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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References
Pollock, J.D., Wu, D.-Y. & Satterlee, J.S. Molecular neuroanatomy: a generation of progress. Trends Neurosci. 37, 106–123 (2014).
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).
Arenkiel, B.R. & Ehlers, M.D. Molecular genetics and imaging technologies for circuit-based neuroanatomy. Nature 461, 900–907 (2009).
Van Essen, D.C. & Ugurbil, K. The future of the human connectome. Neuroimage 62, 1299–1310 (2012).
Lichtman, J.W., Livet, J. & Sanes, J.R. A technicolour approach to the connectome. Nat. Rev. Neurosci. 9, 417–422 (2008).
Ekstrand, M.I. et al. Molecular profiling of neurons based on connectivity. Cell 157, 1230–1242 (2014).
Deisseroth, K. et al. Next-generation optical technologies for illuminating genetically targeted brain circuits. J. Neurosci. 26, 10380–10386 (2006).
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).
Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).
Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
Johnson, M.B. et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62, 494–509 (2009).
Hawrylycz, M.J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
Heintz, N. Gene expression nervous system atlas (GENSAT). Nat. Neurosci. 7, 483 (2004).
Korbel, J.O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).
Park, P.J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).
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).
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).
Licatalosi, D.D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008).
Aebersold, R. & Mann, M. Mass spectrometry–based proteomics. Nature 422, 198–207 (2003).
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).
Ong, S.-E. & Mann, M. Mass spectrometry–based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).
Vidal, M. et al. The human proteome—a scientific opportunity for transforming diagnostics, therapeutics, and healthcare. Clin. Proteomics 9, 6 (2012).
Wilhelm, M. et al. Mass-spectrometry–based draft of the human proteome. Nature 509, 582–587 (2014).
Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).
Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273–299 (2011).
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).
Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).
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).
Bensimon, A., Heck, A.J.R. & Aebersold, R. Mass spectrometry–based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).
Craft, G.E., Chen, A. & Nairn, A.C. Recent advances in quantitative neuroproteomics. Methods 61, 186–218 (2013).
Cox, B. & Emili, A. Tissue subcellular fractionation and protein extraction for use in mass-spectrometry–based proteomics. Nat. Protoc. 1, 1872–1878 (2006).
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).
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
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).
Oppermann, F.S. et al. Large-scale proteomics analysis of the human kinome. Mol. Cell. Proteomics 8, 1751–1764 (2009).
Deribe, Y.L., Pawson, T. & Dikic, I. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672 (2010).
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).
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).
Min, S.-W. et al. Acetylation of tau inhibits its degradation and contributes to tauopathy. Neuron 67, 953–966 (2010).
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).
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).
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).
Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).
Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).
Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186 (2006).
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).
Elvira, G. et al. Characterization of an RNA granule from developing brain. Mol. Cell. Proteomics 5, 635–651 (2006).
Moghaddas Gholami, A. et al. Global proteome analysis of the NCI-60 cell line panel. Cell Reports 4, 609–620 (2013).
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).
Yu, L.-R. et al. Global analysis of the cortical neuron proteome. Mol. Cell. Proteomics 3, 896–907 (2004).
Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).
Phanstiel, D.H. et al. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Methods 8, 821–827 (2011).
Butovsky, O. et al. Identification of a unique TGF-β–dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).
Greenbaum, D., Colangelo, C., Williams, K. & Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003).
Lundberg, E. et al. Defining the transcriptome and proteome in three functionally different human cell lines. Mol. Syst. Biol. 6, 450 (2010).
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).
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).
Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).
Kong, J. & Lasko, P. Translational control in cellular and developmental processes. Nat. Rev. Genet. 13, 383–394 (2012).
Brar, G.A. et al. High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335, 552–557 (2012).
Gonzalez, C. et al. Ribosome profiling reveals a cell-type–specific translational landscape in brain tumors. J. Neurosci. 34, 10924–10936 (2014).
Mercer, T.R., Dinger, M.E. & Mattick, J.S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 10, 155–159 (2009).
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).
Boisvert, F.M. et al. A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol. Cell Proteomics 11, M111.011429 (2012).
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).
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).
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).
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).
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).
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).
Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).
Corominas, R. et al. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat. Commun. 5, 3650 (2014).
Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).
Tran, J.C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254–258 (2011).
Engström, P.G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).
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).
Sakurai, M. et al. A biochemical landscape of A-to-I RNA editing in the human brain transcriptome. Genome Res. 24, 522–534 (2014).
Lomeli, H. et al. Control of kinetic properties of AMPA receptor channels by nuclear RNA editing. Science 266, 1709–1713 (1994).
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).
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).
Burns, C.M. et al. Regulation of serotonin-2C receptor G-protein coupling by RNA editing. Nature 387, 303–308 (1997).
Pickrell, J.K., Gilad, Y. & Pritchard, J.K. Comment on “Widespread RNA and DNA sequence differences in the human transcriptome.” Science 335, 1302 (2012).
Buckland, P.R. Allele-specific gene expression differences in humans. Hum. Mol. Genet. 13, R255–R260 (2004).
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).
Gregg, C. et al. High-resolution analysis of parent-of-origin allelic expression in the mouse brain. Science 329, 643–648 (2010).
Butter, F. et al. Proteome-wide analysis of disease-associated SNPs that show allele-specific transcription factor binding. PLoS Genet. 8, e1002982 (2012).
Holdt, L.M. et al. Quantitative trait loci mapping of the mouse plasma proteome (pQTL). Genetics 193, 601–608 (2013).
Liu, C. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol. Psychiatry 15, 779–784 (2010).
Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).
Agam, G. et al. Knockout mice in understanding the mechanism of action of lithium. Biochem. Soc. Trans. 37, 1121–1125 (2009).
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).
Martens, L. et al. PRIDE: the proteomics identifications database. Proteomics 5, 3537–3545 (2005).
Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006).
Ahmad, Y. & Lamond, A.I. A perspective on proteomics in cell biology. Trends Cell Biol. 24, 257–264 (2014).
Sun, Q. et al. PPDB, the Plant Proteomics Database at Cornell. Nucleic Acids Res. 37, D969–D974 (2009).
Martens, L. et al. A comparison of the HUPO Brain Proteome Project pilot with other proteomics studies. Proteomics 6, 5076–5086 (2006).
Bell, A.W. et al. A HUPO test sample study reveals common problems in mass spectrometry–based proteomics. Nat. Methods 6, 423–430 (2009).
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).
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).
Wang, X. et al. Protein identification using customized protein sequence databases derived from RNA-Seq data. J. Proteome Res. 11, 1009–1017 (2012).
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).
Bayés, A. & Grant, S.G.N. Neuroproteomics: understanding the molecular organization and complexity of the brain. Nat. Rev. Neurosci. 10, 635–646 (2009).
Sun, F. & Cavalli, V. Neuroproteomics approaches to decipher neuronal regeneration and degeneration. Mol. Cell. Proteomics 9, 963–975 (2010).
Gebriel, M. et al. Zebrafish brain proteomics reveals central proteins involved in neurodegeneration. J. Neurosci. Res. 92, 104–115 (2014).
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).
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).
Macaulay, I.C. & Voet, T. Single cell genomics: advances and future perspectives. PLoS Genet. 10, e1004126 (2014).
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).
Liu, X. et al. Molecular imaging of drug transit through the blood-brain barrier with MALDI mass spectrometry imaging. Sci. Rep. 3, 2859 (2013).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Biesemann, C. et al. Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting. EMBO J. 33, 157–170 (2014).
Dammer, E.B. et al. Neuron enriched nuclear proteome isolated from human brain. J. Proteome Res. 12, 3193–3206 (2013).
Spalding, K.L. et al. Dynamics of hippocampal neurogenesis in adult humans. Cell 153, 1219–1227 (2013).
Ernst, A. et al. Neurogenesis in the striatum of the adult human brain. Cell 156, 1072–1083 (2014).
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).
Kavalali, E.T. & Jorgensen, E.M. Visualizing presynaptic function. Nat. Neurosci. 17, 10–16 (2014).
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).
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).
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).
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).
Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).
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).
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).
Bateup, H.S. et al. Cell type–specific regulation of DARPP-32 phosphorylation by psychostimulant and antipsychotic drugs. Nat. Neurosci. 11, 932–939 (2008).
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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DOI: https://doi.org/10.1038/nn.3829
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