Conserved cell types with divergent features in human versus mouse cortex
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Abstract
Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.
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The more-advanced animal brains have the largest numbers of neuron types, with highly complex interactions.
“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
The Results section (Transcriptomic taxonomy of cell types; page not provided) identifies 69 neuronal types in the sampled human cortex, supporting substantial neuronal diversity. But the Results section (Human and mouse cell type homology; page not provided) says some subclasses are more diverse in humans and others in mice. The paper does not establish that more-advanced animal brains have the largest numbers of neuron types or demonstrate highly complex interactions among them. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNuclei were sorted using a standard gating strategy to exclude multiplets.
“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]
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.
“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]
Multiplex FISH is already used to measure the proportions of transcriptomic types in brain tissue.
“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
Single-cell RNA-sequencing studies have systematically characterized and classified cell types in individual regions of isocortex and hippocampus.
“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
Application of scRNA-seq to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types consistent overall with anatomical, physiological and developmental studies but with unmatched granularity.
“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]
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.
“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]
Comparing human with mouse and non-human primates reveals both conserved and distinct cell type signatures, including species-specific differences in proportions of cell populations and in gene expression within homologous cell classes and types.
“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
Patterns of evolutionary conservation and divergence reveal gene regulatory programs that were selected for effects on cognition.
“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
Many genes showed categorical changes in cell-type-specific expression, and rarely a gene marked one cell class in some species but was absent from interneurons in another.
“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]
These four marker genes stably define a core set of neocortical interneuron types in ferrets, mice and primates.
“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]
The expansion of LAMP5+ LHX6+ cells throughout primate neocortex probably explains why this cell type is much more abundant in human temporal lobe than in mouse V1.
“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]
The earlier study found 45 human interneuron clusters, while the marmoset data resolved into 22 clusters.
“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]
Rare cortical GABAergic cell categories, including MEIS2+ cells and putative Cajal-Retzius cells, appeared inconsistently across individual animals and regions.
“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]
The approach provides means to evaluate and compare circuit contributions and function of PV cINs across species regardless of genetic accessibility.
“Notably, this approach provides the means to evaluate and compare the circuit contributions and function of PV cINs across species, regardless of their genetic accessibility44,45.”
From Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans (2020) · cites this paper as [44]
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Calretinin-immunoreactive neurons in the primary visual cortex of dolphin and human brains
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Cellular Structure of the Human Cerebral Cortex
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Rapid Neuromodulation of Layer 1 Interneurons in Human Neocortex
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Cell type discovery and representation in the era of high-content single cell phenotyping
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Self-citationCalretinin-positive Cajal–Retzius cells persist in the adult human neocortex
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Spatiotemporal dynamics of the postnatal developing primate brain transcriptome
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Self-citationCyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis
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“Subpial Fan Cell” — A Class of Calretinin Neuron in Layer 1 of Adult Monkey Prefrontal Cortex
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Self-citationEvaluating stably expressed genes in single cellsPreprint
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A single–cell type transcriptomics map of human tissues
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Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
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An atlas of the protein-coding genes in the human, pig, and mouse brain
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“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).”
BackgroundTranscriptome-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 · see citing works
“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 · see citing works
Self-citationA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
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Construction of a human cell landscape at single-cell level
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Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
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Transcriptomic diversity of cell types across the adult human brain
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Self-citationComparative cellular analysis of motor cortex in human, marmoset and mouse
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Self-citationDevelopment and Functional Diversification of Cortical Interneurons
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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 · see citing works
A comparison of automatic cell identification methods for single-cell RNA sequencing data
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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 663 · see citing works
“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
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Cell stress in cortical organoids impairs molecular subtype specification
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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 · see citing works
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 · see citing works
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 585 · see citing works
“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 · see citing works
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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 · see citing works
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 · see citing works
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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 506 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“…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).”
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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 · see citing works
Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications
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Architectures of neuronal circuits
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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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
TGF-β Signaling in Cellular Senescence and Aging-Related Pathology
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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 · see citing works
“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 · see citing works
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 370 · see citing works
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 343 · see citing works
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 · see citing works
“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 335 · see citing works
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 · see citing works
“, 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 · see citing works
“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 · see citing works
“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 · see citing works
“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 309 · see citing works
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Self-citationHuman cerebral organoids — a new tool for clinical neurology research
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Molecular atlas of the adult mouse brain
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Microglia in Alzheimer’s disease: pathogenesis, mechanisms, and therapeutic potentials
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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 · see citing works
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 278 · see citing works
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 · see citing works
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 · see citing works
“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 267 · see citing works
DNA methylation atlas of the mouse brain at single-cell resolution
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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 277 · see citing works
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 · see citing works
“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 281 · see citing works
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 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
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“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 · see citing works
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 253 · see citing works
“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 256 · see citing works
“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 · see citing works
Marton, R. M., & Pașca, S. (2019) · Trends in Cell Biology · doi:10.1016/j.tcb.2019.11.004 · Cited by 202 · see citing works
“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 246 · see citing works
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 · see citing works
“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 250 · see citing works
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 247 · see citing works
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 226 · see citing works
“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 · see citing works
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 233 · see citing works
“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 · see citing works
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
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Computational Models of Interoception and Body Regulation
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“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 · see citing works
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 219 · see citing works
“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 215 · see citing works
“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 · see citing works
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 210 · see citing works
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 · see citing works
“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 212 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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Genetic mapping of etiologic brain cell types for obesity
Timshel, P. N., Thompson, J. J., & Pers, T. H. (2020) · eLife · doi:10.7554/elife.55851 · Cited by 194 · see citing works
“, 2012), and that broadly defined cell types were conserved between mouse and human (Hodge et al., 2019), analyses of human tissues may identify additional cell types critical to obesity development.”
BackgroundInfluential
- 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 · 2018: 6 · 2017: 1 · 2015: 1
- FWCI
- 64.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- University of Washington
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (12720, 12721)
- National Institutes of Health (5u01mh114812-03, MH114812-02)
- National Institute of Mental Health (U01MH105982, U01MH114812, U19MH114830)
- National Institute of General Medical Sciences (T32GM008497)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- human cerebral cortex, middle temporal gyrus, cortical cell types, excitatory neurons, inhibitory neurons, single-nucleus RNA sequencing, human-mouse comparison, cortical laminar organization, species differences, neuronal morphology, gene expression
- MeSH terms
- RNA-Seq, Adolescent, Adult, Aged, Animals, Astrocytes, Cerebral Cortex, Biological Evolution, Female, Humans, Male, Middle Aged, Neural Inhibition, Neurons, Species Specificity, Principal Component Analysis, Mice, Young Adult, Single-Cell Analysis, Transcriptome
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