Deconstructing replicative senescence heterogeneity of human mesenchymal stem cells at single cell resolution reveals therapeutically targetable senescent cell sub-populations
Open accessAbstract
Cellular senescence is characterised by a state of permanent cell cycle arrest. It is accompanied by often variable release of the so-called senescence-associated secretory phenotype (SASP) factors, and occurs in response to a variety of triggers such as persistent DNA damage, telomere dysfunction, or oncogene activation. While cellular senescence is a recognised driver of organismal ageing, the extent of heterogeneity within and between different senescent cell populations remains largely unclear. Elucidating the drivers and extent of variability in cellular senescence states is important for discovering novel targeted seno-therapeutics and for overcoming cell expansion constraints in the cell therapy industry. Here we combine cell biological and single cell RNA-sequencing approaches to investigate heterogeneity of replicative senescence in human ESC-derived mesenchymal stem cells (esMSCs) as MSCs are the cell type of choice for the majority of current stem cell therapies and senescence of MSC is a recognized driver of organismal ageing. Our data identify three senescent subpopulations in the senescing esMSC population that differ in SASP, oncogene expression, and escape from senescence. Uncovering and defining this heterogeneity of senescence states in cultured human esMSCs allowed us to identify potential drug targets that may delay the emergence of senescent MSCs in vitro and perhaps in vivo in the future.
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Single-cell RNA-sequencing has revealed high levels of heterogeneity in cellular senescence in culture.
“Recently, single-cell RNA-sequencing (scRNA-Seq) has been used to analyze the diversity within senescent cell populations in culture revealing high levels of heterogeneity in cellular senescence [13–15].”
From Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during senescence induction (2025) · cites this paper as [14]
Significant heterogeneity was observed in different senescent cell clusters with different transcriptomic profiles, suggesting existence of senescent cell subpopulations with different cell states that likely exhibit distinct function and biology.
“Despite the carefully controlled uniform experimental conditions, significant heterogeneity was observed in different senescent cell clusters with different transcriptomic profiles, suggesting the existence of senescent cell subpopulations with different cell states that likely would exhibit distinct function and biology.”
From Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during senescence induction (2025) · cites this paper as [14]
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Progeria accelerates adult stem cell aging
Brunauer, R., & Kennedy, B. K. (2015) · Science · doi:10.1126/science.aac4214 · Cited by 15 · see citing works
Lyu, G., Guan, Y., Zhang, C., Zong, L., Sun, L., Huang, X., Huang, L., Zhang, L., Tian, X., Zhou, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06710-3 · Cited by 13 · see citing works
Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancerPreprint
Tousignant, K. D., Rockstroh, A., Poad, B. L. J., Talebi, A., Young, R. S. E., Fard, A. T., Gupta, R., Zang, T., Wang, C., Lehman, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.08.899609 · Cited by 10 · see citing works
Self-citationDharmaratne, M., Kulkarni, A. S., Fard, A. T., & Mar, J. C. (2022) · GigaScience · doi:10.1093/gigascience/giac126 · Cited by 8 · see citing works
Self-citationDerivation of human embryonic stem cell line Genea022
Dumevska, B., Bosman, A., McKernan, R., Schmidt, U., & Peura, T. T. (2016) · Stem Cell Research · doi:10.1016/j.scr.2016.02.011 · Cited by 6 · see citing works
Xue, N., Li, D., Wang, D.-K., Fu, J., & Ju, X. (2012) · PubMed · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Neri, F., Zheng, S., Watson, M. A., Desprez, P. Y., Gerencser, A. A., Campisi, J., Wirtz, D., Wu, P.-H., & Schilling, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.22.600200 · Cited by 7 · see citing works
Neri, F., Zheng, S., Watson, M. A., Desprez, P.-Y., Gerencser, A. A., Campisi, J., Wirtz, D., Wu, P.-H., & Schilling, B. (2025) · Aging · doi:10.18632/aging.206299 · Cited by 6 · see citing works
Pietrogrande, G., Shaker, M. R., Stednitz, S. J., Soheilmoghaddam, M., Aguado, J., Morrison, S. D., Zambrano, S., Tabassum, T., Javed, I., Cooper-White, J. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.29.555421
Self-citation
- Authors
- Fard, A. T., Leeson, H., Perez, J. A., Pietrogrande, G., Power, D., Inclan, C. L. G., Zheng, H., Nelson, C., Soheilmoghaddam, F., Glass, N., Dharmaratne, M., Watson, E. R., Lu, J., Martin, S., Pickett, H., Cooper-White, J., Wolvetang, E., & Mar, J. C.
- Affiliations
- The University of Queensland (AU)
- The University of Sydney (AU)
- Corresponding author
- Wolvetang, Ernst
- Mar, Jessica C.
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-01-27
- OpenAlex
- W4225077734
- Cited by
- 3
- References
- 92
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Health Sciences › Medicine › Genetics › Mesenchymal stem cell research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- replicative senescence, mesenchymal stem cells, senescence-associated secretory phenotype, senescence escape, oncogene expression, aging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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