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      Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis

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          Abstract

          Collagen-producing cells maintain the complex architecture of the lung and drive pathologic scarring in pulmonary fibrosis. Here we perform single-cell RNA-sequencing to identify all collagen-producing cells in normal and fibrotic lungs. We characterize multiple collagen-producing subpopulations with distinct anatomical localizations in different compartments of murine lungs. One subpopulation, characterized by expression of Cthrc1 (collagen triple helix repeat containing 1), emerges in fibrotic lungs and expresses the highest levels of collagens. Single-cell RNA-sequencing of human lungs, including those from idiopathic pulmonary fibrosis and scleroderma patients, demonstrate similar heterogeneity and CTHRC1-expressing fibroblasts present uniquely in fibrotic lungs. Immunostaining and in situ hybridization show that these cells are concentrated within fibroblastic foci. We purify collagen-producing subpopulations and find disease-relevant phenotypes of Cthrc1-expressing fibroblasts in in vitro and adoptive transfer experiments. Our atlas of collagen-producing cells provides a roadmap for studying the roles of these unique populations in homeostasis and pathologic fibrosis.

          Abstract

          Collagen production by lung cells is critical to maintain organ architecture but can also drive pathological scarring. Here the authors perform single cell RNA sequencing of collagen-producing lung cells identifying a subset of pathologic fibroblasts characterized by Cthrc1 expression which are concentrated within fibroblastic foci in fibrotic lungs and show a pro-fibrotic phenotype.

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          A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

          Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
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            Multiple stromal populations contribute to pulmonary fibrosis without evidence for epithelial to mesenchymal transition.

            There are currently few treatment options for pulmonary fibrosis. Innovations may come from a better understanding of the cellular origin of the characteristic fibrotic lesions. We have analyzed normal and fibrotic mouse and human lungs by confocal microscopy to define stromal cell populations with respect to several commonly used markers. In both species, we observed unexpected heterogeneity of stromal cells. These include numerous cells with molecular and morphological characteristics of pericytes, implicated as a source of myofibroblasts in other fibrotic tissues. We used mouse genetic tools to follow the fates of specific cell types in the bleomcyin-induced model of pulmonary fibrosis. Using inducible transgenic alleles to lineage trace pericyte-like cells in the alveolar interstitium, we show that this population proliferates in fibrotic regions. However, neither these cells nor their descendants express high levels of the myofibroblast marker alpha smooth muscle actin (Acta2, aSMA). We then used a Surfactant protein C-CreER(T2) knock-in allele to follow the fate of Type II alveolar cells (AEC2) in vivo. We find no evidence at the cellular or molecular level for epithelial to mesenchymal transition of labeled cells into myofibroblasts. Rather, bleomycin accelerates the previously reported conversion of AEC2 into AEC1 cells. Similarly, epithelial cells labeled with our Scgb1a1-CreER allele do not give rise to fibroblasts but generate both AEC2 and AEC1 cells in response to bleomycin-induced lung injury. Taken together, our results show a previously unappreciated heterogeneity of cell types proliferating in fibrotic lesions and exclude pericytes and two epithelial cell populations as the origin of myofibroblasts.
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              Single-Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis

              SUMMARY Fibroblast heterogeneity has long been recognized in mouse and human lungs, homeostasis, and disease states. However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampers our understanding of the mechanisms of fibrosis. To comprehensively classify fibro-blast populations in the lung using an unbiased approach, single-cell RNA sequencing was performed with mesenchymal preparations from either uninjured or bleomycin-treated mouse lungs. Single-cell transcriptome analyses classified and defined six mesenchymal cell types in normal lung and seven in fibrotic lung. Furthermore, delineation of their differentiation trajectory was achieved by a machine learning method. This collection of single-cell transcriptomes and the distinct classification of fibroblast subsets provide a new resource for understanding the fibroblast landscape and the roles of fibroblasts in fibrotic diseases.
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                Author and article information

                Contributors
                Dean.Sheppard@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 April 2020
                21 April 2020
                2020
                : 11
                : 1920
                Affiliations
                [1 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Lung Biology Center, Department of Medicine, , University of California, San Francisco, ; San Francisco, CA USA
                [2 ]ISNI 0000 0004 0572 4227, GRID grid.431072.3, Abbvie Inc, ; North Chicago, IL USA
                [3 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Centre for Inflammation Research, The Queen’s Medical Research Institute, , University of Edinburgh, ; Edinburgh, UK
                [4 ]ISNI 0000000419368710, GRID grid.47100.32, Section of Pulmonary, Critical Care and Sleep Medicine, , Yale School of Medicine, ; New Haven, CT USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA USA
                [6 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Departments of Medicine and Anesthesia, Cardiovascular Research Institute, , University of California, San Francisco, ; San Francisco, CA USA
                [7 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, , University of California, San Francisco, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0003-3100-6934
                http://orcid.org/0000-0002-2273-4094
                http://orcid.org/0000-0002-7714-8076
                http://orcid.org/0000-0001-5917-4601
                http://orcid.org/0000-0002-6277-2036
                Article
                15647
                10.1038/s41467-020-15647-5
                7174390
                32317643
                3acf0c86-ae0d-4288-8474-38f2beca8b4a
                © The Author(s) 2020

                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 June 2019
                : 20 March 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: HL123423
                Award ID: HL108794
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Funded by: FundRef https://doi.org/10.13039/100006483, AbbVie (AbbVie Inc.);
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                next-generation sequencing,respiratory tract diseases
                Uncategorized
                next-generation sequencing, respiratory tract diseases

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