Single-Nucleus Sequencing of an Entire Mammalian Heart: Cell Type Composition and Velocity

Cells. 2020 Jan 28;9(2):318. doi: 10.3390/cells9020318.

Abstract

: Analyses on the cellular level are indispensable to expand our understanding of complex tissues like the mammalian heart. Single-nucleus sequencing (snRNA-seq) allows for the exploration of cellular composition and cell features without major hurdles of single-cell sequencing. We used snRNA-seq to investigate for the first time an entire adult mammalian heart. Single-nucleus quantification and clustering led to an accurate representation of cell types, revealing 24 distinct clusters with endothelial cells (28.8%), fibroblasts (25.3%), and cardiomyocytes (22.8%) constituting the major cell populations. An additional RNA velocity analysis allowed us to study transcription kinetics and was utilized to visualize the transitions between mature and nascent cellular states of the cell types. We identified subgroups of cardiomyocytes with distinct marker profiles. For example, the expression of Hand2os1 distinguished immature cardiomyocytes from differentiated cardiomyocyte populations. Moreover, we found a cell population that comprises endothelial markers as well as markers clearly related to cardiomyocyte function. Our velocity data support the idea that this population is in a trans-differentiation process from an endothelial cell-like phenotype towards a cardiomyocyte-like phenotype. In summary, we present the first report of sequencing an entire adult mammalian heart, providing realistic cell-type distributions combined with RNA velocity kinetics hinting at interrelations.

Keywords: RNA velocity; cardiomyocytes; cluster analysis; seurat; snRNA-seq.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Biomarkers / metabolism
  • Cell Nucleus / metabolism*
  • Gene Expression Regulation
  • Male
  • Mammals / metabolism*
  • Mice
  • Myocardium / cytology*
  • Single-Cell Analysis*
  • Transcriptome / genetics

Substances

  • Biomarkers