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A strain-programmed patch for the healing of diabetic wounds

Abstract

Diabetic foot ulcers and other chronic wounds with impaired healing can be treated with bioengineered skin or with growth factors. However, most patients do not benefit from these treatments. Here we report the development and preclinical therapeutic performance of a strain-programmed patch that rapidly and robustly adheres to diabetic wounds, and promotes wound closure and re-epithelialization. The patch consists of a dried adhesive layer of crosslinked polymer networks bound to a pre-stretched hydrophilic elastomer backing, and implements a hydration-based shape-memory mechanism to mechanically contract diabetic wounds in a programmable manner on the basis of analytical and finite-element modelling. In mouse and human skin, and in mini-pigs and humanized mice, the patch enhanced the healing of diabetic wounds by promoting faster re-epithelialization and angiogenesis, and the enrichment of fibroblast populations with a pro-regenerative phenotype. Strain-programmed patches might also be effective for the treatment of other forms of acute and chronic wounds.

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Fig. 1: Strain-programmed patch for diabetic skin wounds.
Fig. 2: Design and mechanical properties of the strain-programmed patch.
Fig. 3: Mechanical modulation of human skin wounds.
Fig. 4: Evaluation of diabetic wound healing in a db/db mouse model.
Fig. 5: Immunofluorescence staining analysis of diabetic mouse wounds.
Fig. 6: Transcriptomic analysis of diabetic mouse wounds.
Fig. 7: Evaluation of diabetic wound healing in a human skin model.
Fig. 8: Accelerated diabetic wound healing of in vivo porcine skin.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The RNA-seq data are available from the Gene Expression Omnibus (GEO) database, with accession number GSE154132. Publicly available single-cell RNA-seq data were obtained from GEO (accession number, GSE141814). Additional raw datasets generated during the study are too large to be publicly shared, yet they are available from the corresponding authors on reasonable request.

References

  1. National Diabetes Statistics Report (Centers for Disease Control and Prevention, 2017).

  2. Geiss, L. S. et al. Resurgence of diabetes-related nontraumatic lower extremity amputation in the young and middle-aged adult U.S. population. Diabetes Care 42, 50–54 (2019).

    PubMed  Google Scholar 

  3. Boulton, A. J., Vileikyte, L., Ragnarson-Tennvall, G. & Apelqvist, J. The global burden of diabetic foot disease. Lancet 366, 1719–1724 (2005).

    PubMed  Google Scholar 

  4. Veves, A., Falanga, V., Armstrong, D. G., Sabolinski, M. L. & Apligraf Diabetic Foot Ulcer Study Graftskin, a human skin equivalent, is effective in the management of noninfected neuropathic diabetic foot ulcers: a prospective randomized multicenter clinical trial. Diabetes Care 24, 290–295 (2001).

    CAS  PubMed  Google Scholar 

  5. Marston, W. A., Hanft, J., Norwood, P. & Pollak, R. The efficacy and safety of Dermagraft in improving the healing of chronic diabetic foot ulcers: results of a prospective randomized trial. Diabetes Care 26, 1701–1705 (2003).

    PubMed  Google Scholar 

  6. Wieman, T. J., Smiell, J. M. & Su, Y. Efficacy and safety of a topical gel formulation of recombinant human platelet-derived growth factor-BB (becaplermin) in patients with chronic neuropathic diabetic ulcers. A phase III randomized placebo-controlled double-blind study. Diabetes Care 21, 822–827 (1998).

    CAS  PubMed  Google Scholar 

  7. Tecilazich, F., Dinh, T. & Veves, A. Treating diabetic ulcers. Expert Opin. Pharmacother. 12, 593–606 (2011).

    PubMed  Google Scholar 

  8. Tecilazich, F., Dinh, T. L. & Veves, A. Emerging drugs for the treatment of diabetic ulcers. Expert Opin. Emerg. Drugs 18, 207–217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Singer, A. J. & Clark, R. A. Cutaneous wound healing. N. Engl. J. Med. 341, 738–746 (1999).

    CAS  PubMed  Google Scholar 

  10. George Broughton, I., Janis, J. E. & Attinger, C. E. The basic science of wound healing. Plast. Reconstr. Surg. 117, 12S–34S (2006).

    PubMed  Google Scholar 

  11. Gurtner, G. C., Werner, S., Barrandon, Y. & Longaker, M. T. Wound repair and regeneration. Nature 453, 314–321 (2008).

    CAS  PubMed  Google Scholar 

  12. Wong, V. W. et al. A mechanomodulatory device to minimize incisional scar formation. Adv. Wound Care 2, 185–194 (2013).

    Google Scholar 

  13. Eming, S. A., Martin, P. & Tomic-Canic, M. Wound repair and regeneration: mechanisms, signaling, and translation. Sci. Transl. Med. 6, 265sr266 (2014).

    Google Scholar 

  14. Barnes, L. A. et al. Mechanical forces in cutaneous wound healing: emerging therapies to minimize scar formation. Adv. Wound Care 7, 47–56 (2018).

    Google Scholar 

  15. Harn, H. I. C. et al. The tension biology of wound healing. Exp. Dermatol. 28, 464–471 (2019).

    PubMed  Google Scholar 

  16. Falanga, V. Wound healing and its impairment in the diabetic foot. Lancet 366, 1736–1743 (2005).

    PubMed  Google Scholar 

  17. Loots, M. A. et al. Differences in cellular infiltrate and extracellular matrix of chronic diabetic and venous ulcers versus acute wounds. J. Investig. Dermatol. 111, 850–857 (1998).

    CAS  PubMed  Google Scholar 

  18. Brem, H. & Tomic-Canic, M. Cellular and molecular basis of wound healing in diabetes. J. Clin. Investig. 117, 1219–1222 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Theocharidis, G. et al. Single cell transcriptomic landscape of diabetic foot ulcers. Nat. Commun. 13, 181 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang, J. H.-C., Thampatty, B. P., Lin, J.-S. & Im, H.-J. Mechanoregulation of gene expression in fibroblasts. Gene 391, 1–15 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Gurtner, G. C. et al. Improving cutaneous scar formation by controlling the mechanical environment: large animal and phase I studies. Ann. Surg. 254, 217–225 (2011).

    PubMed  Google Scholar 

  22. Li, J. et al. Tough adhesives for diverse wet surfaces. Science 357, 378–381 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Blacklow, S. et al. Bioinspired mechanically active adhesive dressings to accelerate wound closure. Sci. Adv. 5, eaaw3963 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Yuk, H. et al. Dry double-sided tape for adhesion of wet tissues and devices. Nature 575, 169–174 (2019).

    CAS  PubMed  Google Scholar 

  25. Kelley, F. N. & Bueche, F. Viscosity and glass temperature relations for polymer‐diluent systems. J. Polym. Sci. 50, 549–556 (1961).

    CAS  Google Scholar 

  26. Frisch, H., Wang, T. & Kwei, T. Diffusion in glassy polymers. II. J. Polym. Sci. A‐2 7, 879–887 (1969).

    CAS  Google Scholar 

  27. Mao, X., Yuk, H. & Zhao, X. Hydration and swelling of dry polymers for wet adhesion. J. Mech. Phys. Solids 137, 103863 (2020).

    CAS  Google Scholar 

  28. Lendlein, A. & Langer, R. Biodegradable, elastic shape-memory polymers for potential biomedical applications. Science 296, 1673–1676 (2002).

    PubMed  Google Scholar 

  29. Mather, P. T., Luo, X. & Rousseau, I. A. Shape memory polymer research. Annu. Rev. Mater. Res. 39, 445–471 (2009).

    CAS  Google Scholar 

  30. Meng, H. & Li, G. A review of stimuli-responsive shape memory polymer composites. Polymer 54, 2199–2221 (2013).

    CAS  Google Scholar 

  31. Chen, X., Yuk, H., Wu, J., Nabzdyk, C. S. & Zhao, X. Instant tough bioadhesive with triggerable benign detachment. Proc. Natl Acad. Sci. USA 117, 15497–15503 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Upton, D., Solowiej, K., Hender, C. & Woo, K. Stress and pain associated with dressing change in patients with chronic wounds. J. Wound Care 21, 53–61 (2012).

    CAS  PubMed  Google Scholar 

  33. Than, U. T. T., Guanzon, D., Leavesley, D. & Parker, T. Association of extracellular membrane vesicles with cutaneous wound healing. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18050956 (2017).

  34. Flynn, C., Taberner, A. & Nielsen, P. Mechanical characterisation of in vivo human skin using a 3D force-sensitive micro-robot and finite element analysis. Biomech. Model. Mechanobiol. 10, 27–38 (2011).

    PubMed  Google Scholar 

  35. Berezovsky, A. B. et al. Primary contraction of skin grafts: a porcine preliminary study. Plast. Aesthet. Res. 25, 22–26 (2015).

    Google Scholar 

  36. Joodaki, H. & Panzer, M. B. Skin mechanical properties and modeling: a review. Proc. Inst. Mech. Eng. H 232, 323–343 (2018).

    PubMed  Google Scholar 

  37. Grada, A., Mervis, J. & Falanga, V. Research techniques made simple: animal models of wound healing. J. Investig. Dermatol. 138, 2095–2105.e1 (2018).

    CAS  PubMed  Google Scholar 

  38. Scherer, S. S. et al. Wound healing kinetics of the genetically diabetic mouse. Wounds 20, 18–28 (2008).

    PubMed  Google Scholar 

  39. Volk, S. W. & Bohling, M. W. Comparative wound healing—are the small animal veterinarian’s clinical patients an improved translational model for human wound healing research? Wound Repair Regen. 21, 372–381 (2013).

    PubMed  Google Scholar 

  40. Chen, L., Mirza, R., Kwon, Y., DiPietro, L. A. & Koh, T. J. The murine excisional wound model: contraction revisited. Wound Repair Regen. 23, 874–877 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. Wang, X. T., McKeever, C. C., Vonu, P., Patterson, C. & Liu, P. Y. Dynamic histological events and molecular changes in excisional wound healing of diabetic DB/DB mice. J. Surg. Res. 238, 186–197 (2019).

    CAS  PubMed  Google Scholar 

  42. Hinz, B. Formation and function of the myofibroblast during tissue repair. J. Investig. Dermatol. 127, 526–537 (2007).

    CAS  PubMed  Google Scholar 

  43. Rinkevich, Y. et al. Skin fibrosis. Identification and isolation of a dermal lineage with intrinsic fibrogenic potential. Science 348, aaa2151 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. Mascharak, S. et al. Preventing Engrailed-1 activation in fibroblasts yields wound regeneration without scarring. Science 372, eaba2374 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Jiang, D. et al. Two succeeding fibroblastic lineages drive dermal development and the transition from regeneration to scarring. Nat. Cell Biol. 20, 422–431 (2018).

    CAS  PubMed  Google Scholar 

  46. Shook, B. A. et al. Dermal adipocyte lipolysis and myofibroblast conversion are required for efficient skin repair. Cell Stem Cell 26, 880–895.e6 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Plikus, M. V. et al. Regeneration of fat cells from myofibroblasts during wound healing. Science 355, 748–752 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Joshi, N. et al. Comprehensive characterization of myeloid cells during wound healing in healthy and healing-impaired diabetic mice. Eur. J. Immunol. 50, 1335–1349 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Mariani, E., Lisignoli, G., Borzi, R. M. & Pulsatelli, L. Biomaterials: foreign bodies or tuners for the immune response? Int. J. Mol. Sci. https://doi.org/10.3390/ijms20030636 (2019).

  50. Krzyszczyk, P., Schloss, R., Palmer, A. & Berthiaume, F. The role of macrophages in acute and chronic wound healing and interventions to promote pro-wound healing phenotypes. Front. Physiol. 9, 419 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. Gay, D. et al. Phagocytosis of Wnt inhibitor SFRP4 by late wound macrophages drives chronic Wnt activity for fibrotic skin healing. Sci. Adv. 6, eaay3704 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Acharya, P. S. et al. Fibroblast migration is mediated by CD44-dependent TGFβ activation. J. Cell Sci. 121, 1393–1402 (2008).

    CAS  PubMed  Google Scholar 

  53. Ruiz-Ederra, J. & Verkman, A. Aquaporin-1-facilitated keratocyte migration in cell culture and in vivo corneal wound healing models. Exp. Eye Res. 89, 159–165 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Xie, T. et al. Single-cell deconvolution of fibroblast heterogeneity in mouse pulmonary fibrosis. Cell Rep. 22, 3625–3640 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Stojadinovic, O. & Tomic-Canic, M. Human ex vivo wound healing model. Methods Mol. Biol. 1037, 255–264 (2013).

    PubMed  Google Scholar 

  56. Gherardini, J., van Lessen, M., Piccini, I., Edelkamp, J. & Bertolini, M. Human wound healing ex vivo model with focus on molecular markers. Methods Mol. Biol. 2154, 249–254 (2020).

    CAS  PubMed  Google Scholar 

  57. Summerfield, A., Meurens, F. & Ricklin, M. E. The immunology of the porcine skin and its value as a model for human skin. Mol. Immunol. 66, 14–21 (2015).

    CAS  PubMed  Google Scholar 

  58. Chen, K. et al. Disrupting biological sensors of force promotes tissue regeneration in large organisms. Nat. Commun. 12, 5256 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Martínez‐Santamaría, L. et al. The regenerative potential of fibroblasts in a new diabetes‐induced delayed humanised wound healing model. Exp. Dermatol. 22, 195–201 (2013).

    PubMed  Google Scholar 

  60. Ding, J. & Tredget, E. E. in Fibrosis. Methods in Molecular Biology Vol. 1627 (ed. Rittié, L.) 65–80 (Humana Press, 2017).

  61. Démarchez, M., Hartmann, D. J., Herbage, D., Ville, G. & Pruniéras, M. Wound healing of human skin transplanted onto the nude mouse: II. An immunohistological and ultrastructural study of the epidermal basement membrane zone reconstruction and connective tissue reorganization. Dev. Biol. 121, 119–129 (1987).

    PubMed  Google Scholar 

  62. Driver, V. R. et al. A clinical trial of Integra Template for diabetic foot ulcer treatment. Wound Repair Regen. 23, 891–900 (2015).

    PubMed  Google Scholar 

  63. Baltzis, D., Eleftheriadou, I. & Veves, A. Pathogenesis and treatment of impaired wound healing in diabetes mellitus: new insights. Adv. Ther. 31, 817–836 (2014).

    CAS  PubMed  Google Scholar 

  64. Castleberry, S. A. et al. Self-assembled wound dressings silence MMP-9 and improve diabetic wound healing in vivo. Adv. Mater. 28, 1809–1817 (2016).

    CAS  PubMed  Google Scholar 

  65. Shibata, S. et al. Adiponectin regulates cutaneous wound healing by promoting keratinocyte proliferation and migration via the ERK signaling pathway. J. Immunol. 189, 3231–3241 (2012).

    CAS  PubMed  Google Scholar 

  66. Gao, M. et al. Acceleration of diabetic wound healing using a novel protease-anti-protease combination therapy. Proc. Natl Acad. Sci. USA 112, 15226–15231 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Brown, R. L., Breeden, M. P. & Greenhalgh, D. G. PDGF and TGF-alpha act synergistically to improve wound healing in the genetically diabetic mouse. J. Surg. Res. 56, 562–570 (1994).

    CAS  PubMed  Google Scholar 

  68. Smiell, J. M. et al. Efficacy and safety of becaplermin (recombinant human platelet-derived growth factor-BB) in patients with nonhealing, lower extremity diabetic ulcers: a combined analysis of four randomized studies. Wound Repair Regen. 7, 335–346 (1999).

    CAS  PubMed  Google Scholar 

  69. Leal, E. C. et al. Substance P promotes wound healing in diabetes by modulating inflammation and macrophage phenotype. Am. J. Pathol. 185, 1638–1648 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Tellechea, A. et al. Topical application of a mast cell stabilizer improves impaired diabetic wound healing. J. Investig. Dermatol. 140, 901–911 e911 (2020).

    CAS  PubMed  Google Scholar 

  71. Stone, R. C. et al. A bioengineered living cell construct activates an acute wound healing response in venous leg ulcers. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aaf8611 (2017).

  72. Theocharidis, G. et al. Integrated skin transcriptomics and serum multiplex assays reveal novel mechanisms of wound healing in diabetic foot ulcers. Diabetes 69, 2157–2169 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Asp, M., Bergenstrahle, J. & Lundeberg, J. Spatially resolved transcriptomes-next generation tools for tissue exploration. Bioessays 42, e1900221 (2020).

    PubMed  Google Scholar 

  74. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  75. Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).

    PubMed  PubMed Central  Google Scholar 

  76. Cunningham, F. et al. Ensembl 2019. Nucleic Acids Res. 47, D745–D751 (2019).

    CAS  PubMed  Google Scholar 

  77. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  78. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  79. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  80. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Koch Institute Swanson Biotechnology Center for technical support, specifically K. Cormier and the Histology Core for the histological processing and analysis. This work was supported by Defense Advanced Research Projects Agency (DARPA) (5(GG0015670) to X.Z. and A.V.) and Department of Defense Congressionally Directed Medical Research Programs (CDMRP) (PR200524P1 to X.Z. and A.V.). A.V. received funding from the National Rongxiang Xu Foundation. G.T. received a George and Marie Vergottis Foundation Postdoctoral Fellowship. H.Y. acknowledges the financial support from Samsung Scholarship. H.R. acknowledges the financial support from Kwanjeong Educational Foundation Scholarship.

Author information

Authors and Affiliations

Authors

Contributions

H.Y., G.T., A.V. and X.Z. designed the study. H.Y. conceived the idea and developed the materials and method for the strain-programmed patch. H.Y., H.R. and C.S.N. designed the in vitro and ex vivo experiment. H.Y. and H.R. conducted the in vitro and ex vivo experiment and analysis. L.W. and C.F.G. designed and conducted the theoretical and numerical modelling and analysis. H.Y. and J.W. designed and conducted the in vivo biocompatibility experiment. G.T., I.M., L.C. and A.V. designed and conducted the in vivo diabetic mouse wound healing experiment and analysis. G.T., M.C., B.S. and A.V. designed and conducted the in vivo diabetic porcine wound healing experiment. G.T., I.M., B.S., L.Z. and E.W. designed and conducted the in vivo diabetic humanized mice wound healing experiment. G.T., I.M., Z.L., E.W. and N.J. completed the flow cytometry, histology, immunofluorescence, gene expression and human skin ex vivo experiment analyses. A.K. performed histology assessment and scoring. X.-L.K., N.K. and I.S.V. performed sequencing analysis. H.Y. and G.T. prepared the figures. G.T., H.Y., X.Z. and A.V. wrote the manuscript with inputs from all authors.

Corresponding authors

Correspondence to Hyunwoo Yuk, Aristidis Veves or Xuanhe Zhao.

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Competing interests

H.Y., H.R., X.Z., G.T. and A.V. are the inventors of a patent application (U.S. Application No. 63/148,901) that covers the design and mechanism of the strain-programmed patch for diabetic wound healing. H.Y., C.S.N. and X.Z. have a financial interest in SanaHeal, Inc., a biotechnology company focused on the development of medical devices for surgical sealing and repair. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Rapid wet adhesion and on-demand detachment of the strain-programmed patch.

a, Rapid wet adhesion and on-demand detachment of the strain-programmed patch. b,c, Chemistry of the physical and covalent crosslinks for rapid wet adhesion (b) and on-demand detachment (c) of the strain-programmed patch.

Extended Data Fig. 2 Anisotropically strain-programmed patch.

a, Closure of an incisional wound on porcine skin by the anisotropically strain-programmed patch. b,c, Theoretical and experimental values of contraction (\(\lambda _{{{{\mathrm{patch}}}}}^{{{{\mathrm{shrink}}}}}\)) (b) and nominal contractile stress (c) generated by programmed strain release upon hydration of the anisotropically strain-programmed patch with varying \(\lambda _{{{{\mathrm{patch}}}}}^{{{{\mathrm{pre}}}}1}\). Values in b,c represent the mean and the standard deviation (n = 4; independent samples). Scale bars, 10 mm (a).

Extended Data Fig. 3 On-demand detachment of the strain-programmed patch.

a, On-demand detachment of the strain-programmed patch adhered on a porcine skin by application of a detachment solution. b, Interfacial toughness of the strain-programmed patch adhered on porcine skin 5 min after applying PBS or the detachment solution. Values in b represent the mean and the standard deviation (n = 3; independent samples). Statistical significance and p values are determined by two-sided t-test.

Extended Data Fig. 4 In vitro and in vivo biocompatibility of the strain-programmed patch.

a, Representative LIVE/DEAD assay images and the cell viability of mouse embryonic fibroblasts (mEFs) for control (DMEM) and the strain-programmed patch after 24-h culture. DMEM, Dulbecco’s modified eagle medium. b-f, Representative histological images for the subcutaneously implanted strain-programmed patch (b), Coseal (c), Dermabond cyanoacrylate (CA) adhesive (d), strain-programmed patch after on-demand detachment (e), and sham surgery (f) after 2 weeks post-implantation stained with hematoxylin and eosin (H&E). g, Degree of inflammation of various groups evaluated by a blinded pathologist (0, normal; 1, mild; 2, moderate; 3, severe; 4, very severe) after 2 weeks post-implantation. Skin side and implant side in the histological images are indicated by arrows. SM, skeletal muscle; FC, fibrous capsule. All experiments are repeated four biological replicates with similar results. Values in a,g represent the mean and the standard deviation (n = 4; independent samples). Statistical significance and p values are determined by two-sided t-test; ns, not significant; * p < 0.05. Scale bars, 200 µm (a-f).

Extended Data Fig. 5 Proliferation and apoptosis of the wound cells.

a,b, Quantification of proliferation marker Ki67+ cells in the dermis (a) and the epidermis (b) of day 5 (D5) wounds. c, Quantification of apoptosis marker active Caspase-3+ cells in the dermis of D5 wounds. d,e, Quantification of proliferation marker Ki67+ cells in the dermis (d) and the epidermis (e) of day 10 (D10) wounds. f, Quantification of apoptosis marker active Caspase-3+ cells in the dermis of D10 wounds. Values represent the mean and the standard deviation (n = 10 in a and b; n = 7 in c and f; n = 10 for TD Only, 8 for No Strain and 10 for Strain in d; n = 10 for TD Only, 8 for No Strain and 8 for Strain in e; independent samples). Statistical significance and p values are determined by one-way ANOVA followed by Tukey’s multiple comparison test; ns, not significant.

Extended Data Fig. 6 Flow-cytometric quantification of major immune cell populations and macrophage polarized states in D5 wounds.

a-m, Single-cell suspensions were generated from excised wound and peri-wound tissues and stained for the indicated cell surface proteins. Percentage of immune cells (a), neutrophils (b), monocytes (c), macrophages (d), dendritic cells (DCs) (e), and T-cells (f) and T-cell subsets (g,h). Percentage of macrophages expressing markers CD86 (i), CD80 (j), CD163 (k), CD206 (l), and CD301b (m). Each data point represents pooled cells from two mice (four wounds). Values represent the mean and the standard deviation (n = 6 for TD Only, 7 for No Strain and 7 for Strain; independent samples). Statistical significance and p values are determined by one-way ANOVA followed by Fisher’s least significant difference (LSD) post-hoc test; ns, not significant.

Extended Data Fig. 7 Flow-cytometric quantification of major immune cell populations and macrophage polarized states in D10 wounds.

a-m, Single-cell suspensions were generated from excised wound and peri-wound tissues and stained for the indicated cell surface proteins. Percentage of immune cells (a), neutrophils (b), monocytes (c), macrophages (d), dendritic cells (DCs) (e), and T-cells (f) and T-cell subsets (g,h). Percentage of macrophages expressing markers CD86 (i), CD80 (j), CD163 (k), CD206 (l), and CD301b (m). Each data point represents pooled cells from two mice (four wounds). Values represent the mean and the standard deviation (n = 6 for TD Only, 7 for No Strain and 7 for Strain; independent samples). Statistical significance and p values are determined by one-way ANOVA followed by Fisher’s least significant difference (LSD) post-hoc test; ns, not significant.

Extended Data Fig. 8 Ex vivo human skin wound-healing model with pre-tension.

a-c, Ex vivo human skin with speckles and corresponding digital image correlation (DIC) analysis results without pre-tension (a) and with pre-tension in x-direction (b) and in y-direction (c). ROI, region of interest. d,e, Representative images of an ex vivo human skin culture setup with pre-tension before (d) and after (e) the strain-programmed patch application. f, Quantification of the open wound length on day 4 (D4) post-injury. Values in f represent the mean and the standard deviation (n = 9 wounds for No Strain and 13 wounds for Strain from 2 individual patients’ skin; independent samples). Statistical significance and p values are determined by two-sided t-test. Scale bars, 5 mm (d,e).

Extended Data Fig. 9 Immunostaining analysis of diabetic in vivo wound healing of porcine skin.

a,b, Representative immunohistochemistry images for CD31 (a) and quantification of CD31 + vessels per unit area (b) on day 7 (D7). c,d, Representative immunohistochemistry images for CD31 (c) and quantification of CD31 + vessels per unit area (d) on day 14 (D14). e,f, Representative immunofluorescence images for αSMA (e) and quantification of αSMA + cells per unit area (f) on D7. g, Representative immunofluorescence images for αSMA on D14. In immunohistochemistry images, the NOVA Red peroxidase substrate chromogenic stain was used. In immunofluorescence images, blue fluorescence corresponds to cell nuclei stained with 4′,6-diamidino-2-phenylindole (DAPI); green fluorescence corresponds to the expression of αSMA. Experimental groups are Tegaderm (TD) only, no strain (\(\lambda _{{{{\mathrm{patch}}}}}^{{{{\mathrm{pre}}}}} = 1\)) and strain-programmed (\(\lambda _{{{{\mathrm{patch}}}}}^{{{{\mathrm{pre}}}}} = 1.3\)) patch for both D7 and D14. Values in b,d,f represent the mean and the standard deviation (n = 6; independent samples). Statistical significance and p values are determined by one-way ANOVA followed by Fisher’s least significant difference (LSD) post-hoc test; ns, not significant. Scale bars, 100 µm (a,c,e,g).

Extended Data Fig. 10 Accelerated diabetic in vivo wound healing of humanized mouse skin.

a, Schematic illustrations for the xenotransplantation procedure, diabetes induction, and experimental plan. W, week. b, Representative images from Day 5 wounds with Masson’s trichrome stain (MTS). Red triangles denote wound margins. c-g, Quantification of the wound closure expressed as % of open wound compared to Day 0 (c), the re-epithelialization expressed as % (d), the hyperproliferative epidermis (HPE) area (e), the number of CD31 + vessels per unit area (f), and the number of αSMA + cells per unit area (g) on Day 5. Values in c-g represent the mean and the standard deviation (n = 4 in c, f, g; n = 4 for No Strain and 3 for Strain in d and e; independent samples). Statistical significance and p values are determined by two-sided t-test. Scale bars, 5 mm (a); 250 µm (b). Parts of (a) were created with BioRender.com.

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Supplementary Information

Supplementary methods, discussion, figures, references and video captions.

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Supplementary Dataset 1

Complete lists of differentially expressed genes from RNA-seq data.

Supplementary Dataset 2

Complete list of the antibody cocktails for flow cytometry.

Supplementary Video 1

Rapid adhesion and closure of an ex vivo porcine skin wound by the strain-programmed patch.

Supplementary Video 2

Rapid adhesion and closure of an incisional skin wound by the anisotropically strain-programmed patch.

Supplementary Video 3

On-demand removal of the adhered patch from ex vivo porcine skin wound by applying a detachment solution.

Supplementary Video 4

Rapid adhesion and wound closure in diabetic mouse skin by the strain-programmed patch.

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Theocharidis, G., Yuk, H., Roh, H. et al. A strain-programmed patch for the healing of diabetic wounds. Nat. Biomed. Eng (2022). https://doi.org/10.1038/s41551-022-00905-2

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