Novel method for investigating mechanism of pericyte‑to‑fibroblast transition in ischemic heart failure

Abstract

Disclosed in the present invention is a novel method for investigating a mechanism of pericyte‑to‑fibroblast transition in ischemic heart failure, comprising the following steps: S1, sample preparation; S2, single-cell expression matrix construction; S3, cell quality control; S4, cell type annotation; S5, cell subtype annotation; S6, cell metabolism analysis; S7, cell differentiation trajectory analysis; and S8, cell-cell communication analysis. Single-cell sequencing is performed on hearts of healthy mice and IHF mice, pericytes and fibroblasts are identified from cardiac transcriptional profiles of the healthy mice and the IHF mice, and then potential differences in cell-cell interaction signals are investigated; moreover, core regulatory factors that cause dysfunction in pericyte signal communication are revealed, providing potential therapeutic targets for the treatment of ischemic heart failure. Rarres2 and Tnfsf12 genes can serve as therapeutic targets for the treatment of myocardial fibrosis in ischemic heart failure.

Description

A new method for studying the mechanism of pericardial cell to fibroblast conversion in ischemic heart failure Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically involving a new method for studying the mechanism of cardiac pericytes transforming into fibroblasts in ischemic heart failure. Background Technology

[0002] Heart failure is a serious cardiovascular disease that poses a significant threat to human health. Ischemic heart failure is one of the main types of heart failure, and its pathogenesis is complex, involving changes at multiple cellular and molecular levels.

[0003] In recent years, research has revealed that pericytes possess stem cell-like characteristics, capable of differentiating into fibroblasts and other cell types under specific conditions. These transdifferentiation processes play a crucial role in tissue repair and regeneration. Particularly in the heart, the pericyte-fibroblast transition mechanism plays a key role in the pathogenesis of ischemic heart failure. Fibroblasts participate in extracellular matrix remodeling, promote angiogenesis, and wound healing after cardiac injury. However, pericyte dysfunction or disrupted signaling communication with other cell types (such as endothelial cells, macrophages, and myofibroblasts) can lead to abnormal tissue repair, persistent damage, and heart failure, thereby contributing to the occurrence and development of cardiac fibrosis.

[0004] Research into this transition mechanism is crucial for a deeper understanding of the pathogenesis of ischemic heart failure. Unveiling the molecular mechanisms of the pericyte-fibroblast transition can provide a theoretical basis for developing new therapeutic strategies. Currently, some studies have indicated that certain cytokines, signaling pathways, and transcription factors may be involved in the pericyte-fibroblast transition. However, further research is needed to elucidate the specific details and regulatory networks of this transition mechanism.

[0005] Single-cell RNA sequencing (scRNA-seq) technology can comprehensively reveal the transcriptional heterogeneity of various cells in the cardiac myocardium, and is an effective method for systematically analyzing the pericyte-fibroblast transition mechanism's role in the development of malignant cardiac fibrosis. This invention stems from this.

[0006] Summary of the Invention

[0007] To address at least one of the aforementioned technical problems, this invention provides a novel method for studying the mechanism of pericyte-to-fibroblast conversion in the heart of ischemic heart failure. Single-cell sequencing is performed on the myocardium of healthy mice and IHF mice to screen pericytes and fibroblasts in the cardiac transcriptional atlases of healthy mice and IHF mice. This allows for the exploration of potential differences in cell interaction signals and the identification of core regulatory factors that cause dysfunction in pericyte functional signal communication, providing potential therapeutic targets for the treatment of ischemic heart failure.

[0008] To solve the above technical problems, the technical solution of the present invention is as follows:

[0009] The purpose of this invention is to provide a novel method for studying the mechanism of cardiac pericyte to fibroblast conversion in ischemic heart failure, comprising the following steps:

[0010] S1. Sample preparation; S2. Single-cell expression matrix construction; S3. Cell quality control; S4. Cell type annotation; S5. Cell subtype annotation; S6. Cell metabolism analysis; S7. Cell differentiation trajectory analysis; S8. Cell communication analysis.

[0011] S1 Sample Preparation: Six 8-week-old SPF-grade male C57BL / 6 mice were randomly divided into a sham-operated group (Sham group) and an IHF group. In the IHF group, left anterior descending coronary artery ligation (LAD) was used to induce myocardial infarction and fibrosis in the heart of the C57BL / 6 male mice, mimicking the pathological process of ischemic heart failure (IHF) in humans. Both groups of mice were fed standard feed and additive-free drinking water.

[0012] S2 single-cell expression matrix construction: Three biological replicates were taken from each of the collected heart tissues, Sham group and IHF group, and single-cell sequencing (scRNA-seq) was performed on the Illumina NovaSeq6000 platform. The sequencing depth was about 56×, that is, the data volume of a single sample was about 150Gb.

[0013] S3 cell quality control defines cells with a median UMI of less than 1000 or greater than 5000 and a median gene count of less than 500 as low-quality cells. After filtering out these low-quality cells, high-quality data is obtained for downstream analysis.

[0014] S4 cell type annotation, using classic cell marker gene annotation, identified a total of 8 cell types, including endothelial cells (Flt1, Cdh5, Egfl7, Pecam1), fibroblasts (Pdgfra, Gsn, Dcn, Timp1, Col1a2, Col3a1), B cells (Ighm, Iglc2, Cd79a, Cd79b), T cells (Trbc1, Cd3e, Cd3d), macrophages (Adgre1, C1qa, C1qb, Csf1r, Mrc1, C3ar1), smooth muscle cells (Acta2, Tagln, Myh11, Lmod1), perivascular cells (Kcnj8, Steap4, Abcc9, Vtn, Colec11), and neutrophils (Csf3r, Retnlg, S100a8, S100a9, Hdc, Slpi). The marker genes required for cell type identification exhibit good cell type specificity, and the cell annotation and clustering are accurate and reasonable, meeting the requirements of data analysis. Based on the above data, it was found that the number of fibroblasts in the myocardium of IHF mice was 1.18 times that of the Sham group, and the number of pericytes in the myocardium of Sham mice was 3.18 times that of the IHF group. The Itga6 gene, a force-sensitive receptor, was specifically expressed in pericytes of fibrotic hearts. In conclusion, it is preliminarily determined that the expression of the Itga6 gene in pericytes of mouse myocardium is closely related to the occurrence and development of myocardial fibrosis.

[0015] S5 cell subset annotation was used to classify the endothelial cells and fibroblasts annotated in S4 into cell subsets, and a total of vascular endothelial cells (Eng, Plvap, Jam2, Flt1, Cdh5, Egfl7, Pecam1), matrix fibroblasts (Pnisr), inflammatory fibroblasts (Ccl2, Ccl7) and myofibroblasts (Dkk3, Ptn, Fbln1, Pdlim3, Postn) were identified.

[0016] S6. Cellular Metabolism Analysis: The scMetabolism function was used to analyze the differences in cellular metabolism among pericardial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts in the hearts of mice in the Sham and IHF groups. The results showed that 23 metabolic pathways, including tyrosine metabolism, tryptophan metabolism, oxidative phosphorylation, fatty acid degradation, and butyrate metabolism, differed between the pericardial cells of mice in the Sham and IHF groups.

[0017] S7. Cell differentiation trajectory analysis: Using the monocle2 function, we studied the major genes that induce the differentiation of pericytes into myofibroblasts in myocardium of ischemic heart failure and analyzed their regulatory mechanisms in depth. The results showed that pericytes can undergo two differentiation pathways by regulating related genes in cell fate 1 and 2, namely "pericytes → matrix fibroblasts → myofibroblasts → inflammatory fibroblasts" and "pericytes → matrix fibroblasts".

[0018] S8 cell communication analysis reveals that intercellular communication is crucial for the coordination of organismal functions and the development of disease. Disease arises when cells fail to interact correctly or decode molecular information inappropriately. To investigate the interaction characteristics of pericytes, vascular endothelial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts in the myocardium of patients with malignant heart disease (IHF), intercellular communication analysis was performed based on scRNA-seq cell subset annotation information obtained in section "S5," combined with the CellChat ligand receptor database. The results showed that, by statistically analyzing the receptor-ligand pairs among different cell populations, the interaction frequency between pericytes and myofibroblasts was reduced in the myocardium of patients with IHF. By comparing the communication patterns of peripheral cells and other cell subpopulations in the Sham group and IHF heart cardiomyocytes, it was found that the Rarres2-Cmklr1 and Tnfsf12-Tnfrsf12a signaling pathways emitted by peripheral cells in IHF heart cardiomyocytes were inhibited. The abnormal closure of the Rarres2-Cmklr1 and Tnfsf12-Tnfrsf12a signaling pathways is the main cause of malignant fibrosis in IHF heart cardiomyocytes.

[0019] Compared with the prior art, the advantages of the present invention are:

[0020] This invention performed single-cell sequencing on the myocardium of healthy and IHF mice, screening pericytes and fibroblasts in the cardiac transcriptional profiles of these two mice to explore potential differences in cell-cell interaction signals and reveal core regulatory factors causing impaired signaling communication in pericytes, providing potential therapeutic targets for the treatment of ischemic heart failure. Cell communication analysis revealed that abnormal closure of the Rarres2-Cmklr1 and Tnfsf12-Tnfrsf12a signaling pathways is the main cause of malignant fibrosis in the myocardium of IHF patients. It is hypothesized that abnormal expression of the Rarres2 and Tnfsf12 genes is the primary cause of impaired signaling communication between pericytes and fibroblasts, and that the Rarres2 and Tnfsf12 genes could serve as therapeutic targets for myocardial fibrosis in ischemic heart failure. Attached Figure Description

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

[0022] Figure 1 shows the single-cell transcriptional patterns of mouse hearts in the Sham and IHF groups; where A is a bubble chart of mouse heart tissue cells; B is a UMAP map of the distribution of various cell types; C is a heatmap of the top 20 highly variable genes (HVGs) in mouse heart tissue cells; D is the proportion of various cell types in the Sham and IHF groups; and E is a violin plot of the Itga6 gene in the Sham and IHF groups.

[0023] Figure 2 shows the classification of cardiac cell subsets in mice in the Sham and IHF groups; where A is a bubble chart annotating mouse cardiac tissue cell subsets; B is a UMAP map showing the distribution of each cell subset; and C is the proportion of each cell subset in the Sham and IHF groups.

[0024] Figure 3 shows the metabolic activity analysis of cardiac cell subsets in mice from the Sham and IHF groups.

[0025] Figure 4 shows the differentiation trajectory analysis of cardiac cell subsets in mice in the Sham and IHF groups; where A in the figure is a diagram of the differentiation degree of each cell subset in mouse cardiac tissue; B in the figure is a diagram of the cell differentiation trajectory of each cell subset in the Sham group; C in the figure is a diagram of the cell differentiation trajectory of each cell subset in the IHF group; and D in the figure is a heatmap of the genes driving the differentiation of peripheral cells in mouse cardiac myocardium.

[0026] Figure 5 shows the cardiac cell communication analysis of mice in the Sham and IHF groups. In the figure, A and D represent the statistical frequency of cardiac cell interactions between mice in the Sham and IHF groups, respectively; B and E represent the interaction intensity of cardiac signaling pathways between mice in the Sham and IHF groups, respectively; C and F represent the regulatory loop diagrams of receptor genes in cardiac signaling pathways between mice in the Sham and IHF groups, respectively; the size of the circles in the bubble diagram represents the number of receptor pairs for that cell type. The larger the circle diameter, the more receptor pairs between cells, and the darker the circle color, the greater the probability of receptor communication between cells. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0028] The novel method for studying the mechanism of cardiac pericyte to fibroblast conversion in ischemic heart failure, provided by embodiments of the present invention, includes the following steps:

[0029] I. Sample Preparation

[0030] To clarify the pathogenesis of ischemic heart failure (IHF) and accurately screen key therapeutic targets for IHF, C57BL / 6 mice were used as experimental animals. The IHF mouse model reported in the literature (Golforoush P, 2020 Basic Res Cardiol, 30; 115(6):73.) was used for the experiment. The specific procedures were as follows: Mice were fasted for 12 hours before surgery. The mice were anesthetized, fixed, shaved, and disinfected. A longitudinal incision of approximately 1.5 cm was made about 1-2 mm from the left sternal border. The chest wall muscles were bluntly dissected layer by layer. The thoracic cavity was quickly entered through the 4th intercostal space. Hemostatic forceps were used to open the intercostal spaces, and the heart was gently squeezed with the left hand to eject from the opening in coordination with the heartbeat. An 8-0 suture needle was used to ligate the left atrial appendage 1-2 mm below the left atrial appendage and 0.5 mm beside the pulmonary artery conus, passing through the anterior descending coronary artery. Successful ischemia was indicated when the outer surface of the anterior wall of the left ventricle turned pale. Cardiac tissue is a typical heterogeneous tissue, and the results of single-cell dissociation directly affect the quality of single-cell transcriptome sequencing. We optimized the single-cell dissociation technique for cardiac tissue, achieving a cell concentration of 8.0 × 10⁻⁶ cells / cell. 5 The cell count was [value missing] / mL, with an activity of 90.5%, and no obvious impurities were observed in the microscopic field of view. In summary, the number and activity of the captured cells meet the requirements for single-cell transcriptome sequencing.

[0031] II. Construction of Single-Cell Expression Matrix

[0032] Fastp software was used to perform basic quality checks and filtering on the raw reads after the data was processed, resulting in high-quality, clean reads suitable for analysis. The filtering criteria were: (1) removing reads containing polyA (base sequences); (2) removing reads with more than 3 N bases; and (3) removing low-quality reads (reads with a quality value of Q ≤ 5 accounting for more than 20% of the total reads). Subsequently, Syngenomics software was used... (v1.17.0) Performs cell quality control, detection, and alignment with the reference genome (mm9) to obtain the raw expression matrix. The specific steps are as follows: First, install CeleScope using the command `pip install celescope`. Prepare the raw FASTQ sequencing data and create a project directory to store analysis results and intermediate files. In the project directory, create a configuration file (e.g., `config.yaml`). This file should include the reference genome path (the file containing the mm9 reference genome), sample information (the paths to the R1 and R2 files for each sample in the Sham and IHF groups), and analysis parameters (in this study, these include: barcode, cutadapt, star, featureCounts, and count). Then, run the analysis using the command `celescope multi config.yaml`. CeleScope automatically performs cell quality control steps, including removing low-quality cells and reads, generating a quality control report, and using STAR for genome alignment, aligning the reads to the reference genome mm9. Next, CeleScope uses the featureCounts tool to calculate gene expression levels and generate the raw expression matrix. The final results typically include directories for the raw and filtered expression matrices (e.g., raw_feature_bc_matrix / and filtered_feature_bc_matrix / ). After analysis, go to the output directory to view the generated quality control report, alignment results, and expression matrices, and download the expression matrix file for subsequent analysis and visualization.

[0033] III. Cell quality control

[0034] Cells with a median UMI of less than 1000 or greater than 5000, and a median gene count of less than 500, were defined as low-quality cells. After filtering out these low-quality cells, high-quality data were obtained for downstream analysis.

[0035] IV. Cell Type Annotation

[0036] After quality control, high-quality cells were retained for downstream analysis. The Sctransform function was then used to standardize the data and screen for hypervariable genes. The specific steps are as follows: First, a generalized linear model (GLM) was used to fit the relationship between the expression level of each gene and sequencing depth, calculating the regression coefficients (β0 and β1) and the negative binomial distribution parameter θ for each gene using a quasi-Poisson distribution. This step used the R function glm for fitting and estimated the θ value using the theta.ml function in the MASS package. Second, a smoothing function (ksmooth) was used to correct the fitting results to prevent overfitting of low-expression genes. This process included calculating the logarithmic mean and variance of each gene and filtering outliers, optimizing the smoothing effect using the bandwidth adjustment parameter (bw_adjust). Finally, the Pearson residuals for each gene were calculated based on the fitted mean (μ) and standard deviation (σ), ensuring that the residual value did not exceed the square root of the total cell count. The corrected read count matrix was generated using the Pearson residuals and the median sequencing depth. The 2000 genes with the largest expression fluctuations among cells were selected as highly variable genes (HVGs). Further, we used the Harmony function (https: / / github.com / immunogenomics / harmony) to remove potential batch effects between samples. The specific steps are as follows: First, a Seurat object is created and standard PCA analysis is performed, while setting condition variables. Then, the RunHarmony function is called, passing the Seurat object and specifying the variables to be integrated. Key parameters include group.by.vars (specifying which variable to group by; in this study, group is used to distinguish between the Sham and IHF groups), max.iter.harmony (setting the maximum number of iterations, default is 10), lambda (integration strength parameter, default value is 1, generally between 0.5 and 2), theta (cluster diversity penalty parameter, default value is 2, larger values ​​lead to higher diversity), dims.use (PCA dimensions used, default is all dimensions), and sigma (width of soft k-means clustering, default value is 0.1). Finally, the Harmony-corrected embedding results are obtained and viewed. Subsequently, we performed dimensionality reduction on the integrated data using Principal Component Analysis (PCA). The steps of PCA dimensionality reduction are as follows: First, the VariableFeatures function is used to extract highly variable genes identified after standardization from the dataset. Then, when calling RunPCA, these highly variable genes are used as input features for PCA calculation.The RunPCA function, by default, calculates and stores the first 50 principal components. The ElbowPlot function from the Seurat package is used to visualize the decrease in PC variance, and the first 15 principal components are selected for downstream analysis. The FindNeighbors and FindClusters functions are then used to cluster all cells, with the latter having a resolution of 0.2. The specific steps are as follows: First, the default Annoy algorithm is called using the NNHelper function to calculate the 20 nearest neighbors for each cell. The Annoy algorithm accelerates the search by building a tree-structured index and uses Euclidean distance as the metric. Next, a Shared Nearest Neighbor (SNN) is calculated based on Jaccard similarity, recording the nearest neighbor overlap for each cell and pruning according to a default threshold of 1 / 15. The calculated nearest neighbor matrix (NN) and shared nearest neighbor matrix (SNN) are converted into Graph objects and stored in the graphs slot of the Seurat object, named RNA_nn and RNA_snn. The FindClusters function optimizes cell group identification through the shared nearest neighbor (SNN) module. Then, the function initialization parameters are set, such as `graph.name` (defaults to `RNA_snn`), `resolution` (0.5 in this study), and `algorithm` (defaults to the original Louvain algorithm). Next, the function checks the validity of the graph objects and calls the corresponding clustering function according to the specified algorithm: the Louvain algorithm uses `RunModularityClustering` implemented in C++, and the Leiden algorithm uses `RunLeiden` implemented in Python. During the clustering process, the function optimizes the modularization function to determine cell clusters and handles outliers, reassigning individual cells to the densest populations. Finally, the function adds the clustering results to the metadata of the Seurat object, updates the `Idents` and `seurat_clusters` columns, and logs the results. Two-dimensional data visualization of the principal component analysis data is performed using the dimensionality reduction method of Uniform Manifold Approximation and Projection (UMAP). The specific computation is as follows: the R package `uwot` is called, and UMAP dimensionality reduction is implemented using the specified dimensionality reduction result (the top 10 dimensions of PCA in this study). When executing UMAP, the `uwot` method calls the `uwot::umap` function, passing in the data matrix and parameters. The function converts the UMAP output to a specified format, sets column names such as `UMAP_1` and `UMAP_2`, creates a `DimReduc` object to store the results, and saves the UMAP model as needed. Finally, the function updates the dimensionality reduction result of the `Seurat` object, adds a new `DimReduc` object, and logs the changes.Using classic cell marker gene annotation, a total of endothelial cells (Flt1, Cdh5, Egfl7, Pecam1), fibroblasts (Pdgfra, Gsn, Dcn, Timp1, Col1a2, Col3a1), B cells (Ighm, Iglc2, Cd79a, Cd79b), T cells (Trbc1, Cd3e, Cd3d), and macrophages (Adgre1, C1q) were identified. Eight cell types were identified, including a, C1qb, Csf1r, Mrc1, C3ar1, smooth muscle cells (Acta2, Tagln, Myh11, Lmod1), pericytes (Kcnj8, Steap4, Abcc9, Vtn, Colec11), and neutrophils (Csf3r, Retnlg, S100a8, S100a9, Hdc, Slpi) (see Figures 1A to 1C). The marker genes required for cell type identification exhibited good cell type specificity, and the cell annotation clusters were accurate and reasonable, meeting the requirements for data analysis. Based on the above data, it was found that the number of fibroblasts in the myocardium of IHF mice was 1.18 times that of the Sham group, and the number of pericytes in the myocardium of Sham mice was 3.18 times that of the IHF group (see Figure 1D). The Itga6 gene, a force-sensitive receptor for pericytes, was specifically expressed in fibrotic hearts (see Figure 1E).

[0037] V. Cell Subpopulation Annotations

[0038] Cell subpopulation typing was performed on the endothelial cells and fibroblasts annotated in S4, and vascular endothelial cells (Eng, Plvap, Jam2, Flt1, Cdh5, Egfl7, Pecam1), matrix fibroblasts (Pnisr), inflammatory fibroblasts (Ccl2, Ccl7) and myofibroblasts (Dkk3, Ptn, Fbln1, Pdlim3, Postn) were identified (see Figure 2).

[0039] VI. Cellular Metabolic Analysis

[0040] Single-cell metabolism (scMetabolism) refers to the study of cellular metabolic processes at the individual cell level. Analyzing the metabolism of single cells provides a deeper understanding of cellular function, physiological states, and disease mechanisms. It helps reveal the metabolic adaptation of cells under different environmental conditions and the relationship between cellular metabolism and disease development. In the biomedical field, single-cell metabolism research is of great significance for disease diagnosis, treatment, and drug development. This study uses single-cell transcriptome data from ischemic heart failure cardiac tissue for analysis. The dataset includes various cell types such as pericytes and fibroblasts. Required software packages, including scMetabolism, Seurat, and ggplot2, were installed and loaded. Seurat was used for data integration, and single-cell metabolic activity was quantified using scMetabolism. The AUCell method was selected for assessing metabolic activity, and the KEGG metabolic pathway was chosen. A metabolic activity score matrix was extracted for subsequent analysis and visualization. The DimPlot function was used to visualize the metabolic pathways of interest. The specific operation is as follows: The DotPlot function is called, passing in the Seurat object `seurat_obj`, the module list `mods`, and the grouping criterion `group.by` (in this study, `cell_type` is used as the setting object) to generate a DotPlot plot, thus visually displaying the metabolic state among different cell populations. Differences in the activity and percentage of various cellular metabolic pathways were compared between the Sham and IHF groups. The results showed that 23 metabolic pathways, including tyrosine metabolism, tryptophan metabolism, oxidative phosphorylation, fatty acid degradation, and butyrate metabolism, differed in pericardial cells of the heart of mice in the Sham and IHF groups (see Figure 3).

[0041] VII. Analysis of Cell Differentiation Trajectory

[0042] Monocle2 is a widely used single-cell transcriptome analysis tool designed to reveal the developmental trajectory and dynamic changes of cells. It can process high-throughput single-cell RNA sequencing data and helps researchers understand the transcriptional state and function of cells at different time points or under different conditions by constructing relationship maps between cells. We used Monocle2 (v2.26.0) to perform pseudo-time series analysis on mouse cardiac pericardial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts. Monocle2 introduces a strategy of sorting individual cells within a pseudo-timeframe, utilizing the asynchronous processes of individual cells to place them on trajectories corresponding to biological processes such as cell differentiation. Specifically, in this study, we first used the VariableFeatures function in Seurat to identify 2000 HVGs among cells and constructed differentiation trajectories based on these 2000 HVGs. Subsequently, we used the DifferentialGeneTest function in Monocle2 to infer the gene set whose expression changes over pseudo-time. We used Monocle2 functions to study the major genes inducing the differentiation of pericardial cells into myofibroblasts in ischemic heart failure cardiomyocytes, and to further elucidate their regulatory mechanisms. The results showed that pericytes can differentiate into two pathways by regulating related genes in cell fate 1 and 2 (see Tables 1 and 2), namely “pericytes → matrix fibroblasts → myofibroblasts → inflammatory fibroblasts” and “pericytes → matrix fibroblasts” (see Figure 4).

[0043] Table 1. Analysis of cell fate-related gene signaling pathways.

[0044] Table 2. Analysis of cell fate-related gene signaling pathways.

[0045] VIII. Cell Communication Analysis

[0046] Intercellular communication is crucial for the coordination of biological functions and the development of disease. Disease arises when cells fail to interact correctly or decode molecular information inappropriately. To investigate the interaction characteristics of different cell types in the heart of patients with malignant fibrosis (IHF), this study used CellChat software to infer, analyze, and visualize intercellular communication networks from single-cell RNA sequencing (scRNA-seq) data. First, the necessary R libraries, including CellChat and patchwork, were loaded. A CellChat object was created from the digital gene expression matrix and cell tag information. The CellChat object was initialized by calling the createCellChat function and passing in the gene expression data and cell metadata. Specifically, the createCellChat function first used the cell-annotated Seurat data as input. Next, group.by was specified to define the cell group, with RNA as the default data type. Then, using these parameters, the createCellChat function created a CellChat object for subsequent intercellular communication analysis. Next, a database for ligand-receptor interactions was set up, selecting the human database CellChatDB.mouse, and intercellular communication analysis was performed using only a subset of secreted signals. The subset of the CellChat database was set as the default database for the object by calling the subsetDB function. To infer intercellular communication networks, we used a "trimean" statistical method to calculate the average gene expression level for each cell group, yielding fewer but stronger interactions. Specifically, for each cell type, the first quartile (Q1), median, and third quartile (Q3) were calculated, and Tukey's trimean formula (Q1 + 2 * Median + Q3) / 4 was applied. This process was performed on all cell types in the list, resulting in a trimean value for each cell type, reflecting the central trend in gene expression levels across cell types. When calculating the communication probability, the computeCommunProb function was called with the default parameter type set to "triMean" to calculate the average gene expression level for each cell group. Furthermore, cells in each cell group with a minimum number of cells less than 10 for intercellular communication were filtered out. Next, we use the computeCommunProbPathway function to calculate the communication probability at the signaling pathway level and aggregate the intercellular communication network. The specific process is as follows: set "triMean" as the default parameter and adjust several parameters (including whether to use the original data, whether to consider the size of the cell population, and whether to introduce spatial distance constraints, etc.) to adapt to the data characteristics.Based on the set parameters, `computeCommunProbPathway` updates the `CellChat` object after execution, calculating the communication probability and statistical significance (p-value) of each signaling pathway. By calling the `netVisual_aggregate` function, we generated communication network diagrams for different signaling pathways and used bubble diagrams to illustrate the interactions between different cell populations. The results showed that, by counting the number of receptor-ligand pairs among different cell populations, the interaction frequency between pericytes and myofibroblasts in the myocardium of IHF heart was reduced. Comparing the communication patterns of pericytes with other cell subpopulations in the Sham group and the IHF heart myocardium revealed that the Rarres2-Cmklr1 and Tnfsf12-Tnfrsf12a signaling pathways emitted by pericytes in the IHF heart myocardium were inhibited. The abnormal closure of the Rarres2-Cmklr1 and Tnfsf12-Tnfrsf12a signaling pathways is the main cause of malignant fibrosis in the IHF heart myocardium (see Figure 5). It is further speculated that the abnormal expression of Rarres2 and Tnfsf12 genes is the main cause of the dysfunction of signal communication between pericytes and fibroblasts, and that Rarres2 and Tnfsf12 genes can be used as therapeutic targets for the treatment of myocardial fibrosis in ischemic heart failure.

[0047] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A novel method for studying the mechanism of pericyte-to-fibroblast transformation in ischemic heart failure, characterized by: Includes the following steps: S1. Sample preparation: SPF-grade mice were used as experimental animals. The sham surgery group (Sham group) and the ischemic heart failure (IHF group) group were designed. The IHF group was induced by ligation of the left anterior descending coronary artery to induce myocardial infarction and fibrosis pathological changes to simulate the pathological process of human ischemic heart failure. Mice in both the Sham and IHF groups were given normal feed and additive-free drinking water. Heart tissues were collected from mice in the Sham and IHF groups respectively. S2. Single-cell expression matrix construction: Single-cell sequencing was performed on collected heart tissue using the Illumina NovaSeq 6000 platform, with a sequencing depth of approximately 56×. The sequencing was then performed using Syngenomics software. Perform a reference genome alignment to obtain the original expression matrix; S3. Cell quality control: Cells with a median UMI of less than 1000 or greater than 5000 and a median gene count of less than 500 are defined as low-quality cells. After filtering out these low-quality cells, high-quality cell data is obtained for downstream analysis. S4. Cell type annotation: After using the classic cell marker gene annotation, a total of 8 cell types were identified, including endothelial cells, fibroblasts, B cells, T cells, macrophages, smooth muscle cells, perivascular cells and neutrophils, and scRNA-seq cell type annotation information was obtained. S5. Cell Subpopulation Annotation: Cell subpopulation typing was performed on the endothelial cells and fibroblasts annotated in step S4. Vascular endothelial cells, matrix fibroblasts, inflammatory fibroblasts and myofibroblasts were identified, and scRNA-seq cell subpopulation annotation information was obtained. S6. Cell Metabolism Analysis: The differences in cell metabolism among pericardial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts in the heart of mice in the Sham group and IHF group were analyzed using the scMetabolism function. S7. Cell differentiation trajectory analysis: Using the monocle2 function, we studied the major genes that cause the differentiation of myocardial peripheral cells into myofibroblasts in ischemic heart failure and analyzed their regulatory mechanisms in depth. S8. Cell Communication Analysis: Based on the scRNA-seq cell subset annotation information obtained in step S5, intercellular communication analysis was performed on cardiac pericardial cells, vascular endothelial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts using the CellChat ligand receptor database.

2. The novel method according to claim 1, characterized in that, In step S1, the collected cardiac tissue underwent single-cell dissociation, and the cell concentration after dissociation was 8.0 × 10⁻⁶. 5 / mL, activity 90.5%.

3. The new method according to claim 1, characterized in that, In step S2, Fastp software is used to perform basic quality checks and filtering on the raw data after it is taken off the machine, so as to obtain high-quality data that can be used for analysis. The filtering criteria are as follows: (1) Remove Reads containing polyA; (2) Remove Reads with more than 3 N; (3) Remove low-quality Reads, wherein the low-quality Reads are Reads in which the number of bases with a quality value Q less than or equal to 5 accounts for more than 20% of the total Reads.

4. The new method according to claim 1, characterized in that, In step S2, the steps for operating the original expression matrix include: constructing a reference genome catalog using the mkref command, which requires genome sequence and gene annotation information; once the reference genome catalog is constructed, using the celescope rna command to analyze the samples; Provide sample information, which includes sample ID, analysis type, and reagent version; Based on the information filtering and barcode correction of read1 sequence, the corrected barcode and the original UMI sequence are added to the ID of read2; Perform quality control on the data to ensure the quality of the sequences; Use STAR to align the reads2 sequence to the reference genome and generate an alignment file; The FeatureCounts tool is used to count the alignment results in each cell based on the alignment file. UMI counting and cell number assessment were performed, and finally, an expression matrix was generated.

5. The novel method according to claim 1, characterized in that, Step S4 includes the following specific operations: The Sctransform function was used to standardize and screen for hypervariable genes in quality-controlled cell data. Among them, the 2000 genes with the largest expression fluctuations among cells were selected as hypervariable genes. Use the Harmony function to remove potential batch effects between samples; Principal component analysis was used to reduce the dimensionality of the integrated data. The ElbowPlot function in the Seurat package was used to visualize the decrease in PC variance. The first 15 principal components were selected for downstream analysis. The FindNeighbors and FindClusters functions were used to cluster all cells in sequence, with the latter having a resolution of 0.

2. Two-dimensional data visualization is performed on the principal component analysis data by using uniform manifold approximation and projection dimensionality reduction methods.

6. The novel method according to claim 1, characterized in that, Step S6 includes the following specific operations: Data integration was performed using Seurat, and single-cell metabolic activity was quantified using scMetabolism. The AUCell method was selected for metabolic activity assessment, with the KEGG metabolic pathway chosen. A metabolic activity score matrix was extracted for subsequent analysis and visualization. Use the DimPlot function to visualize metabolic pathways of interest.

7. The novel method according to claim 1, characterized in that, In step S7, Monocle2 was used to perform pseudo-temporal analysis on mouse cardiac pericardial cells, matrix fibroblasts, inflammatory fibroblasts, and myofibroblasts. Monocle introduced a strategy of sorting individual cells in pseudo-time, taking advantage of the asynchronous processes of individual cells to place them on trajectories corresponding to biological processes such as cell differentiation.

8. The novel method according to claim 7, characterized in that, The specific operations include: First, the VariableFeatures function in Seurat was used to identify 2000 HVGs between cells, and differentiation trajectories were constructed based on these 2000 HVGs; Subsequently, the DifferentialGeneTest function in Monocle2 was used to infer the set of genes whose expression changed over time.

9. The novel method according to claim 1, characterized in that, In step S8, CellChat software was used to infer, analyze, and visualize the intercellular communication network of single-cell RNA sequencing data. A database for ligand-receptor interactions was set up, using the human database CellChatDB.mouse, and intercellular communication analysis was performed using only a subset of secreted signals. The subset of the CellChat database was set as the default database for the object by calling the subsetDB function. The trimean statistical method was used to calculate the average gene expression level for each cell type to generate fewer but stronger interactions. The computeCommunProb function was called when calculating communication probabilities, with the default type parameter set to triMean, to calculate the average gene expression level for each cell group. Cells in each cell group with a minimum number of cells less than 10 for intercellular communication were filtered out. The computeCommunProbPathway function was used to calculate the communication probability at the signaling pathway level and aggregate the intercellular communication network. The netVisual_aggregate function was called to generate communication network diagrams for different signaling pathways, and bubble charts were used to illustrate the interactions between different cell populations.

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