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Conserved cell types with divergent features in human versus mouse cortex
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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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Allometric rules for mammalian cortical layer 5 neuron biophysics
Beaulieu-Laroche, L., Brown, N. J., Hansen, M., Toloza, E. H. S., Sharma, J., Williams, Z. M., Frosch, M. P., Cosgrove, G. R., Cash, S. S., & Harnett, M. T. (2021) · Nature · doi:10.1038/s41586-021-04072-3 · Cited by 141
SPACEL: deep learning-based characterization of spatial transcriptome architectures
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“…Spoint ’ s performance in cell type deconvolution on real ST experimental data, we used two datasets as input: a 10X Visium dataset for the human dorsolateral prefrontal cortex (DLPFC) that contains 12 ST slices 34 and a human brain scRNA-seq dataset down-loaded from the Allen Brain Map 51 .”
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scMD facilitates cell type deconvolution using single-cell DNA methylation references
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Aiding and Abetting Anhedonia: Impact of Inflammation on the Brain and Pharmacological Implications
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“For example, there are human-specific aspects of neurodevelopment (Letinic and Rakic, 2001; Rakic, 2009; Clowry et al., 2010; Zeng et al., 2012; Hansen et al., 2013; Paredes et al., 2016; Hodge et al., 2019) that limit the generalizability and/or scope of studies undertaken with animal model systems and the feasibilit…”
BackgroundDifferential Encoding in Prefrontal Cortex Projection Neuron Classes Across Cognitive Tasks
Lui, J. H., Nguyen, N. D., Grutzner, S. M., Darmanis, S., Peixoto, D., Wagner, M. J., Allen, W. E., Kebschull, J. M., Richman, E. B., Ren, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.046 · Cited by 124
Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex
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“Whereas most neurons from these subclasses are distinct, neurons from some of the finer transcriptomic types in both mice (26) and humans (1) have gene expression profiles partially consistent with both SST and PVALB subclasses.”
BackgroundMethodInfluentialSelf-citationCell type ontologies of the Human Cell Atlas
Osumi-Sutherland, D., Xu, C., Keays, M., Levine, A. P., Kharchenko, P. V., Regev, A., Lein Ed, S., & Teichmann, S. A. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00787-7 · Cited by 118
Self-citationPyramidal neuron subtype diversity governs microglia states in the neocortex
Stogsdill, J. A., Kim, K., Binan, L., Farhi, S. L., Levin, J. Z., & Arlotta, P. (2022) · Nature · doi:10.1038/s41586-022-05056-7 · Cited by 121
A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons
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SARS-CoV-2 promotes microglial synapse elimination in human brain organoids
Samudyata, S., Oliveira, A. O., Malwade, S., de Sousa, N. R., Goparaju, S. K., Gracias, J., Orhan, F., Steponaviciute, L., Schalling, M., Sheridan, S. D., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01786-2 · Cited by 125
Comprehensive evaluation of deconvolution methods for human brain gene expression
Sutton, G. J., Poppe, D., Simmons, R. K., Walsh, K., Nawaz, U., Lister, R., Gagnon-Bartsch, J. A., & Voineagu, I. (2022) · Nature Communications · doi:10.1038/s41467-022-28655-4 · Cited by 122
“CA (Cell Atlas): Count-level exon expression data for NeuN + sorted adult nuclei from the middle temporal gyrus were acquired from the Human Cell Atlas 41 .”
MethodInfluentialCell segmentation-free inference of cell types from in situ transcriptomics data
Park, J., Choi, W., Tiesmeyer, S., Long, B., Borm, L. E., Garren, E. J., Nguyen, T. N., Tasic, B., Codeluppi, S., Graf, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23807-4 · Cited by 119
Self-citationAdvancing models of neural development with biomaterials
Roth, J. G., Huang, M. S., Li, T. L., Feig, V. R., Jiang, Y., Cui, B., Greely, H. T., Bao, Z., Pașca, S. P., & Heilshorn, S. C. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00496-y · Cited by 122
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Tunable hydrogel viscoelasticity modulates human neural maturation
Roth, J. G., Huang, M. S., Navarro, R. S., Akram, J., LeSavage, B. L., & Heilshorn, S. C. (2023) · Science Advances · doi:10.1126/sciadv.adh8313 · Cited by 122
“Given the anatomical and physiological differences between murine and human brain development ( 23 – 25 ), as well as the observation that stress relaxation is implicated in human brain development and disease ( 17 , 26 ), there exists a need for studies to characterize how stress relaxation…”
BackgroundEvolution of cortical neurons supporting human cognition
Galakhova, A. A., Hunt, S., Wilbers, R., Heyer, D. B., de Kock, C. P. J., Mansvelder, H. D., & Goriounova, N. A. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.08.012 · Cited by 127
“The human brain contains human-specialized pyramidal neuron types [19,21,97,98], but even types homologous to mouse show a larger diversity in physiology, morphology, and gene expression features [19,21].”
BackgroundReproducible generation of human midbrain organoids for in vitro modeling of Parkinson’s disease
Nickels, S., Modamio, J., Mendes-Pinheiro, B., Monzel, A. S., Betsou, F., & Schwamborn, J. C. (2020) · Stem Cell Research · doi:10.1016/j.scr.2020.101870 · Cited by 129
Single-nuclei isoform RNA sequencing unlocks barcoded exon connectivity in frozen brain tissue
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Landmarks of human embryonic development inscribed in somatic mutations
Bizzotto, S., Dou, Y., Ganz, J., Doan, R. N., Kwon, M., Bohrson, C. L., Kim, S. N., Bae, T., Abyzov, A., NIMH Brain Somatic Mosaicism Network, et al. (2021) · Science · doi:10.1126/science.abe1544 · Cited by 117
Neuropathogenesis-on-chips for neurodegenerative diseases
Amartumur, S., Nguyen, H. M., Huynh, T., Kim, T. S., Woo, R., Oh, E., Kim, K. K., Lee, L. P., & Heo, C. (2024) · Nature Communications · doi:10.1038/s41467-024-46554-8 · Cited by 131
“Additionally, there are inevitable limitations due to the differences between humanand animal brains at the cellular and tissue levels 20,21 .”
BackgroundSchwarz, N., Uysal, B., Welzer, M., Bahr, J. C., Layer, N., Löffler, H., Stanaitis, K., Ashraf, H. P., Weber, Y. G., Hedrich, U. B. S., et al. (2019) · eLife · doi:10.7554/elife.48417 · Cited by 120
“Especially the spines have been proven to be plastic and to appear and disappear on time scales of days in the developing (Holtmaat and Svoboda, 2009; Lendvai et al., 2000) and adult brain (Trachtenberg et al., 2002).”
BackgroundDiversity of transcriptomic microglial phenotypes in aging and Alzheimer's disease
Boche, D., & Gordon, M. N. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12389 · Cited by 122
A dopamine gradient controls access to distributed working memory in the large-scale monkey cortex
Froudist‐Walsh, S., Bliss, D. P., Ding, X., Rapan, L., Niu, M., Knoblauch, K., Zilles, K., Kennedy, H., Palomero‐Gallagher, N., & Wang, X. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.024 · Cited by 122
Anatomical structures, cell types and biomarkers of the Human Reference Atlas
Börner, K., Teichmann, S. A., Quardokus, E. M., Gee, J. C., Browne, K., Osumi-Sutherland, D., Herr, B. W., Bueckle, A., Paul, H., Haniffa, M. A., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00788-6 · Cited by 123
Self-citationMiyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115
“Single-nucleus RNA-sequencing (snRNA-seq) has revealed that brain cell populations are heterogeneous at the molecular level 1–6 .”
Mossink, B., Negwer, M., Schubert, D., & Nadif Kasri, N. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03714-5 · Cited by 114
“While mice have many advantages as model organisms and many features are conserved down to the cellular level [331], some features appear to be unique to the human lineage, for example specialized cell-types such as outer radial glia cells [448], subpial interlaminar astrocytes [449], or the recently described rosehip…”
BackgroundThe Parvalbumin Hypothesis of Autism Spectrum Disorder
Filice, F., Janickova, L., Henzi, T., Bilella, A., & Schwaller, B. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.577525 · Cited by 118
“More recent classifications based on single-cell or single-nucleus RNA-sequencing (RNA-seq) (Hodge et al., 2019), patch sequencing (Patch-seq) yielding morphoelectric (met-type) and transcriptomic (t-type) datasets (Gouwens et al.”
MethodThree-dimensional genome rewiring in loci with human accelerated regions
Keough, K. C., Whalen, S., Inoue, F., Przytycki, P. F., Fair, T., Deng, C., Steyert, M., Ryu, H., Lindblad-Toh, K., Karlsson, E., et al. (2023) · Science · doi:10.1126/science.abm1696 · Cited by 113
“Finally, we repeated the CellWalker analysis using single-cell ATAC-seq and RNA-seq from the human adult brain ( 68 , 69 ) and heart ( 70 ).”
MethodShared Transcriptional Signatures in Major Depressive Disorder and Mouse Chronic Stress Models
Scarpa, J. R., Fatma, M., Loh, Y. E., Traore, S. R., Stefan, T., Chen, T. H., Nestler, E. J., & Labonté, B. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.029 · Cited by 114
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“Ontology enrichments were calculated by g:Pro fi ler with FDR corrected p -values. f Enrichment of ASD downregulated neuronal co-expression modules with neuronal cell-type markers identi fi ed from single-nuclei RNA sequencing 22 .”
MethodInfluentialThe Epigenome in Neurodevelopmental Disorders
Reichard, J., & Zimmer‐Bensch, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.776809 · Cited by 118
A brain cell atlas integrating single-cell transcriptomes across human brain regions
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Microglial trogocytosis and the complement system regulate axonal pruning in vivo
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Differential assembly diversifies GABAA receptor structures and signalling
Sente, A., Desai, R., Naydenova, K., Malinauskas, T., Jounaïdi, Y., Miehling, J., Zhou, X., Masiulis, S., Hardwick, S. W., Chirgadze, D. Y., et al. (2022) · Nature · doi:10.1038/s41586-022-04517-3 · Cited by 112
Joglekar, A., Hu, W., Zhang, B., Narykov, O., Diekhans, M., Marrocco, J., Balacco, J., Ndhlovu, L. C., Milner, T. A., Fedrigo, O. D., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01616-4 · Cited by 126
“No statistical methods were used to predetermine sample sizes (for example, cell number in single-cell experiments), but our sample sizes are similar to those reported in previous publications 65,66 .”
Whole-brain comparison of rodent and human brains using spatial transcriptomics
Beauchamp, A., Yee, Y., Darwin, B. C., Raznahan, A., Mars, R. B., & Lerch, J. P. (2022) · eLife · doi:10.7554/elife.79418 · Cited by 122
Evolution of DNA methylation in the human brain
Jeong, H., Mendizabal, I., Berto, S., Chatterjee, P., Layman, T. S., Usui, N., Toriumi, K., Douglas, C., Singh, D., Huh, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21917-7 · Cited by 114
Chameh, H. M., Rich, S., Wang, L., Chen, F., Zhang, L., Carlen, P. L., Tripathy, S. J., & Valiante, T. A. (2021) · Nature Communications · doi:10.1038/s41467-021-22741-9 · Cited by 110
Spatially resolved transcriptomics in neuroscience
Close, J. L., Long, B. R., & Zeng, H. (2021) · Nature Methods · doi:10.1038/s41592-020-01040-z · Cited by 94
Self-citationMullane, K., & Williams, M. J. (2020) · Biochemical Pharmacology · doi:10.1016/j.bcp.2020.113945 · Cited by 109
Advances in microfluidic in vitro systems for neurological disease modeling
Holloway, P. M., Willaime‐Morawek, S., Siow, R. C. M., Barber, M., Owens, R. M., Sharma, A. D., Rowan, W. C., Hill, E. J., & Zagnoni, M. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24794 · Cited by 101
“…and morphological complexity, glutamate responses, and immune responsivity) (Chandrasekaran et al., 2016), and numerous differences noted in electrical activity (Beaulieu- Laroche et al., 2018), gene expression and morphology (Hodge et al., 2019) between homologous human and rodent neurons.”
BackgroundKalmbach, B. E., Hodge, R. D., Jorstad, N. L., Owen, S. F., de Frates, R. A., Yanny, A. M., Dalley, R. A., Mallory, M., Graybuck, L. T., Radaelli, C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.030 · Cited by 106
Self-citationMachine learning-assisted neurotoxicity prediction in human midbrain organoids
Monzel, A. S., Hemmer, K., Kaoma, T., Smits, L. M., Bolognin, S., Lucarelli, P., Rosety, I., Žagare, A., Antony, P. M. A., Nickels, S., et al. (2020) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2020.05.011 · Cited by 104
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
Johansen, N., & Quon, G. (2019) · Genome biology · doi:10.1186/s13059-019-1766-4 · Cited by 104
Self-citationTrujillo, C. A., Adams, J. W., Negraes, P. D., Carromeu, C., Tejwani, L., Acab, A., Tsuda, B., Thomas, C. A., Sodhi, N., Fichter, K. M., et al. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202012523 · Cited by 101
“Notably, the most different systems are glutamatergic, serotoninergic, and cholinergic (Hodge et al, 2019; Sjostedt et al, 2020).”
BackgroundApplications of brain organoids in neurodevelopment and neurological diseases
Sun, N., Meng, X., Liu, Y., Song, D., Jiang, C., & Cai, J. (2021) · Journal of Biomedical Science · doi:10.1186/s12929-021-00728-4 · Cited by 103
“More importantly, there are differences in the structure between human and mouse brains; for example, (1) the inner fiber layer (IFL) and outer subventricular zone (OSVZ) in the human brain are absent in the mouse brain [9]; (2) the human cortex is expanded relative to the mouse with a > 1000-fold increase in the area…”
BackgroundSelective vulnerability of layer 5a corticostriatal neurons in Huntington’s disease
Pressl, C., Mätlik, K., Kus, L., Darnell, P., Luo, J., Paul, M. R., Weiss, A. R., Liguore, W. A., Carroll, T. S., Davis, D. A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.009 · Cited by 101
Jaffe, A. E., Hoeppner, D. J., Saito, T., Blanpain, L. T., Ukaigwe, J., Burke, E. E., Collado‐Torres, L., Tao, R., Tajinda, K., Maynard, K. R., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0604-z · Cited by 96
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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