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A biomechanical switch regulates the transition towards homeostasis in oesophageal epithelium

Nature Cell Biology volume 23, pages 511–525 (2021)Cite this article

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Abstract

Epithelial cells rapidly adapt their behaviour in response to increasing tissue demands. However, the processes that finely control these cell decisions remain largely unknown. The postnatal period covering the transition between early tissue expansion and the establishment of adult homeostasis provides a convenient model with which to explore this question. Here, we demonstrate that the onset of homeostasis in the epithelium of the mouse oesophagus is guided by the progressive build-up of mechanical strain at the organ level. Single-cell RNA sequencing and whole-organ stretching experiments revealed that the mechanical stress experienced by the growing oesophagus triggers the emergence of a bright Krüppel-like factor 4 (KLF4) committed basal population, which balances cell proliferation and marks the transition towards homeostasis in a yes-associated protein (YAP)-dependent manner. Our results point to a simple mechanism whereby mechanical changes experienced at the whole-tissue level are integrated with those sensed at the cellular level to control epithelial cell fate.

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Fig. 1: The postnatal mouse oesophagus as a model of rapid tissue expansion transitioning towards homeostasis.
Fig. 2: KLF4 marks the emergence of an early committed population in the oesophageal epithelium.
Fig. 3: Single-cell transcriptional profiling defines transition towards homeostasis.
Fig. 4: Homeostatic transition coincides with tissue spatial reorganization.
Fig. 5: Differential growth generates longitudinal tissue strain that is sensed at the cellular level.
Fig. 6: YAP nuclear localization promotes increased levels of basal commitment through KLF4.
Fig. 7: Changes in tissue mechanics influence basal KLF4 expression.

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Data availability

The scRNA-seq data that support the findings of this study have been deposited in the ArrayExpress repository under accession code E-MTAB-8662. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Statistical analysis of the scRNA-seq data was performed using R codes developed for this study. Image segmentation and computational image analysis were performed using Python codes developed and/or adapted for this study. All codes for the computational analysis are available at https://github.com/BenSimonsLab/McGinn_Nat-Cell-Biol_2021.

References

  1. Varga, J. & Greten, F. R. Cell plasticity in epithelial homeostasis and tumorigenesis. Nat. Cell Biol. 19, 1133–1141 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Tata, P. R. et al. Dedifferentiation of committed epithelial cells into stem cells in vivo. Nature 503, 218–223 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Van Es, J. H. et al. Dll1+ secretory progenitor cells revert to stem cells upon crypt damage. Nat. Cell Biol. 14, 1099–1104 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ito, M. et al. Stem cells in the hair follicle bulge contribute to wound repair but not to homeostasis of the epidermis. Nat. Med. 11, 1351–1354 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Ge, Y. et al. Stem cell lineage infidelity drives wound repair and cancer. Cell 169, 636–650.e14 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yui, S. et al. YAP/TAZ-dependent reprogramming of colonic epithelium links ECM remodeling to tissue regeneration. Cell Stem Cell 22, 35–49.e7 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Que, J., Garman, K. S., Souza, R. F. & Spechler, S. J. Pathogenesis and cells of origin of Barrett’s esophagus. Gastroenterology 157, 349–364.e1 (2019).

    Article  PubMed  Google Scholar 

  8. Guiu, J. et al. Tracing the origin of adult intestinal stem cells. Nature 570, 107–111 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Andersen, M. S. et al. Tracing the cellular dynamics of sebaceous gland development in normal and perturbed states. Nat. Cell Biol. 21, 924–932 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Alcolea, M. P. & Jones, P. H. Tracking cells in their native habitat: lineage tracing in epithelial neoplasia. Nat. Rev. Cancer 13, 161–171 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Tata, P. R. et al. Developmental history provides a roadmap for the emergence of tumor plasticity. Dev. Cell 44, 679–693.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Donati, G. et al. Wounding induces dedifferentiation of epidermal Gata6+ cells and acquisition of stem cell properties. Nat. Cell Biol. 19, 603–613 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rosekrans, S. L., Baan, B., Muncan, V. & van den Brink, G. R. Esophageal development and epithelial homeostasis. Am. J. Physiol. Gastrointest. Liver Physiol. 309, G216–G228 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Doupe, D. P. et al. A single progenitor population switches behavior to maintain and repair esophageal epithelium. Science 337, 1091–1093 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mammoto, T. et al. Mechanochemical control of mesenchymal condensation and embryonic tooth organ formation. Dev. Cell 21, 758–769 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Barriga, E. H., Franze, K., Charras, G. & Mayor, R. Tissue stiffening coordinates morphogenesis by triggering collective cell migration in vivo. Nature 554, 523–527 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, J. et al. The strength of mechanical forces determines the differentiation of alveolar epithelial cells. Dev. Cell 44, 297–312.e5 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Shyer, A. E. et al. Villification: how the gut gets its villi. Science 342, 212–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shyer, A. E., Huycke, T. R., Lee, C., Mahadevan, L. & Tabin, C. J. Bending gradients: how the intestinal stem cell gets its home. Cell 161, 569–580 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shyer, A. E. et al. Emergent cellular self-organization and mechanosensation initiate follicle pattern in the avian skin. Science 357, 811–815 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Heckel, E. et al. Oscillatory flow modulates mechanosensitive klf2a expression through trpv4 and trpp2 during heart valve development. Curr. Biol. 25, 1354–1361 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Vermot, J. et al. Reversing blood flows act through klf2a to ensure normal valvulogenesis in the developing heart. PLoS Biol. 7, e1000246 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Gilmour, D., Rembold, M. & Leptin, M. From morphogen to morphogenesis and back. Nature 541, 311–320 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Segel, M. et al. Niche stiffness underlies the ageing of central nervous system progenitor cells. Nature 573, 130–134 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Biggs, L. C., Kim, C. S., Miroshnikova, Y. A. & Wickstrom, S. A.Mechanical forces in the skin: roles in tissue architecture, stability, and function. J. Invest. Dermatol. 140, 284–290 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. 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 

  28. LeGoff, L. & Lecuit, T. Mechanical forces and growth in animal tissues. Cold Spring Harb. Perspect. Biol. 8, a019232 (2015).

    Article  PubMed  Google Scholar 

  29. Petridou, N. I., Spiro, Z. & Heisenberg, C. P. Multiscale force sensing in development. Nat. Cell Biol. 19, 581–588 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Hardman, M. J., Sisi, P., Banbury, D. N. & Byrne, C. Patterned acquisition of skin barrier function during development. Development 125, 1541–1552 (1998).

    Article  CAS  PubMed  Google Scholar 

  31. Liu, K. et al. Sox2 cooperates with inflammation-mediated Stat3 activation in the malignant transformation of foregut basal progenitor cells. Cell Stem Cell 12, 304–315 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. DeWard, A. D., Cramer, J. & Lagasse, E. Cellular heterogeneity in the mouse esophagus implicates the presence of a nonquiescent epithelial stem cell population. Cell Rep. 9, 701–711 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tetreault, M. P. et al. Esophageal squamous cell dysplasia and delayed differentiation with deletion of Krüppel-like factor 4 in murine esophagus. Gastroenterology 139, 171–181.e9 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Patel, S., Xi, Z. F., Seo, E. Y., McGaughey, D. & Segre, J. A. Klf4 and corticosteroids activate an overlapping set of transcriptional targets to accelerate in utero epidermal barrier acquisition. Proc. Natl Acad. Sci. USA 103, 18668–18673 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 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 

  36. Fortunel, N. O. et al. KLF4 inhibition promotes the expansion of keratinocyte precursors from adult human skin and of embryonic-stem-cell-derived keratinocytes. Nat. Biomed. Eng. 3, 985–997 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Kypriotou, M., Huber, M. & Hohl, D. The human epidermal differentiation complex: cornified envelope precursors, S100 proteins and the ‘fused genes’ family. Exp. Dermatol. 21, 643–649 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Mesa, K. R. et al. Homeostatic epidermal stem cell self-renewal is driven by local differentiation. Cell Stem Cell 23, 677–686.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kulukian, A. & Fuchs, E. Spindle orientation and epidermal morphogenesis. Phil. Trans. R. Soc. Lond. B Biol. Sci. 368, 20130016 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  43. Alcolea, M. P. et al. Differentiation imbalance in single oesophageal progenitor cells causes clonal immortalization and field change. Nat. Cell Biol. 16, 615–622 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Driskell, R. R. et al. Distinct fibroblast lineages determine dermal architecture in skin development and repair. Nature 504, 277–281 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Crisera, C. A., Maldonado, T. S., Longaker, M. T. & Gittes, G. K. Defective fibroblast growth factor signaling allows for nonbranching growth of the respiratory-derived fistula tract in esophageal atresia with tracheoesophageal fistula. J. Pediatr. Surg. 35, 1421–1425 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Botchkarev, V. A. & Sharov, A. A. BMP signaling in the control of skin development and hair follicle growth. Differentiation 72, 512–526 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Park, S. et al. B-cell translocation gene 2 (Btg2) regulates vertebral patterning by modulating bone morphogenetic protein/Smad signaling. Mol. Cell. Biol. 24, 10256–10262 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li, X. & Cao, X. BMP signaling and skeletogenesis. Ann. NY Acad. Sci. 1068, 26–40 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Blanco, S. et al. Stem cell function and stress response are controlled by protein synthesis. Nature 534, 335–340 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Koria, P. & Andreadis, S. T. Epidermal morphogenesis: the transcriptional program of human keratinocytes during stratification. J. Invest. Dermatol. 126, 1834–1841 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Sadagurski, M. et al. Insulin-like growth factor 1 receptor signaling regulates skin development and inhibits skin keratinocyte differentiation. Mol. Cell. Biol. 26, 2675–2687 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang, C. et al. Wnt/β-catenin signaling is critical for dedifferentiation of aged epidermal cells in vivo and in vitro. Aging Cell 11, 14–23 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Joost, S. et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 3, 221–237.e9 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Haake, A. R. & Cooklis, M. Incomplete differentiation of fetal keratinocytes in the skin equivalent leads to the default pathway of apoptosis. Exp. Cell. Res. 231, 83–95 (1997).

    Article  CAS  PubMed  Google Scholar 

  56. Ge, Y. & Fuchs, E. Stretching the limits: from homeostasis to stem cell plasticity in wound healing and cancer. Nat. Rev. Genet. 19, 311–325 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Philippeos, C. et al. Spatial and single-cell transcriptional profiling identifies functionally distinct human dermal fibroblast subpopulations. J. Invest. Dermatol. 138, 811–825 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Nystrom, A. & Bruckner-Tuderman, L. Matrix molecules and skin biology. Semin. Cell Dev. Biol. 89, 136–146 (2019).

    Article  PubMed  CAS  Google Scholar 

  59. Ghaleb, A. M. & Yang, V. W. Krüppel-like factor 4 (KLF4): what we currently know. Gene 611, 27–37 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wei, D., Kanai, M., Huang, S. & Xie, K. Emerging role of KLF4 in human gastrointestinal cancer. Carcinogenesis 27, 23–31 (2006).

    Article  CAS  PubMed  Google Scholar 

  61. Birsoy, K., Chen, Z. & Friedman, J. Transcriptional regulation of adipogenesis by KLF4. Cell Metab. 7, 339–347 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Morgan, J. T., Raghunathan, V. K., Chang, Y. R., Murphy, C. J. & Russell, P. Wnt inhibition induces persistent increases in intrinsic stiffness of human trabecular meshwork cells. Exp. Eye Res. 132, 174–178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mendez, M. G. & Janmey, P. A. Transcription factor regulation by mechanical stress. Int. J. Biochem. Cell Biol. 44, 728–732 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Morse, E. M. et al. PAK6 targets to cell–cell adhesions through its N-terminus in a Cdc42-dependent manner to drive epithelial colony escape. J. Cell Sci. 129, 380–393 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Kopf, J., Petersen, A., Duda, G. N. & Knaus, P. BMP2 and mechanical loading cooperatively regulate immediate early signalling events in the BMP pathway. BMC Biol. 10, 37 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang, H., Pasolli, H. A. & Fuchs, E. Yes-associated protein (YAP) transcriptional coactivator functions in balancing growth and differentiation in skin. Proc. Natl Acad. Sci. USA 108, 2270–2275 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Maglic, D. et al. YAP-TEAD signaling promotes basal cell carcinoma development via a c-JUN/AP1 axis. EMBO J. 37, e98642 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Schutte, U. et al. Hippo signaling mediates proliferation, invasiveness, and metastatic potential of clear cell renal cell carcinoma. Transl. Oncol. 7, 309–321 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Shen, J. et al. Hippo component YAP promotes focal adhesion and tumour aggressiveness via transcriptionally activating THBS1/FAK signalling in breast cancer. J. Exp. Clin. Cancer Res. 37, 175 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. He, C. et al. YAP forms autocrine loops with the ERBB pathway to regulate ovarian cancer initiation and progression. Oncogene 34, 6040–6054 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Xin, M. et al. Regulation of insulin-like growth factor signaling by Yap governs cardiomyocyte proliferation and embryonic heart size. Sci. Signal 4, ra70 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Moreno-Vicente, R. et al. Caveolin-1 modulates mechanotransduction responses to substrate stiffness through actin-dependent control of YAP. Cell Rep. 25, 1622–1635.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Echarri, A. & Del Pozo, M. A. Caveolae—mechanosensitive membrane invaginations linked to actin filaments. J. Cell Sci. 128, 2747–2758 (2015).

    CAS  PubMed  Google Scholar 

  74. Steed, E. et al. klf2a couples mechanotransduction and zebrafish valve morphogenesis through fibronectin synthesis. Nat. Commun. 7, 11646 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Goddard, L. M. et al. Hemodynamic forces sculpt developing heart valves through a KLF2–WNT9B paracrine signaling axis. Dev. Cell 43, 274–289.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhao, B. et al. Angiomotin is a novel Hippo pathway component that inhibits YAP oncoprotein. Genes Dev. 25, 51–63 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Das Thakur, M. et al. Ajuba LIM proteins are negative regulators of the Hippo signaling pathway. Curr. Biol. 20, 657–662 (2010).

    Article  CAS  PubMed  Google Scholar 

  78. Misra, J. R. & Irvine, K. D. The Hippo signaling network and its biological functions. Annu. Rev. Genet. 52, 65–87 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Yuan, Y. et al. YAP1/TAZ–TEAD transcriptional networks maintain skin homeostasis by regulating cell proliferation and limiting KLF4 activity. Nat. Commun. 11, 1472 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Verstreken, C. M., Labouesse, C., Agley, C. C. & Chalut, K. J. Embryonic stem cells become mechanoresponsive upon exit from ground state of pluripotency. Open Biol. 9, 180203 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Aragona, M. et al. Mechanisms of stretch-mediated skin expansion at single-cell resolution. Nature 584, 268–273 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Candi, E., Schmidt, R. & Melino, G. The cornified envelope: a model of cell death in the skin. Nat. Rev. Mol. Cell Biol. 6, 328–340 (2005).

    Article  CAS  PubMed  Google Scholar 

  83. Hatzfeld, M., Keil, R. & Magin, T. M. Desmosomes and intermediate filaments: their consequences for tissue mechanics. Cold Spring Harb. Perspect. Biol. 9, a029157 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Liu-Chittenden, Y. et al. Genetic and pharmacological disruption of the TEAD–YAP complex suppresses the oncogenic activity of YAP. Genes Dev. 26, 1300–1305 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Wang, L. et al. Unbalanced YAP–SOX9 circuit drives stemness and malignant progression in esophageal squamous cell carcinoma. Oncogene 38, 2042–2055 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. Nowlan, N. C., Francis-West, P. & Nelson, C. Mechanics of development. Phil. Trans. R Soc. Lond. B Biol. Sci 373, 20170316 (2018).

    Article  Google Scholar 

  87. Fiore, V. F. et al. Mechanics of a multilayer epithelium instruct tumour architecture and function. Nature 585, 433–439 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Huycke, T. R. et al. Genetic and mechanical regulation of intestinal smooth muscle development. Cell 179, 90–105.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 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 

  90. Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–183 (2011).

    Article  CAS  PubMed  Google Scholar 

  91. Benham-Pyle, B. W., Pruitt, B. L. & Nelson, W. J. Cell adhesion. Mechanical strain induces E-cadherin-dependent Yap1 and β-catenin activation to drive cell cycle entry. Science 348, 1024–1027 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Gudipaty, S. A. et al. Mechanical stretch triggers rapid epithelial cell division through Piezo1. Nature 543, 118–121 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Elbediwy, A. et al. Integrin signalling regulates YAP and TAZ to control skin homeostasis. Development 143, 1674–1687 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Schlegelmilch, K. et al. Yap1 acts downstream of α-catenin to control epidermal proliferation. Cell 144, 782–795 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Imajo, M., Ebisuya, M. & Nishida, E. Dual role of YAP and TAZ in renewal of the intestinal epithelium. Nat. Cell Biol. 17, 7–19 (2015).

    Article  CAS  PubMed  Google Scholar 

  96. Gokhin, D. S., Ward, S. R., Bremner, S. N. & Lieber, R. L. Quantitative analysis of neonatal skeletal muscle functional improvement in the mouse. J. Exp. Biol. 211, 837–843 (2008).

    Article  CAS  PubMed  Google Scholar 

  97. Domenga, V. et al. Notch3 is required for arterial identity and maturation of vascular smooth muscle cells. Genes Dev. 18, 2730–2735 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Chao, C. M., Moiseenko, A., Zimmer, K. P. & Bellusci, S. Alveologenesis: key cellular players and fibroblast growth factor 10 signaling. Mol. Cell. Pediatr. 3, 17 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  99. White, R. B., Bierinx, A. S., Gnocchi, V. F. & Zammit, P. S. Dynamics of muscle fibre growth during postnatal mouse development. BMC Dev. Biol. 10, 21 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Dekoninck, S. et al. Defining the design principles of skin epidermis postnatal growth. Cell 181, 604–620.e22 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Paszek, M. J. et al. The cancer glycocalyx mechanically primes integrin-mediated growth and survival. Nature 511, 319–325 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Mort, R. L. et al. Fucci2a: a bicistronic cell cycle reporter that allows Cre mediated tissue specific expression in mice. Cell Cycle 13, 2681–2696 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Jansson, L. & Larsson, J. Normal hematopoietic stem cell function in mice with enforced expression of the Hippo signaling effector YAP1. PLoS ONE 7, e32013 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. 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 

  105. Burns, E. R., Scheving, L. E., Fawcett, D. F., Gibbs, W. M. & Galatzan, R. E. Circadian influence on the frequency of labeled mitoses method in the stratified squamous epithelium of the mouse esophagus and tongue. Anat. Rec. 184, 265–273 (1976).

    Article  CAS  PubMed  Google Scholar 

  106. 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 

  107. 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 

  108. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Subelj, L. & Bajec, M. Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 83, 036103 (2011).

    Article  PubMed  CAS  Google Scholar 

  110. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank members of the Alcolea and Simons laboratories and B. Colom (Sanger Institute) for comments and suggestions; the staff of the University Biomedical Services, Gurdon Institute; P. Humphreys at the imaging core facilities at the Jeffrey Cheah Biomedical Centre; A. Sossick, R. Butler and N. Lawrence at the Gurdon Institute Imaging Facility; the CRUK Cambridge Institute Genomics Core Facility; J. Larsson (tetO-YAPS127A); I. J. Jackson (Fucci2a); the NIHR Cambridge BRC Cell Phenotyping Hub for FACS support; and the Jeffrey Cheah Biomedical Centre core facilities. We thank T. Savin, E. Hannezo, B. Ladoux, G. Charras, S. Hénon, N. Harmand, G. Duclos and J.-B. Lugagne for insight on deep-learning-based image analysis and/or tissue mechanics. This work was mainly supported by funding from the Wellcome Trust and Royal Society (105942/Z/14/Z to M.P.A.) and a core support grant from the Wellcome Trust (203151/Z/16/Z) and MRC (MC_PC_17230) to the Wellcome–MRC Cambridge Stem Cell Institute. J.M. was supported by a CRUK Cambridge Cancer Centre PhD fellowship. A.H. was supported by a Wellcome Trust Junior Interdisciplinary Research Fellowship (098357/Z/12/Z) and Herchel Smith Postdoctoral Research Fellowship. This work also received support from the Novo Nordisk Foundation (NNF18CC0033666 to K.K. and NNF17OC0028730 and NNF17CC0027852 to K.B.J.), Human Frontier Science Program (LT000092/2016-L to S.H.), Basic Science Research Program (NRF-2014R1A6A3A01005675 to S.H.), National Institutes of Health (ZIA BC 011763 to R.I.-B.) and Wellcome PhD stutentship (220088/Z/20/Z to F.J.E.). C.V. was supported by the Engineering and Physical Sciences Research Council (1506089). The Royal Society and European Research Council (CellFateTech; 772798) supported K.J.C. B.D.S. acknowledges funding from the Wellcome Trust (098357/Z/12/Z and 219478/Z/19/Z) and Royal Society in the form of an E. P. Abraham Research Professorship (RP\R1\180165). A.H., S.H. and B.D.S. acknowledge support from core funding to the Wellcome Trust/CRUK Gurdon Institute (203144/Z/16/Z and C6946/A24843).

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Authors and Affiliations

  1. Wellcome–MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Jamie McGinn, Adrien Hallou, Seungmin Han, Frances J. England, Christophe Verstreken, Kevin J. Chalut, Benjamin D. Simons & Maria P. Alcolea

  2. Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Centre, Cambridge, UK

    Jamie McGinn & Maria P. Alcolea

  3. Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK

    Adrien Hallou, Seungmin Han & Benjamin D. Simons

  4. Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK

    Adrien Hallou

  5. Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark

    Kata Krizic, Svetlana Ulyanchenko & Kim B. Jensen

  6. Novo Nordisk Foundation Center for Stem Cell Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Kata Krizic, Svetlana Ulyanchenko & Kim B. Jensen

  7. Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Ramiro Iglesias-Bartolome

  8. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK

    Benjamin D. Simons

Authors
  1. Jamie McGinn
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  2. Adrien Hallou
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  3. Seungmin Han
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  4. Kata Krizic
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  5. Svetlana Ulyanchenko
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  6. Ramiro Iglesias-Bartolome
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  7. Frances J. England
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  8. Christophe Verstreken
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  9. Kevin J. Chalut
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  10. Kim B. Jensen
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  11. Benjamin D. Simons
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  12. Maria P. Alcolea
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Contributions

The experiments were designed, validated and conducted by J.M., F.J.E., R.I.-B. and M.P.A., with J.M. and F.J.E. working under the supervision of M.P.A. K.K. and S.U. carried out experiments under the supervision of K.B.J. A.H. developed the deep-learning-based image segmentation and other computational image analysis pipelines to quantify cell fluorescence levels, cell shape, tissue spatial organization and SHG images. S.H. guided the experimental design for scRNA-seq and performed analysis of the data. A.H. and S.H. were both under the supervision of B.D.S. C.V. designed and provided expertise on the 3D-printed stretcher device with input from K.J.C. A.H., R.I.-B. and K.J.C. provided insights and technical expertise on tissue mechanics and stretching experiments. K.B.J. and B.D.S. supervised parts of the study and provided expertise in the epithelial stem cell field. J.M. and M.P.A. conceived of and coordinated the project, supervised the experiments and wrote the manuscript with input from A.H. and B.D.S. All authors reviewed and edited the final manuscript. A.H., R.I.-B., K.J.C., K.B.J., B.D.S. and M.P.A. acquired the funding.

Corresponding author

Correspondence to Maria P. Alcolea.

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The authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks John Connelly 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

Extended Data Fig. 1 Postnatal characterization.

a, Diagram illustrating longitudinal oesophageal orientation from proximal to distal as marked by orange diamond. b, and c, Oesophageal tissue growth in length and width over time, respectively. Data expressed as mean ± SEM and analysed using one-way ANOVA with Tukey’s multiple comparisons test (n = 103 mice; ####P < 0.0001 relative to P70; ****P < 0.0001 relative to P7). d, Images showing animal body growth throughout postnatal development. e, Representative images showing EdU+ basal cells 24 hours post-labelling in P14 and P49 from Fig. 1e-g. f, Graphical representation of differential basal cell production rate throughout postnatal development. See Methods; data expressed as mean ± SEM; n=3 mice. g, 3D rendered z-stacks showing split confocal channels from Fig. 1i. h, Typical 3D rendered confocal z-stacks showing tilted side views from Fig. 1i. Yellow arrows indicate immature epithelial barrier. i, Representative side views of confocal z-stacks showing the thickening of the oesophageal epithelium (OE). j, Representative H&E sections of the oesophagus showing increasing cornification, as delimited by dotted lines. k, Quantification of the cornified thickness. n=3 mice; Micrometer, µm. Data expressed as mean ± SEM and analysed using one-way ANOVA with Tukey’s multiple comparisons test (*P < 0.05, **P < 0.01, ***p < 0.001 relative to P7). Scale bars. S1d(2 cm); S1e,g-j(20 µm). Stainings. Blue, DAPI; cyan, EdU; green, KRT14; greyscale, KRT4. All data derived from wild-type C57BL/6J mice. Dashed lines indicate basement membrane. Dotted lines in graphs indicate P28. Orange diamonds depict longitudinal orientation of the oesophagus where indicated. Source data are provided.

Source data

Extended Data Fig. 2 KLF4 basal cell prolife in FUCCI2a mice.

a, Representative confocal z-stacks showing side views of OE wholemounts from Fig. 2a. Dashed lines indicate basement membrane; dotted lines mark the upper limit of the OE; white arrows indicate basal KLF4+ cells. Red, KLF4. b, In vivo protocol. Oesophagi from FUCCI2a mice were collected at time points indicated. Schematic indicating expression pattern of fluorescent proteins in FUCCI2a mouse model. c, Confocal images showing basal views of typical FUCCI2a OE wholemounts in (b). Orange diamonds indicate longitudinal orientation of the oesophagus. White dashed lines indicate mVenus+ cells; green, mVenus; red, mCherry; greyscale, KLF4. d and e, Correlation between KLF4 protein expression and reporter fluorescent proteins mCherry (d)/mVenus (e) in the basal layer from (b) and (c). n=3 mice; Scale bars 20 µm. Parts of (b) were drawn by using and/or adapting diagrams from Servier Medical Art. Source data are provided.

Source data

Extended Data Fig. 3 Single-cell RNA sequencing annotation.

a, Flow cytometry gating strategy for isolation of OE cells. Oesophageal cell suspensions (i) were gated to sort the single (ii) viable (iii) population, enriched for epithelial cells (iv; EpCam+/CD45-). Cells were isolated from 15 mice (P7), 12 mice (P28), and 9 (adults). Representative plots from adult sample are shown. b, UMAP representing cell clusters based on louvain clustering. c, UMAP representing the distribution of cell clusters in (b) after integrating data from different time points (see Methods). d, UMAP showing expression of representative makers for basal (left panel) and differentiated cells (right panel) in OE. e, Heatmap showing expression of representative marker genes for basal cells, cell cycle, and differentiation for the 17 clusters shown in (b in lower bar). Expression values were log2-transformed normalized UMIs followed by scaling and averaging across cells in the same clusters. f, Violin plots showing expression of representative epithelial (basal vs. differentiated) and cell cycle markers at different postnatal stages split by annotated cell cohorts in Fig. 3c. g, Violin plots showing expression of genes associated with regeneration and homeostasis for basal and differentiated cell types at distinct postnatal stages. Basal cells include both cycling and resting cells. h, UMAP showing spatial distribution of distinct cell cycle phases, annotated using cell cycle analysis by R package scran (v 1.12.1) combined with manual curation based on genes in (e). i, Violin plots showing expression of representative cell cycle genes at postnatal stages split by cell cycle cohorts identified in (h). Colour scheme for cell cycle phases as in (h). j, In vivo protocol. Mice were treated with EdU 2 hours prior culling at indicated time points. k, Typical 3D rendered confocal side views showing basal EdU population (see Methods). Dashed white lines, basement membrane. Dotted white lines, upper OE limit. Blue, DAPI; cyan, EdU; scale bar 10 µm. For violin plots in (f), (g) and (i), expression level represents log2-transformed normalized UMIs, dotted lines indicate the median of the distribution. Colour bars of UMAPs in (d) denotes expression range. Parts of (j) were drawn by using and/or adapting diagrams from Servier Medical Art.

Extended Data Fig. 4 Single-cell RNA sequencing expression profile.

a, Distinctive patterns of gene expression in basal cells as defined in (Fig. 3c). Grey, relative expression profiles of individual genes belonging to each pattern. Solid coloured lines, median values at each time point. To calculate the relative expression profiles, log2-transformed normalized UMIs were scaled and averaged across all basal cells at each time point and adjusted compared to the value at P7. b, Heatmap representing expression of individual genes belonging to the 4 patterns in (a). For expression values, log2-transformed normalised UMIs were scaled and averaged across all basal cells for each cluster and time point. The table on the right shows selected GO terms for major Pattern 2 and Pattern 4, corresponding p-values and representative genes. Closely related GO terms are grouped together. See Supplementary Table 3 for GO analysis result for all 4 expression patterns. c and d, UMAPs showing expression of genes related to key biological processes from Gene Ontology analysis for Patterns 2 (c) and 4 (d) in (b). e, Violin plots showing expression of Klf4 in cells of individual clusters at P7 (left), P28 (middle) and Adult (right). f, Expression of relevant genes along the pseudotime trajectory from basal resting to differentiated cells for P7. Left panel, YAP target genes (Cyr61, Ctgf, Thbs1) and genes associated with a response to mechanical stimuli (Cav1, Klf2, Dcn). Right panel, depicts KLF4 target genes (Krt4, Krt13, Cdnk1a, Cebpb). For violin plots in (e), expression level means log2-transformed normalized UMIs and dotted lines indicate the median of the distribution. Colour bars of UMAPs in (c) and (d) indicate log2-transformed normalized UMIs. Gene expression in (f) is represented as auto-scaled, log2-transformed normalized UMIs smoothed using a rolling mean along its trajectory with a window size of 5% of cells. Two bars on the top denotes the arrangement of cells according to pseudotime and clusters in Extended Data Fig. 3b, respectively.

Extended Data Fig. 5 Deep Learning based segmentation.

a, Protocol for in situ fixation of the oesophagus, and b, Images from in situ oesophagi compared with oesophagi fixed immediately after dissection confirm that basal cell shape was not affected by tissue harvesting (Supplementary to Fig. 4a). Blue, DAPI; green, KRT14. Scale bar, 20 µm. c, Schematic depicting deep learning based segmentation principle. Manually or semi-automatically annotated ‘ground truth’ images were used to train a U-Net convolutional neural network. Training of the network was assessed on validation images and iteratively optimized until the achievement of satisfactory automated segmentation. d, Schematic of pipeline utilised for segmentation of single z-slice confocal images of OE basal layer. Nuclear segmentation was based on DAPI staining (blue). Mask overlay shows the match between the binary mask and the original fluorescence image. Scale bar, 20 µm. e, Schematic describing the computation of Voronoi diagrams of the tissue. Single z-slice confocal images of the OE basal layer are segmented as described in (d). Cell centroids are computed using the binary mask. Delaunay triangulation of cells was performed using cell centroid coordinates. Voronoi diagrams are calculated as the dual of Delaunay triangulation of cells in the tissue and overlaid onto the original fluorescence image. Scale bar, 20 µm. f, Cell shape anisotropy tensor at P14 and P49 (supplementary to Fig. 4d). n=3 mice. Orientation is colour-coded. Results from a representative experiment are shown; n=3 mice. g, Violin plots showing the distribution in cell shape anisotropy throughout postnatal development. n=2052–2594 cells from 3 animals per time point. Black dashed line, median. One-way ANOVA with Tukey’s multiple comparisons test (****P < 0.0001 relative to P7). h, Bidimensional structure factor at P14 and P49 (supplementary to Fig. 4e). Changes in the dashed white outline (from ellipse to circle) depict a transition from anisotropic to isotropic spatial distribution over time; n=3 mice. i, Structure factor shape anisotropy distribution as shown in (h) and Fig. 4e. Box plots show median and quartiles; and whiskers, minima/maxima. Individual measurements from n=3 mice; (ns, not significant; ***P < 0.001, ****P < 0.0001 relative to P7). All data derived from wild-type C57BL/6J mice. Parts of (a) were drawn by using and/or adapting diagrams from Servier Medical Art. Source data are provided.

Source data

Extended Data Fig. 6 Oesophageal tissue strain and second harmonic generation.

a, In situ and immediate ex vivo images of oesophageal tubes at P28 and P49 (supplementary to Fig. 5b). White dashed lines delineate oesophageal tube; scale bar 1 cm. b, Representative images showing the size of combined and separate oesophageal layers; full oesophageal tube (Tube), epithelial composite (Epi) and muscle layer (Muscle); scale bar 5 mm. c, Longitudinal tissue strain relative to muscle, represented as percentage. Data expressed as mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test (P7 n=16, P28 and Adult n=9; **P < 0.01 relative to P7; ns, not significant). d, Measure of the stomach perimeter over time. Mean values ± SEM; n=3 mice. Two-way ANOVA, with Tukey’s multiple comparisons test (ns, not significant). Millimetre, mm. e, Basal confocal view of EdU+ cells (cyan) in wholemounts of typical squamous stomach epithelium from (f; Blue, DAPI). f, In vivo protocol. Mice were treated with a single EdU injection 24 h prior stomach collection at the time points indicated. g, Quantification of EdU+ basal cells per field from (f). Presented as mean ± SD; n = 3. h, Representative views of stroma underlying OE basement membrane using second harmonic generation (SHG). Left panels, collagen in magenta. Middle panels, colour map of SHG signal intensity. Right panels, colour-coded local orientation map of SHG signal. Scale bar 100 µm. i, Representative histograms depicting orientation distribution of collagen fibres in (h). n=3 mice. j, Basal view of representative OE wholemounts at P14. Green, YAP; greyscale, B-Catenin (BCat). Scale bar 10 µm. k and l, Quantification of basal nuclear and cytoplasmic staining of YAP and DAPI, respectively (see Methods; supplementary to Fig. 5m). A total of 20 cells for 3 different animals were measured. All data derived from wild-type C57BL/6J mice. g, k and l was performed using two-tailed unpaired t test (****P < 0.0001; ns, not significant). Box plots show median and quartiles; whiskers, 0.1 and 0.99 percentiles. Orange diamonds depict longitudinal orientation of the oesophagus where indicated. Parts of (f) were drawn by using and/or adapting diagrams from Servier Medical Art. Source data are provided.

Source data

Extended Data Fig. 7 Changes in tissue mechanics at cellular level.

a, Model of parts required for 3D printed stretcher. Scale bar, 1 cm. b, In vitro protocol. Oesophagi were exposed to 40% stretch using stretcher (Fig. 7a) and treated with/without 25 µm blebbistatin (BLEBB) for 48 hours. c, Individual basal cell areas. Data analysis was performed using Two-way ANOVA with Tukey’s multiple comparisons test (n=3 mice; *P < 0.05, ****P < 0.0001; ns, not significant; black indicates significance between control vs. BLEBB conditions; grey indicates statistical differences between stretching conditions). d, Confocal basal views of typical organ cultures after 48 hour BLEBB treatment in vitro from (b). Blue represents DAPI, greyscale shows phalloidin staining. Scale bar, 20 µm. e, In vitro protocol. Oesophagi stretched at the indicated levels, treated with EdU for 1 hour, and kept in vitro as whole-organ cultures for a 24 hours. f, Confocal basal views showing EdU (cyan) and phosphohistone H3 (PhosphoH3 in magenta) staining in cultures from (e). Scale bar, 20 µm. g and h, Basal quantification of EdU+ and PhosphoH3+ cells expressed as percentage of DAPI+ cells from (e). Mean ± SD One-way ANOVA with Tukey’s multiple comparisons test (n=3 mice; **P < 0.01, ****P < 0.0001 relative to Control; ns, not significant). i, In vivo protocol. Oesophageal wholemounts from Rosa26-mT/mG mice were mechanically peeled, separating cornified suprabasal layers or underlying stroma (see Methods). j, Individual area of basal cells. Data analysis was performed using One-way ANOVA with Tukey’s multiple comparisons test (n=3 mice; ****P < 0.0001 relative to full tissue). k, Confocal basal views of typical OE wholemounts after mechanical separation. Red, mTmG; scale bar, 10 µm. l, Basal cell density, expressed in number of cells per field after BLEBB treatment in Fig. 7e. Mean ± SD; n=3 mice. Unpaired t test (ns, not significant). m, Basal quantification of EdU+ cells expressed as percentage of DAPI+ cells after BLEBB treatment in Fig. 7e. Mean ± SD; n=3 mice. Unpaired t test (ns, not significant). Data derived from wild-type C57BL/6J mice, unless otherwise stated. Individual points show individual measurements, greyscale indicates values from each of 3 mice. Parts of (b, e) were drawn by using and/or adapting diagrams from Servier Medical Art. Source data are provided.

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Supplementary information

Supplementary Information

Supplementary Note 1

Reporting Summary

Peer Review Information

Supplementary Table 1

Quality control statistics for the scRNA-seq data. Basic statistics on the quality of the scRNA-seq data. The statistics were calculated after filtering out low-quality cells (see Methods for low-quality criteria).

Supplementary Table 2

Tab 1: Expression patterns in basal cells. Clustering of expression profiles of the DEGs between P7, P28 and adult stages for basal cells (see Extended Data Fig. 4a,b). For cluster membership, refer to Tab 2 (Expression pattern) and ExpressionPattern_BasalCell.R in the GitHub repository (https://github.com/BenSimonsLab/McGinn_Nat-Cell-Biol_2021/tree/main/scRNAseq/Source). Tab 2: Expression pattern. Four major patterns of DEGs for basal cells along time points. The heatmap shows four major expression patterns of DEGs at the P28 and adult stages compared with P7, as shown in Extended Data Fig. 4a,b. The relative expression values of 1,738 DEGs were clustered using k-means clustering (k = 10). The relative expression values of the DEGs in each k-means cluster were averaged to give the representative expression values displayed in the heatmap. The size numbers on the right indicates the number of genes belonging to each k-means cluster. The ten k-means clusters were again grouped into four major patterns. The colour bar represents the averaged z-transformed normalized UMI values.

Supplementary Table 3

Gene Ontology analysis of basal cell expression patterns. Expanded version of the table showing Gene Ontology analysis in Extended Data Fig. 4b. P values were determined based on a one-sided Fisher’s exact test in DAVID version 6.8 (https://david.ncifcrf.gov/).

Supplementary Table 4

Supplementary Table 4 Tab 1: Expression patterns of basal resting cells. Expression patterns of DEGs at the P7, P28 and adult stages for resting basal cells, related to Fig. 3f. Expression values of DEGs between representative clusters of resting basal cells at the P7, P28 and adult stages are shown. The expression values were calculated by averaging auto-scaled normalized UMIs in a cluster or group of clusters, as indicated. Clusters 1, 2, 6, 7 and 12 are defined in Extended Data Fig. 3. The 1,729 DEGs were clustered using k-means clustering (k = 6). The six k-means clusters were classified into three major expression patterns (see Tab 2 (K-means clustering) for k-means clusters and expression patterns). Also refer to ExpressionPattern_RestingBasalCell.R in the GitHub repository (https://github.com/BenSimonsLab/McGinn_Nat-Cell-Biol_2021/tree/main/scRNAseq/Source). Tab 2: K-means clustering. Heatmap showing the clustering of marker genes for resting basal cell clusters. Three major expression patterns of DEGs between representative clusters of resting basal cells at the P7, P28 and adult stages (Fig. 3f) are shown. The expression values of the DEGs in each k-means cluster were averaged to give the representative expression values displayed in the heatmap. The size numbers on the right indicate the number of genes belonging to each k-means cluster. The six k-means clusters were again grouped into three major patterns. The colour bar shows the averaged expression value in a k-means cluster. Tabs 3–5. Gene Ontology analysis results for expression patterns 1–3. These tabs show the results of Gene Ontology analysis (biological process terms) for 748 genes in expression pattern 1 (Tab 3), 163 genes in expression pattern 2 (Tab 4) and 818 genes in expression pattern 3 (Tab 5) using DAVID version 6.8 (https://david.ncifcrf.gov/). P values were determined based on a one-sided Fisher’s exact test. P values were adjusted using Bonferroni, Benjamini and FDR methods. Refer to DAVID (https://david.ncifcrf.gov/content.jsp?file=functional_annotation.html) for details relating to each column.

Supplementary Table 5

List of antibodies. Complete list of antibodies used.

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McGinn, J., Hallou, A., Han, S. et al. A biomechanical switch regulates the transition towards homeostasis in oesophageal epithelium. Nat Cell Biol 23, 511–525 (2021). https://doi.org/10.1038/s41556-021-00679-w

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