Classification method and device for key cell types and subtypes of thoracic aortic dissection

Abstract

The invention provides a classification method and device for key cell types and subtypes of a thoracic aortic dissection. The method comprises the following steps: preprocessing a thoracic aortic dissection and a normal contrast tissue sample to obtain cell types; determining suspected key cell types of the thoracic aortic dissection according to the proportion of cell types in the aortic dissection and the normal control tissue sample, and obtaining cell subtypes corresponding to the suspected key cell types of the thoracic aortic dissection based on gene expression data of the suspected key cell types; determining the suspected key cell subtypes of the thoracic aortic dissection according to the cell subtypes corresponding to the suspected key cell types of the thoracic aortic And according to the suspected key cell type of the thoracic aortic dissection and the suspected key cell subtype of the thoracic aortic dissection, determining the key cell type and subtype of the arterial dissection. Through the method, the key cell type and subtype of the thoracic aortic dissection can be determined.

Description

Technical Field

[0001] This document relates to the field of cell classification technology, and in particular to a method and device for classifying key cell types and subtypes of thoracic aortic dissection. Background Art

[0002] Thoracic Aortic Dissection (TAD) is an extremely dangerous cardiovascular disease characterized by a tear in the aortic intima, which leads to the formation of a vascular "false lumen" in the aortic wall. The huge blood flow pressure causes the tear to extend rapidly, and the false lumen compresses, eventually leading to the rupture of the vascular wall. TAD in young patients is often related to genetic factors (such as double aortic valves), connective tissue diseases (such as Marfan syndrome, Ehlews-Danlos syndrome, etc.) or extreme physical activity, and its pathogenesis is significantly different from that of other patients. Cell types / subtypes play a key role in the occurrence and development of the disease, but there is currently a lack of in-depth research on key cell types / subtypes in patients with thoracic aortic dissection. Traditional research methods also usually rely on macroscopic analysis of tissue samples, which makes it difficult to accurately identify and analyze specific cell types / subtypes. Summary of the invention

[0003] The present invention provides a method and device for classifying key cell types and subtypes of thoracic aortic dissection, aiming to solve the above-mentioned problems.

[0004] The embodiment of the present invention provides a method for classifying key cell types and subtypes of thoracic aortic dissection, comprising:

[0005] S1. Obtaining gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocessing the gene expression data to obtain cell types;

[0006] S2, obtaining the ratio of the cell types in the aortic dissection and normal control tissue samples, and determining the cell types with an increased ratio higher than a preset value as suspected key cell types for thoracic aortic dissection;

[0007] S3. Based on the gene expression data of the suspected key cell types, standardization, normalization, and dimensionality reduction cluster analysis are performed to obtain cell subtypes corresponding to the suspected key cell types of thoracic aortic dissection;

[0008] S4, determining the suspected key cell subtype of thoracic aortic dissection according to the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection;

[0009] S5. According to the suspected key cell types and subtypes of thoracic aortic dissection, the key cell types and subtypes of thoracic aortic dissection are determined through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

[0010] The embodiment of the present invention provides a classification device for key cell types and subtypes of thoracic aortic dissection, including:

[0011] A data acquisition module, used to acquire gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocess the gene expression data to obtain cell types;

[0012] A suspected key cell type determination module is used to obtain the ratio of the cell types in the aortic dissection and normal control tissue samples, and determine the cell type with an increased ratio higher than a preset value as a suspected key cell type for thoracic aortic dissection;

[0013] A corresponding cell subtype determination module performs standardization, normalization, and dimensionality reduction cluster analysis based on the gene expression data of the suspected key cell type to obtain the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection;

[0014] Suspected key cell subtypes, used to determine the suspected key cell subtypes of thoracic aortic dissection according to the cell subtypes corresponding to the suspected key cell types of thoracic aortic dissection;

[0015] The key cell type and subtype determination module is used to determine the key cell types and subtypes of aortic dissection based on the suspected key cell types of thoracic aortic dissection and the suspected key cell subtypes of thoracic aortic dissection through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

[0016] By using the embodiments of the present invention, gene expression data of tissue samples of thoracic aortic dissection and normal controls are processed, and key cell types and subtypes of arterial dissection are determined through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis, which helps to improve the understanding of the onset of thoracic aortic dissection in patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate one or more embodiments of this specification 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 recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1A flow chart of a method for classifying key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention;

[0019] Figure 2 A specific flow chart of a method for classifying key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention;

[0020] Figure 3 A specific schematic diagram of a device for classifying key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention;

[0021] Figure 4 Schematic diagram of a device for classifying key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0023] Method Embodiment

[0024] According to an embodiment of the present invention, a method for classifying key cell types and subtypes of thoracic aortic dissection is provided. Figure 1 Flow chart of the classification method of key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention. Figure 1 As shown, the embodiment of the present invention Figure 1 The classification method of key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention specifically includes:

[0025] S1. Obtaining gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocessing the gene expression data to obtain cell types;

[0026] Preprocessing the gene expression data to obtain cell types specifically includes:

[0027] According to the gene expression data of single cells, all single cells of each tissue sample are filtered, standardized, normalized, and dimension reduction clustered to obtain multiple cell types, wherein the cell types include at least one of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells, and Schwann cells of the neural sheath.

[0028] S2, obtaining the ratio of the cell types in the aortic dissection and normal control tissue samples, and determining the cell types with an increased ratio higher than a preset value as suspected key cell types for thoracic aortic dissection;

[0029] Further, the proportions of the same cell types / subtypes in the vascular wall tissues of thoracic aortic dissection and genuine control are compared, and the cell types / subtypes with significantly increased proportions are identified as suspected key cell types / subtypes, including:

[0030] The proportions of the same cell types in thoracic aortic dissection and normal control vascular wall tissues were compared, and the cell types with significantly increased proportions were identified as suspected key cell types, including: by counting the number of single cells, the number of cells of cell type x in sample y was Z(x,y), and the number of cells of non-cell type x in sample y was z(x,y). Wherein, x=a,b,c,d,e,f,h,i,j,k,l; wherein a is a smooth muscle cell, b is a fibroblast, c is an endothelial cell, d is a myeloid cell, e is a T cell, f is a B cell, g is a plasma cell, h is a dendritic cell, i is a NK / T cell, j is a mast cell, k is a mesenchymal cell or a pericyte, and l is a Schwann cell of the nerve sheath; y=1,2, y=1 represents the thoracic aortic dissection sample, and y=2 represents the normal control vascular wall tissue; Z(x,1), z(x,1), Z(x,2), and z(x,2) are used to construct a two-dimensional contingency table, and the significance P value is calculated using the chi-square test. If P<0.05, the cell type x is determined to be a suspected key cell type associated with aortic dissection.

[0031] S3. Based on the gene expression data of the suspected key cell types, standardization, normalization, and dimensionality reduction cluster analysis are performed to obtain cell subtypes corresponding to the suspected key cell types of thoracic aortic dissection;

[0032] The cell subtype type includes at least one of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle, and contractile smooth muscle.

[0033] S4, determining the suspected key cell subtype of thoracic aortic dissection according to the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection;

[0034] Specifically, the proportion of the same cell subtypes in thoracic aortic dissection and normal control vascular wall tissues is compared, and the cell subtype with a significantly increased proportion is determined as a suspected key cell subtype, including: extracting the gene expression data of the suspected key cell type separately for separate standardization and dimensionality reduction cluster analysis to obtain the cell subtype corresponding to each cell type. The cell types include at least one or more of the following: fibroblast smooth muscle, inflammatory smooth muscle, stress smooth muscle 2, stress smooth muscle 3, and contractile smooth muscle. By counting the number of single cells, the number of cells of cell subtype x in sample y is Z(x,y), and the number of cells of non-cell subtype x in sample y is z(x,y); wherein, x=a,b,c,d,e; a is fibroblastic smooth muscle, b is inflammatory smooth muscle, c is stress-type smooth muscle 2, d is stress-type smooth muscle 3, and e is contractile smooth muscle; y=1,2, y=1 represents the thoracic aortic dissection sample, and y=2 represents the normal control vascular wall tissue; a two-dimensional contingency table is constructed with Z(x,1), z(x,1), Z(x,2), and z(x,2), and the significance P value is calculated using the chi-square test. If P<0.05, the cell subtype x is determined to be a suspected key cell subtype associated with aortic dissection.

[0035] S5. According to the suspected key cell types and the suspected key cell subtypes of thoracic aortic dissection, the key cell types / subtypes of aortic dissection are determined through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

[0036] The S5 specifically includes:

[0037] Conducting pathway and function enrichment analysis, specifically including: conducting pathway and function enrichment analysis based on differentially expressed genes between the suspected key cell type / subtype of thoracic aortic dissection and other cell types / subtypes, and analyzing pathways and cell functions that are significantly enriched in the suspected key cell type / subtype of thoracic aortic dissection compared with other cell types / subtypes;

[0038] Based on the differentially expressed genes of the suspected key cell types / subtypes of thoracic aortic dissection in the vascular wall tissue of the tissue samples of thoracic aortic dissection and normal controls, pathway and function enrichment analysis is performed to analyze the unique enrichment pathways and cell functions of the suspected key cell types / subtypes of thoracic aortic dissection in thoracic aortic dissection samples;

[0039] Conducting trajectory analysis, specifically including: using the gene expression data of the suspected key cell type / subtype of thoracic aortic dissection to perform trajectory inference, inferring the source and potential differentiation direction of the suspected key cell type / subtype of thoracic aortic dissection;

[0040] The gene weighted co-expression network analysis specifically includes: calculating the correlation coefficient between each pair of genes in the gene expression data matrix of the single cell, determining the threshold of the correlation coefficient using a soft threshold method, constructing a weighted network between genes according to the determined threshold, identifying highly interconnected gene expression modules in the weighted network, and selecting gene expression modules that are highly expressed in suspected key cell types / subtypes according to the expression of the gene expression modules in various cell types / subtypes;

[0041] Performing cell communication analysis, specifically including: obtaining the interaction ligand receptors between the suspected key cell type / subtype of thoracic aortic dissection and other cell types / subtypes based on the cell communication analysis;

[0042] Based on the above pathway and functional enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis, the genes, ligands, and pathways found were matched with the preset genes, ligands, and pathways related to the characterization of aortic dissection. The key cell types / subtypes of thoracic aortic dissection had the highest overlap.

[0043] Figure 2 This is a specific flow chart of the classification method of key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention. A specific implementation method of the embodiment of the present invention is as follows:

[0044] Step S11, based on single-cell transcriptome sequencing, multiple thoracic aortic dissection tissues and donated normal control vascular wall tissues are sequenced and tested to obtain gene expression data of single cells in each tissue sample.

[0045] Step S12, according to the gene expression data of the single cells, all single cells of all samples are filtered, standardized, normalized, and dimension reduction clustered to obtain multiple single cell types; wherein the single cell types include at least one or more of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells or pericytes and neural sheath Schwann cells.

[0046] Step S13, comparing the proportions of the same cell types in the thoracic aortic dissection and the normal control vascular wall tissue, and determining the cell type with a significantly increased proportion as a suspected key cell type.

[0047] Step S14, separately extract the gene expression data of suspected key cell types, perform standardization, normalization, and dimensionality reduction cluster analysis to obtain multiple single-cell subtypes; wherein the single-cell subtypes include at least one or more of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle 2, stress-type smooth muscle 3, and contractile smooth muscle.

[0048] Step S15, comparing the ratios of the same cell subtypes in the thoracic aortic dissection and the normal control vascular wall tissue, and determining the cell subtype with a mutually increased ratio as a suspected key cell subtype.

[0049] Step S16, matching the genes, ligands, and pathways found based on pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis with the preset genes, ligands, and pathways related to arterial dissection characterization, and the key cell type / subtype has the highest overlap.

[0050] In this embodiment, the sample data used are: 23 patients clinically diagnosed with acute type A thoracic aortic dissection (ICD: I71.000x011) who visited the People's Hospital of Xinjiang Uygur Autonomous Region from March 2023 to December 2023 and 3 patients who voluntarily donated aortic tissue due to accidental death.

[0051] In this embodiment, single-cell transcriptome sequencing uses microfluidics, oil droplet encapsulation and Barcode labeling technologies to achieve high-throughput cell capture technology, which can obtain transcriptome information for each cell. Cell transcriptome sequencing, by detecting the total expression of all mRNA in a single cell, can obtain data with higher "resolution" than ordinary transcriptomes, accurately analyze the gene expression of each cell, accurately distinguish cell populations, and compare cell populations; it can also more finely and accurately reflect the state of tissues, thereby finding key cell populations related to prognosis and treatment. The specific process of single-cell transcriptome sequencing is: on a known microfluidic platform, gel beads with barcodes and primers and single cells are encapsulated in oil droplets; in each oil droplet, the gel beads are dissolved, the cells are lysed to release mRNA, and barcoded cDNA for sequencing is produced by reverse transcription; after the liquid oil droplet is destroyed, the cDNA is subsequently constructed as a library, and finally, the library is sequenced and detected using a second-generation sequencing platform, so that a large amount of single-cell gene expression data can be obtained at one time, and expression sequencing can be achieved at the single-cell level.

[0052] Gene expression data reflects the abundance of gene transcription products mRNA in cells measured directly or indirectly. These data can be used to analyze which genes have changed in expression, the correlation between genes, and how gene activity is affected under different conditions. They play an important role in medical clinical diagnosis, drug efficacy judgment, and revealing the mechanism of disease occurrence.

[0053] Through single-cell transcriptome sequencing, tens of thousands of single cells can be obtained from a sample, and the number of genes is usually tens of thousands. This high-dimensional data needs to be standardized and normalized to eliminate the magnitude differences between different gene expression levels and ensure that the data can be compared and analyzed on the same platform. Standardization refers to adjusting the dimensions of different data so that the data can be compared on the same scale; normalization is to scale the data to a specific range, usually between 0 and 1.

[0054] After data standardization and normalization, clustering is needed to simplify the number of cells and dimensionality reduction is needed to visualize the data. Dimensionality reduction refers to simplifying complex high-dimensional data information into easier-to-read low-dimensional data information; clustering refers to classifying data according to certain standards. After obtaining the reduced-dimensional data, the cells are classified and grouped according to the clustering algorithm to obtain cell types and cell subtypes. Among them, the cell types include at least one or more of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells or pericytes and Schwann cells of the nerve sheath; cell subtypes include at least one or more of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle 2, stress-type smooth muscle 3, contractile smooth muscle.

[0055] When comparing the proportion of each cell type / subtype in each type of sample, the cell type / subtype with an increased proportion in the thoracic artery dissection patient group will be identified as the suspected cell type / subtype.

[0056] Compare the proportions of the same cell types in the thoracic aortic dissection and normal control vascular wall tissue, and identify the cell types with significantly increased proportions as suspected key cell types, including: by counting the number of single cells, the number of cells of cell type x in sample y is Z(x,y), and the number of cells of non-cell type x in sample y is z(x,y). Among them, x=a,b,c,d,e,f,h,i,j,k,l; a is smooth muscle cell, b is fibroblast, c is endothelial cell, d is myeloid cell, e is T cell, f is B cell, g is plasma cell, h is dendritic cell, i is NK / T cell, j is mast cell, k is mesenchymal cell or pericyte, l is nerve sheath Schwann cell; y=1,2, y=1 represents the thoracic aortic dissection sample, y=2 represents the normal control vascular wall tissue; Z(x,1), z(x,1), Z(x,2), z(x,2) are constructed into a two-dimensional contingency table, and the significance P value is calculated by chi-square test. If P<0.05, the cell type x is determined to be a suspected key cell type related to aortic dissection. The results show that smooth muscle cells are suspected key cell types.

[0057] Comparing the proportion of the same cell subtypes in the thoracic aortic dissection and the normal control vascular wall tissue, determining the cell subtype with the increased proportion as the suspected key cell subtype, including: extracting the gene expression data of smooth muscle cells separately for separate standardization and dimensionality reduction clustering analysis to obtain the cell subtype corresponding to each cell type. The cell types include at least one or more of the following: fibroblast smooth muscle, inflammatory smooth muscle, stress smooth muscle 2, stress smooth muscle 3, and contractile smooth muscle. By counting the number of single cells, the number of cells of cell subtype x in sample y is Z(x,y), and the number of cells of non-cell subtype x in sample y is z(x,y); wherein, x=a,b,c,d,e; a is fibroblastic smooth muscle, b is inflammatory smooth muscle, c is stress-type smooth muscle 2, d is stress-type smooth muscle 3, and e is contractile smooth muscle; y=1,2, y=1 represents the thoracic aortic dissection sample, and y=2 represents the normal control vascular wall tissue; construct a two-dimensional contingency table with Z(x,1), z(x,1), Z(x,2), and z(x,2), and calculate the significant P value using the chi-square test. If P<0.05, it is determined that cell subtype x is a suspected key cell subtype associated with aortic dissection. In this embodiment, it is found that stress-type smooth muscle 2 and stress-type smooth muscle 3 are suspected key cell subtypes.

[0058] Pathway and functional enrichment analysis refers to a method of analyzing whether a group of genes appear more frequently than the random level at a certain functional node. It is used to interpret the biological knowledge represented by a group of genes and reveal what role they play inside or outside the cell. Commonly used gene enrichment analyses include GO Term functional enrichment, KEGG Pathway enrichment, and MSigDB gene set enrichment.

[0059] Furthermore, the functional annotation of cell types / subtypes is to use bioinformatics methods and tools to perform high-throughput annotation of the biological functions of all genes in the genome. Based on the functional status of each single cell type / subtype obtained through gene enrichment analysis, etc., the functional annotation of cell types / subtypes is achieved, and the functional status of each cell type / subtype can be intuitively understood.

[0060] Cell trajectory analysis is a method for studying changes in gene expression during cell development and differentiation. By applying algorithms such as Monocle 2, RNA Velocity or Slingshot, the molecular mechanisms of cell fate determination are revealed. The purpose is to identify the critical period of cell state transition, predict the path of cell differentiation, and discover genes and signaling pathways that regulate cell fate. The specific process of using cell trajectory analysis is generally: using pseudo-time sorting, graph-based clustering or machine learning algorithms to analyze the continuity of cell states, and construct a cell state transition graph, where each "node" represents a cell state and "edge" represents the transition between states. In this embodiment, it was found that the suspected key cell subtypes stress-type smooth muscle 2 and stress-type smooth muscle 3 were separate differentiation trajectories, differentiated from contractile smooth muscle.

[0061] Gene weighted co-expression network analysis is a systems biology method used to study gene sets and their interactions in gene expression data. By constructing a weighted network between genes, WGCNA can identify highly co-varied gene modules and explore the association between these modules and specific biological traits or phenotypes.

[0062] Furthermore, the above-mentioned “weighting” means that different weights are given to different gene pairs when constructing a co-expression network, which is usually based on the confidence or biological importance of their co-expression.

[0063] In this example, GO enrichment analysis of different gene co-expression modules was performed, and it was found that genes related to DNA damage, hypoxia, endoplasmic reticulum misfolding, and oxidative stress were specifically expressed in stress-type smooth muscle 2 and stress-type smooth muscle 3, and the expression level was higher in the thoracic aortic dissection patient group. This result shows that stress-type smooth muscle 2 and stress-type smooth muscle 3 no longer have normal contractile function and tend to be abnormal.

[0064] Cell communication analysis is to infer the interaction between different cells by statistically analyzing the expression and pairing of receptors and ligands (i.e., the receiver and sender of signals) in different cell types, combined with molecular information databases. Cell communication analysis can help understand the interaction between cells, analyze the communication network between cells, reveal the interaction between various cells during development and during the occurrence and development of diseases, and thus explore potential therapeutic targets for diseases. Furthermore, the interaction between cells can be displayed in the form of heat maps (mainly used to display the expression of ligands and receptors) or signal pathway networks (mainly used to display the interaction strength between ligands and receptors).

[0065] In this example, for the interaction ligand receptors of stress-type smooth muscle 2, stress-type smooth muscle 3 and other cell types / subtypes, it was found that MIF and THBS2 related ligand receptors were specifically highly expressed in the thoracic aortic dissection group, the former was related to macrophage recruitment; the latter was related to the polarization state of macrophages. The above two characteristics are both unique characteristics of thoracic aortic dissection.

[0066] It can be understood that in this embodiment, through the above-mentioned pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis, it is determined that the stress-type smooth muscle 2 and stress-type smooth muscle 3, which have an increased proportion in the thoracic aortic dissection group, are derived from the contractile smooth muscle, which no longer has the contractile function itself, and is in a state of DNA damage, endoplasmic reticulum misfolding, and oxidative stress, and highly expresses the MIF and THBS2 genes, and has the potential to promote macrophage recruitment and macrophage polarization. In summary, the stress-type smooth muscle 2 and stress-type smooth muscle 3 are the key cell subtypes of thoracic aortic dissection.

[0067] The embodiments of the present invention have the following beneficial effects:

[0068] The present invention is based on single-cell transcriptome sequencing, and sequencing and detection are performed on multiple thoracic aortic dissections and donated normal control vascular wall tissues to obtain gene expression data of single cells in all tissue samples; according to the gene expression data of single cells, all single cells of the left and right tissue samples are filtered, standardized, normalized, and clustered by dimensionality reduction to obtain multiple cell types / subtypes; the proportions of different cell types / subtypes in the above two types of samples are compared, and the cell type / subtype with a significantly increased proportion is determined as the key cell type / subtype; based on the obtained suspected key cell types / subtypes, pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, cell communication analysis, etc. are further performed to determine the key cell types / subtypes. The single-cell transcriptome sequencing technology is used to perform expression sequencing on tissue samples from patients with thoracic aortic dissection, which can accurately capture the gene expression information of individual cells, systematically restore the molecular map of aortic dissection tissue, reveal the dynamic changes of cell heterogeneity and cell state, and provide the possibility for identifying and analyzing key cell types / subtypes; at the same time, the equipment of the present invention has powerful data processing capabilities, which can quickly integrate and analyze a large amount of single-cell sequencing data, providing an efficient research tool for research; the interpretation of key cell types / subtypes in patients with thoracic aortic dissection provides a new perspective and data support for the study of its pathological mechanism.

[0069] Device Embodiment

[0070] According to an embodiment of the present invention, a classification device for key cell types and subtypes of thoracic aortic dissection is provided. Figure 4 Schematic diagram of a classification device for key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention, Figure 4 As shown, the embodiment of the present invention Figure 4 The classification device for key cell types and subtypes of thoracic aortic dissection according to an embodiment of the present invention specifically includes:

[0071] A data acquisition module 40 is used to acquire gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocess the gene expression data to obtain cell types;

[0072] The cell types include at least one or more of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells or pericytes and Schwann cells of the nerve sheath. The expression data of the suspected key cell types are extracted, standardized, normalized, and dimensionally reduced cluster analysis is performed to obtain multiple cell subtypes, wherein the cell subtypes include at least one or more of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle 2, stress-type smooth muscle 3, and contractile smooth muscle.

[0073] Furthermore, the data acquisition module 40 is specifically used to: separate the tissue into single cells, encapsulate the single cells and barcoded microbeads in droplets through microfluidic technology; lyse the cells in the droplets and reverse transcribe RNA into cDNA; amplify the cDNA; perform end repair, linker addition and purification on the amplified cDNA to construct a sequencing library; use a sequencing platform to sequence the constructed library to obtain expression data of the corresponding genes of the cells corresponding to the sample.

[0074] A suspected key cell type determination module 42 is used to obtain the ratio of the cell types in the aortic dissection and normal control tissue samples, and determine the cell type with an increased ratio higher than a preset value as a suspected key cell type for thoracic aortic dissection;

[0075] Furthermore, the method in the ratio comparison module includes:

[0076] The suspected key cell type determination module 42 is specifically used to: compare the proportions of the same cell types in the thoracic aortic dissection and the normal control vascular wall tissue, and determine the cell type with a significantly increased proportion as the suspected key cell type, including: by counting the number of single cells, the number of cells of cell type x in sample y is Z(x,y), and the number of cells of non-cell type x in sample y is z(x,y). Wherein, x=a,b,c,d,e,f,h,i,j,k,l; a is smooth muscle cell, b is fibroblast, c is endothelial cell, d is myeloid cell, e is T cell, f is B cell, g is plasma cell, h is dendritic cell, i is NK / T cell, j is mast cell, k is mesenchymal cell or pericyte, l is nerve sheath Schwann cell; y=1,2, y=1 represents the thoracic aortic dissection sample, y=2 represents the normal control vascular wall tissue; Z(x,1), z(x,1), Z(x,2), z(x,2) are used to construct a two-dimensional contingency table, and the significance P value is calculated using the chi-square test. If P<0.05, the cell type x is determined to be a suspected key cell type associated with aortic dissection.

[0077] The corresponding cell subtype determination module 44 performs standardization, normalization, and dimensionality reduction cluster analysis based on the gene expression data of the suspected key cell type to obtain the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection;

[0078] Furthermore, the corresponding cell subtype determination module 44 includes:

[0079] According to the gene expression data of the single cell, all cells are subjected to dimensionality reduction clustering analysis and matched with preset cell gene markers to obtain a plurality of cell types and subtypes.

[0080] Suspected key cell subtype 46, used to determine the suspected key cell subtype of thoracic aortic dissection according to the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection;

[0081] Suspected key cell subtype 46 is specifically used to compare the proportion of the same cell subtype in the thoracic aortic dissection and the normal control vascular wall tissue, and determine the cell subtype with the increased proportion as the suspected key cell subtype, including: extracting the gene expression data of the suspected key cell type separately for separate standardization and dimensionality reduction cluster analysis to obtain the cell subtype corresponding to each cell type. Among them, the types of the cell types include at least one or more of the following: fibroblast smooth muscle, inflammatory smooth muscle, stress smooth muscle 2, stress smooth muscle 3, contractile smooth muscle. By counting the number of single cells, the number of cells of cell subtype x in sample y is Z(x,y), and the number of cells of non-cell subtype x in sample y is z(x,y); wherein, x=a,b,c,d,e; a is fibroblastic smooth muscle, b is inflammatory smooth muscle, c is stress-type smooth muscle 2, d is stress-type smooth muscle 3, and e is contractile smooth muscle; y=1,2, y=1 represents the thoracic aortic dissection sample, and y=2 represents the normal control vascular wall tissue; a two-dimensional contingency table is constructed with Z(x,1), z(x,1), Z(x,2), and z(x,2), and the significance P value is calculated using the chi-square test. If P<0.05, the cell subtype x is determined to be a suspected key cell subtype associated with aortic dissection.

[0082] The key cell type and subtype determination module 48 is used to determine the key cell types and subtypes of aortic dissection based on the suspected key cell types of thoracic aortic dissection and the suspected key cell subtypes of thoracic aortic dissection through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

[0083] Furthermore, the key cell type and subtype determination module 48 specifically includes:

[0084] Functional annotation module: Based on the differentially expressed genes between the suspected key cell types / subtypes and other cell types / subtypes, pathway and function enrichment analysis is performed to analyze the pathways and cell functions that are significantly enriched in the suspected key cell types / subtypes compared with other cell types / subtypes. Based on the differentially expressed genes of the suspected key cell types / subtypes in thoracic aortic dissection samples and normal control vascular wall tissues, pathway and function enrichment analysis is performed to analyze the enriched pathways and cell functions that are unique to the suspected key cell types / subtypes in thoracic aortic dissection samples.

[0085] Trajectory analysis module: using the gene expression data of the suspected key cell type / subtype to perform trajectory inference, inferring the origin and potential differentiation direction of the suspected key cell type / subtype.

[0086] Gene weighted co-expression network analysis module: Calculate the correlation coefficient between each pair of genes in the gene expression data matrix of the single cell, use the soft threshold method to determine the threshold of the correlation coefficient, build a weighted network between genes based on the determined threshold, and identify highly interconnected gene expression modules in the weighted network. According to the expression of the obtained gene modules in each cell type / subtype, select the gene expression modules that are highly expressed in suspected key cell types / subtypes.

[0087] Cell communication module: Based on cell communication analysis, the interaction ligands between the suspected key cell type / subtype and other cell types / subtypes are obtained.

[0088] The genes, ligands, receptors, and pathways found based on the above-mentioned functional annotation module, trajectory analysis module, gene weighted co-expression network analysis module, and cell communication module are matched with the preset genes, ligands, receptors, and pathways related to the characterization of arterial dissection, and the key cell types / subtypes have the highest overlap.

[0089] Figure 3 A specific implementation of the embodiment of the present invention is as follows: Figure 3 The classification device for key cell types and subtypes of thoracic aortic dissection according to the embodiment of the present invention specifically includes:

[0090] The transcriptome sequencing module 21 is used to perform sequencing detection on multiple thoracic aortic dissections, aortic aneurysms and donated normal control vascular wall tissues based on single-cell transcriptome sequencing to obtain gene expression data of single cells in each tissue sample.

[0091] The clustering module 22 is used to perform dimensionality reduction clustering analysis on all cells of each tissue sample according to the gene expression data of the single cell to obtain multiple cell types; wherein the cell types include at least one or more of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells or pericytes and nerve sheath Schwann cells; the cell subtypes include at least one or more of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle 2, stress-type smooth muscle 3, and contractile smooth muscle.

[0092] The cell ratio comparison module 23 is used to compare the ratios of the same cell types / subtypes in the thoracic aortic dissection and the normal control vascular wall tissue, and determine the cell types / subtypes with significantly increased ratios as key cell types / subtypes.

[0093] Furthermore, the ratio comparison module is specifically used to: compare the ratios of the same cell types / subtypes in the thoracic aortic dissection and normal control vascular wall tissues, and determine the cell types / subtypes with significantly increased ratios as key cell types / subtypes;

[0094] Furthermore, the device comprises:

[0095] Functional annotation module 24, for performing pathway and function enrichment analysis based on differentially expressed genes between the key cell type / subtype and other cell types / subtypes, and analyzing pathways and cell functions that are significantly enriched in the key cell type / subtype compared with other cell types / subtypes;

[0096] Furthermore, the device comprises:

[0097] A trajectory analysis module 25, for analyzing and reproducing the evolution trajectory of the key cell according to the expression of the differential genes between the key cell type / subtype and other cell types / subtypes;

[0098] Furthermore, the device comprises:

[0099] The gene weighted co-expression network analysis module 26 is used to obtain a gene co-expression module based on the differentially expressed genes between the key cell type / subtype and other cell types / subtypes through weighted co-expression network analysis, and further define the key cell type / subtype according to the pathway and function enrichment of the differentially expressed genes in the co-expression module.

[0100] The cell communication analysis module 27 is used to obtain the interaction probability between cell types / subtypes and determine the interaction mechanism between a cell type / subtype and other cell types / subtypes.

[0101] The embodiments of the present invention have the following beneficial effects:

[0102] The present invention is based on single-cell transcriptome sequencing, and sequencing and detection are performed on multiple thoracic aortic dissections and donated normal control vascular wall tissues to obtain gene expression data of single cells in all tissue samples; according to the gene expression data of single cells, all single cells of the left and right tissue samples are filtered, standardized, normalized, and clustered by dimensionality reduction to obtain multiple cell types / subtypes; the proportions of different cell types / subtypes in the above two types of samples are compared, and the cell type / subtype with a significantly increased proportion is determined as the key cell type / subtype; based on the obtained suspected key cell types / subtypes, pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, cell communication analysis, etc. are further performed to determine the key cell types / subtypes. The single-cell transcriptome sequencing technology is used to perform expression sequencing on tissue samples from patients with thoracic aortic dissection, which can accurately capture the gene expression information of individual cells, systematically restore the molecular map of aortic dissection tissue, reveal the dynamic changes of cell heterogeneity and cell state, and provide the possibility for identifying and analyzing key cell types / subtypes; at the same time, the equipment of the present invention has powerful data processing capabilities, which can quickly integrate and analyze a large amount of single-cell sequencing data, providing an efficient research tool for research; the interpretation of key cell types / subtypes in patients with thoracic aortic dissection provides a new perspective and data support for the study of its pathological mechanism.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for classifying key cell types and subtypes of thoracic aortic dissection, characterized in that include: S1. Obtaining gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocessing the gene expression data to obtain cell types; S2, obtaining the ratio of the cell types in the aortic dissection and normal control tissue samples, and determining the cell types with an increased ratio higher than a preset value as suspected key cell types for thoracic aortic dissection; S3. Based on the gene expression data of the suspected key cell types, standardization, normalization, and dimensionality reduction cluster analysis are performed to obtain cell subtypes corresponding to the suspected key cell types of thoracic aortic dissection; S4, determining the suspected key cell subtype of thoracic aortic dissection according to the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection; S5. According to the suspected key cell types and subtypes of thoracic aortic dissection, the key cell types and subtypes of thoracic aortic dissection are determined through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

2. The method according to claim 1, characterized in that The preprocessing of the gene expression data to obtain the cell type specifically includes: Based on the gene expression data of single cells, all single cells in each tissue sample are filtered, standardized, normalized, and dimensionally reduced cluster analysis is performed to obtain multiple cell types.

3. The method according to claim 1, characterized in that The cell types include at least one of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells and neural sheath Schwann cells.

4. The method according to claim 1, characterized in that: The cell subtype type includes at least one of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle, and contractile smooth muscle.

5. The method according to claim 1, characterized in that The S5 specifically includes: Conducting pathway and function enrichment analysis, specifically including: conducting pathway and function enrichment analysis based on differentially expressed genes between the suspected key cell types and subtypes of thoracic aortic dissection and other cell types and subtypes, and analyzing pathways and cell functions that are significantly enriched in the suspected key cell types and subtypes of thoracic aortic dissection compared with other cell types and subtypes; Based on the differentially expressed genes of the suspected key cell types and subtypes of thoracic aortic dissection in the vascular wall tissue of the tissue samples of thoracic aortic dissection and normal control, pathway and function enrichment analysis was performed to analyze the unique enrichment pathways and cell functions of the suspected key cell types and subtypes of thoracic aortic dissection in thoracic aortic dissection samples; Conducting trajectory analysis, specifically including: using the gene expression data of the suspected key cell types and subtypes of thoracic aortic dissection to perform trajectory inference, inferring the sources and potential differentiation directions of the suspected key cell types and subtypes of thoracic aortic dissection; The gene weighted co-expression network analysis specifically includes: calculating the correlation coefficient between each pair of genes in the gene expression data matrix of the single cell, determining the threshold of the correlation coefficient using a soft threshold method, constructing a weighted network between genes according to the determined threshold, identifying highly interconnected gene expression modules in the weighted network, and selecting gene expression modules that are highly expressed in suspected key cell types and subtypes according to the expression of the gene expression modules in various cell types and subtypes; Performing cell communication analysis, specifically including: obtaining the interaction ligands and receptors between the suspected key cell types and subtypes of thoracic aortic dissection and other cell types and subtypes based on the cell communication analysis; Based on the above pathway and functional enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis, the genes, ligands, and pathways found were matched with the preset genes, ligands, and pathways related to the characterization of aortic dissection. The key cell types and subtypes of thoracic aortic dissection had the highest overlap.

6. A classification device for key cell types and subtypes of thoracic aortic dissection, characterized in that: include: A data acquisition module, used to acquire gene expression data of tissue samples of thoracic aortic dissection and normal controls, and preprocess the gene expression data to obtain cell types; A suspected key cell type determination module is used to obtain the ratio of the cell types in the aortic dissection and normal control tissue samples, and determine the cell type with an increased ratio higher than a preset value as a suspected key cell type for thoracic aortic dissection; A corresponding cell subtype determination module performs standardization, normalization, and dimensionality reduction cluster analysis based on the gene expression data of the suspected key cell type to obtain the cell subtype corresponding to the suspected key cell type of thoracic aortic dissection; Suspected key cell subtypes, used to determine the suspected key cell subtypes of thoracic aortic dissection according to the cell subtypes corresponding to the suspected key cell types of thoracic aortic dissection; The key cell type and subtype determination module is used to determine the key cell types and subtypes of aortic dissection based on the suspected key cell types of thoracic aortic dissection and the suspected key cell subtypes of thoracic aortic dissection through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis.

7. The device according to claim 6, characterized in that The data acquisition module is specifically used for: Based on the gene expression data of single cells, all single cells in each tissue sample are filtered, standardized, normalized, and dimensionally reduced cluster analysis is performed to obtain multiple cell types.

8. The device according to claim 6, characterized in that The cell types include at least one of the following: smooth muscle cells, fibroblasts, endothelial cells, myeloid cells, T cells, B cells, plasma cells, dendritic cells, NK / T cells, mast cells, mesenchymal cells and neural sheath Schwann cells.

9. The device according to claim 6, characterized in that The cell subtype type includes at least one of the following: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle, and contractile smooth muscle.

10. The device according to claim 6, characterized in that The key cell type and subtype determination module is specifically used for: Conducting pathway and function enrichment analysis, specifically including: conducting pathway and function enrichment analysis based on differentially expressed genes between the suspected key cell types and subtypes of thoracic aortic dissection and other cell types and subtypes, and analyzing pathways and cell functions that are significantly enriched in the suspected key cell types and subtypes of thoracic aortic dissection compared with other cell types and subtypes; Based on the differentially expressed genes of the suspected key cell types and subtypes of thoracic aortic dissection in the vascular wall tissue of the tissue samples of thoracic aortic dissection and normal control, pathway and function enrichment analysis was performed to analyze the unique enrichment pathways and cell functions of the suspected key cell types and subtypes of thoracic aortic dissection in thoracic aortic dissection samples; Conducting trajectory analysis, specifically including: using the gene expression data of the suspected key cell types and subtypes of thoracic aortic dissection to perform trajectory inference, inferring the sources and potential differentiation directions of the suspected key cell types and subtypes of thoracic aortic dissection; The gene weighted co-expression network analysis specifically includes: calculating the correlation coefficient between each pair of genes in the gene expression data matrix of the single cell, determining the threshold of the correlation coefficient using a soft threshold method, constructing a weighted network between genes according to the determined threshold, identifying highly interconnected gene expression modules in the weighted network, and selecting gene expression modules that are highly expressed in suspected key cell types and subtypes according to the expression of the gene expression modules in various cell types and subtypes; Performing cell communication analysis, specifically including: obtaining the interaction ligands and receptors between the suspected key cell types and subtypes of thoracic aortic dissection and other cell types and subtypes based on the cell communication analysis; Based on the above pathway and functional enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis, the genes, ligands, and pathways found were matched with the preset genes, ligands, and pathways related to the characterization of aortic dissection. The key cell types and subtypes of thoracic aortic dissection had the highest overlap.

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