Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy

Abstract

Background: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Infiltration and alterations in non-cardiomyocytes of the human heart involve crucially in the occurrence of DCM and associated immunotherapeutic approaches.

Methods: We constructed a single-cell transcriptional atlas of DCM and normal patients. Then, the xCell algorithm, EPIC algorithm, MCP counter algorithm, and CIBERSORT method were applied to identify DCM-related cell types with a high degree of precision and specificity using RNA-seq datasets. We further analyzed the heterogeneity among cell types, performed trajectory analysis, examined transcription factor regulatory networks, investigated metabolic heterogeneity, and conducted intercellular communication analysis. Finally, we used bulk RNA-seq data to confirm the roles of M2-like2 subpopulations and GAS6 in DCM.

Results: We integrated and analyzed Single-cell sequencing (scRNA-seq) data from 7 DCM samples and 3 normal heart tissue samples, totaling 70,958 single-cell data points. Based on gene-specific expression and prior marker genes, we identified 9 distinct subtypes, including fibroblasts, endothelial cells, myeloid cells, pericytes, T/NK cells, smooth muscle cells, neuronal cells, B cells, and cardiomyocytes. Using machine learning methods to quantify bulk RNA-seq data, we found significant differences in fibroblasts, T cells, and macrophages between DCM and normal samples. Further analysis revealed high heterogeneity in tissue preference, gene expression, functional enrichment, immunodynamics, transcriptional regulatory factors, metabolic changes, and communication patterns in fibroblasts and myeloid cells. Among fibroblast subpopulations, proliferative F3 cells were implicated in the fibroblast transition process in DCM, while myofibroblast F6 cells promoted the fibroblast transition to a late cell state in DCM. Additionally, two subpopulations of M2 macrophages, M2-like1 and M2-like2, were identified with distinct features. The M2-like2 cell subpopulation, which was enriched in glycolysis and fatty acid metabolism, involved in inflammation inhibition and fibrosis promotion. Cell‒cell communication analysis indicated the GAS6-MERTK axis might exhibit interaction between M2 macrophage and M2-like1 macrophage. Furthermore, deconvolution analysis for bulk RNA-seq data revealed a significant increase in M2-like2 subpopulations in DCM, suggesting a more important role for this cell population in DCM.

Conclusions: We revealed the heterogeneity of non-cardiomyocytes in DCM and identified subpopulations of myofibroblast and macrophages engaged in DCM, which suggested a potential significance of non-cardiomyocytes in treatment of DCM.

Keywords: Bulk RNA sequencing (Bulk RNA-seq); Dilated cardiomyopathy (DCM); Heterogeneity; Non-cardiomyocytes; Single-cell RNA sequencing (scRNA-seq).

Fig. 1

Landscape of DCM and normal samples based on single-cell data. A t-SNE plot displaying 70,958 cells from DCM and normal samples. B Dot plot showing the proportional expression levels of typical markers used for identifying each cell type. The color key from gray to purple represents low to high expression levels. The size of the dots represents the percentage of cells expressing the gene. C Distribution of cell subtypes in DCM and normal samples. D Differential genes in each subtype between DCM and normal samples. Red represents upregulated genes, and blue represents downregulated genes. Genes with P < 0.05 and log₂ fold change > 0.25 were considered significantly upregulated, while genes with P < 0.05 and log₂ fold change < −0.25 were considered significantly downregulated. E Spearman correlation analysis between cell types. F Functional comparison of biological processes between B cells and myeloid cells (upper) and between cardiomyocytes and fibroblasts (lower)

Fig. 2

Landscape and features of fibroblasts in DCM and normal samples. A t-SNE plot displaying 26,435 fibroblasts. B t-SNE plot displaying the distribution of fibroblasts in DCM and normal samples. C Preference of tissue morbidity rates estimated by Ro/e scores for each fibroblast type. Ro/e > 1 indicates a significant preference for the tissue. D Distribution of fibroblast subtypes in DCM and normal samples. E Dot plot showing the expression levels of fibroblast state markers used to identify each subtype. F Top left, pseudotime trajectory of six fibroblast subtypes; top right, pseudotime trajectory of six fibroblast subtypes with pseudotime; bottom left, distribution of DCM and normal samples in the pseudotime trajectory of six fibroblast subtypes; bottom right, cell state of the pseudotime trajectory of six fibroblast subtypes. G Infiltration levels of fibroblast subtypes in DCM and normal samples calculated based on deconvolution methods. H CytoTRACE analysis of the differentiation potential of fibroblast subtypes. I Left, heatmap depicting differentially expressed genes in fibroblasts based on the pseudotime trajectory; blue to red represent low to high expression patterns, respectively; right, GO analysis for Cluster 3. J Dynamic expression of differentially expressed genes within pseudotime. The data were sorted based on pseudotime

Fig. 3

Landscape and features of T cells and NK cells in DCM and normal samples. A t-SNE plot displaying 4865 T cells and NK cells. B, C Feature plots showing the expression of canonical cell marker genes used to define CD4 + cells and CD8 + cells. D Heatmap showing the tissue morbidity rates estimated by Ro/e scores for each cell type. E Distribution of cell subtypes in DCM and normal samples. F Mean differential gene expression values for each cell subtype. The color scale from black to red indicates increasing significance of differential expression. G Infiltration levels of T cell subtypes in DCM and normal samples calculated based on deconvolution methods. Red represents DCM, blue represents normal samples, and P < 0.05 indicates significant difference. H Significantly differentially expressed genes in the CD4-C1-CDKN1A subtype compared to the other subtypes. Red indicates upregulation relative to other subtypes, and blue indicates downregulation. Rows represent the mean fold change, where a log2 fold change > 1 indicates upregulation and < 1 indicates downregulation. Columns represent the significance of subgroup differences calculated by t tests, where P < 0.05 indicates significant difference. I Similarly, significantly differentially expressed genes in the CD8-C2-FGFBP2 subtype compared to the other subtypes were identified. J Enriched functions and pathways associated with the top 10 genes in the CD4-C1-CDKN1A subgroup. K Enriched functions and pathways associated with the top 10 genes in the CD8-C2-FGFBP2 subgroup. L Heatmap showing the top 10 DEGs between the NK1 and NK2 subtypes. M Distribution of significantly highly expressed genes in the NK1 and NK2 subtypes. N Preference of NK1 and NK2 subtypes in tissue estimated by Ro/e scores

Fig. 4

Landscape and features of myeloid cells in DCM and normal samples. A t-SNE plot displaying 11,908 myeloid cells. B t-SNE plot showing the distribution of myeloid cells in DCM and normal samples. C Mean differential gene expression values for each cell subtype. The color scale from black to red indicates increasing significance of differential expression. D Feature plots showing the expression of canonical cell marker genes used to define each cluster. E Violin plots showing the proportional expression levels of typical markers used for identifying each cell type. F Distribution of myeloid cell subtypes in DCM and normal samples. G Preference of myeloid cell subtypes in tissue estimated by Ro/e scores. H–N Pathway and functional enrichment of the (H) cDC-like1, (I) cDC-like2, (J) cDC-like3, (K) Monocyte, (L) M1-like, (M) M2-like1 and (N) M2-like2 subpopulations

Fig. 5

Comparison of M2-like1 and M2-like2. A Comparison of the biological functions of M2-like1 and M2-like2. B Differential genes in myeloid cell subtypes between DCM and normal samples. Red represents upregulated genes, and blue represents downregulated genes. Genes with P < 0.05 and log twofold change > 0.25 were considered significantly upregulated, while genes with P < 0.05 and log twofold change < − 0.25 were considered significantly downregulated. C ssGSEA evaluation of M2-like1 and M2-like2 infiltration levels in the GSE1145 and GSE141910 bulk-seq datasets and comparison of infiltration levels between DCM and normal samples. D Pseudotime trajectory of monocytes and macrophages. E Top: CytoTRACE analysis of myeloid cell differentiation potential; Bottom: Pseudotime density distribution of monocytes and macrophages. F Differential gene modules and functions of cell fate 1 and cell fate 2 at branch point one. G Heatmap showing the levels of transcription factors in myeloid cells. H Top 10 compounds with negative similarity scores for drug-disease pairs. The x-axis shows the top 10 compounds with negative similarity scores for the drug–disease pairs. The y-axis shows the similarity scores of drug–disease pairs

Fig. 6

Metabolic specificity of cell types. A DCM (left) and Normal(right) metabolic activity of 9 cell types. B Left: Top 20 metabolic pathway activities in myeloid cells; Right: All myeloid cells metabolic pathway activities. C Differential metabolic pathways between M2-like1 and M2-like2. D Correlations between phenotype scores and metabolic activity scores in M2-like1 macrophages. Red circles indicate P < 0.05, Spearman's rho > 0.3. E Correlations between phenotype scores and metabolic activity scores in M2-like2 macrophages. Red circles indicate P < 0.05, Spearman’s rho > 0.3. F Phenotype scores of monocytes and macrophages

Fig. 7

Cell communication in cell types. A Circular plot showing the number of interactions between cell types in DCM patients. The thickness of the lines is proportional to the number of ligand‒receptor interactions, and the loops represent autocrine loops. B Detailed view of ligand‒receptor interactions in cell types involved in DCM. C Inferred number of ligand‒receptor interactions between DCM and normal samples. D Total information flow and ranking of important signaling pathways in the network differences between DCM and normal samples. E Inferred GAS signaling network. Edge width represents the communication probability. F Relative contribution of each ligand‒receptor pair to the overall communication network of the GAS signaling pathway. G Expression levels of ligands and receptors contributing to the GAS signaling pathway in myeloid cells. H Comparison of cell-incoming signaling patterns between DCM and normal samples. The colors are proportional to the contribution scores calculated from pattern recognition analysis. Higher scores indicate richer signaling pathways in the corresponding cell population