Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions
Open accessAbstract
Dedicated stem cells ensure postnatal growth, repair and homeostasis of skeletal muscle. Following injury, muscle stem cells (MuSCs) exit from quiescence and divide to reconstitute the stem cell pool and give rise to muscle progenitors. The transcriptomes of pooled MuSCs have provided a rich source of information for describing the genetic programs of distinct static cell states; however, bulk microarray and RNA sequencing provide only averaged gene expression profiles, blurring the heterogeneity and developmental dynamics of asynchronous MuSC populations. Instead, the granularity required to identify distinct cell types, states, and their dynamics can be afforded by single cell analysis. We were able to compare the transcriptomes of thousands of MuSCs and primary myoblasts isolated from homeostatic or regenerating muscles by single cell RNA sequencing. Using computational approaches, we could reconstruct dynamic trajectories and place, in a pseudotemporal manner, the transcriptomes of individual MuSC within these trajectories. This approach allowed for the identification of distinct clusters of MuSCs and primary myoblasts with partially overlapping but distinct transcriptional signatures, as well as the description of metabolic pathways associated with defined MuSC states.
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Our data are consistent with previous reports of decreased Pax7 with activation at the ensemble level, when considering only the mean expression of Pax7 at the quiescent and activated time points.
“Our data are consistent with previous reports of decreased Pax7 with activation at the ensemble level (Cornelison and Wold, 1997; De Micheli et al., 2020; Dell'Orso et al., 2019), when we consider only the mean expression of Pax7 at the quiescent and activated time points (Fig. S3C).”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Dell'Orso et al., 2019
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References 83 · Cited by 243
- Authors
- Dell'Orso, S., Juan, A. H., Ko, K.-D., Naz, F., Perovanovic, J., Gutierrez-Cruz, G., Feng, X., & Sartorelli, V.
- Affiliations
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NIH 1 Genome Technology Unit , , Bethesda, MD 208292 , USA
- National Institutes of Health (US)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NIH 2 Laboratory of Muscle Stem Cells and Gene Regulation , , Bethesda, MD 208292 , USA
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2019-04-11
- Volume
- 146
- Issue
- 12
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- PMC
- PMC6602351
- OpenAlex
- W2923948921
- Cited by
- 243
- References
- 83
- Citations by year
- 2026: 20 · 2025: 25 · 2024: 39 · 2023: 39 · 2022: 41 · 2021: 36 · 2020: 35 · 2019: 7 · 2018: 1
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Development (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR041126, AR041164)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- skeletal muscle stem cells, muscle regeneration, muscle homeostasis, single-cell transcriptomics, myoblasts, stem cell quiescence, metabolic pathways, gene expression signatures, mice
- MeSH terms
- RNA-Seq, Animals, Cell Separation, Flow Cytometry, Homeostasis, Leukocytes, Mononuclear, Mice, Inbred C57BL, Regeneration, Software, Stem Cells, Cluster Analysis, Sequence Analysis, RNA, Muscle, Skeletal, Computational Biology, Oligonucleotide Array Sequence Analysis, Genomics, Muscle Development, Mice, Single-Cell Analysis, Transcriptome
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