Method for annotating vesicle cell subpopulation based on single vesicle membrane proteomics

Abstract

The invention discloses a method for annotating a vesicle cell subset based on monovesicle membrane proteomics, and belongs to the technical field of vesicle cell annotation. The method comprises the following steps: 1, carrying out exosome collection on a plasma sample to obtain a sample; 2, coding and labeling the antibody probe, and adding a protein tag and a labeled DNA sequence into the antibody probe to obtain a labeled antibody probe; 3, preparing a corresponding combined product; 4, adding a labeled antibody probe into the sample to obtain an exosome compound; step 5, capturing an exosome conjugate through CTB; 6, adding the binding product into the exosome conjugate to generate a sequence; 7, constructing a sequence library and sequencing to obtain a sequencing sequence; 8, removing a sequencing sequence with low expression quantity to obtain an exosome expression data matrix, and standardizing the exosome expression data matrix; 9, carrying out clustering analysis to find out an exosome core subgroup; and step 10, determining the source of the exosome core subgroup, and annotating the single vesicle subgroup.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vesicle cell annotation, and particularly relates to a method for annotating vesicle cell subgroups based on single-vesicle membrane proteomics. BACKGROUND

[0002] Single-vesicle membrane proteomics is based on mass spectrometry technology, and through deep analysis of proteins on a single vesicle membrane, cell biology and disease matrix can be deeply studied, including neurodegenerative diseases, cancer and infectious diseases. Single-vesicle membrane proteomics is a trace, high-throughput and target membrane protein single-vesicle solution, which can not only analyze the membrane protein expression of exosomes from the perspectives of overall protein expression and subpopulation analysis. Single-vesicle membrane proteomics has no particle size preference, and can detect the expression information of 240+ membrane proteins of millions of exosomes at the single-vesicle level, and analyze the heterogeneity of vesicles.

[0003] Cell annotation is a core step in single-cell genomics data analysis, which refers to the process of matching and labeling cell clusters generated by high-throughput sequencing with known cell types, states or functions through bioinformatics methods. The process of mapping unlabeled cell populations to a biological classification system.

[0004] At present, in the analysis link of single-vesicle membrane proteomics sequencing data, there is still no vesicle annotation. This link has the following deficiencies: 1) Insufficient coverage of vesicle subgroups and lack of functional states: existing tools still have no report on single-vesicle markers, and often ignore the annotation of key functional states such as activation, exhaustion and aging. In addition, low-abundance marker genes may not be accurately detected due to data sparseness, further affecting the accuracy of annotation.

[0005] 2) Data sparseness and noise: the high dimensionality and low detection sensitivity of single-vesicle data result in a sparse gene expression matrix, and there is technical noise (such as amplification bias), which affects the accuracy of annotation. Batch effect: technical variations between different experimental batches or platforms may introduce systematic bias, leading to inconsistent annotation results across datasets.

[0006] 3) Annotation tool diversity and lack of standardization: different annotation tools (such as Seurat, SingleR, CellID) rely on different algorithms (clustering, machine learning, reference alignment), resulting in inconsistent results and lack of uniform standards. Different researchers may have significant differences in the annotation of the same cell type, affecting the comparability and reproducibility of the results. Manual annotation relies on expert experience, while automated tools (such as CellMarker, SingleR) have limited ability to identify rare cell types.

[0007] 4) Difficulty in identifying unknown vesicle types: For rare vesicle types found in serum or tissues, existing marker gene-based methods are difficult to accurately classify, and prior knowledge or new tool development is required. In some tools, it is still difficult to be effectively distinguished.

[0008] Therefore, a method for annotating vesicle cell subgroups based on single vesicle membrane proteomics is needed to address the heterogeneity of exosomes, find core subgroups, and address the problems of exosome heterogeneity and tracing. SUMMARY

[0009] To achieve the above purpose, the following technical solutions are adopted: A method for annotating vesicle cell subgroups based on single vesicle membrane proteomics, comprising the following steps: Step one: collecting exosomes from plasma samples to obtain samples; Step two: DNA fragment coding labeling is performed on the antibody probe, and protein tags and labeled DNA sequences are added to the antibody probe to obtain a labeled antibody probe; Step three: preparing a binding product of a complex tag that is complementary to and recognizes the labeled DNA sequence in step two; Step four: adding the labeled antibody probe in step two to the sample in step one to combine the sample with the antibody probe to obtain an exosome complex; Step five: adding the exosome complex in step four and the cholera toxin B subunit to capture the exosome complex combined with the labeled antibody probe; Step six: adding the binding product in step three to the exosome complex in step five to generate sequences of exosome tags, protein tags, and labeled DNA tags; Step seven: performing PCR amplification on the sequences of exosome tags, protein tags, and labeled DNA tags in step six to form DNA fragments, adding adapters to construct a library, sequencing the library, and obtaining sequencing sequences containing exosome tag, protein tag, and labeled DNA tag sequences; Step eight: filtering the sequencing sequences in step seven to remove low-expression sequencing sequences, and counting the types and quantities of proteins containing the same exosome tag to obtain an exosome expression data matrix, and standardizing the exosome expression data matrix; Step nine: based on the standardized exosome expression data matrix, performing cluster analysis on all exosome expression data matrices to find an exosome core subgroup; Step ten: determining the cells from which the exosome core subgroup is derived, and annotating the single vesicle subgroup through labeled DNA proteins.

[0010] As a further improvement of the technical solution, in step eight, the filtered exosome expression data matrix is standardized by SCTransform.

[0011] As a further improvement of the technical solution, in step nine, the exosome expression data matrix is analyzed by PCA technology for dimension reduction, and then normalized to identify the vesicle cluster.

[0012] As a further improvement of the technical solution, in step nine, the subpopulation is classified according to the differentially up-regulated expression proteins of each single vesicle subpopulation relative to other single vesicle subpopulations.

[0013] As a further improvement of the technical solution, in step two, the DNA fragments are encoded and labeled by PBA technology.

[0014] As a further improvement of the technical solution, in step three, the binding product of the complex tag is prepared by rolling circle replication with circular DNA.

[0015] As a further improvement of the technical solution, in step seven, two different PCR primers are used for PCR amplification to form double-stranded DNA fragments.

[0016] As a further improvement of the technical solution, in step five, the cholera toxin B subunit is fixed on the well plate, and the exosome complex in step four is added to the well plate. The cholera toxin B subunit captures the exosome complex combined with the labeled antibody probe, while the unbound exosome and antibody probe are washed.

[0017] As a further improvement of the technical solution, in step one, the plasma sample is filtered through a 0.8um filter, eluted in a gel exclusion column, and then added to the column for exosome collection. Finally, the sample is washed with PBS, and after washing, the column is sealed.

[0018] Compared with the prior art, the beneficial effects of the present application are as follows: The present application has higher robustness, integrates multi-omics data, is suitable for establishing a standardized annotation process, and can guarantee the accuracy of cell annotation while reducing the complexity of manual annotation. The single vesicle membrane proteomics solves the heterogeneity problem of exosomes, finds the core subpopulation, and solves the two big problems of exosome heterogeneity and tracing, which can improve the recognition ability of new single vesicle subpopulation. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A PBA flowchart is provided for the method of annotating vesicle cell subpopulation based on single vesicle membrane proteomics of the present application; Figure 2 A data graph of PBA analysis of sEVs number of AD patients and normal control group is provided for the present application; Figure 3A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 4 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 5 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 6 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 1 ; Figure 7 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 2 ; Figure 8 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 3 ; Figure 9 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 10 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 11 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 12 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 13 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 14 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 15 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 1 ; Figure 16 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 2 ; Figure 17 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 18 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 19 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 20 A data graph of the number of proteins in AD patients and normal control groups analyzed by PBA provided by the present application is provided; Figure 21 AddModuleScore plot of macrophage provided by the present application; Figure 22 AddModuleScore plot of natural killer cell provided by the present application; Figure 23 AddModuleScore plot of oligodendrocyte provided by the present application; Figure 24 AddModuleScore plot of pericyte provided by the present application; Figure 25 AddModuleScore plot of CCR4+ T cell provided by the present application.

[0020] Figure 26 AddModuleScore plot of CD3E+ T cell provided by the present application; Figure 27 AddModuleScore plot of CD8A+ T cell provided by the present application; Figure 28 AddModuleScore plot of NKT cell provided by the present application; Figure 29 AddModuleScore plot of SIGLEC9 microglia provided by the present application; Figure 30 AddModuleScore plot of SIRPA microglia provided by the present application; Figure 31 AddModuleScore plot of CSF3R microglia provided by the present application. DETAILED DESCRIPTION

[0021] The present application is further illustrated by the following examples, but the present application is not limited to the scope of the examples. The experimental methods not specifically regulated in the following examples are selected according to the product instructions. All other examples obtained by those of ordinary skill in the art based on the described examples of the present application are within the scope of the present application without the need for creative labor. Unless otherwise defined, the technical terms or scientific terms used herein shall have the usual meaning understood by those skilled in the art in the field to which the present application belongs.

[0022] Example: A method for annotating subpopulations of vesicle cells based on single vesicle membrane proteomics, comprising the following steps: Step one: collect exosomes from the plasma sample to obtain a sample; the exosome collection process is as follows: (1) plasma pretreatment: the plasma is filtered through a 0.8 um filter, and after filtration, to reduce sample loss, 250 ul of PBS is used to flush the filter to make the filtrate volume about 1 ml, and it is ready for use in the incubator; (2) column equilibration: take the gel exclusion column out of the incubator at four degrees, place it on the exclusion rack, remove the upper and lower plugs, and then flush it with 0.1M PBS, flushing for 10 ml; (3) sample loading: after the column is well balanced and the liquid flow is exhausted, add 1 ml of pretreated plasma to the column; (4) exosome collection: after the sample is completely added to the column, the liquid at the sieve plate is 5, and no liquid is discharged from the lower outlet, then place a 2 ml collection tube under the exclusion column, add 0.1M PBS for elution, and collect exosomes, 1 ml each time, and a total of 2 ml; (5) exosome condensation: pour all the collected exosomes into a 100 kd ultrafiltration tube, centrifuge at 4000g for 2 min, and the remaining liquid is about 200 ul; (6) column recovery: after the exclusion column has collected 2 ml of exosome components, it is continuously flushed with 0.1M PBS, and the flushing is 10 ml; (7) column sealing: after flushing, flush with 5 ml of 20% ethanol, then add 1 ml of 20% ethanol to block the upper and lower outlets of the column, complete the column sealing, and store the column at 4°C; Step two: encode and label the antibody probe with a DNA fragment by PBA technology, add a protein tag and a labeled DNA sequence to the antibody probe to obtain a labeled antibody probe; each antibody probe is coupled with an oligonucleotide sequence, and the nucleic acid sequence contains a unique protein tag to label specific antibodies and DNA molecules for protein quantification; the labeled DNA sequence is a UMI DNA sequence; Step three: prepare a binding product carrying a complex tag complementary to and recognizing the labeled DNA sequence in step two by rolling circle replication with a circular DNA; Step four: add the labeled antibody probe in step two to the sample in step one to combine the sample with the antibody probe to obtain an exosome complex; Step five: capture the exosome complex combined with the labeled antibody probe with cholera toxin B subunit; the cholera toxin B subunit is fixed on the well plate, the exosome complex in step four is added to the well plate, and the cholera toxin B subunit captures the exosome complex combined with the labeled antibody probe, while washing the unbound exosomes and antibody probes; Step six: add the binding product in step three to the exosome complex in step five to generate the sequences of exosome tags, protein tags, and labeled DNA tags; Step seven: PCR amplification of the sequences of the exosome tag, protein tag and marker DNA tag in step six to form DNA fragments, add adapters to construct a library, sequence the library, and obtain sequencing sequences containing the sequences of the exosome tag, protein tag and marker DNA tag; two different PCR primers are used for PCR amplification to form double-stranded DNA fragments; Step eight: Due to the highly sparse matrix in the single-vessel level protein expression profile in each sample, first filter the proteins that do not meet the presence in at least 20 vesicles to solve the computational limitations caused by a large number of zeros, filter the sequencing sequences in step seven, perform quality control, use Fast QC to filter out low-expression sequencing sequences, remove unmap sequences by comparing sequencing sequences and designed marker DNA tag sequences, obtain aligned sequences; then remove duplicate sequences by comparing UMI sequences in the DNA fragments, the obtained data is the de-duplicated data; finally, determine the corresponding protein antibody code by antibody probe comparison, determine the type of protein, quantify the protein by UMI number, calculate the number of single vesicles detected by protein tag comparison, and count the number and type of proteins containing the same protein tag to obtain the exosome expression data matrix; then use the pivot_wider function of the tidyr package to convert the data from long format to wide format. Then replace the NA value by using the mutate_all function in the dplyr package, create a Seurat object by using the CreateSeuratObject function of the Seurat package, and then perform SCTransform standardization processing on the exosome expression data matrix to eliminate the influence of differences in detection data volume; Step nine: Based on the normalized exosome expression data matrix, perform PCA analysis for dimension reduction, and use the RunHarmony function for normalization processing, then use the clustree (version: 0.5.1) package to explore the influence of different clustering resolutions, help to select the appropriate number of clusters, and use the FindClusters function of the Seurat package to identify vesicle clusters, so as to find the core subpopulation of exosomes; use the RunUMAP function to perform uniform manifold approximation and projection (UMAP), and then use the DimPlot function to plot the vesicle clusters; Step ten: Determine the source of the exosome core subpopulation of cells, and annotate the single vesicle subpopulation by labeled DNA proteins; use the FindAllMarkers function of the Seurat package to find the differentially expressed proteins of each single vesicle subpopulation relative to other single vesicle subpopulations, these differentially expressed proteins are the potential marker proteins of each vesicle subpopulation, use the DotPlot, FeaturePlot and DoHeatmap functions of the Seurat package to plot the marker proteins, and classify the subpopulations according to the marker proteins, and determine the source of the cells by integrating existing tools such as CellMarker, PangaoDB and cell taxonomy database and consulting relevant literature, use known marker proteins to compare with the protein expression profile of single vesicles in the sample, select highly expressed markers as the final identified single vesicle type.

[0023] Effect verification: Functional enrichment analysis: In order to further understand the role of these marker proteins in the pathological mechanism of AD (Alzheimer's disease), we use the ClusterProfiler package to perform GO (including biological process, cellular component and molecular function) and KEGG pathway enrichment analysis on these differentially expressed proteins in the normal control group and AD (Alzheimer's disease) patients. KEGG is a database resource that provides information on the advanced functions and uses of biological systems at the molecular level, such as cells, organisms and ecosystems, especially large-scale molecular data sets generated by genome sequencing and other high-throughput experimental techniques.

[0024] AddModuleScore scoring: Use the AddMouduleScore function of the Seurat package to score the annotated single vesicle subpopulation, first select a background gene set similar in size to the target gene set, and calculate the average expression score of each protein in each cell using the AddModuleScore function, that is, calculate the module score of each vesicle on each vesicle type in turn, the module with the highest score is marked as the cell type of the cell, and the expression of the vesicle type marker protein in the AD (Alzheimer's disease) and normal control sample is visualized by FeaturePlot.

[0025] As Figures 1 to 8 shown, the PBA single vesicle membrane proteomics PBA technology encodes labeled antibodies through DNA fragments, and can assign a unique DNA fragment label to each vesicle, thereby achieving single vesicle proteome detection through multiple immune recognition and single vesicle coding Figure 1). By DNA fragment sequencing and bioinformatics analysis of AD (Alzheimer's disease) and normal control samples, tens to millions of vesicles in each sample can be analyzed, and the expression data of multiple proteins on each vesicle can be obtained Figures 2 to 4 ). Volcano plot results show that NCR1 and NES protein expression is elevated Figure 5 ). In the enrichment analysis results, biological processes (Biological Process, BP) are enriched in immune response, viral life cycle and transmission process, and cell adhesion response Figure 6 ) and cellular components (Cellular Component, CC) are mainly enriched in extracellular plasma membrane, focal adhesion, membrane microdomain, secretory granule membrane, membrane raft, endocytic vesicle, matrix plasma membrane and integrin complex Figure 7 ) and molecular functions (Molecular Function, MF) are mainly enriched in integrin binding, viral receptor activity, exogenous protein binding, cytokine binding, immune receptor activity and growth factor binding Figure 8 ). KEGG pathway analysis shows that these proteins are significantly enriched in hematopoietic cell line, cell adhesion molecule, PI3K-Akt signaling pathway and extracellular matrix receptor interaction pathway Figure 9 ), which may participate in the pathological mechanism of AD (Alzheimer's disease) by regulating cell inflammatory response, proliferation and immunity.

[0026] Note: The figure is a PBA flowchart; Figure 2 The number of sEVs detected in each sample; Figure 3 The number of proteins detected in each sample; Figure 4 The number of proteins detected in each sEV; Figure 5 Proteomics volcano plot (normal and AD comparison); Figures 6 to 8 GO enrichment analysis; Figure 9 KEGG enrichment analysis.

[0027] Single vesicle dimensionality reduction and clustering visualization: as shown in Figure 10 , 49 vesicle subgroups can be identified by dimensionality reduction clustering analysis and batch effect removal using the Harmony package, and visualized by UMAP plot Figure 10 ).

[0028] Single vesicle type differential protein type: as shown in Figure 11 , the differential proteins are plotted using DotPlot Figure 11 ), and the subgroups are annotated according to the marker proteins Figure 12 and Figure 13), the core subgroups include single-vessel subgroups such as natural killer cells (NK cells), astrocytes, dendritic cells, endothelial cells, pericytes, NKT cells, macrophages, microglia, oligodendrocytes, and T cells. Meanwhile, the proportions of different types of vesicles in AD and normal control samples were compared, and it was found that the proportions of NK cell and astrocyte vesicles were higher in AD samples Figure 14 . In addition, the NCR1 protein in natural killer cell vesicles in AD was significantly reduced, and the NES protein in astrocyte vesicles was significantly increased Figure 15 and Figure 16 . These are consistent with existing research results, indicating that our results have a certain reliability. In order to verify the rationality and accuracy of the identification of vesicle types, the marker genes of known cell types were visualized in a heatmap Figure 17 ), the identified vesicle types and known cell type marker proteins are consistent.

[0029] Note: In Figure 11 , the bubble chart shows the marker proteins of single-vessel subgroups, the larger the dot, the higher the proportion of expressing the protein in the vesicle subgroup, and the darker the color, the higher the expression of the protein in the subgroup; Figure 12 shows the distribution of each vesicle subgroup in AD patients; Figure 13 shows the distribution of each subgroup in AD patients and normal control groups, and each region of different color represents a vesicle subgroup; Figure 14 shows the proportion distribution of each vesicle subgroup, and the larger the area, the greater the proportion of the subgroup; Figure 15 and Figure 16 map known marker genes to annotated cell subgroups, and the darker the color, the higher the expression in the subgroup, and show the distribution of NCR1 and NES proteins; Figure 17 is a marker protein expression heatmap, and the redder the color, the higher the protein expression in the annotated cell.

[0030] AddModuleScore scoring results: as shown in Figures 18 to 31 , map known marker genes to annotated cell subgroups, and the darker the red color, the higher the score of the module marked as the higher the reliability of the vesicle type Figures 18 to 31 , as shown in the figure, the annotation results of the present application are reliable.

[0031] In addition, it needs to be explained that functions used in the application process are all software systems, and are not improved, and are not the improvement points of the application, which will not be described in detail here.

[0032] The above is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the present application.

Claims

1. A method of annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics, characterized in that, The method comprises the following steps: Step 1: collecting exosomes from a plasma sample to obtain a sample; Step 2: encoding and labeling DNA fragments of an antibody probe, adding a protein tag and a labeled DNA sequence to the antibody probe to obtain a labeled antibody probe; Step 3: preparing a binding product of a complex tag that is complementary to and recognizes the labeled DNA sequence in step 2; Step 4: adding the labeled antibody probe in step 2 to the sample in step 1 to combine the sample and the antibody probe, and obtaining an exosome complex; Step 5: adding the exosome complex in step 4 and a cholera toxin B subunit to the exosome complex in step 4 to capture the exosome complex combined with the labeled antibody probe; Step 6: adding the binding product in step 3 to the exosome complex in step 5 to generate a sequence of the exosome tag, the protein tag and the labeled DNA tag; Step 7: performing PCR amplification on the sequence of the exosome tag, the protein tag and the labeled DNA tag in step 6 to form DNA fragments, adding adapters to construct a library, sequencing the library, and obtaining a sequencing sequence containing the sequence of the exosome tag, the protein tag and the labeled DNA tag; Step 8: filtering the sequencing sequence in step 7 to remove low-expression sequencing sequences, and counting the types and quantities of proteins containing the same exosome tag to obtain an exosome expression data matrix, and meanwhile standardizing the exosome expression data matrix; Step 9: based on the standardized exosome expression data matrix, performing cluster analysis on all exosome expression data matrices to find an exosome core subpopulation; Step 10: determining the cells from which the exosome core subpopulation is derived, and annotating the single-vesicle subpopulation by the labeled DNA protein.

2. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics according to claim 1, characterized in that, In step 8, the filtered exosome expression data matrix is standardized by SCTransform.

3. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 9, the exosome expression data matrix is analyzed by PCA technology to reduce the dimension, and then normalized to identify the vesicle cluster.

4. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 9, each single-vesicle subpopulation is classified according to the differentially up-regulated expression proteins thereof relative to other single-vesicle subpopulations.

5. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 2, the antibody probe is labeled by DNA fragment coding by PBA technology.

6. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 3, the binding product of the complex tag is prepared by rolling circle replication of the looped DNA.

7. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 7, two different PCR primers are used for PCR amplification to form double-stranded DNA fragments.

8. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 5, the cholera toxin B subunit is fixed on a well plate, the exosome complex in step 4 is added to the well plate, the cholera toxin B subunit captures the exosome complex combined with the labeled antibody probe, and the unbound exosomes and antibody probes are washed.

9. The method for annotating subpopulations of vesicle cells based on single- vesicle membrane proteomics of claim 1, wherein, In step 1, the plasma sample is filtered, eluted in a gel exclusion column, and then collected in the column to obtain the sample, and finally the sample is washed with PBS, and after the washing is completed, the column is sealed.

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