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.

Hair follicle (HF) stem cells (SCs) reside in the bulge region of the hair follicle, 1-3 which is located at the lower permanent portion of HF from which the temporary portion, known as the bulb, periodically emerges. Coordinated activity between the HFSCs and surrounding microenvironment drives the cyclic regeneration of the HF bulb through various phases: growth (anagen), regression (catagen) and quiescence (telogen). 4,5 During early and mid-anagen phase,

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.

No abstract

Multiple myeloma (MM) is a plasma cell malignancy characterized by clinical and genomic heterogeneity. Recurrent IgH translocations, copy number abnormalities and somatic mutations have been reported to participate in myelomagenesis; however no universal driver of the disease has been identified. Here, we hypothesize that transcriptional deregulation is critical for MM pathogenesis and the maintenance of the MM cell state. In order to capture signatures of transcription factor engagement with the myeloma epigenome, we performed the assay for transposase-accessible chromatin sequencing (ATAC sequencing), deep RNA sequencing in 23 primary myeloma samples and 5 normal plasma cell samples (NPC) from healthy donors along with whole genome sequencing and H3K27ac ChIP-seq in a cohort of these primary MM samples. We identified 22,603 variable accessible loci between MM and NPC and correlated impact of these on expression of associated genes using RNA-seq data. Together with robust differential analysis of open chromatin regions and nuclease-accessibility footprints to identify discrete transcription factor binding events, we have discerned the myeloma-specific open chromatin landscape, identified transcription factor dependencies and potential new myeloma drivers. In our dataset we observe a vast number of loci with heterogeneous chromatin states across the sample cohort, and the majority of the open chromatin sites identified are unique to a single sample. However, distinct variable chromatin accessibility signatures indicative of the MM chromatin state when compared to normal plasma cells were observed. Remarkably, we observed more frequent recurrent loss of variable accessible loci compared to gains. In addition, specific open chromatin profiles evident in hyperdiploid and non-hyperdiploid MM were also identified. Accessibility footprinting revealed MM-specific enrichment for transcription factors known to be essential for MM cell survival including Interferon Regulatory Factors (IRFs), Nuclear Factor Kappa B (NFkB), Ikaros, and Sp1. Interestingly, we also identify the myocyte enhancer factor 2 (MEF2) family of transcription factors as being specifically enriched in open chromatin regions in MM cells. Using a CRISPR-Cas9 knockout system, we identify the MEF2 family member MEF2C as essential for MM cell proliferation and survival. MEF2C is significantly overexpressed at the RNA level in our study as well as in several independent cohorts and is a central enhancer-localized transcription factor in MM core regulatory circuitry as determined by H3K27ac ChIP-sequencing profiles of primary MM samples. In order to evaluate MEF2C as a therapeutic target, we used small molecule inhibitors targeting MEF2C activity via inhibition of MEF2C phosphorylation using inhibitors of salt-induced kinases (SIK) and microtubule-associated protein/microtubule affinity regulating kinases (MARK). SIK/MARK have been described to specifically activate MEF2C. SIK and MARK inhibition resulted in both dose- and time-dependent inhibition of MM cell growth and survival in a panel of 12 MM cell lines with various genotypic and phenotypic characteristics, revealing a potential approach to targeting the dysregulated gene regulatory state of myeloma. To conclude, here we identify here an altered chromatin accessibility landscape in multiple myeloma that likely contributes to oncogenic transcription states through the activity of transcription factors such as MEF2C, representing a new MM dependency and potential therapeutic target. Disclosures Anderson: Millennium Takeda: Consultancy; C4 Therapeutics: Equity Ownership, Other: Scientific founder; Bristol Myers Squibb: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; OncoPep: Equity Ownership, Other: Scientific founder. Young:Camp4 Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Syros Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Omega Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Munshi:OncoPep: Other: Board of director.