The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Sorted by citations
Items 401–500 of 1,009
Neural cognitive signals during spontaneous movements in the macaque
Tremblay, S., Testard, C., DiTullio, R. W., Inchauspé, J., & Petrides, M. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01220-4 · Cited by 41
Gamma oscillation plasticity is mediated via parvalbumin interneurons
Hadler, M. D., Tzilivaki, A., Schmitz, D., Alle, H., & Geiger, J. R. P. (2024) · Science Advances · doi:10.1126/sciadv.adj7427 · Cited by 45
Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images
Wernersson, E., Gelali, E., Girelli, G., Wang, S., Castillo, D., Langseth, C. M., Verron, Q., Nguyen, H. Q., Chattoraj, S., Casals, A. M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02294-7 · Cited by 44
Koskeridis, F., Fancy, N. N., Tan, P. F., Meena, D., Εvangelou, Ε., Elliott, P., Wang, D. Y. Q., Matthews, P. M., Dehghan, A., & Tzoulaki, I. (2024) · Nature Communications · doi:10.1038/s41467-024-53452-6 · Cited by 46
Schoonover, K. E., Dienel, S. J., & Lewis, D. A. (2020) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2020.100015 · Cited by 45
“Importantly, chandelier cells exclusively use GAD67 for GABA synthesis, whereas basket cells use both GAD65 and GAD67 [97], a difference supported by single cell-transcriptomic findings [128].”
BackgroundPrimate Neuronal Connections are Sparse as Compared to Mouse
Wildenberg, G. A., Rosen, M. C., Lundell, J., Paukner, D., Freedman, D. J., & Kasthuri, N. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.109709 · Cited by 49
How many markers are needed to robustly determine a cell's type?
Fischer, S., & Gillis, J. (2021) · iScience · doi:10.1016/j.isci.2021.103292 · Cited by 44
Shi, Y., Wu, X., Zhou, J., Cui, W., Wang, J., Hu, Q., Zhang, S., Han, L., Zhou, M., Luo, J., et al. (2022) · Advanced Science · doi:10.1002/advs.202104112 · Cited by 42
Self-citationExploring the neurogenic differentiation of human dental pulp stem cells
Al‐Maswary, A. A., O’Reilly, M., Holmes, A. P., Walmsley, A. D., Cooper, P. R., & Scheven, B. A. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277134 · Cited by 43
“, [67] and the authors interpreted that this may occur due to the genetic and physiological differences between mice and human [69,70].”
BackgroundCasingal, C. R., Descant, K. D., & Anton, E. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.034 · Cited by 46
Campos, L., Arokiaraj, C. M., Chuapoco, M. R., Chen, X., Goeden, N., Gradinaru, V., & Fox, A. S. (2023) · Current Research in Neurobiology · doi:10.1016/j.crneur.2023.100086 · Cited by 42
Kim, M.-H., Radaelli, C., Thomsen, E. R., Monet, D., Chartrand, T., Jorstad, N. L., Mahoney, J. T., Taormina, M. J., Long, B., Baker, K. S., et al. (2023) · eLife · doi:10.7554/elife.81863 · Cited by 46
“Advances in single cell transcriptomics have demonstrated a highly complex cellular architecture in the mouse and human cortex, with 45 types of inhibitory interneurons reported that can be discriminated using genome-wide gene expression ( Hodge et al., 2019 ).”
InfluentialSelf-citationLibé‐Philippot, B., Lejeune, A., Wierda, K. D. B., Louros, N. N., Erkol, E., Vlaeminck, I., Beckers, S., Gaspariunaite, V., Bilheu, A., Konstantoulea, K., et al. (2022) · Cell · doi:10.1016/j.cell.2023.11.028 · Cited by 44
Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus
Lei, Y., Liang, X., Sun, Y., Yao, T. S., Gong, H., Chen, Z., Gao, Y., Wang, H., Wang, R., Huang, Y., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.01.003 · Cited by 44
Tracking single-cell evolution using clock-like chromatin accessibility loci
Xiao, Y., Jin, W., Ju, L., Fu, J., Wang, G., Yu, M., Chen, F., Qian, K., Wang, X., & Zhang, Y. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02241-z · Cited by 46
“…known ‘inside-out’ developmental paradigm of the cortex 72 , the earlier-aged GluN5 specifically expresses the layer V/VI marker genes SCUBE1 and SEMA3E 73 , whereas the younger GluN4 population expresses similarly higher levels of the layer III/IV marker genes NTNG1 and MME 73 (Supplementary Fig.”
Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics
Pentimalli, T. M., Karaiskos, N., & Rajewsky, N. (2024) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-111523-023417 · Cited by 45
“While single-cell technologies reveal the extent of cell types and states across different brain regions (47, 48), they cannot accurately map the spatial organization of neuronal and nonneuronal cells into layers and circuits (49).”
Distinctive Structural and Molecular Features of Myelinated Inhibitory Axons in Human Neocortex
Micheva, K. D., Chang, E. F., Nana, A. L., Seeley, W. W., Ting, J. T., Cobbs, C., Lein, E. S., Smith, S. J., Weinberg, R. J., & Madison, D. V. (2018) · eNeuro · doi:10.1523/eneuro.0297-18.2018 · Cited by 44
Self-citationKucheryavenko, A. S., Chernomyrdin, N. V., Gavdush, A. A., Алексеева, А. И., Nikitin, P. V., Dolganova, I. N., Karalkin, P. A., Khalansky, A. S., Spektor, I. E., Skorobogatiy, M. A., et al. (2021) · Biomedical Optics Express · doi:10.1364/boe.432758 · Cited by 42
Genetic Mechanisms Underlying the Evolution of Connectivity in the Human Cortex
Schmidt, E. R. E., & Polleux, F. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.787164 · Cited by 49
“However, a large number of different neuronal cell types exist in mammalian cortex, and even cortical PNs themselves are not a homogenous class of neurons (Hodge et al., 2019).”
BackgroundYuan, W., Ma, S., Brown, J. R., Kim, K., Murek, V., Trastulla, L., Meissner, A., Lodato, S., Shetty, A. S., Levin, J. Z., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01123-4 · Cited by 42
Largest GWAS (N=1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune CellsPreprint
Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., Martinsen, A. E., et al. (2020) · medRxiv · doi:10.1101/2020.11.20.20235275 · Cited by 42
Cerina, M., Piastra, M. C., & Frega, M. (2023) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/acce12 · Cited by 44
The Pig as a Translational Animal Model for Biobehavioral and Neurotrauma Research
Netzley, A. H., & Pelled, G. (2023) · Biomedicines · doi:10.3390/biomedicines11082165 · Cited by 42
“(2019) has demonstrated considerable differences in the expression of genes among similar cell types within mouse and human brains [24].”
BackgroundShamul, J. G., Wang, Z., Gong, H., Ou, W., White, A. M., Garcia, D. P. M., Gu, S., Clyne, A. M., Quiñones‐Hinojosa, A., & He, X. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01250-2 · Cited by 43
“Nonetheless, the physiology, anatomy, immunology and biology may be very different between animals and humans, compromising the translational value of animal models 215–224 .”
Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans
Russo, S., Claar, L. D., Furregoni, G., Marks, L. C., Krishnan, G. P., Zauli, F. M., Hassan, G., Solbiati, M., d’Orio, P., Mikulan, E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58717-2 · Cited by 54
“There are many differences between homo sapiens and mus musculus , including neuronal population, gene expression, and brain size 64 – 66 .”
Origin, Development, and Synaptogenesis of Cortical Interneurons
Llorca, A., & Deogracias, R. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.929469 · Cited by 46
“Although this repertory of cell identities was first and most intensely examined in the rodent cortex, recent RNAseq data indicate that it is conserved in primate species, including humans (Hodge et al., 2019).”
BackgroundNeurexin-2: An inhibitory neurexin that restricts excitatory synapse formation in the hippocampus
Lin, P.-Y., Chen, L. Y., Jiang, M., Trotter, J. H., Seigneur, E. M., & Südhof, T. C. (2023) · Science Advances · doi:10.1126/sciadv.add8856 · Cited by 45
DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution
Liu, H., Zhou, J., Tian, W., Luo, C., Bartlett, A., Aldridge, A., Lucero, J., Osteen, J. K., Nery, J. R., Chen, H., et al. (2020) · doi:10.1101/2020.04.30.069377 · Cited by 290
A single-cell multi-omic atlas spanning the adult rhesus macaque brain
Chiou, K. L., Huang, X., Bohlen, M. O., Tremblay, S., DeCasien, A. R., O’Day, D. R., Spurrell, C. H., Gogate, A. A., Zintel, T. M., Andrews, M. G., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh1914 · Cited by 44
Tobon, A., Shyti, R., Villa, C. E., Cheroni, C., Fuentes-Bravo, P., Trattaro, S., Caporale, N., Troglio, F., Tenderini, E., Mihailovich, M., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh2726 · Cited by 40
Progress in Brain‐Compatible Interfaces with Soft Nanomaterials
Jeong, Y., Lee, H. E., Shin, A., Kim, D., Lee, K. J., & Kim, D. (2020) · Advanced Materials · doi:10.1002/adma.201907522 · Cited by 40
Mulvey, B., Lagunas, T., & Dougherty, J. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.011 · Cited by 41
CaMKIIα-Positive Interneurons Identified via a microRNA-Based Viral Gene Targeting Strategy
Keaveney, M. K., Rahsepar, B., Tseng, H.-A., Fernandez, F. R., Mount, R. A., Ta, T. L., White, J. A., Berg, J., & Han, X. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2570-19.2020 · Cited by 39
Pluvinage, J. V., Sun, J., Claes, C., Flynn, R. A., Haney, M. S., Iram, T., Meng, X., Lindemann, R. C., Riley, N. M., Danhash, E., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abg2919 · Cited by 40
Multi-cellular communities are perturbed in the aging human brain and Alzheimer’s disease
Cain, A., Taga, M., McCabe, C., Green, G., Hekselman, I., White, C. C., Lee, D. I., Gaur, P., Rozenblatt-Rosen, O., Zhang, F., et al. (2020) · doi:10.1101/2020.12.22.424084 · Cited by 143
Li, H., Wang, X., Wang, Y., Zhang, M., Fan, H. D., Wang, H., Cui, A., Zhao, J., Ji, W., & Chen, Y. (2022) · Cell Regeneration · doi:10.1186/s13619-022-00118-7 · Cited by 38
SOTIP is a versatile method for microenvironment modeling with spatial omics data
Yuan, Z., Li, Y., Shi, M., Yang, F., Gao, J. T., Yao, J., & Zhang, M. Q. (2022) · Nature Communications · doi:10.1038/s41467-022-34867-5 · Cited by 40
Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
Qian, J., Liao, J., Liu, Z., Chi, Y., Yin, F., Zheng, Y., Shao, X., Liu, B.-Q., Cui, Y., Guo, W., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38121-4 · Cited by 45
“We next perform scSpace to reconstruct the pseudo-space of human middle temporal gyrus (MTG) snRNA-seq data generated by Hodge et al. 30 , which contain 15,928 cells, including 10,708 excitatory neurons, 4297 inhibitory neurons, and 923 non-neuronal cells.”
InfluentialNeurogenesis in primates versus rodents and the value of non-human primate models
Zhang, R., Quan, H., Wang, Y., & Luo, F. (2023) · National Science Review · doi:10.1093/nsr/nwad248 · Cited by 47
“…outer radial glia (oRG) cell numbers in non-human primates, especially in human cortex, driving layer 2/3 upper-layer (UL) neuron generation [ 60 , 232 ]; varied oRG cell types in primates [ 33 ]; an increase in the number of cell divisions for basal intermediate progenitor cells (basal IPs) and…”
BackgroundSun, E., Ma, R., Negredo, P. N., Brunet, A., & Zou, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02184-y · Cited by 43
“S1A) paired with single-cell or single-nuclei RNAseq datasets (spanning Smart-seq, Drop-seq, and 10X Chromium technologies) from the same organism and tissue regions [24, 26, 15, 19, 9, 45, 16, 20, 36, 13, 4, 17, 48, 42].”
Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum
Lei, Y., Liu, Y., Wang, M., Yuan, N., Hou, Y., Ding, L., Zhu, Z., Wu, Z., Li, C., Zheng, M., et al. (2025) · Cell · doi:10.1016/j.cell.2025.02.037 · Cited by 43
Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia
Albert, K., Niskanen, J., Kälvälä, S., & Lehtonen, Š. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094334 · Cited by 40
“To study human diseases, human iPSCs are required since, despite some evolutionarily conserved similarities, rodent and human brains differ from each other [36].”
BackgroundGomes, C., VanderWall, K. B., Pan, Y., Lu, X., Lavekar, S. S., Huang, K.-C., Fligor, C. M., Harkin, J., Zhang, C., Cummins, T., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.05.006 · Cited by 39
“…astrocyte modulation of RGCs in glaucoma is due to studies using rodent models, important differences exist between rodents and humans, including a low degree of conservation of both RGCs as well as astrocytes (Hodge et al., 2019; Oberheim et al., 2009; Peng et al., 2019; Zhang et al., 2016b).”
BackgroundIntegrative inference of brain cell similarities and differences from single-cell genomicsPreprint
Welch, J. D., Kozareva, V., Ferreira, A. N., Vanderburg, C. R., Martin, C., & Macosko, E. Z. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/459891 · Cited by 40
Dumas, G., Malesys, S., & Bourgeron, T. (2021) · Genome Research · doi:10.1101/gr.262113.120 · Cited by 38
“2017), (B) human cortical layers (Table S6; Hodge et al. 2019; Tasic et al. 2018) and (C) the mouse cerebellum (Table S6; Carter et al.”
Layer 1 neocortex: Gating and integrating multidimensional signals
Huang, S., Wu, S. J., Sansone, G., Ibrahim, L. A., & Fishell, G. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.041 · Cited by 43
“4 5, 105 In the latter, L1 interneurons can be classified into two main NDNF+ interneuron-mediated inhibition NDNF+ interneurons are the majority of L1 interneurons.”
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
Li, K., Li, J., Tao, Y., & Wang, F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae171 · Cited by 42
“This iterative process is repeated for T steps until the 48 278 [30] final prediction is obtained when t = 0: where (cid:2) t ∼ N ( 0 , I ) .”
MethodWang, C., Wang, S., Xue, Y., Zhong, Y., Li, H., Hou, X., Kang, D. D., Liu, Z., Tian, M., Wang, L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02487-7 · Cited by 53
Rethinking Matrigel: The Complex Journey to Matrix Alternatives in Organoid Culture
Wolff, L., & Hendrix, S. (2025) · Advanced Science · doi:10.1002/advs.202508734 · Cited by 44
Delevich, K. M., Okada, N. J., Rahane, A., Zhang, Z., Hall, C. D., & Wilbrecht, L. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhz325 · Cited by 36
“neurons that are homologous to the cell types studied here (Lake et al. 2016; Hodge et al. 2019), although it has been argued that PT-type neurons are more abundant in mice”
BackgroundSimplified Brain Organoids for Rapid and Robust Modeling of Brain Disease
Ha, J., Kang, J. S., Lee, M., Baek, A., Kim, S., Chung, S., Lee, M., & Kim, J. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.594090 · Cited by 40
“Furthermore, recent advances in single-cell transcriptome revealed divergent gene expression between homolog cell types of human versus mouse cortex associated with neuronal connectivity, signaling, neurotransmitters, and cell-type-specific markers, implying functional variations between the two species (Hodge et al.,…”
BackgroundScaling up reproducible research for single-cell transcriptomics using MetaNeighbor
Fischer, S., Crow, M., Harris, B. D., & Gillis, J. A. (2021) · Nature Protocols · doi:10.1038/s41596-021-00575-5 · Cited by 36
Large Animal Models of Glioma: Current Status and Future Prospects
Hicks, W. H., Bird, C. E., Pernik, M. N., Haider, A. S., Dobariya, A., Abdullah, K. G., Aoun, S. G., Bentley, R. T., Cohen‐Gadol, A., Bachoo, R. M., et al. (2021) · Anticancer Research · doi:10.21873/anticanres.15347 · Cited by 44
Increased Alu RNA processing in Alzheimer brains is linked to gene expression changes
Cheng, Y., Saville, L., Gollen, B., Alvarez-Veronesi, A., Mohajerani, M. H., Joseph, J. T., & Zovoilis, A. (2021) · EMBO Reports · doi:10.15252/embr.202052255 · Cited by 35
“Available from: [https://celltypes.brain-map.org/rnaseq/human_ctx_sma rt-seq] based on (Tasic et al , 2018; Hodge et al , 2019).”
Next generation human brain models: engineered flat brain organoids featuring gyrification
Rothenbücher, T. S. P., Gürbüz, H., Pereira, M. P., Heiskanen, A. R., Emnéus, J., & Martı́nez-Serrano, A. (2021) · Biofabrication · doi:10.1088/1758-5090/abc95e · Cited by 37
“Besides, due to structural, and metabolic differences between species, animals serve as a very limitedmodel for the human brain [1].”
Translational Block in Stroke: A Constructive and “Out-of-the-Box” Reappraisal
Lourbopoulos, A. I., Mourouzis, I. S., Xinaris, C., Zerva, N., Filippakis, K., Pavlopoulos, A., & Pantos, C. I. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.652403 · Cited by 40
Challenges and Opportunities of Targeting Astrocytes to Halt Neurodegenerative Disorders
Valori, C. F., Possenti, A., Brambilla, L., & Rossi, D. (2021) · Cells · doi:10.3390/cells10082019 · Cited by 39
Persistent DNA damage associated with ATM kinase deficiency promotes microglial dysfunction
Bourseguin, J., Cheng, W., Talbot, E., Hardy, L., Lai, J. F., Jeffries, A. M., Lodato, M. A., Lee, E. A., & Khoronenkova, S. V. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac104 · Cited by 36
Hu, Y., Jiang, Y., Behnan, J., Ribeiro, M. M., Kalantzi, C., Zhang, M.-D., Lou, D., Häring, M., Sharma, N., Okawa, S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm6340 · Cited by 40
Wightman, D. P., Savage, J. E., Tissink, E., Romero, C., Jansen, I. E., & Posthuma, D. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.03.004 · Cited by 34
scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data
Ji, X., Tsao, D., Bai, K., Tsao, M., Xing, L., & Zhang, X. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad030 · Cited by 40
Time-aligned hourglass gastrulation models in rabbit and mouse
Mayshar, Y., Raz, O., Cheng, S., Ben-Yair, R., Hadas, R., Reines, N., Mittnenzweig, M., Ben-Kiki, O., Lifshitz, A., Tanay, A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.04.037 · Cited by 34
“Previous comparative single-cell atlas works analyzed either highly diverged species with non-alignable genomes,(38,39) or in the case of mammals, using lower resolution data.(19,30,40,41) These analyses thereforewere restricted to qualitative and coarse grain comparisons.”
D’Aloia, A., Pastori, V., Blasa, S., Campioni, G., Peri, F., Sacco, E., Ceriani, M., Lecchi, M., & Costa, B. (2024) · Cell Death Discovery · doi:10.1038/s41420-023-01790-7 · Cited by 36
“Speci fi cally, marked variations were observed in the proportions of neuronal and non-neuronal cells and in the transcription of genes associated with neuronal structure and activity [3].”
Eles, J. R., Stieger, K. C., & Kozai, T. D. Y. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc29c · Cited by 36
The impact of (ab)normal maternal environment on cortical development
Vasistha, N. A., & Khodosevich, K. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102054 · Cited by 38
Barth, M., Bacioglu, M., Schwarz, N., Novotny, R., Brandes, J., Welzer, M., Mazzitelli, S., Häsler, L. M., Schweighauser, M., Wuttke, T. V., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00471-2 · Cited by 35
Cellular resolution anatomical and molecular atlases for prenatal human brains
Ding, S., Royall, J. J., Lesnar, P., Facer, B. A. C., Smith, K. A., Wei, Y., Brouner, K., Dalley, R. A., Dee, N., Dolbeare, T. A., et al. (2021) · The Journal of Comparative Neurology · doi:10.1002/cne.25243 · Cited by 36
Self-citationCell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
Liu, Y. H., Smith, S. J., Mihalaş, Ş., Shea‐Brown, E., & Sümbül, U. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111821118 · Cited by 43
A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases
Zeighami, Y., Bakken, T. E., Nickl‐Jockschat, T., Peterson, Z. J., Jegga, A. G., Miller, J. A., Schulkin, J. A., Evans, A. C., Lein, E. S., & Hawrylycz, M. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002058 · Cited by 29
Self-citationEarly auditory processing dysfunction in schizophrenia: Mechanisms and implications
Dondé, C., Kantrowitz, J. T., Medalia, A. A., Saperstein, A. M., Balla, A., Sehatpour, P., Martı́nez, A., O’Connell, M. N., & Javitt, D. C. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105098 · Cited by 41
Ugarte, G., Piña, R., Contreras, D., Godoy, F., Rubio, D., Rozas, C., Zeise, M. L., Vidal, R., Escobar, J., & Morales, B. (2023) · Biology · doi:10.3390/biology12091241 · Cited by 35
“Comparative single-cell transcriptomic analysis of human and mouse cortical neurons show that serotonin receptor subunits (HTR3A and HTR3B) are among the most-divergent genes in terms of expression [87].”
Xu, J., Erlendsson, S., Singh, M., Holling, G. A., Regier, M., Ibiricu, I., Einstein, J., Hantak, M. P., Day, G. S., Piquet, A. L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.009 · Cited by 38
A study of gene expression in the living human brain
Liharska, L. E., Park, Y. J., Ziafat, K., Wilkins, L., Silk, H., Linares, L. M., Thompson, R. C., Vornholt, E., Sullivan, B., Cohen, V. V., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03163-1 · Cited by 36
Repeated head trauma causes neuron loss and inflammation in young athletes
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. (2025) · Nature · doi:10.1038/s41586-025-09534-6 · Cited by 57
“Next, owing to the known dysfunction and degeneration of neurons and synaptic dysfunction following head trauma and in neurodegenerative disease, we examined neurons, labelling subclusters using known layer-specific markers 12,26–31 (Fig.”
The application of in vitro ‐derived human neurons in neurodegenerative disease modeling
D’Souza, G. X., Rose, S. E., Knupp, A., Nicholson, D. A., Keene, C. D., & Young, J. E. (2020) · Journal of Neuroscience Research · doi:10.1002/jnr.24615 · Cited by 36
“Large-scale single cell transcriptome studies of the human brain, such as those done by Hodge and colleagues (Hodge et al., 2019), provide data about specific neuronal subtypes and will certainly influence hiPSC differentiation protocols to further refine in vitro cell types. hiPSCs have great…”
BackgroundSelf-citationBrain organoids: Human 3D models to investigate neuronal circuits assembly, function and dysfunction
Tambalo, M., & Lodato, S. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147028 · Cited by 36
Comprehensive in situ mapping of human cortical transcriptomic cell types
Langseth, C. M., Gyllborg, D., Miller, J. A., Close, J., Long, B., Lein, E. S., Hilscher, M. M., & Nilsson, M. F. (2021) · Communications Biology · doi:10.1038/s42003-021-02517-z · Cited by 33
Self-citationCardiac Investigations in Sudden Unexpected Death in DEPDC5 ‐Related Epilepsy
Bacq, A., Roussel, D., Bonduelle, T., Zagaglia, S., Maletic, M., Ribierre, T., Adle‐Biassette, H., Marchal, C., Jennesson, M. M., Gourfinkel‐An, I., et al. (2021) · Annals of Neurology · doi:10.1002/ana.26256 · Cited by 35
“Using a tagged HA-Depdc5 mouse, we show that Depdc5 protein is highly expressed in the cortex, and in both excitatory and inhibitory neurons, but not in glial cells, in accord with mouse and human single-cell RNA sequencing data.(51) As expected for an mTOR repressor,(24) Depdc5 colocalized with lysosomes.”
ResultLi, H., Li, H., Zhou, J., & Gao, X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac605 · Cited by 35
Medalla, M., Mo, B., Nasar, R. T., Zhou, Y., Park, J., & Luebke, J. I. (2023) · The Journal of Comparative Neurology · doi:10.1002/cne.25514 · Cited by 33
Multi-layered maps of neuropil with segmentation-guided contrastive learning
Dorkenwald, S., Li, P. H., Januszewski, M., Berger, D. R., Maitin-Shepard, J., Bodor, A. L., Collman, F., Schneider-Mizell, C. M., da Costa, N. M., Lichtman, J. W., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02059-8 · Cited by 30
“Here, we to compute adjusted logits (3).”
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018820 · Cited by 35
Self-citationLarge-scale neurophysiology and single-cell profiling in human neuroscience
Lee, A. T., Chang, E. F., Paredes, M. F., & Nowakowski, T. J. (2024) · Nature · doi:10.1038/s41586-024-07405-0 · Cited by 35
“What differs across species are cell-type-specific molecular profiles as well as spatial distribution, including across the layers of the cerebral cortex 136 , and deeper analyses have begun to unravel differences in cell-subtype diversity 137,138 .”
Persistent sodium currents in neurons: potential mechanisms and pharmacological blockers
Müller, P., Draguhn, A., & Egorov, A. V. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02980-7 · Cited by 36
“In any case, it is unlikely that Na v 1.8 and Na v 1.9 are responsible for I NaP in cortical neurons, as single cell transcriptomics of human and mouse cortex show no expression of both SCN10A (Na v 1.8) and SCN11A (Na v 1.9) [90].”
BackgroundInfluentialMatera, A., Compagnion, A.-C., Pedicone, C., Kotah, J. M., Ivanov, A., Monsorno, K., Labouèbe, G., Leggio, L., Pereira-Iglesias, M., Beule, D., et al. (2025) · Immunity · doi:10.1016/j.immuni.2024.11.003 · Cited by 34
“102,103 MGE interneurons could be split into two populations, based on Sst and Pvalb expression, while CGE interneurons formed two clusters, which could be split based on Cck and Vip expression.”
Ho, A. M., Coombes, B. J., Nguyen, T. T. L., Liu, D., McElroy, S. L., Singh, B., Nassan, M. M., Colby, C. L., Larrabee, B. R., Weinshilboum, R. M., et al. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1982 · Cited by 34
“For celltype-specific gene expression patterns in the human brain, we conducted searches in the Allen Brain Map Transcriptomics Explorer,(23) which contains transcriptomic data of sorted single-cell nuclei from human cortical regions.(24,25) We also searched for potential methylation quantitative trait loci among our…”
MethodSelf-citationABHD4-dependent developmental anoikis safeguards the embryonic brain
László, Z. I., Lele, Z., Zöldi, M., Miczán, V., Mógor, F., Simon, G. M., Mackie, K., Kacskovıcs, I., Cravatt, B. F., & Katona, I. (2020) · Nature Communications · doi:10.1038/s41467-020-18175-4 · Cited by 33
Learning for single-cell assignment
Duan, B., Zhu, C., Chuai, G., Tang, C., Chen, X., Chen, S., Fu, S., Li, G., & Liu, Q. (2020) · Science Advances · doi:10.1126/sciadv.abd0855 · Cited by 34
NECAB1 and NECAB2 are Prevalent Calcium-Binding Proteins of CB1/CCK-Positive GABAergic Interneurons
Miczán, V., Kelemen, K., Glavinics, J. R., László, Z. I., Barti, B., Kenesei, K., Kisfali, M., & Katona, I. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa326 · Cited by 30
“Although further experimental evidence is required to establish whether the NECAB1/parvalbumin-immunopositive cells indeed represent chandelier cells, it is suggestive that the new Allen Institute single-cell RNA-seq dataset based on ∼1.2 million cortical cells (Yao et al. 2020) contains a unique population of parvalb…”
BackgroundMethodInfluentialEngineered neural circuits for modeling brain physiology and neuropathology
Bang, S., Hwang, K. S., Jeong, S., Cho, I., Choi, N., Kim, J., & Kim, H. N. (2021) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.06.024 · Cited by 32
HTCA: a database with an in-depth characterization of the single-cell human transcriptome
Pan, L., Shan, S., Tremmel, R., Li, W., Liao, Z., Shi, H., Chen, Q., Zhang, X., & Li, X. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac791 · Cited by 34
Single cell epigenomic atlas of the developing human brain and organoids
Ziffra, R. S., Kim, C. N., Wilfert, A., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Kreimer, A., Pollard, K. S., Ament, S. A., et al. (2019) · doi:10.1101/2019.12.30.891549 · Cited by 33
Kooijmans, R. N., Sierhuis, W., Self, M. W., & Roelfsema, P. R. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa068 · Cited by 31
Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence
Salem, N. A., Manzano, L. A., Keist, M. W., Ponomareva, O. Y., Roberts, A. J., Roberto, M., & Mayfield, R. D. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106361 · Cited by 33
“The full list of cell specific differentially expressed genes is shown in Supplementary Table 5.”
BackgroundMultimodal cortical neuronal cell type classification
Mao, X., & Staiger, J. F. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02923-2 · Cited by 36
“The journey from intuitive morphological criteria to cutting-edge transcriptomic profiling to multimodal classification represents a remarkable evolution in our quest to unravel the intricacies of the brain’s cellular landscape [12].”
BackgroundGraph Fourier transform for spatial omics representation and analyses of complex organs
Chang, Y., Liu, J., Jiang, Y. F., Ma, A., Yeo, Y. Y., Guo, Q., McNutt, M. E., Krull, J. E., Rodig, S. J., Barouch, D. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51590-5 · Cited by 32
“(v) A total of 86 signature genes in the cortical region shared by humans and mice were collected from Hodge ’ s study 26 .”
Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
Meyerhoff, J. L., Muhie, S. Y., Chakraborty, N., Naidu, L., Sowe, B., Hammamieh, R., Jett, M., & Gautam, A. (2021) · Journal of Visualized Experiments · doi:10.3791/61941 · Cited by 38
Srinivasan, C., Phan, B. N., Lawler, A. J., Ramamurthy, E., Kleyman, M., Brown, A. R., Kaplow, I. M., Wirthlin, M. E., & Pfenning, A. R. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2534-20.2021 · Cited by 34
Transcriptional glucocorticoid effects in the brain: Finding the relevant target genes
Meijer, O. C., Buurstede, J. C., Viho, E. M. G., Amaya, J. M., Koning, A.-S. C. A. M., van der Meulen, M., van Weert, L. T. C. M., Paul, S. N., Kroon, J., & Koorneef, L. L. (2022) · Journal of Neuroendocrinology · doi:10.1111/jne.13213 · Cited by 34
“Mouse single-cell expression data of the cortex and hippocampus are now publicly available as a resource from the Allen Institute.39 We recently used the mouse Allen Brain Atlas data to describe MR and GR gene expression in the four main types of glutamatergic neurons, the five main types of GABAergic neurons, and non…”
MethodMao, S., Su, J., Wang, L., Bo, X., Li, C., & Chen, H. (2023) · Genome Research · doi:10.1101/gr.277491.122 · Cited by 32
“…biomarkers and build “ aging clocks, ” namely, predictors of chronological age and biological age, in various omics data sets and aging systems (Hannum et al. 2013; Horvath 2013; Peters et al. 2015; Levine et al. 2018; Lu et al. 2019; Belsky et al. 2020; Moaddel et al. 2021; Buckley et al. 2023).”
Background
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
