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

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          Abstract

          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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          Most cited references73

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          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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                Author and article information

                Journal
                9809671
                Nat Neurosci
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                27 January 2021
                01 January 2021
                21 December 2020
                15 February 2021
                : 24
                : 1
                : 47-60
                Affiliations
                [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
                Article
                EMS114908
                10.1038/s41593-020-00757-6
                7116742
                33349711
                a4b5f277-5bf5-4212-af21-c85ce187a7d8

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