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Human melanocyte development and melanoma dedifferentiation at single-cell resolution

Nature Cell Biology volume 23, pages 1035–1047 (2021)Cite this article

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Abstract

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.

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Fig. 1: Melanocyte transcriptomic profiles differ based on development and anatomical location.
Fig. 2: Characterization of divergent pigment developmental trajectories in volar and non-volar melanocytes.
Fig. 3: Anatomical site-specific melanocyte subpopulation enrichment arises during development and persists in adulthood.
Fig. 4: Defining transcriptomic programs specific to human melanocyte development.
Fig. 5: Evaluation of the expression of melanocyte developmental programs from mammalian models in human non-volar cutaneous melanocyte developmental groups.
Fig. 6: Identification of distinct patterns of developmental programs reacquired in metastasized melanomas.
Fig. 7: Reacquisition of specific developmental programs in heterogeneous melanoma is prognostic.

Data availability

All healthy human skin scRNA-seq data generated for this study has been deposited in the Gene Expression Omnibus (GEO) database repository and are available under accession number GSE151091. Human melanoma datasets were obtained from publicly accessible repositories: GSE65904, GSE72056, GSE115978, dbGAP phs001036.v1.p1, TCGA Research Network (https://www.cancer.gov/tcga), and the Single Cell portal (https://portals.broadinstitute.org/single_cell/study/melanoma-immunotherapy-resistance). All other data supporting the findings of this study are available from the corresponding authors on reasonable request; lead contact: R.L.J.-T. Source data are provided with this paper.

Code availability

Jupyter notebooks with detailed analysis scripts are available at GitHub (https://github.com/danledinh/human_melanocytes)83.

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Acknowledgements

We thank the staff at the University of California, San Francisco Program for Breakthrough Biomedical Research Sandler Fellows Program for funding (to R.L.J.-T.); the staff at the University of California, San Francisco Biospecimen Resource Program for support with tissue acquisition; N. Neff and M. Tan for assistance with library quality control and sequencing; the staff at the Huntsman Cancer Institute Bioinformatic Analysis Shared Resource and University of Utah Center for High Performance Computing for supporting analyses of acral tumour samples at the Huntsman Cancer Institute; and the staff at Life Science Editors for critical editing of the manuscript. Funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Daniel Le

    Present address: Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech Inc, South San Francisco, CA, USA

  2. Ashley Maynard

    Present address: Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

  3. These authors contributed equally: Rachel L. Belote, Daniel Le.

Authors and Affiliations

  1. Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    Rachel L. Belote & Robert L. Judson-Torres

  2. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Daniel Le, Ashley Maynard & Spyros Darmanis

  3. Department of Dermatology, University of California, San Francisco, CA, USA

    Ursula E. Lang

  4. Department of Pathology, University of California, San Francisco, CA, USA

    Ursula E. Lang

  5. Department of Urology and Division of Pediatric Urology, University of California, San Francisco, CA, USA

    Adriane Sinclair & Laurence Baskin

  6. Bioinformatics Shared Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    Brian K. Lohman

  7. Department of Otolaryngology–Head and Neck Surgery, University of California, San Francisco, CA, USA

    Vicente Planells-Palop & Aaron D. Tward

  8. Department of Dermatology, University of Utah, Salt Lake City, UT, USA

    Robert L. Judson-Torres

  9. Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA

    Robert L. Judson-Torres

  10. Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech Inc, South San Francisco, CA, USA

    Spyros Darmanis

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Contributions

Conceptualization: R.L.B., D.L., A.D.T, S.D. and R.L.J.-T. Methodology: R.L.B. and A.M. Validation: R.L.B. and D.L. Formal analysis: R.L.B. and D.L. Investigation: R.L.B., D.L. and A.M. Resources: U.E.L., A.S., B.K.L., V.P.-P., L.B. and A.D.T. Data curation: D.L., A.M. and B.K.L. Writing—original draft: R.L.B. and R.L.J.-T. Writing—review and editing: D.L., A.M., U.E.L. and S.D. Visualization: R.L.B. and D.L. Supervision: S.D. and R.L.J.-T. Funding acquisition: S.D. and R.L.J.-T.

Corresponding authors

Correspondence to Spyros Darmanis or Robert L. Judson-Torres.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Single cell RNA sequencing quality control, cell-type specific markers, and donor age.

a) FACS gate protocol for representative sort. Melanocytes (blue circles in live, scatter, and singlets) were isolated as KIT+ cells from the CD11c- gate. b) Fraction of cells from each indexed FACS gate assignment. c) Number of reads and d) number of genes per cell for all 14,370 sequenced cells. Dashed line: quality control threshold, cells with < 50,000 reads and < 500 genes were excluded from further analysis. e) Genes expressed in more than 3 cells (dashed line) were included for subsequent analysis. f) Cell-type specific gene expression overlay on UMAPs. Genes indicated in upper left corner of each plot. g-i) UMAPs with donor age overlay for g) adult, h) neonatal, and i) fetal cells. j) Heatmap of expression values (row z-score) for all cell types in our dataset of differentially expressed genes (DEG)s from melanocyte clusters identified in previous fresh from human skin sequencing studies10,11.

Source data

Extended Data Fig. 2 Characterization of cell cycle state.

a) UMAPs of cycling cell program score used to determine which cells were designated as b) cycling (blue, in G2 & M phase) vs non-cycling (red). c) Fraction of cycling and non-cycling cells for each cell type identified in Fig. 1b.

Source data

Extended Data Fig. 3 FACS BSC is a correlate of relative melanocyte pigmentation.

a) Representative FACS plots of BSC and FSC for melanocytes from three non-volar cutaneous skin donors of varying skin pigmentation levels: light (L), light-medium (LM), and medium (M). b) Increase in BSC corresponds to increase in pigmentation. Mean raw BSC value with corresponding histogram for melanocytes isolated from non-volar cutaneous skin donors.

Source data

Extended Data Fig. 4 NTRK2 and HPGD expression (summarized in Fig. 3d) by anatomic site and donor age.

Expression level of v-mel gene, NTRK2, and c-mel gene, HPGD, in volar melanocytes compared to non-volar cutaneous melanocytes at each age (n=22 donors). Interquartile range with median, standard deviation, and outliers (grey circles).

Source data

Extended Data Fig. 5 Pseudotime and pairwise differential expression analysis of developmental ages and groups.

Pseudotime and pairwise differential expression analysis of developmental ages and groups. a) Melanocytes cluster by developmental age in diffusion component space DC1 and DC2. b) Pseudotime overlay onto DC space. c) Progression from fetal to adult through an intermediate neonatal transcriptional state. Diffusion pseudotime from b) is plotted for each cell, binned by donor age. d) Volcano plot highlighting the top ten DEGs between MSC (yellow) and FET (teal) non-volar cutaneous melanocyte populations. e) Volcano plot showing the top ten DEGs between FET (teal) and ADT (magenta) non-volar cutaneous melanocytes. (d-e) DEGs determined by Two-sided Wilcoxon Rank Sum Test and adjusted p-value computed using Benjamini-Hochberg multiple testing procedure. f) Heatmap visualization of the relative expression (column z score) of DEGs from (d) and (e) for all four non-volar cutaneous developmental groups. Both MSC and FET were enriched for known developmental genes (SOX11, LYPD1) and genes involved in extracellular matrix establishment/remodeling (COL1A2, PXDN). The ADT group expressed genes involved in innate immunity, inflammation and regulating apoptosis/cell stress in other cell types and tissues (HLAs, APOD, CLU, LGALS3). The NEO group exhibited high expression of a subset of genes from both the FET and ADT stages, consistent with neonatal melanocytes being an intermediate developmental state. See Supplementary Table 4 for the full list.

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Extended Data Fig. 6 Identification of enriched biological processes in MSC, FET, NEO and ADT melanocytes.

Significantly enriched biological processes between temporally adjacent developmental groups a) FET vs MSC; b) NEO vs FET; and c) ADT vs NEO. Each dot represents an individual GO-bp term, plotted according to their associated NES. Dot color correspond to the FDR q-value for each GO-bp term and size corresponds to number of enriched genes from each GO-bp term.

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Extended Data Fig. 7 Biological processes associated with DevMel transcriptional programs.

PercayAI, an augmented artificial intelligence software platform, identified biological concepts (processes/pathways) associated with the positively correlated genes in each DevMel transcriptional program. Two dimensional representation of biological themes (circles) comprised of genes related by associated biological concepts arrange in three dimensional space based on relatedness of each themes for a) prg[MSC]; b) prg[FET]; c) prg[NEO]; and d) prg[ADT]. Highly related themes are connected by grey lines.

Extended Data Fig. 8 Characterization of melanoma cells and tumors classified by in situ human melanocyte developmental programs.

a-b) Density plots showing the expression of a) the Widmer et al. from ref. 61 invasive and proliferative programs and b) the Tirosh et al. from ref. 58 AXL and MITF programs for individual cells in MALADT, MALNEO, MALFET and MALMSC groups. c) Pairwise Fisher (one-sided) exact test showing negative log10 adjusted (Bonferroni multiple testing) p-values for the gene set enrichment analysis conducted using TCGA et al., 2015, Cirenajwis et al., 2015 and Tsoi et al., 2018 gene signatures. Significant enrichment determined as adjusted p-value < 0.05. d) Heatmap showing the relative expression levels (row z score) of WNT5A high, TP53 high slow cycling cell associated genes in each normal melanocyte and MAL developmental group. e) Heatmap showing the relative expression levels (row z score) of the four minimal residual disease states identified by Rambow et al., 2018 in each normal melanocyte and MAL developmental group. f) Pairwise Fisher (one-sided) exact test showing negative log10 adjusted (Bonferroni multiple testing) p-values for clinicopathological feature and transcriptional categorization within each SKCM group (SKCMADT, SKCMNEO, SKCMFET, SKCMMSC). There is little to no difference in the enrichment of pigment level, mutation category, or tissue origin between SKCM groups in Fig. 7. g) Heatmap showing the relative expression levels (row z-score) of immune infiltration program, immune evasion program and FDA-approved therapeutic targets in SKCM groups. h) The MALNEO signature is enriched for genes down regulated in tumors that respond to Nivolumab treatment (green text). Pairwise Fisher (one-sided) exact test showing negative log10 adjusted (Bonferroni multiple testing) p-values for the gene set enrichment analysis conducted using previously identified prognostic signatures (Supplementary Table 8).

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Supplementary Tables 1–8 in a single Excel file.

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Belote, R.L., Le, D., Maynard, A. et al. Human melanocyte development and melanoma dedifferentiation at single-cell resolution. Nat Cell Biol 23, 1035–1047 (2021). https://doi.org/10.1038/s41556-021-00740-8

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