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      Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans

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          Summary

          Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.

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          Highlights

          • A small set of genes is depleted in microglial nuclei relative to single cells

          • This set is enriched for microglial activation genes, including APOE and SPP1

          • This depletion is confirmed in publicly available datasets

          • Single-nucleus sequencing is not suited for the detection of human microglial activation

          Abstract

          Thrupp et al. demonstrate the depletion of a small population of genes in nuclei relative to cells in human microglia by using single-nucleus and single-cell sequencing. This population is enriched for microglial activation genes, suggesting that single-nucleus sequencing is not suited for the detection of microglial activation in humans.

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          Most cited references21

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          DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

          Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as “doublets,” which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool—Doublet-Finder—that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell’s proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present “best practices” for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with “hybrid” expression features. scRNA-seq data interpretation is confounded by technical artifacts known as doublets—single-cell transcriptome data representing more than one cell. Moreover, scRNA-seq cellular throughput is purposefully limited to minimize doublet formation rates. By identifying cells sharing expression features with simulated doublets, DoubletFinder detects many real doublets and mitigates these two limitations.
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            An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

            Gene set enrichment analysis is a widely used tool for analyzing gene expression data. However, current implementations are slow due to a large number of required samples for the analysis to have a good statistical power. In this paper we present a novel algorithm, that efficiently reuses one sample multiple times and thus speeds up the analysis. We show that it is possible to make hundreds of thousands permutations in a few minutes, which leads to very accurate p-values. This, in turn, allows applying standard FDR correction procedures, which are more accurate than the ones currently used. The method is implemented in a form of an R package and is freely available at \url{https://github.com/ctlab/fgsea}.
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              Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and - independent cellular responses in Alzheimer’s disease

              Glia have been implicated in Alzheimer’s disease (AD) pathogenesis. Variants of the microglia receptor TREM2 increase AD risk and activation of “disease-associated microglia” (DAM) is dependent on TREM2 in mouse models of AD. We surveyed gene expression changes associated with AD pathology and TREM2 in 5XFAD mice and human AD by snRNA-seq. We confirmed the presence of Trem2-dependent DAM and identified a novel Serpina3n + C4b + reactive oligodendrocyte population in mice. Interestingly, remarkably different glial phenotypes were evident in human AD. Microglia signature was reminiscent of IRF8-driven reactive microglia in peripheral nerve injury. Oligodendrocyte signatures suggested impaired axonal myelination and metabolic adaptation to neuronal degeneration. Astrocyte profiles indicated weakened metabolic coordination with neurons. Notably, the reactive phenotype of microglia was less palpable in TREM2 R47H and R62H carriers than in non-carriers, demonstrating a TREM2 requirement in both mouse and human AD, despite the marked species-specific differences.
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                Author and article information

                Contributors
                Journal
                Cell Rep
                Cell Rep
                Cell Reports
                Cell Press
                2211-1247
                29 September 2020
                29 September 2020
                29 September 2020
                : 32
                : 13
                : 108189
                Affiliations
                [1 ]Centre for Brain and Disease Research, Flanders Institute for Biotechnology (VIB), Leuven, Belgium
                [2 ]Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
                [3 ]UK Dementia Research Institute at University College London, University College London, London, UK
                [4 ]UK Dementia Research Institute at Imperial College London and Department of Brain Sciences, Imperial College London, London, UK
                [5 ]Department of Neurosciences, Research Group Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium
                Author notes
                []Corresponding author bart.destrooper@ 123456kuleuven.vib.be
                [∗∗ ]Corresponding author mark.fiers@ 123456kuleuven.vib.be
                [6]

                Lead Contact

                Article
                S2211-1247(20)31178-5 108189
                10.1016/j.celrep.2020.108189
                7527779
                32997994
                02cb6e56-f155-43dc-a094-2bf1ba85c391
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 26 February 2020
                : 19 June 2020
                : 2 September 2020
                Categories
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                Cell biology
                microglia,activation,alzheimer’s disease,single-nucleus rna-seq,single-cell rna-seq,microglial activation,arm

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