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      Cell-type-specific profiling of brain mitochondria reveals functional and molecular diversity

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          Abstract

          Mitochondria vary in morphology and function in different tissues; however, little is known about their molecular diversity among cell types. Here we engineered MitoTag mice, which express a Cre recombinase-dependent green fluorescent protein targeted to the outer mitochondrial membrane, and developed an isolation approach to profile tagged mitochondria from defined cell types. We determined the mitochondrial proteome of the three major cerebellar cell types (Purkinje cells, granule cells and astrocytes) and identified hundreds of mitochondrial proteins that are differentially regulated. Thus, we provide markers of cell-type-specific mitochondria for the healthy and diseased mouse and human central nervous systems, including in amyotrophic lateral sclerosis and Alzheimer's disease. Based on proteomic predictions, we demonstrate that astrocytic mitochondria metabolize long-chain fatty acids more efficiently than neuronal mitochondria. We also characterize cell-type differences in mitochondrial calcium buffering via the mitochondrial calcium uniporter (Mcu) and identify regulator of microtubule dynamics protein 3 (Rmdn3) as a determinant of endoplasmic reticulum-mitochondria proximity in Purkinje cells. Our approach enables exploring mitochondrial diversity in many in vivo contexts.

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          A gene expression atlas of the central nervous system based on bacterial artificial chromosomes.

          The mammalian central nervous system (CNS) contains a remarkable array of neural cells, each with a complex pattern of connections that together generate perceptions and higher brain functions. Here we describe a large-scale screen to create an atlas of CNS gene expression at the cellular level, and to provide a library of verified bacterial artificial chromosome (BAC) vectors and transgenic mouse lines that offer experimental access to CNS regions, cell classes and pathways. We illustrate the use of this atlas to derive novel insights into gene function in neural cells, and into principal steps of CNS development. The atlas, library of BAC vectors and BAC transgenic mice generated in this screen provide a rich resource that allows a broad array of investigations not previously available to the neuroscience community.
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            A translational profiling approach for the molecular characterization of CNS cell types.

            The cellular heterogeneity of the brain confounds efforts to elucidate the biological properties of distinct neuronal populations. Using bacterial artificial chromosome (BAC) transgenic mice that express EGFP-tagged ribosomal protein L10a in defined cell populations, we have developed a methodology for affinity purification of polysomal mRNAs from genetically defined cell populations in the brain. The utility of this approach is illustrated by the comparative analysis of four types of neurons, revealing hundreds of genes that distinguish these four cell populations. We find that even two morphologically indistinguishable, intermixed subclasses of medium spiny neurons display vastly different translational profiles and present examples of the physiological significance of such differences. This genetically targeted translating ribosome affinity purification (TRAP) methodology is a generalizable method useful for the identification of molecular changes in any genetically defined cell type in response to genetic alterations, disease, or pharmacological perturbations.
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              Is Open Access

              1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data

              Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, the presence of protein domains or the membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.
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                Author and article information

                Journal
                Nature Neuroscience
                Nat Neurosci
                Springer Science and Business Media LLC
                1097-6256
                1546-1726
                September 9 2019
                Article
                10.1038/s41593-019-0479-z
                31501572
                d5851a8e-7793-4b2d-b256-56532bdd44c0
                © 2019

                http://www.springer.com/tdm

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