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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 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
“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
“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]
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
“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
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
“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]
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
“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
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
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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Zalocusky, K. A., Najm, R., Taubes, A. L., Hao, Y., Yoon, S. Y., Koutsodendris, N., Nelson, M. R., Rao, A., Bennett, D. A., Bant, J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00851-3 · Cited by 189
Enhancer viruses for combinatorial cell-subclass-specific labeling
Graybuck, L. T., Daigle, T. L., Sedeño-Cortés, A. E., Walker, M., Kalmbach, B., Lenz, G. H., Morin, E., Nguyen, T. N., Garren, E., Bendrick, J. L., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.011 · Cited by 187
Self-citationRecruitment of GABAergic Interneurons in the Barrel Cortex during Active Tactile Behavior
Yu, J., Hu, H., Agmon, A., & Svoboda, K. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.027 · Cited by 186
Self-citationAn atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185
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 188
Neuronal circuits in barrel cortex for whisker sensory perception
Staiger, J. F., & Petersen, C. C. H. (2021) · Physiological Reviews · doi:10.1152/physrev.00019.2019 · Cited by 165
Canzio, D., Nwakeze, C. L., Horta, A., Rajkumar, S. M., Coffey, E. L., Duffy, E. E., Duffié, R., Monahan, K., O’Keeffe, S., Simon, M. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.008 · Cited by 175
Early postnatal development of pyramidal neurons across layers of the mouse medial prefrontal cortex
Kroon, T., van Hugte, E., van Linge, L., Mansvelder, H. D., & Meredith, R. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-41661-9 · Cited by 161
Bhattacherjee, A., Djekidel, M. N., Chen, R., Chen, W., Tuesta, L. M., & Zhang, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-12054-3 · Cited by 174
Circuitry Underlying Experience-Dependent Plasticity in the Mouse Visual System
Hooks, B. M., & Chen, C. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.031 · Cited by 176
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 168
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 192
A comparative atlas of single-cell chromatin accessibility in the human brain
Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173
Self-citationSingle nucleus multi-omics identifies human cortical cell regulatory genome diversity
Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 173
Self-citationTranscriptional and spatial resolution of cell types in the mammalian habenula
Hashikawa, Y., Hashikawa, K., Rossi, M. A., Basiri, M. L., Liu, Y., Johnston, N. L., Ahmad, O. R., & Stuber, G. D. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.011 · Cited by 167
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 185
Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits
Colquitt, B. M., Merullo, D. P., Konopka, G., Roberts, T. F., & Brainard, M. S. (2021) · Science · doi:10.1126/science.abd9704 · Cited by 167
Cobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168
Toward Circuit Mechanisms of Pathophysiology in Depression
Spellman, T., & Liston, C. M. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20030280 · Cited by 180
The neuropeptidergic connectome of C. elegans
Ripoll-Sánchez, L., Watteyne, J., Sun, H., Fernandez, R. W., Taylor, S. R., Weinreb, A., Bentley, B. L., Hammarlund, M., Miller, D. M., Hobert, O., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.043 · Cited by 172
Fan, X., Fu, Y., Zhou, X., Sun, L., Yang, M., Wang, M., Chen, R., Wu, Q., Yong, J., Dong, J., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz2978 · Cited by 163
Transcriptomic comparison of human and mouse brain microvessels
Song, H. W., Foreman, K. L., Gastfriend, B. D., Kuo, J. S., Palecek, S. P., & Shusta, E. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-69096-7 · Cited by 166
Unique homeobox codes delineate all the neuron classes of C. elegans
Reilly, M. B., Cros, C., Varol, E., Yemini, E., & Hobert, O. (2020) · Nature · doi:10.1038/s41586-020-2618-9 · Cited by 157
Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
Smith, S. J., Sümbül, U., Graybuck, L. T., Collman, F., Seshamani, S., Gala, R., Gliko, O., Elabbady, L., Miller, J. A., Bakken, T. E., et al. (2019) · eLife · doi:10.7554/elife.47889 · Cited by 156
Self-citationGenetic dissection of the glutamatergic neuron system in cerebral cortex
Matho, K. S., Huilgol, D., Galbavy, W., He, M., Kim, G., An, X., Lu, J., Wu, P., Di Bella, D. J., Shetty, A. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03955-9 · Cited by 155
Self-citationSingle-cell DNA methylation and 3D genome architecture in the human brain
Tian, W., Zhou, J., Bartlett, A., Zeng, Q., Liu, H., Castanon, R. G., Kenworthy, M., Altshul, J., Valadon, C., Aldridge, A., et al. (2023) · Science · doi:10.1126/science.adf5357 · Cited by 154
Self-citationEfficient 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
Wan, X., Xiao, J., Tam, S. S. T., Cai, M., Sugimura, R., Wang, Y., Wan, X., Lin, Z., Wu, A. R., & Yang, C. (2023) · Nature Communications · doi:10.1038/s41467-023-43629-w · Cited by 166
Song, Y.-H., Yoon, J., & Lee, S. (2021) · Experimental & Molecular Medicine · doi:10.1038/s12276-021-00580-4 · Cited by 154
Insights into Alzheimer’s disease from single-cell genomic approaches
Murdock, M. H., & Tsai, L.-H. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01222-2 · Cited by 148
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
Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network
Whitesell, J. D., Liska, A., Coletta, L., Hirokawa, K. E., Bohn, P., Williford, A., Groblewski, P. A., Graddis, N., Kuan, L., Knox, J. E., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.011 · Cited by 139
Self-citationGeldhof, V., de Rooij, L. P. M. H., Sokol, L., Amersfoort, J., Schepper, M. D., Rohlenová, K., Hoste, G., Vanderstichele, A., Delsupehe, A.-M., Isnaldi, E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33052-y · Cited by 145
MARS: discovering novel cell types across heterogeneous single-cell experiments
Brbić, M., Žitnik, M., Wang, S., Pisco, A. O., Altman, R. B., Darmanis, S., & Leskovec, J. (2020) · Nature Methods · doi:10.1038/s41592-020-00979-3 · Cited by 144
Intermediate progenitors and Tbr2 in cortical development
Hevner, R. F. (2019) · Journal of Anatomy · doi:10.1111/joa.12939 · Cited by 144
Yu, H., Miao, W., Ji, E., Huang, S., Jin, S., Zhu, X., Liu, M.-Z., Sun, Y.-G., Xu, F., & Yu, X. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.022 · Cited by 144
Mediodorsal and Ventromedial Thalamus Engage Distinct L1 Circuits in the Prefrontal Cortex
Anastasiades, P. G., Collins, D. P., & Carter, A. G. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.031 · Cited by 139
Brain-wide cell-type-specific transcriptomic signatures of healthy ageing in mice
Jin, K., Yao, Z., van Velthoven, C. T. J., Kaplan, E. S., Glattfelder, K. J., Barlow, S. T., Boyer, G., Carey, D., Casper, T., Chakka, A. B., et al. (2025) · Nature · doi:10.1038/s41586-024-08350-8 · Cited by 152
Self-citationWhole-body integration of gene expression and single-cell morphology
Vergara, H. M., Pape, C., Meechan, K., Zinchenko, V., Genoud, C., Wanner, A. A., Mutemi, K. N., Titze, B., Templin, R., Bertucci, P. Y., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.017 · Cited by 146
Cortical Somatostatin Interneuron Subtypes Form Cell-Type Specific Circuits
Wu, S. J., Sevier, E., Dwivedi, D., Saldi, G.-A., Hairston, A., Yu, S. Q., Abbott, L., Choi, D. H., Sherer, M., Qiu, Y., et al. (2022) · Neuron · doi:10.1016/j.neuron.2023.05.032 · Cited by 139
PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells
Stassen, S. V., Siu, D. M. D., Lee, K. C. M., Ho, J. W. K., So, H. K., & Tsia, K. K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa042 · Cited by 131
Davoudian, P. A., Shao, L.-X., & Kwan, A. C. (2023) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00637 · Cited by 134
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118
Genome-wide spatial expression profiling in formalin-fixed tissues
Gracia Villacampa, E., Larsson, L., Mirzazadeh, R., Kvastad, L., Andersson, A., Mollbrink, A., Kokaraki, G., Monteil, V., Schultz, N., Appelberg, K. S., et al. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100065 · Cited by 132
Vision-dependent specification of cell types and function in the developing cortex
Cheng, S., Butrus, S., Tan, L., Xu, R., Sagireddy, S., Trachtenberg, J. T., Shekhar, K., & Zipursky, S. L. (2022) · Cell · doi:10.1016/j.cell.2021.12.022 · Cited by 136
Putative cell type discovery from single-cell gene expression data
Miao, Z., Moreno, P., Huang, N., Papatheodorou, I., Brazma, A., & Teichmann, S. A. (2020) · Nature Methods · doi:10.26226/morressier.5ebd45acffea6f735881af34 · Cited by 133
Direct neuronal reprogramming: Fast forward from new concepts toward therapeutic approaches
Bocchi, R., Masserdotti, G., & Götz, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.023 · Cited by 135
Genes, Cells and Brain Areas of Intelligence
Goriounova, N. A., & Mansvelder, H. D. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00044 · Cited by 148
Osseward, P. J., Amin, N. D., Moore, J. D., Temple, B. A., Barriga, B. K., Bachmann, L. C., Beltran, F., Gullo, M., Clark, R. C., Driscoll, S. P., et al. (2021) · Science · doi:10.1126/science.abe0690 · Cited by 115
Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics
Ratz, M., von Berlin, L., Larsson, L., Martin, M., Westholm, J. O., La Manno, G., Lundeberg, J., & Frisén, J. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01011-x · Cited by 127
Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex
Sermet, B. S., Truschow, P., Feyerabend, M., Mayrhofer, J. M., Oram, T. B., Yizhar, O., Staiger, J. F., & Petersen, C. C. H. (2019) · eLife · doi:10.7554/elife.52665 · Cited by 132
Differential Encoding in Prefrontal Cortex Projection Neuron Classes Across Cognitive Tasks
Lui, J. H., Nguyen, N. D., Grutzner, S. M., Darmanis, S., Peixoto, D., Wagner, M. J., Allen, W. E., Kebschull, J. M., Richman, E. B., Ren, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.046 · Cited by 124
Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex
Lee, B. R., Dalley, R., Miller, J. A., Chartrand, T., Close, J., Mann, R., Mukora, A., Ng, L., Alfiler, L., Baker, K., et al. (2023) · Science · doi:10.1126/science.adf6484 · Cited by 125
Self-citationCortical and Striatal Circuits in Huntington’s Disease
Blumenstock, S., & Dudanova, I. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00082 · Cited by 128
A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons
Okaty, B. W., Sturrock, N., Lozoya, Y. E., Chang, Y., Senft, R. A., Lyon, K. A., Alekseyenko, O. V., & Dymecki, S. M. (2020) · eLife · doi:10.7554/elife.55523 · Cited by 122
Sun, Y.-C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J., & Zador, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00842-4 · Cited by 124
Comprehensive evaluation of deconvolution methods for human brain gene expression
Sutton, G. J., Poppe, D., Simmons, R. K., Walsh, K., Nawaz, U., Lister, R., Gagnon-Bartsch, J. A., & Voineagu, I. (2022) · Nature Communications · doi:10.1038/s41467-022-28655-4 · Cited by 122
Cell segmentation-free inference of cell types from in situ transcriptomics data
Park, J., Choi, W., Tiesmeyer, S., Long, B., Borm, L. E., Garren, E. J., Nguyen, T. N., Tasic, B., Codeluppi, S., Graf, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23807-4 · Cited by 119
Self-citationSingle-nuclei isoform RNA sequencing unlocks barcoded exon connectivity in frozen brain tissue
Hardwick, S. A., Hu, W., Joglekar, A., Fan, L., Collier, P. G., Foord, C., Balacco, J., Lanjewar, S., Sampson, M. M., Koopmans, F., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01231-3 · Cited by 119
Herrero-Navarro, Á., Puche-Aroca, L., Moreno-Juan, V., Sempere-Ferràndez, A., Espinosa, A., Susín, R., Torres-Masjoan, L., Leyva-Díaz, E., Karow, M., Figueres-Oñate, M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abe8978 · Cited by 117
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118
“49.”
InfluentialDecoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain
Androvic, P., Kirdajova, D., Tureckova, J., Zucha, D., Rohlova, E., Abaffy, P., Kriska, J., Valny, M., Anderova, M., Kubista, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107777 · Cited by 118
Extraction of Distinct Neuronal Cell Types from within a Genetically Continuous Population
Kim, E. J., Zhang, Z., Huang, L., Ito-Cole, T., Jacobs, M. W., Juavinett, A. L., Senturk, G., Hu, M., Ku, M., Ecker, J. R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.018 · Cited by 103
Anatomical and single-cell transcriptional profiling of the murine habenular complex
Wallace, M. L., Huang, K. W., Hochbaum, D., Hyun, M., Radeljic, G., & Sabatini, B. L. (2020) · eLife · doi:10.7554/elife.51271 · Cited by 113
Miyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115
Mossink, B., Negwer, M., Schubert, D., & Nadif Kasri, N. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03714-5 · Cited by 114
stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics
Chen, S., Boheng, Z., Chen, X., Xuegong, Z., & Rui, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab298 · Cited by 114
Xu, W. Q., Yang, W., Zhang, Y., Chen, Y., Hong, N., Zhang, Q., Wang, X. J., Hu, Y., Song, K., Jin, W., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01601-4 · Cited by 118
Three-dimensional genome rewiring in loci with human accelerated regions
Keough, K. C., Whalen, S., Inoue, F., Przytycki, P. F., Fair, T., Deng, C., Steyert, M., Ryu, H., Lindblad-Toh, K., Karlsson, E., et al. (2023) · Science · doi:10.1126/science.abm1696 · Cited by 113
The Epigenome in Neurodevelopmental Disorders
Reichard, J., & Zimmer‐Bensch, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.776809 · Cited by 118
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data
Jerby‐Arnon, L., & Regev, A. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01288-0 · Cited by 119
A brain cell atlas integrating single-cell transcriptomes across human brain regions
Chen, X., Huang, Y., Huang, L., Huang, Z., Hao, Z.-Z., Xu, L., Xu, N., Li, Z., Mou, Y., Ye, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03150-z · Cited by 118
Differential assembly diversifies GABAA receptor structures and signalling
Sente, A., Desai, R., Naydenova, K., Malinauskas, T., Jounaïdi, Y., Miehling, J., Zhou, X., Masiulis, S., Hardwick, S. W., Chirgadze, D. Y., et al. (2022) · Nature · doi:10.1038/s41586-022-04517-3 · Cited by 112
A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture
Llorca, A., Ciceri, G., Beattie, R., Wong, F. K., Diana, G., Serafeimidou‐Pouliou, E., Fernández-Otero, M., Streicher, C., Arnold, S. J., Meyer, M. P., et al. (2019) · eLife · doi:10.7554/elife.51381 · Cited by 112
The development and evolution of inhibitory neurons in primate cerebrum
Schmitz, M. T., Sandoval, K., Chen, C. P., Mostajo-Radji, M. A., Seeley, W. W., Nowakowski, T. J., Ye, C. J., Paredes, M. F., & Pollen, A. A. (2022) · Nature · doi:10.1038/s41586-022-04510-w · Cited by 111
Neuronal types in the mouse amygdala and their transcriptional response to fear conditioning
Hochgerner, H., Singh, S., Tibi, M., Lin, Z., Skarbianskis, N., Admati, I., Ophir, O., Reinhardt, N., Netser, S., Wagner, S., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01469-3 · Cited by 110
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778
Activity-dependent modulation of synapse-regulating genes in astrocytes
Farhy-Tselnicker, I., Boisvert, M., Liu, H., Dowling, C. C., Erikson, G. A., Blanco‐Suárez, E., Farhy, C., Shokhirev, M. N., Ecker, J. R., & Allen, N. J. (2021) · eLife · doi:10.7554/elife.70514 · Cited by 108
New Insights Into Cholinergic Neuron Diversity
Ahmed, N. Y., Knowles, R., & Dehorter, N. (2019) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2019.00204 · Cited by 108
Functional and anatomical specificity in a higher olfactory centre
Frechter, S., Bates, A. S., Tootoonian, S., Dolan, M.-J., Manton, J., Jamasb, A. R., Kohl, J., Bock, D., & Jefferis, G. (2019) · eLife · doi:10.7554/elife.44590 · Cited by 105
Kalmbach, B. E., Hodge, R. D., Jorstad, N. L., Owen, S. F., de Frates, R. A., Yanny, A. M., Dalley, R. A., Mallory, M., Graybuck, L. T., Radaelli, C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.030 · Cited by 106
Self-citationA Dendrite-Focused Framework for Understanding the Actions of Ketamine and Psychedelics
Savalia, N. K., Shao, L.-X., & Kwan, A. C. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.11.008 · Cited by 104
Patch-seq: Past, Present, and Future
Lipovsek, M., Bardy, C., Cadwell, C. R., Hadley, K., Kobak, D., & Tripathy, S. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1653-20.2020 · Cited by 104
Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., Jaquier, A. T., King, J., Kumbhar, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30214-w · Cited by 102
Selective vulnerability of layer 5a corticostriatal neurons in Huntington’s disease
Pressl, C., Mätlik, K., Kus, L., Darnell, P., Luo, J., Paul, M. R., Weiss, A. R., Liguore, W. A., Carroll, T. S., Davis, D. A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.009 · Cited by 101
Rbfox1 Mediates Cell-type-Specific Splicing in Cortical Interneurons
Wamsley, B., Jaglin, X. H., Favuzzi, E., Quattrocolo, G., Nigro, M. J., Yusuf, N., Khodadadi‐Jamayran, A., Rudy, B., & Fishell, G. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.026 · Cited by 101
Extracellular Spike Waveform Dissociates Four Functionally Distinct Cell Classes in Primate Cortex
Trainito, C., von Nicolai, C., Miller, E. K., & Siegel, M. (2019) · Current Biology · doi:10.1016/j.cub.2019.07.051 · Cited by 99
SpiceMix enables integrative single-cell spatial modeling of cell identity
Chidester, B., Zhou, T., Alam, S., & Ma, J. (2023) · Nature Genetics · doi:10.1038/s41588-022-01256-z · Cited by 102
Stereotyped transcriptomic transformation of somatosensory neurons in response to injury
Nguyen, M. Q., Pichon, C. E. L., & Ryba, N. J. P. (2019) · eLife · doi:10.7554/elife.49679 · Cited by 94
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 104
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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
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