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Single-cell proteomic landscape of the developing human brain

Nature Biotechnology (2026)Cite this article

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Abstract

Profiling protein abundance and dynamics at single-cell resolution in complex human tissues is challenging. Given the discordance between transcript and protein abundance observed in studies of the human cerebral cortex, we developed an optimized workflow that combines label-free single-cell mass spectrometry with precise sample preparation to resolve quantitative proteomes of individual cells from the developing human brain. Our method achieves deep proteomic coverage (~800 proteins per cell) even in small immature prenatal human neurons (diameter ~7–10 μm, ~50 pg protein), capturing major brain cell types and enabling proteome-wide characterization at single-cell resolution. We document extensive transcriptome–proteome discordance across cell types, particularly in genes associated with neurodevelopmental disorders. Proteins exhibit markedly higher cell-type specificity than their mRNA counterparts, underscoring the importance of proteomic-level analysis. By reconstructing developmental trajectories from radial glia to excitatory neurons at the proteomic level, we identify dynamic, stage-specific protein co-expression modules and pinpoint the intermediate progenitor-to-neuron transition as a genetically vulnerable phase associated with autism.

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Fig. 1: Label-free single-cell proteome profiling in the human developing brain.
Fig. 2: Proteomics-based cell map of the prenatal human neocortex.
Fig. 3: Generating scRNA-seq data from the same tissue samples.
Fig. 4: Experimental validation of RNA–protein discordance.
Fig. 5: mRNA–protein discordance.
Fig. 6: Protein dynamics across human neurodevelopment.

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Data availability

Processed single-cell proteome and single-cell transcriptome data are available at an interactive portal (https://cell.ucsf.edu/SCProteome/). Single-cell proteomics raw data have been deposited to the ProteomeXchange Consortium (PXD071075)91. The single-cell transcriptome data are available in the Gene Expression Omnibus repository (GSE310125)92. The reference single-cell RNA-seq atlas of the developing human brain39 is accessible at https://cell.ucsf.edu/snMultiome/. Bulk proteome data were provided in Supplementary Tables 2 and 3. Bulk transcriptome data were sourced from the BrainSpan atlas (https://www.brainspan.org/static/download.html). The list of short-lived proteins was obtained from a previous study62. Synaptic genes were identified using SynaptomeDB42, and the SFARI gene list was downloaded from the SFARI Gene database (https://gene.sfari.org/database/human-gene/). Data for mutations associated with autism and NDDs were sourced from a previous study66. Protein pLI scores were obtained from gnomAD93, and evolutionary rates were retrieved from the Ensembl genome browser (v99) through biomart94. The polysome TrIP-seq data were retrieved from previous study57. The list of human transcription factors is available at https://humantfs.ccbr.utoronto.ca/download/v_1.01/TF_names_v_1.01.txt.

Code availability

The code used for data analysis is available on GitHub (https://github.com/SingleCellProteomics/Brain)95.

References

  1. Gry, M. et al. Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genomics 10, 365 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Jiang, L. et al. A quantitative proteome map of the human body. Cell 183, 269–283 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Takemon, Y. et al. Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney. eLife 10, e62585 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ghazalpour, A. et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 7, e1001393 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tracey, L. J., An, Y. & Justice, M. J. CyTOF: an emerging technology for single-cell proteomics in the mouse. Curr. Protoc. 1, e118 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Anand, S., Samuel, M., Ang, C.-S., Keerthikumar, S. & Mathivanan, S. Label-based and label-free strategies for protein quantitation. Methods Mol. Biol. 1549, 31–43 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Virant-Klun, I., Leicht, S., Hughes, C. & Krijgsveld, J. Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Mol. Cell. Proteomics 15, 2616–2627 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang, Z., Liu, P.-K. & Li, L. A tutorial review of labeling methods in mass spectrometry-based quantitative proteomics. ACS Meas. Sci. Au 4, 315–337 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Brenes, A., Hukelmann, J., Bensaddek, D. & Lamond, A. I. Multibatch TMT reveals false positives, batch effects and missing values. Mol. Cell. Proteomics 18, 1967–1980 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Phua, S.-X., Lim, K.-P. & Goh, W. W.-B. Perspectives for better batch effect correction in mass-spectrometry-based proteomics. Comput. Struct. Biotechnol. J. 20, 4369–4375 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guzman, U. H. et al. Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition. Nat. Biotechnol. 42, 1855–1866 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Brunner, A. et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. Mol. Syst. Biol. 18, e10798 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schoof, E. M. et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nat. Commun. 12, 3341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Furtwängler, B. et al. Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics. Science 390, eadr8785 (2025).

    Article  PubMed  Google Scholar 

  21. Lombard-Banek, C., Reddy, S., Moody, S. A. & Nemes, P. Label-free quantification of proteins in single embryonic cells with neural fate in the cleavage-stage frog (Xenopus laevis) embryo using capillary electrophoresis electrospray ionization high-resolution mass spectrometry (CE-ESI-HRMS). Mol. Cell. Proteomics 15, 2756–2768 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bubis, J. A. et al. Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity. Nat. Methods 22, 510–519 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mun, D.-G. et al. Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry. Commun. Biol. 7, 884 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mun, D.-G. et al. Optimizing single cell proteomics using trapped ion mobility spectrometry for label-free experiments. Analyst 148, 3466–3475 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sanchez-Avila, X. et al. Easy and accessible workflow for label-free single-cell proteomics. J. Am. Soc. Mass Spectrom. 34, 2374–2380 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fang, W. et al. A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition. Anal. Chim. Acta 1251, 341038 (2023).

    Article  CAS  PubMed  Google Scholar 

  27. Guo, Y. et al. Single-cell quantitative proteomic analysis of human oocyte maturation revealed high heterogeneity in in vitro-matured oocytes. Mol. Cell. Proteomics 21, 100267 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tsai, C.-F. et al. Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics. Commun. Biol. 4, 265 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cong, Y. et al. Improved single-cell proteome coverage using narrow-bore packed nanoLC columns and ultrasensitive mass spectrometry. Anal. Chem. 92, 2665–2671 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lombard-Banek, C., Moody, S. A., Manzini, M. C. & Nemes, P. Microsampling capillary electrophoresis mass spectrometry enables single-cell proteomics in complex tissues: developing cell clones in live Xenopus laevis and zebrafish embryos. Anal. Chem. 91, 4797–4805 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dou, M. et al. High-throughput single cell proteomics enabled by multiplex isobaric labeling in a nanodroplet sample preparation platform. Anal. Chem. 91, 13119–13127 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ai, L. et al. Single-cell proteomics reveals specific cellular subtypes in cardiomyocytes derived from human iPSCs and adult hearts. Mol. Cell. Proteomics 24, 100910 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kreimer, S. et al. High-throughput single-cell proteomic analysis of organ-derived heterogeneous cell populations by nanoflow dual-trap single-column liquid chromatography. Anal. Chem. 95, 9145–9150 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rosenberger, F. A. et al. Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nat. Methods 20, 1530–1536 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Matzinger, M., Müller, E., Dürnberger, G., Pichler, P. & Mechtler, K. Robust and easy-to-use one-pot workflow for label-free single-cell proteomics. Anal. Chem. 95, 4435–4445 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Ye, Z. et al. Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics. Nat. Methods 22, 499–509 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Géminard, C., de Gassart, A. & Vidal, M. Reticulocyte maturation: mitoptosis and exosome release. Biocell 26, 205–215 (2002).

    Article  PubMed  Google Scholar 

  39. Wang, L. et al. Molecular and cellular dynamics of the developing human neocortex. Nature 647, 169–178 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shi, Y. et al. Mouse and human share conserved transcriptional programs for interneuron development. Science 374, eabj6641 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang, L. et al. A cross-species proteomic map reveals neoteny of human synapse development. Nature 622, 112–119 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pirooznia, M. et al. SynaptomeDB: an ontology-based knowledgebase for synaptic genes. Bioinformatics 28, 897–899 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Perez, J. D. et al. Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons. eLife 10, e63092 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Li, L. T., Jiang, G., Chen, Q. & Zheng, J. N. Ki67 is a promising molecular target in the diagnosis of cancer (review). Mol. Med. Rep. 11, 1566–1572 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Heidebrecht, H. J., Buck, F., Haas, K., Wacker, H. H. & Parwaresch, R. Monoclonal antibodies Ki-S3 and Ki-S5 yield new data on the ‘Ki-67’ proteins. Cell Prolif. 29, 413–425 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Tani, H. et al. Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals. Genome Res. 22, 947–956 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  49. Hevner, R. F. et al. Tbr1 regulates differentiation of the preplate and layer 6. Neuron 29, 353–366 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Raju, C. S. et al. Secretagogin is expressed by developing neocortical GABAergic neurons in humans but not mice and increases neurite arbor size and complexity. Cereb. Cortex 28, 1946–1958 (2017).

    Article  PubMed Central  Google Scholar 

  51. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Drivas, T. G. et al. A second cohort of CHD3 patients expands the molecular mechanisms known to cause Snijders Blok-Campeau syndrome. Eur. J. Hum. Genet. 28, 1422–1431 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Washbourne, P. Synapse assembly and neurodevelopmental disorders. Neuropsychopharmacology 40, 4–15 (2015).

    Article  PubMed  Google Scholar 

  54. Sapir, T., Frotscher, M., Levy, T., Mandelkow, E.-M. & Reiner, O. Tau’s role in the developing brain: implications for intellectual disability. Hum. Mol. Genet. 21, 1681–1692 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Fiock, K. L., Smalley, M. E., Crary, J. F., Pasca, A. M. & Hefti, M. M. Increased tau expression correlates with neuronal maturation in the developing human cerebral cortex. eNeuro 7, ENEURO.0058–20.2020 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Wang, J. et al. Benchmarking informatics workflows for data-independent acquisition single-cell proteomics. Nat. Commun. 16, 10276 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Blair, J. D., Hockemeyer, D., Doudna, J. A., Bateup, H. S. & Floor, S. N. Widespread translational remodeling during human neuronal differentiation. Cell Rep. 21, 2005–2016 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Thumuluri, V. et al. NetSolP: predicting protein solubility in Escherichia coli using language models. Bioinformatics 38, 941–946 (2022).

    Article  CAS  PubMed  Google Scholar 

  59. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Nagore, L. I. et al. Purification and characterization of transcription factors. Mass Spectrom. Rev. 32, 386–398 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mauger, O., Lemoine, F. & Scheiffele, P. Targeted intron retention and excision for rapid gene regulation in response to neuronal activity. Neuron 92, 1266–1278 (2016).

    Article  CAS  PubMed  Google Scholar 

  62. Li, J. et al. Proteome-wide mapping of short-lived proteins in human cells. Mol. Cell 81, 4722–4735 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Blake, J. A. et al. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res. 42, D810–D817 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Zhou, X. et al. Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes. Nat. Genet. 54, 1305–1319 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Park, C. Y. et al. Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk. Nat. Genet. 53, 166–173 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

    Article  CAS  PubMed  Google Scholar 

  69. Webster, E. et al. De novo PHIP-predicted deleterious variants are associated with developmental delay, intellectual disability, obesity, and dysmorphic features. Cold Spring Harb. Mol. Case Stud. 2, a001172 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kampmeier, A. et al. PHIP-associated Chung-Jansen syndrome: report of 23 new individuals. Front. Cell Dev. Biol. 10, 1020609 (2022).

    Article  PubMed  Google Scholar 

  71. Mattioli, F. et al. De novo frameshift variants in the neuronal splicing factor NOVA2 result in a common C-terminal extension and cause a severe form of neurodevelopmental disorder. Am. J. Hum. Genet. 106, 438–452 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  73. Melliou, S. et al. Regionally defined proteomic profiles of human cerebral tissue and organoids reveal conserved molecular modules of neurodevelopment. Cell Rep. 39, 110846 (2022).

    Article  CAS  PubMed  Google Scholar 

  74. Machol, K. et al. Expanding the spectrum of BAF-related disorders: de novo variants in SMARCC2 cause a syndrome with intellectual disability and developmental delay. Am. J. Hum. Genet. 104, 164–178 (2019).

    Article  CAS  PubMed  Google Scholar 

  75. Schanze, I. et al. NFIB haploinsufficiency is associated with intellectual disability and macrocephaly. Am. J. Hum. Genet. 103, 752–768 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Beck, D. B. et al. A recurrent de novo CTBP1 mutation is associated with developmental delay, hypotonia, ataxia, and tooth enamel defects. Neurogenetics 17, 173–178 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Gingold, H. et al. A dual program for translation regulation in cellular proliferation and differentiation. Cell 158, 1281–1292 (2014).

    Article  CAS  PubMed  Google Scholar 

  78. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kelleher, R. J. & Bear, M. F. The autistic neuron: troubled translation? Cell 135, 401–406 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hooshmandi, M., Wong, C. & Khoutorsky, A. Dysregulation of translational control signaling in autism spectrum disorders. Cell. Signal. 75, 109746 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Sanes, J. R. & Zipursky, S. L. Synaptic specificity, recognition molecules, and assembly of neural circuits. Cell 181, 536–556 (2020).

    Article  CAS  PubMed  Google Scholar 

  83. Díaz-Villanueva, J. F., Díaz-Molina, R. & García-González, V. Protein folding and mechanisms of proteostasis. Int. J. Mol. Sci. 16, 17193–17230 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Clapier, C. R., Iwasa, J., Cairns, B. R. & Peterson, C. L. Mechanisms of action and regulation of ATP-dependent chromatin-remodelling complexes. Nat. Rev. Mol. Cell Biol. 18, 407–422 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article  CAS  PubMed  Google Scholar 

  87. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  91. PRIDE. Single-cell proteomic landscape of the developing human brain. www.ebi.ac.uk/pride/archive/projects/PXD071075/ (2025).

  92. Gene Expression Omnibus. Single-cell transcriptomic profiling of the developing human brain (GW 13, 15, and 19). www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE310125 (2025).

  93. Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

    Article  CAS  PubMed  Google Scholar 

  94. Kinsella, R. J. et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford) 2011, bar030 (2011).

    Article  PubMed  Google Scholar 

  95. Tianzhi, W. Single-cell proteomic landscape in the developing human brain. GitHub github.com/SingleCellProteomics/Brain (2025).

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Acknowledgements

This work was supported by the SFARI grant (AN-AR-Gene Therapies-01010017 to J.L., A.R.K. and M.S.).

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  1. These authors contributed equally: Tianzhi Wu, Lihua Jiang.

Authors and Affiliations

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

    Tianzhi Wu, Tanzila Mukhtar, Li Wang, Cheng Wang, Arnold R. Kriegstein & Jingjing Li

  2. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Lihua Jiang, Ruiqi Jian, Tiffany Trinh & Michael Snyder

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Contributions

J.L., L.J. and T.W. designed the study. L.W., T.M. and A.R.K. helped with sample collection. T.W., L.J., T.M., R.J., T.T., J.L. and M.S. were responsible for single-cell proteomic data production. L.W. was responsible for generating single-cell RNA-seq data. T.M. documented immunostaining validation. T.W. and T.M. conducted the immunostaining data analysis. T.W. and J.L. conducted the single-cell data analysis. J.L., A.R.K. and M.S. were responsible for funding. J.L., T.W., L.W., L.J. and A.R.K. were involved in writing the original draft and performing the revision. All authors contributed to the review and editing of the paper.

Corresponding authors

Correspondence to Arnold R. Kriegstein, Michael Snyder or Jingjing Li.

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Competing interests

A.R.K. is a cofounder, consultant and director of Neurona Therapeutics. J.L. is a cofounder of SensOmics and serves on its scientific advisory board. M.S. is a cofounder of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos and NiMo, and serves on the scientific advisory boards of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos, NiMo and Genapsys. The other authors declare no competing interests.

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Supplementary Note (quantitative data analysis) and Supplementary Figs. 1–21.

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Wu, T., Jiang, L., Mukhtar, T. et al. Single-cell proteomic landscape of the developing human brain. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-025-02980-7

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