Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A cross-species proteomic map reveals neoteny of human synapse development

Nature volume 622, pages 112–119 (2023)Cite this article

Subjects

Abstract

The molecular mechanisms and evolutionary changes accompanying synapse development are still poorly understood1,2. Here we generate a cross-species proteomic map of synapse development in the human, macaque and mouse neocortex. By tracking the changes of more than 1,000 postsynaptic density (PSD) proteins from midgestation to young adulthood, we find that PSD maturation in humans separates into three major phases that are dominated by distinct pathways. Cross-species comparisons reveal that human PSDs mature about two to three times slower than those of other species and contain higher levels of Rho guanine nucleotide exchange factors (RhoGEFs) in the perinatal period. Enhancement of RhoGEF signalling in human neurons delays morphological maturation of dendritic spines and functional maturation of synapses, potentially contributing to the neotenic traits of human brain development. In addition, PSD proteins can be divided into four modules that exert stage- and cell-type-specific functions, possibly explaining their differential associations with cognitive functions and diseases. Our proteomic map of synapse development provides a blueprint for studying the molecular basis and evolutionary changes of synapse maturation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Changes in PSD composition during human neocortical development.
Fig. 2: Protein modules of the developing human PSD.
Fig. 3: Comparison of PSD development across humans, macaques and mice.
Fig. 4: Increase in RhoGEF proteins promotes neoteny of human synapses.
Fig. 5: Association of human PSD modules with cognitive functions and brain disorders.

Similar content being viewed by others

Data availability

All raw proteomic data were deposited to ProteomeXchange through MassIVE (human PFC dataset: MSV000091887 and PXD042067; human V1 dataset: MSV000091888 and PXD042068; macaque dataset: MSV000091889 and PXD042069; mouse dataset: MSV000091890 and PXD042071). All processed data are available in Supplementary Tables 1–13 and at an online interactive portal (https://liwang.shinyapps.io/PSD_development_explorer). The reference proteomes (Homo sapiens UP000005640_9606, Macaca mulatta UP000006718_9544 and Mus musculus UP000000589_10090) can be downloaded from UniProt (https://www.uniprot.org/proteomes). The PPI data can be downloaded from the BioGRID database (https://thebiogrid.org/). Bulk-level RNA-seq data of developing human brain can be downloaded from the BrainSpan database (http://www.brainspan.org) or from the PsychENCODE website (http://evolution.psychencode.org/). Protein domain information can be downloaded from the SMART database (https://smart.embl.de/). Single-cell and single-nucleus RNA-seq data from developing and adult human neocortex can be downloaded from the UCSC Cell Browser (https://cells.ucsc.edu). GWAS summary statistics for human cognitive function can be downloaded from the GWAS ATLAS resource (https://atlas.ctglab.nl). LOEUF scores, missense z-scores and synonymous z-scores for all human genes can be downloaded from the gnomAD database (https://gnomad.broadinstitute.org). De novo variants in neurodevelopmental disorders can be obtained from denovo-db (https://denovo-db.gs.washington.edu/denovo-db). GWAS summary statistics for psychiatric disorders can be downloaded from The Psychiatric Genomics Consortium database (https://www.med.unc.edu/pgc).

Code availability

Code used for data analysis in this manuscript is available at GitHub (https://github.com/alexwang1001/PSD_development).

References

  1. Südhof, T. C. Towards an understanding of synapse formation. Neuron 100, 276–293 (2018).

  2. Schmidt, E. R. E. & Polleux, F. Genetic mechanisms underlying the evolution of connectivity in the human cortex. Front. Neural Circuits 15, 787164 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Zoghbi, H. Y. & Bear, M. F. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb. Perspect. Biol. 4, a009886 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Frankle, W. G., Lerma, J. & Laruelle, M. The synaptic hypothesis of schizophrenia. Neuron 39, 205–216 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Shankar, G. M. & Walsh, D. M. Alzheimer’s disease: synaptic dysfunction and Aβ. Mol. Neurodegener. 4, 964075 (2009).

    Article  Google Scholar 

  6. Craig, A. M. & Boudin, H. Molecular heterogeneity of central synapses: afferent and target regulation. Nat. Neurosci. 4, 569–578 (2001).

    Article  CAS  PubMed  Google Scholar 

  7. Harris, K. M. & Weinberg, R. J. Ultrastructure of synapses in the mammalian brain. Cold Spring Harb. Perspect. Biol. 4, 7 (2012).

    Article  Google Scholar 

  8. Uezu, A. et al. Identification of an elaborate complex mediating postsynaptic inhibition. Science 353, 1123–1129 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sheng, M. & Kim, E. The postsynaptic organization of synapses. Cold Spring Harb. Perspect. Biol. 3, a005678 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  11. Swulius, M. T., Kubota, Y., Forest, A. & Waxham, M. N. Structure and composition of the postsynaptic density during development. J. Comp. Neurol. 518, 4243–4260 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Petralia, R. S., Sans, N., Wang, Y. X. & Wenthold, R. J. Ontogeny of postsynaptic density proteins at glutamatergic synapses. Mol. Cell. Neurosci. 29, 436–452 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Harris, K. M., Jensen, F. E. & Tsao, B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation. J. Neurosci. 12, 2685–2705 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. van Zundert, B., Yoshii, A. & Constantine-Paton, M. Receptor compartmentalization and trafficking at glutamate synapses: a developmental proposal. Trends Neurosci. 27, 428–437 (2004).

    Article  PubMed  Google Scholar 

  15. Elias, G. M., Elias, L. A. B., Apostolides, P. F., Kriegstein, A. R. & Nicoll, R. A. Differential trafficking of AMPA and NMDA receptors by SAP102 and PSD-95 underlies synapse development. Proc. Natl Acad. Sci. USA 105, 20953–20958 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gray, J. A. et al. Distinct modes of AMPA receptor suppression at developing synapses by GluN2A and GluN2B: single-cell NMDA receptor subunit deletion in vivo. Neuron 71, 1085–1101 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Li, J. et al. Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders. Nat. Neurosci. 20, 1150–1161 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kaizuka, T. et al. Developmental dynamics of the postsynaptic proteome to understand synaptic maturation and dysmaturation. Preprint at bioRxiv https://doi.org/10.1101/2022.05.05.490828 (2022).

  19. Elston, G. N., Benavides-Piccione, R. & DeFelipe, J. The pyramidal cell in cognition: a comparative study in human and monkey. J. Neurosci. 21, RC163 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bayés, À. 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).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Benavides-Piccione, R., Ballesteros-Yáñez, I., DeFelipe, J. & Yuste, R. Cortical area and species differences in dendritic spine morphology. J. Neurocytol. 31, 337–346 (2002).

    Article  PubMed  Google Scholar 

  23. Shibata, M. et al. Hominini-specific regulation of CBLN2 increases prefrontal spinogenesis. Nature 598, 489–494 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Loomba, S. et al. Connectomic comparison of mouse and human cortex. Science 377, eabo0924 (2022).

    Article  CAS  PubMed  Google Scholar 

  25. Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108, 13281–13286 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu, X. et al. Extension of cortical synaptic development distinguishes humans from chimpanzees and macaques. Genome Res. 22, 611–622 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang, L. et al. An autism-linked missense mutation in SHANK3 reveals the modularity of Shank3 function. Mol. Psychiatry 25, 2534–2555 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  29. Roy, M. et al. Proteomic analysis of postsynaptic proteins in regions of the human neocortex. Nat. Neurosci. 21, 130–141 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Pickard, L., Noel, J., Henley, J. M., Collingridge, G. L. & Molnar, E. Developmental changes in synaptic AMPA and NMDA receptor distribution and AMPA receptor subunit composition in living hippocampal neurons. J. Neurosci. 20, 7922–7931 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  35. Velmeshev, D. et al. Single-cell analysis of prenatal and postnatal human cortical development. Preprint at bioRxiv https://doi.org/10.1101/2022.10.24.513555 (2022).

  36. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wu, L. J., Li, X., Chen, T., Ren, M. & Zhuo, M. Characterization of intracortical synaptic connections in the mouse anterior cingulate cortex using dual patch clamp recording. Mol. Brain 2, 32 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Workman, A. D., Charvet, C. J., Clancy, B., Darlington, R. B. & Finlay, B. L. Modeling transformations of neurodevelopmental sequences across mammalian species. J. Neurosci. 33, 7368–7383 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Laubach, M., Amarante, L. M., Swanson, K. & White, S. R. What, if anything, is rodent prefrontal cortex? eNeuro 5, 315–333 (2018).

    Article  Google Scholar 

  40. Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997).

    Article  CAS  PubMed  Google Scholar 

  41. Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Carlin, R. K., Grab, D. J. & Siekevitz, P. Postmortem accumulation of tubulin in postsynaptic density preparations. J. Neurochem. 38, 94–100 (1982).

    Article  CAS  PubMed  Google Scholar 

  43. Schanzenbächer, C. T., Langer, J. D. & Schuman, E. M. Time- and polarity-dependent proteomic changes associated with homeostatic scaling at central synapses. eLife 7, e33322 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhu, F. et al. Cell-type-specific visualisation and biochemical isolation of endogenous synaptic proteins in mice. Eur. J. Neurosci. 51, 793–805 (2020).

    Article  PubMed  Google Scholar 

  45. Bygrave, A. M. et al. Btbd11 supports cell-type-specific synaptic function. Cell Rep. 42, 112591 (2023).

  46. Kawaguchi, Y., Karube, F. & Kubota, Y. Dendritic branch typing and spine expression patterns in cortical nonpyramidal cells. Cereb. Cortex 16, 696–711 (2006).

    Article  PubMed  Google Scholar 

  47. Charrier, C. et al. Inhibition of SRGAP2 function by its human-specific paralogs induces neoteny during spine maturation. Cell 149, 923–935 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fossati, M. et al. SRGAP2 and its human-specific paralog co-regulate the development of excitatory and inhibitory synapses. Neuron 91, 356–369 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  Google Scholar 

  51. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    Article  CAS  PubMed  Google Scholar 

  52. Walikonis, R. S. et al. Identification of proteins in the postsynaptic density fraction by mass spectrometry. J. Neurosci. 20, 4069–4080 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Shin, J. B. et al. Molecular architecture of the chick vestibular hair bundle. Nat. Neurosci. 16, 365–374 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, X. et al. Proteome-wide identification of ubiquitin interactions using UbIA-MS. Nat. Protoc. 13, 530–550 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).

    MathSciNet  MATH  Google Scholar 

  57. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 17–22 (2016).

    Article  Google Scholar 

  59. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Liberzon, A. et al. The Molecular Signatures Database hallmark gene set Collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, 535–539 (2006).

    Article  Google Scholar 

  64. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. (2006).

  66. Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).

    Article  CAS  PubMed  Google Scholar 

  67. Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  69. Keenan, A. B. et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 47, W212–W224 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Speir, M. L. et al. UCSC Cell Browser: visualize your single-cell data. Bioinformatics 37, 4578–4580 (2021).

  71. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  72. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).

    Article  Google Scholar 

  73. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  75. Turner, T. N. et al. denovo-db: a compendium of human de novo variants. Nucleic Acids Res. 45, D804–D811 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Heyne, H. O. et al. De novo variants in neurodevelopmental disorders with epilepsy. Nat. Genet. 50, 1048–1053 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Rees, E. et al. De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia. Nat. Neurosci. 23, 179–184 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Howrigan, D. P. et al. Exome sequencing in schizophrenia-affected parent–offspring trios reveals risk conferred by protein-coding de novo mutations. Nat. Neurosci. 23, 185–193 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  80. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Demontis, D. et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat. Genet. 55, 198–208 (2023).

    Article  CAS  PubMed  Google Scholar 

  83. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank NIH NeuroBioBank, the University of Maryland School of Medicine Brain and Tissue Bank for providing postmortem brain tissue samples; L. Kohlstaedt for help in mass spectrometry data analysis; and S. Sanders, R. Nicoll, N. Parikshak, T. Mukhtar, M. Song and H. Zoghbi for comments on the manuscript. This work used the Vincent J. Proteomics/Mass Spectrometry Laboratory at UC Berkeley, supported in part by NIH S10 Instrumentation grant S10RR025622. This study was supported by National Institute of Neurological Disorders and Stroke grant R35NS097305 to A.R.K., National Institute of Mental Health grant K99MH131832 to L.W., National Human Genome Research Institute grant HG010898 to N.S. and National Institute of Mental Health grant MH124619 to N.S.

Author information

Authors and Affiliations

  1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA

    Li Wang, Li Zhou, Arantxa Cebrián-Silla, Shaohui Wang, Qiuli Bi, Matthew L. White, Brandon Ho, Tao Li, Yonatan Perez, Mercedes F. Paredes, Alex A. Pollen, Jingjing Li, Xianhua Piao, Arturo Alvarez-Buylla & Arnold R. Kriegstein

  2. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Li Wang, Li Zhou, Shaohui Wang, Qiuli Bi, Matthew L. White, Brandon Ho, Yonatan Perez, Mercedes F. Paredes, Alex A. Pollen, Jingjing Li & Arnold R. Kriegstein

  3. Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, USA

    Kaifang Pang & Zhandong Liu

  4. Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

    Kaifang Pang & Zhandong Liu

  5. Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA

    Arantxa Cebrián-Silla, Ethan A. Winkler & Arturo Alvarez-Buylla

  6. Laboratory of Comparative Neurobiology, Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia and CIBERNED, Valencia, Spain

    Susana González-Granero & José Manuel García-Verdugo

  7. Gilead Sciences, Foster City, CA, USA

    Jiani Li

  8. Department of Pathology, University of California San Francisco, San Francisco, CA, USA

    Eric J. Huang

  9. Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA

    Rothem Kovner & Nenad Sestan

  10. Department of Chemistry, University of Massachusetts Lowell, Lowell, MA, USA

    Pengyuan Liu

  11. Division of Neonatology, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

    Xianhua Piao

  12. Newborn Brain Research Institute, University of California San Francisco, San Francisco, CA, USA

    Xianhua Piao

Authors
  1. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaifang Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arantxa Cebrián-Silla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susana González-Granero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shaohui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qiuli Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew L. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Brandon Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiani Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yonatan Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Eric J. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ethan A. Winkler
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mercedes F. Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Rothem Kovner
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Nenad Sestan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Alex A. Pollen
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Pengyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jingjing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Xianhua Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. José Manuel García-Verdugo
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Arturo Alvarez-Buylla
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Zhandong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Arnold R. Kriegstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: L.W.; data curation: L.W.; formal analysis: L.W.; funding acquisition: L.W., N.S., A.R.K.; investigation: L.W., L.Z., A.C.-S., S.G.-G., S.W., M.L.W., B.H., T.L.; methodology: L.W., K.P., L.Z., A.C.-S., S.G.-G., Jiani Li, P.L., Jingjing Li, J.M.G.-V.; resources: S.W., Y.P., E.J.H., E.A.W., M.F.P., R.K., N.S., A.A.P., A.R.K.; software: L.W., K.P.; supervision: X.P., J.M.G.-V., A.A.-B., Z.L., A.R.K.; visualization: L.W., K.P.; writing (original draft): L.W.; writing (review and editing): all authors.

Corresponding authors

Correspondence to Li Wang or Arnold R. Kriegstein.

Ethics declarations

Competing interests

A.R.K. is a co-founder, consultant and director of Neurona Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Javier DeFelipe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Isolation of PSDs from immature and mature human cortices.

a, Western blot analysis of different subcellular fractions of a GW23 sample demonstrating enrichment of PSD proteins and depletion of presynaptic SYP and cytoplasmic GAPDH in the PSD fraction. Enrichment of PSD proteins after fractionation was validated for all samples subjected to mass spectrometry analysis. b, Electron micrographs of the PSD fraction isolated from a GW23 sample (scale bar: 200 nm). Arrows denote structures resembling the PSD. The experiment was performed on one sample. c, Western blot analysis of purified PSDs from different age groups demonstrating changes in GRIN2B and DLG4 during development. The experiment was performed once. d, Correlation between PSD yield and synapse number of developing human prefrontal cortex. e, Venn diagram showing the overlap between Year18_22 samples in this study and the human PSD proteomes published in Roy et al., 2017 and Bayés et al., 2011. f, UpSet plot describing the number of identified proteins and their overlaps at each age group. g, PCA plots of the samples colored by various covariates. h, Variance explained by individual covariates (n = 1765 proteins). Boxplot center: median; hinges: the 25th and 75th percentiles; whiskers: 1.5 × inter-quartile range. i, Abundance patterns of GRIN2A, GRIN2B, DLG3, and DLG4. j, Colocalization of RPS6, CTNNB1, GDI1, or CFL1 with DLG4 in second-trimester human neocortex (n = 5, 5, 5 and 5 samples, scale bar: 10 μm or 5 μm as indicated in the figure). Data are presented as mean values ± s.e.m.

Extended Data Fig. 2 Examples of PSD protein abundance patterns in human cortical development.

a, Abundance patterns of representative PSD proteins. b, Examples of PSD protein paralogs that undergo reciprocal developmental changes. c, Abundance patterns of DBN1 and DBNL. d, Immunofluorescent intensity of DBN1 and DBNL at DLG4 loci in human neocortex (n = 3 and 3 samples, scale bar: 2 μm). Data are presented as mean values ± s.e.m. The P value was obtained from unpaired two-tailed t test; *P < 0.05. e, Original large field of view images for producing processed images in panel d (scale bar: 10 μm or 5 μm as indicated in the figure).

Extended Data Fig. 3 Protein modules of the developing human PSD.

a, Kernel density estimation of the null distributions of protein-protein interaction (PPI) numbers assuming no enrichment of PPI in individual modules; the vertical red lines indicate the observed PPI numbers in each module. The exact P value was derived from the one-tailed permutation test. b, SynGO biological pathway enrichment analysis of each module. Nominal P values from Fisher’s exact test were adjusted by the Benjamini and Hochberg method. c, PPI-co-abundance network of the four PSD modules. d, PPI-co-abundance network of each module highlighting proteins in enriched pathways. e, The normalized average shortest path lengths of pathways in individual modules. The asterisks denote that the average shortest path length is significantly shorter within pathway proteins than between pathway and non-pathway proteins. The P values were obtained from one-tailed Wilcoxon rank sum test; *P < 0.01.

Extended Data Fig. 4 Protein domains in PSD proteins.

a, Protein domains in individual PSD proteins. The rows are clustered based on the Jaccard distance. b, Abundance patterns of RhoGAPs and RhoGEFs not listed in Fig. 2e.

Extended Data Fig. 5 Changes in PSD composition during human V1 development.

a, Schematic illustrating the developmental stages of samples in the human V1 dataset. b, PCA plots of samples in the human V1 dataset colored by their age groups. c, Hierarchical clustering of the samples in the human V1 dataset based on proteins with differential abundance. d, Gene set enrichment analysis (GSEA) for individual age groups across species. NES, normalized enrichment score. Nominal P values were adjusted by the Benjamini and Hochberg method. e, Scaled abundance patterns (module eigengene values) of four protein modules in the human V1 dataset. f, Similarity matrices representing pairwise Pearson correlations between human PFC and human V1 samples.

Extended Data Fig. 6 Transcription of PSD proteins.

a, Preservation of human PSD modules in the bulk RNA-seq data. b, Uniform manifold approximation and projection (UMAP) plots showing the distribution of age groups and cell types in the single-nucleus RNA-seq data from developing human neocortex. c, UMAP plots showing the expression patterns of neuronal subtype-specific markers in the single-nucleus RNA-seq data from developing human neocortex. d, Standardized expression values of genes in the blue (n = 301 genes) and yellow (n = 218 genes) modules in individual neuronal subtypes of the adult human neocortex. Boxplot center: median; hinges: the 25th and 75th percentiles; whiskers: 1.5 × inter-quartile range. e, Standardized expression values of top TFs predicted to regulate the blue (95 genes) and yellow (97 genes) modules in individual neuronal subtypes of the adult human neocortex. Boxplot center: median; hinges: the 25th and 75th percentiles; whiskers: 1.5 × inter-quartile range.  EN_IT, excitatory intratelencephalic neuron; EN_non-IT, excitatory non-intratelencephalic neuron; IN_MGE, inhibitory neuron derived from the medial ganglionic eminence; IN_CGE, inhibitory neuron derived from the caudal ganglionic eminence.

Extended Data Fig. 7 Changes in PSD composition during macaque and mouse neocortical development.

a, UpSet plot describing the number of identified proteins and their overlaps at each age group of macaque and mouse datasets. b, PCA plots of the macaque and mouse samples colored by their age groups. c, Hierarchical clustering of macaque and mouse samples based on proteins with differential abundance. d, Preservation of Human PSD modules in macaque and mouse PSD proteomic data. e, Gene set enrichment analysis (GSEA) for individual age groups across human V1, macaque, and mouse samples. NES, normalized enrichment score. Nominal P values were adjusted by the Benjamini and Hochberg method. f, Similarity matrices representing pairwise Pearson correlations between human V1, macaque, and mouse samples. g, Standardized abundance patterns of proteins in the four PSD modules (n = 127, 169, 159 and 178 proteins) across regions and species along the humanized age based on the human V1 dataset. Data are presented as mean values ± s.e.m. The macaque and mouse illustrations were created with BioRender.com.

Extended Data Fig. 8 RhoGEF levels and activities during synapse development.

a, Immunoblots and quantification of ARHGEF7 and PREX1 in the PSD and homogenate of developing human cortex (n = 4, 4 and 4 samples). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA with Holm-Sidak’s multiple comparisons test; *P < 0.05, **P < 0.01, ***P < 0.001. b, Immunoblots and quantification of ARHGEF7 and PREX1 in the PSD of developing mouse cortex (n = 2, 2, 2, 2, 2, 2 and 2 samples). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA; ns, not significant. c, Original large field of view images for producing processed images in Fig. 4b (scale bar: 10 μm or 5 μm as indicated in the figure). d, Comparison of adult human PSD samples from postmortem brain tissues and neurosurgical biopsy tissues by western blot analysis (n = 4 and 3 samples). e, Immunoblots and quantification of ARHGEF7, PREX1, phospho-PAK, PAK, phospho-CFL1, and CFL1 in the synaptosomes of cultured primary human (n = 3 and 3 samples) and mouse cortical neurons (n = 3 and 3 samples) at indicated days in vitro (DIV). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA with Holm-Sidak’s multiple comparisons test; *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant. The human and mouse illustrations were created with BioRender.com.

Extended Data Fig. 9 Manipulation of Rho GTPase regulators alters synapse development in human and mouse cortical neurons.

a, Original large field of view images for producing processed images in Fig. 4c (scale bar: 50 μm). b, Immunostaining of dendrites from primary mouse cortical neurons cultured 8 days in vitro. Neurons were transfected with mEGFP-C1 and vectors expressing mCherry, mCherry-ARHGEF7, or mCherry-RASGRF2 (n = 20, 20 and 20 neurons from 4 cultures, scale bar: 5 μm). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA with Holm-Sidak’s multiple comparisons test; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. c, Original large field of view images for producing processed images in panel b (scale bar: 50 μm). d, Quantification of mRNA levels of ARHGAP23 and SRGAP1 in HEK293T cells transfected with control shRNAs (shControl), two shRNAs targeting ARHGAP23 (shARGGAP23−1 and shARGGAP23−2), or two shRNAs targeting SRGAP1 (shSRGAP1−1 and shSRGAP1−2) (n = 3 cultures for all conditions). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA with Holm-Sidak’s multiple comparisons test; ****P < 0.0001. e,f, Immunostaining of dendrites from primary human cortical neurons cultured six weeks in vitro. Neurons were transfected with mEGFP-C1 and vectors co-expressing turbo-RFP (tRGP) and shControl, shARGGAP23−1, shARGGAP23−2, shSRGAP1−1 or shSRGAP1−2 (n = 20, 22, 20, 20 and 20 neurons from 4 cultures, scale bar: 5 μm). Data are presented as mean values ± s.e.m. The P values were obtained from one-way ANOVA with Holm-Sidak’s multiple comparisons test; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. g, Original large field of view images for producing processed images in panel e (scale bar: 50 μm). h, Original large field of view images for producing processed images in Fig. 4e (scale bar: 50 μm).

Extended Data Fig. 10 Association of PSD modules with cognitive functions and brain disorders.

a, PPI-co-abundance network of the turquoise module with activity-dependent proteins highlighted. b, Distribution of gnomAD synonymous Z-scores of genes in each category (n = 265, 313, 402, 224, 561, 1765 and 12892 genes). Boxplot center: median; hinges: the 25th and 75th percentiles; whiskers: 1.5 × inter-quartile range. The P values were obtained from Kruskal–Wallis test with Dunn’s multiple comparisons test; ns, not significant. c, Percentage of rare variants located at PSD module genes in subjects with or without neurodevelopmental disorders. d, PPI-co-abundance network of the turquoise module with genes carrying neurodevelopmental disorder-linked (at least 3) de novo missense variants or (at least 2) PTVs highlighted. e, PPI-co-abundance network of the brown module with genes carrying psychiatric disorder-linked common variants highlighted. f, PPI-co-abundance network of the yellow module with genes downregulated in psychiatric disorders highlighted. g, Volcano plots for misexpressed genes after the onset of psychiatric disorders in PSD modules. Red dots indicate differentially expressed genes with the Benjamini–Hochberg adjusted P values < 0.05.

Supplementary information

Supplementary Information

Table of contents for the supplementary files, and Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–13.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Pang, K., Zhou, L. et al. A cross-species proteomic map reveals neoteny of human synapse development. Nature 622, 112–119 (2023). https://doi.org/10.1038/s41586-023-06542-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06542-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research