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      Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution

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          SUMMARY

          Microglia, the tissue-resident macrophages in the brain, are damage sensors that react to nearly any perturbation, including neurodegenerative diseases such as Alzheimer’s disease (AD). Here, using single-cell RNA sequencing, we determined the transcriptome of more than 1,600 individual microglia cells isolated from the hippocampus of a mouse model of severe neurodegeneration with AD-like phenotypes and of control mice at multiple time points during progression of neurodegeneration. In this neurodegeneration model, we discovered two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively. Furthermore, our work identified previously unobserved heterogeneity in the response of microglia to neurodegeneration, discovered disease stage-specific microglia cell states, revealed the trajectory of cellular reprogramming of microglia in response to neurodegeneration, and uncovered the underlying transcriptional programs.

          In Brief

          Mathys et al. use single-cell RNA sequencing to determine the phenotypic heterogeneity of microglia during the progression of neurodegeneration. They identify multiple disease stage-specific cell states, including two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively.

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          Most cited references16

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          Gamma frequency entrainment attenuates amyloid load and modifies microglia.

          Changes in gamma oscillations (20-50 Hz) have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies is unclear. Here we show reduced, behaviourally driven gamma oscillations before the onset of plaque formation or cognitive decline in a mouse model of Alzheimer's disease. Optogenetically driving fast-spiking parvalbumin-positive (FS-PV)-interneurons at gamma (40 Hz), but not other frequencies, reduces levels of amyloid-β (Aβ)1-40 and Aβ 1-42 isoforms. Gene expression profiling revealed induction of genes associated with morphological transformation of microglia, and histological analysis confirmed increased microglia co-localization with Aβ. Subsequently, we designed a non-invasive 40 Hz light-flickering regime that reduced Aβ1-40 and Aβ1-42 levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. Our findings uncover a previously unappreciated function of gamma rhythms in recruiting both neuronal and glial responses to attenuate Alzheimer's-disease-associated pathology.
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            Single cell RNA Seq reveals dynamic paracrine control of cellular variation

            High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis, and function of gene expression variation between seemingly identical cells. Here, we sequence single-cell RNA-Seq libraries prepared from over 1,700 primary mouse bone marrow derived dendritic cells (DCs) spanning several experimental conditions. We find substantial variation between identically stimulated DCs, in both the fraction of cells detectably expressing a given mRNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a “core” module of antiviral genes is expressed very early by a few “precocious” cells, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analyzing DCs from knockout mice, and modulating secretion and extracellular signaling, we show that this response is coordinated via interferon-mediated paracrine signaling. Surprisingly, preventing cell-to-cell communication also substantially reduces variability in the expression of an early-induced “peaked” inflammatory module, suggesting that paracrine signaling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations use to establish complex dynamic responses.
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              DNA damage primes the type I interferon system via the cytosolic DNA sensor STING to promote anti-microbial innate immunity.

              Dysfunction in Ataxia-telangiectasia mutated (ATM), a central component of the DNA repair machinery, results in Ataxia Telangiectasia (AT), a cancer-prone disease with a variety of inflammatory manifestations. By analyzing AT patient samples and Atm(-/-) mice, we found that unrepaired DNA lesions induce type I interferons (IFNs), resulting in enhanced anti-viral and anti-bacterial responses in Atm(-/-) mice. Priming of the type I interferon system by DNA damage involved release of DNA into the cytoplasm where it activated the cytosolic DNA sensing STING-mediated pathway, which in turn enhanced responses to innate stimuli by activating the expression of Toll-like receptors, RIG-I-like receptors, cytoplasmic DNA sensors, and their downstream signaling partners. This study provides a potential explanation for the inflammatory phenotype of AT patients and establishes damaged DNA as a cell intrinsic danger signal that primes the innate immune system for a rapid and amplified response to microbial and environmental threats.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                6 October 2017
                10 October 2017
                16 October 2017
                : 21
                : 2
                : 366-380
                Affiliations
                [1 ]Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [2 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [3 ]Department of Cellular Genetics, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
                [4 ]Center for Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
                [5 ]Biogen, 225 Binney Street, Cambridge, MA 02142, USA
                [6 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [7 ]Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140, USA
                [8 ]Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                Author notes
                [* ]Correspondence: lhtsai@ 123456mit.edu
                [9]

                Lead Contact

                Article
                NIHMS907763
                10.1016/j.celrep.2017.09.039
                5642107
                29020624
                e0295d73-af40-4afc-9d3c-8423317afa18

                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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