Biomarkers based on single-cell protein modification, their detection and analysis methods, and applications

Abstract

This invention discloses a biomarker based on single-cell protein modification, its detection and analysis method, and its application. The method includes: obtaining tissue samples and control tissues; characterizing immune cells in the tissue samples and control tissues; labeling cells with antibodies containing cell markers; characterizing protein modifications with a generalized modified antibody; comparing expression differences between the tissue samples and control tissues under different cell types; sorting out immune cells in the tissue samples that show expression differences compared to the control tissues; performing whole-proteomics and modified proteomics analysis on the sorted immune cells; and screening for proteins and modified proteins with significant differences; using the obtained proteins and modified proteins with significant differences as the detection antibody set, which is the biomarker based on single-cell protein modification. This method can obtain biomarkers based on single-cell protein modification, enabling the characterization of protein modifications at the single-cell level.

Description

Technical Field

[0001] This invention relates to the field of immunology, and in particular to a biomarker based on single-cell protein modification, its detection and analysis method, and its application. Background Technology

[0002] The immune microenvironment refers to the local environment present in tumors or other pathological tissues, encompassing various immune cells, cytokines, chemical signaling molecules, extracellular matrix components, and other cell types. This environment significantly impacts disease progression, treatment response, and patient prognosis. For example, the expression of PD-1 protein on T cells in tumors can predict patient prognosis; FASN protein, involved in lipid metabolism by macrophages in fatty liver, plays a crucial role in subsequent metastasis to cirrhosis; and autoimmune diseases such as lupus are caused by the overexpression of antibody proteins by B cells in the immune system. Therefore, accurately characterizing protein expression on immune cells within the immune microenvironment is essential for disease treatment, patient survival, and overall health.

[0003] There are two main technical approaches for characterizing the immune microenvironment: one is sequencing-based single-cell technology, including but 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, including metalloantibody-based mass cytometry and imaging mass cytometry, as well as phenocyder-Fusion technology using fluorescent antibodies. While single-cell sequencing technology can effectively characterize cell states, the correlation between mRNA expression and protein abundance is not high, meaning that single-cell sequencing technology cannot be directly used to predict protein abundance. Antibody-based single-cell protein technology has an upper limit of approximately 500 antibody combinations, which is limited compared to the expression of tens of thousands of proteins within a cell, indicating a limited number of proteins that can be characterized.

[0004] Due to the complexity of cell types involved in immune microenvironment activities, characterizing this complex environment using single-cell level techniques is particularly important. Mass cytometry and imaging mass cytometry based on mass spectrometry and metal-coupled antibody platforms can conveniently characterize protein expression and its spatial location in known cell subtypes. However, systematic detection and analysis methods for protein modifications closely related to protein function still lack. Therefore, providing an analytical method based on single-cell protein modification biomarkers has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0005] This invention discloses a biomarker based on single-cell protein modification, its detection and analysis method, and its application, in order to solve the technical problem of the lack of systematic detection and analysis methods for protein modification in related technologies.

[0006] To solve the above problems, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for detecting and analyzing biomarkers based on single-cell protein modification.

[0007] The present invention provides a method for detecting and analyzing biomarkers of single-cell protein modification, comprising the following steps: Step 100: Obtain tissue samples and control tissues, characterize immune cells in tissue samples and control tissues, wherein cells are labeled with antibodies of cell markers, protein modifications are characterized with universally modified antibodies, and expression differences of each cell type are compared between tissue samples and control tissues. Step 200: Immune cells that express different values ​​from control tissues are sorted out from the tissue samples. The sorted immune cells are subjected to whole proteomics and modified proteomics, and proteins with significant differences and modified proteins are screened out. Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as the detection antibody group, which is a biomarker based on single-cell protein modification.

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

[0009] According to an optional implementation, 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 implementation, mass cytometry characterization is selected when the weight of the tissue sample and control tissue is greater than or equal to 1g, and imaging mass cytometry characterization is selected when the weight of the tissue sample and control tissue is less than 1g; and / or mass cytometry characterization is selected when the tissue sample and control tissue are cellular fluids, and imaging mass cytometry characterization is selected when the tissue sample and control tissue are solids.

[0011] According to an optional implementation, in step 200, the following steps are used to determine whether there are significant differences in protein and modified protein pathways between the tissue sample and the control tissue: The names of proteins in tissue samples and control tissues are represented by gene names; Genes were sorted in descending order of expression level in tissue samples and control tissues. Select the gene set to be analyzed from the gene set in the public database, and calculate the enrichment score of the gene set to be analyzed at the top or bottom of the sorting list based on the sorting results; The enrichment scores are normalized to obtain the single-sample gene enrichment scores for each gene set to be analyzed. The mean difference in gene enrichment scores for each gene set to be analyzed between tissue samples and control tissues was calculated using the t-test. If p < 0.05, the gene set to be analyzed was considered to have a significant difference between tissue samples and control tissues.

[0012] According to one optional implementation, the set of genes to be analyzed is derived from public databases such as MsigDB, KEGG, GeneOntology, and / or Reactome.

[0013] According to an optional implementation, in step 200, proteins with significant differences and modified proteins are identified through the following steps: For the gene set with p < 0.05 in the t-test, extract the proteins and modified proteins belonging to the differentially expressed pathways; The abundance difference of each protein and modified protein in the tissue sample and control tissue is calculated, and proteins or modified sites with an abundance difference of 1 or more at a preset value are identified as proteins or modified proteins with significant differences.

[0014] According to one alternative implementation, proteins or modified sites with an abundance difference of 1.5 times or more are defined as proteins or modified proteins with significant differences.

[0015] A second aspect of the present invention provides a biomarker based on single-cell protein modification.

[0016] This invention is based on single-cell protein modification biomarkers, wherein the biomarkers 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 based on single-cell protein modification biomarkers as described in any of the technical solutions of this invention.

[0017] A third aspect of the invention provides an application of a biomarker based on single-cell protein modification.

[0018] The application of single-cell protein modification-based biomarkers as described in any of the technical solutions of this invention in cell typing, disease detection, and / or prediction of patient response to drugs.

[0019] The technical solution adopted in this invention can achieve the following beneficial effects: This invention provides a method for detecting and analyzing biomarkers of single-cell protein modification. By comparing the expression differences of different cell types in tissue samples and control tissues, sorting out immune cells in tissue samples that express different values ​​from those in control tissues, and screening out proteins with significant differences and modified proteins, biomarkers based on single-cell protein modification can be obtained. This allows for the characterization of protein modification at the single-cell level and solves the technical problem of the lack of systematic detection and analysis methods for protein modification in related technologies.

[0020] This invention provides a detection and analysis method based on single-cell protein modification biomarkers, which can characterize protein modifications at the single-cell level. This helps to reveal the heterogeneity within cell populations, accurately identify and classify different cell types, and also facilitates a deeper understanding of cell function, revealing functional changes of cells under different physiological and pathological states, and discovering new biomarkers and therapeutic targets. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a graph showing the comparison of the level of generalized post-translational modification in hepatitis B complicated with hepatocellular carcinoma between different cell subtypes in cancerous tissue (CT) and adjacent normal tissue (ANT) by mass cytometry according to an embodiment of this application. Figure 2 This is a comparison of ssGSEA between myeloid cells and adjacent cells in hepatocellular carcinoma complicated with hepatitis B by imaging mass cytometry according to an embodiment of this application. Figure 3 This is a graph showing the expression results of various phosphorylation indicators in various myeloid cell lines of cancer tissue and adjacent normal tissue in the embodiments of this application; Figure 4 This is a graph showing the expression results of the positive rates of various phosphorylation indicators in various myeloid cell lines of cancer tissue and adjacent tissue in the embodiments of this application; Figure 5 This is a graph showing the expression results of known immune checkpoint antibodies in the high-expression and low-expression groups of each phosphorylation index in the embodiments of this application. Figure 6 This is one of the expression results of the combination of each phosphorylation index with known immune checkpoint antibodies in the high expression group and low expression group of each phosphorylation index in the embodiments of this application. Figure 7This is the second graph showing the expression results of the combination of each phosphorylation index with known immune checkpoint antibodies in the high-expression and low-expression groups of each phosphorylation index in the embodiments of this application. Figure 8 This is a graph showing the patient's response to the biomarkers in the embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] Because the characteristic peptides containing protein modification sites constitute a very small percentage of the total characteristic peptides in the protein, traditional mass spectrometry techniques require millions of cells for purification and enrichment of these modification sites, a requirement that traditional single-cell techniques cannot meet. Therefore, traditional single-cell protein studies and space-based single-cell protein studies cannot characterize protein modifications at the omics level, even though protein modification is a direct indicator of protein function and can be used to determine protein function.

[0026] Therefore, this application provides a biomarker based on single-cell protein modification, its detection and analysis method, and its application. This method obtains a biomarker based on single-cell protein modification by comparing the expression differences of different cell types in tissue samples and control tissues, sorting out immune cells in tissue samples that express different values ​​from those in control tissues, and screening out proteins and modified proteins with significant differences. This allows for the characterization of protein modification at the single-cell level.

[0027] Specifically, the detection and analysis method for single-cell protein modification biomarkers in this application includes the following steps: Step 100: Obtain tissue samples and control tissues, characterize immune cells in tissue samples and control tissues, wherein cells are labeled with antibodies of cell markers, protein modifications are characterized with universally modified antibodies, and expression differences of each cell type are compared between tissue samples and control tissues. Step 200: Immune cells that express different values ​​from control tissues are sorted out from the tissue samples. The sorted immune cells are subjected to whole proteomics and modified proteomics, and proteins with significant differences and modified proteins are screened out. Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as the detection antibody group, which is a biomarker based on single-cell protein modification.

[0028] This application provides biomarkers for single-cell protein modifications, enabling the characterization of protein modifications at the single-cell level and addressing the technical problem of lacking systematic detection and analysis methods for protein modifications in related technologies. This application's ability to characterize protein modifications at the single-cell level helps reveal heterogeneity within cell populations, accurately identify and classify different cell types, and also facilitates a deeper understanding of cell function, revealing functional changes in cells under different physiological and pathological states, and discovering new biomarkers and therapeutic targets.

[0029] Preferably, in step 100, the protein is modified by 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 control tissue. More preferably, when the weight of the tissue sample and control tissue is greater than or equal to 1g, mass cytometry characterization is selected; when the weight of the tissue sample and control tissue is less than 1g, imaging mass cytometry characterization is selected. When the tissue sample and control tissue are cellular fluids, mass cytometry characterization is selected; when the tissue sample and control tissue are solids, imaging mass cytometry characterization is selected. Exemplary examples of cellular fluids include, but are not limited to, PBMCs, pleural effusion, ascites, and other cell-rich fluids.

[0031] This application selects the characterization method based on the weight and / or state of the tissue sample and control tissue. When the weight of the tissue sample and control tissue is greater than or equal to 1g, dissociation of the tissue can obtain a large number of cells, and mass cytometry can obtain information of the entire tissue, which is highly representative. When the weight of the tissue sample and control tissue is less than 1g, imaging mass cytometry can obtain additional spatial information, which is highly dimensional.

[0032] Preferably, in step 200, the following steps are used to determine whether there are significant differences in protein and modified protein pathways between the tissue sample and the control tissue: The names of proteins in tissue samples and control tissues are represented by gene names; Genes were sorted in descending order of expression level in tissue samples and control tissues. Select the gene set to be analyzed from the gene set in the public database, and calculate the enrichment score of the gene set to be analyzed at the top or bottom of the sorting list based on the sorting results; The enrichment scores are normalized to obtain the single-sample gene enrichment scores for each gene set to be analyzed. The mean difference in gene enrichment scores for each gene set to be analyzed between tissue samples and control tissues was calculated using the t-test. If p < 0.05, the gene set to be analyzed was considered to have a significant difference between tissue samples and control tissues.

[0033] This application uses the above steps to determine whether there are significant differences in protein 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, it uses a gene sequencing algorithm, so the batch effect is small; third, it outputs high resolution and generates continuous enrichment scores, supporting fine quantification of biological states; and fourth, the background noise can be adjusted by exponential weighting to enhance the contribution of core driver genes.

[0034] Preferably, the gene set to be analyzed is derived from public databases such as MsigDB, KEGG, GeneOntology, and / or Reactome. However, it is not limited to these; the gene set to be analyzed may also be derived from a custom database.

[0035] Preferably, in step 200, proteins with significant differences and modified proteins are identified through the following steps: For the gene set with p < 0.05 in the t-test, extract the proteins and modified proteins belonging to the differentially expressed pathways; The abundance difference of each protein and modified protein in the tissue sample and control tissue is calculated, and proteins or modified sites with an abundance difference of 1 or more at a preset value are identified as proteins or modified proteins with significant differences.

[0036] For example, proteins or modified sites with an abundance difference of 1.5 times or more are considered as proteins or modified proteins with significant differences.

[0037] For example, the abundance of each protein and modified protein can be calculated using the maxquant software.

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

[0039] This application is based on single-cell protein modification markers, which 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 single-cell protein modification markers in any of the technical solutions of this application.

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

[0041] The following detailed description, in conjunction with specific embodiments and application scenarios, illustrates the single-cell protein modification-based biomarkers, their detection and analysis methods, and their applications provided in this application.

[0042] This embodiment is based on biomarkers for single-cell protein modification, their detection and analysis methods, and applications, including the following steps: Step 100: Obtain tissue samples and control tissues, characterize immune cells in tissue samples and control tissues, wherein cells are labeled with antibodies of cell markers, protein modifications are characterized with universally modified antibodies, and expression differences of each cell type are compared between tissue samples and control tissues.

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

[0044] Tissue samples and control tissues were cut into 0.5–1 gram pieces to obtain tissue samples and control tissues. The obtained tissue samples and control tissues were washed twice with pre-chilled PBS. The washed tissues were then placed in tissue preservation solution (MACS® Tissue Storage Solution, 130-100-008) and transported to the laboratory chilled, where they were stored at 4°C. After this step, the tissue samples and control tissues could be used for subsequent cell dissociation experiments. Cell dissociation experiments were completed within 24 hours.

[0045] The cell dissociation assay includes the following steps: After removing tissue samples and control tissues from the tissue preservation solution, place them in centrifuge tubes and wash 2-3 times with ice-cold PBS to remove residual blood. Using sterile scissors in PBS, trim tissue blocks, selecting structurally intact tissue weighing approximately 0.2 grams, avoiding blood clots. After removing PBS and unwanted tissue, gently cut the tissue into 1 cubic millimeter pieces. Mix 5 mL of tissue separation buffer (200 μL of enzyme H, 100 μL of enzyme R, and 25 μL of enzyme A in 4.7 mL of RPML; all enzymes are from the MACS® Tumor Dissociation Kit, 130-095-929) with the obtained tissue and stir in a 37°C water bath for 8 to 10 minutes. During this time, use a 5 mL Pasteur pipette to aspirate approximately 10 times every two minutes to ensure thorough mixing of the tissue with the enzyme solution, further dissociate the tissue, and monitor its dissociation. When approximately 80% of the tissue block has decreased in volume and appears loose, indicating complete dissociation, the tissue fluid is poured into a 70-micron cell filter placed on ice above a 50 mL centrifuge tube to terminate the enzymatic reaction and collect the cells. The cell filter is rinsed with approximately 20 mL of DMEM solution (HyClone, SH30243.01) to maximize cell recovery by aspirating and filtering as much cell suspension as possible. The cell suspension collected in the 50 mL centrifuge tube is then centrifuged at 300 g for 5 minutes at room temperature, the supernatant is discarded, and the cells are washed once with DMEM solution, followed by further discarding of the supernatant. The cells are resuspended in cryopreservation medium (10% DMSO mixed with 90% FBS, FBS from Gibco) and counted. Cell counts are sampled and tested for viability using trypan blue staining; a count exceeding 3 million cells with a viability exceeding 90% is considered acceptable. The counted cell suspension was then collected into 1.5 mL cryovials and placed in a programmed cooling box, stored at -80°C, and subsequently transferred to a liquid nitrogen tank for storage.

[0046] For cellular fluids, both the experimental and control groups can be stored and transported at 4°C. Centrifuge both fluids at 300g for 15 minutes, discard the supernatant, and resuspend twice in PBS. Centrifuge at 500g for 5 minutes, discarding the supernatant. Resuspend the cells at the bottom of the test tube in 1ml of cryopreservation buffer (10% DMSO mixed with 90% FBS, FBS from Gibco). Count and sample the cells, staining for viability with trypan blue. A cell count exceeding 3 million and a viability exceeding 90% is considered acceptable. Transfer the cell suspension to a programmed cooling box and store at -80°C, subsequently transferring to liquid nitrogen for storage.

[0047] Step 120: Characterize immune cells in tissue samples and control tissues, wherein cells are labeled with antibodies that are cell markers and protein modifications are characterized with antibodies that are universally modified.

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

[0049] The first step is to identify the antibody combination. Taking a study of hepatitis B complicated with liver cancer as an example, this will clarify the antibody selection method. Antibodies mainly consist of three parts: immune cells, non-immune cells, and modified antibodies.

[0050] Immune cells are differentiated using a series of antibodies against 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 cell population screening; CD19 specifically marks B lymphocytes; the CD3 and CD4 combination identifies helper T cell subsets, with CD45RO+CCR7+ characterizing CD4 central memory T cells (CD4TCM), CD45RO+CCR7+ characterizing CD4 effector memory T cells (CD4TEM), and CD4+CD25+ phenotype-specifically marking regulatory T cells (Tregs); the CD3 and CD8 combination identifies cytotoxic T cells. Differential expression of 45RO and CCR7 was used to classify CD8TCM and CD8TEM subsets; co-expression of TCRγδ and CD56 was used to identify CD56+γδ T cells; CD56 single-positive markers were used to label natural killer (NK) cells; the CD14+CD16- phenotype was used to label classical monocytes (Mono), and the CD14+CD16+ phenotype was used to define the CD16+ monocyte subset; the CD11b+CD66b+ combination was used to specifically label neutrophils; and co-expression of CD11c and HLA-DR was used to identify dendritic cells. Furthermore, the CD14-CD68- phenotype combined with CD1a+ features was used to define conventional dendritic cells (cDC), while the CD14+CD68+ combination combined with CD163+ features was used to label myeloid dendritic cells (mDC); macrophages were specifically identified by strong CD68 expression combined with the CD14+CD16+ phenotype, clearly distinguishing them from CD16+ monocytes.

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

[0052] For the use of modified antibodies, the antibodies used in the study of hepatitis B and liver cancer include modified tyrosine phosphorylation antibodies, modified acetylation antibodies, modified lactylation antibodies, and modified ubiquitination antibodies.

[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, subsequent experimental procedures do not differentiate between solid tissue and cellular fluid. For samples to be analyzed using mass cytometry, the experimental procedure is as follows: The target sample was removed from the liquid nitrogen tank, rapidly thawed and revived in a 37°C water bath, and pretreated for analysis. Cells were then washed and stained with cisplatin-Live / Dead staining agent (Fluidigm) at a 1:10000 dilution. Cells were pooled and labeled with surface markers using metal-conjugated antibodies, then fixed with 1.6% formaldehyde, permeated with 100% methanol, and stained with metal-conjugated antibodies targeting intracellular molecules. All 41 antibodies were either conjugated in the laboratory according to the manufacturer's (Fluidigm) operating procedures or pre-conjugated directly from Fluidigm. Antibodies suitable for flow cytometry were also suitable for imaging mass cytometry after metal conjugation. Finally, an iridium-containing dye (DNA inserter) was added to identify individual cells. The cells were then washed and diluted with EQFourElement CalibrationBeads (Fluidigm) for signal normalization. Data were collected using a Helios (Fluidigm) system equipped with CyTOF® 6.7 system control software, following the manufacturer's instructions. The sample collection event rate was 300–500 events / second, with noise suppression applied. Data were classified to identify cellular events (high DNA expression) and exclude dead cells (cisplatin positive). The data then proceeded to the data analysis workflow.

[0055] For single-cell processing, all CyTOF data were transformed using the arcsinh function with a cofactor of 5, implemented by the cytofAsinh function in cytofkitv1.11.3, and the data were scaled to the range of 0-1. Clustering was performed using Rphenographv0.99.1 with parameter K=45, a tool available on GitHub. Heatmaps were created using ComplexHeatmapv2.10.0 to display the median z-score (range 0 to 1) of marker expression in cells within each cluster. For dimensionality reduction, t-random neighborhood embeddings (t-SNE) were visualized using the runTSNE function in scranv1.18.7, a function used to explore phenotypic diversity among cell populations, and further analyzed using dittoSeqv1.6.0. Cell types were defined based on the expression of indicators for each cell cluster. Based on cell type and clustering results, the proportion of each cell type in each sample was calculated, and t-tests were used to compare differences between tissue samples and control tissues.

[0056] Figure 1 An example result is given, showing the statistical differences in the abundance of generalized post-translational modifications across different cells. For example... Figure 1 As shown in the figure, the horizontal axis represents the post-translational modification type, and the vertical axis represents the cell subtype. The size of the dot represents the expression rate of the corresponding cell subtype under that post-translational modification type, and the intensity of the dot's color represents the relative expression level between the two groups; the redder the dot, the higher the expression level, and the bluer the color, the lower the expression level. The left half of the figure represents cancerous tissue (CT), and the right half represents adjacent normal tissue (ANT). A black circle around the dot indicates that after a t-test, the p-value between the two groups is less than 0.05, meaning there is a significant difference.

[0057] For samples to be detected using imaging mass cytometry, the experimental procedure is as follows: Imaging mass cytometry is suitable for paraffin-embedded tissue blocks. The dewaxing, antigen retrieval, and antibody labeling methods mentioned below are applicable to paraffin immunohistochemistry slides and tissue microarray immunohistochemistry slides with anti-detachment properties. For anti-detachment slides, the section thickness should be 3-4 μm. In the slide pretreatment stage, the sample is first baked in a 58-62℃ oven for 1.5-2.5 hours, followed by dewaxing with fresh xylene in a fume hood for 15-25 minutes. After dewaxing, a gradient rehydration treatment is performed: sequentially using 100%, 95%, 80%, and 70% ethanol, each step lasting 4-6 minutes. The initial washing is completed in a Coplin staining jar using Maxpar deionized water and a 50-100 rpm orbital shaker for 4-6 minutes. In the antigen retrieval stage, the slide is immersed in retrieval solution (50ml conical tube) preheated to 94-98℃ for 28-32 minutes for heat-induced epitope exposure, with the tube cap partially open. The retrieval solution is then allowed to cool naturally to 68-72℃ using a gradient cooling program and maintained for 8-12 minutes. A second wash is performed using fresh Maxpar PBS buffer with two 8-12 minute shaking washes (50-100 rpm). In the sample blocking stage, the area is delineated with a hydrophobic pen and blocked at room temperature for 40-50 minutes in a humidity-controlled chamber using blocking solution containing 2.5-3.5% BSA. In the antibody incubation stage, an antibody working solution containing 0.4-0.6% BSA is prepared according to the detection system and incubated at 2-6℃ in a humidity-controlled environment for 12-16 hours. Antibodies for imaging mass cytometry can be obtained via immunohistochemical antibody-metal conjugation, with the conjugation procedure identical to that described in the mass cytometry section, or purchased directly from the supplier. Washing was performed sequentially using Maxpar PBS buffer containing 0.18-0.22% Triton X-100 (twice, 7-9 minutes, 30-50 rpm) and pure Maxpar PBS buffer (twice, 8-10 minutes). For the labeling and staining phase, apply 300-500 μl / cm² of the solution. 2 Iridium-based working solution (Maxpar PBS diluted 1:300-1:500) was used for staining at room temperature for 25-35 minutes. Final processing included washing with Maxpar deionized water for 4-6 minutes, followed by air drying at 18-25°C for 20-40 minutes. Microscopic observation and image acquisition were then performed using the Hyperion imaging system. Raw data were integrated and stored using Fluidigm's commercial acquisition software.

[0058] Data analysis followed this workflow: First, the raw MCD files acquired by the Hyperion imaging system were preprocessed using the IMCSegmentation Pipeline and converted into multi-channel TIFF images. Semi-supervised automated feature extraction was then performed using Ilastik software, followed by standardized cell segmentation and feature quantization using CellProfiler. Subsequently, single-cell data were extracted using imcRtools (v1.9.0), and multi-channel images were read and segmented using Cytommaper. After batch effect correction using Harmony (v0.1.1), Rphenograph (v0.99.1) was used for cell subpopulation clustering and the expression of post-translational modifications in each cell subtype. A t-test was used to compare the differences between tissue samples and control tissues. The results of imaging mass cytometry regarding the differences in post-translational modifications among cell subtypes were presented in the same format as those of mass cytometry, and will not be elaborated further here.

[0059] Step 200: Immune cells that express different values ​​from control tissues are sorted out from the tissue samples. The sorted immune cells are subjected to whole proteomics and modified proteomics, and proteins with significant differences and modified proteins are screened out.

[0060] Specifically, this step includes the following process: Step 210: Sorting out immune cells in the tissue sample that show different expression compared to the control tissue.

[0061] Taking myeloid cells derived from hepatitis B complicated with hepatocellular carcinoma as an example, the myeloid cells were isolated using the EasySep™ Human Myeloid Positive Selection Kit II (Stemcell, catalog number #17893). This 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 (10% antibody mixture, 2% fetal bovine serum, 1% penicillin / streptomycin, 1% DNase solution in PBS), incubated at room temperature for 10 minutes, and then transferred to polystyrene FACS tubes. The tubes were attached to a magnet and incubated for 5 minutes. After discarding the cell solution, the tubes were removed from the magnet and resuspended in 2.5 mL of FBS solution (2% fetal bovine serum, 1% penicillin / streptomycin, 1% DNase solution in PBS). The tubes were again attached to the magnet and incubated for 5 minutes. After discarding 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: Perform whole proteomics and modified proteomics on the sorted immune cells, and screen out proteins and modified proteins with significant differences.

[0063] Cell samples were collected, washed with PBS, and stored at -80°C. Proteins were digested and phosphorylated peptides were enriched using the EasyPept™ PTM Phosphorylated Peptide Enrichment Kit (omicsolution, OSFP0005_200UG). A phosphatase inhibitor (Reagent0) was added at a 1:100 ratio. For isolated cell samples, 40 μl of Reagent0 was added to the cell pellet. The mixture was vortexed and vigorously pipetted 15–20 times with a 200 μl pipette tip, then lysed on ice for 10 minutes. The mixture was centrifuged at 12000 g for 20 minutes at 4°C, and the supernatant was collected. BCA protein quantification was performed on the supernatant. Based on the quantification results, 300 μg of protein was taken for further processing. The protein solution was added to S-plates, and 200 μl of Reagent0 was added to each well sequentially, mixing 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, cool the sample 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 digestion, add 30 μl of Reagent E to each well, mix, and terminate the digestion reaction. Precipitation may occur at this stage. Centrifuge the mixture at 20,000 g for 1 minute and collect the supernatant for subsequent desalting. Activate the desalting column by adding 1 ml of methanol, 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 with 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. The desalted sample was quantified using a peptide quantification kit. Based on the quantification results, 200 μg of peptide was taken and concentrated using a vacuum centrifuge (temperature below 30°C recommended). The EnrichmentTip was inserted into the adapter and placed on a waste plate. Reagent F (150 μl) was added to the dried peptide and mixed thoroughly. Then, Reagent G (50 μl) was added to the EnrichmentTip and centrifuged. After discarding the eluent, the adapter with the EnrichmentTip was transferred to a new waste plate. The sample was loaded onto the EnrichmentTip and centrifuged, and the eluent was repeated once. Reagent F and Reagent G (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.Samples were eluted using Reagent H1 and Reagent H2 (50 μl each), and then concentrated using a vacuum centrifuge (below 30°C is recommended). Reagent 0 and Reagent A through Reagent G correspond one-to-one with Reagent 0, Reagent A through Reagent G in the EasyPept™ PTM Phosphorylated Peptide Enrichment Kit, respectively.

[0064] Mass spectrometry analysis was performed using a QExactive HF-X hybrid quadrupole-orbit trap mass spectrometer equipped with a ThermoeasynLC-1200 system. The LC-MS / MS system consisted of 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 nanophase gradient was as follows: transition from 2% buffer B (80% ACN + 0.1% FA in water) to 8% within 3 min, from 8% to 40% within 78 min, from 40% to 95% within 2 min, and held at 95% within 7 min. The mass spectrometry settings were as follows: ESI+ ion source, DDA primary scan mode, scan range 400-1200 m / z, resolution 60,000 m / z 200, AGC 3e6, maximum IT 30 ms; full scan mode, TOPN 20, resolution 30,000 m / z 200, AGC 1e5, maximum IT 50 ms. MS2 activation type was HCD, isolation window 1.6Th, normalized collision energy 28. Mass spectrometry data search was performed using MaxQuant software with the following parameters: variable modifications included oxidation (M), fixed modifications included carbamate methylation (C), digestion mode was trypsin / P, label-free quantification used LFQ (label-free quantification), and FAST files were obtained from Uniprot (https: / / www.uniprot.org / ) for review of human proteins. The LFQ intensity in the MaxQuant search results represents the original protein quantification.

[0065] Subsequent data analysis used log2-transformed LFQ intensity. 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, selecting "Homosapiens" as the species, "C2" as the category, and "CP:KEGG" as the subcategory. In the ssGSEA analysis, only proteins with quantitative values ​​in all samples were included. First, the names of proteins in the sample table were represented by their gene names. Then, the ssGSEA method was used to sort the protein expression matrix of each sample from high to low expression level. Based on the sorting results, the enrichment degree (ES value score) of a specific gene set at the top / bottom of the sorted list was calculated. Afterwards, the ES values ​​between samples were normalized using z-scores to obtain the ssGSEA score for the comparability of each gene set. The t-test was used to calculate the mean difference in ssGSEA scores for each gene set between tissue samples and control tissues, and a significant difference between the gene sets was considered to exist between tissue samples and control tissues if p < 0.05. Specific gene sets were obtained from public databases, including but not limited to MSigDB, KEGG, GeneOntology, and Reactome. Pathways with no significant differences were excluded, while pathways with significant differences were selected as the basis for selecting significantly different proteins and modifying differentially expressed sites. Figure 2 To analyze the ssGSEA results obtained from hepatitis B combined with liver cancer samples, a heatmap was used. The x-axis of each cell represents the sample number, and the y-axis represents the corresponding pathway. The intensity of the fill color represents the enrichment level of the pathway; the redder the color, the higher the relative enrichment, and the bluer the color, the lower the relative enrichment. Only pathways with a t-test p-value < 0.05 between two groups were included in the graph. Then, the proteins and phosphorylation modification sites of the differentially expressed pathways were extracted, and their abundance was compared between groups. Proteins or phosphorylation sites with an abundance difference of more than 1.5-fold (p-value < 0.05 in the t-test) were defined as significantly differentially expressed proteins or phosphorylation sites.

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

[0067] Specifically, this step includes the following process: After completing step 200 of the analysis for patients with hepatitis B and liver cancer, a significant difference was found between groups in the Adipocytokine signaling pathway (p < 0.05). The abundance of proteins and phosphorylated peptides belonging to this pathway was then screened from the proteomic and phosphoproteomic results, and a t-test was performed between groups. Proteins such as STAT1 and its phosphorylation sites showed significant differences between groups, with abundance folds greater than 1.5-fold or less than 2 / 3 (p < 0.05). STAT1 and its phosphorylation sites were then classified as significantly differentially modified proteins or phosphorylation sites. This search was performed for each differentially expressed pathway to construct a differentially expressed site library. TRIM28 and HSP27 were obtained using the same method. TRIM28 originated from the Apoptosis pathway, while HSP27 originated from the Mismatch repair pathway. Therefore, p-STAT1, p-HSP27, and p-TRIM28, three proteins with altered post-translational modifications due to phosphorylation, were used as characterization targets for single-cell-level post-translational modifications in imaging mass cytometry.

[0068] For patients with hepatitis B and hepatocellular carcinoma (HCC), paraffin-embedded tissue sections from 37 patients (samples obtained from the Chongqing University Cancer Hospital Sample Bank) were used to characterize the three phosphorylation post-translational modifications. These 37 patients provided tissue samples from 12 peri-cancer tissues and 36 from the cancer center. Of the cancer center tissues, 14 patients received first-line "T+A" therapy. "T+A" therapy refers to the immuno-anti-angiogenic combination therapy of atezolizumab (anti-PD-L1 antibody) and bevacizumab (anti-VEGF antibody), the first approved regimen for first-line treatment of advanced hepatocellular carcinoma (HCC). Of these 14 patients, nine responded to the drug within six months of treatment (i.e., disease progression-free), and five did not respond within six months (i.e., disease progression). Progression clinically signifies treatment failure.

[0069] These tissues were tested, and single-cell post-translational modifications were characterized using imaging mass cytometry. The imaging mass cytometry procedure is not detailed here, as it is consistent with the imaging mass cytometry in step 100, the only difference being the choice of antibodies. Since the proteomic study in step 200 focused on myeloid cells, only the results for myeloid cells are presented here to demonstrate the practicality of the characterization. The following antibody combination was used to label myeloid cells and their cell types: CD45, CD33, CD11b, CD68, CD1163, CD86, CD80, CD11c, CD206, and HLA-DR. CD45 is a universal 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 as co-stimulatory molecules on dendritic cells and activated monocytes; CD11c is a key marker for dendritic cells; CD206 is mainly found on M2 macrophages; and HLA-DR is expressed on antigen-presenting cells such as dendritic cells and macrophages. Using these antibodies, different types of myeloid cells can be labeled in tissue samples. Using the aforementioned antibodies along with three phosphorylated antibodies—p-STAT1, p-TRIM28, and p-HSP27—and in conjunction with known immune checkpoint antibodies such as MHCⅠ, MHCⅡ, CD80, CD86, LAG3, CD24, CD28, CTLA-4, CD47, MERTK, PD-1, PD-L1, Siglec-10, SirPa, TIM3, and THBS1, single-cell characterization of phosphorylated modifications and known immune checkpoints can be achieved on myeloid cells.

[0070] The characterization results can compare the differences in post-translational modifications of single-cell myeloid cells between cancer and adjacent normal cells, and compare the differences in post-translational modifications of myeloid cells and expressed immune checkpoints between "T+A" responsive and non-responsive patients. Figure 3 The differences in various phosphorylation markers between carcinoma and adjacent normal cell lines were shown on example tissue sections, allowing for observation. Figure 3 It can be observed that all three phosphorylation post-translational modification indicators are elevated in cancer cells. Figure 4 The study demonstrates the difference in the positive rates of phosphorylation markers expressed in tissues surrounding and at the center of all cancers. From... Figure 4 It can be observed that phosphorylation expression is most differentiated on macrophage M2, and it increases significantly in cancer tissue. Figure 5This indicates that there were no significant differences 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 cells between the high-response and low-response groups. When the positivity or non-positivity of p-STAT1, p-TRIM28, and p-HSP27 on macrophage M2 cells was combined to re-type macrophages, all negative were classified as P0, single positive as P1, double positive as P2, and triple positive as P3. This allows for the classification of macrophages from... Figure 6 The study found that the high-response group had more P2 and P3 types, which could also be seen from the tissue section images. Figure 7 Simultaneously, an AUC analysis was performed on the patient's response, from... Figure 8 Patients with a higher-than-mean proportion of the M2-P2 type showed no recurrence in AUC analysis. Therefore, the proportion of the M2-P2 type can be used to predict patient response to medication.

[0071] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0072] Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than 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 merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for screening biomarkers based on single-cell protein modification, characterized in that, Includes 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 with antibodies containing cell markers, protein modifications are characterized with universally modified antibodies, and the expression differences of each cell type in the tissue samples and control tissues are compared. Specifically, immune cells in tissue samples and control tissues are characterized in the following ways: the characterization method is selected based on the weight and / or state of the tissue samples and control tissues, and mass cytometry is selected when the weight of the tissue samples and control tissues is greater than or equal to 1g, and imaging mass cytometry is selected when the weight of the tissue samples and control tissues is less than 1g; and / or mass cytometry is selected when the tissue samples and control tissues are cellular fluids, and imaging mass cytometry is selected when the tissue samples and control tissues are solids. Protein modification can be categorized into several types, including phosphorylation, ubiquitination, acetylation, methylation, glycosylation, sulfation, and fatty acylation. Step 200: Identify immune cells in the tissue samples that show differential expression compared to control tissues. Perform whole proteomics and modified proteomics on the identified immune cells, and screen for proteins and modified proteins that show significant differences. The following steps were used to determine whether there were significant differences in protein and modified protein pathways between tissue samples and control tissues: The names of proteins in tissue samples and control tissues are represented by gene names; Genes were sorted in descending order of expression level in tissue samples and control tissues. Select the gene set to be analyzed from the gene set in the public database, and calculate the enrichment score of the gene set to be analyzed at the top or bottom of the sorting list based on the sorting results; The enrichment scores are normalized to obtain the single-sample gene enrichment scores for each gene set to be analyzed. The mean difference of the single-sample gene enrichment score of each gene set to be analyzed in tissue samples and control tissues was calculated using the t-test method, and if p < 0.05, it was considered that there was a significant difference between the gene sets to be analyzed in tissue samples and control tissues. The following steps were used to identify proteins with significant differences and modified proteins: For the gene set with p < 0.05 in the t-test, extract the proteins and modified proteins belonging to the differentially expressed pathways; The abundance difference of each protein and modified protein in the tissue sample and control tissue is calculated, and proteins or modified sites with an abundance difference of 1 or more are regarded as proteins or modified proteins with significant differences. Step 300: The proteins and modified proteins with significant differences obtained in step 200 are used as the detection group, which is a biomarker based on single-cell protein modification. The biomarkers are used for disease detection and / or prediction of patient response to medication.

2. The method for screening biomarkers based on single-cell protein modification according to claim 1, characterized in that, The set of genes to be analyzed is derived from public databases MsigDB, KEGG, Gene Ontology, and / or Reactome.

3. The method for screening biomarkers based on single-cell protein modification according to claim 1, characterized in that, Proteins or modified sites with an abundance difference of 1.5 times or more are considered as proteins or modified proteins with significant differences.

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