E. Ahrne, L. Molzahn, T. Glatter, and A. Schmidt, Critical assessment of proteome-wide labelfree absolute abundance estimation strategies, Proteomics, vol.13, pp.2567-2578, 2013.

D. Baek, J. Villen, C. Shin, F. D. Camargo, S. P. Gygi et al., The impact of microRNAs on protein output, Nature, vol.455, pp.64-71, 2008.

M. Baumert, K. Takei, J. Hartinger, P. M. Burger, G. Fischer-von-mollard et al., P29: a novel tyrosine-phosphorylated membrane protein present in small clear vesicles of neurons and endocrine cells, J. Cell Biol, vol.110, pp.1285-1294, 1990.

R. Belizaire, C. Komanduri, K. Wooten, M. Chen, C. Thaller et al., Characterization of synaptogyrin 3 as a new synaptic vesicle protein, J. Comp. Neurol, vol.470, pp.266-281, 2004.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Series B, vol.57, pp.289-300, 1995.

F. M. Boisvert, Y. Ahmad, M. Gierlinski, F. Charriere, D. Lamont et al., A quantitative spatial proteomics analysis of proteome turnover in human cells, Mol. Cell Proteomics, vol.11, pp.111-011429, 2012.

B. M. Bolstad, R. A. Irizarry, M. Astrand, and T. P. Speed, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics, vol.19, pp.185-193, 2003.

T. Bonaldi, T. Straub, J. Cox, C. Kumar, P. B. Becker et al., Combined use of RNAi and quantitative proteomics to study gene function in Drosophila, Mol. Cell, vol.5, pp.762-772, 2008.

J. M. Burkhart, M. Vaudel, S. Gambaryan, S. Radau, U. Walter et al., The first comprehensive and quantitative analysis of human platelet protein composition allows the comparative analysis of structural and functional pathways, Blood, vol.120, pp.73-82, 2012.

B. C. Carlyle, R. R. Kitchen, J. E. Kanyo, E. Z. Voss, M. Pletikos et al., A multiregional proteomic survey of the postnatal human brain, Nat. Neurosci, vol.20, pp.1787-1795, 2017.

B. Cox, T. Kislinger, and A. Emili, Integrating gene and protein expression data: pattern analysis and profile mining, Methods, vol.35, pp.303-314, 2005.

U. Distler, J. Kuharev, P. Navarro, Y. Levin, H. Schild et al., Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics, Nat. Methods, vol.11, pp.167-70, 2014.

S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed, Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Stat. Sin, vol.12, pp.111-139, 2002.

H. Ehmann, H. Hartwich, C. Salzig, N. Hartmann, M. Clément-ziza et al., Time-dependent gene expression analysis of the developing superior olivary complex, J. Biol. Chem, vol.288, pp.25865-25879, 2013.

O. El-far and M. Seagar, A role for V-ATPase subunits in synaptic vesicle fusion?, J. Neurochem, vol.117, pp.603-612, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00581211

H. Eng, K. Lund, and R. B. Campenot, Synthesis of beta-tubulin, actin, and other proteins in axons of sympathetic neurons in compartmented cultures, J. Neurosci, vol.19, pp.1-9, 1999.

N. Fortelny, C. M. Overall, P. Pavlidis, and G. V. Freue, Can we predict protein from mRNA levels?, Nature, vol.547, pp.19-20, 2017.

S. Frykman, Y. Teranishi, J. Y. Hur, A. Sandebring, N. G. Yamamoto et al., Identification of two novel synaptic gamma-secretase associated proteins that affect amyloid beta-peptide levels without altering Notch processing, Neurochem. Int, vol.61, pp.108-118, 2012.

B. Futcher, G. I. Latter, P. Monardo, C. S. Mclaughlin, and J. I. Garrels, A sampling of the yeast proteome, Mol. Cell Biol, vol.19, pp.7357-7368, 1999.

J. Geiger, J. M. Burkhart, S. Gambaryan, U. Walter, A. Sickmann et al., Response: platelet transcriptome and proteome -relation rather than correlation, Blood, vol.121, pp.5257-5258, 2013.

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

Y. Gunawardana, S. Fujiwara, A. Takeda, J. Woo, C. Woelk et al., Outlier detection at the transcriptome-proteome interface, Bioinformatics, vol.31, pp.2530-2536, 2015.

S. P. Gygi, Y. Rochon, B. R. Franza, and R. Aebersold, Correlation between protein and mRNA abundance in yeast, Mol. Cell Biol, vol.19, pp.1720-1730, 1999.

C. E. Holt and E. M. Schuman, The central dogma decentralized: new perspectives on RNA function and local translation in neurons, Neuron, vol.80, pp.648-657, 2013.

W. Huber, A. Von-heydebreck, H. Sultmann, A. Poustka, and M. Vingron, Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics, vol.18, pp.96-104, 2002.

M. Jovanovic, M. S. Rooney, and P. Mertins, Dynamic profiling of the protein life cycle in response to pathogens, Immunogenetics, vol.347, p.1259038, 2015.

H. Jung, B. C. Yoon, and C. E. Holt, Axonal mRNA localization and local protein synthesis in nervous system assembly, maintenance and repair, Nat. Rev. Neurosci, vol.13, pp.308-324, 2012.

B. Kaltwaßer, T. Schulenborg, F. Beck, M. Klotz, K. H. Schafer et al., Developmental changes of the protein repertoire in the rat auditory brainstem: a comparative proteomics approach in the superior olivary complex and the inferior colliculus with DIGE and iTRAQ, J. Proteomics, vol.79, pp.43-59, 2013.

Z. Khan, M. J. Ford, D. A. Cusanovich, A. Mitrano, J. K. Pritchard et al., Primate transcript and protein expression levels evolve under compensatory selection pressures, Science, vol.342, pp.1100-1104, 2013.

M. Larance, Y. Ahmad, K. J. Kirkwood, T. Ly, and A. I. Lamond, Global subcellular characterization of protein degradation using quantitative proteomics, Mol. Cell Proteomics, vol.12, pp.638-650, 2013.

J. J. Li, P. J. Bickel, and M. D. Biggin, System wide analyses have underestimated protein abundances and the importance of transcription in mammals, PeerJ, vol.2, p.270, 2014.

L. P. Lim, N. C. Lau, P. Garrett-engele, A. Grimson, J. M. Schelter et al., Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs, Nature, vol.433, pp.769-773, 2005.

Y. Liu, A. Beyer, and R. Aebersold, On the dependency of cellular protein levels on mRNA abundance, Cell, vol.165, pp.535-550, 2016.

Y. Liu, M. Gonzalez-porta, S. Santos, A. Brazma, J. C. Marioni et al., Impact of alternative splicing on the human proteome, Cell Rep, vol.20, pp.1229-1241, 2017.

P. Lu, C. Vogel, R. Wang, X. Yao, and E. M. Marcotte, Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation, Nat. Biotechnol, vol.25, pp.117-124, 2007.

R. Lu, F. Markowetz, R. D. Unwin, J. T. Leek, E. M. Airoldi et al., Systems-level dynamic analyses of fate change in murine embryonic stem cells, Nature, vol.462, pp.358-362, 2009.

H. Lui, J. Zhang, and S. R. Makinson, Progranulin deficiency promotes circuit-specific synaptic pruning by microglia via complement activation, Cell, vol.165, pp.921-935, 2016.

T. Maier, M. Guell, and L. Serrano, Correlation of mRNA and protein in complex biological samples, FEBS Lett, vol.583, pp.3966-3973, 2009.

M. S. Malmierca and T. A. Hackett, Structural organization of the ascending auditory pathway, The Oxford Handbook of Auditory Science: The Auditory Brain, vol.2, pp.9-42, 2010.

S. Marguerat, A. Schmidt, S. Codlin, W. Chen, R. Aebersold et al., Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells, Cell, vol.151, pp.671-683, 2012.

J. Mata, S. Marguerat, and J. Bähler, Post-transcriptional control of gene expression: a genome-wide perspective, Trends Biochem. Sci, vol.30, pp.506-514, 2005.

M. Melone, S. Ciappelloni, and F. Conti, A quantitative analysis of cellular and synaptic localization of GAT-1 and GAT-3 in rat neocortex, Brain Struct. Funct, vol.220, pp.885-897, 2015.

C. P. Moritz, E. Eckstein, S. Tenzer, and E. Friauf, Neuroproteomics in the auditory brainstem: candidate proteins for ultrafast and precise information processing, Mol. Cell Neurosci, vol.64, pp.9-23, 2015.

Y. Moriyama, M. Maeda, and M. Futai, The role of V-ATPase in neuronal and endocrine systems, J. Exp. Biol, vol.172, pp.171-178, 1992.

L. Nie, G. Wu, D. E. Culley, J. C. Scholten, and W. Zhang, Integrative analysis of transcriptomic and proteomic data: challenges, solutionsa and applications, Critic Rev Biotechnol, vol.27, pp.63-75, 2007.

A. Ori, B. H. Toyama, M. S. Harris, T. Bock, M. Iskar et al., Integrated transcriptome and proteome analyses reveal organ-specific proteome deterioration in old rats, Cell Syst, vol.1, pp.224-237, 2015.

S. H. Payne, The utility of protein and mRNA correlation, Trends Biochem. Sci, vol.40, pp.1-3, 2015.

. Rdevelopmentcoreteam, R: A language and environment for statistical computing. R Foundation for Statistical Computiong Vienna, 2011.

I. Rivals, L. Personnaz, L. Taing, and M. C. Potier, Enrichment or depletion of a GO category within a class of genes: which test?, Bioinformatics, vol.23, pp.401-407, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00801557

B. R. Schofield, Structural organization of the descending auditory pathway, The Oxford Handbook of Auditory Science: The Auditory Brain, vol.2, pp.43-64, 2010.

B. Schwanhäusser, D. Busse, N. Li, G. Dittmar, J. Schuchhardt et al., Global quantification of mammalian gene expression control, Nature, vol.473, pp.337-342, 2011.

M. Selbach, B. Schwanhäusser, N. Thierfelder, Z. Fang, R. Khanin et al., Widespread changes in protein synthesis induced by microRNAs, Nature, vol.455, pp.58-63, 2008.

A. B. Shyu, M. F. Wilkinson, and A. Van-hoof, Messenger RNA regulation: to translate or to degrade, EMBO J, vol.27, pp.471-481, 2008.

M. Takahashi, K. Tomizawa, S. C. Fujita, K. Sato, T. Uchida et al., A brain-specific protein p25 is localized and associated with oligodendrocytes, neuropil, and fiber-like structures of the CA3 hippocampal region in the rat brain, J. Neurochem, vol.60, pp.228-235, 1993.

A. M. Taylor, D. C. Dieterich, H. T. Ito, S. A. Kim, and E. M. Schuman, Microfluidic local perfusion chambers for the visualization and manipulation of synapses, Neuron, vol.66, pp.57-68, 2010.

Q. Tian, S. B. Stepaniants, and M. Mao, Integrated genomic and proteomic analyses of gene expression in mammalian cells, Mol. Cell Proteomics, vol.3, pp.960-969, 2004.

Y. Tomari and P. D. Zamore, Perspective: machines for RNAi, Genes Dev, vol.19, pp.517-529, 2005.

T. E. Steward and O. , Demonstration of local protein-synthesis within dendrites using a new cell-culture system that permits the isolation of living axons and dendrites from their cell-bodies, J. Neurosci, vol.12, pp.762-772, 1992.

K. C. Verhoeckx, S. Bijlsma, E. M. De-groene, R. F. Witkamp, J. Van-der-greef et al., A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line, Proteomics, vol.4, pp.1014-1028, 2004.

C. Vogel and E. M. Marcotte, Insights into the regulation of protein abundance from proteomic and transcriptomic analyses, Nat. Rev. Genet, vol.13, pp.227-232, 2012.

D. O. Wang, K. C. Martin, and R. S. Zukin, Spatially restricting gene expression by local translation at synapses, Trends Neurosci, vol.33, pp.173-182, 2010.

K. M. Waters, J. G. Pounds, and B. D. Thrall, Data merging for integrated microarray and proteomic analysis, Brief Funct. Genomic Proteomic, vol.5, pp.261-272, 2006.

M. Wilhelm, J. Schlegl, and H. Hahne, Mass-spectrometry-based draft of the human proteome, Nature, vol.509, pp.582-587, 2014.

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng et al., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation, Nucleic Acids Research, vol.30, p.15, 2002.

J. Q. Zheng, T. K. Kelly, B. Chang, S. Ryazantsev, A. K. Rajasekaran et al., A functional role for intra-axonal protein synthesis during axonal regeneration from adult sensory neurons, J. Neurosci, vol.21, pp.9291-9303, 2001.

K. H. Zivraj, Y. C. Tung, M. Piper, L. Gumy, J. W. Fawcett et al., Subcellular profiling reveals distinct and developmentally regulated repertoire of growth cone mRNAs, J. Neurosci, vol.30, pp.15464-15478, 2010.