Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics
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Abstract
Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.
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Up to 50 distinct transcriptional signatures can be distinguished among inhibitory neurons in the cerebral cortex, though the final number may change with sequencing depth and analytical methods.
“Current estimates suggest that up to 50 different types of GABAergic neurons may populate the cerebral cortex”
From Development and Functional Diversification of Cortical Interneurons (2018) · cites this paper as Harris et al., 2018
Continuous gene expression variation within discrete interneuron types suggests that some cellular heterogeneity may be relatively common for some interneuron types.
“More generally, the identification of continuous gene expression variation within discrete types of cortical interneurons (Harris et al., 2018; Tasic et al., 2017) suggests that a certain degree of cellular heterogeneity might be relatively common for some interneuron types.”
From Development and Functional Diversification of Cortical Interneurons (2018) · cites this paper as Harris et al., 2018
Although all deep NGCs belong to the Lamp5 subclass, some are derived from the CGE and some from the MGE, as in the hippocampus.
“Our results suggest that although all deep NGCs belong to the Lamp5 subclass, some are derived from the CGE and some from the MGE, as in the hippocampus25–27.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [27]
Recent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds.
“Consistent with our interpretation, recent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds27,37–39.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [27]
Continuous variation has frequently been observed even in transcriptomic data from isolated cells.
“Even in transcriptomic data from isolated cells, continuous variations were frequently observed (Stanley et al., 2020; Harris et al., 2018; Tasic et al., 2018).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Harris et al., 2018
Comparing this taxonomy with six previous studies, they found generally good but variable correspondences.
“Comparing this taxonomy with six previous studies (Cembrowski et al., 2018; Harris et al., 2018; Saunders et al., 2018; Tasic et al., 2018; Yao et al., 2020; Zeisel et al., 2018), we found generally good but variable correspondences”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Harris et al., 2018
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 Harris et al., 2018
The CTX-HPF GABAergic taxonomy corresponds well with a previous scRNA-seq study of CA1 interneurons, particularly for some HPF-specific clusters identified here.
“Our CTX-HPF GABAergic taxonomy corresponds well with a previous scRNA-seq study of CA1 interneurons (Harris et al., 2018), particularly for some HPF-specific clusters identified here”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Harris et al., 2018
Neurons display substantial multi-dimensional variations, some of which are discrete and others continuous.
“First, neurons display substantial multi-dimensional variations, of which some are discrete whereas others seem continuous13,14.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [13]
Neurons that belong to the same recognized subclass or type frequently show considerable variation-both discrete and continuous-across several modalities.
“Even neurons recognized as members of a given subclass or type often manifest substantial variation (discrete and continuous) along multiple modalities.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [13]
Single-cell transcriptomic data contain both discrete and continuous forms of variation.
“For example, single-cell transcriptome data sets manifest discrete as well as continuous variations13,22.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [13]
There is coexistence of discrete and continuous variations of cell features across modalities at the lower branch level.
“In this context, another important finding, consistent with and building on multiple other studies9,11,68,69, is the coexistence of discrete and continuous variations of cell features across modalities at the lower branch level.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [68]
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Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
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 · see citing works
Interneurons of the hippocampus
Freund, T. F., & Buzsáki, G. (1998) · Hippocampus · doi:10.1002/(sici)1098-1063(1996)6:4 · Cited by 3,423 · see citing works
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 · see citing works
Self-citationInterneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136 · see citing works
Self-citationNeuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations
Klausberger, T., & Somogyi, P. (2008) · Science · doi:10.1126/science.1149381 · Cited by 2,050 · see citing works
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
GARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,357 · see citing works
Hu, H., Gan, J., & Jonas, P. (2014) · Science · doi:10.1126/science.1255263 · Cited by 1,197 · see citing works
Small-sample estimation of negative binomial dispersion, with applications to SAGE data
Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm030 · Cited by 1,073 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Habib, N., Basu, A., Avraham-Davidi, I., Burks, T., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., Weitz, D. A., Rozenblatt-Rosen, O., et al. (2017) · doi:10.1101/115196 · Cited by 1,069 · see citing works
Principles of connectivity among morphologically defined cell types in adult neocortex
Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A. S., Patel, S., & Tolias, A. S. (2015) · Science · doi:10.1126/science.aac9462 · Cited by 908 · see citing works
Hippocampal GABAergic Inhibitory Interneurons
Pelkey, K. A., Chittajallu, R., Craig, M. T., Tricoire, L., Wester, J. C., & McBain, C. J. (2017) · Physiological Reviews · doi:10.1152/physrev.00007.2017 · Cited by 889 · cited 1× · see citing works
“distinct connectivity, firing patterns, and molecular content (Bezaire and Soltesz, 2013; Freund and Buzsaki, 1996; Klausberger and Somogyi, 2008; Pelkey et al., 2017; Somogyi, 2010; Wheeler et al., 2015).”
Buhl, E. H., Halasy, K., & Somogyi, P. (1994) · Nature · doi:10.1038/368823a0 · Cited by 728 · see citing works
Self-citationParvalbumin-expressing basket-cell network plasticity induced by experience regulates adult learning
Donato, F., Rompani, S. B., & Caroni, P. (2013) · Nature · doi:10.1038/nature12866 · Cited by 703 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Hefft, S., & Jonas, P. (2005) · Nature Neuroscience · doi:10.1038/nn1542 · Cited by 583 · see citing works
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3445 · Cited by 581 · see citing works
Gulyás, A. I., Hájos, N., & Freund, T. F. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-10-03397.1996 · Cited by 512 · see citing works
Model-based clustering of high-dimensional data: A review
Bouveyron, C., & Brunet-Saumard, C. (2012) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.12.008 · Cited by 470 · see citing works
Klausberger, T., Marton, L. F., O'Neill, J., Huck, J. H. J., Dalezios, Y., Fuentealba, P., Suen, W. Y., Papp, E., Kaneko, T., Watanabe, M., et al. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.3269-05.2005 · Cited by 469 · see citing works
Self-citationHipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons
Cembrowski, M. S., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · eLife · doi:10.7554/elife.14997 · Cited by 422 · see citing works
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · see citing works
Spiegel, I., Mardinly, A. R., Gabel, H. W., Bazinet, J. E., Couch, C. H., Tzeng, C. P., Harmin, D. A., & Greenberg, M. E. (2014) · Cell · doi:10.1016/j.cell.2014.03.058 · Cited by 416 · cited 3× · see citing works
“Do such cells represent discrete classes with sharp inter-class boundaries, or do they represent points along a continuum? Furthermore, while a cell’s large-scale axonal and dendritic structure likely remains fixed throughout life, both gene expression and electrophysiological properties can be modified by factors suc…”
A Generalization of Principal Components Analysis to the Exponential Family
Collins, M., Dasgupta, S., & Schapire, R. E. (2001) · Cited by 383 · see citing works
Bezaire, M. J., & Soltesz, I. (2013) · Hippocampus · doi:10.1002/hipo.22141 · Cited by 373 · see citing works
Neuronal Diversity in GABAergic Long-Range Projections from the Hippocampus
Jinno, S., Klausberger, T., Marton, L. F., Dalezios, Y., Roberts, J. D. B., Fuentealba, P., Bushong, E. A., Henze, D., Buzsáki, G., & Somogyi, P. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1847-07.2007 · Cited by 369 · see citing works
Self-citationHow many subtypes of inhibitory cells in the hippocampus?
Parra, P., Gulyás, A., & Miles, R. (1998) · Neuron · doi:10.1016/s0896-6273(00)80479-1 · Cited by 369 · see citing works
The organization of two new cortical interneuronal circuits
Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L., & Zhu, J. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3305 · Cited by 338 · see citing works
Spatial Gene-Expression Gradients Underlie Prominent Heterogeneity of CA1 Pyramidal Neurons
Cembrowski, M. S., Bachman, J. L., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.013 · Cited by 328 · see citing works
Acsády, L., Görcs, T. J., & Freund, T. F. (1996) · Neuroscience · doi:10.1016/0306-4522(95)00609-5 · Cited by 310 · see citing works
A Blueprint for the Spatiotemporal Origins of Mouse Hippocampal Interneuron Diversity
Tricoire, L., Pelkey, K. A., Erkkila, B. E., Jeffries, B. W., Yuan, X., & McBain, C. J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0323-11.2011 · Cited by 292 · see citing works
Pawelzik, H., Hughes, D. I., & Thomson, A. M. (2002) · Journal of Comparative Neurology · doi:10.1002/cne.10118 · Cited by 262 · see citing works
Neurogliaform Neurons Form a Novel Inhibitory Network in the Hippocampal CA1 Area
Price, C. J., Cauli, B., Kovacs, E. R., Kulik, A., Lambolez, B., Shigemoto, R., & Capogna, M. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.1135-05.2005 · Cited by 255 · see citing works
Fuentealba, P., Begum, R., Capogna, M., Jinno, S., Márton, L. F., Csicsvari, J., Thomson, A., Somogyi, P., & Klausberger, T. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.034 · Cited by 255 · see citing works
Self-citationEcker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 254 · see citing works
Cell-specific STORM super-resolution imaging reveals nanoscale organization of cannabinoid signaling
Dudok, B., Barna, L., Ledri, M., Szabó, S. I., Szabadits, E., Pintér, B., Woodhams, S. G., Henstridge, C. M., Balla, G. Y., Nyilas, R., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3892 · Cited by 227 · see citing works
Somogyi, J., Baude, A., Omori, Y., Shimizu, H., Mestikawy, S. E., Fukaya, M., Shigemoto, R., Watanabe, M., & Somogyi, P. (2004) · European Journal of Neuroscience · doi:10.1111/j.0953-816x.2003.03091.x · Cited by 207 · see citing works
Self-citationIdentification of a population of sleep-active cerebral cortex neurons
Gerashchenko, D., Wisor, J. P., Burns, D., Reh, R. K., Shiromani, P. J., Sakurai, T., de la Iglesia, H. O., & Kilduff, T. S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803125105 · Cited by 207 · see citing works
Handbook of Brain Microcircuits
(2010) · Oxford University Press eBooks · doi:10.1093/med/9780195389883.001.0001 · Cited by 197 · see citing works
Inhibitory CA1-CA3-Hilar Region Feedback in the Hippocampus
Attila Gabor, S. I. K., Ylinen, A., Penttonen, M., & Buzsáki, G. (1994) · Science · doi:10.1126/science.8085161 · Cited by 197 · see citing works
Acsády, L., Arabadzisz, D., & Freund, T. F. (1996) · Neuroscience · doi:10.1016/0306-4522(95)00610-9 · Cited by 187 · see citing works
Mouse cortical inhibitory neuron type that coexpresses somatostatin and calretinin
Xu, X., Roby, K. D., & Callaway, E. M. (2006) · The Journal of Comparative Neurology · doi:10.1002/cne.21101 · Cited by 185 · see citing works
Ogiwara, I., Iwasato, T., Miyamoto, H., Iwata, R., Yamagata, T., Mazaki, E., Yanagawa, Y., Tamamaki, N., Hensch, T. K., Itohara, S., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt331 · Cited by 183 · see citing works
Sensory experience regulates cortical inhibition by inducing IGF1 in VIP neurons
Mardinly, A. R., Spiegel, I., Patrizi, A., Centofante, E., Bazinet, J. E., Tzeng, C. P., Mandel-Brehm, C., Harmin, D. A., Adesnik, H., Fagiolini, M., et al. (2016) · Nature · doi:10.1038/nature17187 · Cited by 176 · see citing works
Common Origins of Hippocampal Ivy and Nitric Oxide Synthase Expressing Neurogliaform Cells
Tricoire, L., Pelkey, K. A., Daw, M. I., Sousa, V. H., Miyoshi, G., Jeffries, B., Cauli, B., Fishell, G., & McBain, C. J. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5123-09.2010 · Cited by 171 · see citing works
Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch
Dehorter, N., Ciceri, G., Bartolini, G., Lim, L., del Pino, I., & Marín, O. (2015) · Science · doi:10.1126/science.aab3415 · Cited by 169 · see citing works
Katona, L., Lapray, D., Viney, T. J., Oulhaj, A., Borhegyi, Z., Micklem, B. R., Klausberger, T., & Somogyi, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.007 · Cited by 169 · see citing works
Self-citationA Generalization of Principal Component Analysis to the Exponential Family
Collins, M., Dasgupta, S., & Schapire, R. E. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0084 · Cited by 162 · see citing works
Network state-dependent inhibition of identified hippocampal CA3 axo-axonic cells in vivo
Viney, T. J., Lasztóczi, B., Katona, L., Crump, M., Tukker, J. J., Klausberger, T., & Somogyi, P. (2013) · Nature Neuroscience · doi:10.1038/nn.3550 · Cited by 155 · see citing works
Self-citationHippocampome.org: a knowledge base of neuron types in the rodent hippocampus
Wheeler, D. W., White, C. M., Rees, C. L., Komendantov, A. O., Hamilton, D. J., & Ascoli, G. A. (2015) · eLife · doi:10.7554/elife.09960 · Cited by 153 · see citing works
Daw, M. I., Tricoire, L., Erdelyi, F., Szabo, G., & McBain, C. J. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5760-08.2009 · Cited by 151 · see citing works
Cope, D. W., Maccaferri, G., Márton, L. F., Roberts, J. D. B., Cobden, P. M., & Somogyi, P. (2002) · Neuroscience · doi:10.1016/s0306-4522(01)00440-7 · Cited by 144 · see citing works
Self-citationPopulations of hippocampal inhibitory neurons express different levels of cytochrome <i>c</i>
Gulyás, A. I., Buzsáki, G., Freund, T. F., & Hirase, H. (2006) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2006.04814.x · Cited by 142 · see citing works
Tyan, L., Chamberland, S., Magnin, E., Camiré, O., Francavilla, R., David, L. S., Deisseroth, K., & Topolnik, L. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3813-13.2014 · Cited by 140 · see citing works
Ferraguti, F., Klausberger, T., Cobden, P., Baude, A., Roberts, J. D. B., Szucs, P., Kinoshita, A., Shigemoto, R., Somogyi, P., & Dalezios, Y. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.2547-05.2005 · Cited by 137 · see citing works
Self-citationBasket cell dichotomy in microcircuit function
Armstrong, C., & Soltész, I. (2011) · The Journal of Physiology · doi:10.1113/jphysiol.2011.223669 · Cited by 136 · see citing works
Multiple repressive mechanisms in the hippocampus during memory formation
Cho, J., Yu, N., Choi, J., Sim, S.-E., Kang, S. J., Kwak, C., Lee, S., Kim, J., Choi, D. I., Kim, V. N., et al. (2015) · Science · doi:10.1126/science.aac7368 · Cited by 135 · see citing works
Losonczy, A., Zhang, L., Shigemoto, R., Somogyi, P., & Nusser, Z. (2002) · The Journal of Physiology · doi:10.1113/jphysiol.2002.020024 · Cited by 129 · see citing works
Self-citationDual origins of functionally distinct O-LM interneurons revealed by differential 5-HT3AR expression
Chittajallu, R., Craig, M. T., McFarland, A., Yuan, X., Gerfen, S., Tricoire, L., Erkkila, B., Barron, S. C., Lopez, C. M., Liang, B. J., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3538 · Cited by 119 · see citing works
Lee, S.-H., Foldy, C., & Soltesz, I. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.6238-09.2010 · Cited by 114 · see citing works
Parvalbumin Deficiency and GABAergic Dysfunction in Mice Lacking PGC-1α
Lucas, E. K., Markwardt, S. J., Gupta, S., Meador‐Woodruff, J. H., Lin, J. D., Overstreet‐Wadiche, L., & Cowell, R. M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0698-10.2010 · Cited by 104 · see citing works
GABAergic projections from the hippocampus to the retrosplenial cortex in the rat
Miyashita, T., & Rockland, K. S. (2007) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2007.05745.x · Cited by 102 · see citing works
Cohen, S. M., Ma, H., Kuchibhotla, K. V., Watson, B. O., Buzsáki, G., Froemke, R. C., & Tsien, R. W. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.001 · Cited by 100 · see citing works
Neurogliaform and Ivy Cells: A Major Family of nNOS Expressing GABAergic Neurons
Armstrong, C., Krook-Magnuson, E., & Soltesz, I. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00023 · Cited by 88 · see citing works
Lu, J., Tomfohr, J. K., & Kepler, T. B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-165 · Cited by 79 · see citing works
Takács, V. T., Freund, T. F., & Gulyás, A. I. (2008) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2008.06319.x · Cited by 78 · see citing works
Chevée, M., Robertson, J. D., Cannon, G. H., Brown, S. P., & Goff, L. A. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.046 · Cited by 76 · see citing works
Structural organization of long-range GABAergic projection system of the hippocampus
Jinno, S. (2009) · Frontiers in Neuroanatomy · doi:10.3389/neuro.05.013.2009 · Cited by 75 · see citing works
Vruwink, M., Schmidt, H. H. H. W., Weinberg, R. J., & Burette, A. (2001) · The Journal of Comparative Neurology · doi:10.1002/cne.1413 · Cited by 72 · see citing works
Frazer, S., Prados, J., Niquille, M., Cadilhac, C., Markopoulos, F., Gomez, L., Tomasello, U., Telley, L., Holtmaat, A., Jabaudon, D., et al. (2017) · Nature Communications · doi:10.1038/ncomms14219 · Cited by 72 · cited 3× · see citing works
“Interneuron-selective (I-S) interneurons comprise at least 3 subtypes specifically targeting other inhibitory neurons, and expressing one or both of Vip and Calb2 (Acsady et al., 1996a, 1996b; Freund and Buzsaki, 1996; Gulyás et al., 1996).”
Lucas, E. K., Dougherty, S., McMeekin, L. J., Reid, C. S., Dobrunz, L. E., West, A. B., Hablitz, J. J., & Cowell, R. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1222-14.2014 · Cited by 65 · see citing works
Total number of neurons in the layers of the human entorhinal cortex
West, M. J., & Slomianka, L. (1998) · Hippocampus · doi:10.1002/(sici)1098-1063(1998)8:1 · Cited by 57 · see citing works
Blasco‐Ibáñez, J. M., Martinez-Guijarro, F. J., & Freund, T. F. (1998) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.1998.00190.x · Cited by 56 · see citing works
Hippocampus: Intrinsic Organization
Somogyi, P. (2010) · Oxford University Press eBooks · doi:10.1093/med/9780195389883.003.0015 · Cited by 52 · see citing works
Self-citationFuentealba, P., Tomioka, R., Dalezios, Y., Márton, L. F., Studer, M., Rockland, K. S., Klausberger, T., & Somogyi, P. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2052-08.2008 · Cited by 51 · see citing works
Self-citationKatona, L., Micklem, B., Borhegyi, Z., Swiejkowski, D. A., Valenti, O., Viney, T. J., Kotzadimitriou, D., Klausberger, T., & Somogyi, P. (2017) · Hippocampus · doi:10.1002/hipo.22696 · Cited by 50 · see citing works
Self-citationJinno, S., & Kosaka, T. (2004) · Neuroscience · doi:10.1016/j.neuroscience.2004.01.027 · Cited by 35 · see citing works
Multiple embryonic origins of nitric oxide synthase-expressing GABAergic neurons of the neocortex
Magno, L., Oliveira, M. G., Mucha, M. W., Rubin, A. N., & Kessaris, N. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00065 · Cited by 31 · see citing works
Self-citationAcsády, L., Pascual, M., Rocamora, N., Soriano, E., & Freund, T. F. (2000) · Neuroscience · doi:10.1016/s0306-4522(00)00091-9 · Cited by 25 · see citing works
Hippocampus: Intrinsic Organization
Somogyi, P., & Klausberger, T. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190636111.003.0017 · Cited by 22 · see citing works
Self-citationTranscriptional Architecture of Synaptic Communication Delineates Cortical GABAergic Neuron IdentityPreprint
Paul, A., Crow, M., Raudales, R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/180034 · Cited by 6 · see citing works
Chevée, M., Robertson, J. D., Cannon, G. H., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/157149 · Cited by 3 · see citing works
Finch (1996) · doi:10.1002/(sici)1098-1063(1996)6:5
Flagged first, then by citations
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The art of using t-SNE for single-cell transcriptomics
Kobak, D., & Berens, P. (2019) · Nature Communications · doi:10.1038/s41467-019-13056-x · Cited by 1,118 · see citing works
A 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
Development 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 · see citing works
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
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-citationMolecularly 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
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
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
“Consistent with our interpretation, recent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds 27,37–39 .”
Cell 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
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
Mickelsen, L. E., Bolisetty, M. T., Chimileski, B. R., Fujita, A., Beltrami, E. J., Costanzo, J., Naparstek, J., Robson, P., & Jackson, A. C. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0349-8 · Cited by 317 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 254 · see citing works
Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons
Cembrowski, M. S., & Spruston, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0125-5 · Cited by 258 · see citing works
Probabilistic cell typing enables fine mapping of closely related cell types in situ
Qian, X., Harris, K. D., Hauling, T., Nicoloutsopoulos, D., Muñoz‐Manchado, A. B., Skene, N., Hjerling‐Leffler, J., & Nilsson, M. F. (2019) · Nature Methods · doi:10.1038/s41592-019-0631-4 · Cited by 230 · see citing works
Self-citationThe 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
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 · see citing works
Diversity of Interneurons in the Dorsal Striatum Revealed by Single-Cell RNA Sequencing and PatchSeq
Muñoz-Manchado, A. B., Bengtsson Gonzales, C., Zeisel, A., Munguba, H., Bekkouche, B., Skene, N. G., Lönnerberg, P., Ryge, J., Harris, K. D., Linnarsson, S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.053 · Cited by 229 · see citing works
Self-citationPfisterer, 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 224 · see citing works
Distinct subnetworks of the thalamic reticular nucleus
Li, Y., López-Huerta, V. G., Adiconis, X., Levandowski, K. M., Choi, S., Simmons, S., Arias-García, M. A., Guo, B., Yao, A. Y., Blosser, T. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2504-5 · Cited by 208 · see citing works
The journey from melanocytes to melanoma
Centeno, P. P., Pavet, V., & Marais, R. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00565-7 · Cited by 202 · see citing works
Udakis, M., Pedrosa, V., Chamberlain, S. E. L., Claudia Clopath, A., & Mellor, J. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18074-8 · Cited by 195 · see citing works
Ion Channel Degeneracy, Variability, and Covariation in Neuron and Circuit Resilience
Goaillard, J., & Marder, E. E. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092920-121538 · Cited by 178 · see citing works
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows
Salas, S. M., Kuemmerle, L. B., Langseth, C. M., Tiesmeyer, S., Avenel, C., Hu, T., Rehman, H., Grillo, M., Czarnewski, P., Helgadóttir, S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02617-2 · Cited by 185 · see citing works
A transcriptomic axis predicts state modulation of cortical interneurons
Bugeon, S., Duffield, J. A. M., Dipoppa, M., Ritoux, A., Prankerd, I., Nicoloutsopoulos, D., Orme, D., Shinn, M., Peng, H., Forrest, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04915-7 · Cited by 177 · see citing works
Self-citationThe relationship between the gut microbiome and host gene expression: a review
Nichols, R. G., & Davenport, E. (2020) · Human Genetics · doi:10.1007/s00439-020-02237-0 · Cited by 166 · see citing works
The role of inhibitory circuits in hippocampal memory processing
Topolnik, L., & Tamboli, S. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00599-0 · Cited by 156 · see citing works
Continuous and Discrete Neuron Types of the Adult Murine Striatum
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C., & Quake, S. R. (2020) · Neuron · doi:10.1016/j.neuron.2019.11.004 · Cited by 158 · see citing works
Geiller, T., Vancura, B., Terada, S., Troullinou, E., Chavlis, S., Tsagkatakis, G., Tsakalides, P., Ócsai, K., Poirazi, P., Rózsa, B. J., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.013 · Cited by 159 · see citing works
Mirabella, F., Desiato, G., Mancinelli, S., Fossati, G., Rasile, M., Morini, R., Markicevic, M., Grimm, C., Amegandjin, C. A., Termanini, A., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.10.006 · Cited by 148 · see citing works
Complementary networks of cortical somatostatin interneurons enforce layer specific control
Naka, A., Veit, J., Shababo, B., Chance, R. K., Risso, D., Stafford, D., Snyder, B., Egladyous, A., Chu, D., Sridharan, S., et al. (2019) · eLife · doi:10.7554/elife.43696 · Cited by 117 · see citing works
Hippocampal GABAergic interneurons and memory
Tzilivaki, A., Tukker, J. J., Maier, N., Poirazi, P., Sammons, R. P., & Schmitz, D. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.016 · Cited by 145 · see citing works
Emerging links between cerebrovascular and neurodegenerative diseases—a special role for pericytes
Lendahl, U., Nilsson, P., & Betsholtz, C. (2019) · EMBO Reports · doi:10.15252/embr.201948070 · Cited by 141 · see citing works
Sun, Y.-C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J. A., & Zador, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00842-4 · Cited by 124 · see citing works
Imaging transcriptomics of brain disorders
Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A., & Fornito, A. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.10.002 · Cited by 113 · see citing works
Yamawaki, N., Li, X., Lambot, L., Ren, L. Y., Radulović, J., & Shepherd, G. M. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0355-x · Cited by 110 · see citing works
Reynolds, R. H., Botía, J. A., Nalls, M. A., Noyce, A. J., Nicolas, A., Cookson, M., Bandrés‐Ciga, S., Gibbs, J. R., Hernandez, D. G., Singleton, A., et al. (2019) · npj Parkinson s Disease · doi:10.1038/s41531-019-0076-6 · Cited by 100 · see citing works
Diversity and Function of Somatostatin-Expressing Interneurons in the Cerebral Cortex
Riedemann, T. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20122952 · Cited by 94 · see citing works
Microcircuits for spatial coding in the medial entorhinal cortex
Tukker, J. J., Beed, P. S., Brecht, M., Kempter, R., Moser, E. I., & Schmitz, D. (2021) · Physiological Reviews · doi:10.1152/physrev.00042.2020 · Cited by 89 · see citing works
Chartrand, T., Dalley, R., Close, J., Goriounova, N. A., Lee, B. R., Mann, R., Miller, J. A., Molnar, G., Mukora, A., Alfiler, L., et al. (2023) · Science · doi:10.1126/science.adf0805 · Cited by 88 · see citing works
Isoform cell-type specificity in the mouse primary motor cortex
Booeshaghi, A. S., Yao, Z., van Velthoven, C. T. J., Smith, K. A., Tasic, B., Zeng, H., & Pachter, L. S. (2021) · Nature · doi:10.1038/s41586-021-03969-3 · Cited by 81 · see citing works
Extensive and spatially variable within-cell-type heterogeneity across the basolateral amygdala
O’Leary, T. P., Sullivan, K. E., Wang, L., Clements, J., Lemire, A. L., & Cembrowski, M. S. (2020) · eLife · doi:10.7554/elife.59003 · Cited by 75 · see citing works
Cortical disinhibitory circuits: cell types, connectivity and function
Kullander, K., & Topolnik, L. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.009 · Cited by 83 · see citing works
Delorme, J. E., Wang, L., Kuhn, F. R., Kodoth, V., Ma, J., Martinez, J. D., Raven, F., Toth, B. A., Balendran, V., Vega-Medina, A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2019318118 · Cited by 77 · see citing works
Functional Access to Neuron Subclasses in Rodent and Primate Forebrain
Mehta, P., Kreeger, L., Wylie, D. C., Pattadkal, J. J., Lusignan, T., Davis, M. J., Turi, G. F., Li, W.-K., Whitmire, M. P., Chen, Y., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.011 · Cited by 67 · see citing works
Marissal, T., Salazar, R. F., Bertollini, C., Mutel, S., Roo, M. D., Rodríguez, I., Müller, D., & Carleton, A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0225-y · Cited by 77 · see citing works
Tossell, K., Yu, X., Giannos, P., Soto, B. A., Nollet, M., Yustos, R., Miracca, G., Vicente, M., Miao, A., Hsieh, B., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01430-4 · Cited by 82 · see citing works
Perez, J. D., Dieck, S. T., Alvarez‐Castelao, B., Tushev, G., Chan, I. C. W., & Schuman, E. M. (2021) · eLife · doi:10.7554/elife.63092 · Cited by 76 · see citing works
Human pallial MGE-type GABAergic interneuron cell therapy for chronic focal epilepsy
Bershteyn, M., Bröer, S., Parekh, M. B., Maury, Y., Havlicek, S., Kriks, S., Fuentealba, L. C., Lee, S., Zhou, R., Subramanyam, G., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.08.013 · Cited by 75 · see citing works
Nguyen, R., Venkatesan, S., Binko, M. A., Bang, J. Y., Cajanding, J. D., Briggs, C., Sargin, D., Imayoshi, I., Lambe, E. K., & Kim, J. C. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1919-19.2020 · Cited by 72 · see citing works
Guet-McCreight, A., Skinner, F. K., & Topolnik, L. (2020) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.00032 · Cited by 64 · see citing works
Wester, J. C., Mahadevan, V., Rhodes, C. T., Calvigioni, D., Venkatesh, S., Maric, D., Hunt, S., Yuan, X. Q., Zhang, Y., Petros, T. J., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.036 · Cited by 66 · see citing works
openTSNE : A Modular Python Library for t-SNE Dimensionality Reduction and Embedding
Poličar, P. G., Stražar, M., & Zupan, B. (2024) · Journal of Statistical Software · doi:10.18637/jss.v109.i03 · Cited by 210 · see citing works
An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types
Yao, Z., Liu, H., Xie, F., Fischer, S., Booeshaghi, A. S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Pinto-Duarte, A., Bartlett, A., et al. (2020) · doi:10.1101/2020.02.29.970558 · Cited by 397 · see citing works
Ripple-selective GABAergic projection cells in the hippocampus
Szabo, G. G., Farrell, J. S., Dudok, B., Hou, W., Ortiz, A. L., Varga, C., Moolchand, P., Gülsever, C. İ., Gschwind, T., Dimidschstein, J., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.04.002 · Cited by 61 · see citing works
A multimodal cell census and atlas of the mammalian primary motor cortexPreprint
BRAIN Initiative Cell Census Network (BICCN), Adkins, R. S., Aldridge, A. I., Allen, S. W., Ament, S. A., An, X., Armand, E. J., Ascoli, G. A., Bakken, T. E., Bandrowski, A. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.19.343129 · Cited by 60 · see citing works
PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes
Παπαδόπουλος, Ν., Parra, R. G., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz078 · Cited by 57 · see citing works
Que, L., Lukacsovich, D., Luo, W., & Földy, C. (2021) · Nature Communications · doi:10.1038/s41467-020-20328-4 · Cited by 58 · see citing works
Hippocampal hyperactivity in a rat model of Alzheimer’s disease
Sosulina, L., Mittag, M., Geis, H., Hoffmann, K., Klyubin, I. V., Qi, Y., Steffen, J., Friedrichs, D., Henneberg, N., Fuhrmann, F., et al. (2021) · Journal of Neurochemistry · doi:10.1111/jnc.15323 · Cited by 51 · see citing works
Guo, C., Huson, V., Macosko, E. Z., & Regehr, W. G. (2021) · Nature Communications · doi:10.1038/s41467-021-22893-8 · Cited by 50 · see citing works
Hippocampal hub neurons maintain distinct connectivity throughout their lifetime
Bocchio, M., Gouny, C., Angulo‐García, D., Toulat, T., Tressard, T., Quiroli, E., Baude, A., & Cossart, R. (2020) · Nature Communications · doi:10.1038/s41467-020-18432-6 · Cited by 48 · see citing works
Bomkamp, C., Tripathy, S. J., Gonzales, C. B., Hjerling‐Leffler, J., Craig, A. M., & Pavlidis, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007113 · Cited by 42 · see citing works
Self-citationParadoxical network excitation by glutamate release from VGluT3+ GABAergic interneurons
Pelkey, K. A., Calvigioni, D., Fang, C., Vargish, G., Ekins, T., Auville, K., Wester, J. C., Lai, M., Mackenzie-Gray Scott, C., Yuan, X., et al. (2020) · eLife · doi:10.7554/elife.51996 · Cited by 46 · see citing works
Structured inhibitory activity dynamics in new virtual environments
Arriaga, M. A., & Han, E. B. (2019) · eLife · doi:10.7554/elife.47611 · Cited by 48 · see citing works
Mosher, C. P., Wei, Y., Kamiński, J., Nandi, A., Mamelak, A. N., Anastassiou, C. A., & Rutishauser, U. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.02.027 · Cited by 44 · see citing works
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44 · see citing works
Palacios-Filardo, J., Udakis, M., Brown, G. A., Tehan, B. G., Congreve, M., Nathan, P. J., Brown, A., & Mellor, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-25280-5 · Cited by 49 · see citing works
Winterer, J., Lukacsovich, D., Que, L., Sartori, A. M., Luo, W., & Földy, C. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14549 · Cited by 40 · see citing works
Pai, E. L.-L., Chen, J., Darbandi, S. F., Cho, F. S., Chen, J., Lindtner, S., Chu, J. S. F., Paz, J. T., Vogt, D. L., Paredes, M. F., et al. (2020) · eLife · doi:10.7554/elife.54903 · Cited by 43 · see citing works
Almog, Y., Fadila, S., Brusel, M., Mavashov, A., Anderson, K., & Rubinstein, M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.105209 · Cited by 38 · see citing works
Predictive and robust gene selection for spatial transcriptomics
Covert, I., Gala, R., Wang, T., Svoboda, K., Sümbül, U., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37392-1 · Cited by 40 · see citing works
Specific Hippocampal Interneurons Shape Consolidation of Recognition Memory
da Cruz, J. F. O., Busquets-García, A., Zhao, Z., Varilh, M., Lavanco, G., Bellocchio, L., Robin, L. M., Cannich, A., Julio‐Kalajzić, F., Lesté-Lasserre, T., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2020.108046 · Cited by 40 · see citing works
Consistent cross-modal identification of cortical neurons with coupled autoencoders
Gala, R., Budzillo, A., Baftizadeh, F., Miller, J. A., Gouwens, N. W., Arkhipov, A. S., Murphy, G. J., Tasic, B., Zeng, H., Hawrylycz, M., et al. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00030-1 · Cited by 40 · see citing works
Jeong, N., & Singer, A. C. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102604 · Cited by 40 · see citing works
Neuronal chemo‐architecture of the entorhinal cortex: A comparative review
Kobro‐Flatmoen, A., & Witter, M. P. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14511 · Cited by 35 · see citing works
Lee, D. R., Rhodes, C. T., Mitra, A., Zhang, Y., Maric, D., Dale, R. K., & Petros, T. J. (2022) · eLife · doi:10.7554/elife.71864 · Cited by 39 · see citing works
Functional specialization of hippocampal somatostatin-expressing interneurons
Chamberland, S., Grant, G., Machold, R. P., Nebet, E. R., Tian, G., Stich, J. L., Hanani, M., Kullander, K., & Tsien, R. W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306382121 · Cited by 38 · see citing works
Luo, X. M., Muñoz-Pino, E., Francavilla, R., Vallée, M., Droit, A., & Topolnik, L. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01883-z · Cited by 37 · see citing works
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 35 · see citing works
Neurexin-3 defines synapse- and sex-dependent diversity of GABAergic inhibition in ventral subiculum
Boxer, E. E., Seng, C., Lukacsovich, D., Kim, J., Schwartz, S. L., Kennedy, M. J., Földy, C., & Aoto, J. N. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110098 · Cited by 35 · see citing works
Neurogliaform cells dynamically decouple neuronal synchrony between brain areas
Sakalar, E., Klausberger, T., & Lasztóczi, B. (2022) · Science · doi:10.1126/science.abo3355 · Cited by 35 · see citing works
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 29 · see citing works
Jones, E. A. A., Rao, A. T., Zilberter, M. Y., Djukic, B., Bant, J. S., Gillespie, A. K., Koutsodendris, N., Nelson, M. R., Yoon, S. Y., Huang, K., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110159 · Cited by 34 · see citing works
Hippocampal cholecystokinin-expressing interneurons regulate temporal coding and contextual learning
Guerrero, D. K. R., Balueva, K., Barayeu, U., Baracskay, P., Gridchyn, I., Nardin, M., Roth, C. N., Wulff, P., & Csicsvari, J. L. (2024) · Neuron · doi:10.1016/j.neuron.2024.03.019 · Cited by 33 · see citing works
Entorhinal cortex–hippocampal circuit connectivity in health and disease
Hernández‐Frausto, M., & Vivar, C. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1448791 · Cited by 34 · see citing works
Single-cell transcriptomics as a framework and roadmap for understanding the brain
Cembrowski, M. S. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108353 · Cited by 32 · see citing works
Novel long-range inhibitory nNOS-expressing hippocampal cells
Wick, Z. C., Tetzlaff, M. R., & Krook-Magnuson, E. I. (2019) · eLife · doi:10.7554/elife.46816 · Cited by 33 · see citing works
Automated identification of the mouse brain’s spatial compartments from in situ sequencing data
Partel, G., Hilscher, M. M., Milli, G., Solorzano, L., Klemm, A. H., Nilsson, M. F., & Wählby, C. (2020) · BMC Biology · doi:10.1186/s12915-020-00874-5 · Cited by 33 · see citing works
NMDA receptor function in inhibitory neurons
Booker, S. A., & Wyllie, D. J. A. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108609 · Cited by 33 · see citing works
A community-based transcriptomics classification and nomenclature of neocortical cell types
Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0685-8 · Cited by 32 · see citing works
Self-citationTamboli, S., Singh, S. P., Topolnik, D., Barkat, M. E. A., Radhakrishnan, R., Guet-McCreight, A., & Topolnik, L. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114115 · Cited by 33 · see citing works
Rovira-Esteban, L., Gunduz‐Cinar, O., Bukalo, O., Limoges, A., Brockway, E. T., Müller, K., Fenno, L. E., Kim, Y. S., Ramakrishnan, C., Andrási, T., et al. (2019) · eNeuro · doi:10.1523/eneuro.0220-19.2019 · Cited by 32 · see citing works
Venkadesh, S., Komendantov, A. O., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007462 · Cited by 32 · see citing works
Wang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 32 · see citing works
Hernández‐Vivanco, A., Cano-Adamuz, N., Sánchez-Aguilera, A., González‐Alonso, A., Rodríguez-Fernández, A., Azcoitia, Í., de la Prida, L. M., & Méndez, P. (2022) · Nature Communications · doi:10.1038/s41467-022-31635-3 · Cited by 33 · see citing works
Hernández‐Frausto, M., Bilash, O. M., Masurkar, A. V., & Basu, J. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1223891 · Cited by 33 · see citing works
Browne, T. J., Gradwell, M. A., Iredale, J. A., Madden, J. F., Callister, R. J., Hughes, D. I., Dayas, C. V., & Graham, B. A. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00032 · Cited by 30 · see citing works
Magno, L., Asgarian, Z., Pendolino, V., Velona, T., Mackintosh, A. I., Lee, F., Stryjewska, A., Zimmer, C., Guillemot, F., Farrant, M., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109249 · Cited by 28 · see citing works
Self-citationNeuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons
Shepherd, G. M., Marenco, L. N., Hines, M. L., Migliore, M., McDougal, R. A., Carnevale, N. T., Newton, A. J. H., Surles-Zeigler, M. C., & Ascoli, G. A. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00025 · Cited by 29 · see citing works
Li, R., & Quon, G. (2019) · Genome biology · doi:10.1186/s13059-019-1806-0 · Cited by 28 · see citing works
- Authors
- Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J.
- Affiliations
- National Hospital for Neurology and Neurosurgery (GB)
- Karolinska Institutet (SE)
- University College London (GB)
- University of Oxford (GB)
- Corresponding author
- Harris, Kenneth D.
- ORCID
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-06-18
- Volume
- 16
- Issue
- 6
- Pages
- e2006387
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC6029811
- OpenAlex
- W2951619011
- Cited by
- 276
- References
- 92
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 30 · 2023: 31 · 2022: 21 · 2021: 61 · 2020: 44 · 2019: 36 · 2018: 9 · 2017: 1
- FWCI
- 13.34
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-07-03)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (108726, 108726/Z/15/Z)
- European Commission (322304)
- Knut och Alice Wallenbergs Stiftelse
- Vetenskapsrådet (2014-03863, 2018-)
- Medical Research Council (MC_UU_12024, MC_UU_12024/4, MR/R011567/1)
- Biotechnology and Biological Sciences Research Council (BB/N009061/1)
- FP7 Ideas: European Research Council (261063)
- 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 › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- hippocampal CA1, GABAergic interneurons, single-cell transcriptomics, latent factor analysis, dendritic targeting, interneurons, gene expression clusters
- MeSH terms
- Action Potentials, Algorithms, Animals, Dendrites, Interneurons, Mice, Transgenic, Models, Neurological, Synaptic Transmission, Vasoactive Intestinal Peptide, Sequence Analysis, RNA, Pyramidal Cells, Chemokines, CXC, Mice, Receptor-Interacting Protein Serine-Threonine Kinases, Receptor-Interacting Protein Serine-Threonine Kinase 2, CA1 Region, Hippocampal, Single-Cell Analysis, GABAergic Neurons, Transcriptome
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