34
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Cross-tissue immune cell analysis reveals tissue-specific features in humans**

      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

          Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. Here, we surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of 360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multi-tissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis and antigen receptor sequencing.

          Related collections

          Most cited references94

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

          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            SCANPY : large-scale single-cell gene expression data analysis

            Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

              We investigated SARS-CoV-2 potential tropism by surveying expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors. We co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission. These genes are co-expressed in nasal epithelial cells with genes involved in innate immunity, highlighting the cells' potential role in initial viral infection, spread and clearance. The study offers a useful resource for further lines of inquiry with valuable clinical samples from COVID-19 patients and we provide our data in a comprehensive, open and user-friendly fashion at www.covid19cellatlas.org.
                Bookmark

                Author and article information

                Journal
                0404511
                Science
                Science
                Science (New York, N.Y.)
                0036-8075
                1095-9203
                12 May 2022
                13 May 2022
                13 May 2022
                17 May 2022
                : 376
                : 6594
                : eabl5197
                Affiliations
                [1 ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
                [2 ]Department of Clinical Neurosciences, University of Cambridge
                [3 ]Department of Systems Biology, Columbia University Irving Medical Center
                [4 ]Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK
                [5 ]Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK
                [6 ]Department of Microbiology and Immunology, Columbia University Irving Medical Center
                [7 ]Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
                [8 ]West Suffolk Hospital NHS Trust, Bury Saint Edmunds, UK
                [9 ]Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
                [10 ]Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
                [11 ]Department of Anaesthesia, University of Cambridge, Cambridge, UK
                [12 ]Chan Zuckerberg Biohub, San Francisco, CA, USA
                [13 ]Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
                [14 ]Theory of Condensed Matter, Cavendish Laboratory, Department of Physics, University of Cambridge, JJ Thomson Ave, Cambridge CB3 0HE, UK
                Author notes
                [**]

                This manuscript has been accepted for publication in Science. This version has not undergone final editing. Please refer to the complete version of record at http://www.sciencemag.org/ [ sciencemag.org]. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS.

                [#]

                Co-first authors

                Article
                EMS144957
                10.1126/science.abl5197
                7612735
                35549406
                51a5b130-0fc5-428a-9eb2-07bc97bf3487

                This work is licensed under a CC BY 4.0 International license.

                History
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article