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      The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments

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          Abstract

          Neutrophils are implicated in multiple homeostatic and pathological processes, but whether functional diversity requires discrete neutrophil subsets is not known. Here, we apply single-cell RNA sequencing to neutrophils from normal and inflamed mouse tissues. Whereas conventional clustering yields multiple alternative organizational structures, diffusion mapping plus RNA velocity discloses a single developmental spectrum, ordered chronologically. Termed here neutrotime, this spectrum extends from immature pre-neutrophils, largely in bone marrow, to mature neutrophils predominantly in blood and spleen. The sharpest increments in neutrotime occur during the transitions from pre-neutrophils to immature neutrophils and from mature marrow neutrophils to those in blood. Human neutrophils exhibit a similar transcriptomic pattern. Neutrophils migrating into inflamed mouse lung, peritoneum and joint maintain the core mature neutrotime signature together with new transcriptional activity that varies with site and stimulus. Together, these data identify a single developmental spectrum as the dominant organizational theme of neutrophil heterogeneity.

          Abstract

          Differentiating neutrophil functional states is difficult. Here the authors show, using single cell RNA-sequencing and trajectory analyses, that mouse neutrophils can be presented as a transcriptome continuum rather than discrete subsets, but are affected by inflammation to express distinct transcriptional states.

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          Most cited references78

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            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.
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              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.
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                Author and article information

                Contributors
                peter.nigrovic@childrens.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 May 2021
                17 May 2021
                2021
                : 12
                : 2856
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA USA
                [2 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Department of Medicine V, Hematology, Oncology and Rheumatology, , Heidelberg University Hospital, ; Heidelberg, Germany
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Immunology, Department of Microbiology and Immunobiology, , Harvard Medical School, ; Boston, MA USA
                [4 ]GRID grid.410370.1, ISNI 0000 0004 4657 1992, Rheumatology Section, , VA Boston Healthcare System, ; Boston, MA USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Immunology, Boston Children’s Hospital, , Harvard Medical School, ; Boston, MA USA
                [6 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Microbiology & Immunology, , University of California San Francisco, ; San Francisco, CA USA
                [7 ]GRID grid.1042.7, The Walter and Eliza Hall Institute of Medical Research, ; Parkville, VIC Australia
                [8 ]GRID grid.418158.1, ISNI 0000 0004 0534 4718, Department of Cancer Immunology, Genentech, ; South San Francisco, CA USA
                [9 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Immunology, , Harvard Medical School, ; Boston, MA USA
                [10 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [11 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Stem Cell and Regenerative Biology, , Harvard University, ; Cambridge, MA USA
                [12 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Immunology Institute and Tisch Cancer Institute, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [13 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pathology and Immunology, , Washington University School of Medicine, ; St. Louis, MO USA
                [14 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Division of Biological Sciences, , University of California San Diego, ; La Jolla, CA USA
                [15 ]GRID grid.168645.8, ISNI 0000 0001 0742 0364, Department of Pathology, , University of Massachusetts Medical School, ; Worcester, MA USA
                [16 ]GRID grid.185006.a, ISNI 0000 0004 0461 3162, La Jolla Institute for Immunology, ; La Jolla, CA USA
                [17 ]GRID grid.7489.2, ISNI 0000 0004 1937 0511, Department of Life Sciences, , Ben-Gurion University of the Negev, ; Be’er Sheva, Israel
                [18 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Statistics, , University of British Columbia, ; Vancouver, BC Canada
                [19 ]GRID grid.66859.34, Broad Institute of Massachusetts Institute of Technology and Harvard, ; Cambridge, MA USA
                [20 ]GRID grid.509459.4, ISNI 0000 0004 0472 0267, YCI Laboratory for Immunological Transcriptomics, , RIKEN Center for Integrative Medical Sciences, ; Kanagawa, Japan
                Author information
                http://orcid.org/0000-0002-6491-2573
                http://orcid.org/0000-0002-9388-2801
                http://orcid.org/0000-0002-2126-3702
                Article
                22973
                10.1038/s41467-021-22973-9
                8129206
                34001893
                3344e28c-f752-4e04-9772-41f39d3ea5c7
                © 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 May 2020
                : 1 April 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001645, Boehringer Ingelheim Fonds (Stiftung für medizinische Grundlagenforschung);
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R24AI072073
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000069, U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS);
                Award ID: R01AR073201
                Award ID: R21AR076630
                Award ID: P30AR070253
                Award ID: R56AR065538
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: R21HL150575
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012051, Lupus Research Alliance (Lupus Research Alliance, Inc.);
                Award ID: Target Identification in Lupus Grant
                Award Recipient :
                Categories
                Article
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                © The Author(s) 2021

                Uncategorized
                computational biology and bioinformatics,developmental biology,immunogenetics,neutrophils

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