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      A transcriptomic atlas of aged human microglia

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          Abstract

          With a rapidly aging global human population, finding a cure for late onset neurodegenerative diseases has become an urgent enterprise. However, these efforts are hindered by the lack of understanding of what constitutes the phenotype of aged human microglia—the cell type that has been strongly implicated by genetic studies in the pathogenesis of age-related neurodegenerative disease. Here, we establish the set of genes that is preferentially expressed by microglia in the aged human brain. This HuMi_Aged gene set captures a unique phenotype, which we confirm at the protein level. Furthermore, we find this gene set to be enriched in susceptibility genes for Alzheimer’s disease and multiple sclerosis, to be increased with advancing age, and to be reduced by the protective APOEε2 haplotype . APOEε4 has no effect. These findings confirm the existence of an aging-related microglial phenotype in the aged human brain and its involvement in the pathological processes associated with brain aging.

          Abstract

          Aging is associated with various changes in the brain, including transcription alteration. Here, Bradshaw and colleagues describe the transcriptome of aged human cortical microglia, and show age-related gene expression as related to neurodegeneration.

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          Most cited references32

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
          Article 0 comments Cited 12458 times – based on 0 reviews     Review now
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            voom: precision weights unlock linear model analysis tools for RNA-seq read counts

            Charity Law, Yunshun Chen, Wei Shi … (2014)
            New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
            Article 0 comments Cited 1530 times – based on 0 reviews     Review now
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              Full-length RNA-seq from single cells using Smart-seq2.

              Simone Picelli, Omid R. Faridani, Asa K Björklund … (2014)
              Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.
              Article 0 comments Cited 1104 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Philip L. De Jager:
                ORCID: http://orcid.org/0000-0002-8057-2505
                pld2115@cumc.columbia.edu
                Elizabeth M. Bradshaw: emb2280@cumc.columbia.edu
                Journal
                Journal ID (nlm-ta): Nat Commun
                Journal ID (iso-abbrev): Nat Commun
                Title: Nature Communications
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2041-1723
                Publication date (Electronic): 7 February 2018
                Publication date PMC-release: 7 February 2018
                Publication date Collection: 2018
                Volume: 9
                Electronic Location Identifier: 539
                Affiliations
                [1 ]ISNI 0000 0001 2285 2675, GRID grid.239585.0, Center for Translational & Computational Neuroimmunology, Department of Neurology, , Columbia University Medical Center, ; New York City, NY 10032 USA
                [2 ]GRID grid.66859.34, Program in Medical and Population Genetics, , Broad Institute, ; Cambridge, MA 02142 USA
                [3 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, School of Mathematics and Statistics, , The University of Sydney, ; Sydney, New South Wales 2006 Australia
                [4 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Massachusetts General Hospital, ; Boston, MA 02114 USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                [6 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Pacific Northwest National Laboratory, ; Richland, WA 99354 USA
                [7 ]ISNI 0000 0001 0705 3621, GRID grid.240684.c, Rush Alzheimer’s Disease Center, , Rush University Medical Center, ; Chicago, IL 60612 USA
                Author information
                http://orcid.org/0000-0002-5253-4747
                http://orcid.org/0000-0002-3679-6579
                http://orcid.org/0000-0001-5108-2227
                http://orcid.org/0000-0003-3735-2643
                http://orcid.org/0000-0003-4076-151X
                http://orcid.org/0000-0002-8057-2505
                Article
                Publisher ID: 2926
                DOI: 10.1038/s41467-018-02926-5
                PMC ID: 5803269
                PubMed ID: 29416036
                SO-VID: d0e48896-d57d-4341-8989-af366d9b1196
                Copyright © © The Author(s) 2018
                License:

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 23 June 2017
                Date accepted : 9 January 2018
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

                ScienceOpen disciplines: Uncategorized
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