Marker combination for predicting death risk of sepsis patient during hospitalization and application

Abstract

The invention relates to the field of medical treatment, and discloses a marker combination for predicting the death risk of a sepsis patient during hospitalization and application. The marker combination comprises six T cell subgroups / functional states related to sepsis specificity, and the six T cell subgroups / functional states comprise Treg-FOXP3 + CTLA4 + IL2RA +; the TM is CD8 + Tem-FGFBP2; nKT CCL5 < + > GNLY < + >; the preparation method comprises the following steps of: performing Thelper22-AHR; the method comprises the following steps of: adding a CD8A + Teffector-GZMK; the invention relates to a CD8A + Tem-GZMK cell subset. Six marker genomes of T cell subgroups / functional states obtained through screening have OER specificity at different time points of sepsis patients, the six T cell subgroups / functional states can serve as a set of T cell subgroups related to sepsis specificity, and the T cell subgroups have high application value in sepsis immunotherapy.

Description

Technical Field

[0001] The present application relates to the medical field, and more specifically, to a marker combination and application for predicting the risk of death in sepsis patients during hospitalization. Background Art

[0002] Single-cell RNA-sequencing (scRNA-seq) deconstructs immune cell heterogeneity at single-cell resolution, revealing the dynamic changes in the immune microenvironment in sepsis, and providing strong technical support for precise diagnosis, disease course monitoring, and personalized treatment. In recent years, studies have used scRNA-seq to systematically analyze the peripheral blood and organ tissues of patients with sepsis, revealing the molecular mechanisms of immunosuppression in sepsis and potential therapeutic targets. In particular, monocytes and T cells in the peripheral blood of patients with sepsis showed significant dysfunction, accompanied by abnormal activation of the PD-1 / PD-L1 signaling pathway, suggesting the possible therapeutic value of immune checkpoint inhibitors. In addition, the study also discovered a group of new T cell subsets associated with immune recovery that express anti-inflammatory factors during the sepsis recovery phase and play an important role in the restoration of intestinal barrier function. Through single-cell analysis of sepsis-related organs (such as the lungs, liver, and kidneys), the study revealed the differentiation trajectories of macrophage subsets during organ damage and their role in fibrotic repair. For example, the gene expression of M2 macrophages is significantly correlated with the degree of tissue fibrosis. scRNA-seq also has unique value in distinguishing sepsis caused by different pathogens. Sepsis caused by bacterial infection shows more activation of immune cell subsets associated with the inflammatory cascade, while sepsis related to fungal infection is accompanied by more obvious immunosuppressive characteristics. These studies provide an important theoretical basis for more accurate sepsis classification and targeted treatment, and also help to analyze the effectiveness of pathogen-specific treatment.

[0003] In recent years, immunomodulatory therapies have become a hot topic in sepsis research. These approaches primarily regulate the immune response, restore immune homeostasis, and improve multi-organ function. These therapies offer advantages such as high specificity and targeted delivery, including immune checkpoint inhibitors, cytokine therapy, and adoptive cell transfer. Among these, adoptive cell transfer, particularly T-cell-targeted immunotherapy, has shown great potential. This approach uses gene editing to introduce genetic material containing specific antigen recognition domains and T-cell activation signals into T cells, enabling them to recognize specific pathogens or abnormal signals in sepsis patients, leading to activation. These cells then release perforins, granzymes, and other proteins to eliminate infected or abnormal cells, while also secreting cytokines to modulate systemic immune status, alleviate immunosuppression, and promote patient recovery. However, the application of T-cell-based immunotherapy in sepsis remains challenging. First, T-cell subsets in sepsis are highly heterogeneous, and their functional status dynamically changes with infection type, disease stage, and the host immune microenvironment. Second, the abundance of inflammatory factors and metabolic disturbances in the sepsis microenvironment may inhibit T-cell infiltration and activation. Furthermore, long-term immunosuppression can lead to T cell failure, limiting the effectiveness of treatment. Therefore, overcoming these limitations, such as by optimizing gene editing strategies and enhancing the persistence and efficacy of T cells in the infected microenvironment, has become an important issue that needs to be addressed in advancing the application of T cell-based immunotherapy in sepsis. Summary of the Invention

[0004] The goal of this invention is to use scRNA-seq technology to screen 262 different T cell subsets and functional states to identify highly sepsis-specific T cell subsets and functional states. These T cell subsets and functional states may have stronger anti-infection effects when used in immunotherapy. It also aims to provide effective solutions and new ideas for predicting adverse sepsis outcomes and immunotherapy.

[0005] In order to achieve the above-mentioned invention objectives, this application adopts the following technical solutions:

[0006] First, the present application screened out 6 T cell subsets / functional states that are highly specific to sepsis from 262 different T cell subsets / functional states, including Treg-FOXP3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL5+GNLY+; Thelper22-AHR; CD8A+Teffector-GZMK; CD8A+Tem-GZMK cell subsets.

[0007] In a second aspect, the present application provides the use of the above-mentioned marker combination in the preparation of a product for predicting the risk of death in sepsis patients during hospitalization.

[0008] In a third aspect, the present application provides a screening method comprising the following steps:

[0009] a) Researchers should prepare quality-controlled sepsis peripheral blood single-cell RNA sequencing data (rds format files) and organize the marker genomic information of the T cell subsets to be evaluated into a standard file, which should include the cell type / subtype name, marker gene and expression status (negative / positive expression) in csv format;

[0010] b) Import the two prepared input data files into R language and perform cell counting statistics corresponding to different T cell subsets and different T cell subsets in different single cell samples using the OER program package we wrote;

[0011] c) evaluating the number and specificity of 262 T cell subsets / functional states to identify candidate T cell subsets / functional states with significantly altered abundance in the peripheral blood of sepsis patients compared to healthy subjects;

[0012] d) Automatically sort out OER-specific T cell subsets / functional states at different time points in sepsis patients using OER program packages and single-cell data from candidate T cell subsets / functional states (e.g., attached Figure 3 );

[0013] e) Centrifuge approximately 2-3 ml of peripheral blood from a sepsis patient at 800 x g for 5 minutes, remove the upper plasma layer, add 2 ml of red blood cell lysis buffer (containing 150 mM NH4Cl, 10 mM KHCO3, 100 μM EDTA), lyse the sample at room temperature for 5 minutes, centrifuge at 1000 rpm for 10 minutes, and discard the supernatant; add 2 ml of PBS solution, centrifuge at 1000 rpm for 10 minutes, and discard the supernatant;

[0014] f) Surface flow cytometry antibody labeling was performed by adding antibodies at a ratio of 1:200 in PBS containing 1% FBS. Labeling methods for several markers to be tested are as follows: FSV510-L / D, APC-CY7-CD45, FITC-CD3, PE-CY7-CTLA4, PE-CD25, and the cells were resuspended in a volume of 500 μl. Labeling was then performed at 4°C for 30 minutes.

[0015] g) Wash cells with 1% FBS-PBS, centrifuge at 1500 rpm for 5 minutes, discard the supernatant, permeabilize and fix with 100 μl of membrane, wash twice with 250 μl of membrane fixation solution, incubate with AF647-FOXP3 antibody with 100 μl of solution, dissolve with antibody and incubate with 100 μl of solution, and wash once with 2 ml of solution;

[0016] h) Resuspend the cells in 1 ml of 1% FBS-PBS;

[0017] i) Flow cytometry analysis of the CD3+CD45+FOXP3+CTLA4+CD25+ T cell population with positive live cell staining.

[0018] In summary, this application has the following beneficial effects:

[0019] 1) The method of this application first provides a comprehensive classification of 262 T cell subsets / functional states in single-cell sequencing studies;

[0020] 2) The method of this application screened 63 T cell subsets related to the long-term prognosis of sepsis and 38 T cell subsets related to sepsis disease progression;

[0021] 3) The method of the present application further screened six T cell subset / functional status marker gene sets that were OER-specific at different time points in sepsis patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 : Using the OER program, 262 T cell marker gene sets were analyzed in single-cell sequencing data from peripheral blood of healthy subjects and patients with sepsis. Cells with >0 T cell subset / functional state marker gene sets were included in subsequent analysis. The number of T cell subsets detected in different samples was determined. A single dark blue circle represents a cell subset detected only in that sample type, while a dark blue circle and vertical line represent cell subsets detected in two or more sample types. The dark blue bar on the left represents the total number of T cell subsets / functional states detected in that sample type.

[0023] Figure 2 We evaluated the differences in the proportions of peripheral blood T cell subsets and functional status between healthy subjects and patients with sepsis at different time points (first day of hospitalization, third day of hospitalization, and discharge). P < 0.05 was considered statistically significant.

[0024] Figure 3 We assessed OER-specificity in the peripheral circulation of healthy subjects and patients with sepsis at different disease stages using 63 marker genes associated with long-term sepsis prognosis and 38 marker genes associated with sepsis progression, representing T cell subsets and functional states. Red squares represent OER-specific marker genes, while green squares represent non-OER-specific marker genes, i.e., OER-associated or OER-referenced marker genes.

[0025] Figure 4: The ROC curve showed that the proportions of six T cell subsets (Treg-FOXP3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL5+GNLY+; Thelper22-AHR; CD8A+Teffect or-GZMK; CD8A+Tem-GZMK cell subsets) jointly predicted the risk of death in patients with sepsis during hospitalization.

[0026] Figure 5 : Multivariate Cox risk regression and nomogram showed that a model based on the proportions of six T cell subsets (Treg-FOXP3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL 5+GNLY+; Thelper22-AHR; CD8A+Teffector-GZMK; CD8A+Tem-GZMK cell subsets) was developed in a cohort of patients with sepsis to predict the risk of death during hospitalization.

[0027] Figure 6 : PCA dimensionality reduction results of the model development based on the proportions of six T cell subsets (Treg-FOX P3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL5+GNLY+; Thelper22-AH R; CD8A+Teffector-GZMK; CD8A+Tem-GZMK cell subsets) in the peripheral blood of patients with sepsis in the validation cohort.

[0028] Figure 7 :Treg in peripheral blood of patients with sepsis and healthy subjects fci Detection of cell subpopulation ratios. The left side shows the flow cytometry characteristic plot, and the right side shows the quantitative statistical plot. "*" indicates a significant difference, P < 0.05; "**" indicates P < 0.01. DETAILED DESCRIPTION

[0029] The technical solutions and effects of the present application are further described in detail below with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining the present invention, and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present invention, not all of its components.

[0030] scRNA-seq technology has shown great advantages in exploring new T cell subpopulations and T cell characteristics associated with disease risk. In previous studies, T cells were usually divided into multiple subpopulations based on the expression characteristics of key transcription factors, surface molecules and effector molecules in their biological functions or developmental pathways, such as cytotoxic T cells, memory T cells, effector T cells, helper T cells and regulatory T cells. However, this classification method is still insufficient in clinical and scientific research. By integrating previous single-cell sequencing data, we summarized 262 T cell subpopulations and their functional status classified according to differences in the expression of marker gene groups, providing a new perspective for a deeper understanding of the diversity of T cells.

[0031] In previous work, we developed an R language-based package OER to count the number of different T cell subsets in different single-cell sequencing samples and evaluate their specificity. First, we analyzed and compared the T cell abundance corresponding to 262 T cell subsets / functional status marker genomes in the single-cell transcriptome data of healthy subjects (45 cases) and septic patients at different time points, including the first day of admission (33 cases), the third day of admission (24 cases) and discharge (15 cases). The results showed that 194 T cell subsets were detected in healthy subjects, 192 T cell subsets were detected in the peripheral circulation of septic patients on the first day of admission, 189 T cell subsets were detected in the peripheral circulation of septic patients on the third day of admission, and 189 T cell subsets were detected in the peripheral circulation of septic patients at discharge. As shown in the attached figure Figure 1 As shown in the results, further comparison revealed that five T cell subsets / functional states were only detected in healthy subjects (TEarly-cm, Tfh-CXCL13+CXCR5+, Tfh-CXCL13+ICOS+, Thelper1-CXCL13, Thelper17-CXCR6); three T cell subsets / functional states were only detected in the peripheral blood circulation of sepsis patients at different disease stages (Texhausted-FOXP3, TFCGR3B, Thelper22-AHR); and one T cell subset / functional state was only detected in the peripheral circulation of healthy subjects and sepsis patients at discharge (CD8+TTNF).

[0032] Next, we compared the abundance of T cell subsets / functional states in the peripheral blood of sepsis patients and healthy subjects at different time points. The results showed that there were significant statistical differences in the abundance of 57 T cell subsets / functional states in the peripheral blood of sepsis patients on the first day of admission and healthy subjects; there were significant statistical differences in the abundance of 57 T cell subsets / functional states in the peripheral blood of sepsis patients on the third day of admission and healthy subjects; there were significant statistical differences in the abundance of 71 T cell subsets / functional states in the peripheral blood of sepsis patients at discharge and healthy subjects. Among them, the proportions of 38 T cell subsets / functional states were significantly different from those of healthy subjects on both the first and third days of admission, which are considered to be T cell subsets related to disease progression; the proportions of 63 T cell subsets / functional states were significantly different from those of healthy subjects at discharge, which are considered to be T cell subsets related to long-term prognosis of the disease (such as those in the attached table). Figure 2 ).

[0033] We further examined the OER specificity of these 63 long-term prognosis-related and 38 disease progression-related T cell subset marker gene sets in sepsis patients at different time points. The results focused on 6 T cell subsets / functional states that were OER-specific only in healthy subjects, or only at the peak of disease progression, including Treg-FOXP3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL5+GNLY+; Thelper22-AHR; CD8A+Teffector-GZMK; CD8A+Tem-GZMK cell subsets. It should be noted that the marker gene set-specific T cell subsets here do not represent T cell subsets that are only detected in the peripheral blood of sepsis patients. In the peripheral blood of sepsis patients, the expression levels of the marker gene set and the expression levels of other T cell subsets have a small overlap, and the credibility of the cell annotation is high, which is determined by the evaluation principle of the OER algorithm (as shown in the attached figure). Figure 3 ). In order to evaluate the relationship between the six T cell subsets we screened and the clinical outcomes of patients with sepsis, we conducted subsequent analysis. First, we used the proportions of the six T cell subsets mentioned above as joint indicators for multivariate logistic regression analysis to predict the survival outcomes of patients during hospitalization, and drew the ROC curve. The area under the curve (Area under curve, AUC = 0.8714, P = 0.009) showed that the proportions of the six T cell subsets can effectively predict the survival outcomes of patients during hospitalization (such as the attached Figure 4To further explore the weight of these six T cell proportions as predictive factors, we constructed a multivariate Cox risk regression model and compared the weights of these six T cell subsets in predicting the survival outcomes of sepsis patients during hospitalization through a nomogram. The results showed that the lower the proportion of Treg-FOXP3+CTLA4+IL2RA+(Tregfci), the higher the corresponding score, and the greater the risk of death during hospitalization (Appendix Figure 5 ). To verify the above results, we further conducted a validation analysis in an independent external cohort of sepsis patients. The data used for validation came from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ), and the dataset number is GSE167363. This dataset contains peripheral blood single-cell transcriptome sequencing data from 10 sepsis patients, of which 4 samples were from sepsis patients who eventually died and 6 samples were from surviving sepsis patients. In this dataset, we identified and quantified the proportions of the above 6 T cell subsets in each sample and performed principal component analysis (PCA). The results showed that the relative proportions of these 6 T cell subsets were combined to significantly distinguish samples from dead and surviving patients (see Appendix). Figure 6 At the same time, we analyzed the Tregs in the peripheral blood of sepsis patients by flow cytometry, which were jointly labeled by FOXP3, CTLA4 and IL2RA. fci The results showed that the proportion of Treg cell subsets in the peripheral blood of patients with sepsis fci The proportion of cell subpopulations was significantly reduced (see Appendix Figure 7 ).

[0034] In summary, the six T cell subsets / functional states screened out based on single-cell sequencing can be regarded as a group of T cell subsets specifically related to sepsis, which has high application value in sepsis immunotherapy.

[0035] Example

[0036] The implementation process of this application is as follows:

[0037] a) Researchers should prepare quality-controlled sepsis peripheral blood single-cell RNA sequencing data (rds format files) and organize the marker genomic information of the T cell subsets to be evaluated into a standard file, which should include the cell type / subtype name, marker gene and expression status (negative / positive expression) in csv format;

[0038] b) Import the two prepared input data files into R language and use the OER package to perform cell count statistics for different T cell subsets and different T cell subsets in different single cell samples (as shown in the table below);

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] c) evaluating the number and specificity of 262 T cell subsets / functional states to identify candidate T cell subsets / functional states with significantly altered abundance in the peripheral blood of sepsis patients compared to healthy subjects;

[0046] d) Automatically sort out OER-specific T cell subsets / functional states at different time points in sepsis patients using OER program packages and single-cell data from candidate T cell subsets / functional states (e.g., attached Figure 3 );

[0047] e) Centrifuge approximately 2-3 ml of peripheral blood from a sepsis patient at 800 x g for 5 minutes, remove the upper plasma layer, add 2 ml of red blood cell lysis buffer (containing 150 mM NH4Cl, 10 mM KHCO3, 100 μM EDTA), lyse the sample at room temperature for 5 minutes, centrifuge at 1000 rpm for 10 minutes, and discard the supernatant; add 2 ml of PBS solution, centrifuge at 1000 rpm for 10 minutes, and discard the supernatant;

[0048] f) Surface flow cytometry antibody labeling was performed by adding antibodies at a ratio of 1:200 in PBS containing 1% FBS. Labeling methods for several markers to be tested are as follows: FSV510-L / D, APC-CY7-CD45, FITC-CD3, PE-CY7-CTLA4, PE-CD25, and the cells were resuspended in a volume of 500 μl. Labeling was then performed at 4°C for 30 minutes.

[0049] g) Wash cells with 1% FBS-PBS, centrifuge at 1500 rpm for 5 minutes, discard the supernatant, permeabilize and fix with 100 μl of membrane, wash twice with 250 μl of membrane fixation solution, incubate with AF647-FOXP3 antibody with 100 μl of solution, dissolve with antibody and incubate with 100 μl of solution, and wash once with 2 ml of solution;

[0050] h) Resuspend the cells in 1 ml of 1% FBS-PBS;

[0051] i) Flow cytometry analysis of the CD3+CD45+FOXP3+CTLA4+CD25+ T cell population with positive live cell staining.

[0052] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

Claims

1. A marker combination for predicting the risk of death in patients with sepsis during hospitalization, characterized in that: The marker combination includes 6 T cell subsets / functional states specifically associated with sepsis, including Treg-FOXP3+CTLA4+IL2RA+; CD8+Tem-FGFBP2; NKTCCL5+GNLY+; Th elper22-AHR; CD8A+Teffector-GZMK; and CD8A+Tem-GZMK cell subsets.

2. Use of the marker combination according to claim 1 in the preparation of a product for predicting the risk of death in sepsis patients during hospitalization.

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