Marker based on single-cell protein modification as well as detection and analysis method and application thereof

Abstract

The invention discloses a marker based on single-cell protein modification as well as a detection and analysis method and application thereof. The method comprises the following steps: acquiring a tissue sample and a control tissue, characterizing immune cells in the tissue sample and the control tissue, marking the cells by using an antibody of a cell marker, characterizing protein modification by using a generic modified antibody, and comparing expression differences of the tissue sample and the control tissue under various cell types; sorting immune cells which are different from the control tissue expression in the tissue sample, making the sorted immune cells into a whole proteome and a modified proteome, and screening out proteins and modified proteins which are remarkably different; and taking the obtained protein and modified protein with significant difference as a detection antibody group, wherein the detection antibody group is a marker based on single-cell protein modification. According to the method, a marker based on single cell protein modification can be obtained, so that protein modification can be characterized on the single cell level.

Description

Technical Field

[0001] The present invention relates to the field of immunotechnology, and in particular to a marker based on single cell protein modification and a detection and analysis method and application thereof. Background Art

[0002] The immune microenvironment refers to the local environment present in tumors or other pathological tissues, which includes a variety of immune cells, cytokines, chemical signaling molecules, extracellular matrix components and other cell types. This environment has an important impact on the progression of the disease, treatment response and patient prognosis. For example: the expression of PD-1 protein on T cells in tumors can be used to predict the patient's prognosis; the FASN protein involved in lipid metabolism in macrophages in fatty liver plays a key role in the subsequent metastasis to cirrhosis; autoimmune diseases such as lupus erythematosus are caused by excessive expression of antibody proteins by B cells in the immune system. Therefore, correctly characterizing the protein expression on immune cells in the immune microenvironment is crucial for the treatment of the disease, as well as the survival and overall health of the patient.

[0003] There are two main technical routes for characterizing the immune microenvironment in related technologies. One is sequencing-based single-cell technology, which includes but is not limited to single-cell DNA sequencing, single-cell RNA sequencing, and single-cell ATAC sequencing; the other is antibody-based single-cell protein technology, which includes metal antibody-based mass spectrometry flow cytometers and imaging mass spectrometry flow cytometers, as well as fluorescent antibody phenocyder-Fusion technology. Although single-cell sequencing technology can well characterize cell status, the correlation between mRNA expression and protein abundance is not high, resulting in single-cell sequencing technology cannot be directly used to predict protein abundance. For antibody-based single-cell protein technology, the upper limit of antibody combinations is about 500. Compared with the expression of tens of thousands of proteins in cells, the number that can be characterized by antibody-based single-cell protein technology is limited.

[0004] Due to the complexity of cell types involved in immune microenvironment activities, it is particularly important to characterize the complex immune microenvironment using single-cell level technology. Mass spectrometry flow cytometry technology and imaging mass spectrometry flow cytometry technology based on mass spectrometry and metal-coupled antibody platforms can easily characterize protein expression on known cell subtypes and their spatial locations. However, protein modifications that are closely related to protein function still lack systematic detection and analysis methods. Therefore, providing an analysis method based on markers of single-cell protein modifications has become a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0005] The present invention discloses a marker based on single cell protein modification and a detection and analysis method and application thereof, so as to solve the technical problem of lack of systematic detection and analysis method for protein modification in related technologies.

[0006] In order to solve the above problems, the present invention adopts the following technical solutions: The first aspect of the present invention provides a method for detecting and analyzing markers based on single cell protein modification.

[0007] The present invention provides a method for detecting and analyzing markers based on single cell protein modification, comprising the following steps: Step 100: obtaining a tissue sample and a control tissue, characterizing immune cells in the tissue sample and the control tissue, wherein cells are labeled using antibodies to cell markers, protein modifications are characterized using pan-modified antibodies, and expression differences under various cell types of the tissue sample and the control tissue are compared; Step 200: sorting out immune cells in the tissue sample that have expression differences with the control tissue, making a full proteome and a modified proteome of the sorted immune cells, and screening out proteins and modified proteins with significant differences; Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as a detection antibody group, wherein the detection antibody group is a marker based on single cell protein modification.

[0008] According to an optional embodiment, in step 100, the ubiquitination of the protein is one or more of phosphorylation, ubiquitination, acetylation, methylation, glycosylation, sulfation, and fatty acylation.

[0009] According to an optional embodiment, in step 100, the characterization method is selected based on the weight and / or state of the tissue sample and the control tissue.

[0010] According to an optional embodiment, when the weight of the tissue sample and the control tissue is greater than or equal to 1g, mass spectrometry cytometry characterization is selected, and when the weight of the tissue sample and the control tissue is less than 1g, imaging mass spectrometry cytometry characterization is selected; and / or when the tissue sample and the control tissue are cellular body fluids, mass spectrometry cytometry characterization is selected, and when the tissue sample and the control tissue are solid, imaging mass spectrometry cytometry characterization is selected.

[0011] According to an optional embodiment, in step 200, it is determined whether there are significant differences in proteins and modified protein pathways between the tissue sample and the control tissue by the following steps: The names of proteins in tissue samples and control tissues were represented by gene names; Genes were ranked from high to low according to their expression levels in tissue samples and control tissues; Selecting a gene set to be analyzed from a public database gene set, and calculating the enrichment score of the gene set to be analyzed at the top or bottom of the sorted list based on the sorting result; Normalizing the enrichment scores to obtain a single-sample gene enrichment score for each gene set to be analyzed; The t-test method was used to calculate the mean difference of the single-sample gene enrichment score of each gene set to be analyzed in the tissue sample and the control tissue, and if p<0.05, it was considered that there was a significant difference between the gene set to be analyzed and the control tissue.

[0012] According to an optional embodiment, the gene set to be analyzed is derived from the public databases MsigDB, KEGG, GeneOntology and / or Reactome.

[0013] According to an optional embodiment, in step 200, proteins and modified proteins with significant differences are determined by the following steps: For gene sets with p < 0.05 in the t-test, proteins and modified proteins belonging to the differential pathways were extracted; The abundance difference of each of the proteins and modified proteins in the tissue sample and the control tissue is calculated, and the proteins or modified sites with abundance differences above a preset value are regarded as proteins or modified proteins with significant differences.

[0014] According to an optional embodiment, proteins or modification sites with abundance differences of 1.5 times or more are regarded as proteins or modified proteins with significant differences.

[0015] The second aspect of the present invention provides a marker based on single cell protein modification.

[0016] The present invention is based on a marker for single cell protein modification, wherein the marker is a protein and a modified protein with significant differences, and the protein and the modified protein with significant differences are obtained by the detection and analysis method for the marker based on single cell protein modification described in any technical solution of the present invention.

[0017] The third aspect of the present invention provides an application of a marker based on single cell protein modification.

[0018] The application of the marker based on single cell protein modification described in any technical solution of the present invention in cell typing, disease detection and / or prediction of patient response to drug.

[0019] The technical solution adopted by the present invention can achieve the following beneficial effects: The present invention provides a method for detecting and analyzing markers based on single-cell protein modification. By comparing the expression differences under each cell typing between tissue samples and control tissues, sorting out immune cells with differences in expression between tissue samples and control tissues, and screening out proteins and modified proteins with significant differences, markers based on single-cell protein modification can be obtained, so that protein modification can be characterized at the single-cell level, solving the technical problem of the lack of systematic detection and analysis methods for protein modification in related technologies.

[0020] The detection and analysis method of the markers based on single-cell protein modification of the present invention can characterize protein modification at the single-cell level, thereby helping to reveal the heterogeneity within the cell population, accurately identify and classify different cell types, and also facilitate in-depth understanding of cell functions, revealing the functional changes of cells under different physiological and pathological states, and discovering new biomarkers and therapeutic targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is a comparison result diagram of the pan-post-translational modification levels in hepatitis B combined with liver cancer between various cell subtypes of cancer tissue (CT) and adjacent tissue (ANT) on mass spectrometry flow cytometry in the embodiment of the present application; Figure 2 This is a comparison result diagram of ssGSEA of cancer and adjacent myeloid cells in hepatitis B combined with liver cancer on imaging mass spectrometry flow cytometry in an embodiment of the present application; Figure 3 This is a graph showing the expression results of various phosphorylation indicators in various myeloid cell lines in cancer tissues and paracancerous tissues in the examples of the present application; Figure 4 It is a graph showing the expression results of the positive rates of various phosphorylation indicators in various myeloid cell lines in cancer tissues and adjacent tissues in the examples of the present application; Figure 5 It is a graph of the expression results of each known immune checkpoint antibody in the high expression group and the low expression group of each phosphorylation index in the examples of the present application; Figure 6 It is one of the expression result graphs of the combination of each phosphorylation index and a known immune checkpoint antibody in the high expression group and the low expression group of each phosphorylation index in the embodiment of the present application; Figure 7This is the second graph of the expression results of the combination of each phosphorylation index and a known immune checkpoint antibody in the high expression group and the low expression group of each phosphorylation index in the embodiment of the present application; Figure 8 It is a graph showing the patient's response to the biomarkers of the embodiments of the present application. DETAILED DESCRIPTION

[0023] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0024] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0025] Since the characteristic peptides at the modification sites on proteins account for a very low proportion compared to the characteristic peptides contained in the entire protein, the number of cells required for purification and enrichment of modification sites based on traditional mass spectrometry technology is in the millions, which cannot be met by traditional single-cell technology. Therefore, traditional single-cell protein research and spatial single-cell protein research cannot characterize protein modification at the omics level, while protein modification is one of the direct indicators of protein function and can be used to determine the function of a protein.

[0026] To this end, the present application provides a marker based on single-cell protein modification and a detection and analysis method and application thereof. The method can obtain a marker based on single-cell protein modification by comparing the expression differences between tissue samples and control tissues under each cell typing, sorting out immune cells in tissue samples that have differences in expression from control tissues, and screening out proteins and modified proteins with significant differences, so that protein modifications can be characterized at the single-cell level.

[0027] Specifically, the present application is based on the detection and analysis method of single cell protein modification markers, comprising the following steps: Step 100: obtaining a tissue sample and a control tissue, characterizing immune cells in the tissue sample and the control tissue, wherein cells are labeled using antibodies to cell markers, protein modifications are characterized using pan-modified antibodies, and expression differences under various cell types of the tissue sample and the control tissue are compared; Step 200: sorting out immune cells in the tissue sample that have expression differences with the control tissue, making a full proteome and a modified proteome of the sorted immune cells, and screening out proteins and modified proteins with significant differences; Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as a detection antibody group, wherein the detection antibody group is a marker based on single cell protein modification.

[0028] This application can obtain markers based on single-cell protein modification, making it possible to characterize protein modification at the single-cell level, solving the technical problem of the lack of systematic detection and analysis methods for protein modification in related technologies. This application can characterize protein modification at the single-cell level, thereby helping to reveal the heterogeneity within the cell population, accurately identify and classify different cell types, and also help to gain a deeper understanding of cell functions, reveal the functional changes of cells under different physiological and pathological conditions, and discover new biomarkers and therapeutic targets.

[0029] Preferably, in step 100, the ubiquitination of the protein is one or more of phosphorylation, ubiquitination, acetylation, methylation, glycosylation, sulfation, and fatty acylation.

[0030] Preferably, in step 100, the characterization method is selected based on the weight and / or state of the tissue sample and the control tissue. More preferably, when the weight of the tissue sample and the control tissue is greater than or equal to 1g, mass spectrometry characterization is selected, and when the weight of the tissue sample and the control tissue is less than 1g, imaging mass spectrometry characterization is selected. When the tissue sample and the control tissue are cellular body fluids, mass spectrometry characterization is selected, and when the tissue sample and the control tissue are solid, imaging mass spectrometry characterization is selected. Exemplarily, cellular body fluids include but are not limited to body fluids rich in cells such as PBMC, pleural effusion, and peritoneal effusion.

[0031] The present application selects a characterization method based on the weight and / or state of the tissue sample and the control tissue. When the weight of the tissue sample and the control tissue is greater than or equal to 1 g, a large number of cells can be obtained by dissociating the tissue, and information of the entire tissue can be obtained using mass spectrometry flow cytometry, which is highly representative. When the weight of the tissue sample and the control tissue is less than 1 g, additional spatial information can be obtained using imaging mass spectrometry flow cytometry, and the data dimension is high.

[0032] Preferably, in step 200, the following steps are performed to determine whether there are significant differences in proteins and modified protein pathways between the tissue sample and the control tissue: The names of proteins in tissue samples and control tissues were represented by gene names; Genes were ranked from high to low according to their expression levels in tissue samples and control tissues; Selecting a gene set to be analyzed from a public database gene set, and calculating the enrichment score of the gene set to be analyzed at the top or bottom of the sorted list based on the sorting result; Normalizing the enrichment scores to obtain a single-sample gene enrichment score for each gene set to be analyzed; The t-test method was used to calculate the mean difference of the single-sample gene enrichment score of each gene set to be analyzed in the tissue sample and the control tissue, and if p<0.05, it was considered that there was a significant difference between the gene set to be analyzed and the control tissue.

[0033] This application adopts the above steps to determine whether there are significant differences in proteins and modified protein pathways between tissue samples and control tissues, which has the following advantages: first, it can be used to analyze single samples; second, a gene sorting algorithm is used, so the batch effect is small; third, high-resolution output generates continuous enrichment scores to support fine quantification of biological states; fourth, background noise can be adjusted through exponential weights to enhance the contribution of core driver genes.

[0034] Preferably, the gene set to be analyzed is derived from the public databases MsigDB, KEGG, GeneOntology and / or Reactome. Not limited thereto, the gene set to be analyzed may also be derived from a custom database.

[0035] Preferably, in step 200, proteins and modified proteins with significant differences are determined by the following steps: For gene sets with p < 0.05 in the t-test, proteins and modified proteins belonging to the differential pathways were extracted; The abundance difference of each of the proteins and modified proteins in the tissue sample and the control tissue is calculated, and the proteins or modified sites with abundance differences above a preset value are regarded as proteins or modified proteins with significant differences.

[0036] Exemplarily, proteins or modification sites with abundance differences of 1.5 times or more are regarded as proteins or modified proteins with significant differences.

[0037] Exemplarily, the abundance of each of the proteins and modified proteins can be calculated using maxquant software.

[0038] The present application screens out proteins or modified proteins with significant differences based on abundance differences, and can perform quantitative analysis on proteins or modified proteins to accurately identify differences in protein or modified protein expression in different samples.

[0039] The present application is based on a marker of single-cell protein modification, which is a protein and a modified protein with significant differences, and the protein and the modified protein with significant differences are obtained by a detection and analysis method of a marker based on single-cell protein modification according to any technical solution in the present application.

[0040] Any technical solution in the present application is based on the application of markers of single-cell protein modification in cell typing, disease detection and / or prediction of patient response to drugs.

[0041] The markers based on single cell protein modification and their detection and analysis methods and applications provided in the present application are described in detail below in conjunction with specific embodiments and their application scenarios.

[0042] This embodiment is based on single cell protein modification markers and detection and analysis methods and applications thereof, including the following steps: Step 100: Obtain tissue samples and control tissues, characterize immune cells in the tissue samples and control tissues, wherein cells are labeled using antibodies to cell markers, protein modifications are characterized using pan-modified antibodies, and expression differences under various cell types between the tissue samples and control tissues are compared.

[0043] Specifically, this step includes the following process: Step 110: Obtain tissue samples and control tissues.

[0044] The tissue samples and control tissues were cut into 0.5-1 gram tissue blocks to obtain tissue samples and control tissues. The obtained tissue samples and control tissues were washed twice with pre-cooled PBS. The washed tissues were placed in tissue preservation solution (MACS® tissue storage solution, 130-100-008) and transported to the laboratory on ice and stored at 4°C. After this step, the tissue samples and control tissues can be used for subsequent cell dissociation experiments. The cell dissociation experiment was completed within 24 hours.

[0045] The cell dissociation experiment included the following process: After removing the tissue samples and control tissues from the tissue preservation solution, they were placed in a centrifuge tube and washed 2-3 times with ice-cold PBS to remove residual blood. Using sterile scissors in PBS, the tissue pieces were trimmed, and the tissues with intact structure and weighing about 0.2 grams were selected to avoid blood clots. After removing the PBS and unnecessary tissues, the tissues were gently cut into 1 cubic millimeter pieces with scissors. 5 ml of tissue dissociation buffer (200 μl of enzyme H, 100 μl of enzyme R and 25 μl of enzyme A mixed in 4.7 ml of RPML; all enzymes were from the MACS® Tumor Dissociation Kit, 130-095-929) was mixed with the obtained tissues and stirred in a 37°C water bath for 8 to 10 minutes. During this period, a 5 ml Pasteur pipette was used to pipette up and down about 10 times every two minutes to ensure that the tissue was fully mixed with the enzyme solution, further disaggregate the tissue and monitor its dissociation. When approximately 80% of the tissue pieces have reduced in volume and look loose, indicating complete dissociation, the tissue fluid is poured into a 70-micron cell strainer placed on a 50-ml centrifuge tube on ice to terminate the enzyme reaction and collect the cells. Rinse the cell strainer with approximately 20 ml of DMEM solution (HyClone, SH30243.01), and maximize cell recovery by aspirating and filtering as much cell suspension as possible. The cell suspension collected in the 50-ml centrifuge tube was then centrifuged at 300 g for 5 minutes at room temperature, the supernatant was discarded, and the cells were washed once with DMEM solution, after which the supernatant was discarded. The cells were resuspended in cryopreservative solution (10% DMSO mixed with 90% FBS, FBS from Gibco) and counted. The cells were counted and randomly checked, and the cells were stained with trypan blue for survival. The count exceeded 3 million and the viability exceeded 90%, which was qualified. The counted cell suspension was then collected into 1.5 ml cryovials, placed in a program-controlled cooling box, stored in a -80°C freezer, and subsequently transferred to a liquid nitrogen tank for storage.

[0046] For cellular body fluids, the experimental group body fluids and the control group body fluids can be stored and transported at 4°C. Centrifuge the experimental group body fluids and the control group body fluids at 300g for 15 minutes, remove the supernatant, and then resuspend and wash twice with PBS. Use 500g speed for washing and centrifugation for 5 minutes, and discard the supernatant. Use 1ml of cryopreservative solution (10% DMSO mixed with 90% FBS, FBS from Gibco) to resuspend the cells centrifuged at the bottom of the test tube, then count and sample the cells, and use trypan blue to stain the cells for survival. The number of cells exceeds 3 million and the viability exceeds 90%, which is qualified. Then transfer the cell suspension to a program-controlled cooling box, store it in a -80°C refrigerator, and then transfer it to a liquid nitrogen tank for storage.

[0047] Step 120: Characterizing immune cells in tissue samples and control tissues, wherein cells are labeled using antibodies to cell markers and protein modifications are characterized using pan-modified antibodies.

[0048] Cells were labeled with metal antibodies and analyzed in CytoFlash.

[0049] The first thing to confirm is the combination of antibodies. Taking the study of hepatitis B combined with liver cancer as an example, the selection method of antibodies is explained. Antibodies are mainly divided into three parts: immune cells, non-immune cells and modified antibodies.

[0050] Immune cells are distinguished using a series of antibodies including CD45, CD3, CD56, CD19, CD66b, CD11c, HLA-DR, CD68, CD14, TCRgd, CD4, CD8, FceRI, CD11b, CD16, CD25, CD45RO and CCR7. For example, CD45 is used as a pan-immune cell marker for initial screening of cell populations, and CD19 specifically marks B lymphocytes; CD3 and CD4 are combined to identify helper T cell subsets, among which CD45RO+CCR7+ characteristics define CD4 central memory T cells (CD4TCM), CD45RO+CCR7+ characteristics define CD4 effector memory T cells (CD4TEM), and CD4+CD25+ phenotype-specific markers regulatory T cells (Treg), CD3 and CD8 are combined to define cytotoxic T cells, and CD The difference in 45RO and CCR7 expression was used to divide CD8TCM and CD8TEM subgroups; the co-expression characteristics of TCRγδ and CD56 were used to identify CD56+γδT cells; CD56 single positive marked natural killer cells (NK); CD14+CD16- phenotype marked classical monocytes (Mono), and CD14+CD16+ phenotype defined CD16+ monocyte subgroups; CD11b+CD66b+ combination specifically marked neutrophils; CD11c and HLA-DR co-expression characteristics were used to identify dendritic cells. Conventional dendritic cells (cDC) were further defined by CD14-CD68- phenotype combined with CD1a+ characteristics, while myeloid dendritic cells (mDC) were marked by CD14+CD68+ combination combined with CD163+ characteristics; macrophages were specifically identified by strong positive expression of CD68 combined with CD14+CD16+ phenotype, which was clearly distinguished from CD14+CD16+ CD16+ monocytes.

[0051] For non-immune cells, CD31, FAP and PanCK are used for labeling. CD31 (platelet endothelial cell adhesion molecule, PECAM-1) is used as a specific surface marker for vascular endothelial cells; FAP (fibroblast activation protein) is used to specifically identify activated interstitial fibroblasts; PanCK (pan-spectrum cytokeratin) is used as a typical marker for epithelial-derived cells.

[0052] For Fan-modified antibodies, Fan-tyrosine phosphorylation antibodies, Fan-acetylation modified antibodies, Fan-lactyl modified antibodies and Fan-ubiquitination modified antibodies are used in the research of hepatitis B and liver cancer.

[0053] Step 130: Compare the expression differences of each cell type between the tissue sample and the control tissue.

[0054] Since the tissue has been dissociated into discrete cells, the subsequent experimental process does not distinguish between solid tissue and cellular body fluids. For samples to be detected by mass spectrometry, the experimental process is as follows: Samples of interest were removed from liquid nitrogen, rapidly thawed in a 37°C water bath, and pretreated prior to analysis. Cells were then washed and stained with cisplatin-Live / Dead stain (Fluidigm) at a dilution of 1:10,000. Cells were pooled and labeled with metal-conjugated antibodies for surface markers, then fixed with 1.6% formaldehyde, permeabilized with 100% methanol, and stained with metal-conjugated antibodies against intracellular molecules. All 41 antibodies were either conjugated in the laboratory according to the manufacturer's protocol (Fluidigm) or purchased pre-conjugated directly from Fluidigm. Antibodies that could be used for flow cytometry were also suitable for imaging mass cytometry after metal conjugation. Finally, an iridium-containing dye (DNA intercalator) was added to identify individual cells, and the cells were then washed and diluted with EQFourElementCalibrationBeads (Fluidigm) for signal normalization. Data were collected using a Helios (Fluidigm) equipped with CyTOF® 6.7 system control software according to the manufacturer's instructions. Samples were collected at an event rate of 300-500 events / second and noise suppression was performed. Data were sorted to identify cell events (DNA high expression) and exclude dead cells (cisplatin positive). They then entered the data analysis pipeline.

[0055] For single-cell processing, all CyTOF data were transformed by using the arcsinh function with a cofactor of 5, which was implemented by the cytofAsinh function in cytofkit v1.11.3, and the data were scaled to a range of 0–1. Clustering was performed using Rphenograph v0.99.1 with parameter K = 45, which can be accessed on GitHub. Heatmaps were created using ComplexHeatmap v2.10.0 to display the median z-scores (ranging from 0 to 1) of marker expression for cells in each cluster. For dimensionality reduction, t-random neighbor embedding (t-SNE) was visualized using the runTSNE function in scran v1.18.7, which was used to explore the phenotypic diversity between cell populations and further analyzed using dittoSeq v1.6.0. Cell types were defined based on the expression of each cell cluster. Based on the cell type and clustering results, the proportion of each cell type in each sample was calculated, and the differences between tissue samples and control tissues were compared using t-tests.

[0056] Figure 1 An example of a result is given, which is the difference in abundance of pan-translational modifications in various cells after statistics. Figure 1 As shown, the horizontal axis in the figure represents the post-translational modification type, and the vertical axis represents the cell subtype. The size of the point represents the expression rate of the corresponding cell subtype under the post-translational modification type, and the depth of the color of the point represents the relative expression between the two groups. The redder the color, the higher the expression, and the bluer the color, the lower the expression. The left half of the figure represents cancer tissue (CT), and the right half represents paracancerous tissue (ANT). If there is a black circle around the point, it means that after the t-test between the two groups, the p value is less than 0.05, that is, there is a significant difference.

[0057] For samples to be detected using imaging mass cytometry, the experimental process is as follows: Imaging mass spectrometry flow detection is suitable for paraffin-embedded tissue wax blocks. The dewaxing, antigen retrieval methods and antibody labeling methods mentioned in the following content are all applicable to paraffin immunohistochemistry anti-shedding slides and tissue chip immunohistochemistry anti-shedding slides. The slice thickness requirement for anti-shedding slides is 3-4μm. In the slide pretreatment stage, the sample is first placed in a 58-62℃ oven for 1.5-2.5 hours, and then dewaxed with fresh xylene in a fume hood for 15-25 minutes. After dewaxing, a gradient rehydration treatment is performed: 100%, 95%, 80%, and 70% ethanol are used for step-by-step hydration, and each level is treated for 4-6 minutes. The initial cleaning is completed in a Coplin staining jar using Maxpar deionized water and a 50-100rpm orbital shaker for 4-6 minutes. During the antigen retrieval stage, the slides were immersed in a retrieval solution (50 ml conical tube) preheated to 94-98°C for 28-32 minutes of heat-induced epitope exposure. The tube cap was kept partially open, and then the retrieval solution was naturally cooled to 68-72°C and maintained for 8-12 minutes through a gradient cooling program. The secondary wash was performed twice with fresh Maxpar PBS buffer for 8-12 minutes of shaking washing (50-100 rpm). During the sample blocking stage, the area was circled with a hydrophobic pen, and then blocked at room temperature for 40-50 minutes with a blocking solution containing 2.5-3.5% BSA in a humidity-controlled room. During the antibody incubation stage, an antibody working solution containing 0.4-0.6% BSA was prepared according to the detection system, and incubated at low temperature for 12-16 hours in a humidity-controlled environment at 2-6°C. Imaging mass spectrometry antibodies can be obtained by metal coupling of immunohistochemical antibodies. The coupling process is consistent with the aforementioned mass spectrometry, or purchased directly from suppliers. Washing was done sequentially with Maxpar PBS buffer containing 0.18-0.22% TritonX-100 (twice for 7-9 minutes, 30-50 rpm) and pure Maxpar PBS buffer (twice for 8-10 minutes). 300-500 μl / cm was applied during the labeling and staining phase. 2 Iridium working solution (Maxpar PBS 1:300-1:500 dilution) was used for staining at room temperature for 25-35 minutes. Final treatment included washing with Maxpar deionized water for 4-6 minutes, air drying at 18-25°C for 20-40 minutes, and finally microscopic observation and image acquisition were completed by Hyperion imaging system. The raw data were integrated and stored by Fluidigm's commercial acquisition software.

[0058] The data analysis followed the following process: First, the original mcd files obtained by the Hyperion imaging system were preprocessed using the IMC Segmentation Pipeline, converted into multi-channel TIFF images, and semi-supervised automatic feature extraction was performed using the Ilastik software, and standardized cell segmentation and feature quantification were completed using Cell Profiler. Subsequently, single-cell data were extracted using imcR tools (v1.9.0), and multi-channel images and segmentation were read in combination with Cytommaper. After correcting batch effects with Harmony (v0.1.1), Rphenograph (v0.99.1) was used to cluster cell subpopulations and the expression of post-translational modifications in each cell subtype. The t-test was used to compare the differences between tissue samples and control tissues. The presentation of the results of imaging mass spectrometry on the differences in post-translational modifications of each cell subtype is consistent with mass spectrometry, which will not be repeated here.

[0059] Step 200: Immune cells in the tissue sample that have expression differences with the control tissue are sorted out, the sorted immune cells are subjected to full proteome and modified proteome analysis, and proteins and modified proteins with significant differences are screened out.

[0060] Specifically, this step includes the following process: Step 210: sorting out immune cells in the tissue sample that have expression differences from the control tissue.

[0061] Taking myeloid cells from hepatitis B combined with liver cancer as an example, myeloid cells were isolated by EasySepTM Human Myeloid Positive Selection Kit II (Stemcell, catalog number #17893). The kit consists of CD33 and CD66b antibodies, which can positively select myeloid cells. Specifically, the cell pellet was resuspended in 1 ml of antibody solution (antibody mixture 10%, fetal bovine serum 2%, penicillin / streptomycin 1%, DNase solution 1% in PBS), incubated at room temperature for 10 minutes, and then transferred to a polystyrene FACS tube. The tube was connected to a magnet and incubated for 5 minutes. After pouring off the cell solution, the tube was released from the magnet and resuspended in 2.5 ml of FBS solution (fetal bovine serum 2%, penicillin / streptomycin 1%, DNase solution 1% in PBS). The tube was connected to the magnet again and incubated for 5 minutes. After pouring off the cell solution, the cells were washed in PBS and centrifuged at 1500 rpm for 5 minutes at room temperature, repeated twice. The supernatant was then discarded and the cells were stored at -80°C.

[0062] Step 220: The sorted immune cells are subjected to full proteome and modified proteome analysis, and proteins and modified proteins with significant differences are screened out.

[0063] Cell samples were collected and washed with PBS and stored at -80°C. Proteins were digested and phosphorylated peptides were enriched using the EasyPept™ PTM Phosphopeptide Enrichment Kit (omicsolution, OSFP0005_200UG). Phosphatase inhibitors were added at a ratio of 1:100 (phosphatase inhibitor: Reagent0). For isolated cell samples, 40 μl of Reagent0 was added to the cell pellet. The mixture was mixed using a vortex shaker and vigorously pipetted 15-20 times with a 200 μl pipette tip, and then placed on ice for 10 minutes for lysis. The mixture was centrifuged at 12,000 g for 20 minutes at 4°C, and the supernatant was collected. The supernatant was subjected to BCA protein quantification. Based on the quantification results, 300 μg of protein samples were taken for further processing. The protein solution was added to the S plate, and 200 μl of ReagentA was added to each well in turn and mixed by pipetting. Then add 12 μl of Reagent B to each well, mix and heat at 95°C for 5 minutes (95°C, 1000 rpm). After heating, the sample is cooled to room temperature. Then add 20 μl of Reagent C and 50 μl of Reagent D to each well, mix and incubate at 37°C for 2-3 hours for enzymatic digestion. After enzymatic digestion, add 30 μl of Reagent E to each well, mix and terminate the enzymatic digestion reaction. Precipitation may occur at this stage. The mixture is centrifuged at 20000g for 1 minute and the supernatant is collected for subsequent desalting. Activate the desalting column by adding 1 ml of methanol to the desalting column and repeat once. Then equilibrate the desalting column by adding 1 ml of wash buffer and repeat once. Transfer the desalting column to a new collection tube. Adjust the sample pH to less than 3, load the entire sample onto the desalting column and repeat once. Wash by adding 1 ml of wash buffer and repeat twice (three washes in total). Elution was performed by adding 300 μl of elution buffer to the desalting column and repeated once to obtain a final sample volume of 600 μl. Desalted samples were quantified using a peptide quantification kit. Based on the quantification results, 200 μg of peptides were taken and concentrated using a vacuum centrifuge (temperature below 30 °C is recommended). The EnrichmentTip (enrichment column) was inserted into the adapter and placed on a waste plate. ReagentF (150 μl) was added to the dried peptides and mixed thoroughly. Then ReagentG (50 μl) was added to the EnrichmentTip and centrifuged. After discarding the flow-through, the adapter with the EnrichmentTip was transferred to a new waste plate. The sample was loaded onto the EnrichmentTip and centrifuged, and the flow-through was repeated once. ReagentF and ReagentG (50 μl each) were added to the EnrichmentTip and centrifuged. 50 μl of LC-MS water was added and centrifuged. The adapter with the EnrichmentTip was transferred to a new collection plate.Use ReagentH1 and ReagentH2 (50μl each) to elute the sample, and then use a vacuum centrifuge to concentrate (recommended below 30°C). Among them, Reagent0 and ReagentA~ReagentG correspond to Reagent0 and ReagentA~ReagentG in the EasyPept™ PTM Phosphopeptide Enrichment Kit, respectively.

[0064] Mass spectrometry analysis was performed using a QExactive HF-X hybrid quadrupole-orbitrap mass spectrometer equipped with a Thermoeasy LC-1200 system for separation. The LC-MS / MS system included a C18 capillary trap column (150 μm × 100 mm, 3 μm). Samples were loaded at a flow rate of 600 nl / min using 0.1% FA. The nanoliquid gradient was as follows: transition from 2% buffer B (80% ACN + 0.1% FA in water) to 8% in 3 min, from 8% to 40% in 78 min, from 40% to 95% in 2 min, and hold at 95% in 7 min. The mass spectrometry conditions were set as follows: the ion source was ESI+, the primary scan mode was DDA, the scan range was 400-1200 m / z, the resolution was 60,000@m / z200, the AGC was 3e6, and the maximum IT was 30 ms; the secondary scan mode was full scan, TOPN was 20, the resolution was 30,000@m / z200, the AGC was 1e5, and the maximum IT was 50 ms. The MS2 activation type was HCD, the isolation window was 1.6 Th, and the normalized collision energy was 28. The mass spectrometry data search used MaxQuant software, and the specific parameters were as follows: variable modifications included oxidation (M), fixed modifications included carbamine methylation (C), the digestion mode was trypsin / P, and label-free quantification used LFQ (label-free quantification). The fast file was obtained from Uniprot (https: / / www.uniprot.org / ) for human protein review. The LFQ intensity in the MaxQuant search results was used to represent the original protein quantification.

[0065] Subsequent data analysis used log2-transformed LFQ intensities. To characterize each sample, pathway-related ssGSEA analysis was performed using the GSVAR package (https: / / doi.org / 10.1186 / 1471-2105-14-7). The MsigdbrR package was used to prepare pathway information, and “Homosapiens” was selected as the species, “C2” as the category, and “CP:KEGG” as the subcategory. In the ssGSEA analysis, only proteins that were quantified in all samples were included. First, the names of the proteins in the sample were represented by their gene names. Then, the ssGSEA method was used to sort the protein expression matrix of individual samples from high to low expression, and based on the sorting results, the enrichment of a specific gene set at the top / bottom of the sorted list (ES value score) was calculated. The ES values ​​between samples were then normalized by z-score to obtain the ssGSEA score for the comparability of each gene set. The t-test method was used to calculate the mean difference of ssGSEA scores for each gene set in tissue samples and control tissues, and if p < 0.05, the gene set was considered to be significantly different between tissue samples and control tissues. Specific gene bases are from public databases, including but not limited to MSigDB, KEGG, GeneOntology, and Reactome. Pathways without significant differences were excluded, and pathways with significant differences were selected as the basis for selecting significantly different proteins and modified differential sites. Figure 2 The analysis results of ssGSEA obtained after analyzing the samples of hepatitis B combined with liver cancer are presented in the form of a heat map. The horizontal axis corresponding to each grid is the sample number, the right vertical axis is the corresponding pathway, and the depth of the filling color is the score of the pathway enrichment degree. The redder, the higher the relative enrichment degree, and the bluer, the lower the relative enrichment degree. Only pathways with a t-test result of p<0.05 between the two groups will be used for drawing. After that, the proteins and their phosphorylation modification sites belonging to the differential pathways are listed, and the abundance comparison is compared between the groups. For proteins or phosphorylation sites with an abundance difference of more than 1.5 times when the t-test p value is less than 0.05, they are defined as significantly differentially modified proteins or phosphorylation sites.

[0066] Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as a detection antibody group, wherein the detection antibody group is a marker based on single cell protein modification.

[0067] Specifically, this step includes the following process: After completing the analysis of step 200 for patients with hepatitis B and liver cancer, it was found that the pathway named Adipocytokinesignalingpathway had significant differences between the groups, that is, p<0.05. The abundance of proteins and phosphorylated peptides belonging to this pathway was screened out from the proteome results and phosphorylated proteome results, and a t-test was performed between the groups. It was found that proteins such as STAT1 and the phosphorylation sites of STAT1 had significant differences between the groups, with abundance multiples greater than 1.5 times, or less than 2 / 3, p<0.05. STAT1 and STAT1 phosphorylation sites were classified as significantly differentially modified proteins or phosphorylation sites. Such a search was performed for each differential pathway to construct a differential site library. Subsequent TRIM28 and HSP27 were also obtained by the same method. TRIM28 comes from the Apoptosis pathway, while HSP27 comes from the Mismatchrepair pathway. Therefore, p-STAT1, p-HSP27, and p-TRIM28, three proteins with altered phosphorylation post-translational modifications, were used as targets for characterization of post-translational modifications at the single-cell level using imaging mass spectrometry.

[0068] For patients with hepatitis B and liver cancer, paraffin tissue sections from 37 patients (samples were taken from the sample bank of the Affiliated Cancer Hospital of Chongqing University) were used to characterize the three phosphorylation post-translational modifications. These 37 patients provided 12 tissues around the cancer and 36 tissues from the cancer center. Among them, 14 tissues from the cancer center received first-line "T+A" treatment. "T+A" treatment refers to the immune-antiangiogenic combination therapy of atezolizumab (anti-PD-L1 antibody) combined with bevacizumab (anti-VEGF antibody), which is the first approved regimen for first-line treatment of advanced hepatocellular carcinoma (HCC). Among these 14 patients, nine responded to the drug within six months during treatment, that is, the disease did not progress, and five did not respond to the drug within six months, that is, the disease progressed. Progression means failure of treatment in clinical practice.

[0069] These tissues were tested and single-cell post-translational modifications were characterized using imaging mass cytometry. The experimental process of imaging mass cytometry is not described here. It is consistent with the imaging mass cytometry in step 100, and the only difference is the choice of antibodies. Since the proteome object in step 200 is myeloid cells, only the results of myeloid cells are introduced here to demonstrate the practicality of the characterization. The following antibody combination is used to mark myeloid cells and their cell types: CD45, CD33, CD11b, CD68, CD1163, CD86, CD80, CD11c, CD206 and HLA-DR. CD45 is a pan-marker expressed by all myeloid cells, CD33 and CD11b are mainly expressed on bone marrow cells and monocytes, CD68 and CD163 specifically mark macrophages, CD86 and CD80 are expressed on dendritic cells and activated monocytes as co-stimulatory molecules, CD11c is a key marker for dendritic cells, CD206 mainly appears on M2 macrophages, and HLA-DR is expressed on antigen presenting cells such as dendritic cells and macrophages. Using the above antibodies, different types of myeloid cells can be marked in tissue samples. When using the above antibodies together with the three phosphorylation-modified antibodies p-STAT1, p-TRIM28 and p-HSP27, and in combination with known immune checkpoint antibodies, such as the following MHCⅠ, MHCⅡ, CD80, CD86, LAG3, CD24, CD28, CTLA-4, CD47, MERTK, PD-1, PD-L1, Siglec-10, SirPa, TIM3, and THBS1, the characterization of phosphorylation modifications and known immune checkpoints at the single-cell level on myeloid cells can be completed.

[0070] The characterization results can compare the differences in post-translational modifications of single-cell myeloid cells between cancer and adjacent tissues, and compare the differences in post-translational modifications of myeloid cells and expressed immune checkpoints between "T+A" responding patients and non-responding patients. Figure 3 The differences in phosphorylation indexes between cancer and adjacent myeloid cell lines are shown in the sample tissue sections. Figure 3 It can be found that the three phosphorylation post-translational modification indicators are all elevated in cancer. Figure 4 The results show the difference in the expression of phosphorylation indexes between all tissues around the cancer and tissues at the center of the cancer. Figure 4 It can be observed that the expression difference of phosphorylation is the highest on macrophage M2 and is elevated in cancer tissues. Figure 5It shows that there is no significant difference in MHCⅠ, MHCⅡ, CD80, CD86, LAG3, CD24, CD28, CTLA-4, CD47, MERTK, PD-1, PD-L1, Siglec-10, SirPa, TIM3, and THBS1 on macrophage M2 between the high response group and the low response group. When the positivity of p-STAT1, p-TRIM28, and p-HSP27 are combined on macrophage M2 to retype macrophages, all negative ones are P0 type, single positive ones are P1 type, double positive ones are P2 type, and triple positive ones are P3 type, then it can be seen that Figure 6 It was found that there were more P2 and P3 types in the high response group, and it can also be seen from the tissue section diagram ( Figure 7 ). At the same time, the AUC analysis of the patient's response was performed. Figure 8 It can be found that patients whose proportion of M2-P2 type is higher than the average have no recurrence in the performance of AUC analysis. Therefore, the proportion of M2-P2 type can be used to predict the patient's response to the drug.

[0071] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0072] In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0073] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for detecting and analyzing markers based on single cell protein modification, characterized in that: The steps include: Step 100: obtaining a tissue sample and a control tissue, characterizing immune cells in the tissue sample and the control tissue, wherein cells are labeled using antibodies to cell markers, protein modifications are characterized using pan-modified antibodies, and expression differences under various cell types of the tissue sample and the control tissue are compared; Step 200: sorting out immune cells in the tissue sample that have expression differences with the control tissue, making a full proteome and a modified proteome of the sorted immune cells, and screening out proteins and modified proteins with significant differences; Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as a detection antibody group, wherein the detection antibody group is a marker based on single cell protein modification.

2. The method for detecting and analyzing markers based on single cell protein modification according to claim 1, characterized in that: In step 100, the ubiquitination of the protein is one or more of phosphorylation, ubiquitination, acetylation, methylation, glycosylation, sulfation, and fatty acylation.

3. The method for detecting and analyzing markers based on single cell protein modification according to claim 1, characterized in that: In step 100, a characterization method is selected based on the weight and / or state of the tissue sample and the control tissue.

4. The method for detecting and analyzing markers based on single cell protein modification according to claim 3, characterized in that: When the weight of the tissue sample and the control tissue is greater than or equal to 1 g, select mass spectrometry characterization; when the weight of the tissue sample and the control tissue is less than 1 g, select imaging mass spectrometry characterization; And / or when the tissue sample and the control tissue are cellular body fluids, mass spectrometry characterization is selected, and when the tissue sample and the control tissue are solid, imaging mass spectrometry characterization is selected.

5. The method for detecting and analyzing markers based on single cell protein modification according to claim 1, characterized in that: In step 200, the following steps are performed to determine whether there are significant differences in proteins and modified protein pathways between the tissue sample and the control tissue: The names of proteins in tissue samples and control tissues were represented by gene names; Genes were ranked from high to low according to their expression levels in tissue samples and control tissues; Selecting a gene set to be analyzed from a public database gene set, and calculating the enrichment score of the gene set to be analyzed at the top or bottom of the sorted list based on the sorting result; Normalizing the enrichment scores to obtain a single-sample gene enrichment score for each gene set to be analyzed; The t-test method was used to calculate the mean difference of the single-sample gene enrichment score of each gene set to be analyzed in the tissue sample and the control tissue, and if p<0.05, it was considered that there was a significant difference between the gene set to be analyzed and the control tissue.

6. The method for detecting and analyzing markers based on single cell protein modification according to claim 5, characterized in that: The gene set to be analyzed is derived from the public databases MsigDB, KEGG, GeneOntology and / or Reactome.

7. The method for detecting and analyzing markers based on single cell protein modification according to claim 5, characterized in that: In step 200, proteins and modified proteins with significant differences are determined by the following steps: For gene sets with p < 0.05 in the t-test, proteins and modified proteins belonging to the differential pathways were extracted; The abundance difference of each of the proteins and modified proteins in the tissue sample and the control tissue is calculated, and the proteins or modified sites with abundance differences above a preset value are regarded as proteins or modified proteins with significant differences.

8. The method for detecting and analyzing markers based on single cell protein modification according to claim 7, characterized in that: Proteins or modified sites with abundance differences of 1.5 times or more were considered as proteins or modified proteins with significant differences.

9. A marker based on single cell protein modification, characterized in that: The markers are proteins and modified proteins with significant differences, and the proteins and modified proteins with significant differences are obtained by the detection and analysis method of markers based on single cell protein modification according to any one of claims 1 to 8.

10. Use of the marker based on single cell protein modification according to claim 9 in cell typing, disease detection and / or prediction of patient response to a drug.

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