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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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In the citing paper
“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
“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
The 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
“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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Microglia across evolution: from conserved origins to functional divergence
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“Single-cell RNA-sequencing datasets revealed particularities in gene expression, morphology, proportions, and laminar distributions of cell types in our cerebral cortex (Hodge et al., 2019).”
BackgroundDifferential analysis of binarized single-cell RNA sequencing data captures biological variation
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MethodHuman iPSC-Derived Neurons with Reliable Synapses and Large Presynaptic Action Potentials
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BackgroundEndogenous Acetylcholine and Its Modulation of Cortical Microcircuits to Enhance Cognition
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Premeaux, T. A., Mediouni, S., Léda, A. R., O’Brien, R. L. F., Valente, S. T., Fine, H. A., Nixon, D. F., & Ndhlovu, L. C. (2021) · mBio · doi:10.1128/mbio.00680-21 · Cited by 22
“mouse models have been a useful tool for the understanding of molecular and cellular mechanisms and development of therapeutics for neuropathological infections (50).”
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Prediction error neurons in mouse cortex are molecularly targetable cell typesPreprint
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STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning
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Lu, C., Wei, Y., Abbas, M., Hasi, A., Wang, E., Meng, Z., & Zhang, R. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031479 · Cited by 20
“One potential solution is to filter out one-to-many homologous genes and consider only one-to-one genes as candidates for cell type identification [90–92].”
BackgroundDiscovery of optimal cell type classification marker genes from single cell RNA sequencing data
Liu, A., Peng, B., Pankajam, A. V., Duong, T. E., Pryhuber, G., Scheuermann, R. H., & Zhang, Y. (2024) · BMC Methods · doi:10.1186/s44330-024-00015-2 · Cited by 23
Self-citationBrain-wide alterations revealed by spatial transcriptomics and proteomics in COVID-19 infection
Zhang, T., Li, Y., Pan, L., Sha, J., Bailey, M., Faure-Kumar, E., Williams, C. K., Wohlschlegel, J., Magaki, S., Niu, C., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00730-z · Cited by 20
“Consistent with snRNA-seq studies showing NPR1 expression in astrocytes, deep-layer neurons and endothelial cells 40,75,76 , we observed increased NRP1 immunoreactivity in those cells in patients with COVID-19.”
Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution
Wei, S., Luo, M., Wang, P., Chen, R., Jin, X., Xu, C., Li, C., Lin, X., Xu, Z., Liu, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62793-9 · Cited by 20
“We conducted a comprehensive census of cellular diversity and spatialarrangements, which facilitated the delineationof the cellular layer distributions of cell types and provided much more spatial and transcriptomic information than traditional approaches such as RNAscope 4,15,58 .”
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Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies
Bloomingdale, P., Karelina, T., Cirit, M., Muldoon, S. F., Baker, J. T., McCarty, W. J., Geerts, H., & Macha, S. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12607 · Cited by 23
Louth, E. L., Jørgensen, R. L., Korshoej, A. R., Sørensen, J. C. H., & Capogna, M. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.668980 · Cited by 21
Unroe, K. A., Glover, M. E., Shupe, E. A., Feng, N., & Clinton, S. M. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.07.007 · Cited by 20
Screening of Alzheimer’s Disease With Multiwavelength Stokes Polarimetry in a Mouse Model
Borovkova, M. A., Sieryi, O., Lopushenko, I. V., Kartashkina, N. L., Pahnke, J., Bykov, A. V., & Meglinski, I. V. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3129700 · Cited by 20
“Though there are many similarities between the corresponding regions of the human and mouse brain in terms of architecture and cell types, there are also substantial differences [37], [38].”
BackgroundWhat Makes Organoids Good Models of Human Neurogenesis?
Yang, Q., Hong, Y., Zhao, T., Song, H., & Ming, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.872794 · Cited by 26
Audshasai, T., Coles, J. A., Panagiotou, S., Khandaker, S., Scales, H. E., Kjos, M., Baltazar, M., Vignau, J., Brewer, J. M., Kadioglu, A., et al. (2022) · mBio · doi:10.1128/mbio.01024-22 · Cited by 21
Mendieta, J. P., Sangra, A., Yan, H., Minow, M. A. A., & Schmitz, R. J. (2023) · The Plant Journal · doi:10.1111/tpj.16351 · Cited by 21
Zhang, Y., Miller, J. A., Park, J., Lelieveldt, B. P. F., Long, B., Abdelaal, T., Aevermann, B. D., Biancalani, T., Comiter, C. S., Dzyubachyk, O., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-36638-8 · Cited by 22
Self-citationGlial Cell Modulation of Dendritic Spine Structure and Synaptic Function
Rasia‐Filho, A. A., Calcagnotto, M. E., & Halbach, O. V. B. U. (2023) · Advances in neurobiology · doi:10.1007/978-3-031-36159-3_6 · Cited by 21
scGHOST: identifying single-cell 3D genome subcompartments
Xiong, K., Zhang, R., & Ma, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02230-9 · Cited by 21
Wang, H., Chen, J.-Y., Li, Y., Zhang, X., Liu, X., Lu, Y., He, H., Li, Y., Chen, H., Liu, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51761-4 · Cited by 20
ten Donkelaar, H. J., & Fritzsch, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-26098-8_2 · Cited by 20
Cellular and Synaptic Diversity of Layer 2-3 Pyramidal Neurons in Human IndividualsPreprint
Planert, H., Mittermaier, F. X., Grosser, S., Fidzinski, P., Schneider, U. C., Radbruch, H., Onken, J., Holtkamp, M., Schmitz, D., Alle, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467668 · Cited by 21
Brain Data Standards - A method for building data-driven cell-type ontologies
Tan, S. Z. K., Kır, H., Aevermann, B. D., Gillespie, T., Harris, N. L., Hawrylycz, M., Jorstad, N. L., Lein, E. S., Matentzoglu, N., Miller, J. A., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01886-2 · Cited by 18
“In order to support mapping of data previously annotated with PCL and references to PCL terms in previous publications 3,11,28 , we mapped all original IDs to current OBO standard persistent URLs, using OBO standard mappings for obsoleted terms.”
MethodSelf-citationMich, J. K., Sunil, S., Johansen, N., Martinez, R. A., Liu, J., Gore, B. B., Mahoney, J. T., Leytze, M., Ben‐Simon, Y., Bertagnolli, D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.20.558718 · Cited by 21
Self-citationMagnussen, J. H. (2025) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.70061 · Cited by 21
Homologous laminar organization of the mouse and human subiculum
Bienkowski, M. S., Sepehrband, F., Kurniawan, N. D., Stanis, J., Korobkova, L., Khanjani, N., Clark, K. A., Hintiryan, H., Miller, C. A., & Dong, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-81362-w · Cited by 21
Single-Cell Transcriptomics Supports a Role of CHD8 in Autism
Hoffmann, A., & Spengler, D. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063261 · Cited by 20
Gao, S., Wu, Z., Kannan, J., Mathews, L., Feng, X., Kajigaya, S., & Young, N. S. (2021) · Cells · doi:10.3390/cells10050973 · Cited by 19
“and no major missing homologous classes [10].”
Animal models and human tissue compared to better understand and treat the epilepsies
Milior, G., Morin‐Brureau, M., Pallud, J., Miles, R., & Huberfeld, G. (2023) · Epilepsia · doi:10.1111/epi.17552 · Cited by 20
“11 Transcriptomic analysis of single, cortical microglia revealed comparable, if subtly different, signatures for human and mouse cells.”
Kim, B. I., Kim, D., Schulmann, A., Patel, Y., Caban-Rivera, C., Kim, P., Jambhale, A. D., Johnson, K. R., Feng, N., Xu, Q., et al. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0830-22.2023 · Cited by 20
Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex
Wei, Y., Nandi, A., Jia, X., Siegle, J. H., Denman, D. J., Lee, S. Y., Buchin, A., Geit, W. A. H. V., Mosher, C. P., Olsen, S. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37844-8 · Cited by 18
Glaser, A. K., Chandrashekar, J. V., Vasquez, S., Arshadi, C., Javeri, R., Ouellette, N. D., Jiang, X., Baka, J., Kovács, G., Woodard, M., et al. (2025) · eLife · doi:10.1101/2023.06.08.544277 · Cited by 19
“Large-scale projects based on single cell transcriptomics in the mouse and human brain have revealed a great diversity of neuron types (Hodge et al. 2019; Tasic et al. 2018; Zizhen Yao et al. 2023).”
Self-citationNeuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia
Wang, M., Yan, H., Tian, X., Yue, W., Liu, Y., Fan, L., Hu, K., Sun, Y., Zhao, Y., Lou, J., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00110-3 · Cited by 20
Abdulla, A., Yan, H., Chen, S., Wu, L., Chen, X.-S., Zhang, Y., Zhang, M., Zhuang, T. Y., Ahmad, K. Z., Lin, J., et al. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116754 · Cited by 21
From animal models to human individuality: Integrative approaches to the study of brain plasticity
Hille, M., Kühn, S., Kempermann, G., Bonhoeffer, T., & Lindenberger, U. (2024) · Neuron · doi:10.1016/j.neuron.2024.10.006 · Cited by 23
Hu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 19
Self-citationMechanistic Insights in NeuroD Potentiation of Mineralocorticoid Receptor Signaling
van Weert, L. T. C. M., Buurstede, J. C., Sips, H. C. M., Mol, I. M., Puri, T. A., Damsteegt, R., Roozendaal, B., Sarabdjitsingh, R. A., & Meijer, O. C. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20071575 · Cited by 19
An era of single-cell genomics consortia
Ando, Y., Kwon, A. T.-J., & Shin, J. W. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0409-x · Cited by 20
Similarities and dissimilarities between psychiatric cluster disorders
Smail, M. A., Wu, X., Henkel, N., Eby, H., Herman, J. P., McCullumsmith, R. E., & Shukla, R. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01030-3 · Cited by 17
Neurological pathogenesis of SARS-CoV-2 (COVID-19): from virological features to clinical symptoms
Kase, Y., & Okano, H. (2021) · Inflammation and Regeneration · doi:10.1186/s41232-021-00165-8 · Cited by 20
“org) [42], ACE2 expression does not appear in any neuronal subtype according to the single-nucleus RNA-sequencing profile of the middle temporal gyrus of the human cortex.”
BackgroundVawter, M. P., Schulmann, A., Alhassen, L., Alhassen, W., Hamzeh, A. R., Sakr, J., Pauluk, L., Yoshimura, R. F., Wang, X., Dai, Q., et al. (2019) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyz051 · Cited by 19
Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolutionPreprint
Marinaro, F., Haneklaus, M., Zhang, Z., Strano, A., Evans, L., Handfield, L., Ryan, N. S., Fox, N. C., Hemberg, M., Ramanathan, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.14.202317 · Cited by 18
Bayesian segmentation of spatially resolved transcriptomics dataPreprint
Petukhov, V., Soldatov, R., Khodosevich, K., & Kharchenko, P. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.05.326777 · Cited by 21
Analyzing alternative splicing in Alzheimer’s disease postmortem brain: a cell-level perspective
Farhadieh, M., & Ghaedi, K. (2023) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2023.1237874 · Cited by 18
“After clustering, automatic cell type prediction was performed on cell clusters using a cell type enrichment weighted expression algorithm against previously generated Allen brain reference datasets within EWCE under scFlow functions in R (Hodge et al., 2019).”
MethodChim, S. M., Howell, K., Kokkosis, A. G., Zambrowicz, B., Karalis, K. P., & Pavlopoulos, E. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16101314 · Cited by 22
Single-cell sequencing and organoids: applications in organ development and disease
Li, T., Yin, J., Hao, Y., Gao, W., Li, Q., Feng, Q., Tao, B., Hao, M., Liu, Y., Lin, C., et al. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00364-6 · Cited by 20
iPSC toolbox for understanding and repairing disrupted brain circuits in autism
Chiola, S., Edgar, N. U., & Shcheglovitov, A. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01288-7 · Cited by 18
Ni, R. (2021) · Pharmaceuticals · doi:10.3390/ph14111179 · Cited by 18
Proteome of the Luminal Surface of the Blood–Brain Barrier
Hill, J. J., Haqqani, A. S., & Stanimirovic, D. B. (2021) · Proteomes · doi:10.3390/proteomes9040045 · Cited by 18
A multiparametric calcium signal screening platform using iPSC-derived cortical neural spheroids.
Boutin, M. E., Strong, C. E., Hese, B. V., Hu, X., Itkin, Z., Chen, Y., LaCroix, A. S., Gordon, R., Guicherit, O. M., Carromeu, C., et al. (2022) · SLAS DISCOVERY · doi:10.1016/j.slasd.2022.01.003 · Cited by 18
Small-molecule screen reveals pathways that regulate C4 secretion in stem cell-derived astrocytes
Rapino, F. A., Natoli, T. E., Limone, F., O’Connor, E., Blank, J., Tegtmeyer, M., Chen, W., Norabuena, E. M., Narula, J. G., Hazelbaker, D. Z., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.11.018 · Cited by 18
Molecular and cellular mechanisms of human cortical connectivity
Luria, V., Ma, S., Shibata, M., Pattabiraman, K., & Šestan, N. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102699 · Cited by 18
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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