Lineage-specific regulatory changes in hypertrophic cardiomyopathy unraveled by single-nucleus RNA-seq and spatial transcriptomics

Abstract

Hypertrophic cardiomyopathy (HCM) is the most common cardiac genetic disorder characterized by cardiomyocyte hypertrophy and cardiac fibrosis. Pathological cardiac remodeling in the myocardium of HCM patients may progress to heart failure. An in-depth elucidation of the lineage-specific changes in pathological cardiac remodeling of HCM is pivotal for the development of therapies to mitigate the progression. Here, we performed single-nucleus RNA-seq of the cardiac tissues from HCM patients or healthy donors and conducted spatial transcriptomic assays on tissue sections from patients. Unbiased clustering of 55,122 nuclei from HCM and healthy conditions revealed 9 cell lineages and 28 clusters. Lineage-specific changes in gene expression, subpopulation composition, and intercellular communication in HCM were discovered through comparative analyses. According to the results of pseudotime ordering, differential expression analysis, and differential regulatory network analysis, potential key genes during the transition towards a failing state of cardiomyocytes such as FGF12, IL31RA, and CREB5 were identified. Transcriptomic dynamics underlying cardiac fibroblast activation were also uncovered, and potential key genes involved in cardiac fibrosis were obtained such as AEBP1, RUNX1, MEOX1, LEF1, and NRXN3. Using the spatial transcriptomic data, spatial activity patterns of the candidate genes, pathways, and subpopulations were confirmed on patient tissue sections. Moreover, we showed experimental evidence that in vitro knockdown of AEBP1 could promote the activation of human cardiac fibroblasts, and overexpression of AEBP1 could attenuate the TGFβ-induced activation. Our study provided a comprehensive analysis of the lineage-specific regulatory changes in HCM, which laid the foundation for targeted drug development in HCM.

High‐resolution single‐cell transcriptomic survey of cardiomyocytes from patients with hypertrophic cardiomyopathy

“…In this study, using our high-resolution single-cell RNA-seq data, five distinct clusters of HCM cardiomyocytes were identified. Consistently, a cluster with high-level expression of NPPA and NPPB (HCM_C2) was reported by two other studies 9,11 and the cluster showed response to growth factor (HCM_C3), featured by expression of FGF12 was also identified in Liu et al 9 Additionally, clusters related to viral mRNA translation (HCM_C1), energy homeostasis (HCM_C4) and chromatin remodelling (HCM_C5) were also captured in our data, reflecting the advantages of high-resolution data.…”

“…Interestingly, only 14 DARs were identified in the controls with 2 DARs being overlapped with the 13 differential expressional transcriptional factors, again suggesting that HCM was characterized by dominant gene upregulation (Figure S4d). To validate the result of our SCENIC analysis, we calculated the expression of these TFs and the module score of related regulons in other published human single-cell and singlenucleus RNA-seq data of cardiomyocytes from HCM, 8,9,11 which revealed consistent patterns with our results (Figure 3E,F). Together, our data revealed alterations of HCM cardiomyocytes in gene expression, biological pathways, ligand-receptor pairs and TF networks.…”

“…We also eliminated potential cell contaminants to ensure the data quality (see also Methods, Figure S1b–g). As a result, an average of 6061 genes and 92,369 transcripts (counted as the unique molecular identifier, UMIs) were detected in each cell, which were significantly higher than other published single‐cardiomyocyte (one study) and single‐nucleus (two studies) RNA‐seq data 8,9,11 (Figure 1B). Our data also exhibited a lower fraction of mitochondrial counts than the published single‐cardiomyocyte RNA‐seq data, which also indicated the high quality of our data (Figure 1B).…”

“…As a result, a total of 4 datasets, including 2 single-cardiomyocyte RNA-seq (including this study) and 2 single-nucleus RNA-seq data were collected, which constituted a meta dataset of 65,083 cells (nuclei) from 37 HCM patients and 94,240 cells (nuclei) from 41 normal controls (Figure 4A). 8,9,11 To investigate batch effects among datasets derived from various patients and platforms, consensus genes from different datasets (n = 16,392) were classified into 10 groups based on their expression levels and used to perform unsupervised hierarchical clustering (Figure 4B). We showed that the overall distributions of gene expression were very different between singlecardiomyocyte and single-nucleus RNA-seq data, which was also consistent with the higher Pearson's correlation coefficients within the two groups (Figure 4C).…”

“…3,4 Recently, several scRNA-seq studies on samples from both mouse cardiac hypertrophy models and HCM patients have been reported, however, most of these studies have focused on noncardiomyocyte cell types or analysed nuclei of cardiomyocytes rather than intact cardiomyocytes, as the large cell size of cardiomyocytes hinders the use of standard fluid-or droplet-based devices. [5][6][7][8][9] Since the cell mass of a cardiomyocyte is much larger than its nucleus, single-cardiomyocyte RNA-seq should be able to detect more transcripts per cell which should be complementary to these single-nucleus RNA-seq studies and may provide a more comprehensive characterization of the transcriptome of HCM cardiomyocytes. 10 More recently, a single-cardiomyocyte RNA-seq study for human HCM samples has been reported, but the sequencing depth is still limited.…”