In ultraviolet (UV) radiation–exposed skin, mutations fuel clonal cell growth. The relationship between UV exposure and the accumulation of clonal mutations (CMs) and the correlation between CMs and skin cancer risk are largely unexplored. We characterized 450 individual-matched sun-exposed (SE) and non-SE (NE) normal human skin samples. The number and relative contribution of CMs were significantly different between SE and NE areas. Furthermore, we identified hotspots in TP53, NOTCH1, and GRM3 where mutations were significantly associated with UV exposure. In the normal skin from patients with cutaneous squamous cell carcinoma, we found that the cancer burden was associated with the UV-induced mutations, with the difference mostly conferred by the low-frequency CMs. These findings provide previously unknown information on UV’s carcinogenic effect and pave the road for future development of quantitative assessment of subclinical UV damage and skin cancer risk.

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Background: Advances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation. Results: In this study, we compared the performance of NXT following Fluidigm’s protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93–100% per cell). Conclusions: Our results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm’s protocol using NXT.

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