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Mechanisms of stretch-mediated skin expansion at single-cell resolution

Abstract

The ability of the skin to grow in response to stretching has been exploited in reconstructive surgery1. Although the response of epidermal cells to stretching has been studied in vitro2,3, it remains unclear how mechanical forces affect their behaviour in vivo. Here we develop a mouse model in which the consequences of stretching on skin epidermis can be studied at single-cell resolution. Using a multidisciplinary approach that combines clonal analysis with quantitative modelling and single-cell RNA sequencing, we show that stretching induces skin expansion by creating a transient bias in the renewal activity of epidermal stem cells, while a second subpopulation of basal progenitors remains committed to differentiation. Transcriptional and chromatin profiling identifies how cell states and gene-regulatory networks are modulated by stretching. Using pharmacological inhibitors and mouse mutants, we define the step-by-step mechanisms that control stretch-mediated tissue expansion at single-cell resolution in vivo.

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Fig. 1: Inflated hydrogel mediates skin expansion.
Fig. 2: Clonal analysis of epidermal stem cells during stretch-mediated skin expansion.
Fig. 3: Transcriptional and chromatin remodelling associated with stretch-mediated skin expansion.
Fig. 4: Molecular regulation of stretch-mediated skin expansion.

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Data availability

Data associated with this study have been deposited in the NCBI Gene Expression Omnibus under accession numbers GSE126231, GSE126734 and GSE146637, respectively, for the microarray, ATAC-seq and scRNA-seq. Data supporting the findings of this study are available within the Article (and its Supplementary Information files). Source data are provided with this paper.

Code availability

Custom computer code and algorithms used to generate results that are reported in the paper are available within the article (and its Supplementary Information files) and from the corresponding authors on reasonable request. The code used for the modelling of the clonal data has been deposited in GitHub (available at https://github.com/BenSimonsLab/Aragona_Nature_2020).

References

  1. Zöllner, A. M., Holland, M. A., Honda, K. S., Gosain, A. K. & Kuhl, E. Growth on demand: reviewing the mechanobiology of stretched skin. J. Mech. Behav. Biomed. Mater. 28, 495–509 (2013).

    Article  PubMed  Google Scholar 

  2. Le, H. Q. et al. Mechanical regulation of transcription controls Polycomb-mediated gene silencing during lineage commitment. Nat. Cell Biol. 18, 864–875 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Nava, M. M. et al. Heterochromatin-driven nuclear softening protects the genome against mechanical stress-induced damage. Cell 181, 800–817 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Iskratsch, T., Wolfenson, H. & Sheetz, M. P. Appreciating force and shape—the rise of mechanotransduction in cell biology. Nat. Rev. Mol. Cell Biol. 15, 825–833 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. LeGoff, L. & Lecuit, T. Mechanical forces and growth in animal tissues. Cold Spring Harb. Perspect. Biol. 8, a019232 (2016).

    Article  PubMed Central  Google Scholar 

  6. Kirby, T. J. & Lammerding, J. Emerging views of the nucleus as a cellular mechanosensor. Nat. Cell Biol. 20, 373–381 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Vining, K. H. & Mooney, D. J. Mechanical forces direct stem cell behaviour in development and regeneration. Nat. Rev. Mol. Cell Biol. 18, 728–742 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Blanpain, C. & Fuchs, E. Plasticity of epithelial stem cells in tissue regeneration. Science 344, 1242281 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Obdeijn, M. C., Nicolai, J. P. & Werker, P. M. The osmotic tissue expander: a three-year clinical experience. J. Plast. Reconstr. Aesthet. Surg. 62, 1219–1222 (2009).

    Article  PubMed  Google Scholar 

  10. Wickström, S. A. & Niessen, C. M. Cell adhesion and mechanics as drivers of tissue organization and differentiation: local cues for large scale organization. Curr. Opin. Cell Biol. 54, 89–97 (2018).

    Article  PubMed  CAS  Google Scholar 

  11. Yonemura, S., Wada, Y., Watanabe, T., Nagafuchi, A. & Shibata, M. α-Catenin as a tension transducer that induces adherens junction development. Nat. Cell Biol. 12, 533–542 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Clayton, E. et al. A single type of progenitor cell maintains normal epidermis. Nature 446, 185–189 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Mascré, G. et al. Distinct contribution of stem and progenitor cells to epidermal maintenance. Nature 489, 257–262 (2012).

    Article  ADS  PubMed  CAS  Google Scholar 

  14. Sánchez-Danés, A. et al. Defining the clonal dynamics leading to mouse skin tumour initiation. Nature 536, 298–303 (2016).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  15. Lim, X. et al. Interfollicular epidermal stem cells self-renew via autocrine Wnt signaling. Science 342, 1226–1230 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rompolas, P. et al. Spatiotemporal coordination of stem cell commitment during epidermal homeostasis. Science 352, 1471–1474 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mesa, K. R. et al. Homeostatic epidermal stem cell self-renewal is driven by local differentiation. Cell Stem Cell 23, 677–686 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aragona, M. et al. Defining stem cell dynamics and migration during wound healing in mouse skin epidermis. Nat. Commun. 8, 14684 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  19. Eferl, R. & Wagner, E. F. AP-1: a double-edged sword in tumorigenesis. Nat. Rev. Cancer 3, 859–868 (2003).

    Article  CAS  PubMed  Google Scholar 

  20. Botchkarev, V. A. & Flores, E. R. p53/p63/p73 in the epidermis in health and disease. Cold Spring Harb. Perspect. Med. 4, a015248 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Lopez, R. G. et al. C/EBPα and β couple interfollicular keratinocyte proliferation arrest to commitment and terminal differentiation. Nat. Cell Biol. 11, 1181–1190 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Segre, J. A., Bauer, C. & Fuchs, E. Klf4 is a transcription factor required for establishing the barrier function of the skin. Nat. Genet. 22, 356–360 (1999).

    Article  CAS  PubMed  Google Scholar 

  23. Hopkin, A. S. et al. GRHL3/GET1 and trithorax group members collaborate to activate the epidermal progenitor differentiation program. PLoS Genet. 8, e1002829 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Joost, S. et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 3, 221–237 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Joost, S. et al. Single-cell transcriptomics of traced epidermal and hair follicle stem cells reveals rapid adaptations during wound healing. Cell Rep. 25, 585–597 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Aibar, S. & Gonzalez-Blas, C. B. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Rottner, K., Faix, J., Bogdan, S., Linder, S. & Kerkhoff, E. Actin assembly mechanisms at a glance. J. Cell Sci. 130, 3427–3435 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Duda, M. et al. Polarization of myosin ii refines tissue material properties to buffer mechanical stress. Dev. Cell 48, 245–260 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yang, K. et al. YAP and ERK mediated mechanical strain-induced cell cycle progression through RhoA and cytoskeletal dynamics in rat growth plate chondrocytes. J. Orthop. Res. 34, 1121–1129 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Hirata, H. et al. Actomyosin bundles serve as a tension sensor and a platform for ERK activation. EMBO Rep. 16, 250–257 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Panciera, T., Azzolin, L., Cordenonsi, M. & Piccolo, S. Mechanobiology of YAP and TAZ in physiology and disease. Nat. Rev. Mol. Cell Biol. 18, 758–770 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Posern, G. & Treisman, R. Actin’ together: serum response factor, its cofactors and the link to signal transduction. Trends Cell Biol. 16, 588–596 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Rognoni, E. & Walko, G. The roles of YAP/TAZ and the Hippo pathway in healthy and diseased skin. Cells 8, 411 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  35. Luxenburg, C., Pasolli, H. A., Williams, S. E. & Fuchs, E. Developmental roles for Srf, cortical cytoskeleton and cell shape in epidermal spindle orientation. Nat. Cell Biol. 13, 203–214 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Connelly, J. T. et al. Actin and serum response factor transduce physical cues from the microenvironment to regulate epidermal stem cell fate decisions. Nat. Cell Biol. 12, 711–718 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Aragona, M. et al. A mechanical checkpoint controls multicellular growth through YAP/TAZ regulation by actin-processing factors. Cell 154, 1047–1059 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Whitson, R. J. et al. Noncanonical hedgehog pathway activation through SRF–MKL1 promotes drug resistance in basal cell carcinomas. Nat. Med. 24, 271–281 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Miroshnikova, Y. A. et al. Adhesion forces and cortical tension couple cell proliferation and differentiation to drive epidermal stratification. Nat. Cell Biol. 20, 69–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Mamidi, A. et al. Mechanosignalling via integrins directs fate decisions of pancreatic progenitors. Nature 564, 114–118 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  41. Moreno, E., Valon, L., Levillayer, F. & Levayer, R. Competition for space induces cell elimination through compaction-driven ERK downregulation. Curr. Biol. 29, 23–28 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Vasioukhin, V., Degenstein, L., Wise, B. & Fuchs, E. The magical touch: genome targeting in epidermal stem cells induced by tamoxifen application to mouse skin. Proc. Natl Acad. Sci. USA 96, 8551–8556 (1999).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vasioukhin, V., Bauer, C., Degenstein, L., Wise, B. & Fuchs, E. Hyperproliferation and defects in epithelial polarity upon conditional ablation of alpha-catenin in skin. Cell 104, 605–617 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Xin, M. et al. Hippo pathway effector Yap promotes cardiac regeneration. Proc. Natl Acad. Sci. USA 110, 13839–13844 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Damiani, D., Goffinet, A. M., Alberts, A. & Tissir, F. Lack of Diaph3 relaxes the spindle checkpoint causing the loss of neural progenitors. Nat. Commun. 7, 13509 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Tepole, A. B., Gart, M., Purnell, C. A., Gosain, A. K. & Kuhl, E. The incompatibility of living systems: characterizing growth-induced incompatibilities in expanded skin. Ann. Biomed. Eng. 44, 1734–1752 (2016).

    Article  PubMed  Google Scholar 

  49. Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Ellis, S. J. et al. Distinct modes of cell competition shape mammalian tissue morphogenesis. Nature 569, 497–502 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gonzalez-Roca, E. et al. Accurate expression profiling of very small cell populations. PLoS ONE 5, e14418 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  PubMed  CAS  Google Scholar 

  60. Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  62. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge the animal facility at ULB (Erasme campus), the ULB genomic core facility (F. Libert and A. Lefort), J.-M. Vanderwinden and LiMiF for the help with confocal microscopy, T. Van Brussel for help with 10X genomics, B. Gilbert, W. Declercq and P. Vieugue for helping with the TEWL assay, A. De Groote for performing mice perfusion, and colleagues who provided reagents mentioned in the text. scRNA-seq was performed at the Brussels Interuniversity Genomics High Throughput core and the Genomics Core Leuven. C.B. is an investigator of WELBIO. M.A. is supported by a long-term postdoctoral fellowship of the HFSPO (LT000380/2015-L) and an FNRS fellowship. B.D.S. is supported by a Royal Society EP Abraham Research Professorship and a Wellcome Trust Senior Investigator Award (098357/Z/12/Z). S.H. is supported by a long-term fellowship of the HFSPO (LT000092/2016-L). B.D.S and S.H. acknowledge core funding to the Gurdon Institute from the Wellcome Trust (092096) and CRUK (C6946/A14492). A.S., J.V.H. and T.V. are supported by KU Leuven SymBioSys, Stiching Tegen Kanker, FWO postdoctoral fellowship number 12W7318N and Marie Skłodowska-Curie fellowship number 12O5617N. F.T. is a research director at the FNRS. This work was supported by the FNRS, TELEVIE, the PAI programme, a research grant from the Fondation Contre le Cancer, the ULB foundation, the foundation Bettencourt Schueller, the foundation Baillet Latour and a consolidator grant of the European Research Council (ERC-EXPAND, 616333).

Author information

Authors and Affiliations

Authors

Contributions

M.A. and C.B. designed the experiments. M.A., B.D.S. and C.B. performed data analysis. M.M., A.S., J.V.H. and T.V. performed scRNA-seq and analysis. S.H. helped with data analysis. S.D., S.G. and G.L. helped with experiments and animal follow-up. Y.S. and B.S. performed ATAC-seq analysis. C.D. helped with FACS. P.B. and K.V. performed TEM. F.T. contributed with genetic tools. M.A., B.D.S. and C.B. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Benjamin D. Simons or Cédric Blanpain.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Carien Niessen, Nathan Salomonis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 A mouse model of mechanical stretch-mediated skin expansion.

a, Representative photographs of mice with the skin expander immediately after surgery at day (D) D0, D2, D4 and in control (CTRL) condition. Scale bars, 10 mm. The device was implanted on the back skin of the animals, close to the neck where the rigidity of the proximate cervical spines allows the hydrogel to stretch the skin during the inflation of the expander. Control mice were operated upon similarly but without introducing the hydrogel. b, Timeline of the experiment. CD1 mice were operated to place the expander and followed over time. c, Scheme showing the growth of the hydrogel. The arrows indicate the radius of the hemisphere. d, Hydrogel volume (measured by the height of the hydrogel and calculated as the volume of a hemisphere, see Methods, n = 5 D0, n = 13 D0.5, n = 13 D1, n = 13 D2, n = 7 D3, n = 13 D4, n = 10 D6, n = 6 D8, n = 8 D10, n = 5 D14 mice). en, TEM of ultrathin sections of control (e, gj) and expanded (f, kn) epidermis. In e and f, dashed yellow lines denote dermal-epidermal boundary and boxed area in pink, cerulean, orange and green are shown at higher magnification, respectively, in g and k, h and l, i and m, j and n. Scale bars, 5 μm. g, k, Keratin bundles. h, l, Ultrastructural analysis of cell–cell adhesion. i, m, Desmosomes. j, n, Hemidesmodomes. o, Quantification of the intercellular spacing on images as in h and l. Wilcoxon signed-rank test, two-sided. p, Quantification of the width of the desmosomes as in i and m. q, Quantification of the width of the number of hemidesmosomes per μm in j and n. r, TEWL measurements from n = 3 CD1 mice in CTRL and at different time point during expansion. s, Immunohistochemistry for the adherens junctions (AJ) component β-catenin, n = 3 independent experiments. t, v, Representative images of AJ component p120-catenin (t) and E-cadherin (v) colour-coded for the signal intensity with ImageJ. Protein expression is visualized as a colour gradient going from black to yellow, with black as indicator of no expression and yellow as indicator of maximal expression. Scale bars, 10 μm. u, w, Quantification of the average integrated density signal for p120-catenin (u) and E-cadherin (w). Each data point is the average of 3 sections per mouse (n = 3 mice per condition). oq, The quantifications are made on n = 3 different animals per condition on 10 different samples per mouse and represented as mean + s.e.m. gn, Scale bars, 500 nm. d, pr, u, w, Two-tailed Mann–Whitney test, mean + s.e.m.

Source data

Extended Data Fig. 2 Adhesion remodelling and inflammatory response during stretch-mediated skin expansion.

a, c, e, Representative images of the tight junction (TJ) components ZO-1 (a) and Claudin-1 (c) and of Vinculin (e) colour-coded for the signal intensity with ImageJ. Protein expression is visualized as a colour gradient going from black to yellow, with black as indicator of no expression and yellow as indicator of maximal expression. Scale bars, 10 μm. b, d, f, Quantification of the average integrated density signal for ZO-1 (b), Claudin-1 (d) and Vinculin (f). The number of mice per condition is indicated. g, i, Immunostaining for K14 (red), inflammatory cells stained with CD45 (g) and macrophages stained with CD68 (i) (green) and Hoechst for nuclei (blue) on tissue sections. Scale bars, 10 μm. White arrows indicate positive cells, n = 3 independent experiments. h, j, Percentage of CD45 (h) and CD68 (j) positive cells on the total dermal cells quantified based on the nuclear staining, n = 3 mice per condition, mean per mouse + s.e.m. k, mRNA expression analysis for the indicated gene in Untreated (Unt., black) skin and skin treated with Dexamethasone (Dexa., grey). Fold change is expressed compared to one Unt. sample, n = 3 mice per condition, mean per mouse + s.e.m. l, Maximum intensity projection of confocal pictures showing immunostaining for K14 (red), BrdU (green) and Hoechst for nuclei (blue) 4 h following BrdU administration on whole mount epidermis. Scale bars, 10 μm. m, Proportion of basal cells that are BrdU positive (n = 3,694 cells counted from 3 mice for Untreated and n = 3,764 cells from 3 mice for the Dexamethasone treatment). a, c, e, g, i, Dashed lines indicate the basal lamina. b, d, f, m, Two-tailed Mann–Whitney test, mean per mouse + s.e.m.

Source data

Extended Data Fig. 3 Clonal analysis of epidermal stem cells during homeostasis, TPA treatment and stretch-mediated skin expansion.

a, Genetic labelling strategy used to trace K14 IFE SC in the back skin during homeostasis and stretch-mediated tissue expansion. b, Timeline of the experiment. Krt14-creER-RosaConfetti mice were induced with Tamoxifen at 2 months of age and operated upon 3.5 days after to place the expander. The samples were collected 0, 1, 2, 4, 8, 10 and 14 days after surgery. c, Raw distribution of clone size taken from mouse back skin under normal homeostatic conditions (CTRL) at different time points based on basal (top) and total (bottom) cell number. Note that times are calibrated so that the “day 0” time-point is acquired 3.5 days after Tamoxifen injection, requiring effective chase times to be calibrated accordingly, see b. D0: 115 clones from n = 7 mice; D2: 175 clones from n = 7 mice; D4: 136 clones from n = 5 mice; D8: 159 clones from n = 3 mice; D10: 146 clones from n = 3 mice. d, Time line of the experiment to perform clonal tracing upon TPA treatment. Krt14-creER-RosaConfetti mice were induced with Tamoxifen at 2 months of age and after 3.5 days topically treated with 12-O-Tetradecanoylphorbol-13-acetate (TPA) for 2 consecutive days. The samples were collected 1 and 14 days after treatment. e, Maximum intensity projection of representative confocal pictures showing immunostaining for K14 (red) and BrdU (green) following BrdU administration on whole mount epidermis form mice treated with TPA or with vehicle (CTRL). Hoechst nuclear staining in blue. Scale bars, 20 μm. f, Percentage of BrdU positive cells in control and mice treated with TPA at D1 (n = 5). Two-tailed Mann–Whitney test, mean + s.e.m. g, Raw distribution of clone size taken from mouse back skin during TPA treatment (TPA) based on basal (top) and total (bottom) cell number. D1: 85 clones from n = 4 mice; D14: 54 clones from n = 5 mice. h, Raw distribution of clone size taken from mouse back skin under stretch-mediated tissue expansion (EXP) at different time points based on basal (top) and total (bottom) cell number. As with control, note that times are calibrated so that the “day 0” time-point is acquired 3.5 days after Tamoxifen injection, requiring effective chase times to be calibrated accordingly. D2: 231 clones from n = 4 mice; D4: 197 clones from n = 4 mice; D8: 199 clones from n = 4 mice; D10: 157 clones from n = 4 mice. i, Table showing the abundance (raw counts) of clones by their basal and suprabasal cell composition from the CTRL D2 condition (that is, 5 days post-induction), n = 203 clones from 7 mice. j, Table showing the abundance (raw counts) of clones by their basal and suprabasal cell composition from the EXPD2 condition, n = 283 clones from 4 mice. k, Fit of the one-progenitor model to the average size of persisting clones in control conditions based on the basal (black) and total (blue) cell content. Points show data and lines are the results of the fit to a one-compartment model (see Methods). l, m, o, Fit to the one-progenitor cell model. Clone persistence (l), labelled cell fraction (m), and the distribution of basal (upper) and total (lower) clone size (o). Points show data and lines are the results of the fit to a one-progenitor model. km, o, D0: 115 clones from n = 7 mice; D2: 175 clones from n = 7 mice; D4: 136 clones from n = 5 mice; D8: 159 clones from n = 3 mice; D10: 146 clones from n = 3 mice; D14: 195 clones from n = 4 mice. n, p, Sensitivity analysis of the model fits depicted as a map of the total square-differences of the experimental basal/total clone size data and the respective model predictions as a function of the average division time, 1/λ, and the degree of imbalance towards stem cell loss/replacement, r, (see Methods). Panel n shows the results of one-progenitor model and the CTRL data, p shows the results of two-progenitor model and the CTRL data. These results show both the enhanced accuracy of the two-progenitor model over the one-progenitor model, despite involving the same number of fit parameters. km, Mean + s.d. o, Mean + s.e.m.

Source data

Extended Data Fig. 4 Fit of the data to the two-progenitor model.

a, Fit of the model to the clone size distribution under homeostatic control conditions. Note that, with 1/λ = 4.6 days and r = 0.21, the model faithfully reproduces both the exponential-like clone size distribution and the predominance of clones bearing an even number of basal and total cell numbers. Mean + s.e.m. D0: n = 115 clones from 7 mice; D2: n = 175 clones from 7 mice; D4: n = 136 clones from 5 mice; D8: n = 159 clones from 3 mice; D10: n = 146 clones from 3 mice; D14: n = 195 clones from 4 mice. b, Change of division time (1/λ) during stretch-mediated expansion as parameterised from the measured rate of BrdU incorporation, Fig. 1e. c, Change in the probability of symmetric division (parameter, r) during stretch-mediated skin expansion obtained from a fit of the two-compartment model to the clone size data (for details, see Supplementary Note). d, Corresponding fit of the two-compartment model to the clone size distribution during stretch-mediated expansion. The model accurately reproduces both the exponential-like clone size distribution and the predominance of clones bearing an even number of basal and total cell numbers. Notably, the sharp increase in even-sized clones at long times can only be recovered by limiting the frequency of renewing divisions well below that of the control value. Mean + s.e.m. D2: n = 231 clones from 4 mice; D4: n = 197 clones from 4 mice; D8: n = 199 clones from 4 mice; D10: n = 157 clones from 4 mice. e, Fit of the model to the clone size distribution at D14 under TPA treatment. Note that with 1/λ = 2.3 days and r = 0.15, the model faithfully reproduces both the exponential-like clone size distribution and the predominance of clones bearing an even number of basal and total cell numbers. Mean + s.e.m. D1: n = 85 clones from 4 mice; D14: n = 54 clones from 5 mice. f, g, Sensitivity analysis of the model fits depicted as a map of the total square-differences of the experimental basal/total clone size data and the respective model predictions as a function of the average division time, 1/λ, and the degree of imbalance towards stem cell loss/replacement, r, (see Methods). Panels (f) shows the results of the results of two-progenitor model and the EXP data, and (g) shows the results of two-progenitor model and the TPA data. For the EXP data (f), we have imposed the measured relative variation of the proliferation rate (as inferred from BrdU incorporation) (Fig. 1e and panel (b)) and an inferred relative variation of the r parameter as obtained from a model fit (c), while the two parameters in panel (f) represent variation in the net rates. h, Representative orthogonal confocal sections immunostained for Krt14 (red), Krt10 (green) following short-term BrdU (white) incorporation identifying cells biased for renewal (Krt14+Krt10), cells primed for differentiation (Krt14+Krt10+) and differentiated cells (Krt14Krt10+). i, Percentage of the type of divisions in CTRL (108 divisions from n = 4 mice) and EXP D2 (254 divisions from n = 4 mice) based on short-term BrdU tracing and staining as in h. Two-tailed Mann–Whitney test, mean  + s.e.m.

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Extended Data Fig. 5 Genetic signature of TPA-treated and expanded epidermis.

a, Representative FACS plots showing the strategy used to isolate basal cells. Single living cells were gated by debris exclusion (P1), DAPI exclusion (P2), doublet elimination (P3) and basal IFE Integrin-α6high CD34neg cell were sorted (P4). n = 10 independent experiments. b, c, mRNA expression of genes that were upregulated in basal cells at EXP D4 (n = 3) and in cells treated with TPA (n = 2). These genes are related to a generic stress signature (b), regulating ECM remodelling and cytoskeleton, important for cell survival and cell cycle (c). Bars are mean with s.e.m. d, Representative images of Paxillin immunostaining colour-coded for the signal intensity with ImageJ. Protein expression is visualized as a colour gradient going from black to yellow with black as indicator of no expression and yellow as indicator of maximal expression. Scale bars, 10 μm e, Quantification of the average integrated density signal for Paxillin as in d. Each data point is the average of 3 sections per mouse (n = 3 mice per condition). f, Geometric mean fluorescence intensity for the indicated integrin in CTRL (grey, n = 4 mice) and EXP D4 (red, n = 6 mice) from FACS analysis of basal IFE Integrin-α6high CD34neg cells. gk, ATAC-seq profiles showing increasing accessibility of chromatin regions that are specifically remodelled during mechanical expansion (CTRL in grey and EXP D2 in orange). l, Quantification of the number of cells FOSL1+ in the basal layer related to Fig. 3d. m, Immunostaining on skin sections for JUN (white) in control and EXP D4. n, Quantification of the number of cells JUN+ in the basal layer related to m. o, Quantification of the number of cells p63+ in the +1 layer related to Fig. 3e. p, Quantification of the number of cells KLF4+ in the +1 layer related to Fig. 3f. q, Immunohistochemistry on paraffin sections for c-FOS in control and EXPD4. Scale bars, 20 μm. r, Quantification of the number of cells c-FOSL+ in the basal layer related to q. s, Immunofluorescence on tissue sections for pSTAT3 in green and K14 (red) to identified the epidermis. Scale bars, 20 μm. t, Quantification of the number of cells positive for pSTAT3 in the basal layer related to s. l, n, o, p, r, t, 3 sections quantified per n = number of mice and total number of cells indicated in parentheses d, m, q, s, Dashed lines delineate the basal lamina. e, f, l, np, r, t, Two-tailed Mann–Whitney test, Mean + s.e.m.

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Extended Data Fig. 6 Single-cell RNA sequencing clustering analysis.

a, UMAP graphic representation of the CTRL, EXP D1, EXP D4 and TPA single-cell RNA-seq data, showing the graph-based clustering results annotated by cell type. The proliferating IFE stem cells (PROLIF. IFE SCs) are in light blue, the IFE stem cells cluster are in red (IFE SCs#1) and dark red (IFE SCs#2), the IFE committed cells (IFE CCs) cluster is in pink and the differentiated IFE cells (IFE DIFF.) are in green. The differentiated cells from the infundibulum (INF. DIFF.) are in grey, the stem cells of the infundibulum (INF. SCs.) are in black, the proliferating cells of the infundibulum (PROLIF. INF.) are in plum and the sebaceous gland cluster (SG) is in orange. The IFE stress cells (STRESS) are in dark grey and the cluster of stem cells stretch (SCs STRETCH) in yellow. n = 16,651 cells. b, UMAP of the different samples (CTRL, EXP D1, EXP D4, TPA) using the same integrated projection. n = 4,659 cells CTRL, n = 4,934 cells EXP D1, n = 2,716 cells EXP D4, n = 4,342 cells TPA. ck, UMAP plot of the CTRL sample colored by normalized gene expression values for genes identifying the IFE (c) versus infundibulum (d), the sebaceous gland (e) and the proliferating cells (f). Undifferentiated (g) and more differentiated cells (h) in the IFE identified the SCs cluster (i), the CCs cluster (j) and the differentiated stage (k). Gene expression is visualized as a colour gradient going from grey to yellow with grey as indicator of no expression (that is, expression values below or equal to the 50th percentile for that sample) and yellow as indicator of maximal expression. ck, n = 16,651 cells. l, Table showing the specific marker genes used to annotate the different clusters.

Extended Data Fig. 7 Single-cell RNA sequencing clustering analysis on the IFE cells.

a, Integrated UMAP graphic representation of the IFE cells in CTRL, EXP D1, EXP D4 and TPA single-cell RNA-seq data, showing the graph-based clustering results annotated by cell type. The proliferating stem cells (PROLIF.) are in light blue, the stem cells clusters are in red and dark red (SCs#2), the committed cells (CCs) cluster is in pink and the differentiated cells (DIFF.) are in green. The stress cells (STRESS) are in dark grey and the cluster of stem cells stretch (SCs STRETCH) in yellow. n = 12,747 cells. b, UMAP of the different samples (CTRL, EXP D1, EXP D4, TPA). c, Predicted cell-cycle phases assigned using the cyclone function from scran tool and visualized in the UMAP. Cells in G1 are in light blue, cells in G2/M are in orange and cells in S phase are in red. bd, n = 3,142 cells CTRL, n = 3,756 cells EXP D1, n = 2,145 cells EXP D4, n = 3,704 cells TPA. d, Percentage of cells in the different cycling phase calculated on the total number of cells. eh, UMAP plot colored by normalized gene expression values for the indicated gene and in the indicated sample. Gene expression is visualized as a colour gradient going from grey to yellow with grey as indicator of no expression and yellow as indicator of maximal expression. n = 3,142 cells CTRL, n = 3,756 cells EXP D1.

Extended Data Fig. 8 Pseudotime analysis for single-cell RNA sequencing.

a, UMAP plots coloured by the degree of regulon activation for TFs differentially activated (AUC rank-sum test FDR corrected p-value <0.05) in the different conditions. Colour scaling represents the normalized AUC value of target genes in the regulon being expressed as computed by SCENIC. b, Heat map representation of the top 20 gene expression changes along the inferred pseudotime trajectory computed with Slingshot for the CTRL IFE. c, Heat map representation of the top 20 gene expression changes along the inferred pseudotime homeostatic trajectory computed with Slingshot for the EXP D1 IFE. d, Heat map representation of the top 20 gene expression changes along the inferred pseudotime trajectory computed with Slingshot characterizing the stress state for the EXP D1 IFE. bd, Columns represent cells ordered by their position along the pseudotime trajectory; rows represent genes whose expression profiles show highest correlation (FDR-correted p-value <0.01) with the pseudotime variable, calculated using a generalized additive model (GAM). The colour scaling of the cells represents the normalized expression value of a gene in a particular cell, scaled by Z-score. ad, n = 3,142 cells CTRL, n = 3,756 cells EXP D1.

Extended Data Fig. 9 Cell contractility in stretch-mediated tissue expansion.

a, Scheme of the genetic strategy to delete Diaph3 in the epidermis. b, Protocol to delete Diaph3 during stretch-mediated tissue expansion. c, Orthogonal views of confocal analysis of immunostaining for K14 (red) marking basal cells and Phalloidin (green) to visualize F-actin and Hoechst for nuclei (blue) in whole mounts of IFE in CTRL from a CD1 mouse, EXP D1 from a CD1 or Krt14-cre-DIAPH3fl/fl (Diaph3 cKO) mouse. Scale bars, 10 μm. d, Percentage of cells with F-actin fibres in the apical side of basal cells related to c (n = 4 mice per condition). e, Orthogonal views of confocal analysis of immunostaining for K14 (red) marking basal cells, BrdU (green) and Hoechst for nuclei (blue) in whole mounts of IFE from Krt14-cre-DIAPH3fl/+ (Diaph3 WT) and Krt14-cre-DIAPH3fl/fl (Diaph3 cKO) mice during expansion. Scale bars, 20 μm. Epidermal Diaph3 cKO were born at a Mendelian ratio and did not present obvious pathological phenotypes. n = 3 independent experiments. f, Immunostaining for the basal marker K14 (red) and the suprabasal markers K1 and K10 (green) in Diaph3 WT and Diaph3 cKO mice in EXP D2 and EXP D4. Scale bars, 20 μm. g, Epidermal thickness of Diaph3 WT and Diaph3 cKO mice in EXP D2 and EXP D4 (three measurements taken with ImageJ on two sections per mouse, n = at least 3 mice for the different conditions). h, Scheme of the genetic strategy to delete Myh9 in the epidermis. i, Protocol to delete Myh9 during stretch-mediated tissue expansion. j, Immunohistochemistry for MYH9 in untreated and Tamoxifen induced Krt14-creER-MYH9fl/fl mice. Scale bars, 20 μm. n = 3 independent experiments. k, Orthogonal views of confocal analysis of immunostaining for K14 (white), BrdU (red) and Hoechst for nuclei (blue) in whole mounts of IFE in Myh9 WT and Myh9 cKO mice during expansion. Scale bars, 20 μm. l, Epidermal thickness of Myh9 WT and Myh9 cKO mice in EXP D2 and EXP D4 (three measurements taken with ImageJ on two sections per mouse, n = at least 3 mice for the different conditions). mr, Analysis of adherens junctions in Diaph3 cKO and Myh9 cKO mice. m, o, q, Representative images of adherens junction (AJ) component α-catenin (m), the α18 tension sensitive form of α-catenin (α18-catenin) (o) and Vinculin (q), colour-coded for the signal intensity with ImageJ. Protein expression is visualized as a colour gradient going from black to yellow, with black as indicator of no expression and yellow as indicator of maximal expression. Dashed lines indicate the basal lamina. Scale bars, 10 μm. n, p, r, Quantification of the average integrated density signal for α-catenin (n), α18-catenin (p) and Vinculin (r). Each data point is the average of 3 sections per mouse (n = 5 mice per condition). d, g, l, n, p, r, Two-tailed Mann–Whitney test, mean + s.e.m.

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Extended Data Fig. 10 MEK/ERK/AP1, YAP-TAZ and MAL/SRF regulate stretch-mediated proliferation.

a, b, Protocol for Trametinib or Pimasertib treatment in CD1 mice operated to place the expander and scarified at D2, D4 (a) and D8 (b) after surgery. c, Immunohistochemistry for pERK on paraffin sections of epidermis form CD1 mice untreated or treated with the indicated drug at EXP D2. d, Quantification of the proportion of BrdU positive cells during expansion at the indicated time point in CD1 mice untreated or treated with Trimatenib or Pimasertib (n = at least 3 mice per condition as indicated, total number of cells analysed indicated in parentheses). e, f, Immunohistochemistry for FOSL1 (e) and immunofluorescence for JUN (f) on sections of epidermis from CD1 mice untreated or treated with the indicated drug at EXP D2. g, Epidermal thickness measured with ImageJ on tissue sections at EXP D8 in CD1 mice untreated or treated with the indicated drug (n = 5 mice untreated, n = 4 mice Trametinib, n = 3 mice Pimasertib, 3 measurements on at least 2 sections per mouse). h, Immunostaining (white) for YAP1 on skin sections in the control and in EXP D1. White arrows indicate nuclear localization. i, Quantification of YAP1 subcellular localization, bars and error bars represent the mean and s.e.m. Nuclear (N) > Cytosplasm (C), more YAP1 in nucleus than in cytoplasm, N = C, similar level of YAP1 in nucleus than in cytoplasm, N < C, less YAP1 in nucleus than in cytoplasm (n = 150 cells for all samples except n = 120 for EXP D8). j, Quantification of MAL subcellular localization, presented as mean and s.e.m. N > C, more MAL in nucleus than in cytoplasm, N = C, similar level of MAL in nucleus than in cytoplasm, N < C, less MAL in nucleus than in cytoplasm (n = 150 cells for all samples except n = 120 for EXP D8). k, l, Immunostaining (white) for MAL (k) and JUN (l) on skin sections in the control and in EXP D1. White arrows indicate nuclear localization. m, n, Scheme of the genetic strategy to delete YAP-TAZ in the epidermis (m) and protocol to delete YAP and TAZ in stretch-mediated tissue expansion (n). o, Immunohistochemistry for YAP (top) and TAZ (bottom) in Krt14-creER-YAP-TAZfl/fl mice before and after Tamoxifen administration. p, Orthogonal views of confocal analysis of immunostaining for K14 (red) marking basal cells, BrdU (green) and Hoechst for nuclei (blue) in whole mounts of IFE in YAP-TAZfl/fl (YAP-TAZ WT) or Krt14-creER-YAP-TAZfl/fl (YAP-TAZ cKO) mice at the indicated time point following expansion. q, Epidermal thickness of YAP-TAZ WT and YAP-TAZ cKO mice in EXP D2 and EXP D4 (three measurements taken with ImageJ on two sections per mouse, n = at least 4 mice per condition). r, Protocol to inhibit MAL with the CCG203971 small molecule during stretch-mediated tissue expansion. s, Quantification of MAL subcellular localization in EXP D2 and EXP D4 mice treated or not with the MAL inhibitor. N > C, more MAL in nucleus than in cytoplasm, N = C, similar level of MAL in nucleus than in cytoplasm, N < C, less MAL in nucleus than in cytoplasm (n = 150 cells per condition). Data are presented as mean and s.e.m. t, Orthogonal views of confocal analysis of immunostaining for K14 (red) marking basal cells, BrdU (green) and Hoechst for nuclei (blue) in whole mounts of IFE in mice treated with the MAL inhibitor or with vehicle control (untreated) at the indicated time point following expansion. u, Epidermal thickness of CD1 mice in EXP D2 and EXP D4 treated or not with the MAL inhibitor (three measurements taken with ImageJ on two sections per mouse, n = 3 for untreated mice, n = 5 for treated animals). c, e, f, h, k, l, o, p, t, Scale bars, 20 μm. n = 3 independent experiments. c, e, f, h, k, l, o, Dashed lines delineate the basal lamina. d, g, q, u, Two-tailed Mann–Whitney test, mean + s.e.m.

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Extended Data Fig. 11 Pathways associated with stretch-mediated tissue expansion.

a, Protocol used to delete YAP and TAZ and to inhibit MAL with CCG203971 treatment in Krt14-creER-YAP-TAZfl/fl mice in EXP D2. b, Orthogonal views of immunostaining for K14 (red) to mark basal cells, BrdU (green) and Hoechst for nuclei (blue) on whole mounts of IFE in YAP-TAZ WT untreated mice and YAP-TAZ cKO mice treated with the MAL inhibitor at 2 days after the expander placement. Scale bars, 20 μm. c, Proportion of BrdU positive cells in untreated YAP-TAZ WT mice (48,615 cells from 3 mice) and in YAP-TAZ cKO mice treated with the MAL inhibitor (78,282 cells from 5 mice). Two-tailed Mann–Whitney test, mean + s.e.m. d, Epidermal thickness of YAP-TAZ WT untreated (n = 3) and YAP-TAZ cKO treated with the MAL inhibitor in EXP D2 (n = 5), three measurements taken with ImageJ on two sections per mouse. Two-tailed Mann–Whitney test, mean + s.e.m. e, f, Quantification of YAP1 (e) and MAL (f) subcellular localization, presented as mean and s.e.m. in CTRL and EXP D2. N > C, more protein in nucleus than in cytoplasm, N = C, similar level of protein in nucleus than in cytoplasm, N < C, less protein in nucleus than in cytoplasm (n = 150 cells per condition). g, h, Percentage of the type of divisions in CTRL (g) and EXP D2 (h) in YAP-TAZ WT mice and YAP-TAZ cKO mice based on the short-term BrdU tracing and staining as in Extended Data Fig. 4h. i, j, Percentage of the type of divisions in CTRL (i) and EXP D2 (j) in Untreated mice and with MAL inhibitor based on the short-term BrdU tracing and staining as in Extended Data Fig. 4h. k, l, Percentage of the type of divisions in CTRL (k) and EXP D2 (l) in Untreated mice and with Trametinib based on the short-term BrdU tracing and staining as in Extended Data Fig. 4h. gl, The number of counted divisions is indicated in parenthesis from n = number of mice. Two-tailed Mann–Whitney test, mean + s.e.m.

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Extended Data Fig. 12 Single-cell data analysis after MEK and MAL inhibition.

a, Average size of persisting clones in mice treated with MAL inhibitor during expansion, based on the basal (black) and total (blue) cell content. Points show data and lines denote the results from the fit to the two-compartment model (see main text and Methods). D0: n = 115 clones from 7 mice; D2: n = 86 clones from 3 mice; D4: n = 83 clones from 3 mice. b, Average size of persisting clones in mice treated with Trametinib during expansion, based on the basal (black) and total (blue) cell content. Points show data and lines denote the results from the fit to the two-compartment model (see main text and Methods). D0: n = 115 clones from 7 mice; D2: n = 84 clones from 3 mice; D4: n = 80 clones from 4 mice; D8: n = 81 clones from 3 mice. c, Fit of the model to the clone size distribution during expansion upon MAL inhibition with 1/λ = 3.8 days and r = 0.08. D0: n = 115 clones from 7 mice; D2: n = 86 clones from 3 mice; D4: n = 83 clones from 3 mice. d, Least-square values indicate the sensitivity of the fit parameters in c. e, Fit of the model to the clone size distribution during expansion upon Trametininb treatment with 1/λ = 4.3 days and r = 0.17. D0: n = 115 clones from 7 mice; D2: n = 84 clones from 3 mice; D4: n = 80 clones from 4 mice; D8: n = 81 clones from 3 mice. f, Least-square values indicate the sensitivity of the fit parameters in e. g, Predicted cell-cycle phases assigned using the cyclone function from scran tool of EXP D1 Untreated IFE, EXP D2 IFE treated with the MAL inhibitor and EXP D2 IFE treated with Trametinib. Cells in G1 are in grey, cells in G2/M are in blue and cells in S phase are in red. The percentage of cells in the different cycling phases is calculated on the total number of cells. h, Table showing the values of the percentage of the different cellular clusters in Fig. 4j, k. a, b, Mean + s.d. c, e, Mean + s.e.m.

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Supplementary information

Supplementary Information

Supplementary Note. Two-progenitor model. Notes on the modeling of the clonal data related to Fig. 2 and Extended Data Fig. 3 and 4.

Reporting Summary

Supplementary Table

Supplementary Table 1. ATAC-seq peaks upreagulated during expansion. Peaks showing chromatin region with increased accessibility in EXP D2 (n=1 mouse) compared to CTRL (n=1 mouse). Differential peaks are defined as peaks having at least a twofold change compared to control and being called peak in the expanded condition and contain at least 3 reads per million.

Supplementary Table

Supplementary Table 2. Motif enrichment analysis on ATAC-seq peaks upregulated during expansion. Transcription factor motifs enriched in the ATAC-seq peaks that were upregulated by more than 2-fold in EXP D2 (n=1 mouse) compared to CTRL (n=1 mouse) as determined by Homer analysis using known motif search (3262 target sequences, 46200 background sequences).

Supplementary Table

Supplementary Table 3. Marker genes for clusters identified in CTRL, EXP D1, EXP D4 and TPA samples. Genes differentially expressed in the different clusters in CTRL, EXP D1, EXP D4 and TPA samples. n=4659 cells CTRL, n= 4934 cells EXP D1, n= 2716 cells EXP D4, n= 4342 cells TPA. See Methods for statistical tests used.

Supplementary Table

Supplementary Table 4. Differentially expressed SCENIC regulons in CTRL, EXP D1, EXP D4 and TPA samples. SCENIC analysis on the different clusters in CTRL, EXP D1, EXP D4 and TPA samples. n=4659 cells CTRL, n= 4934 cells EXP D1, n= 2716 cells EXP D4, n= 4342 cells TPA. Wilcoxon rank-sum test FDR corrected p-value < 0.05, see Methods.

Supplementary Table

Supplementary Table 5. Differentially expressed genes CTRL vs EXP D1 SCs populations. n=1290 CTRL SCs, n=700 EXP D1 SCs, n=801 EXP D1 Stretch SCs. P-values are FDR adjusted Wilcoxon rank-sum tests.

Supplementary Table

Supplementary Table 6. Differentially expressed regulons CTRL vs EXP D1 SCs populations. n=1290 CTRL SCs, n=700 EXP D1 SCs, n=801 EXP D1 Stretch SCs. P-values are FDR adjusted Wilcoxon rank-sum tests.

Supplementary Table

Supplementary Table 7. Cell-cluster associations.

Supplementary Table

Supplementary Table 8. Differentially expressed genes EXP D1 vs EXP D2 Treatments. n=3869 EXP D1, n=4762 D2 Mal Inhibitor, n=3254 D2 Tramenitib treated. P-values are FDR adjusted Wilcoxon rank-sum tests.

Supplementary Table

Supplementary Table 9. Differentially expressed regulons EXP D1 vs EXP D2 Treatments. n=3869 EXP D1, n=4762 D2 Mal Inhibitor, n=3254 D2 Tramenitib treated. P-values are FDR adjusted Wilcoxon rank-sum tests.

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Aragona, M., Sifrim, A., Malfait, M. et al. Mechanisms of stretch-mediated skin expansion at single-cell resolution. Nature 584, 268–273 (2020). https://doi.org/10.1038/s41586-020-2555-7

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