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Characterization of cellular senescence in aging skeletal muscle

Nature Aging volume 2, pages 601–615 (2022)Cite this article

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Abstract

Senescence is a cell fate that contributes to multiple aging-related pathologies. Despite profound age-associated changes in skeletal muscle (SkM), whether its constituent cells are prone to senesce has not been methodically examined. Herein, using single-cell and bulk RNA sequencing and complementary imaging methods on SkM of young and old mice, we demonstrate that a subpopulation of old fibroadipogenic progenitors highly expresses p16Ink4a together with multiple senescence-related genes and concomitantly, exhibits DNA damage and chromatin reorganization. Through analysis of isolated myofibers, we also detail a senescence phenotype within a subset of old cells, governed instead by p21Cip1. Administration of a senotherapeutic intervention to old mice countered age-related molecular and morphological changes and improved SkM strength. Finally, we found that the senescence phenotype is conserved in SkM from older humans. Collectively, our data provide compelling evidence for cellular senescence as a hallmark and potentially tractable mediator of SkM aging.

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Fig. 1: Hallmarks of cellular senescence in skeletal muscle.
Fig. 2: scRNA-seq of SkM reveals age-associated changes in mononuclear cell composition and p16 expression.
Fig. 3: A distinct cluster of high p16-expressing FAPs in old skeletal muscle exhibits a senescence phenotype.
Fig. 4: FAPs derived from old skeletal muscle exhibit core properties of senescence.
Fig. 5: Skeletal muscles of old mice have high p21-expressing myofibers with a senescence profile.
Fig. 6: Senolytics improve the molecular phenotype and function of SkM in female mice.
Fig. 7: Aging and cellular senescence in human SkM.

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Data availability

The scRNA-seq (GSE172410), myofiber RNA-seq data (GSE172254) and SkM RNA-seq data (GSE184348) generated in this study are deposited in the Gene Expression Omnibus. Human SkM RNA-seq data are publicly available at the Gene Expression Omnibus (GSE97084).

An interactive website for the SkM scRNA-seq dataset can be found at https://mayoxz.shinyapps.io/Muscle/.

Code availability

Codes and all other data are available from the corresponding author upon reasonable request.

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Acknowledgements

We are grateful for the support of the National Institutes of Health, National Institute on Aging for grants P01 AG062413, R01 AG055529 and R56 AG060907 to N.K.L. and R01 AG068048 and UG3CA 268103 to J.F.P. and T32 AG049672 to D.A.E. This work was also supported by the Glenn Foundation for Medical Research and the Pritzker Foundation (N.K.L.). X.Z. was supported by a Robert and Arlene Kogod Center on Aging Career Development Award. L.H. was supported by NIHR Newcastle Biomedical Research Centre grant awarded to the Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. R.A.F. and D.A.R. are partially supported by the US Department of Agriculture under agreement no. 58-8050-9-004 and by and by National Institutes of Health Boston Claude D Pepper Center (OAIC; 1P30AG031679). Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Department of Agriculture. We also thank members of P01 AG062413 (PI, S.K.) for helpful discussions, J.M. Cunningham and E. Wieben within the Mayo Clinic Genome Analysis Core for RNA-seq, Y. Li within the Division of Computational Biology and Department of Quantitative Health Sciences for assistance with bioinformatic analyses and staff at the Optical Microscopy Core within the Mayo Clinic Center for Cell Signaling in Gastroenterology (P30 DK084567) for guidance and use of imaging equipment.

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Author notes

  1. These authors contributed equally: Xu Zhang, Leena Habiballa.

Authors and Affiliations

  1. Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA

    Xu Zhang, Leena Habiballa, Zaira Aversa, Yan Er Ng, Ayumi E. Sakamoto, Davis A. Englund, Vesselina M. Pearsall, Thomas A. White, Adam J. Hruby, Anthony B. Lagnado, Diana Jurk, Sundeep Khosla, Marissa J. Schafer, João F. Passos & Nathan K. LeBrasseur

  2. Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA

    Xu Zhang, Zaira Aversa, Davis A. Englund & Nathan K. LeBrasseur

  3. NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK

    Leena Habiballa, Antoneta Granic & Avan A. Sayer

  4. Mayo Clinic Graduate School of Biomedical Science, Rochester, MN, USA

    Leena Habiballa & Sarah K. Jachim

  5. School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA

    Matthew M. Robinson

  6. Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center, Tufts University, Boston, MA, USA

    Donato A. Rivas & Roger A. Fielding

  7. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Surendra Dasari

  8. Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA

    Adam J. Hruby, Anthony B. Lagnado, Diana Jurk, Marissa J. Schafer & João F. Passos

  9. AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK

    Antoneta Granic & Avan A. Sayer

  10. Division of Endocrinology, Mayo Clinic, Rochester, MN, USA

    Ian R. Lanza

  11. Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA

    Sundeep Khosla & K. Sreekumaran Nair

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Contributions

Conceptualization was carried out by X.Z. and N.K.L. X.Z., L.H., K.S.N., M.J.S., J.F.P. and N.K.L. were responsible for the methodology. Investigation was conducted by X.Z., L.H., Z.A., Y.E.N., A.E.S., M.M.R., D.A.R., S.D., V.M.P., T.A.W., A.J.H., A.B.L., S.K.J., D.A.E. and M.J.S. Resources were the responsibility of M.R., S.D., I.R.L., R.A.F. and K.S.N. Writing of the original draft was conducted by X.Z., L.H., Z.A., J.F.P. and N.K.L. Reviewing and editing was carried out by X.Z., L.H., Z.A., A.E.S., M.M.R., D.A.R., S.D., V.M.P., T.A.W., Y.E.N., A.B.L., S.K.J., D.A.E., A.G., A.A.S., D.J., I.R.L., S.K., R.A.F., K.S.N., M.J.S., J.F.P. and N.K.L. A.G., A.A.S., D.J., J.F.P. and N.K.L. were responsible for supervision. J.F.P. and N.K.L. were responsible for funding acquisition.

Corresponding authors

Correspondence to João F. Passos or Nathan K. LeBrasseur.

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Extended data

Extended Data Fig. 1 The age-related loss of skeletal mass, strength, and function.

a–c, Comparisons of body weight (a), lean mass (%) (b), and quadriceps (quad) muscle weight (c) between young (n = 9) and old (n = 5) mice. d, e, Representative images of quad muscle cross-sections stained for Laminin (d) and quantification of fiber size and distribution for young and old female mice (n = 4 per group) (e). f–h, Comparison of grip strength normalized to body weight (f), treadmill exercise capacity (g), and Rotarod endurance (h) between young (n = 9) and old (n = 5) mice. Two-tailed unpaired t-test was used; error bars represent s.e.m. **, and *** denote p < 0.01 and 0.001, respectively.

Source data

Extended Data Fig. 2 The age-related increase of lipofuscin in skeletal muscle (SkM).

a,b, Representative images of Sudan Black B (SBB) staining (a) and quantification of the numbers of positive foci per quadriceps section (b) from young and old female mice (n = 4 per age group). Two-tailed unpaired t test was used; error bars represent s.e.m. * and ** denote p < 0.05 and 0.01, respectively.

Source data

Extended Data Fig. 3 Identification of skeletal muscle (SkM) cell populations by single cell RNA-sequencing.

a, Heat map of the genes delineating 10 distinct cell populations in young and old female mice (n = 3 per group). b,c, UMAP plot (b) and dot plot (c) showing the main markers of each cell type. d, Relative abundance of the distinct SkM cell populations in individual mice.

Extended Data Fig. 4 Gene Ontology analysis of the upregulated and downregulated genes in high p16-expressing FAP cluster 3 compared to other FAPs.

BP: biological process; MF: molecular function; CC: cellular component. Benjamini–Hochberg Procedure was used to calculate the FDR adjusted p value.

Extended Data Fig. 5 Gene Ontology analysis of the upregulated and downregulated genes in old p21high myofibers compared to old p21low myofibers.

BP: biological process; MF: molecular function; CC: cellular component. Benjamini–Hochberg Procedure was used to calculate the FDR adjusted p value.

Supplementary information

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Uncropped western blot gel image for Fig. 5b.

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Zhang, X., Habiballa, L., Aversa, Z. et al. Characterization of cellular senescence in aging skeletal muscle. Nat Aging 2, 601–615 (2022). https://doi.org/10.1038/s43587-022-00250-8

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