Identification of Potential circRNA-microRNA-mRNA Regulatory Network in Skeletal Muscle

Circular RNAs (circRNAs) are a newly discovered family of regulatory RNAs generated through backsplicing. Genome-wide profiling of circRNAs found that circRNAs are ubiquitously expressed and regulate gene expression by acting as a sponge for RNA-binding proteins (RBPs) and microRNAs (miRNAs). To identify circRNAs expressed in mouse skeletal muscle, we performed high-throughput RNA-sequencing of circRNA-enriched gastrocnemius muscle RNA samples, which identified more than 1,200 circRNAs. In addition, we have identified more than 14,000 and 15,000 circRNAs in aging human skeletal muscle tissue and satellite cells, respectively. A subset of abundant circRNAs was analyzed by RT-PCR, Sanger sequencing, and RNase R digestion assays to validate their expression in mouse skeletal muscle tissues. Analysis of the circRNA-miRNA-mRNA regulatory network revealed that conserved circNfix might associate with miR-204-5p, a suppressor of myocyte enhancer factor 2c (Mef2c) expression. To support the hypothesis that circNfix might regulate myogenesis by controlling Mef2c expression, silencing circNfix moderately reduced Mef2c mRNA expression and inhibited C2C12 differentiation. We propose that circNfix promotes MEF2C expression during muscle cell differentiation in part by acting as a sponge for miR-204-5p.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.