17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Macrophages promote both injury and repair following myocardial infarction, but discriminating functions within mixed populations remains challenging. Here we used fate mapping and single-cell transcriptomics to demonstrate that at steady state, TIMD4 +LYVE1 +MHC-II loCCR2 resident cardiac macrophages self-renew with negligible blood monocyte input. Monocytes partially replaced resident TIMD4 LYVE1 MHC-II hiCCR2 macrophages and fully replaced TIMD4 LYVE1 MHC-II hiCCR2 + macrophages, revealing a hierarchy of monocyte contribution to functionally distinct macrophage subsets. Ischemic injury reduced TIMD4 + and TIMD4 resident macrophage abundance within infarcted tissue while recruited, CCR2 + monocyte-derived macrophages adopted multiple cell fates, including those nearly indistinguishable from resident macrophages. Despite this similarity, inducible depletion of resident macrophages using a Cx3cr1-based system led to impaired cardiac function and promoted adverse remodeling primarily within the peri-infarct zone, highlighting a non-redundant, cardioprotective role of resident cardiac macrophages. Lastly, we demonstrate the ability of TIMD4 to be used as a durable lineage marker of a subset of resident cardiac macrophages.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Fast unfolding of communities in large networks

          We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              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.
                Bookmark

                Author and article information

                Journal
                100941354
                21750
                Nat Immunol
                Nat. Immunol.
                Nature immunology
                1529-2908
                1529-2916
                7 November 2018
                11 December 2018
                January 2019
                14 June 2019
                : 20
                : 1
                : 29-39
                Affiliations
                [1 ]Toronto General Hospital Research Institute, University Health Network (UHN)
                [2 ]Ted Rogers Centre for Heart Research
                [3 ]University of Toronto, Department of Medicine
                [4 ]University of Toronto, Department of Laboratory Medicine and Pathobiology
                [5 ]University of Toronto, Department of Immunology
                [6 ]Singapore Immunology Network, Agency for Science, Technology and Research
                [7 ]Washington University School of Medicine, Division of Cardiology
                [8 ]Peter Munk Cardiac Centre
                Author notes
                [#]

                First Authors, equal contribution.

                Author contributions

                S.A.D. and J.A.M. designed and performed experiments with the help of X.C-C., S.H., C.K., M.G.A., A.W., L.A., R.Z., R.B., and I.B.. A.M. performed all surgeries. M.H., K.J.L., B.R., F.G., M.I.C., and C.S.R. provided expertise and feedback. S.N. performed the bioinformatics analyses. J.C performed the Mpath analysis. S.E. conceived the study, obtained funding and wrote the manuscript with S.A.D. S.N. and J.A.M..

                [* ]Corresponding Author Slava Epelman, MD, PhD, Toronto Medical Discovery Tower, MaRS Building., 101 College St, 3rd Floor, Room TMDT 3-903, Toronto, ON. Canada. M5G 1L7, slava.epelman@ 123456uhn.ca
                Article
                NIHMS1511273
                10.1038/s41590-018-0272-2
                6565365
                30538339
                b5026f02-72a7-4fca-96db-25c0b4d813b1

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Article

                Immunology
                Immunology

                Comments

                Comment on this article