Single-cell transcriptional profiles in human skeletal muscle
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ABSTRACT:
PROVIDER: PRJNA540859 | ENA |
REPOSITORIES: ENA
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Project description:Skeletal muscle is a complex heterogeneous tissue comprised of diverse muscle fiber and non-fiber cell types that, in addition to movement, influences other systems such as immunity, metabolism and cognition. We investigated gene expression patterns of resident human skeletal muscle cells using single-cell RNA-seq of dissections from vastus lateralis. We generate transcriptome profiles of 11 mononuclear human skeletal muscle mononuclear cell types, including immune, endothelial, pericyte and satellite cells. We delineate two fibro-adipogenic progenitor cell subtypes that may contribute to heterotopic ossification and muscular dystrophy fibrosis under pathological conditions. An important application of cell type signatures is for computational deconvolution of cell type specific changes using data from bulk transcriptome experiments. Analysis of transcriptome data from a 12 week resistance training study using the human skeletal muscle cell-type signatures revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. This characterization of human skeletal muscle cell subtypes will resolve cell type specific changes in large-scale physical activity muscle transcriptome studies and can further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease.
2020-01-16 | GSE130646 | GEO
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Project description:Skeletal muscle is a complex heterogeneous tissue comprised of diverse muscle fiber and non-fiber cell types that, in addition to movement, influences other systems such as immunity, metabolism and cognition. We investigated gene expression patterns of resident human skeletal muscle cells using both single-cell RNA-seq and RNA-seq of single muscle fiber dissections from vastus lateralis. We generated transcriptome profiles of the major multinucleated human skeletal muscle fiber-types as well as 11 human skeletal muscle mononuclear cell types, including immune, endothelial, pericyte and satellite cells. We delineated two fibro-adipogenic progenitor cell subtypes that may contribute to heterotopic ossification and muscular dystrophy fibrosis under pathological conditions. An important application of cell type signatures is for computational deconvolution of cell type specific gene expression changes using data from bulk transcriptome experiments. Analysis of transcriptome data from a 12 week resistance exercise training study using these human skeletal muscle cell-type signatures revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. This characterization of human skeletal muscle cell types will resolve cell-type specific changes in large-scale physical activity muscle transcriptome studies and can further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease.
2020-02-01 | GSE130977 | GEO
Project description:Syncytial skeletal muscle cells contain hundreds of nuclei in a shared cytoplasm. Using single nucleus RNA-sequencing (snRNAseq) of isolated nuclei from muscle fibers, we investigated nuclear heterogeneity and transcriptional dynamics in uninjured and regenerating muscle.
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Project description:While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the emergence of distinct myonuclear populations in postnatal development and their reactivation in aging muscle. Our datasets also provided a platform for discovery of novel genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.
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Project description:Skeletal muscle is a heterogeneous tissue consisting of blood vessels, connective tissue, and muscle fibers. The last are highly adaptive and can change their molecular composition depending on external and internal factors, such as exercise, age, and disease. Thus, examination of the skeletal muscles at the fiber type level is essential to detect potential alterations. Therefore, we established a protocol in which myosin heavy chain isoform immunolabeled muscle fibers were laser microdissected and separately investigated by mass spectrometry to develop advanced proteomic profiles of all murine skeletal muscle fiber types. Our in-depth mass spectrometric analysis revealed unique fiber type protein profiles, confirming fiber type-specific metabolic properties and revealing a more versatile function of type IIx fibers. Furthermore, we found that multiple myopathy-associated proteins were enriched in type I and IIa fibers. To further optimize the assignment of fiber types based on the protein profile, we developed a hypothesis-free machine-learning approach (available at: https://github.com/mpc-bioinformatics/FiPSPi), identified a discriminative peptide panel, and confirmed our panel using a public data set.
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