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Shared and distinct transcriptomic cell types across neocortical areas
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Abstract
The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.
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“We also observed that the NP–CT–L6b-like (NP, near-projecting; CT, corticothalamic; L6b, layer 6b) subclasses emerge as a group highly distinct from the IT–ET-like (IT, intratelencephalic; ET, extratelencephalic) subclasses13,23,26,27, forming two distinct classes, IT–ET Glut and NP–CT–L6b Glut.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“One exception is cortical GABAergic cell types, which are shared across all cortical areas as we previously reported23,26”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While we found that different t-types had different preferred morphologies (Extended Data Table 1), they showed substantial overlap, in agreement with the L5 Pvalb t-types themselves not having clear boundaries4 (Fig. 1d).”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We chose to focus on SST neurons in the brain because this population is known to be diverse and to be composed of several relatively rare subpopulations (Muñoz et al., 2017; Tasic et al., 2018; Tasic et al., 2016), and thus might serve as a good test case.”
From A scalable platform for the development of cell-type-specific viral drivers (2019) · cites this paper as Tasic et al., 2018
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“We assessed Loki Decompose on our in-house triple-negative breast cancer (TNBC) dataset, a human colorectal cancer dataset48 and a brain dataset49,50.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [49]
The cited source says what the claim attributes to it.
In the citing paper
“Notably, the prediction of GABAergic subclasses and their spatial location in both directions appears to have more confusions, especially in the Subpallium-GABA neighbourhood, consistent with the more widespread distribution across multiple cortical areas of many GABAergic cell types23,26.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [26]
The cited source says what the claim attributes to it.
In the citing paper
“Clustering for both 10xv2 and 10xv3 datasets was performed independently using the in-house developed R package scrattch.bigcat (v0.0.5, available via github https://github.com/AllenInstitute/scrattch.bigcat), which is a scaled-up version of R package scrattch.hicat23,26 to deal with the increased size of datasets.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [26]
The cited source says what the claim attributes to it.
In the citing paper
“The anatomical distribution of the marker genes selective for specific t- or met-types have been characterized in a recent MERFISH study in the mouse primary motor cortex (MOp) (Zhang et al., 2020); they may be similarly distributed in the visual cortex since GABAergic t-types are shared across cortical areas (Tasic et al., 2018).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“Recent studies have defined taxonomies of mouse neocortical cell types by single-cell RNA-seq transcriptomic profiling of isolated neurons and glia12–14.”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“An important caveat to these findings is that most analyses performed in these studies are performed on populations of neurons.”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [131]
In the citing paper
“Loki Decompose accurately predicted the distribution of VLMCs, astrocytes, neurons from L2/3, L4/5 and L6 and oligodendrocytes, aligning closely with brain anatomic ref. 49.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [49]
In the citing paper
“VLMCs and astrocytes are concentrated near the cortical surface and pial borders (for example, layer 1), while oligodendrocytes are more prevalent in deeper layers and within white matter tracts49.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [49]
In the citing paper
“High-throughput single-cell sequencing techniques have identified dozens of types of neuron on the basis of their transcriptional profiles3,4,14,15, but linking transcriptomically defined cell types (t-types) to their phenotypes has remained a major challenge16.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“Similarly, cortical studies have identified many intermediate cells with uncertain t-type assignments3,4.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“Within-type distributions of soma depths (Fig. 1b) agreed well with previous data4 and with the layer-specific nomenclature of excitatory t-types, confirming the validity of our t-type assignment.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“In summary, different transcriptomic families had largely distinct morpho-electric phenotypes, in agreement with them being well separated in the transcriptomic space4.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“We observed the same in the Sst subclass, which is known to be transcriptomically4 and morpho-electrically29,30,33 diverse in L5. Here we also found that morpho-electric properties varied continuously across the transcriptomic landscape, with neighbouring t-types consistently showing similar morphologies and similar rebound values (Fig. 4b).”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“the three large transcriptomic groups (CGE-derived interneurons, MGE-derived interneurons, and excitatory neurons) that are perfectly separated from each other with no transcriptomically intermediate cells4.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [4]
In the citing paper
“transcriptomically-defined types of GABAergic interneurons are more readily conserved across cortical brain areas”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
In the citing paper
“Recent advances in the collection and analysis of single cell RNA-sequencing (scRNAseq) data provide a promising new way to define cell types and how they relate to one another”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
In the citing paper
“the number of VISp GABAergic “t-types” (60) and “me-types” (26) - differs by more than a factor of two”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
In the citing paper
“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 Tasic et al., 2018
In the citing paper
“Based on gene expression, Sncg neurons have been linked to Vip+/Cck + basket cells (Tasic et al., 2018).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
In the citing paper
“Glutamatergic neuron types are distinct between visual and motor cortical areas whereas GABAergic neuron types are shared between the two areas (Tasic et al., 2018).”
From What is a cell type and how to define it? (2022) · cites this paper as Tasic et al., 2018
In the citing paper
“The current CTX-HPF GABAergic taxonomy is largely consistent with previous transcriptomic taxonomies derived from cortical areas VISp-ALM (Tasic et al., 2018) and MOp (Yao et al., 2020) at the supertype level but exhibits notable ambiguity at the type/cluster level”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“These studies demonstrate the validity and power of using the highly scalable scRNA-seq approach to generate a comprehensive census of cell types as a foundation for further structural and functional studies of brain circuits.”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“most clusters are shared by all isocortical areas, consistent with our previous observations (Tasic et al., 2018), and also by RHP regions”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“Previously, we analyzed single-cell transcriptomes to define 133 cell types in two isocortical areas, primary visual cortex (VISp) and anterolateral motor cortex (ALM), in mice (Tasic et al., 2018) and found that they have shared GABAergic interneuron types but distinct glutamatergic neuron types.”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“The three ALM L5 PT types previously identified (Economo et al., 2018; Tasic et al., 2018) correspond to cluster #248 (thalamus-projecting cells) in the Npnt supertype and clusters #249 and #252 (medulla-projecting cells) in the Cdh13 supertype, respectively”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“This difference in developmental origins may be the underlying reason for the dichotomy between the extensive regional diversity of glutamatergic types and the sharing of a common set of GABAergic types across isocortical regions”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“In addition to the classical single-cell clustering processing steps provided by other tools such as Seurat (Butler et al., 2018), this package features automatic iterative clustering while ensuring all pairs of clusters, even at the finest level, are separable by fairly stringent differential gene expression criteria (Tasic et al., 2018).”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“Across all isocortical areas, most glutamatergic cell types are shared among multiple areas (Fig. 3B, S2), contrary to what we found in our previous study of two distantly located cortical areas, VISp and ALM”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“This suggests that Sst Calb2 Pdlim is a MC type, in agreement with the conclusions of Tasic et al.25 based on the data from Paul et al.46, and that Sst Hpse Cbln4 is a NMC type, in agreement with Naka et al.47.”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“The quality of the mapping was equally good for V1 and S1 cells (Supplementary Fig. 7), suggesting that the V1 + ALM dataset is a reasonable reference for S1 interneurons, in agreement with the idea that inhibitory transcriptomic cell types are shared across cortical regions25,26.”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“Recent large-scale studies of transcriptomic cell types in mouse and human cortex showed that most interneuron types are shared between cortical areas while the excitatory types are predominantly area-specific25,26.”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“We did not encounter any labeled NGCs in PV-Cre, SST-Cre, or VIP-Cre lines, suggesting that they belonged to the Lamp5 transcriptomic family25.”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“Using Patch-seq, we showed that SOM+ MCs in V1 L4 and SOM+ NMCs in S1 L4 correspond to two different transcriptomic cell types (Sst Calb2 Pdlim and Sst Hpse Cbln4 respectively) previously identified in a large-scale transcriptomic cell atlas25.”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“Tasic et al.25 showed that the majority of transcriptomic inhibitory types are shared between two very different cortical areas (V1 and ALM).”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“the transcriptomic reference dataset25 contained many V1 cells from the Sst Hpse Cbln4 type”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [25]
In the citing paper
“A subsequent large-scale study of ~23,800 cells incorporating multiple transgenic lines targeting neuronal subpopulations suggested 113 cortical cell types; these include ~60 GABAergic types conserved between visual and frontal cortex and 56 glutamatergic types that are mostly distinct between these two areas23 (Fig. 1).”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“This landmark data set represents the most comprehensive survey of cortical transcriptional types, establishing a scaffold for transcriptome-based classification and a working draft of cortical cell taxonomy, including GABAergic interneuron taxonomy.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“The ~60 ‘atomic’ GABAergic t-types at the base of the hierarchy23 are probably fluidic and may well be modified by future scRNAseq and orthogonal data sets.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“For example, the number of Sst transcriptional clusters can vary by a factor of 2 depending on the stringency of clustering (30 when less stringent and 15 when more stringent)23.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“Unexpectedly, a group of Lamp5 subclass interneurons with prominent Lhx6 and Nkx2.1 expression were classified as CGE-derived putative neurogliaform cells (NGFCs)23.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“The same large-scale analysis of adult mouse visual cortex36 used 28 Cre driver lines to enable broad coverage, selective targeting of rare populations and linking to previous single-cell transcriptomic data sets23 — the intrinsic properties of >1,900 neurons (928 spiny and 1,010 sparsely spiny) were analysed and the morphologies of 461 neurons (254 spiny and 207 aspiny) were partially”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“Thus, the extent to which the 60 t-types23 correspond to the 17 e-types and 38 m-types and 46 me-types36 is currently not well established.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [23]
In the citing paper
“Recent studies using transcriptomic characterization based on single-cell RNA-sequencing techniques performed unsupervised classification to generate a taxonomy of transcriptomic types in the mouse neocortex12–14.”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“A diverse set of transgenic mouse lines14–18 were used to ensure broad coverage of excitatory and inhibitory classes across all cortical layers, to enable selective targeting of rare cell populations, and to link our study with other experimental approaches, such as transcriptomic characterization19.”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“We note that we are not yet able to establish clear links to the recently described Sncg inhibitory subclass14”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“In L1-L4, the line is associated mostly with the Pvalb-Vipr2 type (expected to be chandelier cells14) and the Pvalb-Tpbg type (Fig. 7i).”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“It is known that individual t-types can exhibit substantial continuous variation within a type14”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“these cells are expected to have the Sst-Chodl t-type, linked to deep long-range projecting interneurons12,14,36,37 (Fig. 7h).”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“The recent finding of shared t-types among inhibitory cells but different t-types among excitatory cells from separate cortical areas14”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“Large-scale transcriptomic studies have provided informative taxonomies of cortical cell types12–14.”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [14]
In the citing paper
“The application of single-cell RNA sequencing (scRNA-seq) to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types that are consistent overall with knowledge from decades of anatomical, physiological and developmental studies but with an unmatched level of granularity7–11.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“MY-projecting neurons showed clear enrichment for L5 ET cluster 0 (Fig. 4e, Extended Data Fig. 3b), in agreement with scRNA-seq data for anterolateral motor cortex (ALM), part of MOs9,53.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“MERFISH data showed that L4/5 IT and L5 IT cells occupied distinct layers in MOp, and the L4/5 IT type expressed Rspo1 (Fig. 7c), a L4 cell-type marker in sensory cortical areas identified in previous studies9.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“Compared with the previous VISp–ALM transcriptomic taxonomy9, mouse MOp L5 ET_1 type corresponded to the ALM MY-projecting type, whereas MOp L5 ET_2-4 types corresponded to the ALM TH-projecting types37.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“MERFISH data showed that L4/5 IT and L5 IT cells occupied distinct layers in MOp, and the L4/5 IT type expressed Rspo1 (Fig. 7c), a L4 cell-type marker in sensory cortical areas identified in previous studies9.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“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 [9]
In the citing paper
“Substantiating previous studies9,10, our multimodal cross-species study of the primary motor cortex suggests that a general principle of cortical cell-type organization is its hierarchical relationship, whereby high-level classes linked by major branches comprise progressively finer subpopulations connected by minor branches.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
In the citing paper
“Recent scRNA-seq studies sampled >20,000–750,000 cells in individual mouse brain regions (Kozareva et al., 2020; Shekhar et al., 2016; Tasic et al., 2018; Yao et al., 2020a).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“A recurring theme from single-cell studies of the brain is the greater diversity of glutamatergic vs. GABAergic neurons across brain regions (Tasic et al., 2018; Yao et al., 2020b; Zeisel et al., 2015) or species (Bakken et al., 2020; Lake et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“Some cells, such as cortical layer 5 pyramidal tract neurons, may be more likely to die during cell isolation for scRNA-seq (Tasic et al., 2018).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“By infecting axon terminals in a defined brain region with a retrograde virus, the cell bodies of source neurons projecting to that region can be labeled, sorted, and their transcriptomes measured by sc/snRNA-seq (Retro-seq) (Tasic et al., 2018).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“As a result, high-throughput multi-omics studies in mouse cerebral cortex identified 9–22 cell types (Chen et al., 2019b; Zhu et al., 2019), compared with over 100 cell types found using scRNA-seq (Tasic et al., 2018; Yao et al., 2020a, 2020b).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Tasic et al., 2018
In the citing paper
“gene-expression patterns for these conserved types differed by cortical region in marmosets”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [18]
In the citing paper
“which suggests that the genes that encode these markers stably delineate a core repertoire of neocortical interneuron types in these species11,15,18”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [18]
In the citing paper
“for example, somatostatin-expressing cortical interneurons comprise several anatomically, electrophysiologically, and molecularly defined cell types whose dysfunction is associated with neuropsychiatric and neurological disorders (Jiang et al., 2015; Muñoz et al., 2017; Tasic et al., 2018).”
From A scalable platform for the development of cell-type-specific viral drivers (2019) · cites this paper as Tasic et al., 2018
In the citing paper
“the subclasses of excitatory neurons in different layers of cortex (ITL23GL, Cortex L2/3; ITL4GL, Cortex L4; ITL5GL, Cortex L5; PTGL, Cortex PT; NPGL, Cortex NP; ITL6GL, Cortex L6; CTGL, Cortex CT; and L6bGL, Cortex L6b) were clearly identified31”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [31]
In the citing paper
“These studies have also shown the ubiquity of neuron types that have the genetic constituents required for the release of more than one neurotransmitter.6,7,9,40,131”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [131]
In the citing paper
“and are likely the remaining non-MGE origin cells (arising from CGE and/or POA)14–16”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [14]
In the citing paper
“Prior studies indicate that inhibitory neurons show uniform molecular features even between distant cortical areas14.”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [14]
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Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
The Mouse Brain in Stereotaxic Coordinates
Paxinos, G. (2001) · Cited by 12,859
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
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Self-citationA multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,475
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
Specification of Cerebral Cortical Areas
Rakic, P. (1988) · Science · doi:10.1126/science.3291116 · Cited by 3,127
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
Pericytes: Developmental, Physiological, and Pathological Perspectives, Problems, and Promises
Armulik, A., Genové, G., & Betsholtz, C. (2011) · Developmental Cell · doi:10.1016/j.devcel.2011.07.001 · Cited by 2,653
A mesoscale connectome of the mouse brain
Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A. M., et al. (2014) · Nature · doi:10.1038/nature13186 · Cited by 2,612
Self-citationSmart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Fuster, J. M. (1997) · Cited by 2,095
dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering
Galili, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv428 · Cited by 1,826
New tools for studying microglia in the mouse and human CNS
Bennett, M. L., Bennett, F. C., Liddelow, S. A., Ajami, B., Zamanian, J. L., Fernhoff, N. B., Mulinyawe, S. B., Bohlen, C. J., Adil, A., Tucker, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1525528113 · Cited by 1,804
Adult 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
Self-citationNeuronal subtype specification in the cerebral cortex
Molyneaux, B. J., Arlotta, P., Menezes, J. R. L., & Macklis, J. D. (2007) · Nature reviews. Neuroscience · doi:10.1038/nrn2151 · Cited by 1,704
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,557
Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Rudy, B., Fishell, G., Lee, S., & Hjerling‐Leffler, J. (2011) · Developmental Neurobiology · doi:10.1002/dneu.20853 · Cited by 1,420
A Designer AAV Variant Permits Efficient Retrograde Access to Projection Neurons
Tervo, D. G. R., Hwang, B., Viswanathan, S., Gaj, T., Lavzin, M., Ritola, K., Lindo, S. E., Michael, S. C., Кулешова, Е. П., Ojala, D. S., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.021 · Cited by 1,220
Self-citationThe neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176
Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system
Marques, S., Zeisel, A., Codeluppi, S., van Bruggen, D., Mendanha Falcão, A., Xiao, L., Li, H., Häring, M., Hochgerner, H., Romanov, R. A., et al. (2016) · Science · doi:10.1126/science.aaf6463 · Cited by 1,132
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
Molecular Identity of Human Outer Radial Glia during Cortical Development
Pollen, A. A., Nowakowski, T. J., Chen, J., Retallack, H., Sandoval-Espinosa, C., Nicholas, C. R., Shuga, J., Liu, S. J., Oldham, M. C., Diaz, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.004 · Cited by 908
CellProfiler™: Free, Versatile Software for Automated Biological Image Analysis
Lamprecht, M. R., Sabatini, D. M., & Carpenter, A. E. (2007) · BioTechniques · doi:10.2144/000112257 · Cited by 880
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841
Self-citationA molecular census of arcuate hypothalamus and median eminence cell types
Campbell, J. N., Macosko, E. Z., Fenselau, H., Pers, T. H., Lyubetskaya, A., Tenen, D., Goldman, M., Verstegen, A. M. J., Resch, J. M., McCarroll, S. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4495 · Cited by 817
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., & Ritchie, M. E. (2016) · F1000Research · doi:10.12688/f1000research.9005.3 · Cited by 795
Flow of cortical activity underlying a tactile decision in mice
Guo, Z. V., Li, N., Huber, D., Ophir, E., Gutnisky, D., Ting, J. T., Feng, G., & Svoboda, K. (2014) · Neuron · doi:10.1016/j.neuron.2013.10.020 · Cited by 715
Self-citationAttwell, D., Mishra, A., Hall, C. N., O’Farrell, F. M., & Dalkara, T. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15610340 · Cited by 648
Ting, J. T., Daigle, T. L., Chen, Q., & Feng, G. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1096-0_14 · Cited by 631
Self-citationArea Patterning of the Mammalian Cortex
O’Leary, D. D. M., Chou, S., & Sahara, S. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.010 · Cited by 593
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
A motor cortex circuit for motor planning and movement
Li, N., Chen, T., Guo, Z. V., Gerfen, C. R., & Svoboda, K. (2015) · Nature · doi:10.1038/nature14178 · Cited by 571
Self-citationMaintenance of persistent activity in a frontal thalamocortical loop
Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., & Svoboda, K. (2017) · Nature · doi:10.1038/nature22324 · Cited by 529
Self-citationThe Evolution of the Brain, the Human Nature of Cortical Circuits, and Intellectual Creativity
DeFelipe, J. (2011) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2011.00029 · Cited by 527
Molecular taxonomy of major neuronal classes in the adult mouse forebrain
Sugino, K., Hempel, C. M., Miller, M. N., Hattox, A. M., Shapiro, P., Wu, C., Huang, Z. J., & Nelson, S. B. (2006) · Nature Neuroscience · doi:10.1038/nn1618 · Cited by 521
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497
Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation
Harris, J. A., Hirokawa, K. E., Sorensen, S. A., Gu, H., Mills, M., Ng, L. L., Bohn, P., Mortrud, M., Ouellette, B., Kidney, J., et al. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00076 · Cited by 485
Self-citationPerceptual Neuroscience: The Cerebral Cortex
Mountcastle, V. B. (1998) · Cited by 476
Deterministic Progenitor Behavior and Unitary Production of Neurons in the Neocortex
Gao, P., Postiglione, M. P., Krieger, T. G., Hernandez, L., Wang, C., Han, Z., Streicher, C., Papusheva, E., Insolera, R., Chugh, K., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.027 · Cited by 449
Zeng, H., Shen, E. H., Hohmann, J. G., Oh, S. W., Bernard, A., Royall, J. J., Glattfelder, K. J., Sunkin, S. M., Morris, J. A., Guillozet-Bongaarts, A. L., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.052 · Cited by 426
Self-citationTranscriptional 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
The cerebral cortex of the rat
(1991) · Comparative Biochemistry and Physiology Part A Physiology · doi:10.1016/0300-9629(91)90172-9 · Cited by 416
Distinct descending motor cortex pathways and their roles in movement
Economo, M. N., Viswanathan, S., Tasic, B., Bas, E., Winnubst, J., Menon, V., Graybuck, L. T., Nguyen, T. N., Smith, K. A., Yao, Z., et al. (2018) · Nature · doi:10.1038/s41586-018-0642-9 · Cited by 415
Self-citationLiu, H., Agam, Y., Madsen, J. R., & Kreiman, G. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.025 · Cited by 402
Kim, Y., Yang, G. R., Pradhan, K., Venkataraju, K. U., Bota, M., García del Molino, L. C., Fitzgerald, G., Ram, K., He, M., Levine, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.020 · Cited by 389
High-Efficiency FLP and ΦC31 Site-Specific Recombination in Mammalian Cells
Raymond, C. S., & Soriano, P. M. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000162 · Cited by 385
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383
Self-citationNeural mechanisms of movement planning: motor cortex and beyond
Svoboda, K., & Li, N. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.023 · Cited by 347
Self-citationStrategies and Tools for Combinatorial Targeting of GABAergic Neurons in Mouse Cerebral Cortex
He, M., Tucciarone, J., Lee, S., Nigro, M. J., Kim, Y., Levine, J. M., Kelly, S. M., Krugikov, I., Wu, P., Chen, Y., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.08.021 · Cited by 339
Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W., & Callaway, E. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.002 · Cited by 323
Massive loss of Cajal-Retzius cells does not disrupt neocortical layer order
Yoshida, M., Assimacopoulos, S., Jones, K. R., & Grove, E. A. (2006) · Development · doi:10.1242/dev.02209 · Cited by 284
An anatomic gene expression atlas of the adult mouse brain
Ng, L., Bernard, A., Lau, C., Overly, C. C., Dong, H., Kuan, C. L., Pathak, D. S., Sunkin, S. M., Dang, C., Bohland, J. W., et al. (2009) · Nature Neuroscience · doi:10.1038/nn.2281 · Cited by 279
Self-citationThe spatial and temporal origin of chandelier cells in mouse neocortex
Taniguchi, H., Lu, J., & Huang, Z. J. (2013) · Science · doi:10.1126/science.1227622 · Cited by 278
Development, evolution and pathology of neocortical subplate neurons
Hoerder‐Suabedissen, A., & Molnár, Z. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3915 · Cited by 278
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Li, H., Horns, F., Wu, B., Xie, Q., Li, J., Li, T., Luginbuhl, D. J., Quake, S. R., & Luo, L. (2017) · Cell · doi:10.1016/j.cell.2017.10.019 · Cited by 261
Developmental and adult phenotyping directly from mutant embryonic stem cells
George, S. H. L., Gertsenstein, M., Vintersten, K., Korets-Smith, E., Murphy, J. T., Stevens, M. E., Haigh, J. J., & Nagy, A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609277104 · Cited by 258
A Map of Anticipatory Activity in Mouse Motor Cortex
Chen, T., Li, N., Daie, K., & Svoboda, K. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.005 · Cited by 219
Self-citationHevner, R. F., Neogi, T., Englund, C., Daza, R. A. M., & Fink, A. (2003) · Developmental Brain Research · doi:10.1016/s0165-3806(02)00641-7 · Cited by 202
Organization of the connections between claustrum and cortex in the mouse
Wang, Q., Ng, L., Harris, J. A., Feng, D. H., Li, Y., Royall, J. J., Oh, S. W., Bernard, A., Sunkin, S. M., Koch, C., et al. (2016) · The Journal of Comparative Neurology · doi:10.1002/cne.24047 · Cited by 199
Self-citationHnasko, T. S., Perez, F. A., Scouras, A. D., Stoll, E. A., Gale, S. D., Luquet, S. H., Phillips, P. E. M., Kremer, E. J., & Palmiter, R. D. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603081103 · Cited by 198
Bondjers, C., He, L., Takemoto, M., Norlin, J., Asker, N., Hellström, M., Lindahl, P., Betsholtz, C., Bondjers, C., He, L., et al. (2006) · The FASEB Journal · doi:10.1096/fj.05-4944fje · Cited by 193
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Chatterjee, S., Sullivan, H. A., MacLennan, B. J., Xu, R., Hou, Y., Lavin, T. K., Lea, N. E., Michalski, J. E., Babcock, K. R., Dietrich, S., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0091-7 · Cited by 177
Self-citationSmart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · F1000Research · doi:10.7490/f1000research.1094785.1 · Cited by 173
Efficient Algorithms for t-distributed Stochastic Neighborhood EmbeddingPreprint
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S., & Kluger, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.09005 · Cited by 172
Groh, A., Meyer, H. S., Schmidt, E. F., Heintz, N., Sakmann, B. J., & Krieger, P. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp152 · Cited by 171
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Moreno‐Juan, V., Filipchuk, A., Antón-Bolaños, N., Mezzera, C., Gezelius, H., Andrés, B., Rodríguez-Malmierca, L. M., Susín, R., Schaad, O., Iwasato, T., et al. (2017) · Nature Communications · doi:10.1038/ncomms14172 · Cited by 166
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Hoerder‐Suabedissen, A., Hayashi, S., Upton, L., Nolan, Z. T., Casas-Torremocha, D., Grant, E., Viswanathan, S., Kanold, P. O., Clascá, F., Kim, Y., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy036 · Cited by 162
Self-citationDurand, S., Iyer, R., Mizuseki, K., de Vries, S. E. J., Mihalaş, Ş., & Reid, R. C. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1741-16.2016 · Cited by 146
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Chou, S., Babot, Z., Leingärtner, A., Studer, M., Nakagawa, Y., & O’Leary, D. D. M. (2013) · Science · doi:10.1126/science.1232806 · Cited by 144
Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity
Sorensen, S. A., Bernard, A., Menon, V., Royall, J. J., Glattfelder, K. J., Desta, T., Hirokawa, K., Mortrud, M., Miller, J. A., Zeng, H., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht243 · Cited by 131
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Yao, Z., Mich, J. K., Ku, S., Menon, V., Krostag, A.-R., Martinez, R. A., Furchtgott, L., Mulholland, H., Bort, S. J., Fuqua, M. A., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.09.011 · Cited by 130
Self-citationA manual method for the purification of fluorescently labeled neurons from the mammalian brain
Hempel, C. M., Sugino, K., & Nelson, S. B. (2007) · Nature Protocols · doi:10.1038/nprot.2007.416 · Cited by 124
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Hilscher, M. M., Leão, R. N., Edwards, S. J., Leão, K. E., & Kullander, K. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001392 · Cited by 118
Man, K., Kaplan, J., Damásio, H., & Damásio, A. R. (2013) · The Journal of Comparative Neurology · doi:10.1002/cne.23408 · Cited by 117
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Vue, T. Y., Lee, M., Tan, Y. E., Werkhoven, Z., Wang, L., & Nakagawa, Y. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5786-12.2013 · Cited by 93
Hobert, O. (2014) · genesis · doi:10.1002/dvg.22747 · Cited by 90
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Kopatz, J., Beutner, C., Welle, K., Bodea, L., Reinhardt, J., Claude, J., Linnartz‐Gerlach, B., & Neumann, H. (2013) · Glia · doi:10.1002/glia.22501 · Cited by 76
Kirischuk, S., Luhmann, H. J., & Kilb, W. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.06.009 · Cited by 72
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
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He, M., Tucciarone, J., Lee, S., Nigro, M. J., Kim, Y., Levine, J. M., Kelly, S. M., Krugikov, I., Wu, P., Chen, Y., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.009 · Cited by 71
Extracortical origin of some murine subplate cell populations
Pedraza, M., Hoerder‐Suabedissen, A., Albert-Maestro, M. A., Molnár, Z., & Carlos, J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1323816111 · Cited by 65
Cellular mechanisms of temporal sensitivity in visual cortex neurons
Cardin, J. A., Kumbhani, R. D., Contreras, D., & Palmer, L. A. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5279-09.2010 · Cited by 62
Abellán, A., Menuet, A., Dehay, C., Medina, L. M., & Rétaux, S. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp242 · Cited by 56
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Ayoub, A. E., & Kostović, I. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp025 · Cited by 37
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Essen, D. C. V., Donahue, C. J., Dierker, D. L., & Glasser, M. F. (2016) · Research and perspectives in neurosciences · doi:10.1007/978-3-319-27777-6_7 · Cited by 26
Tasic, B., Levi, B. P., & Menon, V. (2017) · Springer eBooks · doi:10.1007/978-3-319-57363-2_18 · Cited by 10
Self-citation[Case of mucocele of the frontal sinus].
Akita, T., & Sugahara, M. (1971) · PubMed · Cited by 4
Research works printed but not linked: 1
- Tasic, B., Levi, B. P. & Menon, V. in Decoding Neural Circuit Structure and Function: Cellular Dissection Using Genetic Model Organisms (eds A. Çelik & M. F. Wernet) 437–468 (Springer International Publishing, New York, 2017).
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Metadata
- Authors
- Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., Penn, O., Bakken, T., Menon, V., Miller, J., Fong, O., Hirokawa, K. E., Lathia, K., Rimorin, C., Tieu, M., Larsen, R., Casper, T., Barkan, E., Kroll, M., Parry, S., Shapovalova, N. V., Hirschstein, D., Pendergraft, J., Sullivan, H. A., Kim, T. K., Szafer, A., Dee, N., Groblewski, P., Wickersham, I., Cetin, A., Harris, J. A., Levi, B. P., Sunkin, S. M., Madisen, L., Daigle, T. L., Looger, L., Bernard, A., Phillips, J., Lein, E., Hawrylycz, M., Svoboda, K., Jones, A. R., Koch, C., & Zeng, H.
- Affiliations
- Allen Institute for Brain Science (US)
- Howard Hughes Medical Institute (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Tasic, Bosiljka
- ORCID
- Tasic, Bosiljka
- Yao, Zizhen
- Graybuck, Lucas T.
- Smith, Kimberly A.
- Nguyen, Thuc Nghi
- Bertagnolli, Darren
- Goldy, Jeff
- Economo, Michael N.
- Bakken, Trygve
- Menon, Vilas
- Miller, Jeremy
- Fong, Olivia
- Hirokawa, Karla E.
- Lathia, Kanan
- Rimorin, Christine
- Tieu, Michael
- Larsen, Rachael
- Casper, Tamara
- Barkan, Eliza
- Kroll, Matthew
- Shapovalova, Nadiya V.
- Hirschstein, Daniel
- Pendergraft, Julie
- Sullivan, Heather A.
- Kim, Tae Kyung
- Szafer, Aaron
- Dee, Nick
- Groblewski, Peter
- Wickersham, Ian
- Cetin, Ali
- Harris, Julie A.
- Levi, Boaz P.
- Sunkin, Susan M.
- Daigle, Tanya L.
- Looger, Loren
- Bernard, Amy
- Phillips, John
- Lein, Ed
- Hawrylycz, Michael
- Svoboda, Karel
- Jones, Allan R.
- Zeng, Hongkui
- Venue
- Nature
- Published
- 2018-10-31
- Volume
- 563
- Issue
- 7729
- Pages
- 72-78
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6456269
- OpenAlex
- W2950976066
- Cited by
- 1,746
- References
- 99
- Citations by year
- 2026: 174 · 2025: 193 · 2024: 244 · 2023: 261 · 2022: 232 · 2021: 267 · 2020: 209 · 2019: 137 · 2018: 26
- FWCI
- 56.81
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OpenBU (Boston University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Universitat Autònoma de Barcelona
- National Institutes of Health
- National Institute of Mental Health (U01MH105982, U19MH114830)
- National Eye Institute (R01EY023173)
- 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
- primary visual cortex, anterior lateral motor cortex, GABAergic neurons, glutamatergic neurons, mouse neocortex, single-cell transcriptomics, retrograde tracing
- MeSH terms
- Animals, Female, Male, Motor Cortex, Organ Specificity, Visual Cortex, Biomarkers, Sequence Analysis, RNA, Glutamic Acid, Neocortex, Gene Expression Profiling, Mice, Single-Cell Analysis, GABAergic Neurons
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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