Abstract
Every cell in the human body has a unique set of somatic mutations, but it remains difficult to comprehensively genotype an individual cell1. Here we describe ways to overcome this obstacle in the context of normal human skin, thus offering a glimpse into the genomic landscapes of individual melanocytes from human skin. As expected, sun-shielded melanocytes had fewer mutations than sun-exposed melanocytes. However, melanocytes from chronically sun-exposed skin (for example, the face) had a lower mutation burden than melanocytes from intermittently sun-exposed skin (for example, the back). Melanocytes located adjacent to a skin cancer had higher mutation burdens than melanocytes from donors without skin cancer, implying that the mutation burden of normal skin can be used to measure cumulative sun damage and risk of skin cancer. Moreover, melanocytes from healthy skin commonly contained pathogenic mutations, although these mutations tended to be weakly oncogenic, probably explaining why they did not give rise to discernible lesions. Phylogenetic analyses identified groups of related melanocytes, suggesting that melanocytes spread throughout skin as fields of clonally related cells that are invisible to the naked eye. Overall, our results uncover the genomic landscapes of individual melanocytes, providing key insights into the causes and origins of melanoma.
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Data availability
Sequence data have been deposited in dbGaP (https://www.ncbi.nlm.nih.gov/gap/) with the accession code phs001979.v1.p1. Individual sample summaries of every single cell clone are available at https://doi.org/10.6084/m9.figshare.11794296.v121. Source data are provided with this paper.
Code availability
Scripts and resources to perform analyses downstream of variant calling are available at https://github.com/elliefewings/Melanocytes_Tang2020.
Change history
19 January 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03105-7
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Acknowledgements
We acknowledge support from the following: National Cancer Institute K22 CA217997 (A.H.S.), Melanoma Research Alliance (A.H.S.), LEO Foundation (A.H.S.), George and Judy Marcus Precision Medicine Fund (A.H.S. and S.T.A.), National Center for Advancing Translational Sciences and the National Institutes of Health through UCSF-CTSI TL1-TR001871 (J.T.), 1R35CA220481 (B.C.B.), Mt. Zion Health Research Fund (A.H.S.), Dermatology Foundation (A.H.S.), the American Federation of Aging Research (A.H.S.), and the NIH Director’s Common Fund DP5 OD019787 (R.L.J.). We thank the tissue donors, whose tissue was obtained through the UCSF Willed Body Program for medical education, and patients who consented to donate surgical discard tissue. Cell sorting was performed in the Laboratory for Cell Analysis of UCSF’s Helen Diller Family Comprehensive Cancer Center which is supported by a National Cancer Institute Cancer Center Support Grant (P30 CA082103).
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Conception and design of the work: A.H.S. Data collection: J.T., D.C., S.L., E.F., H.Z., A.J., R.L.B., A.S.M., S.T.A. Data analysis and interpretation: E.F., J.T., D.C., T.M.T.,R.L.J.-T., B.C.B., A.H.S. Drafting the Article: E.F., J.T., A.H.S. Critical revision of the Article: E.F., J.T., A.H.S., R.L.B., I.Y., S.T.A., R.L.J.–T., B.C.B.
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S.T.A. is an employee at Rakuten Medical and a consultant for Castle Biosciences and Enspectra Health.
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Peer review information Nature thanks Meenhard Herlyn, Inigo Martincorena and Göran Jönsson for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
Extended Data Fig. 1 Establishing the ethnicity of donors and identity of cells in this study.
a, Admixture analysis of donors included in this study alongside participants from the 1000 Genomes Project. Donors in our study were genotypically most similar to European participants from the 1000 Genomes Project. EUR- European (TSI-Toscani in Italia, IBS - Iberian Population in Spain, GBR - British in England and Scotland, CEU - Utah Residents with Northern and Western European Ancestry, FIN - Finnish in Finland), AFR - African, AMR - Latin American, SAS - South Asian, and EAS - East Asian. b, Differential expression analysis comparing cells that were morphologically predicted to be keratinocytes, melanocytes, or fibroblasts (see Fig. 1b for more details). The top 20 differentially expressed genes for each group are shown along with gene ontology terms with significant overlap. c, Cells with melanocyte morphology express higher levels of known melanocyte markers. Bar plots showing gene expression levels of MLANA, TYR, PMEL, and S100B, colored as indicated. A value of 1 is equivalent to the medium FPKM value for that gene across cells. Each quartet of bars corresponds to an individual clone, and clones are rank ordered by their medium normalized gene expression values for these 4 genes. The zoomed inset portrays the 5 melanocyte clones with lowest expression levels of melanocyte markers adjacent to the fibroblast and keratinocyte clones.
Extended Data Fig. 2 Detection of somatic mutations in small clones of skin cells with high specificity and sensitivity.
a, Allelic dropout declines rapidly as a function of clone size. Each data point represents the percent of germline SNP alleles that could not be detected for a given clone as a function of the number of cells within the clone. b, Establishing a VAF cutoff to infer somatic mutations within a clone. The left panel depicts the VAFs for known somatic mutations and known amplification artefacts from a single clone. The right panel depicts a ROC curve, showing the VAF at which sensitivity and specificity of somatic mutation calls would be maximized when inferring the mutational status of variants based on VAF alone. Variants that fell within expressed or phase-able portions of the genome were classified as mutations or artefacts as described (Fig. 1c, d). The remaining variants were inferred based on the VAF cutoff, which maximized sensitivity and specificity of somatic mutation calls. c, d, The specificity (c), and sensitivity (d), of inferred somatic mutations as a function of clone size. The mean specificity and sensitivity of inferred somatic mutations was respectively 98.83% and 98.60% for all clones of at least 5 cells. All trendlines correspond to a moving average.
Extended Data Fig. 3 Contexts of single-base substitutions corroborate the quality of somatic mutation calls.
a, The proportion of somatic mutations identified in chronically sun-exposed, intermittently sun-exposed, and sun-shielded skin that belong to each of the 96 trinucleotide substitution contexts. Note the similarity to signature 7 (shown for reference in c), albeit to a lesser extent in sun-shielded skin cells. b, Tri-nucleotide contexts of variants from sun-exposed skin validated to be somatic mutations by RNA-seq or phasing as well as variants inferred to be somatic mutations by their variant allele frequency (VAF). Note the similarity to signature 7. The tri-nucleotide contexts of remaining variants (assumed to be amplification artefacts) are also shown. c, Predefined mutation signatures shown for reference; Signature 7 (associated with UV-radiation-induced DNA damage)51, and SBS scE and SBS scF, which are associated with single-cell whole genome amplification artefacts18.
Extended Data Fig. 4 Median mutation burden of melanocytes from different anatomic sites.
Mutation burden of melanocytes from physiologically normal skin of six donors across different anatomic sites with varied sun exposure that are rank ordered by median mutation burden (line) within each site. (BCC = Basal Cell Carcinoma, Mel = Melanoma).
Extended Data Fig. 5 Differential expression analysis revealing genes significantly correlating with mutation burden.
a–c, Gene expression versus normalized mutation burden is shown for two top correlative genes (HLA-DPA1 and MDM2) and one (CLEC2B) anti-correlative gene of interest from Supplementary Table 4. Clones included in this analysis are from anatomic sites with greater than 3 standard deviations of mutation burdens among their cells, thus demonstrating a range of mutation burdens. The plotted blue line represents a linear model fit to the data with 95% confidence intervals for that model prediction shown in grey.
Extended Data Fig. 6 Copy number landscape of melanocytes from normal human skin.
Copy number was inferred, as described, and segments (regions of equal copy number) are depicted, here, denoting gains (red) and losses (blue) for each melanocyte (rows). Note that copy number alterations over autosomes were rare, while the loss of one sex chromosome is a common occurrence. All X chromosome deletions in females affect the inactive X (see Supplementary Table 5).
Extended Data Fig. 7 Fields of related melanocytes exist within the skin.
Phylogenetic trees in which each branch corresponds to an individual cell. Mutations that are shared between cells comprise the trunk of each tree and private mutations in each cell form the branches. Trunk and branch lengths are scaled equivalently within each tree but not across trees. The proportion of mutations that can be attributed to UV radiation (CC > TT or (C/T)C>T) is annotated in the bar charts on each tree trunk or branch.
Extended Data Fig. 8 Melanocytes accumulate few mutations in tissue culture.
a, We sequenced a bulk culture of neonatal melanocytes to establish the germline SNPs and somatic mutations in the dominant clones. We continued to passage the cell line for 239 days, genotyping individual clones at the time points indicated to establish the rate at which mutations were acquired in culture. In parallel, Petljak et al.18 performed similar experiments on common cancer cell lines, and we analysed their data from a melanoma cell line (Mewo) included in their study. b, On average, the mutation burden of neonatal melanocytes and Mewo cells respectively increased by 0.090 and 0.086 mutations/Mb for every 2 weeks in tissue culture (we typically cultured melanocytes 2 weeks or less in this study). To put these mutation burdens in perspective, the average mutation burdens of sun-exposed and sun-shielded melanocytes from this study are shown in comparison. Based on these results, we conclude that the brief period of tissue culture contributed little towards the mutation burdens observed in our study.
Supplementary information
Supplementary Table
Supplementary Table 1. A summary of all clones included in this study, including anatomic origins, cell count and identity, and targeted-, exome-, and RNA-sequencing data metrics.
Supplementary Table
Supplementary Table 2. Targeted DNA sequencing panel genes and bait intervals.
Supplementary Table
Supplementary Table 3. All validated and inferred somatic mutations identified in normal melanocytes; includes Oncotator annotations and validation inferences based upon haplotyping and expression data.
Supplementary Table
Supplementary Table 4. All top correlative and anti-correlative, differentially expressed genes from DeSeq2 analyses with an adjusted p value < 0.01, reveals genes significantly correlated with mutation burden. P values were calculated by a two-sided Wald test and corrected for multiple comparisons using a false discovery rate adjustment.
Supplementary Table
Supplementary Table 5. Inference of sex chromosome deletions in females and males based upon allelic dropout and gene expression information.
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Tang, J., Fewings, E., Chang, D. et al. The genomic landscapes of individual melanocytes from human skin. Nature 586, 600–605 (2020). https://doi.org/10.1038/s41586-020-2785-8
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DOI: https://doi.org/10.1038/s41586-020-2785-8
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