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      Defining the lineage of thermogenic perivascular adipose tissue

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          Abstract

          Brown adipose tissue can expend large amounts of energy, and therefore increasing its size or activity is a promising therapeutic approach to combat metabolic disease. In humans, major deposits of brown fat cells are found intimately associated with large blood vessels, corresponding to perivascular adipose tissue (PVAT). However, the cellular origins of PVAT are poorly understood. Here, we determine the identity of perivascular adipocyte progenitors in mice and humans. In mice, thoracic PVAT develops from a fibroblastic lineage, consisting of progenitor cells ( Pdgfra+; Ly6a+; Pparg-) and preadipocytes ( Pdgfra+; Ly6a+; Pparg+), which share transcriptional similarity with analogous cell types in white adipose tissue. Interestingly, the aortic adventitia of adult animals contains a population of adipogenic smooth muscle cells (SMCs) ( Myh11+; Pdgfra-; Pparg+) that contribute to perivascular adipocyte formation. Similarly, human PVAT contains presumptive fibroblastic and SMC-like adipocyte progenitors, as revealed by single nucleus RNAseq. Taken together, these studies define distinct populations of progenitor cells for thermogenic PVAT, providing a foundation for developing strategies to augment brown fat activity.

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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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            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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              Is Open Access

              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Journal
                101736592
                48119
                Nat Metab
                Nat Metab
                Nature metabolism
                2522-5812
                4 May 2021
                12 April 2021
                April 2021
                12 October 2021
                : 3
                : 4
                : 469-484
                Affiliations
                [1. ]Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
                [2. ]Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
                [3. ]Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
                [4. ]Department of Medicine; Renal Electrolyte and Hypertension Division, Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
                [5. ]Center for Molecular Medicine, Maine Medical Center Research Institute, Scarborough, ME, USA
                Author notes
                [*]

                These authors contributed equally to this work

                [#]

                Lead Contact.

                Author Contributions

                A.R.A., A.P.S. and P.S were responsible for conceptualization, data analysis and writing. A.R.A. and A.P.S. contributed equally and conducted the majority of the experiments and carried out the bioinformatics analyses. C.D.H and M.N.A. prepared the human aortic PVAT for snRNAseq analysis. L.C. processed tissue sections and performed staining. F.S., M.D.L. and Y-H.T. performed the analysis of Trpv1 (SMC)-reporter mice. C.O. assisted with experimental procedures. R.S. performed cell capture and library preparation for perinatal single cell datasets. K.S. provided sequencing reagents and key experimental insight. K.B. assisted with data analysis. L.L. obtained and provided human peri aortic PVAT samples.

                Correspondence should be addressed to: Patrick Seale, Perelman School of Medicine, University of Pennsylvania, Smilow Center for Translational Research, 3400 Civic Center Blvd, Rm. 12-105, Philadelphia, PA, 19104. USA, Tel: 215-573-8856, Fax: 215-898-5408, sealep@ 123456pennmedicine.upenn.edu
                Article
                NIHMS1680909
                10.1038/s42255-021-00380-0
                8136151
                33846639
                e6f36345-9968-4f53-8e68-5992aa58dd58

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