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      Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data

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          Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Application of a translational profiling approach for the comparative analysis of CNS cell types.

            Comparative analysis can provide important insights into complex biological systems. As demonstrated in the accompanying paper, translating ribosome affinity purification (TRAP) permits comprehensive studies of translated mRNAs in genetically defined cell populations after physiological perturbations. To establish the generality of this approach, we present translational profiles for 24 CNS cell populations and identify known cell-specific and enriched transcripts for each population. We report thousands of cell-specific mRNAs that were not detected in whole-tissue microarray studies and provide examples that demonstrate the benefits deriving from comparative analysis. To provide a foundation for further biological and in silico studies, we provide a resource of 16 transgenic mouse lines, their corresponding anatomic characterization, and translational profiles for cell types from a variety of central nervous system structures. This resource will enable a wide spectrum of molecular and mechanistic studies of both well-known and previously uncharacterized neural cell populations.
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              Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

              The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal.
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                Author and article information

                Journal
                eNeuro
                eNeuro
                eneuro
                eneuro
                eNeuro
                eNeuro
                Society for Neuroscience
                2373-2822
                20 November 2017
                30 November 2017
                Nov-Dec 2017
                : 4
                : 6
                : ENEURO.0212-17.2017
                Affiliations
                [1 ]Graduate Program in Bioinformatics, University of British Columbia , Vancouver V6T 1Z4, Canada
                [2 ]Department of Psychiatry, University of British Columbia , Vancouver V6T 2A1, Canada
                [3 ]Michael Smith Laboratories, University of British Columbia , Vancouver V6T 1Z4, Canada
                [4 ]Campbell Family Mental Health Research Institute of CAMH
                [5 ]Department of Psychiatry and the Department of Pharmacology and Toxicology, University of Toronto , Vancouver M5S 1A8, Canada
                Author notes

                The authors declare no competing financial interests.

                Author contributions: B.O.M., L.T., S.J.T., E.S., and P.P. designed research; B.O.M., L.T., B.L., and B.R. performed research; B.O.M. contributed unpublished reagents/analytic tools; B.O.M., L.T., S.J.T., and B.L. analyzed data; B.O.M., L.T., and P.P. wrote the paper.

                This work is supported by a NeuroDevNet grant (P.P.); the University of British Columbia Bioinformatics Graduate Training Program (B.O.M.); a Canadian Institutes of Health Research postdoctoral fellowship (S.J.T.); the Campbell Family Mental Health Research Institute of Centre for Addiction and Mental Health (E.S. and B.R.); National Institutes of Health Grants MH077159 (to E.S.) and MH111099 and GM076990 (to P.P.); and an Natural Sciences and Engineering Research Council of Canada Discovery Grant (P.P.).

                Correspondence should be addressed to Paul Pavlidis, 177 Michael Smith Laboratories 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada. E-mail: paul@ 123456msl.ubc.ca .
                Author information
                http://orcid.org/0000-0002-1452-0889
                http://orcid.org/0000-0002-1007-9061
                http://orcid.org/0000-0003-3692-243X
                http://orcid.org/0000-0002-0426-5028
                Article
                eN-MNT-0212-17
                10.1523/ENEURO.0212-17.2017
                5707795
                29204516
                e5bfd5ac-b166-48e2-a882-c71e8734223d
                Copyright © 2017 Mancarci et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 13 June 2017
                : 25 October 2017
                : 31 October 2017
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 87, Pages: 0, Words: 14994
                Funding
                Funded by: NeuroDevNet
                Funded by: UBC Bioinformatics Graduate Training Program
                Funded by: CIHR Post-doctoral fellowship
                Funded by: Campbell Family Mental Health Research Institute of CAMH
                Funded by: National Institutes of Health
                Award ID: MH077159
                Award ID: MH111099
                Award ID: GM076990
                Funded by: NSERC Discovery Grant
                Categories
                7
                7.2
                Methods/New Tools
                Novel Tools and Methods
                Custom metadata
                November/December 2017

                cell type,gene expression,marker gene,microarray,rna sequencing

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