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      New tools for studying microglia in the mouse and human CNS

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          Significance

          Microglia are the tissue resident macrophages of the brain and spinal cord, implicated in important developmental, homeostatic, and disease processes, although our understanding of their roles is complicated by an inability to distinguish microglia from related cell types. Although they share many features with other macrophages, microglia have distinct developmental origins and functions. Here we validate a stable and robustly expressed microglial marker for both mouse and human, transmembrane protein 119 (Tmem119). We use custom-made antibodies against Tmem119 to perform deep RNA sequencing of developing microglia, and demonstrate that microglia mature by the second postnatal week in mice. The antibodies, cell isolation methods, and RNAseq profiles presented here will greatly facilitate our understanding of microglial function in health and disease.

          Abstract

          The specific function of microglia, the tissue resident macrophages of the brain and spinal cord, has been difficult to ascertain because of a lack of tools to distinguish microglia from other immune cells, thereby limiting specific immunostaining, purification, and manipulation. Because of their unique developmental origins and predicted functions, the distinction of microglia from other myeloid cells is critically important for understanding brain development and disease; better tools would greatly facilitate studies of microglia function in the developing, adult, and injured CNS. Here, we identify transmembrane protein 119 (Tmem119), a cell-surface protein of unknown function, as a highly expressed microglia-specific marker in both mouse and human. We developed monoclonal antibodies to its intracellular and extracellular domains that enable the immunostaining of microglia in histological sections in healthy and diseased brains, as well as isolation of pure nonactivated microglia by FACS. Using our antibodies, we provide, to our knowledge, the first RNAseq profiles of highly pure mouse microglia during development and after an immune challenge. We used these to demonstrate that mouse microglia mature by the second postnatal week and to predict novel microglial functions. Together, we anticipate these resources will be valuable for the future study and understanding of microglia in health and disease.

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          Most cited references57

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          Is Open Access

          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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            Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

            R. Edgar (2002)
            The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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              Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

              Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Proceedings of the National Academy of Sciences
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                March 22 2016
                February 16 2016
                March 22 2016
                : 113
                : 12
                Affiliations
                [1 ]Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305;
                [2 ]Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305;
                [3 ]Department of Pharmacology and Therapeutics, University of Melbourne, Melbourne, VIC, Australia, 3010;
                [4 ]Department of Neurology, Stanford University School of Medicine, Stanford, CA 94305;
                [5 ]Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305;
                [6 ]Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, CA 94305;
                [7 ]Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305;
                [8 ]Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305;
                [9 ]University of California, San Francisco Epilepsy Center, University of California, San Francisco, CA 94143;
                [10 ]Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305
                Article
                10.1073/pnas.1525528113
                26884166
                ae8df5ab-041a-4d28-bd18-2bc3b6c7f467
                © 2016

                Free to read

                http://www.pnas.org/preview_site/misc/userlicense.xhtml

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