Strength in numbers: Large-scale integration of single-cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration

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 provide spatial information that is needed to understand the context in which myogenic differentiation occurs. Here, we demonstrate how large-scale integration of new and public single-cell and spatial transcriptomic data can overcome these limitations. We created a large-scale single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 79 public single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting compendium includes nearly 350,000 cells and spans a wide range of ages, injury, and repair conditions. Combined, these data enabled identification of the predominant cell types in skeletal muscle with robust, consensus gene expression profiles, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors marked by stem potential, 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, short-lived 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. This analysis identified the temporal variation in cell subtype colocalized interactions during injury recovery. We will release an interactive public web tool to enable exploration and visualization of this rich single-cell transcriptomic resource. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

A lymphatic-stem cell interactome regulates intestinal stem cell activity

“…For this purpose, we used BayesPrism, a Bayesian statistical model that jointly deconvolves cell type composition and cell type-specific gene expression profiles within each spatial spot by using a scRNA-seq reference from matched tissue as prior information (Figure 4A) (Chu et al, 2021; McKellar et al, 2020). This method significantly outperforms other regression-based tools in bulk RNA-seq deconvolution (Chu et al, 2021; McKellar et al, 2021) (Figure S5A), and its robustness to platform batch effects, technical artefacts and noise made it particularly well suited for spatial deconvolution, treating each Visium spot as a bulk RNA sample.…”

A growth factor–expressing macrophage subpopulation orchestrates regenerative inflammation via GDF-15

“…Given the wide associations of GDF-15 with a variety of biological processes, including pregnancy, metabolism, and inflammation, it is very likely that GDF-15 plays additional roles to those described in our studies ( Tsai et al, 2018 ; Patel et al, 2019 ; Breit et al, 2021 ) and may act on multiple different low(er) affinity receptors on different cell types and in concert with other bone morphogenic proteins or TGF-β family members, as has been demonstrated and postulated for these proteins ( Antebi et al, 2017 ). Some of these receptors, like Tmed1, are expressed in cells present during muscle regeneration ( McKellar et al, 2020 Preprint ). It is also possible that its high-affinity receptor, GFRAL ( Mullican et al, 2017 ; Yang et al, 2017 ), may be expressed on other rare cell types outside of the area postrema in the brain, or more likely, additional receptors for GDF-15 may exist but have not yet been discovered.…”

Signaling pathways regulating the fate of fibro/adipogenic progenitors (FAPs) in skeletal muscle regeneration and disease

“…These results suggest a possible overlap between these two populations as suggested also by the increase in the expression of Ly6a in MABs during aging. Noteworthy, in dystrophic muscles, a transition between these three clusters is observed, implying that the observed heterogeneity is the result of drift between short‐lived transitional states of these progenitors [208,214]. Consistently, in glycerol‐injected muscles, an even more complicated transition involving 11 FAP states results in an either adipogenic or fibrogenic terminal phenotype [181].…”