Introduction Dilated cardiomyopathy (DCM) is the most common cause of heart failure (HF), with a complex aetiology including lifestyle and genetic factors involving pathological changes in multiple cardiac cell types. The ability of single-cell RNA sequencing (scRNA-Seq) to measure gene expression in thousands of individual cells simultaneously provides a way to study the differing pathological changes in cell types within complex tissues. We aimed to detect celltype-specific transcriptomic alterations implicated in DCM through an integrated analysis of publicly available adult heart scRNA-Seq datasets that leveraged recent advancements in single-cell analytical tools.
Methods scRNA-Seq data from an adult human HF dataset containing DCM (n=5) and control (n=14) samples were retrieved from Gene Expression Omnibus (GSE109816, GSE121893) and subjected to an updated bioinformatic workflow. Unsupervised clustering analysis of 10,242 cells was paired with reference celltype mapping from Heart Cell Atlas data to produce a more comprehensive annotation of the HF dataset. Differential expression analysis was performed between DCM and control cells to identify celltype-specific transcriptomic changes in DCM. Bulk RNA-seq was performed on adult human DCM (n=9) and control (n=9) heart tissue to detect whole-tissue changes. Genes differentially expressed in bulk and single-cell data were intersected to generate a list of putative DCM-linked genes, validated in vitro by RT-qPCR in human cardiac fibroblasts.
Results Our single-cell workflow resolved 8 distinct cell populations in the heart, 4 of which were not reported in the original publication associated with the data. The validity of these cell populations was strongly supported by the similarity of their transcriptomic profiles with those of the recently published Heart Cell Atlas study. Expression was altered in DCM in all but the rarest heart cell populations. Genes identified as altered in DCM in bulk RNA-seq were compared with altered genes from each single-cell cardiac cell population. Greatest concordance between the two techniques was noted in fibroblasts, with 14 upregulated and 13 downregulated genes common across both analyses. Several of these genes were independently validated in an in vitro model of TGFβ-treated human cardiac fibroblasts.
Conclusions DCM is a complex pathology involving interactions between multiple cardiac cell populations. Our analysis workflow improved resolution at the single-cell level, providing more accurate recapitulation of in vivo tissue heterogeneity. This unbiased approach has enabled the robust detection of unique disease-relevant transcriptomic alterations in specific cardiac cell populations in DCM.
Conflict of Interest None
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