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Conserved cell types with divergent features in human versus mouse cortex
Open accessPublished version of a preprint
Abstract
Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.
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“As a third example of the great elaboration and specialization associated with consciousness, the more-advanced animal brains have the largest numbers of neuron types, with highly complex interactions (Strausfeld, 2012; Feinberg and Mallatt, 2016a; Hodge et al., 2019).”
From Phenomenal Consciousness and Emergence: Eliminating the Explanatory Gap (2020) · cites this paper as Hodge et al., 2019
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“nuclei were sorted using a standard gating strategy to exclude multiplets24”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [24]
In the citing paper
“8 out of 14 modules showed greater intra-module correlation than a comparable random gene set in the adult human brain dataset from Hodge et al (30) (Fig. 4B).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [30]
In the citing paper
“In addition, multiplex FISH is already being used to measure the proportions of transcriptomic types within a given brain tissue (Hodge et al., 2019; Moffitt et al., 2018; Zhang et al., 2020).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Hodge et al., 2019
In the citing paper
“Single-cell RNA-sequencing (scRNA-seq) studies have systematically characterized and classified cell types in individual regions of isocortex and hippocampus”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Hodge et al., 2019
In the citing paper
“The application of single-cell RNA sequencing (scRNA-seq) to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types that are consistent overall with knowledge from decades of anatomical, physiological and developmental studies but with an unmatched level of granularity7–11.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [10]
In the citing paper
“Substantiating previous studies9,10, our multimodal cross-species study of the primary motor cortex suggests that a general principle of cortical cell-type organization is its hierarchical relationship, whereby high-level classes linked by major branches comprise progressively finer subpopulations connected by minor branches.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [10]
In the citing paper
“Comparing human with mouse and non-human primates reveals both conserved and distinct cell type signatures, including species-specific differences in the proportions of cell populations and in gene expression within homologous cell classes and types (Bakken et al., 2020; Hodge et al., 2019; Krienen et al., 2020).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Hodge et al., 2019
In the citing paper
“Evolutionary conservation and divergence provide insight into the gene regulatory programs that were selected for their impact on cognitive function (Bakken et al., 2020; Hodge et al., 2019; Krienen et al., 2020).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Hodge et al., 2019
In the citing paper
“We observed numerous genes with categorical changes in cell-type-specific expression patterns (including NTNG1, IQGAP2 and TRPC3) and, in rare cases, genes that were a specific marker in one cell class in some species but not detected at all in the interneurons of the other species11”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [11]
In the citing paper
“which suggests that the genes that encode these markers stably delineate a core repertoire of neocortical interneuron types in these species11,15,18”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [11]
In the citing paper
“The expansion of this LAMP5+ LHX6+ cell type throughout the neocortex in diverse primates probably explains a recent observation11 that this cell type is much more abundant in the human temporal lobe than in the mouse V1.”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [11]
In the citing paper
“they obtained 45 human interneuron clusters, whereas our marmoset data resolved to 22 clusters”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [11]
In the citing paper
“However, in agreement with other reports11, these types were inconsistently observed across individual animals and regions, likely due to laminar location and low abundance, and were not analysed further.”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [11]
In the citing paper
“Notably, this approach provides the means to evaluate and compare the circuit contributions and function of PV cINs across species, regardless of their genetic accessibility44,45.”
From Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans (2020) · cites this paper as [44]
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“Gene Expression Data We used the gene expression data of 15,928 human brain nuclei from middle temporal gyrus of human cortex (Hodge et al., 2019).”
MethodDo Animals Feel Pain in a Morally Relevant Sense?
Miller, C. (2020) · Philosophia · doi:10.1007/s11406-020-00254-x · Cited by 9
Review: In vitro Cell Platform for Understanding Developmental Toxicity
Xie, J., Wettschurack, K. W., & Yuan, C. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.623117 · Cited by 11
Migratory cortical interneuron-specific transcriptome abnormalities in schizophrenia
Park, J. M., Liu, D., Park, G.-H., Noh, H., Ni, P., Yin, C., Huang, W., & Chung, S. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.02.054 · Cited by 9
Webster, J. M., Grabowski, T. J., Madhyastha, T. M., Gibbons, L. E., Keene, C. D., & Latimer, C. S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.693242 · Cited by 9
Self-citationValori, C. F., & Neumann, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.724891 · Cited by 9
Rubinstein, C. D., McLean, D. T., Lehman, B. P., Meudt, J. J., Schomberg, D. T., Krentz, K. J., Reichert, J. L., Meyer, M. B., Adams, M., Konsitzke, C. M., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.721045 · Cited by 9
Deep brain electrophysiology in freely moving sheep
Perentos, N., Krstulović, M., & Morton, A. J. (2022) · Current Biology · doi:10.1016/j.cub.2021.12.035 · Cited by 8
Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data
Dong, X., Chowdhury, S., Victor, U., Li, X., & Qian, L. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3173587 · Cited by 11
Design, challenges, and the potential of transcriptomics to understand social behavior
Lim, W. K., & Mathuru, A. S. (2020) · Current Zoology · doi:10.1093/cz/zoaa007 · Cited by 11
Transcriptional Cartography Integrates Multiscale Biology of the Human CortexPreprint
Wagstyl, K., Adler, S., Seidlitz, J., Vandekar, S., Mallard, T. T., Dear, R., DeCasien, A. R., Satterthwaite, T. D., Liu, S., Vértes, P. E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.13.495984 · Cited by 9
Gao, Y., van Velthoven, C. T. J., Lee, C., Thomas, E. D., Bertagnolli, D., Carey, D., Casper, T., Chakka, A. B., Chakrabarty, R., Clark, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.02.616246 · Cited by 9
Self-citationHistone bivalency in CNS developmentPreprint
Mätlik, K., Govek, E., & Hatten, M. E. (2025) · Genes & Development · doi:10.1101/gad.352306.124 · Cited by 10
“…the high-resolution classification of hundreds of distinct cell types and developmental states in the mammalian brain based on their transcriptional profiles (Saunders et al. 2018; Tasic et al. 2018; Hodge et al. 2019; La Manno et al. 2021; Yao et al. 2021; Braun et al. 2023; Siletti et al. 2023).”
Rodríguez-Romaguera, J., Namboodiri, V. M. K., Basiri, M. L., Stamatakis, A. M., & Stuber Garret, D. (2020) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039792 · Cited by 9
“…neuronal subpopulations activated by an acute stimulus such as an acute exposure to morphine, stress or a a social stimulus can be discovered by examining the distribution of immediate-early gene expression across neuronal subtypes (Hrvatin et al., 2018; Kim et al., 2019; Wu et al., 2017).”
BackgroundAnalytical Workflows for Single‐Cell Multiomic Data Using the BD Rhapsody Platform
Li, W., Bazaz, S. R., Mayoh, C., & Salomon, R. (2024) · Current Protocols · doi:10.1002/cpz1.963 · Cited by 9
Technical and biological sources of noise confound multiplexed enhancer AAV screening
Hunker, A. C., Mich, J. K., Taskin, N., Torkelson, A., Cardenas, T., Lalanne, J., Mahoney, J. T., Bertagnolli, D., Chakka, A. B., Chakrabarty, R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72147-8 · Cited by 8
“Single-nucleus isolations were prepped as described previously 25 .”
Self-citationde Geus, M. B., Wu, C., Dodge, H. H., Leslie, S. N., Wang, W., Lam, T. T., Kahle, K. T., Chan, D., Kivisäkk, P., Nairn, A. C., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213375 · Cited by 8
Mapping the developmental profile of ventricular zone–derived neurons in the human cerebellum
Erickson, A. W., Tan, H., Hendrikse, L. D., Millman, J., Thomson, Z., Golser, J., Khan, O. A., He, G., Bach, K., Mishra, A. S., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415425122 · Cited by 9
Endocytome profiling uncovers cell-surface protein dynamics underlying neuronal connectivity
McLaughlin, C. N., Ji, H., Dong, K. X., Xu, C., Wong, K. K. L., Li, Z., Luginbuhl, D. J., Xu, C., Lyu, C., Qin, W., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.01.027 · Cited by 8
Tools and tactics for studying alternative splicing
Sousa‐Luís, R., & Carmo‐Fonseca, M. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00952-4 · Cited by 8
“For samples where whole-cell dissociation is impractical, such as frozen tissues or large, fragile cell types, single-nucleus RNA-seq provides an alternative by profiling transcriptomes from isolated nuclei 81 .”
Fu, Y., Zhou, Y., Zhang, Y., Zhao, B., Zhang, X.-L., Zhang, W., Lu, Y., Lu, A., Zhang, J., & Zhang, J. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-04721-z · Cited by 8
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“, 2018) and the human adult middle temporal gyrus (Hodge et al., 2019) (Figure 1E).”
BackgroundWu, B., Ge, Y., Zhang, W., Chen, S.-D., Xiang, S., Zhang, Y.-R., Ou, Y., Jiang, Y., Tan, L., Cheng, W., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119928 · Cited by 8
“Human cell types were defined in scRNA-seq resources(45-51).”
BackgroundInsights into Neurodegeneration in Parkinson's Disease from Single‐Cell and Spatial Genomics
Kamath, T., & Macosko, E. Z. (2023) · Movement Disorders · doi:10.1002/mds.29374 · Cited by 7
“Application of these tools to the human brain has focused on uncovering processes and cell types that underlie disease.(64) Recent efforts have begun to use these high-dimensional analyses to provide insights into the molecular basis of neurodegenerative disease.”
BackgroundAdams, J. W., Vinokur, A., de Souza, J. S., Austria, C., Guerra, B. S., Herai, R. H., Wahlin, K. J., & Muotri, A. R. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113867 · Cited by 8
“29 Despite this progress, however, recapitulation of aspects of human cortical development by animal models may be suboptimal, 30 a subtle yet perhaps salient difference given the prominent association between humans and social expression.”
BackgroundEvolution is in the details: Regulatory differences in modern human and Neanderthal
Barker, H. R., Parkkila, S., & Tolvanen, M. E. E. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.052 · Cited by 10
Medina-Cano, D., Islam, M. T., Petrova, V., Dixit, S., Balic, Z., Yang, M. G., Stadtfeld, M., Wong, E. S. W., & Vierbuchen, T. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.08.001 · Cited by 9
Modeling schizophrenia with iPS cell technology and disease mouse models
Nakazawa, T. (2021) · Neuroscience Research · doi:10.1016/j.neures.2021.08.002 · Cited by 9
“mice (Hodge et al., 2019), there are several genes with different expression levels between species and the presence of several human-specific genes (Suzuki et al.”
BackgroundProdromidou, K., & Matsas, R. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-04063-7 · Cited by 11
“In addition, divergent genes appear functionally relevant as they are associated with connectivity and signaling [21].”
BackgroundDeleanu, R., Ceafalan, L. C., & Dricu, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413211 · Cited by 8
“…adult cortex samples showing clusters and subclusters of neuronal populations (excitatory neurons—ENs and inhibitory neurons—INs), as well as small non-neuronal clusters of astrocytes (Astros), oligodendrocytes (Oligos) and oligodendrocyte progenitor cells (OPCs; adapted from Hodge et al., 2019).”
BackgroundInfluentialMartini, L., Bardini, R., & Carlo, S. D. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669888 · Cited by 8
Evolution of homology: From archetype towards a holistic concept of cell type
Rusin, L. Y. (2023) · Journal of Morphology · doi:10.1002/jmor.21569 · Cited by 8
“…et al., 2020), as well as the most densely sampled Craniota (Briggs et al., 2018; J. Cao et al. 2019, 2020; Farrell et al., 2018; Han et al., 2020; Hodge et al., 2019; Pijuan‐Sala et al., 2019; Regev et al., 2017; Shafer et al., 2022; TheTabula Muris Consortium, 2018; Tosches et al., 2018; D.…”
BackgroundKearns, N. A., Lobo, M., Genga, R. M. J., Abramowitz, R. G., Parsi, K. M., Min, J., Kernfeld, E. M., Huey, J. D., Kady, J., Hennessy, E., et al. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.08.024 · Cited by 8
Introduction: What Are Dendritic Spines?
Rasia‐Filho, A. A., Calcagnotto, M. E., & Halbach, O. V. B. U. (2023) · Advances in neurobiology · doi:10.1007/978-3-031-36159-3_1 · Cited by 10
Shen, K., Zeppillo, T., & Limón, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-68165-1 · Cited by 8
Selective Neuronal Vulnerability in Alzheimer’s Disease: A Modern Holy Grail
Rexach, J. E., & Geschwind, D. H. (2020) · Neuron · doi:10.1016/j.neuron.2020.08.018 · Cited by 7
Watanabe, M., Haney, J. R., Vishlaghi, N., Turcios, F., Buth, J. E., Gu, W., Collier, A. J., Miranda, O. A., Chen, D., Sabri, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.13.875773 · Cited by 8
“However, it has become increasingly clear that there are numerous differences between the rodent and human brain (Florio and Huttner, 2014; Hodge et al., 2019),”
BackgroundAevermann, B. D., Zhang, Y., Novotny, M. A., Bakken, T. E., Miller, J. A., Hodge, R. D., Lelieveldt, B. P. F., Lein, E. S., & Scheuermann, R. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.23.308932 · Cited by 9
“Unscaled heatmaps of mean gene expression per cluster for both the Hodge and NS-Forest marker sets (Figure 5A) demonstrate that both are characterized by binary expression patterns, having a higher expression along the diagonal versus off-diagonal; however, the Hodge markers have an overall lower mean expression level…”
MethodInfluentialSelf-citationScaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterizationPreprint
Lee, B. R., Budzillo, A., Hadley, K., Miller, J. A., Jarsky, T., Baker, K. S., Hill, D. D., Kim, L., Mann, R., Ng, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.04.369082 · Cited by 8
Self-citationTian, W., Zhou, J., Bartlett, A., Zeng, Q., Liu, H., Castanon, R., Kenworthy, M. A., Altshul, J., Valadon, C., Aldridge, A. I., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.30.518285 · Cited by 8
Self-citationGanz, J., Luquette, L. J., Bizzotto, S., Bohrson, C. L., Jin, H., Miller, M. B., Zhou, Z., Galor, A., Park, P. J., & Walsh, C. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.14.523958 · Cited by 8
Repetitive head impacts induce neuronal loss and neuroinflammation in young athletesPreprint
Butler, M. L. M. D., Pervaiz, N., Breen, K., Calderazzo, S. M., Ypsilantis, P., Wang, Y., Breda, J. C., Mazzilli, S. A., Nicks, R., Spurlock, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.26.586815 · Cited by 8
Spatial Single-cell Analysis Decodes Cortical Layer and Area SpecificationPreprint
Qian, X., Coleman, K., Jiang, S., Kriz, A. J., Marciano, J. H., Luo, C., Cai, C., Manam, M. D., Caglayan, E., Otani, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.05.597673 · Cited by 8
Metadata
- Authors
- Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., Yao, Z., Eggermont, J., Höllt, T., Levi, B. P., Shehata, S. I., Aevermann, B., Beller, A., Bertagnolli, D., Brouner, K., Casper, T., Cobbs, C., Dalley, R., Dee, N., Ding, S.-L., Ellenbogen, R. G., Fong, O., Garren, E., Goldy, J., Gwinn, R. P., Hirschstein, D., Keene, C. D., Keshk, M., Ko, A. L., Lathia, K., Mahfouz, A., Maltzer, Z., McGraw, M., Nguyen, T. N., Nyhus, J., Ojemann, J. G., Oldre, A., Parry, S., Reynolds, S., Rimorin, C., Shapovalova, N. V., Somasundaram, S., Szafer, A., Thomsen, E. R., Tieu, M., Quon, G., Scheuermann, R. H., Yuste, R., Sunkin, S. M., Lelieveldt, B., Feng, D., Ng, L., Bernard, A., Hawrylycz, M., Phillips, J. W., Tasic, B., Zeng, H., Jones, A. R., Koch, C., & Lein, E. S.
- Affiliations
- Allen Institute for Brain Science (US)
- University of California, Davis (US)
- Leiden University Medical Center (NL)
- J. Craig Venter Institute (US)
- University of Washington (US)
- Swedish Medical Center (US)
- Columbia University (US)
- Corresponding author
- Lein, Ed S.
- ORCID
- Hodge, Rebecca D.
- Bakken, Trygve E.
- Miller, Jeremy A.
- Smith, Kimberly A.
- Barkan, Eliza R.
- Graybuck, Lucas T.
- Close, Jennie L.
- Long, Brian
- Johansen, Nelson
- Yao, Zizhen
- Eggermont, Jeroen
- Höllt, Thomas
- Levi, Boaz P.
- Shehata, Soraya I.
- Aevermann, Brian
- Beller, Allison
- Bertagnolli, Darren
- Brouner, Krissy
- Casper, Tamara
- Cobbs, Charles
- Dalley, Rachel
- Dee, Nick
- Ding, Song-Lin
- Ellenbogen, Richard G.
- Fong, Olivia
- Goldy, Jeff
- Gwinn, Ryder P.
- Hirschstein, Daniel
- Keene, C. Dirk
- Ko, Andrew L.
- Lathia, Kanan
- Mahfouz, Ahmed
- Maltzer, Zoe
- Nguyen, Thuc Nghi
- Nyhus, Julie
- Ojemann, Jeffrey G.
- Oldre, Aaron
- Rimorin, Christine
- Shapovalova, Nadiya V.
- Somasundaram, Saroja
- Szafer, Aaron
- Thomsen, Elliot R.
- Tieu, Michael
- Quon, Gerald
- Scheuermann, Richard H.
- Yuste, Rafael
- Sunkin, Susan M.
- Lelieveldt, Boudewijn
- Feng, David
- Ng, Lydia
- Bernard, Amy
- Hawrylycz, Michael
- Phillips, John W.
- Tasic, Bosiljka
- Zeng, Hongkui
- Lein, Ed S.
- Venue
- Nature
- Published
- 2019-08-21
- Volume
- 573
- Issue
- 7772
- Pages
- 61-68
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6919571
- OpenAlex
- W2969948459
- Cited by
- 1,775
- References
- 89
- Citations by year
- 2026: 199 · 2025: 242 · 2024: 275 · 2023: 269 · 2022: 235 · 2021: 289 · 2020: 212 · 2019: 43
- FWCI
- 64.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- University of Washington
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (12720, 12721)
- National Institutes of Health (5u01mh114812-03, MH114812-02)
- National Institute of Mental Health (U01MH105982, U01MH114812, U19MH114830)
- National Institute of General Medical Sciences (T32GM008497)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- human cerebral cortex, middle temporal gyrus, cortical cell types, excitatory neurons, inhibitory neurons, single-nucleus RNA sequencing, human-mouse comparison, cortical laminar organization, species differences, neuronal morphology, gene expression
- MeSH terms
- RNA-Seq, Adolescent, Adult, Aged, Animals, Astrocytes, Cerebral Cortex, Biological Evolution, Female, Humans, Male, Middle Aged, Neural Inhibition, Neurons, Species Specificity, Principal Component Analysis, Mice, Young Adult, Single-Cell Analysis, Transcriptome
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