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Cross-species analysis of adult hippocampal neurogenesis reveals human-specific gene expression but convergent biological processes

Nature Neuroscience volume 28, pages 1820–1829 (2025)Cite this article

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Abstract

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity, learning and memory, but their evolutionary changes across species and specialized features in humans remain poorly understood. Here we performed machine-learning-augmented analysis of published single-nucleus RNA-sequencing datasets and identified macaque imGCs with transcriptome-wide immature neuronal characteristics. Our cross-species comparisons among humans, monkeys, pigs and mice showed few shared (such as DPYSL5), but mostly species-specific gene expression in imGCs that converged onto common biological processes regulating neuronal development. We further identified human-specific transcriptomic features of imGCs and demonstrated the functional roles of human imGC-enriched expression of a family of proton-transporting vacuolar-type ATPase subtypes in the development of imGCs derived from human pluripotent stem cells. Our study reveals divergent gene expression patterns but convergent biological processes in the molecular characteristics of imGCs across species, highlighting the importance of conducting independent molecular and functional analyses for adult neurogenesis in different species.

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Fig. 1: Machine-learning-augmented identification and molecular characteristics of immature neurons in macaque hippocampal scRNA-seq datasets.
Fig. 2: Identification of immature neurons in the postnatal macaque hippocampus across ages and studies.
Fig. 3: Conserved immature neuronal biological processes with divergent gene expression feature in imGCs of different species.
Fig. 4: Species-specific enrichment of imGC molecular features.
Fig. 5: Human-enriched hippocampal immature neuron features and functional roles of a family of genes encoding v-ATPases.

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Data availability

All scRNA-seq or single-nucleus RNA-seq data were previously published (summarized in Supplementary Table 1). Additional information required for reanalyzing the data presented in this study can be obtained from the corresponding authors upon request. Source data are provided with this paper.

Code availability

Scripts for bioinformatic analyses used in this study are available at https://github.com/zhoujoeyyi/imGC_species/.

References

  1. Gage, F. H. Adult neurogenesis in mammals. Science 364, 827–828 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Ming, G. L. & Song, H. Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70, 687–702 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Denoth-Lippuner, A. & Jessberger, S. Formation and integration of new neurons in the adult hippocampus. Nat. Rev. Neurosci. 22, 223–236 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Salta, E. et al. Adult hippocampal neurogenesis in Alzheimer’s disease: a roadmap to clinical relevance. Cell Stem Cell 30, 120–136 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Terreros-Roncal, J. et al. Impact of neurodegenerative diseases on human adult hippocampal neurogenesis. Science 374, 1106–1113 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Miller, S. M. & Sahay, A. Functions of adult-born neurons in hippocampal memory interference and indexing. Nat. Neurosci. 22, 1565–1575 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ge, S. et al. GABA regulates synaptic integration of newly generated neurons in the adult brain. Nature 439, 589–593 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Ge, S., Yang, C. H., Hsu, K. S., Ming, G. L. & Song, H. A critical period for enhanced synaptic plasticity in newly generated neurons of the adult brain. Neuron 54, 559–566 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Christian, K. M., Song, H. & Ming, G. L. Functions and dysfunctions of adult hippocampal neurogenesis. Annu. Rev. Neurosci. 37, 243–262 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li, Y. D., Luo, Y. J. & Song, J. Optimizing memory performance and emotional states: multi-level enhancement of adult hippocampal neurogenesis. Curr. Opin. Neurobiol. 79, 102693 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hochgerner, H., Zeisel, A., Lonnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Shin, J. et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lu, Z. et al. Tracking cell-type-specific temporal dynamics in human and mouse brains. Cell 186, 4345–4364 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gao, Y. et al. Integrative single-cell transcriptomics reveals molecular networks defining neuronal maturation during postnatal neurogenesis. Cereb. Cortex 27, 2064–2077 (2017).

    Article  PubMed  Google Scholar 

  15. Artegiani, B. et al. A single-cell RNA sequencing study reveals cellular and molecular dynamics of the hippocampal neurogenic niche. Cell Rep. 21, 3271–3284 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25, 285–294 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rasetto, N. B. et al. Transcriptional dynamics orchestrating the development and integration of neurons born in the adult hippocampus. Sci. Adv. 10, eadp6039 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang, H. et al. Single-nucleus transcriptomic landscape of primate hippocampal aging. Protein Cell 12, 695–716 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hao, Z. Z. et al. Single-cell transcriptomics of adult macaque hippocampus reveals neural precursor cell populations. Nat. Neurosci. 25, 805–817 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, W. et al. Transcriptome dynamics of hippocampal neurogenesis in macaques across the lifespan and aged humans. Cell Res. 32, 729–743 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Franjic, D. et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron 110, 452–469 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tosoni, G. et al. Mapping human adult hippocampal neurogenesis with single-cell transcriptomics: reconciling controversy or fueling the debate? Neuron https://doi.org/10.1016/j.neuron.2023.03.010 (2023).

    Article  PubMed  Google Scholar 

  25. Zhou, Y. et al. Molecular landscapes of human hippocampal immature neurons across lifespan. Nature 607, 527–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ayhan, F. et al. Resolving cellular and molecular diversity along the hippocampal anterior-to-posterior axis in humans. Neuron 109, 2091–2105 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhong, S. et al. Decoding the development of the human hippocampus. Nature 577, 531–536 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Chen, X. et al. A brain cell atlas integrating single-cell transcriptomes across human brain regions. Nat. Med. https://doi.org/10.1038/s41591-024-03150-z (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ramnauth, A. D. et al. Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation. Cell Rep. 44, 115300 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhou, Y., Su, Y., Ming, G. L. & Song, H. Special properties of adult neurogenesis in the human hippocampus: Implications for its clinical applications. Clin. Transl. Med. 13, e1196 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kempermann, G. et al. Human adult neurogenesis: evidence and remaining questions. Cell Stem Cell 23, 25–30 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou, Y., Song, H. & Ming, G. L. Genetics of human brain development. Nat. Rev. Genet. 25, 26–45 (2024).

    Article  CAS  PubMed  Google Scholar 

  33. Wallace, J. L. & Pollen, A. A. Human neuronal maturation comes of age: cellular mechanisms and species differences. Nat. Rev. Neurosci. 25, 7–29 (2024).

    Article  CAS  PubMed  Google Scholar 

  34. Vanderhaeghen, P. & Polleux, F. Developmental mechanisms underlying the evolution of human cortical circuits. Nat. Rev. Neurosci. 24, 213–232 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Knoth, R. et al. Murine features of neurogenesis in the human hippocampus across the lifespan from 0 to 100 years. PLoS ONE 5, e8809 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sorrells, S. F. et al. Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature 555, 377–381 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kornack, D. R. & Rakic, P. Continuation of neurogenesis in the hippocampus of the adult macaque monkey. Proc. Natl Acad. Sci. USA 96, 5768–5773 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ammothumkandy, A. et al. Altered adult neurogenesis and gliogenesis in patients with mesial temporal lobe epilepsy. Nat. Neurosci. 25, 493–503 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Moreno-Jimenez, E. P. et al. Adult hippocampal neurogenesis is abundant in neurologically healthy subjects and drops sharply in patients with Alzheimer’s disease. Nat. Med. 25, 554–560 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Han, L. et al. Cell transcriptomic atlas of the non-human primate Macaca fascicularis. Nature 604, 723–731 (2022).

    Article  CAS  PubMed  Google Scholar 

  43. Kohler, S. J., Williams, N. I., Stanton, G. B., Cameron, J. L. & Greenough, W. T. Maturation time of new granule cells in the dentate gyrus of adult macaque monkeys exceeds six months. Proc. Natl Acad. Sci. USA 108, 10326–10331 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ngwenya, L. B., Heyworth, N. C., Shwe, Y., Moore, T. L. & Rosene, D. L. Age-related changes in dentate gyrus cell numbers, neurogenesis, and associations with cognitive impairments in the rhesus monkey. Front. Syst. Neurosci. 9, 102 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Hart, J. C., Ellis, N. A., Eisen, M. B. & Miller, C. T. Convergent evolution of gene expression in two high-toothed stickleback populations. PLoS Genet. 14, e1007443 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Foster, C. S. P. et al. Different genes are recruited during convergent evolution of pregnancy and the placenta. Mol. Biol. Evol. 39, msac077 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bontonou, G. et al. Evolution of chemosensory tissues and cells across ecologically diverse drosophilids. Nat. Commun. 15, 1047 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Foote, A. D. et al. Convergent evolution of the genomes of marine mammals. Nat. Genet. 47, 272–275 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. She, R. et al. Comparative landscape of genetic dependencies in human and chimpanzee stem cells. Cell 186, 2977–2994 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Whitmarsh-Everiss, T. & Laraia, L. Small molecule probes for targeting autophagy. Nat. Chem. Biol. 17, 653–664 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Yoshimori, T., Yamamoto, A., Moriyama, Y., Futai, M. & Tashiro, Y. Bafilomycin A1, a specific inhibitor of vacuolar-type H+-ATPase, inhibits acidification and protein degradation in lysosomes of cultured cells. J. Biol. Chem. 266, 17707–17712 (1991).

    Article  CAS  PubMed  Google Scholar 

  54. Huss, M. et al. Concanamycin A, the specific inhibitor of V-ATPases, binds to the Vo subunit c. J. Biol. Chem. 277, 40544–40548 (2002).

    Article  CAS  PubMed  Google Scholar 

  55. Van Galen, P. et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bielecki, P. et al. Skin-resident innate lymphoid cells converge on a pathogenic effector state. Nature 592, 128–132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Peng, Y. R. et al. Molecular classification and comparative taxonomics of foveal and peripheral cells in primate retina. Cell 176, 1222–1237 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ichihara, A. & Yatabe, M. S. The (pro)renin receptor in health and disease. Nat. Rev. Nephrol. 15, 693–712 (2019).

    Article  PubMed  Google Scholar 

  59. Bracke, A. & von Bohlen Und Halbach, O. Roles and functions of Atp6ap2 in the brain. Neural Regen. Res 13, 2038–2043 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schafer, S. T. et al. The Wnt adaptor protein ATP6AP2 regulates multiple stages of adult hippocampal neurogenesis. J. Neurosci. 35, 4983–4998 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Hirose, T. et al. ATP6AP2 variant impairs CNS development and neuronal survival to cause fulminant neurodegeneration. J. Clin. Invest. 129, 2145–2162 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Leeman, D. S. et al. Lysosome activation clears aggregates and enhances quiescent neural stem cell activation during aging. Science 359, 1277–1283 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lange, C. et al. The H+ vacuolar ATPase maintains neural stem cells in the developing mouse cortex. Stem Cells Dev. 20, 843–850 (2011).

    Article  CAS  PubMed  Google Scholar 

  64. Qu, Q. et al. Lithocholic acid binds TULP3 to activate sirtuins and AMPK to slow down ageing. Nature https://doi.org/10.1038/s41586-024-08348-2 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Song, Q., Meng, B., Xu, H. & Mao, Z. The emerging roles of vacuolar-type ATPase-dependent Lysosomal acidification in neurodegenerative diseases. Transl. Neurodegener. 9, 17 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Aoto, K. et al. ATP6V0A1 encoding the a1-subunit of the V0 domain of vacuolar H+-ATPases is essential for brain development in humans and mice. Nat. Commun. 12, 2107 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Guerrini, R. et al. Phenotypic and genetic spectrum of ATP6V1A encephalopathy: a disorder of lysosomal homeostasis. Brain 145, 2687–2703 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wang, M. et al. Transformative network modeling of multi-omics data reveals detailed circuits, key regulators, and potential therapeutics for Alzheimer’s disease. Neuron 109, 257–272 (2021).

    Article  CAS  PubMed  Google Scholar 

  69. Sorrells, S. F. et al. Immature excitatory neurons develop during adolescence in the human amygdala. Nat. Commun. 10, 2748 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Nascimento, M. A. et al. Protracted neuronal recruitment in the temporal lobe of young children. Nature https://doi.org/10.1038/s41586-023-06981-x (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  71. 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 

  72. La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  74. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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 

  77. Yu, W., Clyne, M., Khoury, M. J. & Gwinn, M. Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics 26, 145–146 (2010).

    Article  CAS  PubMed  Google Scholar 

  78. Sun, Y. et al. Brain-wide neuronal circuit connectome of human glioblastoma. Nature https://doi.org/10.1038/s41586-025-08634-7 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Wen, Z. et al. Synaptic dysregulation in a human iPS cell model of mental disorders. Nature 515, 414–418 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kim, N. S. et al. Pharmacological rescue in patient iPSC and mouse models with a rare DISC1 mutation. Nat. Commun. 12, 1398 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chiang, C. H. et al. Integration-free induced pluripotent stem cells derived from schizophrenia patients with a DISC1 mutation. Mol. Psychiatry 16, 358–360 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Huang, W. K. et al. Generation of hypothalamic arcuate organoids from human induced pluripotent stem cells. Cell Stem Cell 28, 1657–1670 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Jgamadze, D. et al. Structural and functional integration of human forebrain organoids with the injured adult rat visual system. Cell Stem Cell 30, 137–152 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Gracia-Diaz, C. et al. Gain and loss of function variants in EZH1 disrupt neurogenesis and cause dominant and recessive neurodevelopmental disorders. Nat. Commun. 14, 4109 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhou, Y. et al. Autocrine Mfge8 signaling prevents developmental exhaustion of the adult neural stem cell pool. Cell Stem Cell 23, 444–452 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Schwarzkopf, M. et al. Hybridization chain reaction enables a unified approach to multiplexed, quantitative, high-resolution immunohistochemistry and in situ hybridization. Development 148, dev.199847 (2021).

    Article  Google Scholar 

  87. Sun, G. J. et al. Latent tri-lineage potential of adult hippocampal neural stem cells revealed by Nf1 inactivation. Nat. Neurosci. 18, 1722–1724 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Sun, G. J. et al. Tangential migration of neuronal precursors of glutamatergic neurons in the adult mammalian brain. Proc. Natl Acad. Sci. USA 112, 9484–9489 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Chan, S. et al. A method for manual and automated multiplex RNAscope in situ hybridization and immunocytochemistry on cytospin samples. PLoS ONE 13, e0207619 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Song, Ming and Zhou laboratories, and S. Zhu (CAS Inst. of Neurosci./CEBSIT) for discussion; K. M. Christian (U Penn) for comments; Z. Xu (CAS Inst. of Genet. & Dev. Biol.), X. Teng (ACD, Bio-Techne), L. Gu (from S.X. lab), C. Chen (from S.X. lab) and L. Xie (from Y. Chen lab at CAS Inst. of Neurosci./CEBSIT) for help on in situ hybridization experiments; B. Temsamrit (U Penn), E. LaNoce (U Penn), A. Garcia-Epelboim (U Penn), G. Alepa (U Penn), A. Angelucci (U Penn) and Y. Lu (CAS Inst. of Neurosci./CEBSIT) for laboratory support; and W. Luo, Q. Wu and Y. Chen from U Penn for providing reagents. Some schematic illustrations were created using images modified from BioRender.com. This work was supported by grants from the National Institutes of Health (R35NS137480 to G.-l.M., R35NS116843 and RF1AG079557 to H.S. and R01NS127913 to Y.S.), the Lieber Institute for Brain Development (to J.E.K., T.M.H. and D.R.W.), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (to G.-l.M.), the National Key Research and Development Program of China (2024YFA1803400 to Y. Zhou), Shanghai Science and Technology Development Funds (24QA2710400 to Y. Zhou), Shanghai Pujiang Program (23PJ1414500 to Y. Zhou), Science and Technology Commission of Shanghai Municipality (22JC1403100 to S.X.) and the National Science Foundation of China (32321003 and 32371072 to S.X.).

Author information

Author notes

  1. These authors contributed equally: Yi Zhou, Yijing Su, Qian Yang and Jiaqi Li.

Authors and Affiliations

  1. Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yi Zhou, Yijing Su, Qian Yang, Yan Hong, Lizhou Wang, Guo-li Ming & Hongjun Song

  2. Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Yi Zhou, Jiaqi Li, Taosha Gao, Yanqing Zhong, Xueting Ma, Mengmeng Jin, Xinglan Liu, Nini Yuan, Longying Yan, Cirong Liu, Zhen Liu, Zhiming Shen, Chao Li, Shengjin Xu & Jie He

  3. University of Chinese Academy of Sciences, Shanghai, China

    Yi Zhou, Jiaqi Li, Xueting Ma, Mengmeng Jin, Xinglan Liu, Cirong Liu, Zhen Liu, Shengjin Xu & Jie He

  4. Department of Oral Medicine, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yijing Su

  5. Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Benjamin C. Kennedy

  6. Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin C. Kennedy & Hongjun Song

  7. Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Angela N. Viaene

  8. Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig & Sudha K. Kessler

  9. The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig

  10. Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig

  11. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ingo Helbig & Sudha K. Kessler

  12. Lieber Institute for Brain Development, The Solomon H. Snyder Department of Neuroscience, Department of Neurology, Department of Genetic Medicine, and Department of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Joel E. Kleinman, Thomas M. Hyde & Daniel R. Weinberger

  13. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    David W. Nauen

  14. Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China

    Zhen Liu & Zhiming Shen

  15. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming & Hongjun Song

  16. Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming & Hongjun Song

  17. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming

  18. The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

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Contributions

Y. Zhou led the project and contributed to all aspects. Y.S., T.G., X.M. and L.W. contributed to computational data analyses. Q.Y. and Y.H. developed the in vitro human hippocampal neuron culture system and Q.Y. contributed to in vitro culture experiments. J.L., Y. Zhong, M.J., X.L., L.Y., C. Li, S.X. and J.H. contributed to histology analysis. B.C.K., A.N.V., I.H., S.K.K., J.E.K., T.M.H., D.W.N. and D.R.W. provided human hippocampal specimens. C. Liu provided marmoset specimens. Y. Zhong, N.Y., Z.L., Z.S. and C. Li provided macaque specimens. Y. Zhou, Y.S., G.-l.M. and H.S. conceived the project and wrote the manuscript with inputs from all authors.

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Correspondence to Yi Zhou, Guo-li Ming or Hongjun Song.

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Extended data

Extended Data Fig. 1 Identification and molecular characteristics of immature neurons in a published macaque hippocampal single-cell RNA sequencing dataset using traditional unsupervised clustering.

a, Gene module analysis of the previously annotated immature dentate granule cells (imGCs) and mature dentate granule cells (mGCs) in an adult macaque hippocampus single-nucleus RNA sequencing dataset20 using a well-recognized published list of mouse immature progeny (neuroblast and imGC)-enriched and mGC-enriched genes11. Box plots as in Fig. 3a. b, Top Gene Ontology (GO) term groups for genes enriched in imGCs (using published annotations with traditional unsupervised clustering20). Schematics created using BioRender.com. Reg., regulation.

Source data

Extended Data Fig. 2 Performance of machine learning model for macaque imGCs and feature extraction and comparison of gene weights defining imGCs in different species.

a, Measuring performance of our machine learning model for macaque datasets. Line plot showing the accuracy score of the machine learning classifier varying with decreasing regularization strength as estimated by cross-validation. The red line shows a 95% confidence interval on the estimation of the accuracy score. #Sum abs (coeffs): sum of the absolute value of regression coefficients. b, Heatmap showing expression of top gene weights in top-scoring cells of each prototype determined by our machine learning model for macaque datasets. The genes listed are the top 25 weights defining macaque imGCs. c, Venn diagram of the positive gene weights defining imGCs in humans, macaques, and mice that were generated by separate machine learning models (weights for human and mouse imGCs were generated in ref. 25). Schematics in c created using BioRender.com. Astro: astrocyte; OPC: oligodendrocyte progenitor cell; mOli: mature oligodendrocyte.

Extended Data Fig. 3 Specificity of our machine learning approach for identification of immature neurons in the macaque brain.

a, The fractions of cells with high similarity scores (pimGC ≥ 0.85) among excitatory dentate granule cell (GC), non-GC excitatory neuron, GABAergic interneuron, and non-neuronal cell clusters in various single-cell or single-nucleus RNA sequencing (scRNA-seq) datasets of the macaque hippocampus. Each dot represents data from one specimen from each study (noted by first author’s last name). Note that all imGCs identified reside in the GC clusters. b, No immature neurons were identified using our machine learning model in a scRNA-seq dataset of two 6-year macaque neocortex (one male and one female)42.

Source data

Extended Data Fig. 4 Shared molecular signatures of immature neurons in the hippocampus of different species.

a, GO network of biological processes associated with imGC-enriched genes in different species in comparison to mGCs, colored by FDR-adjusted P value. Only significantly enriched nodes are displayed (one-sided hypergeometric test, FDR-adjusted P < 0.05). The node size represents the term enrichment significance. Examples of the most substantial terms per group are shown. b, Violin plots showing normalized expression of 8 imGC-enriched genes shared across four species in imGCs and GCs (one-way Wilcoxon rank-sum test). A total of nine shared genes were identified and eight are shown here (see Fig. 3d for the plot for the ninth gene, DPYSL5). Schematics created using BioRender.com.

Extended Data Fig. 5 Enrichment of immature neuronal gene features in imGCs of different species.

a, Expression patterns of nine shared imGC-enriched genes across species in the dentate gyrus (DG) of adult mice, eight of which show enrichment in the neurogenic subgranular zone (except for PROX1). Images are from the Allen Brain in situ hybridization database46 (https://mouse.brain-map.org/). P: postnatal day. b, A second set of sample confocal immunostaining images of DPYSL5 enrichment in imGCs in the hippocampi of infant and adult humans, postnatal macaques and marmosets, and adult mice. Scale bars: 10 µm. Asterisks indicate DPYSL5+ cells among STMN1+PROX1+ imGCs (Fig. 3e). c, Venn diagram depicting the overlap of GC-enriched genes across different species when compared to other cell types within the same datasets. d, A schematic illustration of our working model. In contrast to the traditional concept that a single genetic variant can drive cross-species cellular innovations in immature neuron regulation, our study revealed substantial interspecies variance in highly expressed genes enriched in imGCs, which converged onto conserved biological processes, suggesting imGCs in different species may recruit and use species-unique molecular features to drive similar biological processes regulating neuronal development. Schematics in c and d created using BioRender.com.

Extended Data Fig. 6 A schematic of imGC-enriched feature analysis in different species.

To explore species-specific imGC molecular features in a nonbiased manner, gene expression patterns of each individual gene associated with ‘ion transport’ and ‘synaptic transmission’, the two major GO terms showing the most species-specific enrichment, were plotted (Extended Data Fig. 7). Schematics created using BioRender.com.

Extended Data Fig. 7 Unbiased examination of divergent imGC molecular features across four species.

Red-blue heatmaps depict expression patterns of each individual gene associated with ‘ion transport’ and ‘synaptic transmission’, the two major GO terms showing the most species-specific enrichment. Exemplary genes in these two categories, such as those encoding various ion channels, glutamate receptors, GABA receptors, ATPases and transmembrane proteins, among others are highlighted with square boxes. The color bar represents z scores of gene expression, scaled to range from −2 to 2. Schematics created using BioRender.com.

Extended Data Fig. 8 Role of lysosomal vacuolar-type H+-transporting ATPase in the in vitro human hippocampal immature neuron culture.

a, A schematic illustration of the experimental design. Human induced pluripotent stem cell (iPSC) lines (C65 and WTC11) were differentiated into DCX+PROX1+ hippocampal imGCs before treatment with bafilomycin A1 (BafA1) and concanamycin A (ConA), two specific blockers against lysosomal vacuolar-type H+-transporting ATPases to measure neurite growth and neuronal activities. b–e, Characterization of hippocampal imGC culture derived from two independent iPSC lines. Sample confocal images (b) and quantification of DCX and PROX1 enrichment in the hippocampal neuron in vitro culture (c,d) and its cell death level (e) with different treatments. Scale bar: 10 µm. Asterisks indicate cleaved Caspase 3 (cCasp3)+DCX+PROX1+ imGCs (b). Box colors match treatment conditions. Dots represent data from individual images; the centerline represents the mean, box edges show s.e.m. and whiskers extend to the maximum and minimum values (n = 3 cultures per condition) (c–e). None of the quantifications were statistically significant using ANOVA posthoc test; exact P values in Supplementary Table 7 (c–e).

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Summary of published scRNA-seq or single-nucleus RNA-seq datasets used in the current study.

Supplementary Table 2

Molecular weights defining immature dentate granule cells in macaques.

Supplementary Table 3

GO terms related to the positive gene weights defining macaque immature dentate granule cells.

Supplementary Table 4

Lists of GO terms associated with genes enriched in immature dentate granule cells across four species.

Supplementary Table 5

Lists of GO terms associated with genes uniquely enriched in immature dentate granule cells in four species.

Supplementary Table 6

Summary of human specimens used for histology validation.

Supplementary Table 7

Exact P values for Fig. 5 and Extended Data Fig. 8.

Source data

Source Data Fig. 1

Statistical source data for Fig. 1b.

Source Data Fig. 2

Statistical source data for Fig. 2b.

Source Data Fig. 3

Statistical source data for Fig. 3b,f.

Source Data Fig. 4

Statistical source data for Fig. 4a.

Source Data Fig. 5

Statistical source data for Fig. 5c,e,g.

Source Data Extended Data Fig. 1

Statistical source data for ED Fig. 1.

Source Data Extended Data Fig. 3

Statistical source data for ED Fig. 3a,b.

Source Data Extended Data Fig. 8

Statistical source data for ED Fig. 8c–e.

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Zhou, Y., Su, Y., Yang, Q. et al. Cross-species analysis of adult hippocampal neurogenesis reveals human-specific gene expression but convergent biological processes. Nat Neurosci 28, 1820–1829 (2025). https://doi.org/10.1038/s41593-025-02027-9

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