In humans, epidermal melanocytes are responsible for skin pigmentation, defense against ultraviolet radiation, and the deadliest common skin cancer, melanoma. While 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 1-3 . Thus, the biology of developing and adult human melanocytes remains largely uncharacterized. Here, we used a single-cell enrichment and RNA-sequencing pipeline to study human epidermal melanocytes derived directly from skin, capturing transcriptomes across different anatomic sites, developmental age, sexes, and multiple skin tones. Using donor-matched skin from distinct volar and non-volar anatomic locations, we uncovered subpopulations of melanocytes exhibiting site-specific enrichment that occurs during gestation and persists through adulthood. In addition, we identified human melanocyte differentiation transcriptional programs that are distinct from gene signatures generated from model systems. Finally, we use these programs to define patterns of dedifferentiation that are predictive of melanoma prognosis. Overall, the characterization of human melanocytes fresh from skin revealed new subpopulations, human-specific transcriptional programs, and valuable insights into melanoma dedifferentiation. INTRODUCTION:Epidermal melanocytes, the pigment producing cells of human skin, are responsible for skin tone and orchestrate the primary defense against damage from ultraviolet (UV) radiation. Some anatomic site-specific differences in pigmentation are due to environmental factors, such as the tanning response to UV exposure. Others, like the hypopigmentation at volar sites (such as palms and soles), are present at birth. In adult skin, mesenchymal -epithelial interactions are known to influence anatomic site-specific melanocyte survival and pigment production 4 but melanocyte intrinsic factors that contribute to site-specific specialization remain unclear.Model organisms are powerful tools for investigating melanocyte development. In chick and mouse, a transient, multipotent neural crest cell population gives rise to committed immature melanocyte precursors, called melanoblasts, via two spatially and temporally distinct pathways 2,3 . Such studies focus primarily on melanocytes in skin appendages (hair follicle, feather, and sweat gland). However, despite constituting the predominate subtype in human skin, resident epidermal melanocytes have not been the subject of analogous investigations into developmental trajectories and anatomic-specializations.Melanocytes can give rise to melanomas which present distinct phenotypic and genomic characteristics correlated with primary tumor location 5,6 . Like many cancers, melanoma progression is coupled to dedifferentiation of the cell of origin 7 . The aggressive nature of melanoma is proposed to be rooted in unique attributes of the melanocytic linage 8 . Decoding the transcriptome of epidermal mela...

Every cell in the human body has a unique set of somatic mutations, yet it remains difficult to comprehensively genotype an individual cell. Here, we developed solutions to overcome this obstacle in the context of normal human skin, thus offering the first glimpse into the genomic landscapes of individual melanocytes from human skin. We comprehensively genotyped 133 melanocytes from 19 sites across 6 donors. As expected, sun-shielded melanocytes had fewer mutations than sun-exposed melanocytes. However, within sun-exposed sites, melanocytes on chronically sun-exposed skin (e.g. the face) displayed a lower mutation burden than melanocytes on intermittently sun-exposed skin (e.g. 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 harnessed to measure cumulative sun damage and skin cancer risk. Moreover, melanocytes from healthy skin commonly harbor pathogenic mutations, likely explaining the origins of the melanomas that arise in the absence of a pre-existing nevus. Phylogenetic analyses identified groups of related melanocytes, suggesting that melanocytes spread throughout skin as fields of clonally related cells, invisible to the naked eye. Overall, our study offers an unprecedented view into the genomic landscapes of individual melanocytes, revealing key insights into the causes and origins of melanoma.

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

The use of microRNAs as biomarkers has been proposed for many diseases, including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, a limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study, we applied a machine learning-based pipeline to a dataset consisting of genetic features, clinical features, and next-generation microRNA sequencing from micro-dissected formalin-fixed paraffin embedded melanomas and their adjacent benign precursor nevi. We identified patient age and tumor cellularity as variables that frequently confound the measured expression of potentially diagnostic microRNAs. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to the nevi as a normalization strategy, we developed a model that classified all the available published cohorts with an area under the receiver operating characteristic curve of 0.98. External validation on an independent cohort classified lesions with 81% sensitivity and 88% specificity and was uninfluenced by the tumor content of the sample or patient age.

gene expression profile (GEP) testing in cutaneous melanoma (CM) is rising despite a lack of endorsement as standard of care.OBJECTIVE To develop guidelines within the national Melanoma Prevention Working Group (MPWG) on integration of GEP testing into the management of patients with CM, including (1) review of published data using GEP tests, (2) definition of acceptable performance criteria, (3) current recommendations for use of GEP testing in clinical practice, and (4) considerations for future studies. EVIDENCE REVIEWThe MPWG members and other international melanoma specialists participated in 2 online surveys and then convened a summit meeting. Published data and meeting abstracts from 2015 to 2019 were reviewed.FINDINGS The MPWG members are optimistic about the future use of prognostic GEP testing to improve risk stratification and enhance clinical decision-making but acknowledge that current utility is limited by test performance in patients with stage I disease. Published studies of GEP testing have not evaluated results in the context of all relevant clinicopathologic factors or as predictors of regional nodal metastasis to replace sentinel lymph node biopsy (SLNB). The performance of GEP tests has generally been reported for small groups of patients representing particular tumor stages or in aggregate form, such that stage-specific performance cannot be ascertained, and without survival outcomes compared with data from the American Joint Committee on Cancer 8th edition melanoma staging system international database. There are significant challenges to performing clinical trials incorporating GEP testing with SLNB and adjuvant therapy. The MPWG members favor conducting retrospective studies that evaluate multiple GEP testing platforms on fully annotated archived samples before embarking on costly prospective studies and recommend avoiding routine use of GEP testing to direct patient management until prospective studies support their clinical utility.CONCLUSIONS AND RELEVANCE More evidence is needed to support using GEP testing to inform recommendations regarding SLNB, intensity of follow-up or imaging surveillance, and postoperative adjuvant therapy. The MPWG recommends further research to assess the validity and clinical applicability of existing and emerging GEP tests. Decisions on performing GEP testing and patient management based on these results should only be made in the context of discussion of testing limitations with the patient or within a multidisciplinary group.

Mucosal melanoma (MM) is a rare melanoma subtype that originates from melanocytes within sun-protected mucous membranes. Compared with cutaneous melanoma (CM), MM has worse prognosis and lacks effective treatment options. Moreover, the endogenous or exogenous risk factors that influence mucosal melanocyte transformation, as well as the identity of MM precursor lesions, are ambiguous. Consequently, there remains a lack of molecular markers that can be used for early diagnosis, and therefore better management, of MM. In this review, we first summarize the main functions of mucosal melanocytes. Then, using oral mucosal melanoma (OMM) as a model, we discuss the distinct pathologic stages from benign mucosal melanocytes to metastatic MM, mapping the possible evolutionary trajectories that correspond to MM initiation and progression. We highlight key areas of ambiguity during the genetic evolution of MM from its benign lesions, and the resolution of which could aid in the discovery of new biomarkers for MM detection and diagnosis. We outline the key pathways that are altered in MM, including the MAPK pathway, the PI3K/AKT pathway, cell cycle regulation, telomere maintenance, and the RNA maturation process, and discuss targeted therapy strategies for MM currently in use or under investigation.

Despite significant progress in the development of treatment options, melanoma remains a leading cause of death due to skin cancer. Advances in our understanding of the genetic, transcriptomic, and morphologic spectrum of benign and malignant melanocytic neoplasia have enabled the field to propose biomarkers with potential diagnostic, prognostic, and predictive value. While these proposed biomarkers have the potential to improve clinical decision making at multiple critical intervention points, most remain unvalidated. Clinical validation of even the most commonly assessed biomarkers will require substantial resources, including limited clinical specimens. It is therefore important to consider the properties that constitute a relevant and clinically-useful biomarker-based test prior to engaging in large validation studies. In this review article we adapt an established framework for determining minimally-useful biomarker test characteristics, and apply this framework to a discussion of currently used and proposed biomarkers designed to aid melanoma detection, staging, prognosis, and choice of treatment.

Molecular biomarkers can be powerful tools for aiding in the efficiency and precision of clinical decisionmaking. Feature selection methods, machine-learning, and biostatistics have been applied to discover subsets of molecular markers that identify target classes of clinical cases. For example, in the field of dermatology, these approaches have been used to develop predictive models that identify skin diseases, ranging from melanoma to psoriasis, based upon a variety of biomarkers. However, a continuous increase in the variety and size of datasets from which candidate biomarkers can be derived, and limitations in the computational tools used to analyze them, have hindered the interpretability of biomarker discovery studies. In this article, the various methods of feature selection are described along with the important steps needed to properly validate the performance of the selected methods. Limitations and suggestions toward uses of these methods are discussed.