11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages

      research-article

      Read this article at

      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

          Microglia are resident myeloid cells in the central nervous system (CNS) that control homeostasis and protect CNS from damage and infections. Microglia and peripheral myeloid cells accumulate and adapt tumor supporting roles in human glioblastomas that show prevalence in men. Cell heterogeneity and functional phenotypes of myeloid subpopulations in gliomas remain elusive. Here we show single-cell RNA sequencing (scRNA-seq) of CD11b + myeloid cells in naïve and GL261 glioma-bearing mice that reveal distinct profiles of microglia, infiltrating monocytes/macrophages and CNS border-associated macrophages. We demonstrate an unforeseen molecular heterogeneity among myeloid cells in naïve and glioma-bearing brains, validate selected marker proteins and show distinct spatial distribution of identified subsets in experimental gliomas. We find higher expression of MHCII encoding genes in glioma-activated male microglia, which was corroborated in bulk and scRNA-seq data from human diffuse gliomas. Our data suggest that sex-specific gene expression in glioma-activated microglia may be relevant to the incidence and outcomes of glioma patients.

          Abstract

          Microglia and peripheral myeloid cells are critically involved in the immunopathology of glioblastoma. Here the authors present single-cell sequencing data that assesses the phenotypic composition of CD11b + myeloid cells from a murine model of glioblastoma and suggest enhanced MHCII transcription which they additionally report from human cases of glioblastoma.

          Related collections

          Most cited references76

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
                Bookmark

                Author and article information

                Contributors
                j.mieczkowski@nencki.edu.pl
                b.kaminska@nencki.edu.pl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 February 2021
                19 February 2021
                2021
                : 12
                : 1151
                Affiliations
                [1 ]GRID grid.419305.a, ISNI 0000 0001 1943 2944, Laboratory of Molecular Neurobiology, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, ; Warsaw, Poland
                [2 ]GRID grid.13339.3b, ISNI 0000000113287408, Postgraduate School of Molecular Medicine, , Medical University of Warsaw, ; Warsaw, Poland
                [3 ]GRID grid.419305.a, ISNI 0000 0001 1943 2944, Laboratory of Cytometry, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, ; Warsaw, Poland
                Author information
                http://orcid.org/0000-0003-1464-6382
                http://orcid.org/0000-0002-4988-1086
                http://orcid.org/0000-0001-5079-1566
                http://orcid.org/0000-0002-1630-4389
                http://orcid.org/0000-0001-9611-531X
                http://orcid.org/0000-0001-8710-2808
                http://orcid.org/0000-0002-2091-012X
                http://orcid.org/0000-0002-2642-4616
                Article
                21407
                10.1038/s41467-021-21407-w
                7895824
                33608526
                13d2a6cc-3d22-4c48-bec1-81e58e8b9827
                © The Author(s) 2021

                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
                : 22 August 2019
                : 26 January 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer,computational biology and bioinformatics,immunology,molecular biology
                Uncategorized
                cancer, computational biology and bioinformatics, immunology, molecular biology

                Comments

                Comment on this article