Proteomic analysis of postsynaptic proteins in regions of the human neocortex

  • Nature Neurosciencevolume 21pages130138 (2018)
  • doi:10.1038/s41593-017-0025-9
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The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire, and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. We characterized the compositional signatures in brain regions involved with language, emotion and memory functions. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions.

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  1. 1.

    Brodmann, K. On the comparative localization of the cortex. in Some Papers on the Cerebral Cortex (ed. von Bonin, G.) (Charles C Thomas, Springfield, IL, USA, 1909).

  2. 2.

    DeFelipe, J., Alonso-Nanclares, L. & Arellano, J. I. Microstructure of the neocortex: comparative aspects. J. Neurocytol. 31, 299–316 (2002).

  3. 3.

    Bayés, A. et al. Comparative study of human and mouse postsynaptic proteomes finds high compositional conservation and abundance differences for key synaptic proteins. PLoS One 7, e46683 (2012).

  4. 4.

    Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

  5. 5.

    Bayés, À. et al. Human post-mortem synapse proteome integrity screening for proteomic studies of postsynaptic complexes. Mol. Brain 7, 88 (2014).

  6. 6.

    Coba, M. P. et al. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Sci. Signal. 2, ra19 (2009).

  7. 7.

    Li, J. et al. Long-term potentiation modulates synaptic phosphorylation networks and reshapes the structure of the postsynaptic interactome. Sci. Signal. 9, rs8 (2016).

  8. 8.

    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).

  9. 9.

    Frank, R. A. et al. NMDA receptors are selectively partitioned into complexes and supercomplexes during synapse maturation. Nat. Commun. 7, 11264 (2016).

  10. 10.

    Husi, H., Ward, M. A., Choudhary, J. S., Blackstock, W. P. & Grant, S. G. Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nat. Neurosci. 3, 661–669 (2000).

  11. 11.

    Komiyama, N. H. et al. SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the complex with postsynaptic density 95 and NMDA receptor. J. Neurosci. 22, 9721–9732 (2002).

  12. 12.

    Frank, R. A. W., Zhu, F., Komiyama, N. H. & Grant, S. G. N. Hierarchical organization and genetically separable subfamilies of PSD95 postsynaptic supercomplexes. J. Neurochem. 142, 504–511 (2017).

  13. 13.

    Collins, M. O. et al. Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome. J. Neurochem. 97 (Suppl. 1), 16–23 (2006).

  14. 14.

    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).

  15. 15.

    Li, K. W. et al. Proteomics analysis of rat brain postsynaptic density. Implications of the diverse protein functional groups for the integration of synaptic physiology. J. Biol. Chem. 279, 987–1002 (2004).

  16. 16.

    Peng, J. et al. Semiquantitative proteomic analysis of rat forebrain postsynaptic density fractions by mass spectrometry. J. Biol. Chem. 279, 21003–21011 (2004).

  17. 17.

    Bayés, À. et al. Evolution of complexity in the zebrafish synapse proteome. Nat. Commun. 8, 14613 (2017).

  18. 18.

    Emes, R. D. et al. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nat. Neurosci. 11, 799–806 (2008).

  19. 19.

    Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

  20. 20.

    Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).

  21. 21.

    Le Bihan, T. et al. Label-free quantitative analysis of the casein kinase 2-responsive phosphoproteome of the marine minimal model species Ostreococcus tauri. Proteomics 15, 4135–4144 (2015).

  22. 22.

    Mazzo, F. et al. Reconstitution of synaptic Ion channels from rodent and human brain in Xenopus oocytes: a biochemical and electrophysiological characterization. J. Neurochem. 138, 384–396 (2016).

  23. 23.

    Vaishnavi, S. N. et al. Regional aerobic glycolysis in the human brain. Proc. Natl. Acad. Sci. USA 107, 17757–17762 (2010).

  24. 24.

    Wu, K., Aoki, C., Elste, A., Rogalski-Wilk, A. A. & Siekevitz, P. The synthesis of ATP by glycolytic enzymes in the postsynaptic density and the effect of endogenously generated nitric oxide. Proc. Natl. Acad. Sci. USA 94, 13273–13278 (1997).

  25. 25.

    Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

  26. 26.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  27. 27.

    Brody, A. L. et al. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol. Psychiatry 55, 77–84 (2004).

  28. 28.

    Gowin, J. L., Mackey, S. & Paulus, M. P. Altered risk-related processing in substance users: imbalance of pain and gain. Drug Alcohol Depend. 132, 13–21 (2013).

  29. 29.

    Janes, A. C. et al. Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence. Biol. Psychiatry 67, 722–729 (2010).

  30. 30.

    Brody, A. L. et al. Brain metabolic changes during cigarette craving. Arch. Gen. Psychiatry 59, 1162–1172 (2002).

  31. 31.

    Pocklington, A. J., Cumiskey, M., Armstrong, J. D. & Grant, S. G. The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour. Mol. Syst. Biol. 2, 0023 (2006).

  32. 32.

    Broadhead, M. J. et al. PSD95 nanoclusters are postsynaptic building blocks in hippocampus circuits. Sci. Rep. 6, 24626 (2016).

  33. 33.

    Buckner, R. L. et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci. 25, 7709–7717 (2005).

  34. 34.

    Vlassenko, A. G. et al. Spatial correlation between brain aerobic glycolysis and amyloid-β (Aβ) deposition. Proc. Natl. Acad. Sci. USA 107, 17763–17767 (2010).

  35. 35.

    Weinberger, D. R., Berman, K. F. & Zec, R. F. Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia. I. Regional cerebral blood flow evidence. Arch. Gen. Psychiatry 43, 114–124 (1986).

  36. 36.

    Dalack, G. W., Healy, D. J. & Meador-Woodruff, J. H. Nicotine dependence in schizophrenia: clinical phenomena and laboratory findings. Am. J. Psychiatry 155, 1490–1501 (1998).

  37. 37.

    Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

  38. 38.

    Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).

  39. 39.

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

  40. 40.

    Le Bihan, T., Grima, R., Martin, S., Forster, T. & Le Bihan, Y. Quantitative analysis of low-abundance peptides in HeLa cell cytoplasm by targeted liquid chromatography/mass spectrometry and stable isotope dilution: emphasising the distinction between peptide detection and peptide identification. Rapid Commun. Mass Spectrom. 24, 1093–1104 (2010).

  41. 41.

    Tukey, J. W. Comparing individual means in the analysis of variance. Biometrics 5, 99–114 (1949). 

  42. 42.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

  43. 43.

    Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1–36 (2014).

  44. 44.

    Simpson, T. I., Armstrong, J. D. & Jarman, A. P. Merged consensus clustering to assess and improve class discovery with microarray data. BMC Bioinformatics 11, 590 (2010).

  45. 45.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

  46. 46.

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

  47. 47.

    Marcus, D. S. et al. Informatics and data mining tools and strategies for the human connectome project. Front. Neuroinform. 5, 4 (2011).

  48. 48.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  49. 49.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  50. 50.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  51. 51.

    Gormley, P. et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat. Genet. 48, 856–866 (2016).

  52. 52.

    Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).

  53. 53.

    Pappa, I. et al. A genome-wide approach to children’s aggressive behavior: The EAGLE consortium. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 171, 562–572 (2016).

  54. 54.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

  55. 55.

    Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

  56. 56.

    Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

  57. 57.

    Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  58. 58.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

  59. 59.

    Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  60. 60.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

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Support came from the Medical Research Council (Brain Bank MR/L016400/1) and European Union Seventh Framework Programme (FP7 grant agreement no. 604102) and Horizon 2020 (agreement no. 720270). We thank K. Elsegood for laboratory management, J. DeFelipe for comments on the manuscript, D. Maizels for artwork, and T. Le Bihan and L. Imrie at SynthSys, University of Edinburgh for mass spectrometry sample analysis. The LC-MS QExactive equipment was purchased by a Wellcome Trust Institutional Strategic Support Fund and a strategic award from the Wellcome Trust for the Centre for Immunity, Infection and Evolution (095831/Z/11/Z). Data were extracted from NIFTI (Neuroimaging Informatics Technology Initiative) files using a custom automated script written by J. J. Roy, MEMEX, Inc., Burlington, Ontario, Canada. MRI data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Author information

Author notes

  1. Marcia Roy and Oksana Sorokina contributed equally to this work.


  1. Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

    • Marcia Roy
    • , Nathan Skene
    • , Clémence Simonnet
    • , Colin Smith
    •  & Seth G. N. Grant
  2. School of Informatics, University of Edinburgh, Edinburgh, UK

    • Oksana Sorokina
    •  & J. Douglas Armstrong
  3. Lilly Research Centre, Eli Lilly & Company, Erl Wood Manor, Windlesham, UK

    • Francesca Mazzo
    • , Ruud Zwart
    •  & Emanuele Sher


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C. Smith supplied brain tissue samples; M.R. and C. Simmonet performed biochemistry; F.M., R.Z. and E.S. performed electrophysiology; M.R., O.S. and N.S. performed bioinformatics and statistical analysis; J.D.A. provided supervision; S.G.N.G. conceived and supervised the project, wrote the manuscript and secured funding.

Competing interests

E.S. and R.Z. were employees of Eli Lilly and Co. This work was partially supported by an Eli Lilly and Company postdoctoral fellowship to F.M.

Corresponding author

Correspondence to Seth G. N. Grant.

Integrated Supplementary Information

  1. Supplementary Figure 1 Distribution of ANOVA P-values for differentially expressed genes

    The magnitude of Anova P-value (x-axis) plotted against the maximum fold change (y-axis) for differentially expressed proteins. Dots correspond to specific proteins (total 1215). Green, 149 differentially expressed proteins with level of significance p < 0.05 and fold change >1.5.

  2. Supplementary Figure 2 Overlap between the 1,213 PSD proteins identified in this study and other published synaptic proteomes

    Overlap between the 1,213 PSD proteins identified in this study and other published synaptic proteomes. A) Venn diagram showing the overlap between this study and the human PSD proteomes published in Bayes et al., 2011 and Bayes et al., 2014. B) Venn diagram showing the overlap between this study and the PSD proteomes published in rat and mouse models from Distler et al., 2014, Collins et al., 2006, Fernandez et al., 2009 and Trinidad et al., 2008.

  3. Supplementary Figure 3 Similarity of the PSD proteome between individuals

    Similarity of the PSD proteome between individuals. Individual clustering dendrograms displaying the similarities between the 12 neocortical regions based upon PSD proteome abundances observed for each individual: A) SD025/13, B) SD042/13, C) SD032/13 and D) SD023/13.

  4. Supplementary Figure 4 Results of the Tukey test

    (A) Confidence intervals for the mean PSD protein abundance (Y-axis) plotted for each individual brain A) SD025/13, B) SD042/13, C) SD032/13 and D) SD023/13 (y-axis).  (B)  Pairwise comparison of the PSD proteome abundances in individuals A-D.

  5. Supplementary Figure 5 Effect of individual variation on PPM robustness

    Effect of individual variation on PPM robustness. (A) Bootstrap analysis of PPM cluster robustness with k-means and PAM methods shows all modules were found to be stable (≥0.5 membership robustness). (B) Correlation of the PPMs/clusters across three combined samples (omitting the longest postmortem interval sample) (y-axis) and all four samples (x-axis). (C) Correlation of the PPMs/clusters between three combined samples (omitting the female sample) (y-axis) and all four samples (x-axis). Cr, cluster number; Mr, PPM number.

  6. Supplementary Figure 6 Circos plots showing circular hierarchical clustering of the distribution of PPMs in 12 Bas

    Circos plots showing circular hierarchical clustering of the distribution of PPMs in 12 BAs. Shown left, an overlay of all seven protein modules for all 12 cortical regions and individual plots of PPM1 (middle) and PPM2 (right) illustrate differential distributions. PPM 1 (black), PPM 2 (red), PPM 3 (green), PPM 4 (blue), PPM 5 (cyan) and PPM 6 (magenta) and PPM 7 (yellow). Width of the link is proportional to the fraction of the regional proteins contributed while its color corresponds to the respective PPM.

  7. Supplementary Figure 7 Alignment of BAs in our study with the multimodal parcellated human cortex from the Human Connectome Project (HCP)

    Alignment of Brodmann areas (BAs) in our study with the multi-modal parcellated human cortex from the Human Connectome Project (HCP). A) Line plot showing the trends in abundance for the four myelin proteins (Mog, dark blue; Plp2, red; Omg, green; Pmp2, purple), the average abundance of all four proteins (Avgenes, shown in cyan) and the average myelin abundance detected by MRI reported by Glasser et al., 2016 from all parcels corresponding to BAs (Avmyelin, shown in orange) and from selected parcels, which give the best correlations score (av1, shown in light blue). B) Atlas of 40 HCP regions (annotated) corresponding to BA areas used for synapse proteomics.

  8. Supplementary Figure 8 Correlations between the Postsynaptic Proteome Module (PPM) abundance and task-fMRI scores across 12 BAs

    Significant correlations between the Postsynaptic Proteome Module (PPM) abundance (averaged protein abundance for respective module) values and structural (Thickness, myelin) and task-fMRI scores across 12 Brodmann areas. The coefficients with an absolute value above 0.53 were significant at the FDR level 5%. Scale bar indicates positive and negative correlations. See Methods and supplementary Table 12 for definitions of tfMRI nomenclature.

  9. Supplementary Figure 9 Results of resampling test for PET

    The distribution of correlation scores obtained for 10,000 reshuffled samples is shown for module2/GI and module3/GI pairs. Note that that the observed correlation values (vertical black line) lie far from the random distribution, indicating that the observed correlation with PET data is extremely unlikely to have occurred by chance (resampling-based q-value < 10−4).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–9.

  2. Life Sciences Reporting Summary.

  3. Supplementary Table 1

    Human brain tissue used in this study.

  4. Supplementary Table 2

    LC-MS/MS quantitation and comparison of 1,213 PSD proteins throughout the human neocortex one-way ANOVA.

  5. Supplementary Table 3

    Summary of 149 proteins differentially abundant across the 12 regions of the neocortex.

  6. Supplementary Table 4

    New PSD proteins detected only in this study.

  7. Supplementary Table 5

    Differential stability analysis: correlation of PSD proteome abundances between individual brains.

  8. Supplementary Table 6

    Differential stability analysis: functional enrichment in PSD proteins that correlate between individual brains.

  9. Supplementary Table 7

    LC-MS/MS quantitation and comparison of the human PSD proteome throughout the human neocortex.

  10. Supplementary Table 8

    Ranked abundance of PSD proteins in 12 neocortical regions.

  11. Supplementary Table 9

    TOST-test analysis: proteins that do not change between brain regions.

  12. Supplementary Table 10

    Functional enrichment analysis: proteins that do not change between brain regions.

  13. Supplementary Table 11

    Differential abundance across the human neocortex of key molecules involved in cognition and memory.

  14. Supplementary Table 12

    Composition of protein modules (PMMs).

  15. Supplementary Table 13

    Nomenclature used for the task-fMRI (tfMRI) data. A full list of the tfMRI terms as well as their corresponding full descriptions as per Glasser et al., 2016, Supplementary Information file 3, Table 3 (

  16. Supplementary Table 14

    Correlation between myelin values obtained by LC-MS/MS in this study and the parcellated cortex Glasser et al., 2016 study.

  17. Supplementary Table 15

    Correlation values between functional MRI scores from Glasser et al., 2016 and abundance values of the 1,213 PSD proteins identified in this study.

  18. Supplementary Table 16

    Significant correlation values (P < 0.05, corrected for multiple testing by 10,000 permutations) between functional MRI scores from Glasser et al., 2016 and abundance values of the 1,213 PSD proteins identified in this study. Protein, protein name; funct, functional term from Glasser et al., 2016; pval, P value; corr.val, R2.