Abstract
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.
Highlights
Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells
We curated 88 publicly available mouse skeletal muscle sc/snRNAseq datasets that were generated on the 10x Chromium platform (v2, v3, or v3.1) from PanglaoDB33 and SRA as of January 1, 2021
These comprised 111 individual samples with a total of 503,929 cell barcodes, before quality control and filtering (Figs. 1a and S1, Table S1). These data vary across sex, age (10 days to 30 months of age), chemical injury model, injury-response timepoint (0.5–21 days postinjury [dpi]), and the sample preparation strategy, including whole-muscle dissociations and FACS enrichment of specific cell types (Figs. 1b and S1)
Summary
Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Other studies reported an infrequent sampling of committed myogenic progenitors from whole muscle samples[15,16,17] To overcome these challenges, we used large-scale integration of single-cell transcriptomics data. We leveraged recent improvements in batch-correction algorithms[18,19] to incorporate 88 publicly available sc/snRNAseq datasets from 18 prior studies in our analysis[9,11,15,16,17,20,21,22,23,24,25,26,27,28,29,30,31,32] This led to a dataset that included ~365,000 cells/nuclei after quality filtering and allowed us to study the cellular composition and dynamics in response to skeletal muscle injury over a wide range of experimental conditions. Our analysis brings insights into the dynamics of stromal and immune cell colocalization with transient myogenic cell states
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