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      Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          The murine epidermis with its hair follicles represents an invaluable model system for tissue regeneration and stem cell research. Here we used single-cell RNA-sequencing to reveal how cellular heterogeneity of murine telogen epidermis is tuned at the transcriptional level. Unbiased clustering of 1,422 single-cell transcriptomes revealed 25 distinct populations of interfollicular and follicular epidermal cells. Our data allowed the reconstruction of gene expression programs during epidermal differentiation and along the proximal-distal axis of the hair follicle at unprecedented resolution. Moreover, transcriptional heterogeneity of the epidermis can essentially be explained along these two axes, and we show that heterogeneity in stem cell compartments generally reflects this model: stem cell populations are segregated by spatial signatures but share a common basal-epidermal gene module. This study provides an unbiased and systematic view of transcriptional organization of adult epidermis and highlights how cellular heterogeneity can be orchestrated in vivo to assure tissue homeostasis.

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          Highlights

          • Single-cell RNA-seq analysis identifies 25 populations of epidermal cells
          • Differentiation and spatial gene expression signatures can be defined
          • Interplay of differentiation and spatial signatures explains most heterogeneity
          • Stem cell populations are divided by spatial signatures and only share basal identity

          Abstract

          Joost et al. use high-throughput single-cell RNA-seq to describe gene expression in mouse epidermis and hair follicles at unprecedented detail and explain epidermal heterogeneity as the interplay of differentiation-related and spatial gene expression signatures.

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          Most cited references 72

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

            Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
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              Clustering by passing messages between data points.

              Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.
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                Author and article information

                Affiliations
                [1 ]Department of Biosciences and Nutrition and Center for Innovative Medicine, Karolinska Institutet, Novum, 141 83 Huddinge, Sweden
                [2 ]Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, 171 77 Stockholm, Sweden
                Author notes
                []Corresponding author sten.linnarsson@ 123456ki.se
                [∗∗ ]Corresponding author maria.kasper@ 123456ki.se
                [3]

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                Contributors
                Journal
                Cell Syst
                Cell Syst
                Cell Systems
                Cell Press
                2405-4712
                2405-4720
                28 September 2016
                28 September 2016
                : 3
                : 3
                : 221-237.e9
                S2405-4712(16)30265-4
                10.1016/j.cels.2016.08.010
                5052454
                27641957
                © 2016 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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