Biomarker detection method

Abstract

The invention provides a biomarker detection method which comprises the following steps: carrying out fluorescence labeling on a specific biomarker in a biological sample, and carrying out single-molecule fluorescence imaging on the biomarker to obtain multiple frames of original microscopic fluorescence images; and analyzing and processing the multiple frames of original microscopic fluorescence images so as to extract the super-resolution distribution characteristic information of the specific biomarker on the surface of the biological sample. The method comprises the following steps: processing an original microscopic fluorescence image to obtain positioning table data; rendering positioning points in the positioning table data to generate a super-resolution image; performing convolution operation, opening operation, convolution filtering and region segmentation on the super-resolution image to calculate the area of cells, the number of clusters and the area of a single cluster, determining the density of the clusters and the area ratio of the clusters by using the two, and calculating the closest distance between the clusters by using the center points of the clusters; and the accuracy of a biomarker detection result is greatly improved.

Description

[0001] This application is a divisional application of invention patent application 202411801780.X, which was filed on December 9, 2024, with application number 202411801780.X, and the invention title is "A Super-Resolution Imaging System and a Method for Detecting Biomarkers". Technical Field

[0002] This invention relates to the field of biomedical detection and image processing technology, and more specifically, to a method for detecting biomarkers. Background Technology

[0003] Alzheimer's disease (AD) is the most common form of dementia and is an irreversible neurodegenerative disease. Early diagnosis and early intervention are key to preventing AD. However, commonly used techniques are expensive (Positron Emission Tomography, PET) or somewhat invasive (cerebrospinal fluid biomarker testing), making them difficult for patients to accept.

[0004] In recent years, the detection of blood biomarkers for Alzheimer's disease (AD) has progressed rapidly. Current research on blood testing methods for AD mainly includes plasma and red blood cell assays. Amyloid β-protein (Aβ) is one of the core biomarkers of Alzheimer's disease, exhibiting high affinity for albumin, apolipoproteins, and α2-macroglobulin in plasma. While highly sensitive single-molecule array (SMA) and mass spectrometry-based techniques for detecting Aβ protein in plasma (such as immunoprecipitation-coupled mass spectrometry and antibody-free liquid chromatography-mass spectrometry) can improve the detection limit of Aβ protein, and while numerous studies have been conducted on multiple variables before plasma testing and standardized pre-analytical processing guidelines have been established, the complexity of the plasma matrix and the difficulty in standardizing individual operational details remain challenges. These variations can affect the accuracy of AD biomarker detection results in plasma, thus impacting the clinical applicability of these biomarkers.

[0005] Studies have shown that Aβ protein in cerebrospinal fluid (CSF) can cross the blood-brain barrier and flow into the bloodstream, easily accumulating on the surface of peripheral blood erythrocytes. However, the concentration of biomarkers in the blood is much lower than in CSF, thus requiring highly sensitive techniques for accurate measurement. Currently, methods for detecting AD biomarkers in the blood all have limitations, such as the limited sensitivity of immunofluorescence assays and ELISA; while atomic force microscopy offers ultra-high resolution, its results may be affected by other similar proteins. Specifically, the following drawbacks exist: (1) Complex sample processing procedures: Existing early AD diagnosis methods based on blood biomarkers mainly detect the concentration of AD biomarkers in plasma; a few studies also detect AD biomarkers on the surface of red blood cells using enzyme-linked immunosorbent assay (ELISA) and europium immunoassay. That is, peripheral blood red blood cells can be considered an additional source for screening and detecting AD biomarkers. However, due to the complex blood environment, the detection of target proteins has many steps, complex operation, long time consumption, and high technical requirements. Existing studies have shown that the same plasma sample will have different results when different laboratories use the same or different methods to detect it (detection methods include: enzyme-linked immunosorbent assay (ELISA), single molecule array, IP-LC-MS (Immunoprecipitation-coupled toliquid chromatography mass spectrometry). In other words, the accuracy of plasma-based AD biomarker detection is easily affected by the detection process (human operation error, blood storage conditions, etc.).

[0006] (2) Lack of biological specificity: Some studies have used atomic force microscopy to study the morphology (size, structure, and texture, etc.) of Aβ protein aggregates on the surface of peripheral blood erythrocytes from AD patients and compared them with clinical data to explore the feasibility of using the spatial distribution characteristics of protein aggregates as a diagnostic criterion for AD. However, the detection of erythrocyte surface biomarkers based on atomic force microscopy still has certain problems in terms of accuracy or specificity: the concentration measurement method based on immunoadsorption is easily affected by the sample preparation process, while the morphological measurement using atomic force microscopy lacks biological specificity. On the other hand, although some researchers have used single-molecule localization imaging technology to detect the characteristic protein density of multiple myeloma cells, the number of cells counted is relatively small, and the detection parameter only uses the characteristic protein density as an indicator to judge the disease progression. However, in single-molecule localization imaging, the quenching of the target protein during the experiment will affect the accuracy of the final density detection. In short, as the single-molecule localization imaging process continues, the fluorescent molecules in the bright state will be gradually bleached, and the number of fluorescent molecules collected by the camera will gradually decrease. When the target protein content is low, the number of fluorescent molecules collected may be insufficient to support the acquisition of super-resolution data or affect the quality of super-resolution data, which will affect the accuracy of subsequent algorithms for density detection. Summary of the Invention

[0007] In view of this, the present invention proposes a method for detecting biomarkers, which aims to solve the problems of complex blood biomarker sample processing procedures and low accuracy of biomarker detection results in the prior art.

[0008] This invention proposes a method for detecting biomarkers, comprising the following steps: Specific biomarkers in biological samples are fluorescently labeled, and single-molecule fluorescence imaging is performed on the biomarkers to obtain multiple frames of raw microscopic fluorescence images; The original multi-frame microscopic fluorescence images are analyzed and processed to extract super-resolution distribution characteristics of specific biomarkers on the surface of the biological samples.

[0009] Furthermore, in the above-mentioned biomarker detection method, the super-resolution distribution feature information of specific biomarkers on the surface of the biological sample includes: the density of specific biomarker clusters, the area ratio of specific biomarkers on the surface of the biological sample, and the average value and distribution standard deviation of the nearest neighbor distance of specific biomarker clusters.

[0010] Furthermore, in the above-mentioned biomarker detection method, the analysis and processing of the multiple frames of original microscopic fluorescence images includes: The location table data was obtained by processing multiple frames of original microscopic fluorescence images; The positioning points in the positioning table data are rendered to generate a super-resolution image; The super-resolution image is convolved to obtain a downsampled image to delineate the boundaries of the biological samples, and the total area of ​​the biological samples is calculated. Image opening and convolution filtering are performed on the unsampled super-resolution image to remove small noise. Connectivity analysis is performed on the image after the opening operation to segment the biomarkers in the image into isolated clusters. Statistics are then performed on all the segmented clusters to obtain the number, area, and center point of each biomarker cluster. The density and area ratio of biomarker clusters in the biological sample are determined by using the total area of ​​the biological sample, the number of all biomarker clusters, and the area of ​​each individual cluster. The mean and standard deviation of the nearest neighbor distance of each biomarker cluster are calculated using the center point of each cluster.

[0011] Furthermore, in the above-mentioned biomarker detection method, the biological sample is blood cells.

[0012] Furthermore, in the above-mentioned biomarker detection method, the fluorescent labeling of biological samples with specific biomarkers includes the following steps: Blood cells containing specific biomarkers were extracted, diluted, and then added to a pre-treated confocal culture dish. The dish was left to stand for a period of time to allow the blood cells to adhere to the culture dish. After fixing the blood cells in a confocal culture dish with fixative, the blood cells are then blocked with blocking solution. The primary antibody was incubated with the aforementioned blood cells to specifically recognize and bind to specific biomarkers on the blood cells; The blood cells, which have been incubated with the primary antibody, are incubated with a secondary antibody labeled with a fluorescent dye to complete the fluorescent labeling of the biological sample with specific biomarkers.

[0013] Furthermore, in the above-mentioned biomarker detection method, the specific biomarker is β-amyloid protein.

[0014] Furthermore, in the above-mentioned biomarker detection method, the density of the specific biomarker cluster is determined based on the total area of ​​the biological sample and the number of the biomarker clusters, and the area ratio is determined based on the ratio of the total area of ​​all biomarker clusters to the total area of ​​the biological sample.

[0015] This invention acquires multiple frames of raw microscopic fluorescence images from fluorescently labeled biological samples containing specific biomarkers, analyzes and processes these images to obtain location table information and super-resolution images of specific biomarkers on the surface of the biological samples, and finally uses an algorithm to obtain super-resolution distribution feature information of specific molecules on the surface of the biological samples, thereby greatly improving the accuracy of biomarker detection results. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram illustrating the super-resolution positioning imaging principle in an embodiment of the present invention; Figure 2 A schematic flowchart of the method for detecting AD biomarkers on the surface of red blood cells provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the clustering analysis of multiple frames of original microscopic fluorescence images in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the noise signal filtering principle in an embodiment of the present invention. Figure 5 This is a super-resolution image of Aβ protein on the surface of a single red blood cell in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the process of dividing cell boundaries, removing extracellular signals, and obtaining cell area in an embodiment of the present invention. Figure 7 In this embodiment of the invention, an image opening operation is performed on the super-resolution image that has not been downsampled to remove small noise points before and after the image is processed. Figure 8 This is a clustering effect diagram of super-resolution data of Aβ protein on the surface of a single red blood cell in an embodiment of the present invention after algorithm processing; Figure 9 This is a map showing the nearest neighbor distance distribution of Aβ protein on the surface of a single red blood cell in an embodiment of the present invention; Figure 10 This is the density statistical result of a gradient experiment of Aβ protein on the surface of multiple red blood cells in the embodiments of the present invention; Figure 11 This is the statistical result of the nearest neighbor distance of gradient experiments on Aβ protein on the surface of multiple red blood cells in this embodiment of the invention; Figure 12 This is a statistical result of the protein cluster area ratio of multiple red blood cell membrane surface Aβ protein gradient experiments in the embodiments of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] Immunofluorescence (IF) is a labeling technique widely used in fluorescence microscopy. It achieves in-situ imaging by specifically recognizing and labeling target proteins within cells using antibodies. Single-molecule localization microscopy (SMLM) uses lasers to excite fluorescently labeled target proteins. With the assistance of a special imaging buffer, it achieves random flashing of individual fluorescent molecules of the target protein. SMLM captures these individual fluorescent molecules, acquiring tens of thousands of original images of the flashing dots, reducing overlap effects and thus improving image resolution. Finally, the acquired images are reconstructed using algorithms to obtain super-resolution images and corresponding localization tables. Therefore, SMLM can achieve a spatial resolution of 20-30 nm, far exceeding that of ordinary fluorescence microscopy. Combined with immunofluorescence, it can clearly reveal the distribution of biomarkers on the cell surface, thereby achieving precise density detection.

[0019] See Figure 1 The basic principle of single-molecule localization super-resolution microscopy is as follows: (1) Samples labeled with photoactivated fluorescent probes. Photoactivated fluorescent molecules are an important prerequisite for achieving super-resolution localization imaging. Photoactivated fluorescent molecules can switch between bright and dark states, which is called the light-switching phenomenon. This means that each fluorescent molecule can be imaged in a time-division manner, resulting in spatially isolated and non-overlapping PSFs (point spread functions), avoiding the overlap of fluorescent molecule signals in the diffraction-limited region.

[0020] (2) Activation of fluorescent probes labeled with biological structures. Fluorescent molecules are activated using a laser of a specific wavelength, so that only a small number of randomly and sparsely distributed fluorescent molecules are converted to the bright state at a time. Subsequently, a laser of another wavelength is used to excite the fluorescent molecules, causing them to emit fluorescence. The switching between the bright and dark states of the fluorescent molecules causes them to exhibit a "flickering" phenomenon.

[0021] (3) Detection and localization of single-molecule spatial positions. A detector is used to collect the fluorescence signals emitted by fluorescent molecules in their bright state. Only a small number of single-molecule fluorescence signals are collected per frame because bright-state fluorescent molecules are randomly distributed in space. Since the single-molecule fluorescence signal is relatively weak and the imaging spot covers multiple pixels, the detector must have high sensitivity and a high signal-to-noise ratio. Currently, electron multiplication charge-coupled devices (EMCCDs) and scientific-grade complementary metal-oxide-semiconductor (sCMOS) are commonly used for single-molecule fluorescence imaging. The PSF of the fluorescent molecule collected by the detector can be approximated as a two-dimensional Gaussian distribution. By fitting the image, the Gaussian intensity spectrum of the molecule's horizontal and vertical positions can be obtained, and the peak position is the centroid position of the fluorescent molecule. It should be noted that the imaging throughput of SMLMs does not solely depend on the detector; the photophysical properties of the fluorophore (scintillation rate) are also another important factor.

[0022] (4) Reconstruct the super-resolution image. Repeat the above process extensively to obtain thousands or even tens of thousands of single-molecule images. Using enough single-molecule localization information from these single-molecule images for reconstruction, the final super-resolution image and localization table information can be obtained.

[0023] See Figures 2-12 This invention provides a method for processing images of biological samples with specific biomarkers, comprising the following steps: Step S1: Fluorescently label specific biomarkers in the biological sample and perform single-molecule fluorescence imaging on the biomarkers to obtain multiple frames of raw microscopic fluorescence images.

[0024] Specifically, in this embodiment, 100,000 frames of raw microscopic fluorescence images are obtained. The biological sample is blood cells, such as red blood cells, white blood cells, and platelets. Compared to existing detection methods using mixed solutions like serum and plasma, the blood cell-based detection method reduces interference from signal-free samples, thereby improving detection sensitivity. Furthermore, using single-molecule localization super-resolution imaging to detect multiple (100 or more) blood cells from the same batch of blood samples effectively eliminates the impact of individual cell differences on the detection results and simplifies the sample processing procedure.

[0025] In this embodiment, the biological sample containing a specific biomarker is preferably a blood cell sample. The fluorescence signal of the blood cell sample enriched with the biomarker is captured by a detector to generate a single-frame image. This process is repeated to generate a raw microscopic fluorescence image containing multiple images. The fluorescent molecule signals in each raw microscopic fluorescence image are extracted and processed to generate entries in the localization table data.

[0026] Recombined Figure 2Fluorescent labeling of biological samples with specific biomarkers includes the following steps: Step S11: Extract blood cells with specific biomarkers, dilute them, and then add them to a pretreated confocal culture dish. Let them stand for a period of time to allow the blood cells to adhere to the dish.

[0027] This step, when implemented in practice, may include: (1) Prepare biomarker solutions of different concentrations.

[0028] Among them, the preferred biomarker is Aβ protein, which has good biological specificity.

[0029] In practice, this step includes the following sub-steps: a. Dissolve Aβ1-42 powder in pre-cooled hexafluoroisopropanol (HFIP) solution to a final concentration of 1 mM, and incubate at room temperature for about 1 h to allow the Aβ1-42 powder to dissolve completely.

[0030] b. Nitrogen blowing for about 10 minutes to volatilize HFIP and obtain an Aβ1-42 film, followed by a small amount of dimethyl sulfoxide. Dimethyl sulfoxide (DMSO) dissolves Aβ1-42 films.

[0031] c. Add hydroxyethylpiperazine ethanethioic acid (HEPES) to adjust to the required concentration. If a small amount of crystals are present, they can be dissolved by ultrasonication. After placing in a 4°C refrigerator for 24 hours, dispense the product and store it in a -80°C refrigerator.

[0032] (2) Extract blood cells and incubate blood cells with biomarker solutions of different concentrations for a period of time to enrich the biomarkers on the surface of blood cells. In this embodiment, red blood cells are preferred.

[0033] In practice, this step includes the following sub-steps: a. Red blood cell extraction: Take 4 μL of fresh whole blood from the fingertip and mix it with 6 mL of PBS-GB solution. Impurities in the blood are removed by centrifugation, and the supernatant is removed to obtain concentrated red blood cells.

[0034] Specifically, this step uses peripheral blood from healthy donors to simulate red blood cell samples from AD patients.

[0035] b. Simulated red blood cells from AD patients: Red blood cells were incubated with Aβ protein solutions of different concentrations for about 24 hours to enrich Aβ protein on the surface of the red blood cells. The supernatant was then removed to obtain incubated red blood cells. The cells were then washed twice with an incubation solution without Aβ protein and centrifuged to remove unbound Aβ protein. The resulting concentrated red blood cell solution was the simulated red blood cells from AD patients.

[0036] (3) After processing the confocal culture dish, blood cells with specific biomarkers are diluted and added to the confocal culture dish. The dish is left to stand for a period of time to allow the red blood cells to adhere to the wall.

[0037] In practice, confocal culture dishes with good light transmittance (Mat Tek, P35G-1.5-14-C) were treated with 0.1% poly-L-lysine to facilitate subsequent experiments. Red blood cells simulating AD patients were diluted to the required concentration with DMEM basal medium, and 400 μL of the diluted red blood cell suspension was added to the confocal culture dish. The dish was then incubated for 30 min to allow the red blood cells to adhere to the wall, which can achieve better labeling results.

[0038] Because sample offset occurs during imaging, and the offset of the fluorescent microspheres matches the offset of the target protein on the cell, the fluorescent microspheres do not flicker during imaging, while the target protein on the cell flickers as a single molecule. The cell offset can be corrected using a positioning table of the fluorescent microspheres. In this embodiment, fluorescent microspheres are added to the confocal culture dish before this step. The diameter of the fluorescent microspheres can be 100 nm.

[0039] The specific implementation steps are as follows: add the diluted fluorescent microsphere solution to a confocal culture dish that has been treated with poly-L-lysine, dry it in an oven to allow the fluorescent microspheres to adhere to the confocal culture dish, and then add biological samples for incubation and immunofluorescence labeling.

[0040] Step S12: After fixing the blood cells in the confocal culture dish with fixative, block the blood cells with blocking solution.

[0041] In practice, remove excess incubation solution from the confocal culture dish, add fixative and fix for 15 min, then wash with PBS phosphate buffer to remove excess fixative. After washing, add blocking solution to block the red blood cells.

[0042] Step S13: Incubate the above-mentioned red blood cells with a primary antibody to specifically recognize and bind to the Aβ protein on the blood cells.

[0043] In practice, the primary antibody is diluted with 3% BSA, 200 μL of the primary antibody is added to the well of the dish and incubated for 2 hours, and then washed with PBS phosphate buffer to remove excess primary antibody.

[0044] Step S14: Incubate blood cells that have been incubated with primary antibody using a secondary antibody labeled with a fluorescent dye to complete the fluorescent labeling of biological samples with specific biomarkers.

[0045] In practice, dilute the secondary antibody with 3% BSA, add 200 μL of the secondary antibody to the well of the dish, incubate in the dark for about 40 minutes, and then wash with PBS phosphate buffer to remove excess secondary antibody.

[0046] In this embodiment, the primary antibody is incubated to allow it to locate the target protein. The secondary antibody binds to the primary antibody, which amplifies the fluorescence signal to a certain extent. After fluorescence labeling, the sample in the confocal culture dish can be directly added to the imaging buffer for single-molecule localization imaging, or it can be temporarily stored in a 4°C refrigerator.

[0047] The steps S11-S14 above are used for the preparation and fluorescent labeling of model blood cell samples. For fluorescent labeling of real blood samples from patients, only steps (3)-S14 are required.

[0048] It can be seen that this invention only requires the characteristic measurement and analysis of biomarkers on the surface of blood cells, and completes the detection of biomarkers on the surface of blood cells with high sensitivity, high accuracy and high specificity, thereby reducing the impact of differences in experimental operation procedures on the measurement results and greatly reducing the error pressure on experimental personnel; it requires a small sample volume, only a few microliters of blood are needed to meet the experimental requirements; other substances in the blood have little impact on the results of this experiment.

[0049] Step S2 involves analyzing and processing the multiple frames of original microscopic fluorescence images to extract super-resolution distribution feature information of specific biomarkers on the surface of the biological sample.

[0050] Specifically, the super-resolution distribution characteristics of specific biomarkers on the surface of the biological sample include: the density of specific biomarker clusters, the area ratio of specific biomarkers on the surface of the biological sample, and the average and standard deviation of the nearest neighbor distances of specific biomarker clusters. That is, the detection results are no longer presented as concentration, but rather as the super-resolution distribution characteristics of biomarkers detected on the surface of disease-related blood cells (the density of specific biomarker clusters, the area ratio of specific biomarkers on the surface of the biological sample, and the average and standard deviation of the nearest neighbor distances of specific biomarker clusters), and the features of the super-resolution distribution characteristics can be quantified. In this embodiment, the biomarker features include: the density of specific biomarker clusters, the area ratio of specific biomarkers on the surface of the biological sample, and the average and standard deviation of the nearest neighbor distances of specific biomarker clusters.

[0051] A series of raw fluorescence microscopic images were obtained by repeatedly exposing biological samples with specific biomarkers using a single-molecule localization super-resolution microscopy method. Each exposure activated only a small number of fluorescent molecules, ensuring that the fluorescence signal was sparsely distributed.

[0052] Combination Figure 3 In this embodiment, the analysis and processing of the multiple frames of original microscopic images includes: (1) By processing multiple frames of original microscopic fluorescence images, the position and related information of each fluorescent molecule are determined to obtain the localization table data.

[0053] Specifically, the original image is processed using a single-molecule localization algorithm to obtain localization table data; the localization table includes 12 columns of information (as shown in Table 1), which, in addition to coordinate information, also include the peak signal intensity (PI) and signal-to-noise ratio (SNR) of the localization point.

[0054] In one specific implementation of this embodiment, the localization algorithm used is QC-STORM (QC is an abbreviation for Quality Control), which is a GPU-accelerated method for image preprocessing and molecular recognition, maximum likelihood estimation localization, super-resolution image rendering, and statistical information analysis. Its function is to calculate information such as the position of signal points and the full width at half maximum (FWHM) in the localization microscopic image to form a localization table.

[0055] See Figure 4 In this embodiment, the coordinate relationship between the positioning points in the positioning table can be used to delete noise signals. If no other coordinate point is found within rnm (in this experiment, the r value ranges from 2.5 to 20 nm, preferably 10 nm, and the r value can be adjusted according to the molecular point density) of a positioning point, the signal point is deleted.

[0056] Table 1 sequence describe 1 Peak signal strength 2 X-coordinate (pixel) 3 Y-coordinate (pixel) 4 Z-coordinate (nm) 5 Gaussian fit standard deviation in the X direction 6 Gaussian fitting standard deviation in the Y direction 7 Total number of photons 8 Background intensity 9 Signal-to-noise ratio 10 X-axis positioning accuracy 11 Y-axis positioning accuracy 12 Frames (2) Combination Figure 5 The positioning points in the positioning table data are rendered to generate a super-resolution image.

[0057] In this embodiment, the positioning point refers to the point obtained through a single-molecule positioning algorithm. Each positioning point contains information such as the precise position (x, y) and intensity of the fluorescent molecule.

[0058] In practice, a Gaussian model is used to render the localized points to obtain a super-resolution image with a pixel size of 5 nm. The pixel size of the generated super-resolution image is 5 nm, meaning that each pixel represents a 5 nm × 5 nm region.

[0059] (3) Combination Figure 6 The super-resolution image is convolved to obtain a downsampled image to delineate the boundaries of the biological samples, and the total area of ​​the biological samples is calculated.

[0060] In practice, the super-resolution image obtained in the previous step is convolved with a Gaussian kernel of size 100 pixels to obtain a downsampled image, which is used to delineate cell boundaries. Extracellular signals are then removed based on the cell boundaries, and the cell area is calculated. The pixel size of the image is selected based on the density of biomarker clusters on the cell.

[0061] (4) Combination Figure 7 Image opening and convolution filtering are performed on the super-resolution image that has not been downsampled to remove small noise.

[0062] In practice, a disk structure of 5 pixels (determined according to the cluster density) is used to perform image opening operation on the super-resolution image that has not been downsampled, so as to further remove noise points smaller than 5 pixels in the image.

[0063] By using a disk structure of 5 pixels to perform opening operations, small specks with a diameter of less than 5 pixels in an image can be effectively removed.

[0064] Downsampling: Through convolution operations, the resolution of the image is reduced, but the main structural features are preserved, which facilitates subsequent cell boundary division.

[0065] (5) Combination Figure 8 The image after the opening operation is subjected to connected component analysis to segment the biomarkers in the image into isolated clusters. The number, area and center point of each cluster of all biomarkers are obtained by statistical analysis of all the segmented clusters.

[0066] Specifically, the clusters here can be protein clusters, the biological sample is a cell, and the protein cluster density is calculated according to the formula: protein cluster density = number of protein clusters / cell area.

[0067] (6) Combination Figures 9 to 10 The density and area ratio of biomarker clusters in the biological sample are determined by using the total area of ​​the biological sample and the number and area of ​​all biomarker clusters, and the mean and standard deviation of the nearest neighbor distance of the biomarker cluster are calculated by using the center point of each cluster.

[0068] Existing clustering algorithms developed for single-molecule localization microscopy, such as DBSCAN, ClusterViSu, and FACAM, are based on point cloud data from calibration tables. They only utilize the positional coordinates of localization points, neglecting information such as the intensity of these points. However, cell surface biomarkers are distributed in isolated clusters, and their super-resolution imaging results also correspond to this isolated cluster distribution, meaning cluster classification can be performed based on this image information. Furthermore, the rendering and reconstruction process of super-resolution images utilizes multi-dimensional information such as coordinates, photon counts, and localization accuracy from the localization table. Therefore, compared to clustering methods that only utilize localization point coordinates, clustering methods based on super-resolution images can analyze higher-density data.

[0069] See Figure 2 , Figures 10 to 12 In the control group, healthy red blood cells were directly incubated with DMEM basal medium. In the experimental group, the prepared Aβ protein solution was diluted to different concentrations with DMEM basal medium and then incubated with healthy red blood cells at 37 °C for about 24 h to enrich Aβ protein on the surface of red blood cells. The incubation solution was then discarded, and the incubated red blood cells were used for immunofluorescence labeling experiments. Super-resolution imaging was then used to obtain super-resolution data. Finally, the super-resolution distribution characteristics of Aβ protein on the surface of red blood cells were analyzed using an algorithm. The super-resolution data of 100 red blood cells were randomly selected from each group. The clustering algorithm in this embodiment was used to calculate the size, density, nearest neighbor distance, and area ratio of Aβ protein clusters on the surface of each red blood cell. The statistical results of the density, nearest neighbor distance, and area ratio of the obtained protein clusters are shown below. Figures 10 to 12 As shown in the figure, when red blood cells are incubated with Aβ protein solutions of different concentrations, the density and area ratio of Aβ protein clusters on the surface of red blood cells increase with the increase of incubation concentration, while the nearest neighbor distance of Aβ protein clusters on the surface of red blood cells decreases to a certain extent. This proves that the detection method provided in this embodiment of the invention can effectively and accurately detect the density, area ratio, and mean and standard deviation of the nearest neighbor distance of Aβ protein clusters on the surface of red blood cells.

[0070] As can be seen, the clustering algorithm of this invention can process super-resolution images with high-density biomarkers, providing more valuable detection parameters and quantifying image features. In addition to detecting the cluster density of biomarkers on disease-related cells, it also adds statistical analysis of the nearest neighbor distance and area ratio of biomarkers. These quantitative parameters greatly reduce the impact of target protein quenching on density detection during the imaging process, thereby significantly improving the accuracy of the detection results.

[0071] It should be noted that although this embodiment uses β-amyloid protein as a representative biomarker of Alzheimer's disease (AD) for illustrative purposes, those skilled in the art will understand that this method is also applicable to other AD-related biomarkers, as long as they can be labeled with immunofluorescence and exhibit distinguishable cluster distribution characteristics under single-molecule localization super-resolution microscopy.

[0072] In this embodiment, the spatial distribution characteristics of biomarkers are extracted based on the super-resolution images obtained by single-molecule localization super-resolution microscopy. The process is as follows: The super-resolution data is rendered to generate a super-resolution image. Then, cell segmentation is performed on the image to extract the cell regions to be analyzed. After image filtering and segmentation, isolated signal clusters are separated. Finally, the area and nearest neighbor distance of the signal clusters are statistically analyzed to obtain the clustering results (i.e., the super-resolution distribution characteristics of the target protein).

[0073] It is evident from the above that this embodiment acquires multiple frames of raw microscopic fluorescence images by collecting data from biological samples that have been fluorescently labeled with specific biomarkers, and analyzes and processes these multiple frames of raw microscopic fluorescence images to obtain the localization table information and super-resolution images of specific biomarkers on the surface of the biological samples. Finally, an algorithm is used to obtain the super-resolution distribution feature information of specific molecules on the surface of the biological samples, thereby greatly improving the accuracy of biomarker detection results.

[0074] See again Figure 2The following is a detailed description of the process of acquiring and analyzing super-resolution data in this invention using a specific embodiment: (1) Open the single-molecule localization microscopy imaging system, apply immersion oil to the objective lens, and select the folder. (2) Place the prepared biological sample on the stage, ensuring that the position is within the range that the objective lens can move; fix the sample and do not move it arbitrarily during the process. (3) Immerse the biological sample in the imaging buffer, find the focal plane of the sample in bright field or low laser power, and select the ROI region (the ROI region should contain target cells and fluorescent microspheres). The ROI region refers to the area in the image that needs special attention and processing, containing the structure or feature of interest. (4) Adjust the imaging system, increase the laser power, and make the target protein randomly flash with the assistance of the imaging buffer to acquire tens of thousands of single-molecule fluorescence images. (5) Perform localization processing on the acquired single-molecule fluorescence images to obtain localization table data (such as coordinate position, localization accuracy, and intensity information), and then generate a grayscale image through image rendering. (6) Use the location information of the fluorescent microspheres to correct the lateral offset generated during cell imaging to obtain the corrected localization table data. (7) Finally, clustering algorithm is used to analyze the corrected location table data to obtain the super-resolution distribution characteristics of Aβ protein on the surface of red blood cells (size, density, nearest neighbor distance and area ratio of protein clusters).

[0075] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for detecting biomarkers, characterized in that, Includes the following steps: Specific biomarkers in biological samples are fluorescently labeled, and single-molecule fluorescence imaging is performed on the biomarkers to obtain multiple frames of raw microscopic fluorescence images; The original multi-frame microscopic fluorescence images are analyzed and processed to extract super-resolution distribution characteristics of specific biomarkers on the surface of the biological samples.

2. The method for detecting biomarkers according to claim 1, characterized in that, The super-resolution distribution characteristics of specific biomarkers on the surface of the biological sample include: the density of specific biomarker clusters, the area ratio of specific biomarkers on the surface of the biological sample, and the average and standard deviation of the nearest neighbor distance of specific biomarker clusters.

3. The method for detecting biomarkers according to claim 2, characterized in that, The analysis and processing of the multiple frames of original microscopic fluorescence images includes: The location table data was obtained by processing multiple frames of original microscopic fluorescence images; The positioning points in the positioning table data are rendered to generate a super-resolution image; The super-resolution image is convolved to obtain a downsampled image to delineate the boundaries of the biological samples, and the total area of ​​the biological samples is calculated. Image opening and convolution filtering are performed on the unsampled super-resolution image to remove small noise. Connectivity analysis is performed on the image after the opening operation to segment the biomarkers in the image into isolated clusters. Statistics are then performed on all the segmented clusters to obtain the number, area, and center point of each biomarker cluster. The density and area ratio of biomarker clusters in the biological sample are determined by using the total area of ​​the biological sample, the number of all biomarker clusters, and the area of ​​each cluster. The mean and standard deviation of the nearest neighbor distance of each biomarker cluster, as well as the area ratio of the biomarker on the biological sample, are calculated using the center point of each cluster.

4. The method for detecting biomarkers according to claim 1, characterized in that, The biological sample is blood cells.

5. The method for detecting biomarkers according to claim 4, characterized in that, Fluorescent labeling of biological samples with specific biomarkers includes the following steps: Blood cells containing specific biomarkers were extracted, diluted, and then added to a pre-treated confocal culture dish. The dish was left to stand for a period of time to allow the blood cells to adhere to the culture dish. After fixing the blood cells in a confocal culture dish with fixative, the blood cells are then blocked with blocking solution. The primary antibody was incubated with the aforementioned blood cells to specifically recognize and bind to specific biomarkers on the blood cells; The blood cells, which have been incubated with the primary antibody, are incubated with a secondary antibody labeled with a fluorescent dye to complete the fluorescent labeling of the biological sample with specific biomarkers.

6. The method for detecting biomarkers according to claim 1, characterized in that, The specific biomarker is β-amyloid protein.

7. The method for detecting biomarkers according to claim 1, characterized in that, The density of the specific biomarker cluster is determined based on the total area of ​​the biological sample and the number of the biomarker clusters, and the area ratio is determined based on the ratio of the total area of ​​all biomarker clusters to the total area of ​​the biological sample.

Images