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      Hair-bearing human skin generated entirely from pluripotent stem cells

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          Abstract

          The skin is a multi-layered organ equipped with appendages (i.e. follicles and glands) critical for regulating bodily fluid retention and temperature, guarding against external stresses, and mediating touch and pain sensation 1, 2 . Reconstruction of appendage-bearing skin in cultures and in bioengineered grafts remains an unmet biomedical challenge 39 . Here, we report an organoid culture system that generates complex skin from human pluripotent stem cells. We use step-wise modulation of the TGFβ and FGF signalling pathways to co-induce cranial epithelial cells and neural crest cells within a spherical cell aggregate. During 4–5 months incubation, we observe the emergence of a cyst-like skin organoid composed of stratified epidermis, fat-rich dermis, and pigmented hair follicles equipped with sebaceous glands. A network of sensory neurons and Schwann cells form nerve-like bundles that target Merkel cells in organoid hair follicles, mimicking human touch circuitry. Single-cell RNA-sequencing and direct comparison to foetal specimens suggest that skin organoids are equivalent to human facial skin in the second-trimester of development. Moreover, we show that skin organoids form planar hair-bearing skin when grafted on nude mice. Together, our results demonstrate that nearly complete skin can self-assemble in vitro and be used to reconstitute skin in vivo. We anticipate skin organoids will be foundational to future studies of human skin development, disease modelling, or reconstructive surgery.

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          Most cited references42

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Spatial reconstruction of single-cell gene expression

              Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                30 April 2020
                03 June 2020
                June 2020
                03 December 2020
                : 582
                : 7812
                : 399-404
                Affiliations
                [1 ]Department of Otolaryngology, Boston Children’s Hospital, Boston, Massachusetts, 02115, USA
                [2 ]Department of Plastic and Oral Surgery, Boston Children’s Hospital, Boston, Massachusetts, 02115, USA
                [3 ]F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, Massachusetts, 02115, USA
                [4 ]Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, 02115, USA
                [5 ]Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
                [6 ]Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
                [7 ]Medical Neuroscience Graduate Program, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
                [8 ]Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
                [9 ]Department of Cell and Anatomy, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
                [10 ]Department of Otolaryngology, Stanford University, Palo Alto, CA 94305, USA.
                Author notes

                AUTHOR CONTRIBUTIONS

                J.L. and K.R.K. conceived the study and wrote the manuscript. J.L. performed most of the in vitro experiments and IHC analysis. J.L., C.R., Z.P., T.S., and K.R.K. designed and performed the in vivo experiments. M.S. and A.K. performed IHC and scRNA-seq data analysis. H.G. and Y.L. performed bioinformatic analyses of scRNA-seq data and generated figures. B.M.W. and S.H. performed protocol validation experiments and generated figures. K.R.K. supervised the project, monitored the experiments, and acquired funding. All authors read and approved the final manuscript.

                Article
                NIHMS1589516
                10.1038/s41586-020-2352-3
                7593871
                32494013
                8b5d71ab-ebb7-44e8-8830-b8bd006c82e7

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