Single-cell sequencing technology in skin wound healing

Xu Cheng Cheng , Zi Tong Wang , Rui Wang , Feng Zhao , Shuai Hou , Zhe Wang

Burns & Trauma ›› 2024, Vol. 12 ›› Issue (1) : tkae043

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Burns & Trauma ›› 2024, Vol. 12 ›› Issue (1) :tkae043 DOI: 10.1093/burnst/tkae043
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Single-cell sequencing technology in skin wound healing
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Abstract

Skin wound healing is a complicated biological process that mainly occurs in response to injury, burns, or diabetic ulcers. It can also be triggered by other conditions such as dermatitis and melanoma-induced skin cancer. Delayed healing or non-healing after skin injury presents an important clinical issue; therefore, further explorations into the occurrence and development of wound healing at the cellular and molecular levels are necessary. Single-cell sequencing (SCS) is used to sequence and analyze the genetic messages of a single cell. Furthermore, SCS can accurately detect cell expression and gene sequences. The use of SCS technology has resulted in the emergence of new concepts pertaining to wound healing, making it an important tool for studying the relevant mechanisms and developing treatment strategies. This article discusses the application value of SCS technology, the effects of the latest research on skin wound healing, and the value of SCS technology in clinical applications. Using SCS to determine potential biomarkers for wound repair will serve to accelerate wound healing, reduce scar formation, optimize drug delivery, and facilitate personalized treatments.

Keywords

Single-cell sequencing / Wound healing / DNA sequencing / RNA sequencing / Epigenetic sequencing / Skin / Scar / Diabetic ulcer

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Xu Cheng Cheng, Zi Tong Wang, Rui Wang, Feng Zhao, Shuai Hou, Zhe Wang. Single-cell sequencing technology in skin wound healing. Burns & Trauma, 2024, 12 (1) : tkae043 DOI:10.1093/burnst/tkae043

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References

[1]

Wang PH, Huang BS, Horng HC, Yeh CC, Chen YJ. Wound healing. J Chin Med Assoc 2018; 81: 94-101. https://doi.org/10.1016/j.jcma.2017.11.002.

[2]

O’Meara S, Cullum N, Majid M, Sheldon T. Systematic reviews of wound care management: (3) antimicrobial agents for chronic wounds; (4) diabetic foot ulceration. Health Technol Assess 2000; 4: 1-237.

[3]

Hedlund E, Deng Q. Single-cell RNA sequencing: technical advancements and biological applications. Mol Asp Med 2018; 59: 36-46. https://doi.org/10.1016/j.mam.2017.07.003.

[4]

Wu X, Yang B, Udo-Inyang I, Ji S, Ozog D, Zhou L, et al. Research techniques made simple: single-cell RNA sequencing and its applications in dermatology. J Invest Dermatol 2018; 138: 1004-9. https://doi.org/10.1016/j.jid.2018.01.026.

[5]

Guo S, Dipietro LA. Factors affecting wound healing. J Dent Res 2010; 89: 219-29. https://doi.org/10.1177/0022034509359125.

[6]

Hubler MJ, Peterson KR, Hasty AH. Iron homeostasis: a new job for macrophages in adipose tissue? Trends Endocrinol Metab 2015; 26: 101-9. https://doi.org/10.1016/j.tem.2014.12.005.

[7]

Powers JG, Higham C, Broussard K, Phillips TJ. Wound healing and treating wounds: chronic wound care and management. J Am Acad Dermatol 2016; 74: 607-25quiz 625-6. https://doi.org/10.1016/j.jaad.2015.08.070.

[8]

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. Xu N. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009; 6: 377-82. https://doi.org/10.1038/nmeth.1315.

[9]

Ren H, Zhao F, Zhang Q, Huang X, Wang Z. Autophagy and skin wound healing. Burns. Trauma 2022; 10: tkac003. https://doi.org/10.1093/burnst/tkac003.

[10]

Martin P, Nunan R. Cellular and molecular mechanisms of repair in acute and chronic wound healing. Br J Dermatol 2015; 173: 370-8. https://doi.org/10.1111/bjd.13954.

[11]

Klein CA, Seidl S, Petat-Dutter K, Offner S, Geigl JB, Schmidt-Kittler O, et al. Combined transcriptome and genome analysis of single micrometastatic cells. Nat Biotechnol 2002; 20: 387-92. https://doi.org/10.1038/nbt0402-387.

[12]

Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 2013; 498: 236-40. https://doi.org/10.1038/nature12172.

[13]

Brehm-Stecher BF, Johnson EA. Single-cell microbiology: tools, technologies, and applications. Microbiol Mol Biol Rev 2004; 68: 538-59. https://doi.org/10.1128/MMBR.68.3.538-559.2004.

[14]

Dries R, Chen J, Del Rossi N, Khan MM, Sistig A, Yuan GC. Advances in spatial transcriptomic data analysis. Genome Res 2021; 31: 1706-18. https://doi.org/10.1101/gr.275224.121.

[15]

Deng Q, Ramsköld D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014; 343: 193-6. https://doi.org/10.1126/science.1245316.

[16]

Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, et al. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24: 550-72. https://doi.org/10.1038/s41576-023-00586-w.

[17]

Jovic D, Liang X, Zeng H, Lin L, Xu F, Luo Y. Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med 2022; 12: e694. https://doi.org/10.1002/ctm2.694.

[18]

Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 2018; 50: 1-14. https://doi.org/10.1038/s12276-018-0071-8.

[19]

Telenius H, Pelmear AH, Tunnacliffe A, Carter NP, Behmel A, Ferguson-Smith MA, et al. Cytogenetic analysis by chromosome painting using DOP-PCR amplified flow-sorted chromosomes. Genes Chromosomes Cancer 1992; 4: 257-63. https://doi.org/10.1002/gcc.2870040311.

[20]

Dean FB, Hosono S, Fang L, Wu X, Faruqi AF, Bray-Ward P, et al. Comprehensive human genome amplification using multiple displacement amplification. Proc Natl Acad Sci USA 2002; 99: 5261-6. https://doi.org/10.1073/pnas.082089499.

[21]

Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 2012; 338: 1622-6. https://doi.org/10.1126/science.1229164.

[22]

Chen C, Xing D, Tan L, Li H, Zhou G, Huang L, et al. Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science 2017; 356: 189-94. https://doi.org/10.1126/science.aak9787.

[23]

Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 2012; 30: 777-82. https://doi.org/10.1038/nbt.2282.

[24]

Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014; 9: 171-81. https://doi.org/10.1038/nprot.2014.006.

[25]

Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets. Cell 2015; 161: 1202-14. https://doi.org/10.1016/j.cell.2015.05.002.

[26]

Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 2016; 17: 77. https://doi.org/10.1186/s13059-016-0938-8.

[27]

Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2012; 2: 666-73. https://doi.org/10.1016/j.celrep.2012.08.003.

[28]

Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell 2017; 65: 631-643.e4. https://doi.org/10.1016/j.molcel.2017.01.023.

[29]

Guo H, Zhu P, Wu X, Li X, Wen L, Tang F. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 2013; 23: 2126-35. https://doi.org/10.1101/gr.161679.113.

[30]

Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 2015; 523: 486-90. https://doi.org/10.1038/nature14590.

[31]

Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 2015; 528: 142-6. https://doi.org/10.1038/nature15740.

[32]

Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 2015; 33: 1165-72. https://doi.org/10.1038/nbt.3383.

[33]

Nakamura N, Ruebel K, Long J, Qian X, Zhang H, Lloyd RV.Laser capture microdissection for analysis of single cells. Methods Mol Med 2007; 132: 11-8. https://doi.org/10.1007/978-1-59745-298-4_2.

[34]

Blainey PC.The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol Rev 2013; 37: 407-27. https://doi.org/10.1111/1574-6976.12015.

[35]

Christian R, Janey L, Nandita N, Goudeau D, Thompson B, Poulton N, et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat Protoc 2014; 9: 1038-48. https://doi.org/10.1038/nprot.2014.067.

[36]

Birey F, Andersen J, Makinson CD, Islam S, Wei W, Huber N, et al. Assembly of functionally integrated human forebrain spheroids. Nature 2017; 545: 54-9. https://doi.org/10.1038/nature22330.

[37]

Zeisel A, Hochgerner H, Lonnerberg P, Johnsson A, Memic F, van der Zwan J, et al. Molecular architecture of the mouse nervous system. Cell 2018; 174: 999-1014.e22. https://doi.org/10.1016/j.cell.2018.06.021.

[38]

Lei Y, Tang R, Xu J, Wang W, Zhang B, Liu J, et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol 2021; 14: 91. https://doi.org/10.1186/s13045-021-01105-2.

[39]

Song Y, Milon B, Ott S, Zhao X, Sadzewicz L, Shetty A, et al. A comparative analysis of library prep approaches for sequencing low input translatome samples. BMC Genomics 2018; 19: 696. https://doi.org/10.1186/s12864-018-5066-2.

[40]

Björklund ÅK, Forkel M, Picelli S, Konya V, Theorell J, Friberg D, et al. The heterogeneity of human CD127(+) innate lymphoid cells revealed by single-cell RNA sequencing. Nat Immunol 2016; 17: 451-60. https://doi.org/10.1038/ni.3368.

[41]

Wang X, He Y, Zhang Q, Ren X, Zhang Z. Direct comparative analyses of 10X genomics chromium and Smart-seq2. Genomics Proteomics Bioinformatics 2021; 19: 253-66. https://doi.org/10.1016/j.gpb.2020.02.005.

[42]

Morris TJ, Beck S. Analysis pipelines and packages for Infinium human methylation 450 bead Chip (450k) data. Methods 2015; 72: 3-8. https://doi.org/10.1016/j.ymeth.2014.08.011.

[43]

Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, et al. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21: 31. https://doi.org/10.1186/s13059-020-1926-6.

[44]

Chen G, Ning B, Shi T. Single-cell RNA-Seq technologies and related computational data analysis. Front Genet 2019; 10: 317. https://doi.org/10.3389/fgene.2019.00317.

[45]

Baßler K, Günther P, Schulte-Schrepping J, Becker M, Biernat P. A Bioinformatic toolkit for single-cell mRNA analysis. Methods Mol Biol 2019; 1979: 433-55. https://doi.org/10.1007/978-1-4939-9240-9_26.

[46]

Sankowski R, Monaco G, Prinz M. Evaluating microglial phenotypes using single-cell technologies. Trends Neurosci 2022; 45: 133-44. https://doi.org/10.1016/j.tins.2021.11.001.

[47]

Van de Sande B, Flerin C, Davie K, De Waegeneer M, Hulselmans G, Aibar S, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc 2020; 15: 2247-76. https://doi.org/10.1038/s41596-020-0336-2.

[48]

Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017; 14: 1083-6. https://doi.org/10.1038/nmeth.4463.

[49]

Singhal PK, Sassi S, Lan L, Au P, Halvorsen SC, Fukumura D, et al. Mouse embryonic fibroblasts exhibit extensive developmental and phenotypic diversity. Proc Natl Acad Sci USA 2016; 113: 122-7. https://doi.org/10.1073/pnas.1522401112.

[50]

Daley JM, Brancato SK, Thomay AA, Reichner JS, Albina JE. The phenotype of murine wound macrophages. J Leukoc Biol 2010; 87: 59-67. https://doi.org/10.1189/jlb.0409236.

[51]

Baker AB, Ettenson DS, Jonas M, Nugent MA, Iozzo RV, Edelman ER. Endothelial cells provide feedback control for vascular remodeling through a mechanosensitive autocrine TGF-beta signaling pathway. Circ Res 2008; 103: 289-97. https://doi.org/10.1161/CIRCRESAHA.108.179465.

[52]

Theocharidis G, Tekkela S, Veves A, McGrath JA, Onoufriadis A. Single-cell transcriptomics in human skin research: available technologies, technical considerations and disease applications. Exp Dermatol 2022; 31: 655-73. https://doi.org/10.1111/exd.14547.

[53]

Costa V, Angelini C, De Feis I, Ciccodicola A. Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010; 2010: 853916. https://doi.org/10.1155/2010/853916.

[54]

Wang Z, Gerstein M, Snyder M. Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009; 10: 57-63. https://doi.org/10.1038/nrg2484.

[55]

Stark R, Grzelak M, Hadfield J. Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019; 20: 631-56. https://doi.org/10.1038/s41576-019-0150-2.

[56]

Liu J, Qiu X, Lv Y, Zheng C, Dong Y, Dou G, et al. Apoptotic bodies derived from mesenchymal stem cells promote cutaneous wound healing via regulating the functions of macrophages. Stem Cell Res Ther 2020; 11: 507. https://doi.org/10.1186/s13287-020-02014-w.

[57]

Naderi N, Combellack EJ, Griffin M, Sedaghati T, Javed M, Findlay MW, et al. The regenerative role of adipose-derived stem cells (ADSC) in plastic and reconstructive surgery. Int Wound J 2017; 14: 112-24. https://doi.org/10.1111/iwj.12569.

[58]

Wang Z, Feng C, Liu H, Meng T, Huang WQ, Song KX, et al. Exosomes from circ-Astn1-modified adipose-derived mesenchymal stem cells enhance wound healing through miR-138-5p/SIRT1/FOXO1 axis regulation. World J Stem Cells 2023; 15: 476-89. https://doi.org/10.4252/wjsc.v15.i5.476.

[59]

Sun C, Wang L, Wang H, Huang T, Yao W, Li J, et al. Single-cell RNA-seq highlights heterogeneity in human primary Wharton’s jelly mesenchymal stem/stromal cells cultured in vitro. Stem Cell Res Ther 2020; 11: 149. https://doi.org/10.1186/s13287-020-01660-4.

[60]

Lin K, Yang Y, Cao Y, Liang J, Qian J, Wang X, et al. Combining single-cell transcriptomics and cell tagging to identify differentiation trajectories of human adipose-derived mesenchymal stem cells. Stem Cell Res Ther 2023; 14: 14. https://doi.org/10.1186/s13287-023-03237-3.

[61]

Schwalie PC, Dong H, Zachara M, Russeil J, Alpern D, Akchiche N, et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature 2018; 559: 103-8. https://doi.org/10.1038/s41586-018-0226-8.

[62]

Merrick D, Sakers A, Irgebay Z, Okada C, Calvert C, Morley MP, et al. Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science 2019; 364: eaav2501. https://doi.org/10.1126/science.aav2501.

[63]

Tabib T, Morse C, Wang T, Chen W, Lafyatis R. SFRP2/DPP4 and FMO1/LSP1 define major fibroblast populations in human skin. J Invest Dermatol. 2018; 138: 802-10. https://doi.org/10.1016/j.jid.2017.09.045.

[64]

Wei K, Nguyen HN, Brenner MB. Fibroblast pathology in inflammatory diseases. J Clin Invest 2021; 131:e149538. https://doi.org/10.1172/JCI149538.

[65]

Ugurlu B, Karaoz E. Comparison of similar cells: mesenchymal stromal cells and fibroblasts. Acta Histochem 2020; 122: 151634. https://doi.org/10.1016/j.acthis.2020.151634.

[66]

Sivan U, Jayakumar K, Krishnan LK. Matrix-directed differentiation of human adipose-derived mesenchymal stem cells to dermal-like fibroblasts that produce extracellular matrix. J Tissue Eng Regen Med 2016; 10: E546-58. https://doi.org/10.1002/term.1865.

[67]

Soundararajan M, Kannan S. Fibroblasts and mesenchymal stem cells: two sides of the same coin? J Cell Physiol 2018; 233: 9099-109. https://doi.org/10.1002/jcp.26860.

[68]

Pradhan RN, Krishnamurty AT, Fletcher AL, Turley SJ, Müller S. A bird’s eye view of fibroblast heterogeneity: a pan-disease, pan-cancer perspective. Immunol Rev 2021; 302: 299-320. https://doi.org/10.1111/imr.12990.

[69]

Velnar T, Bailey T, Smrkolj V. The wound healing process: an overview of the cellular and molecular mechanisms. J Int Med Res 2009; 37: 1528-42. https://doi.org/10.1177/147323000903700531.

[70]

Li L, Zhang J, Zhang Q, Zhang D, Xiang F, Jia J, et al. High glucose suppresses keratinocyte migration through the inhibition of p38 MAPK/autophagy pathway. Front Physiol 2019; 10: 24. https://doi.org/10.3389/fphys.2019.00024.

[71]

Theocharidis G, Baltzis D, Roustit M, Tellechea A, Dangwal S, Khetani RS, et al. Integrated skin Transcriptomics and serum multiplex assays reveal novel mechanisms of wound healing in diabetic foot ulcers. Diabetes 2020; 69: 2157- 69. https://doi.org/10.2337/db20-0188.

[72]

Theocharidis G, Thomas BE, Sarkar D, Mumme HL, Pilcher WJR, Dwivedi B, et al. Single cell transcriptomic landscape of diabetic foot ulcers. Nat Commun 2022; 13: 181. https://doi.org/10.1038/s41467-021-27801-8.

[73]

Guerrero-Juarez CF, Dedhia PH, Jin S, Ruiz-Vega R, Ma D, Liu Y, et al. Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. Nature. Communications 2019; 10: 650. https://doi.org/10.1038/s41467-018-08247-x.

[74]

Chen CJ, Kajita H, Takaya K, Aramaki-Hattori N, Sakai S, Asou T, et al. Single-cell RNA-seq analysis reveals cellular functional heterogeneity in dermis between fibrotic and regenerative wound healing fates. Front Immunol 2022; 13: 875407. https://doi.org/10.3389/fimmu.2022.875407.

[75]

Sun LX, Wu S, Zhang XW, Liu WJ, Zhang LJ. Investigation on the growth factor regulatory network of dermal fibroblasts in mouse full-thickness skin defect wounds based on single-cell RNA sequencing. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2022; 38: 629-39Chinese. https://doi.org/10.3760/cma.j.cn501225-20220215-00029.

[76]

Muhl L, Genové G, Leptidis S, Liu J, He L, Mocci G, et al. Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell identification and discrimination. Nat Commun 2020; 11: 3953. https://doi.org/10.1038/s41467-020-17740-1.

[77]

Shim J, Oh SJ, Yeo E, Park JH, Bae JH, Kim SH, et al. Integrated analysis of single-cell and spatial Transcriptomics in keloids: highlights on Fibrovascular interactions in keloid pathogenesis. J Invest Dermatol 2022; 142: 2128-2139.e11. https://doi.org/10.1016/j.jid.2022.01.017.

[78]

Kalucka J, de Rooij LPMH, Goveia J, Rohlenova K, Dumas SJ, Meta E, et al. Single-cell transcriptome atlas of murine endothelial cells. Cell 2020; 180: 764-779.e20. https://doi.org/10.1016/j.cell.2020.01.015.

[79]

Joost S, Zeisel A, Jacob T, Sun X, la Manno G, Lönnerberg P, et al. Single-cell Transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst 2016; 3: 221-237.e9. https://doi.org/10.1016/j.cels.2016.08.010.

[80]

Haensel D, Jin S, Sun P, Cinco R, Dragan M, Nguyen Q, et al. Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell Transcriptomics. Cell Rep 2020; 30: 3932-3947.e6. https://doi.org/10.1016/j.celrep.2020.02.091.

[81]

Charles Jacob HK, Lavania S, Saluja AK. Single cell nuclear sequencing of healthy and diseased pancreas: the coming-of-age of single nucleus RNA sequencing. Gastroenterology 2021; 160: 1014-6. https://doi.org/10.1053/j.gastro.2020.12.028.

[82]

Lake BB, Chen S, Hoshi M, Plongthongkum N, Salamon D, Knoten A, et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 2019; 10: 2832. https://doi.org/10.1038/s41467-019-10861-2.

[83]

Bakken TE, Hodge RD, Miller JA, Yao Z, Nguyen TN, Aevermann B, et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS One 2018; 13: e0209648. https://doi.org/10.1371/journal.pone.0209648.

[84]

Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med 2020; 26: 792-802. https://doi.org/10.1038/s41591-020-0844-1.

[85]

Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol 2019; 30: 23-32. https://doi.org/10.1681/ASN.2018090912.

[86]

Zhu R, Pan X, Wang S, Qiu Z, Gu C, Yao X, et al. Updated skin transcriptomic atlas depicted by reciprocal contribution of single-nucleus RNA sequencing and single-cell RNA sequencing. J Dermatol Sci 2023; 111: 22-31. https://doi.org/10.1016/j.jdermsci.2023.06.005.

[87]

Sun W, Dong H, Balaz M, Slyper M, Drokhlyansky E, Colleluori G, et al. snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature 2020; 587: 98-102. https://doi.org/10.1038/s41586-020-2856-x.

[88]

Shendure J, Balasubramanian S, Church GM, Gilbert W, Rogers J, Schloss JA, et al. Schloss JA. DNA sequencing at 40: past, present and future. Nature 2017; 550: 345-53. https://doi.org/10.1038/nature24286.

[89]

Rantalainen M.Application of single-cell sequencing in human cancer. Brief Funct Genomics 2018; 17: 273-82. https://doi.org/10.1093/bfgp/elx036.

[90]

Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol 2018; 16: 143-55. https://doi.org/10.1038/nrmicro.2017.157.

[91]

Oh J, Byrd AL, Park M, NISC Comparative Sequencing Program, Kong HH, Segre JA.Temporal stability of the human skin microbiome. Cell 2016; 165: 854-66. https://doi.org/10.1016/j.cell.2016.04.008.

[92]

Feng L, Huinan Y, Quan H, Qinxue Z, Qi C, Longlong Y, et al. Application of metagenomics second-generation sequencing technology for the detection of pathogens in burn patients and patients with acute and chronic wound surfaces. Chinese Burn Journal 2021; 37: 764-9.

[93]

Goswami K, Tipton C, Clarkson S, Chang G, Tan TL, Fram B, et al. Fracture-associated microbiome and persistent nonunion: next-generation sequencing reveals new findings. J Orthop Trauma 2022; 36: S40-6. https://doi.org/10.1097/BOT.0000000000002305.

[94]

Senneville E, Lombart A, Beltrand E, Valette M, Legout L, Cazaubiel M, et al. Outcome of diabetic foot osteomyelitis treated nonsurgically: a retrospective cohort study. Diabetes Care 2008; 31: 637-42. https://doi.org/10.2337/dc07-1744.

[95]

Malone M, Fritz BG, Vickery K, Schwarzer S, Sharma V, Biggs N, et al. Analysis of proximal bone margins in diabetic foot osteomyelitis by conventional culture, DNA sequencing and microscopy. APMIS 2019; 127: 660-70. https://doi.org/10.1111/apm.12986.

[96]

Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. Identification of novel genes coding for small expressed RNAs. Science 2001; 294: 853-8. https://doi.org/10.1126/science.1064921.

[97]

Borrow J, Stanton VP Jr, Andresen JM, Becher R, Behm FG, Chaganti RSK, et al. The translocation t(8;16)(p11;p13) of acute myeloid leukaemia fuses a putative acetyltransferase to the CREB-binding protein. Nat Genet 1996; 14: 33-41. https://doi.org/10.1038/ng0996-33.

[98]

Aapola U, Kawasaki K, Scott HS, Ollila J, Vihinen M, Heino M, et al. Isolation and initial characterization of a novel zinc finger gene, DNMT3L, on 21q22.3, related to the cytosine-5-methyltransferase 3 gene family. Genomics 2000; 65: 293-8. https://doi.org/10.1006/geno.2000.6168.

[99]

Yu H, Wang Y, Wang D, Yi Y, Liu Z, Wu M, et al. Landscape of the epigenetic regulation in wound healing. Front Physiol 2022; 13: 949498. https://doi.org/10.3389/fphys.2022.949498.

[100]

Cao J, Yan Q. Histone ubiquitination and deubiquitination in transcription, DNA damage response, and cancer. Front Oncol 2012; 2: 26. https://doi.org/10.3389/fonc.2012.00026.

[101]

Peixoto P, Cartron PF, Serandour AA, Serandour AA, Hervouet E. From 1957 to nowadays: a brief history of epigenetics. Int J Mol Sci 2020; 21: 7571. https://doi.org/10.3390/ijms21207571.

[102]

Abbasi S, Sinha S, Labit E, Rosin NL, Yoon G, Rahmani W, et al. Distinct regulatory programs control the latent regenerative potential of dermal fibroblasts during wound healing. Cell Stem Cell 2021; 28: 581-3. https://doi.org/10.1016/j.stem.2021.02.004.

[103]

Foster DS, Januszyk M, Yost KE, Chinta MS, Gulati GS, Nguyen AT, et al. Integrated spatial multiomics reveals fibroblast fate during tissue repair. Proc Natl Acad Sci USA 2021; 118:e2110025118. https://doi.org/10.1073/pnas.2110025118.

[104]

Wen L, Tang F. Single cell epigenome sequencing technologies. Mol Asp Med 2018; 59: 62-9. https://doi.org/10.1016/j.mam.2017.09.002.

[105]

Lorenz HP, Longaker MT, Perkocha LA, Jennings RW, Harrison MR, Adzick NS. Scarless wound repair: a human fetal skin model. Development 1992; 114: 253-9. https://doi.org/10.1242/dev.114.1.253.

[106]

Rinkevich Y, Walmsley GG, Hu MS, Maan ZN, Newman AM, Drukker M, et al. Identification and isolation of a dermal lineage with intrinsic fibrogenic potential. Science 2015; 348: aaa2151. https://doi.org/10.1126/science.aaa2151.

[107]

Mallery CS Jr, Carrillo M, Mei A, Correia-Branco A, Kashpur O, Wallingford MC. Cellular complexity of Hemochorial placenta: stem cell populations, insights from scRNA-seq, and SARS-CoV-2 susceptibility. Curr Stem Cell Rep 2021; 7: 185-93. https://doi.org/10.1007/s40778-021-00194-6.

[108]

Rodrigues M, Kosaric N, Bonham CA, Gurtner GC. Wound healing: a cellular perspective. Physiol Rev 2019; 99: 665-706. https://doi.org/10.1152/physrev.00067.2017.

[109]

Song B, Zhu Y, Zhao Y, Wang K, Peng Y, Chen L, et al. Machine learning and single-cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGf β1/P-Smad2/3 signalling pathway in hypertrophic scar. Int Wound J 2023; 21: e14481. https://doi.org/10.1111/iwj.14481.

[110]

Wu D, Wang W, Li X, Yin B, Ma Y. Single-cell sequencing reveals the antifibrotic effects of YAP/TAZ in systemic sclerosis. Int J Biochem Cell Biol 2022; 149: 106257. https://doi.org/10.1016/j.biocel.2022.106257.

[111]

Frykberg RG, Banks J. Challenges in the treatment of chronic wounds. Adv Wound Care (New Rochelle) 2015; 4: 560-82. https://doi.org/10.1089/wound.2015.0635.

[112]

Du H, Li S, Lu J, Tang L, Jiang X, He X, et al. Single-cell RNA-seq and bulk-seq identify RAB17 as a potential regulator of angiogenesis by human dermal microvascular endothelial cells in diabetic foot ulcers. Burns Trauma 2023; 11: tkad020. https://doi.org/10.1093/burnst/tkad020.

[113]

He J, Wang JR, Gan WJ, Li GQ, Xin Q, Lin ZP, et al. Analysis of the types and functions of CD34+ cells in full-thickness skin defect wounds of normal mice and diabetic mice by single-cell RNA sequencing. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2024; 40: 230-9. Chinese. https://doi.org/10.3760/cma.j.cn501225-20231130-00217.

[114]

Yakupu A, Zhang D, Guan H, Jiang M, Dong J, Niu Y, et al. Single-cell analysis reveals melanocytes may promote inflammation in chronic wounds through cathepsin G. Front Genet 2023; 14: 1072995. https://doi.org/10.3389/fgene.2023.1072995.

[115]

Wang Z, Zhao F, Chengcheng X, Zhang Q, Ren H, Huang X, et al. Metabolic reprogramming in skin wound healing. Burns & Trauma 2024; 12: tkad047. https://doi.org/10.1093/burnst/tkad047.

[116]

Marcy Y, Ishoey T, Lasken RS, Stockwell TB, Walenz BP, Halpern AL, et al. Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet 2007; 3: 1702-8. https://doi.org/10.1371/journal.pgen.0030155.

[117]

Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, et al. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proc Natl Acad Sci USA 2012; 109: 17454-9. https://doi.org/10.1073/pnas.1213530109.

[118]

Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015; 348: aaa6090. https://doi.org/10.1126/science.aaa6090.

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