The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.
Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells
Open accessAbstract
The hippocampal-entorhinal system supports cognitive functions, has lifelong neurogenic capabilities in many species, and is selectively vulnerable to Alzheimer's disease. To investigate neurogenic potential and cellular diversity, we profiled single-nucleus transcriptomes in five hippocampal-entorhinal subregions in humans, macaques, and pigs. Integrated cross-species analysis revealed robust transcriptomic and histologic signatures of neurogenesis in the adult mouse, pig, and macaque but not humans. Doublecortin (DCX), a widely accepted marker of newly generated granule cells, was detected in diverse human neurons, but it did not define immature neuron populations. To explore species differences in cellular diversity and implications for disease, we characterized subregion-specific, transcriptomically defined cell types and transitional changes from the three-layered archicortex to the six-layered neocortex. Notably, METTL7B defined subregion-specific excitatory neurons and astrocytes in primates, associated with endoplasmic reticulum and lipid droplet proteins, including Alzheimer's disease-related proteins. This resource reveals cell-type- and species-specific properties shaping hippocampal-entorhinal neurogenesis and function.
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“EC-specific subtypes that clustered separately from neocortical subtypes for the same layers were often marked by expression of RELN, TOX3 and GPC5, and contained subtypes from both the lateral (L2 RELN+GPC5+) and medial (L2 TOX3+POSTN+) EC41–43”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [41]
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“Five of the miR-132 targets (BMI1, FOXO1, SPRY1, DPYSL3, CITED2) displaying the highest betweenness centrality, a proxy of network connectivity, have all shown to be involved in cell cycle, and more specifically, in neurogenesis [68–72] (Fig. 5D) (Supplementary Table 7).”
From microRNA-132 attenuates inflammation in induced pluripotent stem cell-derived microglia from Alzheimer’s disease patients (2026) · cites this paper as [70]
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“Although some brain regions relevant to AD have been studied individually at scale or jointly in samples from a few individuals8–16, a comprehensive molecular characterization of region-specific differences in AD is currently lacking”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [13]
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“Five of the miR-132 targets (BMI1, FOXO1, SPRY1, DPYSL3, CITED2) displaying the highest betweenness centrality, a proxy of network connectivity, have all shown to be involved in cell cycle, and more specifically, in neurogenesis [68–72] (Fig. 5D) (Supplementary Table 7).”
From microRNA-132 attenuates inflammation in induced pluripotent stem cell-derived microglia from Alzheimer’s disease patients (2026) · cites this paper as [70]
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“Our data indicate that while there are common ‘core’ miR-132 targets across conditions, possibly involved in ‘housekeeping’ cellular functions, like cell cycle [68–72], there are also context-dependent targets, specific to healthy control or sAD iMGs.”
From microRNA-132 attenuates inflammation in induced pluripotent stem cell-derived microglia from Alzheimer’s disease patients (2026) · cites this paper as [70]
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“Some groups have reported absent or minimal hippocampal neurogenesis in the mature brain using double immunofluorescence targeting markers expressed by neuronal cells at different maturational stages95, 96 and single nucleus transcriptomics97.”
From Dysregulation of adult hippocampal neuroplasticity in major depression: Pathogenesis and therapeutic implications (2022) · cites this paper as [97]
In the citing paper
“The explanatory power of neurogenesis in depression and antidepressant drug effects is complicated by the fact that neurogenesis is only observed in the dentate gyrus, putting a severe limit on the possible depression symptom domains it can impact, and by recent findings that neurogenesis may be very limited or absent in humans [102].”
From Plasticity of synapses and reward circuit function in the genesis and treatment of depression (2023) · cites this paper as [102]
In the citing paper
“We also conducted integration of our ATAC data and scATAC-seq data from human brain samples42 (Fig. 5b), and our RNA data with adult human brain snRNA-seq data43, to show the dominant cell identities and states.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [43]
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Neurogenesis in the adult human hippocampus
Eriksson, P. S., Perfilieva, E., Björk-Eriksson, T., Alborn, A.-M., Nordborg, C., Peterson, D. A., & Gage, F. H. (1998) · Nature Medicine · doi:10.1038/3305 · Cited by 6,369
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
A statistical model for identifying proteins by tandem mass spectrometry.
Nesvizhskii, A., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,902
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- Xu, 2006, ProLuCID, a fast and sensitive tandem mass spectra-based protein identification program, Mol. Cell. Proteomics, 5, S174
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Items 301–308 of 308
Hu, L., Wang, Y.-M., Xie, J., Gu, L., Si, S., Liu, Z.-X., Long, L., Yin, T., Esmailizadeh, A., Wu, F., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2602503123
Self-citationSystematic benchmarking and optimal strategy selection of cross-species integration methods
Wang, R., Zheng, J., Mao, C., Zhang, Y., Ding, Z., & Wang, G. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag490
Slow renewal and fast reset shape adult hippocampal plasticity across two timescales
Zhu, G., Liu, K., Wang, Z., Ge, M., Wang, Y., & Xu, S. (2026) · Trends Open · doi:10.1016/j.treopn.2026.09.005
Can your brain grow new neurons? How researchers are solving a century-old puzzle
Lenharo, M. (2026) · Nature · doi:10.1038/d41586-026-03132-w
Leon, J., Yoshinaga, S., Koya, I., Palafox, G., Ando, Y., Kubo, K., Moody, J., Takao, M., Carninci, P., Shin, J. W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.07.757387
Ding, Q., Wang, P., Yu, H., Xuan, Z., Yang, C., & Wang, K. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1952952
Multiple target strategies in Alzheimer’s disease therapy
Ma, X., Sun, H., & Ye, J. (2026) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2026.10.018
Tissue coordination in ageing and disease
Cleal, K. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103422
Metadata
- Authors
- Franjic, D., Skarica, M., Ma, S., Arellano, J. I., Tebbenkamp, A. T. N., Choi, J., Xu, C., Li, Q., Morozov, Y. M., Andrijevic, D., Vrselja, Z., Spajic, A., Santpere, G., Li, M., Zhang, S., Liu, Y., Spurrier, J., Zhang, L., Gudelj, I., Rapan, L., Takahashi, H., Huttner, A., Fan, R., Strittmatter, S. M., Sousa, A. M. M., Rakic, P., & Sestan, N.
- Affiliations
- Yale University (US)
- Universitat Pompeu Fabra (ES)
- Yale Cancer Center (US)
- ORCID
- Franjic, Daniel
- Ma, Shaojie
- Arellano, Jon I.
- Tebbenkamp, Andrew T.N.
- Choi, Jinmyung
- Xu, Chuan
- Li, Qian
- Morozov, Yury M.
- Andrijevic, David
- Spajic, Ana
- Santpere, Gabriel
- Li, Mingfeng
- Liu, Yang
- Spurrier, Joshua
- Zhang, Le
- Gudelj, Ivan
- Rapan, Lucija
- Takahashi, Hideyuki
- Huttner, Anita
- Fan, Rong
- Strittmatter, Stephen M.
- Rakic, Pasko
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 110
- Issue
- 3
- Pages
- 452-469.e14
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC8813897
- OpenAlex
- W3212895337
- Cited by
- 308
- References
- 130
- Citations by year
- 2026: 48 · 2025: 68 · 2024: 58 · 2023: 74 · 2022: 58
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neuron (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-nd)
- Funders
- “la Caixa” Foundation (100010434)
- Instituto de Salud Carlos III (MS20/00064)
- European Social Fund
- Agencia Estatal de Investigación (PID2019-104700GA-I00)
- Fundacion La Caixa
- National Institutes of Health (MH103339, MH106934, MH109904, MH110926)
- Topics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- adult neurogenesis, single-nucleus transcriptomics, cross-species comparison, doublecortin, METTL7B, excitatory neurons, astrocytes, lipid droplets, endoplasmic reticulum, granule cells
- MeSH terms
- Doublecortin Protein, Animals, Hippocampus, Humans, Macaca, Swine, Mice, Neurogenesis, Transcriptome
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