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Cited by 4Cardiomyopathy and Myosin StudiesSingle-cell and spatial transcriptomicsCardiac electrophysiology and arrhythmias

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Characterising Shared and Specific Cell–Cell Communication in Cardiomyopathy Subtypes From Single‐Cell Transcriptomics Data

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Tao, Wenqi, Gong, Miao, Ke, Zunping·2025·Journal of Cellular and Molecular Medicine
DOI 10.1111/jcmm.70554PMC PMC12061637
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Abstract

Cardiomyopathy encompasses a diverse range of conditions characterised by extensive molecular heterogeneity, particularly the variations in cell–cell communication events such as ligand‐receptor interactions and downstream signalling. Understanding the common and unique features of these intercellular interactions is crucial for advancing targeted treatments. We analysed single‐cell RNA sequencing datasets from the ventricular regions of patients with arrhythmogenic cardiomyopathy (ACM), dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and healthy donors (HD), as well as ischemic cardiomyopathy (ICM). Our analyses focused on cell type‐specific disease preferences, differential gene expression, pathway enrichment and particularly cell–cell communication. We observed that inflammatory, autoimmune, angiogenesis, lymphangiogenesis and fibrotic extracellular matrix deposition are consistently enriched across all four disease subtypes, highlighting their universal significance in disease progression through intercellular interactions. Additionally, we identified subtype‐specific pathways that reflect distinct intercellular communication patterns unique to each disease subtype: arrhythmia‐associated pathways in ACM, chronic inflammation‐related pathways in DCM, ECM remodelling pathways in HCM and ischaemic injury and recovery pathways in ICM.

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FIGURE 1 Identification of Major Cell Compartments in Human Cardiomyopathy and Healthy Donors through Single‐cell Data Integration. (A) Overview of Dataset Integration, Downstream Analysis and Interpretation of Results Using Single‐Cell RNA Sequencing Datasets from Three Independent Studies. (B) UMAP embeddings of ‘celltype.l2’ cell types, split by disease subtype and healthy donor group. Cell types are labelled by abbreviation as follows: Adipocyte (AD), Arterial Endothelial cells (aECs), Atrial Cardiomyocyte cells (aCMs), B cells (B), Capillary Endothelial cells (cECs), Endocardial (Endo), Endothelial cells (ECs), Fibroblasts (FBs), Innate Lymphoid cells (ILCs), Lymphatic Endothelial cells (lECs), Macrophages (MPs), Mast (Mast), Mesothelial cells (MESO), Monocyte/cDC (Mono/cDC), Neuronal cells (N), NK/T cells (NK), Pericytes (Per), Smooth Muscle cells (SMCs), T cells (T), Venous Endothelial cells (vECs) and Ventricular Cardiomyocyte cells (vCMs). (C) Stack violin plots showing the ‘celltype.l2’ annotated cell types specific expression of the top‐ranking candidate marker genes for each identified cell type across disease subtype and healthy donor group.
FIGURE 1 Identification of Major Cell Compartments in Human Cardiomyopathy and Healthy Donors through Single‐cell Data Integration. (A) Overview of Dataset Integration, Downstream Analysis and Interpretation of Results Using Single‐Cell RNA Sequencing Datasets from Three Independent Studies. (B) UMAP embeddings of ‘celltype.l2’ cell types, split by disease subtype and healthy donor group. Cell types are labelled by abbreviation as follows: Adipocyte (AD), Arterial Endothelial cells (aECs), Atrial Cardiomyocyte cells (aCMs), B cells (B), Capillary Endothelial cells (cECs), Endocardial (Endo), Endothelial cells (ECs), Fibroblasts (FBs), Innate Lymphoid cells (ILCs), Lymphatic Endothelial cells (lECs), Macrophages (MPs), Mast (Mast), Mesothelial cells (MESO), Monocyte/cDC (Mono/cDC), Neuronal cells (N), NK/T cells (NK), Pericytes (Per), Smooth Muscle cells (SMCs), T cells (T), Venous Endothelial cells (vECs) and Ventricular Cardiomyocyte cells (vCMs). (C) Stack violin plots showing the ‘celltype.l2’ annotated cell types specific expression of the top‐ranking candidate marker genes for each identified cell type across disease subtype and healthy donor group.CC BY · PubMed Central
FIGURE 2 Characterise Cell Composition in Each Patient Subgroup and Evaluate Cell Type‐Specific Disease Preferences. (A) Stack bar plots showing the distribution of Level_2 annotated cell types across disease subtypes and the healthy group. (B) Heatmap showing disease preference for each annotated cell type, evaluated by the ratio of observed to expected cell counts (Ro/e) value. (C) UMAP plot showing the expression patterns of the ILC marker gene KIT in annotated ILC cells across detected patient groups. (D) UMAP plot showing the expression patterns of the genes, including LY96(MD‐2), CPA3, NRXN1, RYR2, LINC00639 and ITGA8 in annotated Monocyte/cDC, Mast, Atrial Cardiomyocyte, Neuronal, Arterial Endothelial and Smooth Muscle, respectively. Split by disease subtype and healthy donor group. Dot colours represent expression level.
FIGURE 2 Characterise Cell Composition in Each Patient Subgroup and Evaluate Cell Type‐Specific Disease Preferences. (A) Stack bar plots showing the distribution of Level_2 annotated cell types across disease subtypes and the healthy group. (B) Heatmap showing disease preference for each annotated cell type, evaluated by the ratio of observed to expected cell counts (Ro/e) value. (C) UMAP plot showing the expression patterns of the ILC marker gene KIT in annotated ILC cells across detected patient groups. (D) UMAP plot showing the expression patterns of the genes, including LY96(MD‐2), CPA3, NRXN1, RYR2, LINC00639 and ITGA8 in annotated Monocyte/cDC, Mast, Atrial Cardiomyocyte, Neuronal, Arterial Endothelial and Smooth Muscle, respectively. Split by disease subtype and healthy donor group. Dot colours represent expression level.CC BY · PubMed Central
FIGURE 3 Gene Set Enrichment Analysis Reveals Hallmark Gene Sets Across Cardiomyopathy Subtypes and Annotated Cell Types. (A) Dot plot showing Hallmark gene‐set enrichment analysis comparing disease versus healthy controls across four cardiomyopathy subtypes (ACM, DCM, HCM, ICM). The plot is faceted by disease subtype, with the x‐axis representing the normalised enrichment score (NES) and the y‐axis indicating pathway names. Dot size corresponds to the −log10 (p‐value), and dot colour indicates statistical significance. (B) Violin plot showing single‐sample gene set enrichment analysis (ssGSEA) scores for pathways identified in Panel A across annotated cell types. The plot is faceted by enriched pathways from each disease subtype, with the x‐axis and violin colour representing the annotated cell types and the y‐axis indicating the ssGSEA score.
FIGURE 3 Gene Set Enrichment Analysis Reveals Hallmark Gene Sets Across Cardiomyopathy Subtypes and Annotated Cell Types. (A) Dot plot showing Hallmark gene‐set enrichment analysis comparing disease versus healthy controls across four cardiomyopathy subtypes (ACM, DCM, HCM, ICM). The plot is faceted by disease subtype, with the x‐axis representing the normalised enrichment score (NES) and the y‐axis indicating pathway names. Dot size corresponds to the −log10 (p‐value), and dot colour indicates statistical significance. (B) Violin plot showing single‐sample gene set enrichment analysis (ssGSEA) scores for pathways identified in Panel A across annotated cell types. The plot is faceted by enriched pathways from each disease subtype, with the x‐axis and violin colour representing the annotated cell types and the y‐axis indicating the ssGSEA score.CC BY · PubMed Central
FIGURE 4 Summary of cell–cell communication in patients. Interactome analysis was performed on the annotated scRNAseq analysis of disease vs. healthy control using the MultiNicheNet package. Circos plots showing the top 30 ligand‐receptor interactions differentially activated in disease subtypes compared to healthy controls: ACM (A), DCM (B), HCM (C) and ICM (D). All clusters listed in the sender and receiver columns within the respective disease group of the prioritised table were included.
FIGURE 4 Summary of cell–cell communication in patients. Interactome analysis was performed on the annotated scRNAseq analysis of disease vs. healthy control using the MultiNicheNet package. Circos plots showing the top 30 ligand‐receptor interactions differentially activated in disease subtypes compared to healthy controls: ACM (A), DCM (B), HCM (C) and ICM (D). All clusters listed in the sender and receiver columns within the respective disease group of the prioritised table were included.CC BY · PubMed Central
FIGURE 5 Summary of Shared and Disease‐Specific Cell–Cell Communications Across Disease Subtypes. Bubble plots display ligand‐receptor pairs and their differential impact across disease subtypes: (A) shared across three or more disease subtypes, (B) preferentially affected in ACM, (C) preferentially affected in DCM, (D) preferentially affected in HCM and (E) preferentially affected in ICM. Each plot includes scaled ligand activity in receiver cells, scaled cell type activity and curation levels of intercellular communication based on the Omnipath dataset.
FIGURE 5 Summary of Shared and Disease‐Specific Cell–Cell Communications Across Disease Subtypes. Bubble plots display ligand‐receptor pairs and their differential impact across disease subtypes: (A) shared across three or more disease subtypes, (B) preferentially affected in ACM, (C) preferentially affected in DCM, (D) preferentially affected in HCM and (E) preferentially affected in ICM. Each plot includes scaled ligand activity in receiver cells, scaled cell type activity and curation levels of intercellular communication based on the Omnipath dataset.CC BY · PubMed Central
FIGURE 6 Validation of Disease‐Enriched Pathways in Cardiomyopathy Mouse Models Using ssGSEA. Heatmaps showing single‐sample gene set enrichment analysis (ssGSEA) scores for hallmark gene sets in mouse models of cardiomyopathy. Each panel represents one disease subtype: (A) ACM, (B) DCM, (C) HCM and (D) ICM. Rows correspond to annotated hallmark gene sets (left), and columns represent individual samples (top), grouped by disease versus wild type (WT). The colour scale ranges from blue (low ssGSEA score) to red (high ssGSEA score), reflecting pathway activity. This analysis validates the pathways identified through GSEA in human cardiomyopathy by assessing their enrichment in corresponding mouse models.
FIGURE 6 Validation of Disease‐Enriched Pathways in Cardiomyopathy Mouse Models Using ssGSEA. Heatmaps showing single‐sample gene set enrichment analysis (ssGSEA) scores for hallmark gene sets in mouse models of cardiomyopathy. Each panel represents one disease subtype: (A) ACM, (B) DCM, (C) HCM and (D) ICM. Rows correspond to annotated hallmark gene sets (left), and columns represent individual samples (top), grouped by disease versus wild type (WT). The colour scale ranges from blue (low ssGSEA score) to red (high ssGSEA score), reflecting pathway activity. This analysis validates the pathways identified through GSEA in human cardiomyopathy by assessing their enrichment in corresponding mouse models.CC BY · PubMed Central

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Metadata

Authors
Tao, W., Gong, M., & Ke, Z.
Affiliations
  • Shanghai Medical College of Fudan University (CN)
  • Department of Geriatrics, Shanghai Fifth People's Hospital Fudan University Shanghai China
ORCID
Venue
Journal of Cellular and Molecular Medicine
Publisher
Wiley
Published
2025-05
Volume
29
Issue
9
Pages
e70554-e70554
ISSN
  • 1582-1838 (ISSN-L)
  • 1582-4934
DOI
10.1111/jcmm.70554
PMC
PMC12061637
OpenAlex
W4410248941
Cited by
4
References
2
Citations by year
2026: 4
FWCI
2.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Cellular and Molecular Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
cardiomyopathy, intercellular communication, arrhythmogenic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, ischemic cardiomyopathy, ventricular myocytes, ligand-receptor interactions, extracellular matrix remodeling, fibrosis, lymphangiogenesis, chronic inflammation, ischemic injury, disease heterogeneity
MeSH terms
Cardiomyopathy, Dilated, Cell Communication, Humans, Cardiomyopathies, Signal Transduction, Gene Expression Profiling, Single-Cell Analysis, Transcriptome

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CoChat. (2026). Citation check: Characterising Shared and Specific Cell–Cell Communication in Cardiomyopathy Subtypes From Single‐Cell Transcriptomics Data. https://app.cochat.ai/papers/10.1111/jcmm.70554
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Tao, W., Gong, M., & Ke, Z. (2025). Characterising Shared and Specific Cell–Cell Communication in Cardiomyopathy Subtypes From Single‐Cell Transcriptomics Data. Journal of Cellular and Molecular Medicine. https://doi.org/10.1111/jcmm.70554