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      Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis

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          Abstract

          Fibroblasts regulate tissue homeostasis, coordinate inflammatory responses, and mediate tissue damage. In rheumatoid arthritis (RA), synovial fibroblasts maintain chronic inflammation which leads to joint destruction. Little is known about fibroblast heterogeneity or if aberrations in fibroblast subsets relate to pathology. Here, we show functional and transcriptional differences between fibroblast subsets from human synovial tissues using bulk transcriptomics of targeted subpopulations and single-cell transcriptomics. We identify seven fibroblast subsets with distinct surface protein phenotypes, and collapse them into three subsets by integrating transcriptomic data. One fibroblast subset, characterized by the expression of proteins podoplanin, THY1 membrane glycoprotein and cadherin-11, but lacking CD34, is threefold expanded in patients with RA relative to patients with osteoarthritis. These fibroblasts localize to the perivascular zone in inflamed synovium, secrete proinflammatory cytokines, are proliferative, and have an in vitro phenotype characteristic of invasive cells. Our strategy may be used as a template to identify pathogenic stromal cellular subsets in other complex diseases.

          Abstract

          Synovial fibroblasts are thought to be central mediators of joint destruction in rheumatoid arthritis (RA). Here the authors use single-cell transcriptomics and flow cytometry to identify synovial fibroblast subsets that are expanded and display distinct tissue distribution and function in patients with RA.

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          Most cited references45

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            The Molecular Signatures Database (MSigDB) hallmark gene set collection.

            The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.

                Author and article information

                Contributors
                soumya@broadinstitute.org
                mbrenner@research.bwh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                23 February 2018
                23 February 2018
                2018
                : 9
                : 789
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Genetics, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02446 USA
                [3 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA 02142 USA
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, Bioinformatics and Integrative Genomics, , Harvard University, ; Cambridge, MA 02138 USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA 02115 USA
                [6 ]ISNI 0000 0001 2177 007X, GRID grid.415490.d, Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, , Queen Elizabeth Hospital, ; Birmingham, B15 2WB UK
                [7 ]ISNI 0000 0004 0378 8294, GRID grid.62560.37, Department of Orthopedic Surgery, , Brigham and Women’s Hospital, ; Boston, MA 02115 USA
                [8 ]ISNI 0000 0001 2285 8823, GRID grid.239915.5, Arthritis and Tissue Degeneration Program and the David Z. Rosensweig Genomics Research Center, , Hospital for Special Surgery, ; New York, NY 10021 USA
                [9 ]ISNI 000000041936877X, GRID grid.5386.8, Weill Cornell Graduate School of Medical Sciences, ; New York, NY 10021 USA
                [10 ]ISNI 0000 0004 0378 8294, GRID grid.62560.37, Department of Surgery, , Brigham and Women’s Hospital and Harvard Medical School, ; Boston, MA 02115 USA
                [11 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Center for Immunology and Inflammatory Diseases, , Massachusetts General Hospital, ; Charlestown, MA 02114 USA
                [12 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Medicine, , Harvard Medical School, ; Boston, MA 02115 USA
                [13 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Immunology, Department of Medicine, Boston Children’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                [14 ]ISNI 0000000121662407, GRID grid.5379.8, Arthritis Research UK Centre for Genetics and Genomics, Manchester Academic Health Science Centre, , University of Manchester, ; Manchester, M13 9PT UK
                [15 ]ISNI 0000 0001 1014 9130, GRID grid.265073.5, Present Address: Department of Rheumatology, Graduate School of Medical and Dental Sciences, , Tokyo Medical and Dental University (TMDU), ; Tokyo, 113-8519 Japan
                [16 ]Present Address: JW Creagene Corporation, Seongnam-Si, 13202 South Korea
                [17 ]ISNI 0000000122986657, GRID grid.34477.33, Present Address: Division of Rheumatology, , University of Washington, ; Seattle, WA 98109 USA
                Author information
                http://orcid.org/0000-0002-2843-6370
                http://orcid.org/0000-0002-4427-8245
                http://orcid.org/0000-0002-9951-0646
                http://orcid.org/0000-0002-2126-3702
                http://orcid.org/0000-0001-6924-6402
                Article
                2892
                10.1038/s41467-018-02892-y
                5824882
                29476097
                99571bae-1887-4664-be30-106395d8f2ae
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 March 2017
                : 8 January 2018
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