Hedgehog signaling via its ligand DHH acts as cell fate determinant during skeletal muscle regeneration.

Successful muscle regeneration relies on the interplay of multiple cell populations. However, the signals required for this coordinated intercellular crosstalk remain largely unknown. Here, we describe how the Hedgehog (Hh) signaling pathway controls the fate of fibro/adipogenic progenitors (FAPs), the cellular origin of intramuscular fat (IMAT) and fibrotic scar tissue. Using conditional mutagenesis and pharmacological Hh modulators in vivo and in vitro, we identify DHH as the key ligand that acts as a potent adipogenic brake by preventing the adipogenic differentiation of FAPs. Hh signaling also impacts muscle regeneration, albeit indirectly through induction of myogenic factors in FAPs. Our results also indicate that ectopic and sustained Hh activation forces FAPs to adopt a fibrogenic fate resulting in widespread fibrosis. In this work, we reveal crucial post-developmental functions of Hh signaling in balancing tissue regeneration and fatty fibrosis. Moreover, they provide the exciting possibility that mis-regulation of the Hh pathway with age and disease could be a major driver of pathological IMAT formation.

Spatial mapping of the total transcriptome by in situ polyadenylation.

Spatial transcriptomics reveals the spatial context of gene expression, but current methods are limited to assaying polyadenylated (A-tailed) RNA transcripts. Here we demonstrate that enzymatic in situ polyadenylation of RNA enables detection of the full spectrum of RNAs, expanding the scope of sequencing-based spatial transcriptomics to the total transcriptome. We demonstrate that our spatial total RNA-sequencing (STRS) approach captures coding RNAs, noncoding RNAs and viral RNAs. We apply STRS to study skeletal muscle regeneration and viral-induced myocarditis. Our analyses reveal the spatial patterns of noncoding RNA expression with near-cellular resolution, identify spatially defined expression of noncoding transcripts in skeletal muscle regeneration and highlight host transcriptional responses associated with local viral RNA abundance. STRS requires adding only one step to the widely used Visium spatial total RNA-sequencing protocol from 10x Genomics, and thus could be easily adopted to enable new insights into spatial gene regulation and biology.

A Tead1-Apelin axis directs paracrine communication from myogenic to endothelial cells in skeletal muscle.

Apelin (Apln) is a myokine that regulates skeletal muscle plasticity and metabolism and declines during aging. Through a yeast one-hybrid transcription factor binding screen, we identified the TEA domain transcription factor 1 (Tead1) as a novel regulator of the Apln promoter. Single-cell analysis of regenerating muscle revealed that the apelin receptor (Aplnr) is enriched in endothelial cells, whereas Tead1 is enriched in myogenic cells. Knock-down of Tead1 stimulates Apln secretion from muscle cells in vitro and myofiber-specific overexpression of Tead1 suppresses Apln secretion in vivo. Apln secretion via Tead1 knock-down in muscle cells stimulates endothelial cell expansion via endothelial Aplnr. In vivo, Apln peptide supplementation enhances endothelial cell expansion while Tead1 muscle overexpression delays endothelial remodeling following muscle injury. Our work describes a novel paracrine crosstalk in which Apln secretion is controlled by Tead1 in myogenic cells and influences endothelial remodeling during muscle repair.

Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis.

A significant fraction of sudden death in children and young adults is due to viral myocarditis, an inflammatory disease of the heart. In this study, by using integrated single-cell and spatial transcriptomics, we created a high-resolution, spatially resolved transcriptome map of reovirus-induced myocarditis in neonatal mouse hearts. We assayed hearts collected at three timepoints after infection and studied the temporal, spatial and cellular heterogeneity of host-virus interactions. We further assayed the intestine, the primary site of reovirus infection, to establish a full chronology of molecular events that ultimately lead to myocarditis. We found that inflamed endothelial cells recruit cytotoxic T cells and undergo pyroptosis in the myocarditic tissue. Analyses of spatially restricted gene expression in myocarditic regions and the border zone identified immune-mediated cell-type-specific injury and stress responses. Overall, we observed a complex network of cellular phenotypes and spatially restricted cell-cell interactions associated with reovirus-induced myocarditis in neonatal mice.

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.

Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.