Abstract 281: Single Cell Analysis of Postnatal Heart Development and Disease

A fundamental challenge in understanding cardiac biology and disease is that the remarkable heterogeneity in cell-type composition and functional states have not been well characterized at single-cell resolution in maturing and diseased mammalian hearts. Massively parallel single-nucleus RNA sequencing (snRNA-Seq) has emerged as a powerful tool to address these questions by interrogating the transcriptome of tens of thousands of nuclei isolated from fresh or frozen tissues. snRNA-Seq overcomes the technical challenge of isolating intact single cell from complex tissues including the maturing mammalian hearts, reduces biased recovery of easily dissociated cell types and minimizes aberrant gene expression during the whole-cell dissociation. We have recently applied sNucDrop-Seq, a droplet microfluidics-based massively parallel snRNA-Seq method, to investigate the transcriptional landscape of postnatal mouse hearts in both healthy and mitochondrial disease states. By profiling the transcriptome of nearly 20,000 nuclei, we identified major and rare cardiac cell types and revealed significant cellular heterogeneity in the postnatal developing heart. When applied to a mouse model of mitochondrial cardiomyopathy, we uncovered profound cell type-specific modifications of the cardiac transcriptional landscape at single-nucleus resolution. Here, we expanded these earlier studies and used our dataset to further decipher the cardiac cell type-specific gene regulatory networks. Our analysis reveals novels insights into the key nodes of gene networks that control the postnatal development and disease-associated changes of different cardiac cell types. Our ongoing work is using genetic mouse models to validate these findings from single cell analysis.