A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization.

[1]  Bridget Pratt,et al.  How should communities be meaningfully engaged (if at all) when setting priorities for biomedical research? Perspectives from the biomedical research community , 2023, BMC Medical Ethics.

[2]  B. Andersen,et al.  Differential cell composition and split epidermal differentiation in human palm, sole, and hip skin , 2023, Cell reports.

[3]  Karen H. Miga,et al.  Comprehensive variant discovery in the era of complete human reference genomes , 2023, Nature Methods.

[4]  M. Menden,et al.  Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases , 2022, Nature communications.

[5]  S. Joost,et al.  Plasticity of Epithelial Cells during Skin Wound Healing. , 2022, Cold Spring Harbor perspectives in biology.

[6]  S. Nagpure,et al.  Types of Psoriasis and Their Effects on the Immune System , 2022, Cureus.

[7]  A. Heguy,et al.  Interleukin-17 governs hypoxic adaptation of injured epithelium , 2022, Science.

[8]  S. Schuierer,et al.  Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq , 2022, Nature Biotechnology.

[9]  R. Sandberg,et al.  Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress , 2022, Nature Biotechnology.

[10]  K. Qu,et al.  Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution , 2022, Nature Methods.

[11]  Ge Gao,et al.  Multi-omics single-cell data integration and regulatory inference with graph-linked embedding , 2022, Nature Biotechnology.

[12]  Ji Hye Park,et al.  Integrated analysis of single-cell and spatial transcriptomics in keloids: Highlights on fibro-vascular interactions in keloid pathogenesis. , 2022, The Journal of investigative dermatology.

[13]  Malte D. Luecken,et al.  Benchmarking atlas-level data integration in single-cell genomics , 2021, Nature Methods.

[14]  Q. Phan,et al.  Parallel single cell multi-omics analysis of neonatal skin reveals transitional fibroblast states that restricts differentiation into distinct fates. , 2021, The Journal of investigative dermatology.

[15]  Joshua M. Stuart,et al.  VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics , 2021, Nature Communications.

[16]  Fabian J Theis,et al.  Mapping single-cell data to reference atlases by transfer learning , 2021, Nature Biotechnology.

[17]  L. Garza,et al.  Understanding and Harnessing Epithelial‒Mesenchymal Interactions in the Development of Palmoplantar Identity. , 2021, The Journal of investigative dermatology.

[18]  Howard Y. Chang,et al.  Integrated spatial multiomics reveals fibroblast fate during tissue repair , 2021, Proceedings of the National Academy of Sciences.

[19]  Joshua D. Welch,et al.  Iterative single-cell multi-omic integration using online learning , 2021, Nature Biotechnology.

[20]  K. Sarin,et al.  Hidradenitis suppurativa in patients of color is associated with increased disease severity and healthcare utilization: A retrospective analysis of 2 U.S. cohorts , 2021, JAAD international.

[21]  Ariel J. Levine,et al.  Confronting false discoveries in single-cell differential expression , 2021, Nature Communications.

[22]  Q. Nie,et al.  The landscape of cell-cell communication through single-cell transcriptomics. , 2021, Current opinion in systems biology.

[23]  S. Teichmann,et al.  Developmental cell programs are co-opted in inflammatory skin disease , 2021, Science.

[24]  Yifan Zhao,et al.  Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data , 2021, Nature Communications.

[25]  M. Kasper,et al.  Gradual differentiation uncoupled from cell cycle exit generates heterogeneity in the epidermal stem cell layer , 2021, bioRxiv.

[26]  Michael I. Jordan,et al.  Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models , 2021, Molecular systems biology.

[27]  W. Al-Delaimy,et al.  Minority Representation in Clinical Trials in the United States: Trends Over the Past 25 Years. , 2021, Mayo Clinic proceedings.

[28]  Fabian J Theis,et al.  Conditional out-of-distribution generation for unpaired data using transfer VAE. , 2020, Bioinformatics.

[29]  Bryan D. Bryson,et al.  Single Cell and Spatial Transcriptomics Defines the Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas , 2020, bioRxiv.

[30]  J. I. Izpisúa Belmonte,et al.  A Single-Cell Transcriptomic Atlas of Human Skin Aging. , 2020, Developmental cell.

[31]  O. Harismendy,et al.  Deciphering cell–cell interactions and communication from gene expression , 2020, Nature reviews. Genetics.

[32]  Raphael Gottardo,et al.  Integrated analysis of multimodal single-cell data , 2020, Cell.

[33]  Adam L. Maclean,et al.  Single cell transcriptomics of human epidermis identifies basal stem cell transition states , 2020, Nature Communications.

[34]  L. Pachter,et al.  Museum of spatial transcriptomics , 2020, Nature Methods.

[35]  Sheila V. Kusnoor,et al.  Diversity and inclusion for the All of Us research program: A scoping review , 2020, PloS one.

[36]  Adam J. Rubin,et al.  Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma , 2020, Cell.

[37]  A. Tward,et al.  Human melanocyte development and melanoma dedifferentiation at single cell resolution , 2020, bioRxiv.

[38]  Vladimir Yu Kiselev,et al.  Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench , 2020, bioRxiv.

[39]  J. Mallm,et al.  Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming , 2020, Communications Biology.

[40]  K. Plath,et al.  Defining transcriptional signatures of human hair follicle cell states. , 2020, The Journal of investigative dermatology.

[41]  S. Day,et al.  Broadening community engagement in clinical research: Designing and assessing a pilot crowdsourcing project to obtain community feedback on an HIV clinical trial , 2020, Clinical trials.

[42]  Kok Siong Ang,et al.  A benchmark of batch-effect correction methods for single-cell RNA sequencing data , 2020, Genome Biology.

[43]  R. Sandberg,et al.  Single-cell RNA counting at allele and isoform resolution using Smart-seq3 , 2019, Nature Biotechnology.

[44]  T. Swartz,et al.  The Science and Value of Diversity: Closing the Gaps in Our Understanding of Inclusion and Diversity. , 2019, The Journal of infectious diseases.

[45]  Kerstin B. Meyer,et al.  BBKNN: fast batch alignment of single cell transcriptomes , 2019, Bioinform..

[46]  Mohammad Lotfollahi,et al.  scGen predicts single-cell perturbation responses , 2019, Nature Methods.

[47]  T. M. Murali,et al.  Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data , 2019, Nature Methods.

[48]  Bonnie Berger,et al.  Efficient integration of heterogeneous single-cell transcriptomes using Scanorama , 2019, Nature Biotechnology.

[49]  Howard Y. Chang,et al.  Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion , 2019, Nature Biotechnology.

[50]  D. Burgess.  Spatial transcriptomics coming of age , 2019, Nature Reviews Genetics.

[51]  N. Fortunel,et al.  Age-related evolutions of the dermis: Clinical signs, fibroblast and extracellular matrix dynamics , 2019, Mechanisms of Ageing and Development.

[52]  Charles J. Vaske,et al.  Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution , 2018, Cell reports.

[53]  Laleh Haghverdi,et al.  Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.

[54]  R. Atit,et al.  Dermal fibroblast in cutaneous development and healing , 2018, Wiley interdisciplinary reviews. Developmental biology.

[55]  Charlotte Soneson,et al.  Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.

[56]  Yasmine Belkaid,et al.  The human skin microbiome , 2018, Nature Reviews Microbiology.

[57]  Wei Chen,et al.  SFRP2/DPP4 and FMO1/LSP1 Define Major Fibroblast Populations in Human Skin. , 2017, The Journal of investigative dermatology.

[58]  Davis J. McCarthy,et al.  f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq , 2017, Genome Biology.

[59]  H. Swerdlow,et al.  Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.

[60]  Sarah A Teichmann,et al.  Wounding induces dedifferentiation of epidermal Gata6+ cells and acquisition of stem cell properties , 2017, Nature Cell Biology.

[61]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[62]  M. Davis,et al.  Sex differences in the incidence of skin and skin‐related diseases in Olmsted County, Minnesota, United States, and a comparison with other rates published worldwide , 2016, International journal of dermatology.

[63]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[64]  S. Reeder,et al.  Dermal white adipose tissue: a new component of the thermogenic response , 2015, Journal of Lipid Research.

[65]  Lisa M. Nicholson,et al.  Recruitment and retention strategies in clinical studies with low-income and minority populations: Progress from 2004-2014. , 2015, Contemporary clinical trials.

[66]  A. Wheeler,et al.  A Review of Anatomy, Physiology, and Benign Pathology of the Nipple , 2015, Annals of Surgical Oncology.

[67]  Howard Y. Chang,et al.  Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.

[68]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[69]  F. Watt.  Mammalian skin cell biology: At the interface between laboratory and clinic , 2014, Science.

[70]  M. Merritt,et al.  Why We Need Community Engagement in Medical Research , 2014, Journal of Investigative Medicine.

[71]  Howard Y. Chang,et al.  Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.

[72]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[73]  H. Maibach,et al.  Characteristics of the Aging Skin. , 2013, Advances in wound care.

[74]  S. B. Murray,et al.  Reporting, Representation, and Subgroup Analysis of Race and Ethnicity in Published Clinical Trials of Atopic Dermatitis in the United States Between 2000 and 2009 , 2012, Pediatric dermatology.

[75]  M. Darvin,et al.  Ethnic Differences in Skin Physiology, Hair Follicle Morphology and Follicular Penetration , 2012, Skin Pharmacology and Physiology.

[76]  Nick V Grishin,et al.  Phenotypic and genotypic analyses of genetic skin disease through the Online Mendelian Inheritance in Man (OMIM) database. , 2009, The Journal of investigative dermatology.

[77]  P. Cole,et al.  Keloids: pathogenesis, clinical features, and management. , 2009, Seminars in plastic surgery.

[78]  Howard Y. Chang,et al.  A dermal HOX transcriptional program regulates site-specific epidermal fate. , 2008, Genes & development.

[79]  H. Dao,et al.  Gender differences in skin: a review of the literature. , 2007, Gender medicine.

[80]  J. Lademann,et al.  Morphometry of human terminal and vellus hair follicles , 2007, Experimental dermatology.

[81]  Yasuhiro Matsumura,et al.  Toxic effects of ultraviolet radiation on the skin. , 2004, Toxicology and applied pharmacology.

[82]  J. Lamartine.  Towards a new classification of ectodermal dysplasias , 2003, Clinical and experimental dermatology.

[83]  David Botstein,et al.  Diversity, topographic differentiation, and positional memory in human fibroblasts , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[84]  S. Werner,et al.  Wound repair and regeneration , 1994, Nature.

[85]  Mayumi Ito,et al.  Wound healing and skin regeneration. , 2015, Cold Spring Harbor perspectives in medicine.

[86]  Hywel C Williams,et al.  Acne vulgaris , 2012, The Lancet.

[87]  H. Wulf,et al.  Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits. , 2003, Acta dermato-venereologica.

[88]  T. Tuan,et al.  The molecular basis of keloid and hypertrophic scar formation. , 1998, Molecular medicine today.