Title: Towards constructing single-cell gene regulatory networks of skeletal muscle myogenesis
Abstract: The regeneration of adult skeletal muscle from injury and disease is carried out through satellite cells, or muscle stem cells. Typically quiescent and resting on top of muscle cells (myofibers), satellite cells are activated upon injury and undergo asymmetric division to both self-renew their population and proliferate into myoblasts that repair the myofiber. Myoblasts fuse with the damaged fiber and with themselves in order to form new fibers, but satellite cells remain mononucleated. Although the role of satellite cells in regeneration has been established, the mechanisms controlling alternative satellite cell fate decisions have not been described at the level of gene regulatory networks. To address this, we performed single cell/nucleus RNA-seq and ATAC-seq on a differentiating satellite-cell derived skeletal muscle cell line in order to profile gene expression and chromatin accessibility of single cells. We recovered >70,000 cells in our scRNA-seq experiments and >20,000 nuclei in our snATAC-seq experiments from cultures of proliferating myoblasts and differentiated myotubes. We found a subpopulation of myonuclei expressing Mef2c, a transcription factor involved in slow-twitch (oxidative) muscle fiber type specification, and a subpopulation of satellite-cell-like mononucleated cells (MNCs) expressing Col1a1, collagen type Ia. We visualized the localization of these transcripts and found that Mef2c builds up in junctions between myotubes while Col1a1 is sparsely yet highly expressed in a subpopulation of MNCs. Our continuing analysis is focused on identifying downstream targets of Mef2c as well as upstream transcription factors controlling Col1a1 and other co-regulators in order to build gene regulatory networks describing the differentiation of a supposedly homogeneous myoblast culture to these subpopulations. We plan to draw connections between transcription factors and targets using a previously published tool from our lab that relies on self-organizing maps to link scRNA-seq and snATAC-seq data, as well as using our snATAC-seq data for motif enrichment, transcription factor footprinting, and chromatin co-accessibility analysis.