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      Macrophages directly contribute collagen to scar formation during zebrafish heart regeneration and mouse heart repair

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          Abstract

          Canonical roles for macrophages in mediating the fibrotic response after a heart attack include extracellular matrix turnover and activation of cardiac fibroblasts to initiate collagen deposition. Here we reveal that macrophages directly contribute collagen to the forming post-injury scar. Unbiased transcriptomics shows an upregulation of collagens in both zebrafish and mouse macrophages following heart injury. Adoptive transfer of macrophages, from either collagen-tagged zebrafish or adult mouse GFP tpz-collagen donors, enhances scar formation via cell autonomous production of collagen. In zebrafish, the majority of tagged collagen localises proximal to the injury, within the overlying epicardial region, suggesting a possible distinction between macrophage-deposited collagen and that predominantly laid-down by myofibroblasts. Macrophage-specific targeting of col4a3bpa and cognate col4a1 in zebrafish significantly reduces scarring in cryoinjured hosts. Our findings contrast with the current model of scarring, whereby collagen deposition is exclusively attributed to myofibroblasts, and implicate macrophages as direct contributors to fibrosis during heart repair.

          Abstract

          Macrophages mediate the fibrotic response after a heart attack by extracellular matrix turnover and cardiac fibroblasts activation. Here the authors identify an evolutionarily-conserved function of macrophages that contributes directly to the forming post-injury scar through cell-autonomous deposition of collagen.

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          Most cited references46

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          Direct multiplexed measurement of gene expression with color-coded probe pairs.

          We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.
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            Tissue-Resident Macrophage Ontogeny and Homeostasis.

            Defining the origins and developmental pathways of tissue-resident macrophages should help refine our understanding of the role of these cells in various disease settings and enable the design of novel macrophage-targeted therapies. In recent years the long-held belief that macrophage populations in the adult are continuously replenished by monocytes from the bone marrow (BM) has been overturned with the advent of new techniques to dissect cellular ontogeny. The new paradigm suggests that several tissue-resident macrophage populations are seeded during waves of embryonic hematopoiesis and self-maintain independently of BM contribution during adulthood. However, the exact nature of the embryonic progenitors that give rise to adult tissue-resident macrophages is still debated, and the mechanisms enabling macrophage population maintenance in the adult are undefined. Here, we review the emergence of these concepts and discuss current controversies and future directions in macrophage biology.
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              STEM: a tool for the analysis of short time series gene expression data

              Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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                Author and article information

                Contributors
                paul.riley@dpag.ox.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                30 January 2020
                30 January 2020
                2020
                : 11
                : 600
                Affiliations
                [1 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Physiology, Anatomy and Genetics, , University of Oxford, ; Oxford, OX1 3PT UK
                [2 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, , University of Oxford, ; Oxford, OX3 9DS UK
                [3 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, BHF Oxbridge Centre of Regenerative Medicine, , University of Oxford, ; Oxford, UK
                [4 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, UK
                [5 ]Bioinfo, Plantagenet, Canada
                [6 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Sir William Dunn School of Pathology, , University of Oxford, ; Oxford, UK
                [7 ]ISNI 0000 0001 2156 6853, GRID grid.42505.36, Translational Imaging Centre, , University of Southern California, ; Los Angeles, CA USA
                [8 ]ISNI 0000 0001 2179 926X, GRID grid.266756.6, School of Dentistry, University of Missouri-Kansas City, ; Kansas City, MO USA
                [9 ]ISNI 0000 0000 9805 2626, GRID grid.250464.1, Present Address: Molecular Genetics Unit, Okinawa Institute of Science & Technology, ; 1919-1 Tancha, Onna, 904-0495 Japan
                Author information
                http://orcid.org/0000-0003-2956-0311
                http://orcid.org/0000-0002-7488-5898
                http://orcid.org/0000-0003-2023-6304
                http://orcid.org/0000-0002-0936-851X
                http://orcid.org/0000-0001-6355-8691
                http://orcid.org/0000-0003-2856-9410
                http://orcid.org/0000-0002-8046-1688
                http://orcid.org/0000-0001-9289-0263
                http://orcid.org/0000-0002-9862-7332
                Article
                14263
                10.1038/s41467-019-14263-2
                6992796
                32001677
                1a1fcc52-bc04-4b1a-8f4a-43f1a51960f8
                © 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
                : 21 March 2019
                : 27 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000274, British Heart Foundation (BHF);
                Award ID: RM/13/3/30159
                Award ID: RG/13/9/303269
                Award ID: CH/11/1/28798
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 106334/Z/14/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001674, Fondation Leducq;
                Award ID: 14CVD04
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                innate immune cells,cardiac regeneration
                Uncategorized
                innate immune cells, cardiac regeneration

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