Abstract
Cardiac diseases compose a fatal disease category worldwide. Over the past decade, high-throughput transcriptome sequencing of bulk heart tissues has widened our understanding of the onset and progression of cardiac diseases. The recent rise of single-cell RNA sequencing (scRNA-seq) technology further enables deep explorations of their molecular mechanisms in a cell-type-specific manner. However, due to technical difficulties in performing scRNA-seq on heart tissues, there are still few scRNA-seq studies on cardiac diseases. In this study, we demonstrate that an effective alternative could be cell-type-specific computational reconstruction of bulk transcriptomes. An integrative bulk transcriptome dataset covering 110 samples from 12 studies was first constructed by re-analysis of raw sequencing data derived from the heart tissues of four common cardiac disease mouse models (myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy). Based on the single-cell reference covering four major cardiac component cell types and 22 immune cell subtypes, for each sample, the bulk transcriptome was reconstructed into cellular compositions and cell-type-specific expression profiles by CIBERSORTx. Variations in the estimated cell composition revealed elevated abundances of fibroblast and monocyte during myocardial infarction, which were further verified by our flow cytometry experiment. Moreover, through cell-type-specific differential gene expression and pathway enrichment analysis, we observed a series of signaling pathways that mapped to specific cell type in diseases, like MAPK and EGFR1 signaling pathways in fibroblasts in myocardial infarction. We also found an increased expression of several secretory proteins in monocytes which may serve as regulatory factors in cardiac fibrosis. Finally, a ligand–receptor analysis identified key cell types which may serve as hubs in cellular communication in cardiac diseases. Our results provide novel clues for the cell-type-specific signatures of cardiac diseases that would promote better understanding of their pathophysiological mechanisms.
Highlights
The onset and progression of complex cardiac diseases often involve a variety of genes and features through systemic gene expression alterations
Multiple bulk RNA-Seq data could be retrieved by searching the Gene Expression Omnibus (GEO) database (Barrett et al, 2013) with cardiac disease-related keywords
We noted that a reasonable principal component analysis (PCA) clustering result could be observed based on this corrected expression matrix, where samples that belong to the same disease group were clustered together well even with the variations in source datasets and disease modeling approaches (Figure 2A)
Summary
The onset and progression of complex cardiac diseases often involve a variety of genes and features through systemic gene expression alterations. With the popularity of sequencing technologies, RNA sequencing of bulk tissues (bulk RNA-seq) has generated a huge amount of data about transcriptomic alterations in cardiac diseases in the last decade (Wang et al, 2009). Such bulk RNA-seq data sketch the overall transcriptomic landscape of cardiac disease at the whole tissue level, which has provided useful clues for investigating cardiac disease genes and pathways. In order to comprehensively understand the molecular mechanism of cardiac diseases, it is necessary to dissect the bulk transcriptome to the single cell or, at least, to the single cell type level
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