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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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Citations
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
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
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
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Self-citationDerrien, T., Johnson, R., Bussotti, G., Andrea Tanzer, A., Djebali, S., Tilgner, H., Guernec, G., Martin, D., Merkel, A., Knowles, D. G., et al. (2012) · Genome Research · doi:10.1101/gr.132159.111 · Cited by 5,135
A multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,475
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
Cellular Scaling Rules for the Brains of Marsupials: Not as “Primitive” as Expected
Santos, S. E. D., Porfírio, J., Cunha, F., Manger, P. R., Tavares, W. C., Pessôa, L. M., Raghanti, M. A., Sherwood, C. C., & Herculano‐Houzel, S. (2017) · Brain Behavior and Evolution · doi:10.1159/000452856 · Cited by 2,652
Research works printed but not linked: 1
- Lorente de Nó, R. La corteza cerebral del ratón. Trab. Lab. Invest. Bio. (Madrid) 20, 41–78 (1922).
Cited by
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Ross, J. M., Kim, C. N., Allen, D. E., Crouch, E. E., Narsinh, K. H., Cooke, D. L., Abla, A. A., Nowakowski, T. J., & Winkler, E. A. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.600767 · Cited by 66
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Viho, E. M. G., Buurstede, J. C., Mahfouz, A. E., Koorneef, L. L., van Weert, L. T. C. M., Houtman, R., Hunt, H., Kroon, J., & Meijer, O. C. (2019) · Neuroendocrinology · doi:10.1159/000499659 · Cited by 61
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“The single-nucleus profiling SMART-seq dataset of the MTG from Hodge et al. 67 that was used in the comparative analysis of Fang et al. was leveraged as a reference for our selections.”
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Zhang, L., Cheng, Y., Xue, Z., Li, J., Wu, N., Yan, J., Wang, J., Wang, C., Chen, W., Zhou, T., et al. (2021) · Cell Biology and Toxicology · doi:10.1007/s10565-021-09601-4 · Cited by 56
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“However, the mouse brain differs from a human brain in the complexity, proportion, and distribution of different brain areas and their gene expression profiles [13].”
BackgroundGittings, L. M., Alsop, E. B., Antone, J., Singer, M., Whitsett, T. G., Sattler, R., & Keuren‐Jensen, K. R. V. (2023) · Acta Neuropathologica · doi:10.1007/s00401-023-02599-5 · Cited by 58
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“, 2017) cortex are largely region-specific at the level of transcriptomic cell types, with several dozens of excitatory cell types per area (Tasic et al., 2018; Hodge et al., 2019).”
BackgroundEmerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal
Uffelmann, E., & Posthuma, D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.05.022 · Cited by 57
Andersen, J. V., Jakobsen, E., Westi, E. W., Lie, M. E. K., Voss, C. M., Aldana, B. I., Schousboe, A., Wellendorph, P., Bak, L. K., Pinborg, L. H., et al. (2020) · Glia · doi:10.1002/glia.23872 · Cited by 57
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BackgroundHerb, B. R., Glover, H. J., Bhaduri, A., Colantuoni, C., Bale, T. L., Siletti, K., Linnarsson, S., Hodge, R. D., Lein, E. S., Kriegstein, A. R., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf6251 · Cited by 57
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MethodSelf-citationQian, J., Shao, X., Bao, H., Yin, F., Guo, W., Li, C., Li, A., Hua, H., & Fan, X. (2025) · Nature Communications · doi:10.1038/s41467-025-57029-9 · Cited by 62
“Speci fi cally, we fi rst used the human middle temporal gyrus (MTG) scRNA-seq data by Hodge et al. 38 , which contains 75 transcriptomically distinct cell types, as the single-cell reference, and deconvoluted the spots of each DLPFC slice data using Cell2location 39 (Supplementary Fig.”
Sequeira, A., Shen, K., Gottlieb, A., & Limón, A. (2019) · Communications Biology · doi:10.1038/s42003-019-0413-7 · Cited by 59
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BackgroundInfluentialLiu, X., Shen, Q., & Zhang, S. (2022) · Genome Research · doi:10.1101/gr.276868.122 · Cited by 61
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BackgroundMethodSelf-citationMaden, S. K., Kwon, S. H., Huuki-Myers, L. A., Collado‐Torres, L., Hicks, S. C., & Maynard, K. R. (2023) · Genome biology · doi:10.1186/s13059-023-03123-4 · Cited by 58
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Self-citationHuang, A. Y., Li, P., Rodin, R. E., Kim, S. N., Dou, Y., Kenny, C. J., Akula, S. K., Hodge, R. D., Bakken, T. E., Miller, J. A., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006163117 · Cited by 50
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“Consistently, alignments with additional datasets of mature14,16 and developing15 macaques yields similar results Co-clustering excitatory neurons 0.0 1.0 M ac aq ue _c lu st er Human_cluster Ex c L3 L IN C 00 50 7 PS R C 1 Ex c L2 -3 L IN C 00 50 7 R PL 9P 17 Ex c L3 -4 R O R B S EM A 6D Ex c L3 R O R B C A R TP T Ex…”
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BackgroundHulse, B. K., Haberkern, H., Franconville, R., Turner-Evans, D. B., Takemura, S., Wolff, T., Noorman, M., Dreher, M., Dan, C., Parekh, R., et al. (2020) · doi:10.1101/2020.12.08.413955 · Cited by 392
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Chittajallu, R., Auville, K., Mahadevan, V., Lai, M., Hunt, S., Calvigioni, D., Pelkey, K. A., Zaghloul, K. A., & McBain, C. J. (2020) · eLife · doi:10.7554/elife.57571 · Cited by 54
Pelkonen, A., Pistono, C., Klecki, P., Gómez‐Budia, M., Dougalis, A., Konttinen, H., Stanová, I., Fagerlund, I., Leinonen, V., Korhonen, P., et al. (2021) · Cells · doi:10.3390/cells11010106 · Cited by 54
“showed major differences between humans and mice in the proportions, laminar distributions, gene expression and morphology of cortical cells, as well as in the serotonin responsiveness of specific circuits [22].”
BackgroundDiabetes and Its Cardiovascular Complications: Comprehensive Network and Systematic Analyses
Wu, H., Norton, V., Cui, K., Zhu, B., Bhattacharjee, S., Lu, Y. W., Wang, B., Shan, D., Wong, S., Dong, Y., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.841928 · Cited by 59
“This has been shown to be especially true in complex organs and tissues with a high degree of cellular heterogeneity, such as mammalian brains and tumors (50, 51).”
BackgroundThe neuropeptide landscape of human prefrontal cortex
Zhong, W., Barde, S. S., Mitsios, N., Ádori, C., Oksvold, P., von Feilitzen, K., O’Leary, L. A., Csiba, L., Hortobágyi, T., Szocsics, P., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2123146119 · Cited by 52
“From these studies (14,15) we have also adopted the”
BackgroundMethodConsequences and opportunities arising due to sparser single-cell RNA-seq datasets
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2023) · Genome biology · doi:10.1186/s13059-023-02933-w · Cited by 52
“To substantiate our claim, we used BDA [15] to identify the top 100 most differentially expressed genes between two cell populations and the top 100 most stable expressed genes in a 10X dataset [24] as well as a Smart-Seq dataset [29].”
MethodSelf-citationApplications of Brain Organoids for Infectious Diseases
Fan, W., Christian, K. M., Song, H., & Ming, G. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167243 · Cited by 56
“Mice and humans share evolutionarily conserved neural architecture made up of similar types of brain cells.(18) Mouse models can also be genetically modified and disease phenotypes can be studied within an intact organism to understand disease mechanisms and progression.”
BackgroundHeyer, D. B., Wilbers, R., Galakhova, A. A., Hartsema, E. A., Braak, S., Hunt, S., Verhoog, M. B., Muijtjens, M. L., Mertens, E. J., Idema, S., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab330 · Cited by 46
“We and others (Molnár et al. 2008; Wang et al. 2015; Hodge et al. 2019) have repeatedly demonstrated that using access tissue samples, one can study nonpathological properties of human circuits (Testa-Silva et al.”
BackgroundSelf-citationCruz, B. M. D. L., Márkus, R., Malla, S., Haig, M. I., Gell, C., Sang, F., Bellows, E., Sherif, M. A., McLean, D., Lourdusamy, A., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01282-z · Cited by 49
Mehta, N. H., Suss, R. A., Dyke, J. P., Theise, N. D., Chiang, G. C.-Y., Strauß, S., Saint-Louis, L. A., Li, Y., Pahlajani, S. S., Babaria, V., et al. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105776 · Cited by 51
“1000 fold greater size and complexity in the human nervous system (Hodge et al., 2019).”
BackgroundSuresh, H., Crow, M., Jorstad, N. L., Hodge, R. D., Lein, E. S., Dobin, A., Bakken, T. E., & Gillis, J. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02186-7 · Cited by 58
Self-citationAcyl-CoA synthetases as regulators of brain phospholipid acyl-chain diversity
Fernandez, R. F., & Ellis, J. M. (2020) · Prostaglandins Leukotrienes and Essential Fatty Acids · doi:10.1016/j.plefa.2020.102175 · Cited by 53
“Advances in technology have overcome these obstacles with the advent of single-cell transcriptomics to define distinct cell type transcripts from adult human brain [121, 122].”
BackgroundAutomatic deep learning-driven label-free image-guided patch clamp system
Koos, K., Oláh, G., Balassa, T., Mihut, N., Rózsa, M., Ozsvár, A., Tasnádi, E. A., Barzó, P., Faragó, N., Puskás, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21291-4 · Cited by 46
Interneuron Dysfunction and Inhibitory Deficits in Autism and Fragile X Syndrome
Nomura, T. (2021) · Cells · doi:10.3390/cells10102610 · Cited by 51
Local Connectivity and Synaptic Dynamics in Mouse and Human NeocortexPreprint
Campagnola, L., Seeman, S. C., Chartrand, T., Kim, L., Hoggarth, A., Gamlin, C. R., Ito, S., Trinh, J., Davoudian, P. A., Radaelli, C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.31.437553 · Cited by 53
Self-citationKita, Y., Nishibe, H., Wang, Y., Hashikawa, T., Kikuchi, S. S., Mami, U., Yoshida, A., Yoshida, C., Kawase, T., Ishii, S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020125118 · Cited by 52
Choroid plexus mast cells drive tumor-associated hydrocephalus
Li, Y., Di, C., Song, S., Zhang, Y., Lu, Y., Liao, J., Lei, B., Zhong, J., Guo, K., Zhang, N., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.001 · Cited by 55
Organization, Function, and Development of the Mouse Retinogeniculate Synapse
Liang, L., & Chen, C. (2020) · Annual Review of Vision Science · doi:10.1146/annurev-vision-121219-081753 · Cited by 49
From Cell Types to an Integrated Understanding of Brain Evolution: The Case of the Cerebral Cortex
Tosches, M. A. (2021) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-120319-112654 · Cited by 47
Wilmes, M., Pinto-Espinoza, C., Ludewig, P., Stabernack, J., Liesz, A., Nicke, A., Gelderblom, M., Gerloff, C. P., Falzoni, S., Tolosa, E., et al. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02601-z · Cited by 50
“Expression data from the Allen Brain Atlas for mice [18] and humans [19] show that the P2X7 receptor is highly expressed by microglia in humans and rodents (Table 1).”
BackgroundMethodResultInfluentialUltrafast and interpretable single-cell 3D genome analysis with Fast-Higashi
Zhang, R., Zhou, T., & Ma, J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.004 · Cited by 46
Munz, M., Bharioke, A., Kosche, G., Moreno‐Juan, V., Brignall, A. C., Rodrigues, T. M., Graff-Meyer, A., Ulmer, T., Haeuselmann, S., Pavlinić, D., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.025 · Cited by 52
Mapping pediatric brain tumors to their origins in the developing cerebellum
Okonechnikov, K., Joshi, P. K., Sepp, M., Leiss, K., Sarropoulos, I., Murat, F., Sill, M., Beck, P., Chan, K. C.-H., Korshunov, A., et al. (2023) · Neuro-Oncology · doi:10.1093/neuonc/noad124 · Cited by 52
Neurodegeneration-Associated Proteins in Human Olfactory Neurons Collected by Nasal Brushing
Brozzetti, L., Sacchetto, L., Cecchini, M. P., Avesani, A., Perra, D., Bongianni, M., Portioli, C., Scupoli, M. T., Ghetti, B. F., Monaco, S., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00145 · Cited by 49
Hippocampal Hyperactivity as a Druggable Circuit-Level Origin of Aberrant Salience in Schizophrenia
Kätzel, D., Wolff, A. R., Bygrave, A. M., & Bannerman, D. M. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.486811 · Cited by 51
Review of Design Considerations for Brain-on-a-Chip Models
Cameron, T., Bennet, T. J., Rowe, E. M., Anwer, M., Wellington, C. L., & Cheung, K. C. (2021) · Micromachines · doi:10.3390/mi12040441 · Cited by 49
“The human and murine brain also differ considerably in the proportion of gray:white matter, regional organization and gene expression [2].”
BackgroundNandi, A., Chartrand, T., Geit, W. A. H. V., Buchin, A., Yao, Z., Lee, S. Y., Wei, Y., Kalmbach, B. E., Lee, B., Lein, E. S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111176 · Cited by 49
“, 2009), has revealed a multitude of “cell types” (Hodge et al., 2019; Poulin et al., 2016; Zeisel et al., 2015).”
Self-citationAPOE genotype determines cell-type-specific pathological landscape of Alzheimer’s disease
Li, Z., Martens, Y. A., Ren, Y., Jin, Y., Sekiya, H., Doss, S. V., Kouri, N., Castanedes-Casey, M., Christensen, T. A., Miller Nevalainen, L. B., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.02.017 · Cited by 39
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44
Self-citationConsens, M. E., Chen, Y., Menon, V., Wang, Y., Schneider, J. A., De Jager, P. L., Bennett, D. A., Tripathy, S. J., & Felsky, D. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.903175 · Cited by 44
“To build a high-quality foundation for investigating cell subtype proportions - focusing on 257 subclasses of neocortical neurons - in AD, we first established representative marker gene sets 258 using ultra-high depth single-nucleus data RNA sequencing (snRNAseq) data from multiple 259 regions of human cortex collect…”
BackgroundMethodInfluentialSingle-Cell RNA Sequencing in Parkinson’s Disease
Ma, S., & Lim, S. B. (2021) · Biomedicines · doi:10.3390/biomedicines9040368 · Cited by 50
“Consequ ntly, such studies have constructed open-s urce sc/snRNA-s q databas s, including a sin le-c ll atlas of the mid-g st tion human neoco tex [82] and SN [30] and the Allen Brain Cell Types Databas containing the primary motor cortex (M1), multiple cortical areas, middle temporal gyrus (MTG), primary visual corte…”
BackgroundResultInfluentialBae, E., Huang, P., Muller-Greven, G. M., Hambardzumyan, D., Sloan, A. E., Nowacki, A. S., Marko, N. F., Carlin, C. R., & Gladson, C. L. (2022) · Nature Communications · doi:10.1038/s41467-022-31981-2 · Cited by 48
Cross-disorder and disease-specific pathways in dementia revealed by single-cell genomics
Rexach, J. E., Cheng, Y., Chen, L., Polioudakis, D., Lin, L., Mitri, V., Elkins, A., Han, X., Yamakawa, M., Yin, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.019 · Cited by 59
Beau, M., Herzfeld, D. J., Naveros, F., Hemelt, M. E., D’Agostino, F., Oostland, M., Sánchez‐López, Á., Chung, Y. Y., Maibach, M., Kyranakis, S., et al. (2025) · Cell · doi:10.1016/j.cell.2025.01.041 · Cited by 55
Pistollato, F., Forbes-Hernández, T. Y., Iglesias, R. C., Ruíz, R., Elexpuru-Zabaleta, M., Cianciosi, D., Giampieri, F., & Battino, M. A. (2019) · Pharmacological Research · doi:10.1016/j.phrs.2019.104579 · Cited by 49
Brain regions vulnerable and resistant to aging without Alzheimer’s disease
Feng, X., Guo, J., Sigmon, H. C., Sloan, R. P., Brickman, A. M., Provenzano, F. A., Small, S. A., & Initiative, A. D. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234255 · Cited by 55
“Identifying vulnerable and resistant regions, and mapping the aging trajectories of the vulnerable regions, can be used for understanding cellular and molecular mechanisms that underlie normal aging, however these must be taken in light of differences in neuronal laminar distributions and morphology between species [5…”
BackgroundHeterogeneity and Molecular Markers for CNS Glial Cells Revealed by Single-Cell Transcriptomics
Sun, J., Song, Y., Chen, Z., Qiu, J., Zhu, S., Wu, L., & Xing, L. (2021) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-021-01159-3 · Cited by 52
Thompson, J. R., Nelson, E. D., Tippani, M., Ramnauth, A. D., Divecha, H. R., Miller, R. A., Eagles, N. J., Pattie, E. A., Kwon, S., Bach, S. V., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02022-0 · Cited by 58
“Using these DEGs, we annotated GABAergic families corresponding to central ganglionic eminence (CGE) origin (consisting of VIP and HTR3A cell types) based on ADARB2 expression 144 and lack of NXPH1 , a LAMP5 + family of ADARB2 expressing interneurons 154 (consisting of CXCL14, LAMP5.”
Emerging technologies to study glial cells
Hirbec, H., Déglon, N., Foo, L. C., Goshen, I., Grutzendler, J., Hangen, E., Kreisel, T., Linck, N., Muffat, J., Regio, S., et al. (2020) · Glia · doi:10.1002/glia.23780 · Cited by 51
“Human glial cells are quite different from their mouse counterparts (Geirsdottir et al., 2019; Hodge et al., 2019).”
BackgroundColl-Tané, M., Gong, N. N., Belfer, S. J., van Renssen, L. V., Kurtz‐Nelson, E. C., Szuperák, M., Eidhof, I., van Reijmersdal, B., Terwindt, I., Durkin, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abe2626 · Cited by 47
Cell type specificity of glucocorticoid signaling in the adult mouse hippocampus
Viho, E. M. G., Buurstede, J. C., Berkhout, J. B., Mahfouz, A. E., & Meijer, O. C. (2021) · Journal of Neuroendocrinology · doi:10.1111/jne.13072 · Cited by 47
Self-citationReactive astrocytes secrete the chaperone HSPB1 to mediate neuroprotection
Yang, F., Beltran-Lobo, P., Sung, K., Goldrick, C., Croft, C. L., Nishimura, A., Hedges, E., Mahiddine, F., Troakes, C., Golde, T. E., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk9884 · Cited by 46
Kumar, P., Goettemoeller, A. M., Espinosa‐García, C., Tobin, B. R., Tfaily, A., Nelson, R. S., Natu, A., Dammer, E. B., Santiago, J. V., Malepati, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47028-7 · Cited by 53
“…atlas snRNA data was downloaded from https://cells.ucsc.edu/?ds=allen-celltypes+human-cortex+various-cortical-areas&meta=class_label and processed in R to generate a counts per million normalized reference matrix with 47,509 non-excluded cell nuclei assigned to any of 19 cell type clusters 1,72 .”
Zuo, Y., Li, B., Xie, J., Ma, Z., Thirupathi, A., Yu, P., Gao, G., Zhou, J., Zhou, C., Xu, H., et al. (2020) · Neurochemistry International · doi:10.1016/j.neuint.2020.104693 · Cited by 46
Mosher, C. P., Wei, Y., Kamiński, J., Nandi, A., Mamelak, A. N., Anastassiou, C. A., & Rutishauser, U. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.02.027 · Cited by 46
Oguro‐Ando, A., Bamford, R. A., Sital, W., Sprengers, J. J., Zuko, A., Matser, J. M., Oppelaar, H., Sarabdjitsingh, R. A., Joëls, M., Burbach, J. P. H., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01223-y · Cited by 47
Cell-type specific changes in PKC-delta neurons of the central amygdala during alcohol withdrawal
Dilly, G. A., Kittleman, C. W., Kerr, T. M., Messing, R. O., & Mayfield, R. D. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02063-0 · Cited by 47
“These cells did, however, express the interneuron markers Kit and Lhx6 [40], and were thus identified as Htr2a interneurons.”
BackgroundXie, L., Zhang, Y., Hong, H., Xu, S., Cui, L., Wang, S., Li, J., Liu, L., Lin, M., Luo, X., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120524 · Cited by 45
“Although mounting evidence based on animal experiments demonstrates the role of arterial pulsation in glymphatic clearance, the extensive difference between humans and mice emphasizes the importance of validation in the human brain (Hodge et al., 2019).”
Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
Peng, Y., Bjelde, A., Aceituno, P. V., Mittermaier, F. X., Planert, H., Grosser, S., Onken, J., Faust, K., Kalbhenn, T., Simon, M., et al. (2024) · Science · doi:10.1126/science.adg8828 · Cited by 48
Kim, S., Lee, J., Koh, I. G., Ji, J., Kim, H. J., Kim, E., Park, J., Park, J.-E., & An, J. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01328-6 · Cited by 48
Mich, J. K., Ryu, J., Wei, A. D., Gore, B. B., Guo, R., Bard, A. M., Martinez, R. A., Luber, E. M., Liu, J., Bishaw, Y. M., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adn5603 · Cited by 63
Self-citationAlzheimer’s disease, neural stem cells and neurogenesis: cellular phase at single-cell level
Kızıl, Ç., Coşacak, M. İ., & Bhattarai, P. (2019) · Neural Regeneration Research · doi:10.4103/1673-5374.268896 · Cited by 46
“A recent study also supports the importance of finding the homologous cell types in different model organisms to elucidate the universal role of a particular cell type (Hodge et al. 2019).”
Modeling Controlled Cortical Impact Injury in 3D Brain‐Like Tissue Cultures
Liaudanskaya, V., Chung, J. Y., Mizzoni, C., Rouleau, N., Berk, A. N., Wu, L., Turner, J. A., Georgakoudi, I., Whalen, M. J., Nieland, T. J. F., et al. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.202000122 · Cited by 45
Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex
Lei, Y., Cheng, M., Li, Z., Zhuang, Z., Wu, L. L., Sun, Y., Han, L., Huang, Z., Wang, Y., Wang, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34413-3 · Cited by 47
Self-citationHuman in vitro models for understanding mechanisms of autism spectrum disorder
Gordon, A., & Geschwind, D. H. (2020) · Molecular Autism · doi:10.1186/s13229-020-00332-7 · Cited by 45
Xu, J. Q., Liu, Y., Li, H., Tarashansky, A. J., Kalicki, C. H., Hung, R., Hu, Y., Comjean, A., Kolluru, S. S., Wang, B., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2203179119 · Cited by 43
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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