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Decoding neuroproteomics: integrating the genome, translatome and functional anatomy

Abstract

The immense intercellular and intracellular heterogeneity of the CNS presents major challenges for high-throughput omic analyses. Transcriptional, translational and post-translational regulatory events are localized to specific neuronal cell types or subcellular compartments, resulting in discrete patterns of protein expression and activity. A spatial and quantitative knowledge of the neuroproteome is therefore critical to understanding both normal and pathological aspects of the functional genomics and anatomy of the CNS. Improvements in mass spectrometry allow the profiling of proteins at a sufficient depth to complement results from high-throughput genomic and transcriptomic assays. However, there are challenges in integrating proteomic data with other data modalities and even greater challenges in obtaining comprehensive neuroproteomic data with cell-type specificity. Here we discuss how proteomics should be exploited to enhance high-throughput functional genomic analysis by tighter integration of data analyses. We also discuss experimental strategies to achieve finer cellular and subcellular resolution in transcriptomic and proteomic studies of neural tissues.

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Figure 1: Integrated genomic, transcriptomic and proteomic analyses and the central dogma.

References

  1. Pollock, J.D., Wu, D.-Y. & Satterlee, J.S. Molecular neuroanatomy: a generation of progress. Trends Neurosci. 37, 106–123 (2014).

    CAS  PubMed  Google Scholar 

  2. Medland, S.E., Jahanshad, N., Neale, B.M. & Thompson, P.M. Whole-genome analyses of whole-brain data: working within an expanded search space. Nat. Neurosci. 17, 791–800 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Arenkiel, B.R. & Ehlers, M.D. Molecular genetics and imaging technologies for circuit-based neuroanatomy. Nature 461, 900–907 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Van Essen, D.C. & Ugurbil, K. The future of the human connectome. Neuroimage 62, 1299–1310 (2012).

    CAS  PubMed  Google Scholar 

  5. Lichtman, J.W., Livet, J. & Sanes, J.R. A technicolour approach to the connectome. Nat. Rev. Neurosci. 9, 417–422 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ekstrand, M.I. et al. Molecular profiling of neurons based on connectivity. Cell 157, 1230–1242 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Deisseroth, K. et al. Next-generation optical technologies for illuminating genetically targeted brain circuits. J. Neurosci. 26, 10380–10386 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Sunkin, S.M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013).

    CAS  PubMed  Google Scholar 

  9. Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).

    CAS  PubMed  Google Scholar 

  10. Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Johnson, M.B. et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62, 494–509 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Hawrylycz, M.J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Heintz, N. Gene expression nervous system atlas (GENSAT). Nat. Neurosci. 7, 483 (2004).

    CAS  PubMed  Google Scholar 

  14. Korbel, J.O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Park, P.J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Li, J.B. & Church, G.M. Deciphering the functions and regulation of brain-enriched A-to-I RNA editing. Nat. Neurosci. 16, 1518–1522 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R.S. & Weissman, J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Licatalosi, D.D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Aebersold, R. & Mann, M. Mass spectrometry–based proteomics. Nature 422, 198–207 (2003).

    CAS  PubMed  Google Scholar 

  20. Stergachis, A.B., MacLean, B., Lee, K., Stamatoyannopoulos, J.A. & MacCoss, M.J. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 8, 1041–1043 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ong, S.-E. & Mann, M. Mass spectrometry–based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).

    CAS  PubMed  Google Scholar 

  22. Vidal, M. et al. The human proteome—a scientific opportunity for transforming diagnostics, therapeutics, and healthcare. Clin. Proteomics 9, 6 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. Wilhelm, M. et al. Mass-spectrometry–based draft of the human proteome. Nature 509, 582–587 (2014).

    CAS  PubMed  Google Scholar 

  24. Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273–299 (2011).

    CAS  PubMed  Google Scholar 

  26. Altelaar, A.F.M., Munoz, J. & Heck, A.J.R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2013).

    CAS  PubMed  Google Scholar 

  27. Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).

    CAS  PubMed  Google Scholar 

  28. Ahrens, C.H., Brunner, E., Qeli, E., Basler, K. & Aebersold, R. Generating and navigating proteome maps using mass spectrometry. Nat. Rev. Mol. Cell Biol. 11, 789–801 (2010).

    CAS  PubMed  Google Scholar 

  29. Bensimon, A., Heck, A.J.R. & Aebersold, R. Mass spectrometry–based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).

    CAS  PubMed  Google Scholar 

  30. Craft, G.E., Chen, A. & Nairn, A.C. Recent advances in quantitative neuroproteomics. Methods 61, 186–218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Cox, B. & Emili, A. Tissue subcellular fractionation and protein extraction for use in mass-spectrometry–based proteomics. Nat. Protoc. 1, 1872–1878 (2006).

    CAS  PubMed  Google Scholar 

  32. Boisvert, F.-M., Lam, Y.W., Lamont, D. & Lamond, A.I. A quantitative proteomics analysis of subcellular proteome localization and changes induced by DNA damage. Mol. Cell. Proteomics 9, 457–470 (2010).

    CAS  PubMed  Google Scholar 

  33. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    PubMed  Google Scholar 

  34. Naegle, K.M. et al. PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies. Mol. Cell. Proteomics 9, 2558–2570 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Oppermann, F.S. et al. Large-scale proteomics analysis of the human kinome. Mol. Cell. Proteomics 8, 1751–1764 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Deribe, Y.L., Pawson, T. & Dikic, I. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672 (2010).

    CAS  PubMed  Google Scholar 

  37. Edwards, A.V.G., Edwards, G.J., Schwämmle, V., Saxtorph, H. & Larsen, M.R. Spatial and temporal effects in protein post-translational modification distributions in the developing mouse brain. J. Proteome Res. 13, 260–267 (2014).

    CAS  PubMed  Google Scholar 

  38. Seyfried, N.T. et al. Quantitative analysis of the detergent-insoluble brain proteome in frontotemporal lobar degeneration using SILAC internal standards. J. Proteome Res. 11, 2721–2738 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Min, S.-W. et al. Acetylation of tau inhibits its degradation and contributes to tauopathy. Neuron 67, 953–966 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Toffolo, E. et al. Phosphorylation of neuronal lysine-specific demethylase 1LSD1/KDM1A impairs transcriptional repression by regulating interaction with CoREST and histone deacetylases HDAC1/2. J. Neurochem. 128, 603–616 (2014).

    CAS  PubMed  Google Scholar 

  41. Sridharan, R. et al. Proteomic and genomic approaches reveal critical functions of H3K9 methylation and heterochromatin protein-1γ in reprogramming to pluripotency. Nat. Cell Biol. 15, 872–882 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Mirzaei, H. et al. Systematic measurement of transcription factor–DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins. Proc. Natl. Acad. Sci. USA 110, 3645–3650 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186 (2006).

    CAS  PubMed  Google Scholar 

  46. Shawahna, R. et al. Transcriptomic and quantitative proteomic analysis of transporters and drug metabolizing enzymes in freshly isolated human brain microvessels. Mol. Pharm. 8, 1332–1341 (2011).

    CAS  PubMed  Google Scholar 

  47. Elvira, G. et al. Characterization of an RNA granule from developing brain. Mol. Cell. Proteomics 5, 635–651 (2006).

    CAS  PubMed  Google Scholar 

  48. Moghaddas Gholami, A. et al. Global proteome analysis of the NCI-60 cell line panel. Cell Reports 4, 609–620 (2013).

    Google Scholar 

  49. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010).

    CAS  PubMed  Google Scholar 

  50. Yu, L.-R. et al. Global analysis of the cortical neuron proteome. Mol. Cell. Proteomics 3, 896–907 (2004).

    CAS  PubMed  Google Scholar 

  51. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Phanstiel, D.H. et al. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Methods 8, 821–827 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Butovsky, O. et al. Identification of a unique TGF-β–dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    CAS  PubMed  Google Scholar 

  54. Greenbaum, D., Colangelo, C., Williams, K. & Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003).

    PubMed  PubMed Central  Google Scholar 

  55. Lundberg, E. et al. Defining the transcriptome and proteome in three functionally different human cell lines. Mol. Syst. Biol. 6, 450 (2010).

    PubMed  PubMed Central  Google Scholar 

  56. Ingolia, N.T., Lareau, L.F. & Weissman, J.S. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147, 789–802 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Li, J.J., Bickel, P.J. & Biggin, M.D. System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2, e270 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kong, J. & Lasko, P. Translational control in cellular and developmental processes. Nat. Rev. Genet. 13, 383–394 (2012).

    CAS  PubMed  Google Scholar 

  60. Brar, G.A. et al. High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335, 552–557 (2012).

    CAS  PubMed  Google Scholar 

  61. Gonzalez, C. et al. Ribosome profiling reveals a cell-type–specific translational landscape in brain tumors. J. Neurosci. 34, 10924–10936 (2014).

    PubMed  PubMed Central  Google Scholar 

  62. Mercer, T.R., Dinger, M.E. & Mattick, J.S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 10, 155–159 (2009).

    CAS  PubMed  Google Scholar 

  63. Guttman, M., Russell, P., Ingolia, N.T., Weissman, J.S. & Lander, E.S. Ribosome profiling provides evidence that large noncoding RNAs do not encode proteins. Cell 154, 240–251 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Boisvert, F.M. et al. A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol. Cell Proteomics 11, M111.011429 (2012).

    PubMed  Google Scholar 

  65. Aviner, R., Geiger, T. & Elroy-Stein, O. Genome-wide identification and quantification of protein synthesis in cultured cells and whole tissues by puromycin-associated nascent chain proteomics (PUNCH-P). Nat. Protoc. 9, 751–760 (2014).

    CAS  PubMed  Google Scholar 

  66. Wu, J.Q. et al. Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing. Proc. Natl. Acad. Sci. USA 107, 5254–5259 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Nakata, K. et al. DISC1 splice variants are upregulated in schizophrenia and associated with risk polymorphisms. Proc. Natl. Acad. Sci. USA 106, 15873–15878 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Sheynkman, G.M., Shortreed, M.R., Frey, B.L. & Smith, L.M. Discovery and mass spectrometric analysis of novel splice-junction peptides using RNA-Seq. Mol. Cell. Proteomics 12, 2341–2353 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Soares, D.C., Carlyle, B.C., Bradshaw, N.J. & Porteous, D.J. DISC1: structure, function, and therapeutic potential for major mental illness. ACS Chem. Neurosci. 2, 609–632 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Gonzàlez-Porta, M., Frankish, A., Rung, J., Harrow, J. & Brazma, A. Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol. 14, R70 (2013).

    PubMed  PubMed Central  Google Scholar 

  71. Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Corominas, R. et al. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat. Commun. 5, 3650 (2014).

    PubMed  Google Scholar 

  73. Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Tran, J.C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254–258 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Engström, P.G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).

    PubMed  PubMed Central  Google Scholar 

  76. Treutlein, B., Gokce, O., Quake, S.R. & Südhof, T.C. Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. Proc. Natl. Acad. Sci. USA 111, E1291–E1299 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Sakurai, M. et al. A biochemical landscape of A-to-I RNA editing in the human brain transcriptome. Genome Res. 24, 522–534 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lomeli, H. et al. Control of kinetic properties of AMPA receptor channels by nuclear RNA editing. Science 266, 1709–1713 (1994).

    CAS  PubMed  Google Scholar 

  79. Higuchi, M. et al. Point mutation in an AMPA receptor gene rescues lethality in mice deficient in the RNA-editing enzyme ADAR2. Nature 406, 78–81 (2000).

    CAS  PubMed  Google Scholar 

  80. Kawahara, Y. et al. Dysregulated editing of serotonin 2C receptor mRNAs results in energy dissipation and loss of fat mass. J. Neurosci. 28, 12834–12844 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Burns, C.M. et al. Regulation of serotonin-2C receptor G-protein coupling by RNA editing. Nature 387, 303–308 (1997).

    CAS  PubMed  Google Scholar 

  82. Pickrell, J.K., Gilad, Y. & Pritchard, J.K. Comment on “Widespread RNA and DNA sequence differences in the human transcriptome.” Science 335, 1302 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Buckland, P.R. Allele-specific gene expression differences in humans. Hum. Mol. Genet. 13, R255–R260 (2004).

    CAS  PubMed  Google Scholar 

  84. Khan, Z. et al. Quantitative measurement of allele-specific protein expression in a diploid yeast hybrid by LC-MS. Mol. Syst. Biol. 8, 602 (2012).

    PubMed  PubMed Central  Google Scholar 

  85. Gregg, C. et al. High-resolution analysis of parent-of-origin allelic expression in the mouse brain. Science 329, 643–648 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Butter, F. et al. Proteome-wide analysis of disease-associated SNPs that show allele-specific transcription factor binding. PLoS Genet. 8, e1002982 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Holdt, L.M. et al. Quantitative trait loci mapping of the mouse plasma proteome (pQTL). Genetics 193, 601–608 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Liu, C. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol. Psychiatry 15, 779–784 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Agam, G. et al. Knockout mice in understanding the mechanism of action of lithium. Biochem. Soc. Trans. 37, 1121–1125 (2009).

    CAS  PubMed  Google Scholar 

  91. Hangauer, M.J., Vaughn, I.W. & McManus, M.T. Pervasive transcription of the human genome produces thousands of previously unidentified long intergenic noncoding RNAs. PLoS Genet. 9, e1003569 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Martens, L. et al. PRIDE: the proteomics identifications database. Proteomics 5, 3537–3545 (2005).

    CAS  PubMed  Google Scholar 

  93. Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006).

    CAS  PubMed  Google Scholar 

  94. Ahmad, Y. & Lamond, A.I. A perspective on proteomics in cell biology. Trends Cell Biol. 24, 257–264 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Sun, Q. et al. PPDB, the Plant Proteomics Database at Cornell. Nucleic Acids Res. 37, D969–D974 (2009).

    CAS  PubMed  Google Scholar 

  96. Martens, L. et al. A comparison of the HUPO Brain Proteome Project pilot with other proteomics studies. Proteomics 6, 5076–5086 (2006).

    CAS  PubMed  Google Scholar 

  97. Bell, A.W. et al. A HUPO test sample study reveals common problems in mass spectrometry–based proteomics. Nat. Methods 6, 423–430 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Menschaert, G. et al. Deep proteome coverage based on ribosome profiling aids mass spectrometry–based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events. Mol. Cell. Proteomics 12, 1780–1790 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Lopez-Casado, G. et al. Enabling proteomic studies with RNA-Seq: the proteome of tomato pollen as a test case. Proteomics 12, 761–774 (2012).

    CAS  PubMed  Google Scholar 

  100. Wang, X. et al. Protein identification using customized protein sequence databases derived from RNA-Seq data. J. Proteome Res. 11, 1009–1017 (2012).

    CAS  PubMed  Google Scholar 

  101. Wang, X. & Zhang, B. customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search. Bioinformatics 29, 3235–3237 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Bayés, A. & Grant, S.G.N. Neuroproteomics: understanding the molecular organization and complexity of the brain. Nat. Rev. Neurosci. 10, 635–646 (2009).

    PubMed  Google Scholar 

  103. Sun, F. & Cavalli, V. Neuroproteomics approaches to decipher neuronal regeneration and degeneration. Mol. Cell. Proteomics 9, 963–975 (2010).

    CAS  PubMed  Google Scholar 

  104. Gebriel, M. et al. Zebrafish brain proteomics reveals central proteins involved in neurodegeneration. J. Neurosci. Res. 92, 104–115 (2014).

    CAS  PubMed  Google Scholar 

  105. Seo, J.-W., Kim, Y., Hur, J., Park, K.-S. & Cho, Y.-W. Proteomic analysis of primary cultured rat cortical neurons in chemical ischemia. Neurochem. Res. 38, 1648–1660 (2013).

    CAS  PubMed  Google Scholar 

  106. Liu, X. et al. Proteomics reveal energy metabolism and mitogen-activated protein kinase signal transduction perturbation in human Borna disease virus Hu-H1–infected oligodendroglial cells. Neuroscience 268, 284–296 (2014).

    CAS  PubMed  Google Scholar 

  107. Macaulay, I.C. & Voet, T. Single cell genomics: advances and future perspectives. PLoS Genet. 10, e1004126 (2014).

    PubMed  PubMed Central  Google Scholar 

  108. Romanova, E.V., Aerts, J.T., Croushore, C.A. & Sweedler, J.V. Small-volume analysis of cell-cell signaling molecules in the brain. Neuropsychopharmacology 39, 50–64 (2014).

    PubMed  Google Scholar 

  109. Liu, X. et al. Molecular imaging of drug transit through the blood-brain barrier with MALDI mass spectrometry imaging. Sci. Rep. 3, 2859 (2013).

    PubMed  PubMed Central  Google Scholar 

  110. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  PubMed  Google Scholar 

  111. Biesemann, C. et al. Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting. EMBO J. 33, 157–170 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Dammer, E.B. et al. Neuron enriched nuclear proteome isolated from human brain. J. Proteome Res. 12, 3193–3206 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Spalding, K.L. et al. Dynamics of hippocampal neurogenesis in adult humans. Cell 153, 1219–1227 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Ernst, A. et al. Neurogenesis in the striatum of the adult human brain. Cell 156, 1072–1083 (2014).

    CAS  PubMed  Google Scholar 

  115. Holt, C.E. & Schuman, E.M. The central dogma decentralized: new perspectives on RNA function and local translation in neurons. Neuron 80, 648–657 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Kavalali, E.T. & Jorgensen, E.M. Visualizing presynaptic function. Nat. Neurosci. 17, 10–16 (2014).

    CAS  PubMed  Google Scholar 

  117. Cheng, D. et al. Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum. Mol. Cell. Proteomics 5, 1158–1170 (2006).

    CAS  PubMed  Google Scholar 

  118. O'Rourke, N.A., Weiler, N.C., Micheva, K.D. & Smith, S.J. Deep molecular diversity of mammalian synapses: why it matters and how to measure it. Nat. Rev. Neurosci. 13, 365–379 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Portales-Casamar, E. et al. A regulatory toolbox of MiniPromoters to drive selective expression in the brain. Proc. Natl. Acad. Sci. USA 107, 16589–16594 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Jordi, E. et al. Differential effects of cocaine on histone posttranslational modifications in identified populations of striatal neurons. Proc. Natl. Acad. Sci. USA 110, 9511–9516 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Selimi, F., Cristea, I.M., Heller, E., Chait, B.T. & Heintz, N. Proteomic studies of a single CNS synapse type: the parallel fiber/Purkinje cell synapse. PLoS Biol. 7, e83 (2009).

    PubMed  Google Scholar 

  123. Fernández, E. et al. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol. Syst. Biol. 5, 269 (2009).

    PubMed  PubMed Central  Google Scholar 

  124. Bateup, H.S. et al. Cell type–specific regulation of DARPP-32 phosphorylation by psychostimulant and antipsychotic drugs. Nat. Neurosci. 11, 932–939 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by US National Institutes of Health grants DA018343 (A.C.N. and M.B.G.) and DA10044 (A.C.N.) and Department of the Army grant W81XWH-09-1-0434 (A.C.N.). Support was also obtained from the State of Connecticut, Department of Mental Health and Addiction Services.

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Kitchen, R., Rozowsky, J., Gerstein, M. et al. Decoding neuroproteomics: integrating the genome, translatome and functional anatomy. Nat Neurosci 17, 1491–1499 (2014). https://doi.org/10.1038/nn.3829

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