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A multiregional proteomic survey of the postnatal human brain

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Carlyle, Becky C., Kitchen, Robert R., Kanyo, Jean E., Voss, Edward Z., Pletikos, Mihovil, Sousa, André M. M., Lam, TuKiet T., Gerstein, Mark B., Sestan, Nenad, Nairn, Angus C.·2017·Nature Neuroscience

DOI 10.1038/s41593-017-0011-2PMC PMC5894337

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Abstract

Detailed observations of transcriptional, translational and post-translational events in the human brain are essential to improving our understanding of its development, function and vulnerability to disease. Here, we exploited label-free quantitative tandem mass-spectrometry to create an in-depth proteomic survey of regions of the postnatal human brain, ranging in age from early infancy to adulthood. Integration of protein data with existing matched whole-transcriptome sequencing (RNA-seq) from the BrainSpan project revealed varied patterns of protein-RNA relationships, with generally increased magnitudes of protein abundance differences between brain regions compared to RNA. Many of the differences amplified in protein data were reflective of cytoarchitectural and functional variation between brain regions. Comparing structurally similar cortical regions revealed significant differences in the abundances of receptor-associated and resident plasma membrane proteins that were not readily observed in the RNA expression data.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,520
Landscape of transcription in human cells
Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. (2012) · Nature · doi:10.1038/nature11233 · Cited by 5,187
Self-citation
Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180

Show all 59 references

Research works printed but not linked: 1

Steiner, H. & Tseng, K. Y., eds. Handbook of Basal Ganglia Structure and Function (Academic, Cambridge, Massachusetts, USA, 2017).

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Authors: Carlyle, B. C., Kitchen, R. R., Kanyo, J. E., Voss, E. Z., Pletikos, M., Sousa, A. M. M., Lam, T. T., Gerstein, M. B., Sestan, N., & Nairn, A. C.
Affiliations: Yale University (US)
Corresponding author: Nairn, Angus C.
ORCID: Carlyle, Becky C.
Kitchen, Robert R.
Kanyo, Jean E.
Pletikos, Mihovil
Sousa, André M. M.
Lam, TuKiet T.
Gerstein, Mark B.
Sestan, Nenad
Nairn, Angus C.
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2017-11-13
Volume: 20
Issue: 12
Pages: 1787-1795
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/s41593-017-0011-2
PMC: PMC5894337
OpenAlex: W2767910296
Cited by: 143
References: 59
Citations by year: 2026: 12 · 2025: 13 · 2024: 14 · 2023: 19 · 2022: 21 · 2021: 19 · 2020: 20 · 2019: 11 · 2018: 14
FWCI: 5.73
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (unspecified)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (1s10od019967-01, 5r01mh094714-04, 1s10od018034-01)
National Institute on Aging (P50AG047270)
National Institute of Mental Health (R01MH110926, U01MH103339, R01-MH094714, R21MH105881, U01MH103365, U01MH103340, R21MH102791, R01MH105472, P50MH106934, U01MH103392, R01MH105898, R21MH103877, U01MH103346)
National Institute on Drug Abuse (P30DA018343)
Topics: Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: BrainSpan, label-free quantitative proteomics, membrane proteins, plasma membrane proteins, neocortex, brain development, brain aging, cytoarchitecture, cortical regions
MeSH terms: Adolescent, Adult, Aging, Animals, Animals, Newborn, Brain Chemistry, Child, Child, Preschool, Humans, Infant, Infant, Newborn, RNA, RNA, Messenger, Sequence Analysis, RNA, Peptide Library, Gene Expression Profiling, Proteomics, Mice, Tandem Mass Spectrometry, Young Adult, High-Throughput Nucleotide Sequencing, Transcriptome

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A multiregional proteomic survey of the postnatal human brain

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