence obtained by imaging one by one to realize trajectory tracking of single-combination events, and extract the dynamic parameters of the trajectory of each single-combination event; according to the motion characteristics of different events, the events are divided into specific combination events and non-specific interference events; and the range of dynamic parameters corresponding to specific combination events and non-specific interference events is determined. S5) Digitally count the target biological sample: The target biological sample binds to the second specific binder labeled with the signal probe, and then reacts with the detection substrate immobilized with the first specific binder; the detection device performs dynamic continuous imaging of the surface of the detection substrate; the image sequence obtained by imaging is analyzed one by one to realize the trajectory tracking of single binding events, and a unique number is assigned to each single binding event; the kinetic parameters of each single binding event are extracted, and specific binding events and non-specific interference events are divided according to the standard in step S4), non-specific interference events are excluded, and specific binding events are counted; S6) Substitute the specific binding event counts obtained in step S5) into the pre-established standard curve to calculate the concentration of the target biological sample. The method for establishing the standard curve of the target biological is as follows: Prepare a series of target biological standard solutions with known concentrations; for each concentration of standard, count the specific binding events according to step S5), and use the count results as the vertical axis and the corresponding known concentration of the target biological standard as the horizontal axis, and perform linear regression fitting to obtain the standard curve. Preferably, the target biological sample is selected from proteins, nucleic acids, exosomes, viral particles, bacteria, and small molecule compounds.

2. The dynamic single-combination event detection method according to claim 1, characterized in that, In step S4), the trajectory tracking uses a particle tracking algorithm to track the signal probes in the image sequence.

3. The dynamic single-associative event detection method according to claim 2, characterized in that, The particle tracking algorithm uses ImageJ's Trackmate plugin, TrackPy algorithm, or a custom deep learning tracking model; Preferably, the method for tracking the trajectory of a single-linked event by analyzing the image sequence obtained by imaging is as follows: the Trackmate plugin is used to identify single-linked events in different image sequences and match single-linked events in adjacent image sequences. The maximum inter-frame displacement rule is set to determine whether the particles in different image sequences are the same single-linked event. By associating the same single-linked event in different image sequences, the motion trajectory of the single-linked event in the time dimension is obtained. The maximum inter-frame displacement rule states that if the displacement of a single-linked event in two consecutive image sequences is lower than the maximum inter-frame displacement, the single-linked events in the two adjacent image sequences are considered to be the same single-linked event.

4. The dynamic single-associative event detection method according to claim 1, characterized in that, Based on the single-combined event trajectory tracking data obtained from S4, the dynamic parameters of each single-combined event trajectory are extracted; Preferably, the dynamic parameters include kinematic parameters, diffusion characteristic parameters, binding / dissociation dynamic parameters, or their corresponding relationships with time; the dynamic parameters are selected from displacement, velocity, and mean square displacement, and the velocity further includes instantaneous velocity and average velocity; The diffusion characteristic parameters are selected from the diffusion coefficient and diffusion index; the binding / dissociation kinetic parameters are selected from the residence time, binding initiation time, dissociation rate constant and binding rate constant.

5. The dynamic single-combination event detection method according to claim 1, characterized in that, The motion characteristics of different events in step S4) are as follows: i) Free-moving signal probe: Brownian motion, and the trajectory coverage is not limited; ii) Adsorption signal probe: stationary; iii) Detection signal probe: Brownian motion, and the trajectory coverage is limited. Preferably, the method for determining the range of kinetic parameters corresponding to specific binding events and non-specific interference events in step S4) is as follows: A control experiment without biological target samples was conducted. Based on the observed motion characteristics, different events were classified into free motion signal probes and adsorption signal probes. Based on the extracted kinetic parameters of different events, the kinetic parameters of different events were analyzed to obtain the kinetic parameter characteristics and thresholds of non-specific interference events. In experiments involving biological target samples, kinetic parameters of different events are obtained. Based on the characteristics and thresholds of the kinetic parameters of non-specific interference events, non-specific interference events are excluded, and the remaining events are specific binding events. The kinetic parameters of the events identified as specific binding events are extracted and analyzed to obtain the characteristics and thresholds of the kinetic parameters of specific binding events.

6. The dynamic single-associative event detection method according to claim 1, characterized in that, The method for determining the range of kinetic parameters corresponding to specific binding time and non-specific interference events in step S4) is as follows: Detection is performed under conditions where no target molecules are present, and the residence time of the free-moving signal probe and the residence time and tracking start time of the adsorbed signal probe are statistically distributed. A first threshold is set as the high percentile of the residence time distribution of the free-moving signal probe and the tracking start time distribution of the adsorbed signal probe. A second threshold is set as the low percentile of the residence time distribution of the adsorbed signal probe. Based on the obtained kinetic parameter thresholds, the three types of signal probes are classified as follows: events whose motion trajectories exhibit unrestricted Brownian motion and whose residence time is shorter than the first threshold are classified as free-moving signal probes; events whose motion trajectories exhibit extremely restricted, approximately static motion and whose residence time is longer than the second threshold are classified as adsorbed signal probes; events whose motion trajectories exhibit restricted Brownian motion, i.e., having a certain range of motion but confined to a local area, and whose residence time is between the first and second thresholds, are classified as detection signal probes. Preferably, the high percentile is between 90% and 99%, and the low percentile is between 1% and 10%.

7. The dynamic single-combination event detection method according to claim 1, characterized in that, In step S5), the observed single binding events are compared with the above threshold range; events that meet the characteristics of the detection signal probe parameters are determined to be specific binding events; while events that meet the characteristics of the free-moving signal probe or the adsorption signal probe parameters are determined to be non-specific interference events.

8. The dynamic single-combination event detection method according to claim 7, characterized in that, In step S6), events identified as specific binding events are uniquely numbered and digitally counted, and their quantity is statistically analyzed and a quantitative correspondence is established with the target concentration.

9. The dynamic single-combination event detection method according to claim 1, characterized in that, The dynamic continuous imaging described in step S3) adopts a time-resolved acquisition mode, which continuously acquires image sequences through a high-speed imaging device.

10. A detection and analysis apparatus for use in the detection method according to any one of claims 1-9, characterized in that, It includes: A detection substrate, the detection substrate including a functionalized capture interface; An imaging module is used to dynamically image the surface of the detection substrate and acquire image sequences; The analysis and processing module is used to perform trajectory tracking of single binding events, classification and discrimination based on dynamic parameters, digital counting of specific binding events, and quantitative analysis of the biological targets to be tested on the image sequence.

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