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
A single–cell type transcriptomics map of human tissues
Karlsson, M., Zhang, C., Méar, L., Zhong, W., Digre, A., Katona, B., Sjöstedt, E., Butler, L., Odeberg, J., Dusart, P., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh2169 · Cited by 1,768
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222
“Heatmap of Pearson correlation values evaluating the relationship between our derived layer-enriched statistics (y-axis) for 700 genes and (B) layer-enriched statistics from snRNA-seq data in human medial temporal cortex produced by Hodge et al. (Hodge et al., 2019) (these data only profiled layers 1-6 in the gray mat…”
BackgroundMethodInfluentialA taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065
Self-citationA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,203
Self-citationAn atlas of the protein-coding genes in the human, pig, and mouse brain
Sjöstedt, E., Zhong, W., Fagerberg, L., Karlsson, M., Mitsios, N., Adori, C., Oksvold, P., Edfors, F., Limiszewska, A., Hikmet, F., et al. (2020) · Science · doi:10.1126/science.aay5947 · Cited by 1,151
“More recently, brain atlas strategies have been launched on the basis of different approaches: fluorescence-activated cell sorting in mouse (7), antibody-based cell sorting in human (8), single-cell gene expression in mouse (9) and human (10, 11), and covariation analysis of transcriptomics expression (12).”
BackgroundConstruction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087
Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949
Transcriptomic diversity of cell types across the adult human brain
Siletti, K., Hodge, R., Mossi Albiach, A., Lee, K. W., Ding, S.-L., Hu, L., Lönnerberg, P., Bakken, T., Casper, T., Clark, M., et al. (2023) · Science · doi:10.1126/science.add7046 · Cited by 832
Self-citationComparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812
Self-citationDevelopment and Functional Diversification of Cortical Interneurons
Lim, L., Mi, D., Llorca, A., & Marín, O. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.009 · Cited by 714
Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V. (2021) · Nature Genetics · doi:10.1038/s41588-021-00894-z · Cited by 643
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628
Self-citationEffects of COVID-19 on the Nervous System
Iadecola, C., Anrather, J., & Kamel, H. (2020) · Cell · doi:10.1016/j.cell.2020.08.028 · Cited by 674
“Results from human single nuclei RNA-seq databases we mined (Hodge et al., 2019; Lake et al., 2018) are presented in Figures 1 and S1.”
Mitochondrial signal transduction
Picard, M. A., & Shirihai, O. S. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.10.008 · Cited by 688
Cell stress in cortical organoids impairs molecular subtype specification
Bhaduri, A., Andrews, M. G., Mancia Leon, W., Jung, D., Shin, D., Allen, D., Jung, D., Schmunk, G., Haeussler, M., Salma, J., et al. (2020) · Nature · doi:10.1038/s41586-020-1962-0 · Cited by 624
Dysregulation of brain and choroid plexus cell types in severe COVID-19
Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620
A multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522
Self-citationThe Human and Mouse Enteric Nervous System at Single-Cell Resolution
Drokhlyansky, E., Smillie, C. S., Wittenberghe, N. V., Ericsson, M., Griffin, G. K., Eraslan, G., Dionne, D. A., Cuoco, M. S., Goder-Reiser, M. N., Sharova, T. Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.003 · Cited by 606
“Mapping 185 disease risk genes (STAR Methods) onto cells from the human mucosa (Figure 7A) and muscularis (Figure 7B) revealed genes that were enriched in the ENS, even relative to the cortex (Figure 7C) (Hodge et al., 2019).”
Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538
Self-citationKamath, T., Abdulraouf, A., Burris, S. J., Langlieb, J., Gazestani, V., Nadaf, N. M., Balderrama, K., Vanderburg, C., & Macosko, E. Z. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01061-1 · Cited by 552
Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498
Self-citationvan Rheenen, W., van der Spek, R. A. A., Bakker, M. K., van Vugt, J. J. F. A., Hop, P. J., Zwamborn, R. A. J., de Klein, N., Westra, H.-J., Bakker, O. B., Deelen, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00973-1 · Cited by 542
Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.039 · Cited by 478
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532
Molecularly defined and spatially resolved cell atlas of the whole mouse brain
Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06808-9 · Cited by 531
Self-citationMolecular characterization of selectively vulnerable neurons in Alzheimer’s disease
Leng, K., Li, E., Eser, R., Piergies, A., Sit, R., Tan, M., Neff, N., Li, S. H., Rodriguez, R. D., Suemoto, C. K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00764-7 · Cited by 485
Maturation and circuit integration of transplanted human cortical organoids
Revah, O., Gore, F., Kelley, K. W., Andersen, J., Sakai, N., Chen, X., Li, M.-Y., Birey, F., Yang, X., Saw, N. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05277-w · Cited by 504
“We identified and categorized major cell classes through a combination of marker 19–21 , 35 gene expression expression and annotation via integration with reference Differential gene expression analysis between t-hCO and hCO subclasses was performed using a recently developed pseudobulk approach 36…”
Mathys, H., Peng, Z., Boix, C. A., Victor, M. B., Leary, N., Babu, S., Abdelhady, G., Jiang, X., Ng, A. P., Ghafari, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.039 · Cited by 495
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439
Self-citationSliced Human Cortical Organoids for Modeling Distinct Cortical Layer Formation
Qian, X., Su, Y., Adam, C. D., Deutschmann, A. U., Pather, S. R., Goldberg, E. M., Su, K., Li, S., Lu, L., Jacob, F., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.02.002 · Cited by 479
“…immunostaining for GFAP revealed three morphologically distinct astrocyte subtypes, which displayed hallmarks of protoplasmic astrocytes, fibrous astrocytes, and primate/human-specific interlaminar astrocytes, respectively (Figures 4G, S3D, and S3E; Hodge et al., 2019; Oberheim et al., 2009).”
BackgroundParker, K. R., Migliorini, D., Perkey, E., Yost, K. E., Bhaduri, A., Bagga, P., Haris, M., Wilson, N. E., Liu, F., Gabunia, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.022 · Cited by 464
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459
Development and arealization of the cerebral cortex
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408
Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications
Cerneckis, J., Cai, H., & Shi, Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01809-0 · Cited by 452
Marsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 431
Marsh, S. E., Kamath, T., Walker, A. J., Dissing-Olesen, L., Hammond, T. R., Young, A. M. H., Abdulraouf, A., Nadaf, N., Dufort, C., Murphy, S., et al. (2020) · doi:10.1101/2020.12.03.408542 · Cited by 430
Hulse, B. K., Haberkern, H., Franconville, R., Turner-Evans, D., Takemura, S.-Y., Wolff, T., Noorman, M., Dreher, M., Dan, C., Parekh, R., et al. (2021) · eLife · doi:10.7554/elife.66039 · Cited by 392
Sadick, J. S., O’Dea, M. R., Hasel, P., Dykstra, T., Faustin, A., & Liddelow, S. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.008 · Cited by 415
A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
Yao, Z., Liu, H., Xie, F., Fischer, S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Bartlett, A., Behrens, M. M., Van den Berge, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03500-8 · Cited by 398
Self-citationCell segmentation in imaging-based spatial transcriptomics
Petukhov, V., Xu, R. J., Soldatov, R. A., Cadinu, P., Khodosevich, K., Moffitt, J. R., & Kharchenko, P. V. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01044-w · Cited by 412
“Tissue processing was performed as previously described 38 , with some modifications.”
MethodIntegrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397
Self-citationOuter Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma
Bhaduri, A., Di Lullo, E., Jung, D., Müller, S., Crouch, E. E., Espinosa, C. S., Ozawa, T., Alvarado, B., Spatazza, J., Cadwell, C. R., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2019.11.015 · Cited by 419
TGF-β Signaling in Cellular Senescence and Aging-Related Pathology
Tominaga, K., & Suzuki, H. I. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20205002 · Cited by 393
Architectures of neuronal circuits
Luo, L. (2021) · Science · doi:10.1126/science.abg7285 · Cited by 390
Innovations present in the primate interneuron repertoire
Krienen, F. M., Goldman, M., Zhang, Q., C. H. del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2781-z · Cited by 381
“Total number of interneurons from each species met or exceeded comparable published single-cell datasets (e.g. Saunders et al. 2018, Cell; Hodge et al. 2019, Nature) Data exclusions Raw RNA-sequencing reads were quality filtered by standard quality filters (see…”
BackgroundInnovations in Primate Interneuron Repertoire
Krienen, F. M., Goldman, M., Zhang, Q., del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2019) · doi:10.1101/709501 · Cited by 372
A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types
Kozareva, V., Martin, C., Osorno, T., Rudolph, S., Guo, C., Vanderburg, C. R., Nadaf, N. M., Regev, A., Regehr, W. G., & Macosko, E. Z. (2021) · Nature · doi:10.1038/s41586-021-03220-z · Cited by 388
Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo
Chiaradia, I., & Lancaster, M. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00730-3 · Cited by 360
Single-cell multiregion dissection of Alzheimer’s disease
Mathys, H., Boix, C. A., Akay, L. A., Xia, Z., Davila-Velderrain, J., Ng, A. P., Jiang, X., Abdelhady, G., Galani, K., Mantero, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07606-7 · Cited by 343
“First, Spearman rank correlation coefficients between the average expression profiles of neuronal subpopulations previously defined by the Allen Brain Institute 33 and the neuronal subtypes identified in this study were computed using the cor function in R.”
InfluentialCOVID-19 and the Chemical Senses: Supporting Players Take Center Stage
Cooper, K. W., Brann, D. H., Farruggia, M. C., Bhutani, S., Pellegrino, R., Tsukahara, T., Weinreb, C., Joseph, P. V., Larson, E. D., Parma, V., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.032 · Cited by 341
Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329
“, 2017), Allen Institute for Brain Science (AIBS) (Hodge et al., 2019), Tasic (Tasic et al.”
BackgroundA single-cell atlas of the normal and malformed human brain vasculature
Winkler, E. A., Kim, C. N., Ross, J. M., Garcia, J. H., Gil, E., Oh, I., Chen, L. Q., Wu, D., Catapano, J. S., Raygor, K. P., et al. (2022) · Science · doi:10.1126/science.abi7377 · Cited by 324
“Because of biases in cell isolation, existing human brain cell atlas studies have overlooked the cerebrovasculature, and its cellular heterogeneity has been largely unexplored in humans (15, 16).”
In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes
Jin, X., Simmons, S. K., Guo, A., Shetty, A. S., Ko, M., Nguyen, L., Jokhi, V., Robinson, E., Oyler, P., Curry, N., et al. (2020) · Science · doi:10.1126/science.aaz6063 · Cited by 317
“Cell type Adult human brain (Hodge et al 2019) (29)”
BackgroundFranjic, D., Skarica, M., Ma, S., Arellano, J. I., Tebbenkamp, A. T. N., Choi, J., Xu, C., Li, Q., Morozov, Y. M., Andrijevic, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.10.036 · Cited by 308
“InN clusters were first classified to major groups based on the expression of three canonical function markers (PVALB, SST, VIP) as well as LAMP5, a marker mostly representing a group of neurogliaform InN and recently being adopted as a major InN marker (Tasic et al., 2018; Hodge et al., 2019).”
InfluentialMolecular landscapes of human hippocampal immature neurons across lifespan
Zhou, Y. F., Su, Y., Li, S., Kennedy, B. C., Zhang, D. Y., Bond, A. M., Sun, Y. Y., Jacob, F., Lu, L., Hu, P., et al. (2022) · Nature · doi:10.1038/s41586-022-04912-w · Cited by 314
Gyllborg, D., Langseth, C. M., Qian, X., Choi, E., Salas, S. M., Hilscher, M. M., Lein, E. S., & Nilsson, M. F. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa792 · Cited by 308
Self-citationHuman cerebral organoids — a new tool for clinical neurology research
Eichmüller, O. L., & Knoblich, J. A. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00723-9 · Cited by 319
Molecular atlas of the adult mouse brain
Ortiz, C., Navarro, J. F., Jurek, A., Märtin, A., Lundeberg, J., & Meletis, K. (2020) · Science Advances · doi:10.1126/sciadv.abb3446 · Cited by 286
Microglia in Alzheimer’s disease: pathogenesis, mechanisms, and therapeutic potentials
Miao, J., Ma, H., Yang, Y., Liao, Y., Lin, C., Zheng, J., Yu, M., & Lan, J. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1201982 · Cited by 293
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
Paulk, A. C., Kfir, Y., Khanna, A. R., Mustroph, M. L., Trautmann, E. M., Soper, D. J., Stavisky, S. D., Welkenhuysen, M., Dutta, B., Shenoy, K. V., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00997-0 · Cited by 283
Identification of neural oscillations and epileptiform changes in human brain organoids
Samarasinghe, R. A., Miranda, O. A., Buth, J. E., Mitchell, S. A., Ferando, I., Watanabe, M., Allison, T. F., Kurdian, A., Fotion, N. N., Gandal, M. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00906-5 · Cited by 288
Human neocortical expansion involves glutamatergic neuron diversification
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285
Self-citationA connectomic study of a petascale fragment of human cerebral cortex
Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2021) · doi:10.1101/2021.05.29.446289 · Cited by 243
“The input FoV was [403,403,42] voxels centered around the example points, corresponding to an output size of [191,191,14].”
BackgroundSingle-cell epigenomics reveals mechanisms of human cortical development
Ziffra, R. S., Kim, C. N., Ross, J. M., Wilfert, A. B., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Keough, K. C., Shin, D. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03209-8 · Cited by 272
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. (2021) · Nature · doi:10.1038/s41586-020-03182-8 · Cited by 289
Macroscopic gradients of synaptic excitation and inhibition in the neocortex
Wang, X. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0262-x · Cited by 289
Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH
Fang, R., Xia, C., Close, J. L., Zhang, M., He, J., Huang, Z., Halpern, A. R., Long, B., Miller, J. A., Lein, E. S., et al. (2022) · Science · doi:10.1126/science.abm1741 · Cited by 277
“Combining scRNA-seq with microdissection (8), and more recently in situ sequencing targeting 120 genes (9), have revealed the laminar organization of these transcriptionally defined neuronal cell types, in particular the excitatory neurons, in the human MTG.”
BackgroundMethodInfluentialSelf-citationBroad transcriptomic dysregulation occurs across the cerebral cortex in ASD
Gandal, M. J., Haney, J. R., Wamsley, B., Yap, C. X., Parhami, S., Emani, P. S., Chang, N., Chen, G. T., Hoftman, G. D., de Alba, D., et al. (2022) · Nature · doi:10.1038/s41586-022-05377-7 · Cited by 283
Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans
Vormstein-Schneider, D., Lin, J. D., Pelkey, K. A., Chittajallu, R., Guo, B., Arias-Garcia, M. A., Allaway, K., Sakopoulos, S., Schneider, G., Stevenson, O., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0692-9 · Cited by 235
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241
“Comparing human with mouse and non-human primates reveals both conserved and distinct cell type signatures, including species-specific changes in the proportion 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) .”
BackgroundMolecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer’s disease
Otero-Garcia, M., Mahajani, S. U., Wakhloo, D., Tang, W., Xue, Y.-Q., Morabito, S., Pan, J., Oberhauser, J., Madira, A. E., Shakouri, T., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.021 · Cited by 262
Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set
Kebschull, J. M., Richman, E. B., Ringach, N., Friedmann, D., Albarran, E., Kolluru, S. S., Jones, R. C., Allen, W. E., Wang, Y., Cho, S. W., et al. (2020) · Science · doi:10.1126/science.abd5059 · Cited by 262
“2020) and species (Hodge et al. 2019; Bakken et al. 2020; Boldog et al. 2018; Hodge et al. 2020; Tosches et al. 2018; Krienen et al. 2019; Peng et al. 2019; Hoang et al. 2019; Norimoto et al. 2020; Khrameeva et al. 2020) in a system that contains different numbers of homologous regions in”
BackgroundMolecular and cellular evolution of the primate dorsolateral prefrontal cortex
Ma, S., Škarica, M., Li, Q., Xu, C., Risgaard, R. D., Tebbenkamp, A. T. N., Mato-Blanco, X., Kovner, R., Krsnik, Ž., de Martin, X., et al. (2022) · Science · doi:10.1126/science.abo7257 · Cited by 257
“S15A), which has higher sequencing depth using SMART-seq (16).”
BackgroundMethodResultInfluentialConnectomic comparison of mouse and human cortex
Loomba, S., Straehle, J., Gangadharan, V., Heike, N., Khalifa, A., Motta, A., Ju, N., Sievers, M., Gempt, J., Meyer, H. S., et al. (2022) · Science · doi:10.1126/science.abo0924 · Cited by 242
Marton, R. M., & Pașca, S. P. (2019) · Trends in Cell Biology · doi:10.1016/j.tcb.2019.11.004 · Cited by 253
“neuronal subtypes or astrocytes that may influence how these cells interact [7,8].”
BackgroundVirtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders
Disorder, A. S., Disorder, B., Disorder, M. D., Disorder, O.-C., Groups, A. S. E. W., Patel, Y., Parker, N., Shin, J., Howard, D., French, L., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.2694 · Cited by 252
Automated methods for cell type annotation on scRNA-seq data
Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249
“One task likely to be significantly more challenging is the identification of cell subtypes, for example within the neuronal classes, as only few genes may be crucial for their discrimination [57].”
BackgroundUzquiano, A., Kedaigle, A. J., Pigoni, M., Paulsen, B. D. S., Adiconis, X., Kim, K., Faits, T., Nagaraja, S., Antón-Bolaños, N., Gerhardinger, C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.010 · Cited by 257
Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia
Bhuiyan, S. A., Xu, M., Yang, L., Semizoglou, E., Bhatia, P., Pantaleo, K. I., Tochitsky, I., Jain, A., Erdoğan, B., Blair, S. J., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj9173 · Cited by 260
Herring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landín, D. B., Clément, O., Goñi, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.039 · Cited by 242
“In contrast, L2/3-CUX2 and L5/6-THEMIS show maximum eigentrend values after birth, reflecting the increasing concentration of local projections at this time (Kostovi c et al., 2019).”
InfluentialThe diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201
Human brain organogenesis: Toward a cellular understanding of development and disease
Kelley, K. W., & Pașca, S. P. (2021) · Cell · doi:10.1016/j.cell.2021.10.003 · Cited by 244
“This contrasts with comparative adult brain single-cell gene expression analyses, which have identified substantial changes among homologous cell types in human, non-human primates, and rodents (Hodge et al., 2019; Krienen et al., 2020; Bakken et al., 2021).”
BackgroundResultSingle-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 229
Pfisterer, U., Petukhov, V., Demharter, S., Meichsner, J., Thompson, J. J., Batiuk, M. Y., Asenjo-Martínez, A., Vasistha, N. A., Thakur, A., Mikkelsen, J. D., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18752-7 · Cited by 238
Computational Models of Interoception and Body Regulation
Petzschner, F. H., Garfinkel, S. N., Paulus, M. P., Koch, C., & Khalsa, S. S. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.012 · Cited by 227
“In addition, while there are increasingly large-scale endeavors, like the cell census effort of the BRAIN initiative (www.biccn.org), to map and specify cell types in the various brain regions across species [90], there are currently no parallel endeavors for a functional map of the PNS.”
BackgroundGoetz, J. J., Jessen, Z. F., Jacobi, A., Mani, A., Cooler, S., Greer, D., Kadri, S., Segal, J. P., Shekhar, K., Sanes, J. R., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2022.111040 · Cited by 224
Clarke, Z. A., Andrews, T. S., Atif, J., Pouyabahar, D., Innes, B. T., MacParland, S. A., & Bader, G. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00534-0 · Cited by 234
“Typically, one-to-one orthologs are used 100,121 , which better ensures the conservation of function, although it is possible to use one-to-many and many-to-many relationships to increase gene coverage.”
BackgroundLu, T. M., Houghton, S., Magdeldin, T., Durán, J. G. B., Minotti, A. P., Snead, A. M., Sproul, A. A., Nguyen, D.-H. T., Xiang, J., Fine, H. A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016950118 · Cited by 224
“All samples were generated and sequenced by our group with the exception of two brain EC samples and one choroid plexus sample acquired from the Allen Institute Brain Map (37, 38) and Pellegrini et al. (39), respectively.”
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219
Spatial and cell type transcriptional landscape of human cerebellar development
Aldinger, K. A., Thomson, Z., Phelps, I. G., Haldipur, P., Deng, M., Timms, A. E., Hirano, M., Santpere, G., Roco, C. M., Rosenberg, A. B., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00872-y · Cited by 215
Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions
Girskis, K. M., Stergachis, A. B., DeGennaro, E. M., Doan, R. N., Qian, X., Johnson, M. B., Wang, P. P., Sejourne, G. M., Nagy, M. A., Pollina, E. A., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.005 · Cited by 193
“Longer cell cycles are thought to reduce the rate of somatic mutation or maintain DNA quality control and may facilitate formation of the remarkable diversity of neuronal types that constitute the human brain (Boldog et al., 2018; Hodge et al., 2019), but they come at the potential cost of increasing heterochrony, a l…”
BackgroundJung, M., Dourado, M., Maksymetz, J., Jacobson, A. R., Laufer, B. I., Baca, M., Foreman, O., Hackos, D. H., Riol‐Blanco, L., & Kaminker, J. S. (2023) · Nature Communications · doi:10.1038/s41467-023-36014-0 · Cited by 219
Comparative transcriptomics reveals human-specific cortical features
Jorstad, N. L., Song, J. H. T., Exposito-Alonso, D., Suresh, H., Castro-Pacheco, N., Krienen, F. M., Yanny, A. M., Close, J., Gelfand, E., Long, B., et al. (2023) · Science · doi:10.1126/science.ade9516 · Cited by 208
Self-citationEarly Alzheimer’s disease pathology in human cortex involves transient cell states
Gazestani, V., Kamath, T., Nadaf, N. M., Dougalis, A., Burris, S. J., Rooney, B., Junkkari, A., Vanderburg, C., Pelkonen, A., Gomez-Budia, M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.005 · Cited by 209
Transcriptomic cytoarchitecture reveals principles of human neocortex organization
Jorstad, N. L., Close, J., Johansen, N., Yanny, A. M., Barkan, E. R., Travaglini, K. J., Bertagnolli, D., Campos, J., Casper, T., Crichton, K., et al. (2023) · Science · doi:10.1126/science.adf6812 · Cited by 209
“However, cortical areas differ in positional topography, shape and size, laminar and columnar organization, and neuron proportions ( 8 – 10 ).”
BackgroundSelf-citationSingle-cell analysis of prenatal and postnatal human cortical development
Velmeshev, D., Perez, Y., Yan, Z., Valencia, J. E., Castaneda-Castellanos, D. R., Wang, L., Schirmer, L., Mayer, S., Wick, B., Wang, S., et al. (2023) · Science · doi:10.1126/science.adf0834 · Cited by 204
Ayhan, F., Kulkarni, A., Berto, S., Sivaprakasam, K., Douglas, C., Lega, B. C., & Konopka, G. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.003 · Cited by 207
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
