Human melanocyte development and melanoma dedifferentiation at single-cell resolution.
Journal
Nature cell biology
ISSN: 1476-4679
Titre abrégé: Nat Cell Biol
Pays: England
ID NLM: 100890575
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
03
12
2020
accepted:
18
07
2021
pubmed:
4
9
2021
medline:
12
10
2021
entrez:
3
9
2021
Statut:
ppublish
Résumé
In humans, epidermal melanocytes are responsible for skin pigmentation, defence against ultraviolet radiation and the deadliest common skin cancer, melanoma. Although there is substantial overlap in melanocyte development pathways between different model organisms, species-dependent differences are frequent and the conservation of these processes in human skin remains unresolved. Here, we used a single-cell enrichment and RNA-sequencing pipeline to study human epidermal melanocytes directly from the skin, capturing transcriptomes across different anatomical sites, developmental age, sexes and multiple skin tones. We uncovered subpopulations of melanocytes that exhibit anatomical site-specific enrichment that occurs during gestation and persists through adulthood. The transcriptional signature of the volar-enriched subpopulation is retained in acral melanomas. Furthermore, we identified human melanocyte differentiation transcriptional programs that are distinct from gene signatures generated from model systems. Finally, we used these programs to define patterns of dedifferentiation that are predictive of melanoma prognosis and response to immune checkpoint inhibitor therapy.
Identifiants
pubmed: 34475532
doi: 10.1038/s41556-021-00740-8
pii: 10.1038/s41556-021-00740-8
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1035-1047Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Yamaguchi, Y. et al. Mesenchymal-epithelial interactions in the skin: Increased expression of dickkopf1 by palmoplantar fibroblasts inhibits melanocyte growth and differentiation. J. Cell Biol. 165, 275–285 (2004).
pubmed: 15117970
pmcid: 2172049
doi: 10.1083/jcb.200311122
Adameyko, I. et al. Schwann cell precursors from nerve innervation are a cellular origin of melanocytes in skin. Cell 139, 366–379 (2009).
pubmed: 19837037
doi: 10.1016/j.cell.2009.07.049
pmcid: 19837037
Mort, R. L., Jackson, I. J. & Elizabeth Patton, E. The melanocyte lineage in development and disease. Development 142, 620–632 (2015).
pubmed: 25670789
pmcid: 4325379
doi: 10.1242/dev.106567
Hayward, N. K. et al. Whole-genome landscapes of major melanoma subtypes. Nature 545, 175–180 (2017).
pubmed: 28467829
doi: 10.1038/nature22071
pmcid: 28467829
Rabbie, R., Ferguson, P., Molina-Aguilar, C., Adams, D. J. & Robles-Espinoza, C. D. Melanoma subtypes: genomic profiles, prognostic molecular markers and therapeutic possibilities. J. Pathol. 247, 539–551 (2019).
pubmed: 30511391
pmcid: 6492003
doi: 10.1002/path.5213
Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173, 338–354 (2018).
pubmed: 29625051
pmcid: 5902191
doi: 10.1016/j.cell.2018.03.034
Gupta, P. B. et al. The melanocyte differentiation program predisposes to metastasis after neoplastic transformation. Nat. Genet. 37, 1047–1054 (2005).
pubmed: 16142232
pmcid: 1694635
doi: 10.1038/ng1634
Vorstandlechner, V. et al. Deciphering the functional heterogeneity of skin fibroblasts using single-cell RNA sequencing. FASEB J. 34, 3677–3692 (2020).
pubmed: 31930613
doi: 10.1096/fj.201902001RR
pmcid: 31930613
Solé-Boldo, L. et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun. Biol. 3, 188 (2020).
pubmed: 32327715
pmcid: 7181753
doi: 10.1038/s42003-020-0922-4
Cheng, J. B. et al. Transcriptional programming of normal and inflamed human epidermis at single-cell resolution. Cell Rep. 25, 871–883 (2018).
pubmed: 30355494
pmcid: 6367716
doi: 10.1016/j.celrep.2018.09.006
Takahashi, R. et al. Defining transcriptional signatures of human hair follicle cell states. J. Invest. Dermatol. 140, 764–773 (2020).
pubmed: 31676413
doi: 10.1016/j.jid.2019.07.726
pmcid: 31676413
Popescu, D. M. et al. Decoding human fetal liver haematopoiesis. Nature 574, 365–371 (2019).
pubmed: 31597962
pmcid: 6861135
doi: 10.1038/s41586-019-1652-y
Gao, S. et al. Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat. Cell Biol. 20, 721–734 (2018).
pubmed: 29802404
doi: 10.1038/s41556-018-0105-4
pmcid: 29802404
Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).
Sridhar, A. et al. Single-cell transcriptomic comparison of human fetal retina, hPSC-derived retinal organoids, and long-term retinal cultures. Cell Rep. 30, 1644–1659 (2020).
pubmed: 32023475
pmcid: 7901645
doi: 10.1016/j.celrep.2020.01.007
Belote, R. L. & Simon, S. M. Ca
pubmed: 31821412
doi: 10.1083/jcb.201902014
pmcid: 31821412
Norris, A., Todd, C., Graham, A., Quinn, A. G. & Thody, A. J. The expression of the c-kit receptor by epidermal melanocytes may be reduced in vitiligo. Br. J. Dermatol. 134, 299–306 (1996).
pubmed: 8746346
doi: 10.1111/j.1365-2133.1996.tb07618.x
pmcid: 8746346
Randall, V. A., Jenner, T. J., Hibberts, N. A., De Oliveira, I. O. & Vafaee, T. Stem cell factor/c-Kit signalling in normal and androgenetic alopecia hair follicles. J. Endocrinol. 197, 11–23 (2008).
pubmed: 18372228
doi: 10.1677/JOE-07-0522
pmcid: 18372228
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1100 (2013).
pubmed: 24056875
doi: 10.1038/nmeth.2639
pmcid: 24056875
Hsiao, C. J. et al. Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis. Genome Res. 30, 611–621 (2020).
pubmed: 32312741
pmcid: 7197478
doi: 10.1101/gr.247759.118
Lu, R. et al. Transcription factor TCF4 maintains the properties of human corneal epithelial stem cells. Stem Cells 30, 753–761 (2012).
pubmed: 22232078
pmcid: 5610543
doi: 10.1002/stem.1032
Li, Z., Li, Y. & Jiao, J. Neural progenitor cells mediated by H2A.Z.2 regulate microglial development via Cxcl14 in the embryonic brain. Proc. Natl Acad. Sci. USA 116, 24122–24132 (2019).
pubmed: 31712428
pmcid: 6883828
doi: 10.1073/pnas.1913978116
Denecker, G. et al. Identification of a ZEB2-MITF-ZEB1 transcriptional network that controls melanogenesis and melanoma progression. Cell Death Differ. 21, 1250–1261 (2014).
pubmed: 24769727
pmcid: 4085532
doi: 10.1038/cdd.2014.44
Nishikawa, S.-I. & Osawa, M. Generating quiescent stem cells. Pigment Cell Res. 20, 263–270 (2007).
pubmed: 17630959
doi: 10.1111/j.1600-0749.2007.00388.x
pmcid: 17630959
Joshi, S. S. et al. CD34 defines melanocyte stem cell subpopulations with distinct regenerative properties. PLOS Genet. 15, e1008034 (2019).
pubmed: 31017901
pmcid: 6481766
doi: 10.1371/journal.pgen.1008034
Choi, H. R., Park, S. H., Choi, J. W., Kim, D. S. & Park, K. C. A simple assay method for melanosome transfer. Ann. Dermatol. 24, 90–93 (2012).
pubmed: 22363165
pmcid: 3283861
doi: 10.5021/ad.2012.24.1.90
Nakamura, M. et al. Site-specific migration of human fetal melanocytes in volar skin. J. Dermatol. Sci. 78, 143–148 (2015).
pubmed: 25818865
doi: 10.1016/j.jdermsci.2015.03.003
pmcid: 25818865
Cramer, S. F. & Fesyuk, A. On the development of neurocutaneous units—Implications for the histogenesis of congenital, acquired, and dysplastic nevi. Am. J. Dermatopathol. 34, 60–81 (2012).
pubmed: 22197860
doi: 10.1097/DAD.0b013e31822d071a
pmcid: 22197860
Baxter, L. L., Watkins-Chow, D. E., Pavan, W. J. & Loftus, S. K. A curated gene list for expanding the horizons of pigmentation biology. Pigment Cell Melanoma Res. 32, 348–358 (2019).
pubmed: 30339321
doi: 10.1111/pcmr.12743
pmcid: 30339321
Adhikari, K. et al. A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia. Nat. Commun. 10, 358 (2019).
pubmed: 30664655
pmcid: 6341102
doi: 10.1038/s41467-018-08147-0
Crawford, N. G. et al. Loci associated with skin pigmentation identified in African populations. Science 358, eaan8433 (2017).
pubmed: 29025994
pmcid: 5759959
doi: 10.1126/science.aan8433
Han, J. et al. A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS Genet. 4, e1000074 (2008).
pubmed: 18483556
pmcid: 2367449
doi: 10.1371/journal.pgen.1000074
Sturm, R. A. A golden age of human pigmentation genetics. Trends Genet. 22, 464–468 (2006).
pubmed: 16857289
doi: 10.1016/j.tig.2006.06.010
pmcid: 16857289
Antunes, L. C. M. et al. Tropomyosin-related kinase receptor and neurotrophin expression in cutaneous melanoma is associated with a poor prognosis and decreased survival. Oncology 97, 26–37 (2019).
pubmed: 31071716
doi: 10.1159/000499384
pmcid: 31071716
DiVito, K. A., Simbulan-Rosenthal, C. M., Chen, Y. S., Trabosh, V. A. & Rosenthal, D. S. Id2, Id3 and Id4 overcome a Smad7-mediated block in tumorigenesis, generating TGF-β-independent melanoma. Carcinogenesis 35, 951–958 (2014).
pubmed: 24343358
doi: 10.1093/carcin/bgt479
pmcid: 24343358
Yamaguchi, Y. et al. Epithelial-mesenchymal interactions in wounds: treatment of palmoplantar wounds by nonpalmoplantar pure epidermal sheet grafts. Arch. Dermatol. 137, 621–628 (2001).
pubmed: 11346340
pmcid: 11346340
Bolognia, J., Schaffer, J. & Cerroni, L. Dermatology 4th edn (Elsevier, 2018).
Bradford, P. T., Goldstein, A. M., McMaster, M. L. & Tucker, M. A. Acral lentiginous melanoma: Incidence and survival patterns in the United States, 1986-2005. Arch. Dermatol. 145, 427–434 (2009).
pubmed: 19380664
pmcid: 2735055
doi: 10.1001/archdermatol.2008.609
Mahendraraj, K. et al. Malignant melanoma in African-Americans. Medicine 96, e6258 (2017).
pubmed: 28403068
pmcid: 5403065
doi: 10.1097/MD.0000000000006258
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
doi: 10.1038/75556
Carbon, S. et al. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).
doi: 10.1093/nar/gkaa1113
Hou, L., Arnheiter, H. & Pavan, W. J. Interspecies difference in the regulation of melanocyte development by SOX10 and MITF. Proc. Natl Acad. Sci. USA 103, 9081–9085 (2006).
pubmed: 16757562
pmcid: 1482569
doi: 10.1073/pnas.0603114103
Marie, K. L. et al. Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis. Nat. Commun. 11, 333 (2020).
pubmed: 31949145
pmcid: 6965108
doi: 10.1038/s41467-019-14085-2
Rezza, A. et al. Signaling networks among stem cell precursors, transit-amplifying progenitors, and their niche in developing hair follicles. Cell Rep. 14, 3001–3018 (2016).
pubmed: 27009580
pmcid: 4826467
doi: 10.1016/j.celrep.2016.02.078
Sennett, R. et al. An integrated transcriptome atlas of embryonic hair follicle progenitors, their niche, and the developing skin. Dev. Cell 34, 577–591 (2015).
pubmed: 26256211
pmcid: 4573840
doi: 10.1016/j.devcel.2015.06.023
Mica, Y., Lee, G., Chambers, S. M., Tomishima, M. J. & Studer, L. Modeling neural crest induction, melanocyte specification, and disease-related pigmentation defects in hESCs and patient-specific iPSCs. Cell Rep. 3, 1140–1152 (2013).
pubmed: 23583175
pmcid: 3681528
doi: 10.1016/j.celrep.2013.03.025
Osawa, M. et al. Molecular characterization of melanocyte stem cells in their niche. Development 132, 5589–5599 (2005).
pubmed: 16314490
doi: 10.1242/dev.02161
pmcid: 16314490
Lu, Z. et al. Hair follicle stem cells regulate retinoid metabolism to maintain the self-renewal niche for melanocyte stem cells. eLife 9, e52712 (2020).
pubmed: 31898934
pmcid: 6970533
doi: 10.7554/eLife.52712
Saxena, N., Mok, K. W. & Rendl, M. An updated classification of hair follicle morphogenesis. Exp. Dermatol. 28, 332–344 (2019).
pubmed: 30887615
pmcid: 7137758
doi: 10.1111/exd.13913
Gleason, B. C., Crum, C. P. & Murphy, G. F. Expression patterns of MITF during human cutaneous embryogenesis: evidence for bulge epithelial expression and persistence of dermal melanoblasts. J. Cutan. Pathol. 35, 615–622 (2008).
pubmed: 18312434
pmcid: 2935278
doi: 10.1111/j.1600-0560.2007.00881.x
Holbrook, K. A., Underwood, R. A., Vogel, A. M., Gown, A. M. & Kimball, H. The appearance, density and distribution of melanocytes in human embryonic and fetal skin revealed by the anti-melanoma monoclonal antibody, HMB-45. Anat. Embryol. 180, 443–455 (1989).
doi: 10.1007/BF00305119
Hoek, K. S. et al. In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 68, 650–656 (2008).
pubmed: 18245463
doi: 10.1158/0008-5472.CAN-07-2491
pmcid: 18245463
Tsoi, J. et al. Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress. Cancer Cell 33, 890–904 (2018).
pubmed: 29657129
pmcid: 5953834
doi: 10.1016/j.ccell.2018.03.017
Richard, G. et al. ZEB 1-mediated melanoma cell plasticity enhances resistance to MAPK inhibitors. EMBO Mol. Med. 8, 1143–1161 (2016).
pubmed: 27596438
pmcid: 5048365
doi: 10.15252/emmm.201505971
Landsberg, J. et al. Melanomas resist T-cell therapy through inflammation-induced reversible dedifferentiation. Nature 490, 412–416 (2012).
pubmed: 23051752
doi: 10.1038/nature11538
pmcid: 23051752
Grzywa, T. M., Paskal, W. & Włodarski, P. K. Intratumor and intertumor heterogeneity in melanoma. Transl. Oncol. 10, 956–975 (2017).
pubmed: 29078205
pmcid: 5671412
doi: 10.1016/j.tranon.2017.09.007
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018).
pubmed: 6410377
pmcid: 6410377
doi: 10.1016/j.cell.2018.09.006
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 4944528
pmcid: 4944528
doi: 10.1126/science.aad0501
Akbani, R. et al. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).
doi: 10.1016/j.cell.2015.05.044
Cirenajwis, H. et al. Molecular stratification of metastatic melanoma using gene expression profiling—prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget 6, 12297–12309 (2015).
pubmed: 25909218
pmcid: 4494939
doi: 10.18632/oncotarget.3655
Widmer, D. S. et al. Systematic classification of melanoma cells by phenotype-specific gene expression mapping. Pigment Cell Melanoma Res. 25, 343–353 (2012).
pubmed: 22336146
doi: 10.1111/j.1755-148X.2012.00986.x
pmcid: 22336146
Raskin, L. et al. Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis. J. Invest. Dermatol. 133, 2585–2592 (2013).
pubmed: 23633021
pmcid: 4267221
doi: 10.1038/jid.2013.197
Webster, M. R. et al. Wnt5A promotes an adaptive, senescent-like stress response, while continuing to drive invasion in melanoma cells. Pigment Cell Melanoma Res. 28, 184–195 (2015).
pubmed: 25407936
doi: 10.1111/pcmr.12330
pmcid: 25407936
Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855 (2018).
pubmed: 30017245
doi: 10.1016/j.cell.2018.06.025
pmcid: 30017245
Guo, B. et al. Humanin peptide suppresses apoptosis by interfering with Bax activation. Nature 423, 456–461 (2003).
pubmed: 12732850
doi: 10.1038/nature01627
pmcid: 12732850
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
pubmed: 4739640
pmcid: 4739640
doi: 10.1038/nmeth.3337
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
doi: 10.1093/nar/gkx1037
Okamoto, N. et al. A melanocyte-melanoma precursor niche in sweat glands of volar skin. Pigment Cell Melanoma Res. 27, 1039–1050 (2014).
pubmed: 25065272
doi: 10.1111/pcmr.12297
pmcid: 25065272
Nitzan, E., Pfaltzgraff, E. R., Labosky, P. A. & Kalcheim, C. Neural crest and Schwann cell progenitor-derived melanocytes are two spatially segregated populations similarly regulated by Foxd3. Proc. Natl Acad. Sci. USA 110, 12709–12714 (2013).
Goydos, J. S. & Shoen, S. L. in Cancer Treatment and Research Vol. 167 321–329 (Kluwer Academic Publishers, 2016).
Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clinicians 70, 7–30 (2020).
doi: 10.3322/caac.21590
Sennepin, A. et al. The human penis is a genuine immunological effector site. Front. Immunol. 8, 1732 (2017).
pubmed: 29312291
pmcid: 5735067
doi: 10.3389/fimmu.2017.01732
Lezcano, C., Jungbluth, A. A., Nehal, K. S., Hollmann, T. J. & Busam, K. J. PRAME expression in melanocytic tumors. Am. J. Surgical Pathol. 42, 1456–1465 (2018).
doi: 10.1097/PAS.0000000000001134
Drey, E. A., Kang, M. S., McFarland, W. & Darney, P. D. Improving the accuracy of fetal foot length to confirm gestational duration. Obstet. Gynecol. 105, 773–778 (2005).
pubmed: 15802404
doi: 10.1097/01.AOG.0000154159.75022.11
pmcid: 15802404
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
pubmed: 26060301
pmcid: 4466750
doi: 10.1073/pnas.1507125112
Liang, W. S. et al. Integrated genomic analyses reveal frequent TERT aberrations in acral melanoma. Genome Res. 27, 524–532 (2017).
pubmed: 28373299
pmcid: 5378171
doi: 10.1101/gr.213348.116
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
pmcid: 23104886
doi: 10.1093/bioinformatics/bts635
Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
pubmed: 25260700
doi: 10.1093/bioinformatics/btu638
pmcid: 25260700
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 29409532
doi: 10.1186/s13059-017-1382-0
Hsiao, C. J. et al. Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis. Genome Res. 30, 611–621 (2020).
pubmed: 32312741
pmcid: 7197478
doi: 10.1101/gr.247759.118
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
pubmed: 12808457
doi: 10.1038/ng1180
pmcid: 12808457
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).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Jupyter note books: danledinh/human_melanocytes. Zenodo https://doi.org/10.5281/zenodo.5076159 (2021).
Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416 (2018).
pubmed: 29625055
pmcid: 6066282
doi: 10.1016/j.cell.2018.02.052