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      APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

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          SUMMARY

          The E4 allele of Apolipoprotein E ( APOE) is associated with both metabolic dysfunction and a heightened proinflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.

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          In brief

          Lee et al. integrate single-cell and spatially resolved -omics technologies to systematically characterize APOE4’s role in the brain’s response to aging, peripheral inflammatory challenge, and amyloid pathology. E4 microglia display a unique metabolic response to each of these paradigms, with increased aerobic glycolysis and altered expression of lipid metabolism pathways.

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          Most cited references125

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                7 April 2023
                28 March 2023
                03 March 2023
                20 April 2023
                : 42
                : 3
                : 112196
                Affiliations
                [1 ]Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
                [2 ]Sanders Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
                [3 ]Department of Neuroscience, University of Kentucky, Lexington, KY 40536, USA
                [4 ]Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
                [5 ]Department of Biochemistry & Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA
                [6 ]Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA
                [7 ]These authors contributed equally
                [8 ]Senior author
                [9 ]Lead contact
                Author notes
                [* ]Correspondence: josh.morganti@ 123456uky.edu (J.M.M.), johnson.lance@ 123456uky.edu (L.A.J.)

                AUTHOR CONTRIBUTIONS

                L.A.J. and J.M.M. designed the experiments. S.L., N.A.D., J.M.M., and L.A.J. analyzed the data and wrote the paper. N.A.D. completed the metabolic analyses of microglia, including metabolomics, Seahorse assays, and RT-PCR. E.J.A. and J.L.S. performed tissue preparation, sectioning, and staining for ST and immunohistochemistry, respectively. C.T.S., C.M.F., A.E.W., G.M.-S., S.M.M., and H.C.W. assisted with bulk and scRNA-seq analyses. J.M.M. and L.A.J. supervised scRNA-seq and ST workflows, with technical assistance from J.L.S., A.A.G., and D.S.G. R.C.S. oversaw MALDI MSI, with technical and analytical assistance from L.R.G., H.A.C., and T.R.H. All authors read the paper and provided edits.

                Article
                NIHMS1887279
                10.1016/j.celrep.2023.112196
                10117631
                36871219
                91768fc4-50cf-4ace-a0f8-52b055cce1db

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

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                Cell biology
                Cell biology

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