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      Microglia facilitate repair of demyelinated lesions via post-squalene sterol synthesis


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          The repair of inflamed, demyelinated lesions as in multiple sclerosis necessitates the clearance of cholesterol-rich myelin debris by microglia/macrophages and the switch from a pro-inflammatory to an anti-inflammatory lesion environment. Subsequently, oligodendrocytes increase cholesterol levels as a prerequisite for synthesizing new myelin membranes. We hypothesized that lesion resolution is regulated by the fate of cholesterol from damaged myelin combined with oligodendroglial sterol synthesis. By integrating expression profiling, genetics, and comprehensive phenotyping, we found that paradoxically sterol synthesis in myelin-phagocytosing microglia/macrophages determines repair of acutely demyelinated lesions. Rather than producing cholesterol, microglia/macrophages synthesized desmosterol, the immediate cholesterol precursor. Desmosterol activated LXR-signaling to resolve inflammation, creating a permissive environment for oligodendrocyte differentiation. Moreover, LXR-target gene products facilitated the efflux of lipid/cholesterol from lipid-laden microglia/macrophages to support remyelination by oligodendrocytes. Consequently, pharmacological stimulation of sterol synthesis boosted repair of demyelinated lesions, suggesting novel therapeutic strategies for myelin repair in multiple sclerosis.

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            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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              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.

                Author and article information

                Nat Neurosci
                Nat Neurosci
                Nature neuroscience
                27 January 2021
                01 January 2021
                21 December 2020
                15 February 2021
                : 24
                : 1
                : 47-60
                [1 ]Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany
                [2 ]Institute for Neuroimmunology and Multiple Sclerosis Research, University Medical Center Göttingen, Göttingen, Germany
                [3 ]Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences and Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
                [4 ]Institute for Neuropathology, University Medical Centre Göttingen, Göttingen, Germany
                [5 ]Applied Neurobiology Group, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
                [6 ]Institute for Medical Systems Biology, Center for Molecular Neurobiology Hamburg, Hamburg, Germany
                [7 ]Institute of Neuronal Cell Biology, Technical University Munich, German Center for Neurodegenerative Diseases, Munich Cluster of Systems Neurology (SyNergy), München, Germany
                [8 ]Institute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany
                [9 ]Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
                [10 ]Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
                [11 ]Neuroimmunology Research Group, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands
                [12 ]Service Unit for Metabolomics and Lipidomics, Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
                [13 ]Department of Clinical Neurophysiology, University Medical Centre Göttingen, Göttingen, Germany
                Author notes
                [* ] Correspondence and requests for material should be addressed to Gesine Saher saher@ 123456em.mpg.de

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