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

      1 , 1 , 2 , 2 , 3 , 1 , 2 , 4 , 1 , 5 , 1 , 1 , 6 , 1 , 1 , 1 , 7 , 1 , 1 , 4 , 1 , 4 , 1 , 8 , 2 , 2 , 1 , 1 , 1 , 1 , 9 , 10 , 3 , 11 , 1 , 5 , 1 , 9 , 10 , 12 , 13 , 1 , 4 , 3 , 6 , 7 , 2 , 1 , 14
      Science
      American Association for the Advancement of Science (AAAS)

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          Abstract

          Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. 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 multitissue 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.

          Immune cell diversity in the human body

          The human immune system is composed of many different cell types spread across the entire body, but little is currently known about the fine-grained variations in these cell types across organs. Using single-cell genomics, Domínguez Conde et al . examined the gene expression profile of more than 300,000 individual immune cells extracted from 16 different tissues in 12 deceased adult organ donors (see the Perspective by Liu and Zhang). Cell identity was assigned using CellTypist, an automated cell classification tool designed by the authors. In-depth data analysis revealed insights into how the immune system adapts to function effectively in different organ contexts. —LZ and DJ

          Abstract

          An immune cell atlas of human innate and adaptive immune cells across lymphoid, mucosal, and exocrine sites reveals tissue-specific compositions and features.

          Abstract

          INTRODUCTION

          Immune cells that seed peripheral tissues play a vital role in health and disease, yet most studies of human immunity focus on blood-derived cells. Immune cells adapt to local microenvironments, acquiring distinct features and functional specialization. Dissecting these molecular adaptations through the systematic assessment of cells across the human body promises to transform our understanding of the immune system at the organismal level.

          RATIONALE

          To comprehensively assess immune cell types, we collected donor-matched tissues from 12 deceased organ donors. We isolated immune cells and performed single-cell RNA sequencing and paired VDJ sequencing for T cell and B cell receptors, resulting in high-quality data for ~330,000 immune cells. To resolve cell identities, we developed CellTypist, a logistic regression–based framework using stochastic gradient descent learning. This cross-tissue annotation enabled interrogation of shared and tissue-specific expression modules and cell states within myeloid and lymphoid cell lineages.

          RESULTS

          We developed CellTypist by curating and harmonizing public datasets to assemble a comprehensive immune cell type reference database ( https://www.celltypist.org ). CellTypist was then applied to our data, generated across multiple tissues and individuals ( https://www.tissueimmunecellatlas.org/ ). Altogether, we detected 101 immune populations and performed extensive cross-tissue comparisons for each subset. Although macrophages displayed prominent tissue-restricted features, some convergent features were also detected. For example, macrophages expressing erythrophagocytosis-related genes were widely found in spleen, liver, bone marrow, and lymph nodes. Heterogeneity within defined subpopulations was also observed, such as migratory dendritic cell adaptations. Within adaptive immune lineages, we identified tissue-specific distributions of memory populations. Plasma cells showed a restricted tissue distribution, whereas memory B cells were more widely distributed. Similarly, tissue-resident memory T (T RM ) cells were more restricted in distribution compared with central and effector memory T cells. Notably, T RM cells harbored significant diversity, including αβ and γδ lineages, ascertained by VDJ sequencing. Assessment of clonal dynamics revealed the highest clonal expansions in T RM cells and the most frequent clonal sharing between resident and effector memory populations.

          CONCLUSION

          Here we present an immune cell atlas of myeloid and lymphoid lineages across adult human tissues. We developed CellTypist for automated immune cell annotation and performed an in-depth dissection of cell populations, identifying 101 cell types or states from more than one million cells, including previously underappreciated cell states. We also uncovered convergent phenotypes across tissues within given lineages and described tissue adaptation signatures for a number of cell types, including macrophages and resident memory T cells. Together, we have extended our understanding of how human immunity functions as an integrated, cross-tissue network, and we provide the scientific community with several key new resources.

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          Most cited references94

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          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.
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            • Record: found
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            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).
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              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.
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                Author and article information

                Contributors
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                May 13 2022
                May 13 2022
                : 376
                : 6594
                Affiliations
                [1 ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
                [2 ]Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
                [3 ]Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
                [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, New York, NY, USA.
                [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, Cambridge, UK.
                Article
                10.1126/science.abl5197
                51a5b130-0fc5-428a-9eb2-07bc97bf3487
                © 2022
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