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      Transcriptomic atlas and interaction networks of brain cells in mouse CNS demyelination and remyelination

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          SUMMARY

          Demyelination is a hallmark of multiple sclerosis, leukoencephalopathies, cerebral vasculopathies, and several neurodegenerative diseases. The cuprizone mouse model is widely used to simulate demyelination and remyelination occurring in these diseases. Here, we present a high-resolution single-nucleus RNA sequencing (snRNA-seq) analysis of gene expression changes across all brain cells in this model. We define demyelination-associated oligodendrocytes (DOLs) and remyelination-associated MAFB hi microglia, as well as astrocytes and vascular cells with signatures of altered metabolism, oxidative stress, and interferon response. Furthermore, snRNA-seq provides insights into how brain cell types connect and interact, defining complex circuitries that impact demyelination and remyelination. As an explicative example, perturbation of microglia caused by TREM2 deficiency indirectly impairs the induction of DOLs. Altogether, this study provides a rich resource for future studies investigating mechanisms underlying demyelinating diseases.

          In brief

          Hou et al. identify key transcriptional signatures of brain glial and vascular cells during demyelination and remyelination induced by cuprizone using single-nucleus RNA sequencing. They reveal cell-cell interactions that impact these processes and demonstrate that a population of demyelination-associated oligodendrocytes is dependent on a TREM2-mediated microglia response.

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          Most cited references110

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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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              A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease.

              Alzheimer's disease (AD) is a detrimental neurodegenerative disease with no effective treatments. Due to cellular heterogeneity, defining the roles of immune cell subsets in AD onset and progression has been challenging. Using transcriptional single-cell sorting, we comprehensively map all immune populations in wild-type and AD-transgenic (Tg-AD) mouse brains. We describe a novel microglia type associated with neurodegenerative diseases (DAM) and identify markers, spatial localization, and pathways associated with these cells. Immunohistochemical staining of mice and human brain slices shows DAM with intracellular/phagocytic Aβ particles. Single-cell analysis of DAM in Tg-AD and triggering receptor expressed on myeloid cells 2 (Trem2)(-/-) Tg-AD reveals that the DAM program is activated in a two-step process. Activation is initiated in a Trem2-independent manner that involves downregulation of microglia checkpoints, followed by activation of a Trem2-dependent program. This unique microglia-type has the potential to restrict neurodegeneration, which may have important implications for future treatment of AD and other neurodegenerative diseases. VIDEO ABSTRACT.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                9 May 2023
                25 April 2023
                21 March 2023
                23 October 2023
                : 42
                : 4
                : 112293
                Affiliations
                [1 ]Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
                [2 ]Ribeirao Preto Medical School, University of Sao Paulo - Ribeirao Preto, Sao Paulo 14049-900, Brazil
                [3 ]Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
                [4 ]Washington University Center for Cellular Imaging, Washington University School of Medicine, St. Louis, MO 63110, USA
                [5 ]Departments of Cell Biology and Physiology and Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
                [6 ]Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA
                [7 ]Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
                [8 ]Present address: 10X Genomics, Pleasanton, CA 94588, USA
                [9 ]These authors contributed equally
                [10 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                J.H., Y.Z., and M.C. designed the study and interpreted the results. J.H. performed animal experiments. J.H. and A.S. prepared single nuclei suspension samples. J.H., Y.Z., P.S.A., M.T., A.U.A., and T.Q. performed bioinformatic analyses. J.H. conducted immunostaining, and Z.C. and R.M.G. performed quantification. Y.C. provided anti-APOE antibody. S.S., G.S., and J.A.J.F. performed EM sample preparation and imaging. S.G. bred Trem2 −/− mice. D.-H.K. and S.J.V.D. provided Il1rl1 −/− mice. M.N.A. provided guidance on bioinformatic analyses. J.H., Y.Z., S.G., and M.C. wrote and revised the manuscript with feedback from all authors. All authors approved the final version of the manuscript.

                [* ]Correspondence: mcolonna@ 123456wustl.edu
                Article
                NIHMS1895570
                10.1016/j.celrep.2023.112293
                10511667
                36952346
                7c1ff783-0388-4c52-b416-7c6d0f0c22ba

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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