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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.

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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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              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
                Acta Neuropathol
                Acta neuropathologica
                Springer Science and Business Media LLC
                1432-0533
                0001-6322
                May 2021
                : 141
                : 5
                Affiliations
                [1 ] 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 ] Foundational Neuroscience Center, AbbVie Inc, Cambridge, MA, USA.
                [3 ] Department of Biomedical Sciences, Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Wilrijk, Antwerp, Belgium.
                [4 ] Department of Neurology and Alzheimer Center, University of Groningen and University Medical Center Groningen (UMCG), Groningen, the Netherlands.
                [5 ] Faculty of Medicine & Health Sciences, Translational Neurosciences, University of Antwerp, Antwerp, Belgium.
                [6 ] Division of Human Nutrition and Health, Chair group of Nutritional Biology, Wageningen University & Research, Wageningen, the Netherlands.
                [7 ] Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
                [8 ] Neuroscience Discovery, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany.
                [9 ] Department of Pathology and Medical Biology, University Medical Center Groningen (UMCG), University of Groningen, Groningen, the Netherlands.
                [10 ] Department of Neurology, Memory Clinic of Hospital Network Antwerp (ZNA), Middelheim and Hoge Beuken, Antwerp, Belgium.
                [11 ] 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. h.w.g.m.boddeke@umcg.nl.
                [12 ] Center for Healthy Ageing, Department of Cellular and Molecular Medicine, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark. h.w.g.m.boddeke@umcg.nl.
                Article
                10.1007/s00401-021-02263-w
                10.1007/s00401-021-02263-w
                8043951
                33609158
                9715f4cd-11d8-400d-8170-aca39be0b8ba
                History

                Alzheimer’s disease,Amyloid-β,Microglia,Single-nucleus RNA sequencing,Tau

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