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      Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease

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          Abstract

          Alzheimer’s disease (AD) is the most prevalent form of dementia and is characterized by abnormal extracellular aggregates of amyloid-β and intraneuronal hyperphosphorylated tau tangles and neuropil threads. Microglia, the tissue-resident macrophages of the central nervous system (CNS), are important for CNS homeostasis and implicated in AD pathology. In amyloid mouse models, a phagocytic/activated microglia phenotype has been identified. How increasing levels of amyloid-β and tau pathology affect human microglia transcriptional profiles is unknown. Here, we performed snRNAseq on 482,472 nuclei from non-demented control brains and AD brains containing only amyloid-β plaques or both amyloid-β plaques and tau pathology. Within the microglia population, distinct expression profiles were identified of which two were AD pathology-associated. The phagocytic/activated AD1-microglia population abundance strongly correlated with tissue amyloid-β load and localized to amyloid-β plaques. The AD2-microglia abundance strongly correlated with tissue phospho-tau load and these microglia were more abundant in samples with overt tau pathology. This full characterization of human disease-associated microglia phenotypes provides new insights in the pathophysiological role of microglia in AD and offers new targets for microglia-state-specific therapeutic strategies.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00401-021-02263-w.

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          Most cited references35

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                h.w.g.m.boddeke@umcg.nl
                Journal
                Acta Neuropathol
                Acta Neuropathol
                Acta Neuropathologica
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0001-6322
                1432-0533
                20 February 2021
                20 February 2021
                2021
                : 141
                : 5
                : 681-696
                Affiliations
                [1 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, , University of Groningen and University Medical Center Groningen (UMCG), ; Antonius Deusinglaan 1, 9713AV Groningen, the Netherlands
                [2 ]GRID grid.431072.3, ISNI 0000 0004 0572 4227, Foundational Neuroscience Center, , AbbVie Inc, ; Cambridge, MA USA
                [3 ]GRID grid.5284.b, ISNI 0000 0001 0790 3681, Department of Biomedical Sciences, Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, , University of Antwerp, ; Wilrijk, Antwerp, Belgium
                [4 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, Department of Neurology and Alzheimer Center, , University of Groningen and University Medical Center Groningen (UMCG), ; Groningen, the Netherlands
                [5 ]GRID grid.5284.b, ISNI 0000 0001 0790 3681, Faculty of Medicine & Health Sciences, Translational Neurosciences, , University of Antwerp, ; Antwerp, Belgium
                [6 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Division of Human Nutrition and Health, Chair group of Nutritional Biology, , Wageningen University & Research, ; Wageningen, the Netherlands
                [7 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Neuroscience, , Karolinska Institute, ; Stockholm, Sweden
                [8 ]GRID grid.467162.0, ISNI 0000 0004 4662 2788, Neuroscience Discovery, , AbbVie Deutschland GmbH & Co. KG, ; Ludwigshafen, Germany
                [9 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Department of Pathology and Medical Biology, University Medical Center Groningen (UMCG), , University of Groningen, ; Groningen, the Netherlands
                [10 ]GRID grid.416667.4, ISNI 0000 0004 0608 3935, Department of Neurology, , Memory Clinic of Hospital Network Antwerp (ZNA), Middelheim and Hoge Beuken, ; Antwerp, Belgium
                [11 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Center for Healthy Ageing, Department of Cellular and Molecular Medicine, , University of Copenhagen, ; Blegdamsvej 3B, 2200 Copenhagen N, Denmark
                Article
                2263
                10.1007/s00401-021-02263-w
                8043951
                33609158
                9715f4cd-11d8-400d-8170-aca39be0b8ba
                © The Author(s) 2021

                Open AccessThis 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
                : 19 September 2020
                : 6 January 2021
                : 6 January 2021
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2021

                Neurology
                microglia,alzheimer’s disease,single-nucleus rna sequencing,amyloid-β,tau
                Neurology
                microglia, alzheimer’s disease, single-nucleus rna sequencing, amyloid-β, tau

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