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

Abstract

The present application provides a method and device for classifying key cell types and subtypes of thoracic aortic dissection. The method according to the present application includes: pre-processing tissue samples of thoracic aortic dissection and normal controls to obtain cell types; determining suspected key cell types of thoracic aortic dissection based on the ratio of cell types in aortic dissection and normal control tissue samples, obtaining cell subtypes corresponding to suspected key cell types of thoracic aortic dissection based on gene expression data of suspected key cell types; determining suspected key cell subtypes of thoracic aortic dissection based on cell subtypes corresponding to suspected key cell types of thoracic aortic dissection; determining key cell types and subtypes of arterial dissection based on suspected key cell types of thoracic aortic dissection and the suspected key cell subtypes of thoracic aortic dissection. The present application can determine key cell types and subtypes of thoracic aortic dissection.

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" within the aortic wall. The enormous blood flow pressure causes the tear to rapidly extend, compressing the false lumen and ultimately leading to rupture of the vessel wall. TAD in young patients is often associated with genetic factors (such as bivalvular aorta), connective tissue diseases (such as Marfan syndrome and Ehlews-Danlos syndrome), or extreme physical activity, and its pathogenesis differs significantly from that of other patients. Cell types / subtypes play a key role in the development and progression 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 have also generally relied on macroscopic analysis of tissue samples, making 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 problems.

[0004] An 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 tissue sample and the normal control tissue sample, and determining the cell type with an increased ratio higher than a preset value as a suspected key cell type 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 in thoracic aortic dissection;

[0008] S4. Determining the suspected key cell subtype for thoracic aortic dissection based on the cell subtype corresponding to the suspected key cell type for thoracic aortic dissection;

[0009] S5. Based on 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] An embodiment of the present invention provides a device for classifying key cell types and subtypes of thoracic aortic dissection, comprising:

[0011] A data acquisition module is used to obtain 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, configured to obtain the ratio of the cell types in the aortic dissection tissue sample and the normal control tissue sample, 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 thoracic aortic dissection based on the suspected key cell types 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 pathogenesis of patients with thoracic aortic dissection. 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0018] Figure 1Flowchart 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 Detailed 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 technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings 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 making creative efforts should fall within the scope of protection of this document.

[0023] Method Example

[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] Based on the single-cell gene expression data, all single cells in each tissue sample were filtered, normalized, normalized, and subjected to dimensionality reduction cluster analysis to obtain multiple cell types. The cell types included 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 tissue sample and the normal control tissue sample, and determining the cell type with an increased ratio higher than a preset value as a suspected key cell type for thoracic aortic dissection;

[0029] Furthermore, the proportions of the same cell types / subtypes in the vascular wall tissues of thoracic aortic dissection and genuine controls were compared, and the cell types / subtypes with significantly increased proportions were 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 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 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; 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 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; a two-dimensional contingency table is constructed using 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, 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 in 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 for thoracic aortic dissection based on the cell subtype corresponding to the suspected key cell type for thoracic aortic dissection;

[0034] Specifically, the proportions of identical cell subtypes in thoracic aortic dissection and normal control vascular wall tissue are compared, and cell subtypes with significantly increased proportions are identified as suspected key cell subtypes. This includes extracting gene expression data of the suspected key cell types and performing separate normalization and dimensionality reduction cluster analysis to obtain cell subtypes corresponding to each cell type. The cell types 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. 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 using 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, cell subtype x is determined to be a suspected key cell subtype associated with aortic dissection.

[0035] S5. Determine the key cell types / subtypes of thoracic aortic dissection based on the suspected key cell types 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.

[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 and other cell types / subtypes in thoracic aortic dissection, and analyzing pathways and cell functions that are significantly enriched in the suspected key cell type / subtype compared to other cell types / subtypes in thoracic aortic dissection;

[0038] Based on the differentially expressed genes of the suspected key cell types / subtypes in 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 in thoracic aortic dissection samples;

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

[0040] The weighted gene 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 the genes based on 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 based on the expression of the gene expression modules in various cell types / subtypes;

[0041] Performing cell communication analysis, specifically including: obtaining interaction ligands and 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 function 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 aortic dissection characterization. The key cell types / subtypes of thoracic aortic dissection had the highest overlap.

[0043] Figure 2 This is 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. A specific implementation of the embodiment of the present invention is as follows:

[0044] In 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, based on the gene expression data of the single cells, all single cells of all samples are filtered, standardized, normalized, and dimensionality reduced cluster analysis 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 nerve sheath Schwann cells.

[0046] Step S13, comparing the ratios 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 ratio as a suspected key cell type.

[0047] Step S14, separately extract the gene expression data of suspected key cell types, perform standardization, normalization, dimensionality reduction cluster analysis, and obtain multiple single-cell cell subtypes; wherein the single-cell subtypes include at least one or more of the following: fibroblast 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 significantly increased ratio as a suspected key cell subtype.

[0049] In step S16, 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 are matched with the genes, ligands, and pathways associated with the pre-set characterization of arterial dissection. The key cell types / subtypes are those with the highest degree of 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 mRNAs in a single cell, can obtain data with higher "resolution" than ordinary transcriptomes. It can accurately analyze the gene expression of each cell, accurately distinguish cell populations, and compare cell populations. It can also more finely and accurately reflect tissue status, thereby finding key cell populations related to prognosis and treatment. The specific process of single-cell transcriptome sequencing is as follows: 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 dissolve, the cells are lysed to release mRNA, and barcoded cDNA for sequencing is generated by reverse transcription; after the liquid oil droplet is destroyed, the cDNA is subsequently used for library construction. 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, realizing expression sequencing at the single-cell level.

[0052] Gene expression data reflects the abundance of gene transcription products mRNA in cells, which is 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 assessment, and revealing the mechanisms of disease occurrence.

[0053] Single-cell transcriptome sequencing can yield tens of thousands of single cells from a single sample, often containing tens of thousands of genes. This high-dimensional data requires standardization and normalization to eliminate differences in the magnitude of gene expression levels and ensure that data can be compared and analyzed on the same platform. Standardization involves adjusting the dimensions of different data sets so that they can be compared on the same scale; normalization involves scaling the data to a specific range, typically between 0 and 1.

[0054] After data standardization and normalization, clustering is needed to simplify the number of cells and dimensionality reduction is used to visualize the data. Dimensionality reduction refers to simplifying complex, high-dimensional data into more easily readable, low-dimensional data; clustering refers to categorizing data according to certain criteria. After obtaining the reduced-dimensional data, cells are classified and grouped according to clustering algorithms to obtain cell types and cell subtypes. 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, and contractile smooth muscle.

[0055] When comparing the proportions of each cell type / subtype in each type of sample, the cell types / subtypes with increased proportions in the thoracic artery dissection patient group will be identified as suspected cell types / subtypes. These include:

[0056] Comparing the proportions of the same cell types in the thoracic aortic dissection and normal control vascular wall tissues, determining 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). Where x = a, b, c, d, e, f, h, i, j, k, l; a represents smooth muscle cells, b represents fibroblasts, c represents endothelial cells, d represents myeloid cells, e represents T cells, f represents B cells, g represents plasma cells, h represents dendritic cells, i represents NK / T cells, j represents mast cells, k represents mesenchymal cells or pericytes, and l represents Schwann cells of the neural sheath; y = 1, 2, where y = 1 represents the thoracic aortic dissection specimen and y = 2 represents the normal control vascular wall tissue; a two-dimensional contingency table was constructed using Z(x, 1), z(x, 1), Z(x, 2), and z(x, 2). A chi-squared test was used to calculate the significance P value. If P < 0.05, cell type x was identified as a suspected key cell type associated with aortic dissection. The results showed that smooth muscle cells were a suspected key cell type.

[0057] Comparing the proportions of the same cell subtypes in the thoracic aortic dissection and normal control vascular wall tissue, and identifying the cell subtype with an increased proportion as a suspected key cell subtype, includes: extracting gene expression data of smooth muscle cells and performing separate normalization and dimensionality reduction cluster analysis to obtain cell subtypes corresponding to each cell type. The cell types 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. 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); where 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; Z(x,1), z(x,1), Z(x,2), and z(x,2) are constructed into a two-dimensional contingency table, and the significance P value is calculated using the chi-square test. If P < 0.05, cell subtype x is determined to be a suspected key cell subtype associated with aortic dissection. In this example, 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 that analyzes whether a group of genes appear more frequently than random at a certain functional node. It is used to interpret the biological knowledge represented by a group of genes and reveal the roles they play inside or outside the cell. Commonly used gene enrichment analysis methods include GO Term functional enrichment, KEGG Pathway enrichment, and MSigDB gene set enrichment.

[0059] Furthermore, functional annotation of cell types / subtypes utilizes bioinformatics methods and tools to perform high-throughput annotation of the biological functions of all genes in the genome. Based on the functional profiles of individual cell types / subtypes obtained through gene enrichment analysis, functional annotation of cell types / subtypes can be achieved, providing an intuitive understanding of the functional profiles of each cell type / subtype.

[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. Its 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 as follows: using pseudo-time sorting, graph-based clustering or machine learning algorithms to analyze the continuity of cell states, and constructing 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, which were differentiated from contractile smooth muscle.

[0061] Weighted co-expression network analysis (WGCNA) is a systems biology approach 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” refers to giving different weights 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 revealed 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, with higher expression levels in patients with thoracic aortic dissection. This result indicates that stress-type smooth muscle 2 and stress-type smooth muscle 3 no longer have normal contractile function and are tending to be abnormal.

[0064] Cell communication analysis is to infer the interactions 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 interactions between cells, analyze the communication network between cells, and reveal the interactions between various cells during development and the occurrence and development of diseases, thereby exploring potential therapeutic targets for diseases. Furthermore, the interaction relationship 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, we examined the ligands that interact with stress-type smooth muscle 2 and 3 cells and other cell types / subtypes, finding that MIF and THBS2-related ligands were specifically overexpressed in the thoracic aortic dissection group. The former is associated with macrophage recruitment, while the latter is related to macrophage polarization. Both of these characteristics are unique to thoracic aortic dissection.

[0066] It is understandable 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 was determined that the stress-type smooth muscle 2 and stress-type smooth muscle 3, which had an increased proportion in the thoracic aortic dissection group, were derived from contractile smooth muscle, which no longer had contractile function and was in a state of DNA damage, endoplasmic reticulum misfolding, and oxidative stress. They also highly expressed MIF and THBS2 genes and had the potential to promote macrophage recruitment and macrophage polarization. In summary, the stress-type smooth muscle 2 and stress-type smooth muscle 3 are key cell subtypes in thoracic aortic dissection.

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

[0068] Based on single-cell transcriptome sequencing, the present invention performs sequencing and detection on multiple thoracic aortic dissections and donated normal control vascular wall tissues to obtain gene expression data of single cells in all tissue samples; based on the single-cell gene expression data, 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 types / subtypes with significantly increased proportions are identified as key cell types / subtypes; based on the suspected key cell types / subtypes obtained, 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. Using single-cell transcriptome sequencing technology to perform expression sequencing on tissue samples from patients with thoracic aortic dissection 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 and can quickly integrate and analyze large amounts 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 new perspectives and data support for the study of its pathological mechanisms.

[0069] Device embodiment

[0070] According to an embodiment of the present invention, a device for classifying 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 neural sheath. The expression data of suspected key cell types are extracted and standardized, normalized, and subjected to dimensionality reduction cluster analysis 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; end-repair, add linkers and purify 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 corresponding cells in the sample.

[0074] a suspected key cell type determination module 42 for obtaining the ratio of the cell types in the aortic dissection tissue sample and the normal control tissue sample, and determining the cell type with a 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 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 pericyte, and l is a Schwann cell of the neural sheath; 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 using 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, 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] Based on the gene expression data of the single cells, dimensionality reduction cluster analysis is performed on all cells, and they are matched with preset cell gene markers to obtain multiple cell types and subtypes of the cells.

[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 subtypes 46 are specifically used to compare the proportions of the same cell subtypes in the thoracic aortic dissection and normal control vascular wall tissue, and identify the cell subtype with an increased proportion as the suspected key cell subtype, including extracting gene expression data of the suspected key cell type and performing separate normalization 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: fibroblastic smooth muscle, inflammatory smooth muscle, stress-type smooth muscle 2, stress-type 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 using 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, 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 thoracic aortic dissection based on the suspected key cell types 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 between 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: uses the gene expression data of the suspected key cell type / subtype to perform trajectory inference to infer the origin and potential differentiation direction of the suspected key cell type / subtype.

[0086] Gene Weighted Co-expression Network Analysis Module: This module calculates the correlation coefficient between each pair of genes in the single-cell gene expression data matrix. A soft thresholding method is used to determine the correlation coefficient threshold. Based on the threshold, a weighted network of genes is constructed, and highly interconnected gene expression modules are identified within the weighted network. Based on the expression of these gene modules across various cell types / subtypes, gene expression modules that are highly expressed in suspected key cell types / subtypes are selected.

[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 arterial dissection characterization. The key cell types / subtypes have the highest overlap.

[0089] Figure 3 This is a specific implementation of the embodiment of the present invention, such as 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 based on 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 identify 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 the normal control vascular wall tissue, and determine the cell types / subtypes with significantly increased ratios as key cell types / subtypes;

[0094] Furthermore, the device comprises:

[0095] A functional annotation module 24 is configured to perform pathway and function enrichment analysis based on the differentially expressed genes between the key cell types / subtypes and other cell types / subtypes, and analyze the pathways and cell functions that are significantly enriched in the key cell types / subtypes compared to other cell types / subtypes;

[0096] Furthermore, the device comprises:

[0097] a trajectory analysis module 25 for analyzing and reproducing the evolutionary trajectory of the key cells based on the differential gene expression 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 derive 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] Based on single-cell transcriptome sequencing, the present invention performs sequencing and detection on multiple thoracic aortic dissections and donated normal control vascular wall tissues to obtain gene expression data of single cells in all tissue samples; based on the single-cell gene expression data, 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 types / subtypes with significantly increased proportions are identified as key cell types / subtypes; based on the suspected key cell types / subtypes obtained, 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. Using single-cell transcriptome sequencing technology to perform expression sequencing on tissue samples from patients with thoracic aortic dissection 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 and can quickly integrate and analyze large amounts 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 new perspectives and data support for the study of its pathological mechanisms.

[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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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 by 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 tissue sample and the normal control tissue sample, and determining the cell type with an increased ratio higher than a preset value as a suspected key cell type 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 in thoracic aortic dissection; S4. Determining the suspected key cell subtype for thoracic aortic dissection based on the cell subtype corresponding to the suspected key cell type for thoracic aortic dissection; S5. Determine the key cell types and subtypes of thoracic aortic dissection based on the suspected key cell types and subtypes of thoracic aortic dissection through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis; The preprocessing of the gene expression data to obtain cell types specifically includes: Based on the gene expression data of single cells, all single cells in each tissue sample were filtered, standardized, normalized, and subjected to dimensionality reduction cluster analysis to obtain multiple cell types; 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 in thoracic aortic dissection and normal control tissue samples in the vascular wall tissue, pathway and function enrichment analysis was performed to analyze the unique enrichment pathways and cell functions of the suspected key cell types and subtypes in thoracic aortic dissection samples; Performing 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 origin and potential differentiation direction of the suspected key cell types and subtypes of thoracic aortic dissection; The weighted gene 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 the genes based on 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 based on the expression of the gene expression modules in various cell types and subtypes; Performing cell communication analysis, specifically including: obtaining, based on the cell communication analysis, 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 above pathway and function 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 aortic dissection characterization. The key cell types and subtypes of thoracic aortic dissection had the highest overlap.

2. 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 nerve sheath Schwann cells.

3. 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.

4. A classification device for key cell types and subtypes of thoracic aortic dissection, characterized by: include: A data acquisition module is used to obtain 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, configured to obtain the ratio of the cell types in the aortic dissection tissue sample and the normal control tissue sample, 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; A key cell type and subtype determination module, configured to determine the key cell types and subtypes of thoracic aortic dissection based on the suspected key cell types and subtypes of thoracic aortic dissection through pathway and function enrichment analysis, trajectory analysis, gene weighted co-expression network analysis, and cell communication analysis; The data acquisition module is specifically used to: Based on the gene expression data of single cells, all single cells in each tissue sample were filtered, standardized, normalized, and subjected to dimensionality reduction cluster analysis to obtain multiple cell types; 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 in thoracic aortic dissection and normal control tissue samples in the vascular wall tissue, pathway and function enrichment analysis was performed to analyze the unique enrichment pathways and cell functions of the suspected key cell types and subtypes in thoracic aortic dissection samples; Performing 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 origin and potential differentiation direction of the suspected key cell types and subtypes of thoracic aortic dissection; The weighted gene 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 the genes based on 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 based on the expression of the gene expression modules in various cell types and subtypes; Performing cell communication analysis, specifically including: obtaining, based on the cell communication analysis, 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 above pathway and function 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 aortic dissection characterization. The key cell types and subtypes of thoracic aortic dissection had the highest overlap.

5. The device according to claim 4, 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 nerve sheath Schwann cells.

6. The device according to claim 4, 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.

Images