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      Immune microniches shape intestinal T reg function

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          Abstract

          The intestinal immune system is highly adapted to maintaining tolerance to the commensal microbiota and self-antigens while defending against invading pathogens 1, 2 . Recognizing how the diverse network of local cells establish homeostasis and maintains it in the complex immune environment of the gut is critical to understanding how tolerance can be re-established following dysfunction, such as in inflammatory disorders. Although cell and molecular interactions that control T regulatory (T reg) cell development and function have been identified 3, 4 , less is known about the cellular neighbourhoods and spatial compartmentalization that shapes microorganism-reactive T reg cell function. Here we used in vivo live imaging, photo-activation-guided single-cell RNA sequencing 57 and spatial transcriptomics to follow the natural history of T cells that are reactive towards Helicobacter hepaticus through space and time in the settings of tolerance and inflammation. Although antigen stimulation can occur anywhere in the tissue, the lamina propria—but not embedded lymphoid aggregates—is the key microniche that supports effector T reg (eT reg) cell function. eT reg cells are stable once their niche is established; however, unleashing inflammation breaks down compartmentalization, leading to dominance of CD103 +SIRPα + dendritic cells in the lamina propria. We identify and validate the putative tolerogenic interaction between CD206 + macrophages and eT reg cells in the lamina propria and identify receptor–ligand pairs that are likely to govern the interaction. Our results reveal a spatial mechanism of tolerance in the lamina propria and demonstrate how knowledge of local interactions may contribute to the next generation of tolerance-inducing therapies.

          Abstract

          Studies in mice show that effector T regulatory cells in the gut are most functional in the lamina propria, but this homeostatic niche is disrupted in inflammation, suggesting a spatial mechanism of tolerance to commensal microorganisms.

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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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            Inference and analysis of cell-cell communication using CellChat

            Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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              RNA velocity of single cells

              RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
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                Author and article information

                Contributors
                Emily.thornton@ndm.ox.ac.uk
                Fiona.powrie@kennedy.ox.ac.uk
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                3 April 2024
                3 April 2024
                2024
                : 628
                : 8009
                : 854-862
                Affiliations
                [1 ]Kennedy Institute of Rheumatology, NDORMS, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [2 ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, ( https://ror.org/05cy4wa09) Cambridge, UK
                [3 ]Department of Pathology, Oslo University Hospital–Rikshospitalet, ( https://ror.org/00j9c2840) Oslo, Norway
                [4 ]Theory of Condensed Matter, Cavendish Laboratory, Department of Physics, University of Cambridge, ( https://ror.org/013meh722) Cambridge, UK
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Translational Immune Discovery Unit, Weatherall Institute of Molecular Medicine, , University of Oxford, ; Oxford, UK
                [6 ]Nuffield Department of Medicine, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [7 ]GRID grid.417570.0, ISNI 0000 0004 0374 1269, Present Address: Roche Innovation Center Zurich, Pharma Research and Early Development, , F. Hoffmann-La Roche, ; Schlieren, Switzerland
                Author information
                http://orcid.org/0000-0002-8966-9047
                http://orcid.org/0000-0002-6265-999X
                http://orcid.org/0000-0003-0909-2434
                http://orcid.org/0000-0002-0024-2603
                http://orcid.org/0000-0001-5816-1657
                http://orcid.org/0000-0002-6294-6366
                http://orcid.org/0000-0002-0068-2502
                http://orcid.org/0000-0003-3312-5929
                Article
                7251
                10.1038/s41586-024-07251-0
                11041794
                38570678
                b1f9182c-7526-4b70-a2d6-8a6132b6a613
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 August 2022
                : 28 February 2024
                Categories
                Article
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                © Springer Nature Limited 2024

                Uncategorized
                mucosal immunology,cellular signalling networks,peripheral tolerance,lymphocyte differentiation,imaging the immune system

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