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.