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      Distinct mechanisms for sebaceous gland self-renewal and regeneration provide durability in response to injury

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          SUMMARY

          Sebaceous glands (SGs) release oils that protect our skin, but how these glands respond to injury has not been previously examined. Here, we report that SGs are largely self-renewed by dedicated stem cell pools during homeostasis. Using targeted single-cell RNA sequencing, we uncovered both direct and indirect paths by which resident SG progenitors ordinarily differentiate into sebocytes, including transit through a Krt5+PPARγ+ transitional basal cell state. Upon skin injury, however, SG progenitors depart their niche, re-epithelialize the wound, and are replaced by hair-follicle-derived stem cells. Furthermore, following targeted genetic ablation of >99% of SGs from dorsal skin, these glands unexpectedly regenerate within weeks. This regenerative process is mediated by alternative stem cells originating from the hair follicle bulge, is dependent upon FGFR2 signaling, and can be accelerated by inducing hair growth. Altogether, our studies demonstrate that stem cell plasticity promotes SG durability following injury.

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          In brief

          Veniaminova et al. characterize the development, maintenance, and regeneration of sebaceous glands (SGs). Although SGs are largely self-maintained by dedicated stem cells during homeostasis, alternative stem cells enter and regenerate the gland following injury. This regenerative process relies on FGF signaling and can be accelerated by stimulating hair growth.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Reversed graph embedding resolves complex single-cell trajectories

              Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. Applied to two studies of blood development, Monocle 2 revealed that mutations in key lineage transcription factors diverts cells to alternative fates.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                5 October 2023
                26 September 2023
                19 September 2023
                23 October 2023
                : 42
                : 9
                : 113121
                Affiliations
                [1 ]Department of Dermatology, Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
                [2 ]Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA
                [3 ]Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
                [4 ]Department of Dermatology, Department of Cutaneous Immunology and Microbiology, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
                [5 ]These authors contributed equally
                [6 ]Lead contact
                Author notes
                [* ]Correspondence: satwood@ 123456uci.edu (S.X.A.), sunnyw@ 123456umich.edu (S.Y.W.)

                AUTHOR CONTRIBUTIONS

                Conceptualization and methodology, N.A.V., Y.Y.J., A.A.D., S.X.A., and S.Y.W.; investigation, N.A.V., A.H., T.J.H., S.Y.T., M.G., S.N., and S.Y.W.; formal analysis, N.A.V., Y.Y.J., S.X.A., and S.Y.W.; writing – original draft, review & editing, N.A.V., Y.Y.J., S.X.A., and S.Y.W.; funding acquisition and supervision, A.A.D., S.X.A., and S.Y.W.

                Article
                NIHMS1933916
                10.1016/j.celrep.2023.113121
                10591672
                37715952
                46066ccf-f7fe-4343-8f15-ba20b4076b1c

                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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                Cell biology
                Cell biology

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