Method for evaluating platelet stress state

Abstract

The invention discloses a platelet stress state assessment method. The method comprises the following steps: preparing a platelet suspension; incubating the platelet suspension by using an EAAT2 polyclonal primary antibody and a flow fluorescent secondary antibody in combination with a monoclonal antibody of a platelet related marker and / or a monoclonal antibody of Annexin V, and preparing a sample to be detected; detecting a specific signal of the sample through a flow cytometry; and analyzing the specific signal to confirm the stress condition of the sample. According to the method, multi-parameter data of a single platelet can be flexibly processed, and specific clustering analysis of the platelet subgroup is realized in combination with the expression characteristics of the EAAT2 membrane protein. In addition, flexible and efficient statistical analysis can be carried out on inter-group differences of the parameters, correlation between the parameters and the like, and finally personalized accurate diagnosis and analysis of the platelet quality and activation state of the subject are achieved.

Description

Technical Field

[0001] The present application relates to the field of medical testing technology, and in particular, to a method for evaluating platelet stress state. Background Art

[0002] Flow cytometry has significant advantages in analyzing the number, size, morphology, surface molecular expression and activity of platelets. It is easy to operate, including centrifugal separation of platelet-rich plasma, staining, fixation, and on-machine detection. It has little stimulation to platelets, which can minimize the activation of platelets by human manipulation, and can obtain experimental results within 1 to 2 hours, which can better reflect the real-time status and activation degree of the subject's platelets. Due to the stimulation of thrombin, platelets will undergo a fragmentation process that depends on the concentration and time of thrombin. The resulting disintegration of the platelet skeleton and the shedding of components such as membrane proteins will cause changes in platelet volume and an increase in fragments, which ultimately leads to the statistical data of platelet membrane surface markers that cannot truly reflect the current state of platelets, causing people to misinterpret the platelet stress state. Summary of the invention

[0003] In view of the shortcomings of the existing methods, the present application proposes a method for evaluating the platelet stress state to solve the technical problem of evaluation distortion in related technologies.

[0004] The present application provides a method for evaluating platelet stress state, comprising the following steps: preparing platelet suspension; The platelet suspension is incubated with EAAT2 polyclonal primary antibody and flow cytometry fluorescent secondary antibody in combination with monoclonal antibodies of platelet-related markers and / or monoclonal antibodies of Annexin V to prepare a sample to be tested; Detecting specific signals of the sample by flow cytometry; Analysis of the specific signal confirms the stress status of the sample.

[0005] Optionally, analyzing the specific signal comprises: Preliminarily screening the data containing specific signals to obtain first candidate data; Statistical analysis is performed on the first candidate data to obtain statistical results related to the platelet stress state.

[0006] Furthermore, the preliminary screening of the data containing the specific signal to obtain the first candidate data includes: Get FCS files containing data of specific signals; Extracting text segment information and / or data segment information corresponding to a single platelet in the FCS file; Sort the data of individual platelets according to the extracted forward scattered light values ​​and determine the cutoff value; After discarding the data of single platelets larger than the cutoff value, the first candidate data is obtained.

[0007] Optionally, the cutoff value is confirmed by counting the changes in forward scattered light values ​​of healthy people before and after platelet stress; the cutoff value is set to 1000000 (can also be adjusted according to research requirements).

[0008] Further optionally, the performing statistical analysis on the first candidate data includes: Conduct statistical analysis according to the preset different populations corresponding to the samples; Conduct statistical analysis according to the different states of the individuals to which the samples correspond; Statistical analysis was performed according to the status of the samples before and after in vitro stress.

[0009] Optionally, stress modulators used to subject the sample to in vitro stress include: glutamate, arachidonic acid, adenosine diphosphate, collagen, adrenaline, thrombin, thrombin receptor activating enzyme or lipopolysaccharide as activators; excitatory amino acid transporter inhibitors as inhibitors; and inhibitors for preventing fibrin polymerization and thus preventing blood clot formation.

[0010] Optionally, the statistical analysis includes at least one of the following: descriptive statistical analysis, test analysis, correlation analysis, reliability analysis, discriminant analysis and cluster analysis.

[0011] Further optionally, the cluster analysis includes an unsupervised PCA cluster analysis method using a k-means clustering algorithm, and the number of clusters is selected to match different stress states of corresponding platelets.

[0012] Furthermore, the cluster analysis is based on the fluorescence intensity of EAAT2 combined with platelet-related markers and / or Annexin V for clustering; the fluorescence intensity is divided into low fluorescence and high fluorescence according to a threshold value.

[0013] Optionally, the platelet-related markers include at least one of the following: CD61, CD41, CD42b, CD62P, CD63 and PAC-1.

[0014] The beneficial technical effects brought about by the technical solution provided by the embodiment of the present application include: (1) The method of this application can flexibly process the multi-parameter data of a single platelet, and combined with the expression pattern and characteristics of the EAAT2 membrane protein, realize specific clustering analysis of platelet subpopulations. In addition, it can also perform flexible and efficient statistical analysis on the inter-group differences of various parameters and the correlation between parameters, and ultimately realize personalized and accurate diagnosis and analysis of the platelet quality and activation status of the subjects.

[0015] (2) The method of the present application can flexibly extract, process and analyze flow cytometry data based on single platelets, supports conventional statistical analysis methods such as frequency distribution, inter-group comparison, correlation analysis and cluster analysis, and can achieve individualized and accurate diagnosis.

[0016] (3) The method of the present application utilizes EAAT2, combined with CD61 or Annexin V to label platelets, which can effectively distinguish four platelet subpopulations and partially overcome the problem of false changes in the prior art when platelet fragmentation or necrosis increases.

[0017] (4) The method of the present application can selectively perform unsupervised PCA clustering through the k-means clustering algorithm, so that the cell typing of platelets is more refined by adding the analysis of FCA-A (reflecting cell volume) and SSC-A (reflecting particle complexity), and can provide a more in-depth and intuitive platelet heterogeneity evaluation method.

[0018] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 Platelet phenotypes identified by the method of the present application using the EAAT2 membrane protein and CD61 combined labeling; (A) flow density map of unactivated platelets; (B) flow density map of platelets activated by thrombin; (C) approximate positions of platelets in four states in the density map; Figure 2Characteristics of the changes in the volume of healthy human platelets before and after thrombin activation; FSC: forward scattered light, often used to reflect the size of cells; A. Frequency distribution characteristics of FSC-A of small platelets (FSC-A between -20000 and 100000) before and after thrombin activation; B. Frequency distribution characteristics of FSC-A of large platelets (FSC-A between 100000 and 1000000) before and after thrombin activation; C. Comparison of FSC-A results of healthy human platelets before and after thrombin activation. Basal represents the state before thrombin activation, and Thrombin represents the state after thrombin activation; Figure 3 Pearson correlation analysis of the changes in the phenotype of platelet EAAT2 membrane protein using high concentrations of glutamate using the method of the present application; (A) Platelets not incubated with 250 μM glutamate, (B) Platelets incubated with 250 μM glutamate for 30 min, Pearson correlation coefficient result graph based on multi-parameter calculation of a single platelet; FSC-A, forward scatter parameter, commonly used to describe cell size; SSC-A, side scatter parameter, commonly used to describe particle complexity; Figure 4 Platelet flow cytometry plot: AB Single-cell level analysis of the expression characteristics of CD61 and EAAT2 membrane proteins in healthy human platelets under basal conditions and thrombin-activated conditions. CF According to the cell size and activation characteristics of CD61 low-fluorescence platelet subpopulation (CD61+) and CD61 high-fluorescence platelet subpopulation (CD61++), platelets can be roughly divided into three states: low activation state (CD61++, PAC-1 low / CD62P low), high activation state (CD61++, PAC-1 high / CD62P high) and weak reaction state (CD61+, PAC-1 low / CD62P low). GI Expression characteristics of EAAT2 membrane proteins in CD61+ platelet subpopulation and CD61++ platelet subpopulation under basal conditions and thrombin-stimulated conditions; Figure 5 The K-means clustering diagram of platelet phenotype based on CD61 and EAAT2 membrane proteins using the method of the present application. (A) K-means clustering diagram of unactivated platelets; (B) K-means clustering diagram of thrombin-activated platelets; (C) Flow density diagram of unactivated platelets corresponding to Figure A; (D) Flow density diagram of thrombin-activated platelets corresponding to Figure B; Figure 6The expression of Annexin V in four platelet phenotypes co-labeled with EAAT2-CD61 of a certain volunteer using the method of the present application. A. Platelet flow density plot of EAAT2 membrane protein negative isotype control, BC. Platelet flow scatter plot before and after thrombin activation. The ordinate of the AK graph represents the expression level of EAAT2 membrane protein, the abscissa of the AC graph represents the expression level of CD61, and the abscissa of the DK graph represents the expression level of Annexin V. Figures L and M correspond to the K-means clustering graphs of platelets in Figures B and C, respectively, and the percentage of each clustered cell phenotype. DETAILED DESCRIPTION

[0020] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0021] Those skilled in the art will appreciate that, unless specifically stated, the "said" and "the" used herein may also include plural forms. It should be further understood that the term "including" used in the specification of the present application refers to the presence of the features, integers, steps and / or operations, but does not exclude the implementation of other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. The term "and / or" used herein refers to at least one of the items defined by the term, for example, "A and / or B" can be implemented as "A", or as "B", or as "A and B".

[0022] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0023] Platelets are non-nuclear membrane cells, which are non-traditional cells. They are easily stimulated by the extracellular microenvironment and undergo different degrees of skeleton reorganization. The changes in platelet cell phenotype can reflect the stress state of the human body to a certain extent. The platelet's EAAT2 (Excitatory Amino Acid Transporter 2) dominates the platelet's glutamate uptake and is mainly distributed on the platelet membrane and the α-granule membrane of the platelet. When the real-time state of the platelet changes, the distribution of the EAAT2 membrane protein will change significantly with the platelet's skeleton reorganization.

[0024] Existing flow cytometry methods may have the following problems when platelets are severely fragmented or necrotic platelets increase significantly: if fragments are included in the analysis results, the index value will be falsely reduced due to fragmentation. For example, when platelets are stimulated by thrombin, the activation index PAC-1 (antibody that binds to active GPIIb / IIIa integrin, used to measure platelet aggregation status) increases significantly in the early stage of the reaction, but decreases significantly in the later stage of the reaction due to cell rupture; or the index value is falsely increased due to platelet necrosis, for example, due to cell necrosis, cell membrane damage, and intracellular markers are also labeled by fluorescent antibodies. If fragments are not included in the result analysis, the actual degree of platelet loss may be seriously underestimated. These situations will cause the platelet function and status described by the relevant index values ​​to deviate from the truth.

[0025] In order to minimize the interference of sample preparation on platelets, the present application adopts the following method to prepare flow samples for monitoring platelet stress state: (1) Preparing platelet suspension; The freshly collected blood was anticoagulated; the blood was centrifuged at 200 g for 10-15 min at room temperature; the plasma was harvested and allowed to stand for 30 min to obtain platelet-rich plasma; 5 μL of the platelet-rich plasma was added to 95 μL of platelet diluent to obtain a platelet suspension.

[0026] The platelet diluent includes 10 mM HEPES, 135~145 mM NaCl, 2.6~2.8 mM KCl, 11~12 mM NaHCO3, 1~2 mM MgCl2, 0.4~0.5mM Na2HPO4, 5~6mM glucose, and 0.2~0.4% bovine serum albumin, with a pH of pH7.2~pH7.4.

[0027] (2) incubating the platelet suspension with EAAT2 polyclonal primary antibody and flow cytometry fluorescent secondary antibody in combination with monoclonal antibodies to platelet-related markers and / or monoclonal antibodies to Annexin V to prepare a sample to be tested; The EAAT2 polyclonal primary antibody can specifically recognize the extracellular epitope fragments of platelets, and the EAAT2 polyclonal primary antibody can be prepared according to at least one of the extracellular epitope fragments; correspondingly, the flow cytometry fluorescent secondary antibody includes a fluorescent colorimetric marker that can be excited in a flow cytometer, and also includes a segment that can be specifically recognized by the EAAT2 polyclonal primary antibody. The flow cytometry fluorescent secondary antibody in this embodiment is derived from a goat anti-rabbit polyclonal antibody, and specifically can be Anti-rabbit IgG (H+L); Platelet-related markers include CD61, CD41, CD42b, CD62P, CD63, and PAC-1, etc. CD61, also known as GPIIIa, can form a GPIIb / IIIa complex with CD41, participates in platelet aggregation, and is the most commonly used pan-platelet marker. In this embodiment, CD61-PE, i.e., a monoclonal antibody in which CD61 is directly coupled to a PE fluorescent dye, is preferably used. CD41, also known as GPIIb, is an integrin on the surface of platelets that participates in platelet aggregation and thrombosis. CD42b, also known as GPIb, is a von Willebrand factor receptor on the surface of platelets that participates in the adhesion of platelets to damaged blood vessel walls. CD62P, also known as P-selectin, is transferred from the alpha granules inside the platelets to the surface of the platelets after activation and participates in the adhesion of platelets to leukocytes and the inflammatory response. CD63 is a marker for platelet internal granules (alpha granules and dense granules). Increased surface expression of CD63 is associated with platelet activation. PAC-1 is an antibody that binds to active GPIIb / IIIa integrins and is used to measure platelet aggregation status.

[0028] Annexin V, also known as extracellular phosphatidylserine binding protein V or Annexin A5, is a widely used reagent in biological research, especially in the detection of cell apoptosis. The complex of Annexin V and fluorescein can be used for flow cytometry or fluorescence microscopy to detect the apoptotic state of cells.

[0029] The platelet suspension is incubated with the EAAT2 polyclonal primary antibody, CD61-PE and / or Annexin V monoclonal antibodies and flow cytometry fluorescent secondary antibodies in sequence; then the paraformaldehyde is added to fix the sample to form a sample to be tested.

[0030] (3) monitoring the specific signal number of the sample by flow cytometry; This example uses a CytoFLEX flow cytometer for detection. On-machine conditions: CD61 fluorescence selects the PE channel, EAAT2 fluorescence selects the APC channel, APC gain is between 20 and 350, PE is about 300, and compensation is adjusted appropriately according to actual conditions. If the prepared sample cannot be detected on the flow cytometer immediately, it should be protected from light, stored at 4°C, and the detection should be completed within 24 hours. Finally, use CytExport software to analyze the data or extract the corresponding FCS file.

[0031] FCS file, Flow Cytometry Standard File, is a standard format of data files generated by flow cytometers. This format is used to store data generated during flow cytometry analysis. FCS files usually contain the following three parts: Text Header: This part contains detailed information about experimental settings, sample information, instrument parameters, and data analysis methods, usually in the form of key-value pairs, such as "keyword=value"; Data Matrix: This is the core part of the file, containing actual measurement data, each row represents a cell or event, and each column represents a measurement parameter, such as forward scattered light FSC, side scattered light SSC, fluorescence channel, etc.; Metadata: Although metadata is usually included in the text header, it may sometimes exist in other forms to provide additional information about the experiment and analysis.

[0032] (4) Analyzing the specific signal to confirm the stress condition of the sample.

[0033] Previous studies have found that platelet EAAT2 membrane protein can be used to evaluate the quality and activation status of platelets. Figure 1 When EAAT2 membrane protein and CD61 are used to label platelets, four states of platelet subpopulations can be roughly identified: resting platelets, stressed platelets, necrotic platelets, and platelet fragments. Their molecular marker characteristics are: resting platelets—low fluorescence of EAAT2 membrane protein & high fluorescence of CD61; stressed platelets—high fluorescence of EAAT2 membrane protein & high fluorescence of CD61; necrotic platelets—high fluorescence of EAAT2 membrane protein & low fluorescence of CD61; platelet fragments—low fluorescence of EAAT2 membrane protein & low fluorescence of CD61. At different stages of the activation process, the state of platelets has the characteristics of directional transformation, and the direction of transformation is mainly: resting platelets → stressed platelets → necrotic platelets → platelet fragments. Flow cytometric scatter plots can preliminarily classify individual platelets at a two-dimensional level, but other parameter characteristics between different subpopulations are not convenient for statistical analysis.

[0034] In order to overcome the above problems, this application reprocesses the key data in the FCS file to obtain more diverse analysis results.

[0035] One possible implementation method is to extract the data in the FCS file for statistical analysis: (1) Extract and clean data: Extract the values ​​of various parameters of all platelets from the FCS file. (2) Integrate and clean data: Integrate the platelet data of the test population (the data comes from the multi-parameter values ​​of 10,000 platelets per person) and delete the data that may come from platelet aggregates or red and white blood cells. (3) Data analysis: The various parameters of the experimental group can be arbitrarily subjected to frequency distribution, mean comparison, correlation analysis, cluster analysis and other operations according to the research purpose. In addition, for some characteristic parameters (such as CD61), segmentation processing can also be performed to compare and analyze other characteristic parameters of platelets in different segments.

[0036] Optionally, the computer software required for data cleaning and conversion includes at least one of the following: Python (minimum version 3.16), Python toolkit Pandas and FlowCytometryTools, and R language tools.

[0037] The specific operations of data processing are as follows: 1. Using FlowCytometry Tools, construct the program file "fcs2tsv.py" to extract the data from the FCS file. "fcs2tsv.py" contains instructions, including:

[0038] (ii) Use the Perl command line to extract, integrate and clean the data, as follows: First, copy the fcs2tsv.py file to the folder of the FSC file to be analyzed and enter the command in the terminal in the same directory, for example:

[0039] After completing the above data cleaning steps, the first candidate data for subsequent prediction and classification can be obtained.

[0040] The data of cells with FSC values ​​greater than 1,000,000 are deleted based on sorting the data of single platelets according to the extracted FSC values ​​(forward scattered light values), such as Figure 2 The cutoff value of the first candidate data is confirmed by counting the changes in the FSC values ​​of healthy people before and after platelet stress. In this embodiment, 1000000 is selected as the cutoff value. In other embodiments, the cutoff value of the first candidate data should be less than or equal to 1000000 to avoid platelet aggregates, red blood cells, and white blood cells from being included in the statistics.

[0041] The prepared data can be directly used for various traditional statistical analyses, such as frequency distribution, intergroup comparison, multi-parameter correlation analysis, etc. The number of platelets included in the analysis of each sample is 10,000, which is used to analyze the statistical differences of indicators, whether it is the results before and after the intervention or the comparison between different populations, it has extremely high sensitivity. This method can be used for comparison and statistical analysis of platelet characteristics in different states even in a small number of people or even at the individual level.

[0042] One possible implementation is to perform statistical analysis according to different preset populations corresponding to the samples. "Different populations" can be understood as groups artificially divided according to reference standards such as gender, age, region, clinical manifestations, etc. according to clinical statistical needs. By comparing the differences in platelet stress conditions between different groups, the group situation can be understood. For example, based on gender and age, it is possible to examine the changing trend of platelet stress conditions in men and women with age. For example, the degree of influence of diet and geographical conditions on platelet stress conditions can be examined based on the living area. Furthermore, the statistical analysis method is classified according to the content presented in the statistical results, including at least one of the following: descriptive statistical analysis (such as performing chart statistics, calculating mean, median, variance, standard deviation, etc.), test analysis (such as regression analysis, etc.), correlation analysis (such as drawing scatter plots or calculating correlation coefficients, variance analysis, Pearson correlation analysis, etc., such as Figure 3 and Figure 4 as shown), reliability analysis, discriminant analysis and cluster analysis.

[0043] Another possible implementation is to perform statistical analysis according to the different states of the individuals corresponding to the samples. "Different states" can be understood as states that may affect the platelet stress state. For example, within a period of time after an ischemic stroke occurs in a patient, blood samples are collected in units of hours, days or months and analyzed using the method of the present application. The statistical analysis method used is as described above.

[0044] Another possible implementation is to perform statistical analysis according to the state of the sample before and after in vitro stress, and perform in vitro stress on the collected samples, so as to examine the potential stress state or stress reserve capacity of platelets in the sample, and to perform health assessment on the person providing the blood sample, which can be applied to the diagnosis and treatment of stroke, neurodegenerative diseases, autoimmune diseases and cancer. Optionally, the stress regulators used for in vitro stress of the sample include: glutamate, arachidonic acid (AA), adenosine diphosphate (ADP), collagen (Coll), epinephrine (EPI), thrombin, thrombin receptor activating enzyme (TRAP) and lipopolysaccharide (LPS) as activators; excitatory amino acid transporter inhibitors as inhibitors; and GPRP tetrapeptide, an inhibitor for preventing fibrin polymerization and thus preventing blood clot formation. The above stress regulators should be used to intervene or pretreat the sample before adding EAAT2 polyclonal primary antibody and CD61-PE to the sample to be tested.

[0045] Figure 3 The Pearson correlation analysis of the method of the present application using high concentration of glutamate to change the phenotype of platelet EAAT2 membrane protein is used to study the linear relationship between the applied concentration of glutamate and the degree of platelet stress. Previous studies have found that high concentration of glutamate can induce hyperreactivity of platelets, that is, when subjected to traditional activators (such as thrombin, etc.), it can significantly enhance the activation intensity of platelets, but does not directly cause their activation. Figure 3 A is platelets not incubated with 250 μM glutamate. Figure 3 B is a graph of the Pearson correlation coefficient calculated based on the multi-parameters of a single platelet after incubation with 250 μM glutamate for 30 min. The results show that after incubation with high concentrations of glutamate, the platelet volume increases, the cell surface undergoes significant changes, and the expression of EAAT2 and CD61 increases to varying degrees, suggesting that the above factors have a significant linear relationship with the applied concentration of glutamate. In the figure, FSC-A represents the forward scatter parameter, which is often used to describe cell size; SSC-A represents the side scatter parameter, which is often used to describe particle complexity.

[0046] The data collected according to the statistical target of the first candidate data can usually reflect the different activation states of platelets between different samples. Platelets jointly labeled with EAAT2 membrane protein and CD61 can be roughly divided into four states of platelets: resting platelets, stressed platelets, necrotic platelets and platelet fragments: Figure 4 As shown, Figure 4A-4B is a platelet flow cytometry scatter plot, which shows the expression characteristics of CD61 and EAAT2 membrane proteins in healthy human platelets under basal conditions (Basal) and thrombin-activated conditions (0.5 UThrombin). Figure 4 C-4F, according to the cell size and activation characteristics of CD61 low fluorescence platelet subpopulation (CD61+) and CD61 high fluorescence platelet subpopulation (CD61++), platelets can be roughly divided into three states: low activation state (CD61++, PAC-1 low / CD62P low), high activation state (CD61++, PAC-1 high / CD62P high) and weak reaction state (CD61+, PAC-1 low / CD62P low). Figure 4 Expression characteristics of EAAT2 membrane protein in CD61+ platelet subsets and CD61++ platelet subsets under G-4I-based conditions and thrombin-stimulated conditions. .

[0047] Figure 4 The results showed that CD61 can be divided into two groups of platelets: CD61 low fluorescence (CD61+) and CD61 high fluorescence (CD61++). Using platelet activation markers PAC-1 and CD62P, CD61++ platelets can be further divided into low-activated platelets (CD61++, PAC-1 low / CD62P low) and high-activated platelets (CD61++, PAC-1 high / CD62P high). Before and after thrombin activation, the PAC-1 and CD62P signals of CD61+ platelets have no significant difference or only a slight increase, so it can be concluded that the CD61+ platelet group is unresponsive or weakly reactive platelets.

[0048] Based on this, it was speculated that CD61+ platelets may be composed of damaged platelets and platelet fragments. Therefore, Annxin V was further used to verify the four states of platelets. Annxin V is a Ca 2+ Annexin V expression characteristics of four platelet phenotypes using EAAT2-CD61 combined labeling.

[0049] The results are as follows Figure 6 As shown, Figure 6 A is the platelet flow density graph of the EAAT2 membrane protein negative isotype control. Figure 6 B-6C is a flow cytometry scattergram of platelets before and after thrombin activation. Figure 6 The ordinate of A-6K represents the expression level of EAAT2 membrane protein. Figure 6 The abscissa of A-6C represents the expression level of CD61. Figure 6 The abscissa of D-6K represents the expression level of Annexin V.

[0050] Platelets in the P4 region showed low expression of Annxin V, while those in P1, P2, and P3 showed high expression. Based on this, it is believed that platelets in P1~P3 are mainly platelets in the early apoptotic or necrotic state and platelet fragments, while platelets in the P4 region are mainly resting platelets. Platelets with low CD61 fluorescence (i.e., platelets in the P1 and P2 regions) increased significantly after thrombin activation and can be further divided into two phenotypes: high expression of EAAT2 and low expression of EAAT2. Considering that there are abundant EAAT2 proteins in platelets, they can only be recognized by EAAT2 membrane protein antibodies after they are located on the cell membrane surface or after the cell membrane is damaged. Based on this, it is inferred that platelets in the P1 region are necrotic platelets in which both intracellular and extracellular EAAT2 proteins are labeled after the platelet membrane is damaged; the CD61, EAAT2, and Annxin V signals of platelets in the P2 region are significantly reduced, so they may mainly be platelet debris or fragments that have lost a large amount of membrane proteins after activation.

[0051] Figure 4 and Figure 6 The results presented by the flow density map (or scatter plot) fail to include the size of the platelets, particle complexity, and other parameter values, and thus cannot more comprehensively reflect the platelet stress state of the current sample. A large degree of manual judgment is still required, and the information presented in the chart is scattered, making it difficult to further improve the efficiency of automatic statistical analysis and provide evaluation information with more clinical value.

[0052] To solve this problem, the k-means clustering algorithm was further used to perform unsupervised PCA clustering of platelet status, resulting in a more intuitive and detailed cell typing of platelet phenotypes.

[0053] In this embodiment, the specific process and commands of the unsupervised PCA cluster analysis of the k-means clustering algorithm are as follows:

[0054]

[0055] like Figure 5 As shown, the K-means clustering diagram of platelet phenotype based on CD61 and EAAT2 membrane proteins. Figure 5 A is the K-means clustering diagram of unactivated platelets; Figure 5 B is the K-means clustering diagram of thrombin-activated platelets; Figure 5 C is Figure 5 A corresponds to the flow density map of unactivated platelets; Figure 5 D is Figure 5 B corresponds to the flow density map of thrombin-activated platelets. Figure 5C and Figure 5 From the comparison of A, we can see that the flow density map divides the platelets in the sample into three subgroups, and the K-means clustering map can cluster the platelets in the sample into four categories. K-means clustering analysis can identify a group of subtypes that are quite different from other platelets but are smaller in number. Figure 5 D and Figure 5 From the comparison of B, we can see that the flow density map divides the platelets in the sample into three subgroups, and the K-means cluster map can cluster the platelets in the sample into five categories. K-means cluster analysis can further classify the relatively similar platelet subgroups and identify at least one subtype that is similar but actually different. This suggests that the changes that occur in platelets after activation may be more complicated than what is currently understood, and requires more in-depth research.

[0056] Further, refer to Figure 6 , Figure 6 L and Figure 6 M corresponds to Figure 6 B. Figure 6 C K-means clustering diagram of platelets and the percentage of each cell phenotype in the cluster. Figure 6 L and Figure 6 From the comparison with B, we can see that the flow density map divides the platelets in the sample into four subgroups, and the K-means clustering map can cluster the platelets in the sample into five categories. K-means clustering analysis can identify a group of subtypes that are quite different from other platelets but are smaller in number. Figure 6 M and Figure 6 From the comparison of C, we can see that the flow density map divides the platelets in the sample into 4 subgroups, and the K-means cluster map also clusters the platelets in the sample into 4 categories. However, the K-means cluster analysis can further confirm the percentage of each cluster, with a high resolution, and can distinguish a category with a percentage of only 0.02%. This suggests that K-means cluster analysis can be used to classify platelet samples more finely, which is conducive to more effective analysis and interpretation of sample data information, making the evaluation conclusion closer to the actual situation, or conducive to discovering new problems or phenomena.

[0057] In summary, the present application provides a method for evaluating platelet stress state, comprising the following steps: preparing a platelet suspension; incubating the platelet suspension using EAAT2 polyclonal primary antibody and flow cytometry fluorescent secondary antibody, and combining monoclonal antibodies of platelet-related markers and / or monoclonal antibodies of Annexin V to prepare a sample to be tested; detecting the specific signal of the sample by flow cytometry; analyzing the specific signal to confirm the stress state of the sample. The method of the present application can flexibly process the multi-parameter data of a single platelet, and combine the expression pattern and characteristics of the EAAT2 membrane protein to achieve specific clustering analysis of platelet subpopulations. In addition, flexible and efficient statistical analysis can be performed on the inter-group differences of each parameter and the correlation between parameters, ultimately achieving personalized and accurate diagnosis and analysis of the platelet quality and activation state of the subject.

[0058] Those skilled in the art will appreciate that the various operations, methods, steps, measures, and schemes in the processes discussed in this application may be alternated, changed, combined, or deleted. Furthermore, other steps, measures, and schemes in the various operations, methods, and processes discussed in this application may also be alternated, changed, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and schemes in the related art that are similar to those disclosed in this application may also be alternated, changed, rearranged, decomposed, combined, or deleted.

[0059] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0060] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0061] The above is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the scheme of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A method for evaluating platelet stress state, characterized in that: The following steps are involved: preparing platelet suspension; The platelet suspension is incubated with EAAT2 polyclonal primary antibody and flow cytometry fluorescent secondary antibody in combination with monoclonal antibodies of platelet-related markers and / or monoclonal antibodies of Annexin V to prepare a sample to be tested; Detecting specific signals of the sample by flow cytometry; Analysis of the specific signal confirms the stress status of the sample.

2. The evaluation method according to claim 1, characterized in that: The analyzing of the specific signal comprises: Preliminarily screening the data containing specific signals to obtain first candidate data; Statistical analysis is performed on the first candidate data to obtain statistical results related to the platelet stress state.

3. The evaluation method according to claim 2, characterized in that: The preliminary screening of the data containing the specific signal to obtain the first candidate data comprises: Get FCS files containing data of specific signals; Extracting text segment information and / or data segment information corresponding to a single platelet in the FCS file; Sort the data of individual platelets according to the extracted forward scattered light values ​​and determine the cutoff value; After discarding the data of single platelets larger than the cutoff value, the first candidate data is obtained.

4. The evaluation method according to claim 3, characterized in that: The cutoff value is confirmed by statistically analyzing the changes in forward scattered light values ​​of healthy people before and after platelet stress; the cutoff value is not less than 1,000,000.

5. The evaluation method according to claim 2, characterized in that: The performing statistical analysis on the first candidate data includes: Conduct statistical analysis according to the preset different populations corresponding to the samples; Conduct statistical analysis according to the different states of the individuals to which the samples correspond; Statistical analysis was performed according to the status of the samples before and after in vitro stress.

6. The evaluation method according to claim 5, characterized in that: Stress modulators used for in vitro stress of samples include: glutamate, arachidonic acid, adenosine diphosphate, collagen, adrenaline, thrombin, thrombin receptor activating enzyme or lipopolysaccharide as activators; excitatory amino acid transporter inhibitors as inhibitors; and inhibitors for preventing fibrin polymerization and thus preventing blood clot formation.

7. The evaluation method according to claim 5, characterized in that: The statistical analysis includes at least one of the following: descriptive statistical analysis, test analysis, correlation analysis, reliability analysis, discriminant analysis and cluster analysis.

8. The evaluation method according to claim 7, characterized in that: The cluster analysis includes an unsupervised PCA cluster analysis method using k-means clustering, and the number of clusters is selected to match different stress states of corresponding platelets.

9. The evaluation method according to claim 7, characterized in that: The cluster analysis is based on the fluorescence intensity of EAAT2 combined with platelet-related markers and / or Annexin V for clustering; the fluorescence intensity is divided into low fluorescence and high fluorescence according to a threshold value.

10. The evaluation method according to claim 1, wherein: The platelet-related markers include at least one of the following: CD61, CD41, CD42b, CD62P, CD63 and PAC-1.

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