Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

Clearance of p21 highly expressing senescent cells accelerates cutaneous wound healing

Nature Aging volume 5, pages 21–27 (2025)Cite this article

Subjects

Abstract

While senescent cells have detrimental roles in several contexts, they are highly heterogeneous. p16 highly expressing senescent cells have been reported to exert beneficial functions in wound healing. Here we use Xenium spatial transcriptomics to identify a distinct p21 highly expressing senescent population induced on wounding, with a pro-inflammatory profile. We find that clearing p21 highly expressing cells expedites wound closure and is partially mediated by NF-κB inhibition, thus enhancing our understanding of the multifaceted functions of senescence in tissue remodeling.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: p21high cells accumulate during cutaneous wound healing.
Fig. 2: Clearance of p21high cells accelerates wound healing.

Similar content being viewed by others

Data availability

The bulk RNA-seq and Xenium data have been deposited in the Gene Expression Omnibus under accession nos. GSE278527 and GSE278611, respectively. The data that support the findings of this Article are available from the corresponding author upon reasonable request. Source data are provided with this paper.

References

  1. Gasek, N. S., Kuchel, G. A., Kirkland, J. L. & Xu, M. Strategies for targeting senescent cells in human disease. Nat. Aging 1, 870–879 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Demaria, M. et al. An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA. Dev. Cell 31, 722–733 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Baker, D. J. et al. Naturally occurring p16Ink4a-positive cells shorten healthy lifespan. Nature 530, 184–189 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Cohn, R. L., Gasek, N. S., Kuchel, G. A. & Xu, M. The heterogeneity of cellular senescence: insights at the single-cell level. Trends Cell Biol. 33, 9–17 (2023).

    Article  CAS  PubMed  Google Scholar 

  5. Wang, B. et al. An inducible p21-Cre mouse model to monitor and manipulate p21-highly-expressing senescent cells in vivo. Nat. Aging 1, 962–973 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang, L. et al. Targeting p21Cip1 highly expressing cells in adipose tissue alleviates insulin resistance in obesity. Cell Metab. 34, 75–89 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Wang, B. et al. Intermittent clearance of p21-highly-expressing cells extends lifespan and confers sustained benefits to health and physical function. Cell Metab. 36, 1795–1805 (2024).

    Article  CAS  PubMed  Google Scholar 

  8. Zhang, X. et al. Characterization of cellular senescence in aging skeletal muscle. Nat. Aging 2, 601–615 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Andrade, A. M. et al. Role of senescent cells in cutaneous wound healing. Biology 11, 1731 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nair, M. G. et al. Alternatively activated macrophage-derived RELM-α is a negative regulator of type 2 inflammation in the lung. J. Exp. Med. 206, 937–952 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ohtani, N. et al. Visualizing the dynamics of p21Waf1/Cip1 cyclin-dependent kinase inhibitor expression in living animals. Proc. Natl Acad. Sci. USA 104, 15034–15039 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xue, M. & Jackson, C. J. Extracellular matrix reorganization during wound healing and its impact on abnormal scarring. Adv. Wound Care 4, 119–136 (2015).

    Article  Google Scholar 

  13. Hazell, G. G. J. et al. PI16 is a shear stress and inflammation-regulated inhibitor of MMP2. Sci. Rep. 6, 39553 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hsu, I. et al. Serpina3n accelerates tissue repair in a diabetic mouse model of delayed wound healing. Cell Death Dis. 5, e1458 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Feng, X. et al. CCL6 promotes M2 polarization and inhibits macrophage autophagy by activating PI3-kinase/Akt signalling pathway during skin wound healing. Exp. Dermatol. 32, 403–412 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, Y. et al. S100A4 silencing facilitates corneal wound healing after alkali burns by promoting autophagy via blocking the PI3K/Akt/mTOR signaling pathway. Invest. Ophthalmol. Vis. Sci. 61, 19 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jozic, I. et al. Pharmacological and genetic inhibition of caveolin-1 promotes epithelialization and wound closure. Mol. Ther. 27, 1992–2004 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Elenius, V., Götte, M., Reizes, O., Elenius, K. & Bernfield, M. Inhibition by the soluble syndecan-1 ectodomains delays wound repair in mice overexpressing syndecan-1. J. Biol. Chem. 279, 41928–41935 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Takizawa, C. et al. Relationship between gene expression associated with cellular senescence in cells from discarded wound dressings and wound healing: a retrospective cohort study. J. Tissue Viability, https://doi.org/10.1016/j.jtv.2024.07.014 (2024).

    Article  PubMed  Google Scholar 

  20. Chandra, A. et al. Targeted clearance of p21- but not p16-positive senescent cells prevents radiation-induced osteoporosis and increased marrow adiposity. Aging Cell 21, e13602 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Voehringer, D., Liang, H.-E. & Locksley, R. M. Homeostasis and effector function of lymphopenia-induced "memory-like" T cells in constitutively T cell-depleted mice. J. Immunol. 180, 4742–4753 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Heise, N. et al. Germinal center B cell maintenance and differentiation are controlled by distinct NF-κB transcription factor subunits. J. Exp. Med. 211, 2103–2118 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yu, G. T. et al. Clinicopathological and cellular senescence biomarkers in chronic stalled wounds. Int. J. Dermatol. 63, 1227–1235 (2024).

    Article  CAS  PubMed  Google Scholar 

  25. Stojadinovic, O. et al. Molecular pathogenesis of chronic wounds: the role of β-catenin and c-myc in the inhibition of epithelialization and wound healing. Am. J. Pathol. 167, 59–69 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Saul, D. et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat. Commun. 13, 4827 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Joost, S. et al. The molecular anatomy of mouse skin during hair growth and rest. Cell Stem Cell 26, 441–457 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank F. Kaczynski for assistance and colleagues in the UConn Center on Aging for discussion, the UConn’s Center for Genome Innovation genomics core for help with the bulk RNA-seq and Z. Hao for help with histology. This work was supported in part by an NIH F30 Ruth L. Kirschstein Predoctoral Fellowship to N.S.G., an Hevolution/AFAR New Investigator Award in Biology and Geroscience Research to M.X., and NIH grant nos. AG076642, AG079753, AG066679, AG068860 and AG072374 to M.X., no. AG067988 to R.S., no. CA034196 to W.F.F., no. AG013925 to J.L.K. and no. AG082919 to S.P.W.

Author information

Author notes

  1. These authors contributed equally: Nathan S. Gasek, Pengyi Yan, Junyu Zhu.

Authors and Affiliations

  1. UConn Center on Aging, UConn Health, Farmington, CT, USA

    Nathan S. Gasek, Pengyi Yan, Junyu Zhu, Taewan Kim, Lichao Wang, Binsheng Wang, Mingda Sun, Chun Guo, Billy Huggins, Yueying Zhou, Vojtech Parizek & Ming Xu

  2. Department of Genetics and Genome Sciences, UConn Health, Farmington, CT, USA

    Nathan S. Gasek, Pengyi Yan, Junyu Zhu, Taewan Kim, Lichao Wang, Binsheng Wang, Billy Huggins & Ming Xu

  3. Department of Dermatology, Mayo Clinic, Rochester, MN, USA

    K-Raman Purushothaman & Saranya P. Wyles

  4. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    William F. Flynn

  5. Department of Surgery, UConn Health, Farmington, CT, USA

    Roshanak Sharafieh

  6. Xiangya Stomatological Hospital, Central South University, Changsha, China

    Yueying Zhou

  7. Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Tamar Tchkonia & James L. Kirkland

  8. Center for Advanced Gerotherapeutics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Tamar Tchkonia & James L. Kirkland

  9. Division of General Internal Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    James L. Kirkland

Authors
  1. Nathan S. Gasek
    View author publications

    Search author on:PubMed Google Scholar

  2. Pengyi Yan
    View author publications

    Search author on:PubMed Google Scholar

  3. Junyu Zhu
    View author publications

    Search author on:PubMed Google Scholar

  4. K-Raman Purushothaman
    View author publications

    Search author on:PubMed Google Scholar

  5. Taewan Kim
    View author publications

    Search author on:PubMed Google Scholar

  6. Lichao Wang
    View author publications

    Search author on:PubMed Google Scholar

  7. Binsheng Wang
    View author publications

    Search author on:PubMed Google Scholar

  8. William F. Flynn
    View author publications

    Search author on:PubMed Google Scholar

  9. Mingda Sun
    View author publications

    Search author on:PubMed Google Scholar

  10. Chun Guo
    View author publications

    Search author on:PubMed Google Scholar

  11. Billy Huggins
    View author publications

    Search author on:PubMed Google Scholar

  12. Roshanak Sharafieh
    View author publications

    Search author on:PubMed Google Scholar

  13. Yueying Zhou
    View author publications

    Search author on:PubMed Google Scholar

  14. Vojtech Parizek
    View author publications

    Search author on:PubMed Google Scholar

  15. Tamar Tchkonia
    View author publications

    Search author on:PubMed Google Scholar

  16. James L. Kirkland
    View author publications

    Search author on:PubMed Google Scholar

  17. Saranya P. Wyles
    View author publications

    Search author on:PubMed Google Scholar

  18. Ming Xu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.X., N.S.G. and P.Y. conceived and designed the study. N.S.G., P.Y., T.K., L.W., B.W., M.S., C.G., Y.Z., V.P. and B.H. carried out the mouse experiments. M.X., N.S.G., P.Y., J.Z. and W.F.F. carried out the Xenium and bioinformatic analyses. K.P. and S.P.W. carried out the histology work. R.S., T.T., J.L.K. and S.P.W. prepared the paper. M.X., N.S.G. and P.Y. wrote the paper. M.X. supervised the analysis of the experiments and the preparation of the paper.

Corresponding author

Correspondence to Ming Xu.

Ethics declarations

Competing interests

M.X., T.T. and J.L.K. report a competing interest regarding a Mayo Clinic pending patent for the p21Cre mouse model. The other authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Manuel Collado and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 p21 mRNA and protein expression in P mice skins before and after wound injury.

(a) Relative p21 mRNA level in the skins of indicated groups (P-Unwounded without TAM: n = 3, P-Wounded without TAM: n = 4, P-Wounded with TAM: n = 4. n refers to the number of mice). p21 mRNA levels were normalized to Tbp and are presented as fold changes relative to the P-unwounded without TAM group. (b) Western blot showing p21 and β-Actin protein levels in the indicated groups. p21 and β-actin were run on separate gels. (c) Quantification of western blot bands from (b) showing the relative intensity of p21 protein levels normalized to β-Actin. Data are presented as fold changes relative to the P-unwounded without TAM group. P-Unwounded without TAM: n = 3, P-Wounded without TAM: n = 4, P-Wounded with TAM: n = 4. n refers to the number of mice. Results were shown as means ± SEM. Two-tailed, unpaired Student’s t-test.

Source data

Extended Data Fig. 2 Xenium analysis for wound healing.

(a) Heatmap of the top differentially expressed genes defining each cell type cluster. (b) Dimensionality reduction plot (UMAP) of all captured cells from 4 unwounded and 5 wounded skin tissues. (c) Images of all samples for Xenium analysis. (Above) H&E staining. (Below) 11 cell type clusters mapped overlayed on the H&E section. The Xenium experiment was performed once using a tissue microarray including skin samples from four unwounded PT mice and five wounded PT mice.

Extended Data Fig. 3 The distribution of cell types in wounded and unwounded samples.

(a) The cell numbers per 1 mm2 within each cluster for unwounded (n = 4) and wounded (n = 5) tissues. (b) The representative images of the distribution of 11 cell types in unwounded and wounded PT tissues. Results were shown as means ± SEM. Two-tailed, unpaired Student’s t-test.

Source data

Extended Data Fig. 4 p21high and p16high cells are distinct.

(a) UMAP plots showing tdTomato expression levels. (b) UMAP plots showing p16 expression levels. (c) Bubble plots displaying gene enrichment in p21+ cells versus p21- cells in wounded PT samples across 4 cell types. Genes with an adjusted p value less than 0.05 are shown. (d) Bubble plots displaying gene enrichment in p16+ cells versus p16- cells in wounded PT samples across 4 cell types. Two-side MAST test was used for c and d. Multiple comparisons were adjusted using False Discovery Rate (FDR) correction. Genes with an adjusted p value less than 0.05 are shown.

Extended Data Fig. 5 Colocalization of p21+ and tdTomato+ cells.

(a) Representative images of immunofluorescence staining of tdTomato and p21 of wounded PT mice skins 6 days post injury. (b) The percentage of tdT+ p21+ and tdT+ p21- cells per field (averaged from 3 random fields) in wounded PT (n = 4) skins 6 days post injury. Results were shown as means ± SEM. (c) The spatial distribution of tdT+ cells (up) and p21+ cells (down) in wounded tissues. Black dashed line represents the wound margins.

Source data

Extended Data Fig. 6 Senescent features of tdTomato+ cells in PT mice skin after wound injury.

(a) Representative images of immunofluorescence staining of tdTomato of unwounded and wounded PT mice skins 2 days post injury. (b) The percentage of tdT+ cells per field (averaged from 3 random fields) in unwounded PT (n = 3) and in both the inside and outside wounded areas of wounded PT (n = 3) skins. (c) Representative images of immunofluorescence staining of tdTomato and γH2A.X of wounded PT skins 2 days post injury. (d) The number of γH2A.X foci in each tdT+ (n = 81) and tdT- cells (n = 267) from wounded PT skins 2 days post injury. (e) The proportion of tdT+ and tdT- cells with at least one γH2A.X foci in wounded PT mice skin samples (n = 3) 2 days post injury. (f-h) Representative images (f), cell size distribution (g) and average cell size of tdT+ and tdT- cells from five wounded PT skins 2 days post injury. The gating details can be found in the supplementary information file. Results were shown as means ± SEM. Two-tailed, unpaired Student’s t-test: b (PT-unwounded versus PT-wounded inside). Two-tailed, paired Student’s t-test: b (PT-wounded inside versus PT-wounded outside), e and h. Two-tailed, Mann-Whitney U test: d.

Source data

Extended Data Fig. 7 Clearance of p21high cells accelerates wound healing in female mice.

(a) The wound closure curves of PT female mice treated with TAM (n = 8) or corn oil (n = 7) daily from day 1 to day 5 post-injury. Wound area was measured at the indicated days post injury as a percent of initial (Day 0) wound size. (b) The wound closure curve AUCs of corn oil treated PT (n = 7) and TAM treated PT (n = 8) female mice. (c) The wound closure curves of PT (n = 11) and PTD (n = 9) male mice treated with TAM daily from day 1 to day 5 post-injury. Wound area was measured at the indicated days post injury as a percent of initial (Day 0) wound size. (d) The wound closure curve AUCs of PT (n = 11) and PTD (n = 9) male mice. (e) The wound closure curve of PT (n = 16) and PTD (n = 15) female mice with TAM daily from day 1 to day 5 post-injury. Wound area was measured at the indicated days post injury as a percent of initial (Day 0) wound size. P value: Day 3 (0.0045), Day 4 (0.0043), Day 5 (0.0111), Day 7 (0.0269), Day 10 (0.0132). (f) The wound closure curve AUCs of PT (n = 16) and PTD (n = 15) female mice. (g) Bulk RNAseq was performed on PTD wound tissues (n = 5) and PT unwound tissues (n = 3) at the indicated time point. Enriched hallmark gene sets in wound PTD tissues versus unwound tissues with False Discovery Rate (FDR) < 0.05 are shown and considered as statistically significant. Results were shown as means ± SEM. * p < 0.05, ** p < 0.01. Two-tailed, unpaired Student’s t-test.

Source data

Supplementary information

Supplementary Information

Gating information for Extended Data Fig. 6g and Supplementary Discussion.

Reporting Summary

Source data

Source Data

Unprocessed immunoblots.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 1.

Statistical source data.

Source Data Extended Data Fig. 3.

Statistical source data.

Source Data Extended Data Fig. 5.

Statistical source data.

Source Data Extended Data Fig. 6.

Statistical source data.

Source Data Extended Data Fig. 7.

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gasek, N.S., Yan, P., Zhu, J. et al. Clearance of p21 highly expressing senescent cells accelerates cutaneous wound healing. Nat Aging 5, 21–27 (2025). https://doi.org/10.1038/s43587-024-00755-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43587-024-00755-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing