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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The NP-CT-L6b-like subclasses emerge as a group highly distinct from the IT-ET-like subclasses, forming two distinct classes, IT-ET Glut and NP-CT-L6b Glut.
“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]
In “Glutamatergic taxonomy by scRNA-seq and projections” (page unavailable), the paper identifies IT, near-projecting and CT as glutamatergic subclasses and describes L6b types. It does not report that NP–CT–L6b and IT–ET form two distinct classes; “Overall cell type taxonomy” instead uses “glutamatergic” as the class and “L6b” as a subclass. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCortical GABAergic cell types are shared across all cortical areas.
“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]
The Abstract reports that nearly all GABAergic neuron types were shared between the two mouse neocortical areas studied, ALM and VISp. The Overall cell type taxonomy section identifies two VISp-specific exceptions. The claim overstates both the extent of sharing and the number of cortical areas examined. (LLM, conf=0.91)
Experimental — accuracy not yet measuredPrediction 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 types.
“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]
Clustering for both 10xv2 and 10xv3 datasets was performed independently using in-house developed R package scrattch.bigcat.
“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]
Loki Decompose was assessed on an in-house TNBC dataset, a human colorectal cancer dataset and a brain dataset.
“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]
Loki Decompose accurately predicted the distribution of VLMCs, astrocytes, neurons from L2/3, L4/5 and L6 and oligodendrocytes in mouse brain cortex, aligning closely with brain anatomic reference.
“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]
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 tracts.
“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]
High-throughput single-cell sequencing techniques have identified dozens of types of neuron on the basis of their transcriptional profiles.
“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]
Cortical studies have identified many intermediate cells with uncertain t-type assignments.
“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]
Within-type distributions of soma depths agreed well with previous data and with the layer-specific nomenclature of excitatory t-types, confirming the validity of t-type assignment.
“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]
Different transcriptomic families had largely distinct morpho-electric phenotypes, in agreement with them being well separated in the transcriptomic space.
“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]
Different Pvalb t-types had different preferred morphologies but showed substantial overlap.
“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 Sst subclass, morpho-electric properties varied continuously across the transcriptomic landscape, with neighbouring t-types consistently showing similar morphologies and similar rebound values.
“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]
The three large transcriptomic groups (CGE-derived interneurons, MGE-derived interneurons, and excitatory neurons) are perfectly separated from each other with no transcriptomically intermediate cells.
“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]
Transcriptomically-defined types of GABAergic interneurons are more readily conserved across cortical brain areas.
“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
scRNAseq provides a promising new way to define cell types due to high dimensionality and scalability.
“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
The number of VISp GABAergic t-types (60) and me-types (26) differs by more than a factor of two.
“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
Continuous variation has frequently been observed even in transcriptomic data from isolated cells.
“Even in transcriptomic data from isolated cells, continuous variations were frequently observed (Stanley et al., 2020; Harris et al., 2018; Tasic et al., 2018).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tasic et al., 2018
Marker genes selective for particular t- or met-types have been mapped in mouse primary motor cortex by MERFISH, and they may have similar distributions in visual cortex because GABAergic t-types are shared across cortical areas.
“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
Prior work has linked Sncg neurons by gene expression to Vip+/Cck+ basket cells.
“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
Glutamatergic neuron types differ between visual and motor cortical areas, while GABAergic neuron types are shared between these areas.
“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
The current CTX-HPF GABAergic taxonomy is largely consistent with previous transcriptomic taxonomies derived from cortical areas VISp-ALM and MOp at the supertype level but exhibits notable ambiguity at the type/cluster level.
“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
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.
“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
Most clusters are shared by all isocortical areas and also by RHP regions.
“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
Previous analysis of single-cell transcriptomes defined 133 cell types in two isocortical areas, primary visual cortex (VISp) and anterolateral motor cortex (ALM), in mice, and found that they have shared GABAergic interneuron types but distinct glutamatergic neuron types.
“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
The three ALM L5 PT types previously identified correspond to cluster #248 (thalamus-projecting cells) in the Npnt supertype and clusters #249 and #252 (medulla-projecting cells) in the Cdh13 supertype, respectively.
“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
The difference in developmental origins between isocortical glutamatergic neurons (VZ/SVZ) and GABAergic interneurons (MGE/CGE) may be the underlying reason for the dichotomy between extensive regional diversity of glutamatergic types and sharing of a common set of GABAergic types across isocortical regions.
“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
The scrattch.hicat 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.
“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
Across all isocortical areas, most glutamatergic cell types are shared among multiple areas, contrary to previous findings from VISp and ALM.
“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
Sst Calb2 Pdlim is a Martinotti cell type and Sst Hpse Cbln4 is a non-Martinotti cell type.
“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]
The quality of mapping Patch-seq cells to the transcriptomic dataset was equally good for V1 and S1 cells, suggesting the V1 + ALM dataset is a reasonable reference for S1 interneurons.
“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]
Recent large-scale transcriptomic studies showed that most interneuron types are shared between cortical areas while excitatory types are predominantly area-specific.
“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]
No labeled neurogliaform cells were encountered in PV-Cre, SST-Cre, or VIP-Cre lines, suggesting they belonged to the Lamp5 transcriptomic family.
“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]
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 atlas.
“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]
The majority of transcriptomic inhibitory types are shared between two very different cortical areas (V1 and ALM).
“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]
The transcriptomic reference dataset contained many V1 cells from the Sst Hpse Cbln4 type.
“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]
A subsequent large-scale study of about 23,800 cells suggested 113 cortical cell types, including about 60 GABAergic types conserved between visual and frontal cortex and 56 glutamatergic types mostly distinct between these areas.
“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]
This landmark data set is the most comprehensive survey of cortical transcriptional types and establishes a scaffold for transcriptome-based classification and a working draft of cortical cell taxonomy, including GABAergic interneuron taxonomy.
“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]
The approximately 60 atomic GABAergic transcriptomic types at the base of the hierarchy are probably fluidic and may be modified by future scRNAseq and orthogonal data sets.
“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]
The number of Sst transcriptional clusters can vary by a factor of 2 depending on clustering stringency, with 30 clusters under less stringent and 15 under more stringent conditions.
“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]
A group of Lamp5 subclass interneurons with prominent Lhx6 and Nkx2.1 expression were classified as CGE-derived putative neurogliaform cells.
“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]
The same large-scale analysis of adult mouse visual cortex used 28 Cre driver lines to enable broad coverage, selective targeting of rare populations and linking to previous single-cell transcriptomic data sets; intrinsic properties of more than 1,900 neurons were analysed and morphologies of 461 neurons were partially reconstructed.
“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]
The extent to which the 60 t-types correspond to the 17 e-types, 38 m-types and 46 me-types is currently not well established.
“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]
Recent transcriptomic work with single-cell RNA sequencing used unsupervised classification to build a taxonomy of transcriptomic types in mouse neocortex.
“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]
The authors used many transgenic mouse lines to cover excitatory and inhibitory classes across cortical layers, target rare populations, and connect with other approaches like transcriptomic characterization.
“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]
Prior single-cell RNA-seq studies have defined taxonomies of mouse neocortical cell types from isolated neurons and glia.
“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 study was not yet able to establish clear links to the recently described Sncg inhibitory subclass.
“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 L1-L4, the Nkx2-1 line is associated mostly with the Pvalb-Vipr2 type, expected to be chandelier cells, and the Pvalb-Tpbg type.
“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]
Individual t-types can exhibit substantial continuous variation within a type.
“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]
These Nos1/Sst-labeled cells are expected to have the Sst-Chodl t-type, which is linked to deep long-range projecting interneurons.
“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]
Recent work found shared t-types among inhibitory cells but different t-types among excitatory cells from separate cortical areas.
“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]
Large-scale transcriptomic studies have provided informative taxonomies of cortical cell types.
“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]
Application of scRNA-seq to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types consistent overall with anatomical, physiological and developmental studies but with unmatched granularity.
“The application of single-cell RNA sequencing (scRNA-seq) to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types that are consistent overall with knowledge from decades of anatomical, physiological and developmental studies but with an unmatched level of granularity7–11.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
MY-projecting neurons showed clear enrichment for L5 ET cluster 0.
“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]
The L4/5 IT type expressed Rspo1.
“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]
Mouse MOp L5 ET_1 type corresponded to ALM MY-projecting type, whereas MOp L5 ET_2-4 types corresponded to ALM TH-projecting types.
“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]
L4/5 IT and L5 IT cells occupied distinct layers in MOp.
“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]
There is coexistence of discrete and continuous variations of cell features across modalities at the lower branch level.
“In this context, another important finding, consistent with and building on multiple other studies9,11,68,69, is the coexistence of discrete and continuous variations of cell features across modalities at the lower branch level.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
A general principle of cortical cell-type organization is its hierarchical relationship, whereby high-level classes linked by major branches comprise progressively finer subpopulations connected by minor branches.
“Substantiating previous studies9,10, our multimodal cross-species study of the primary motor cortex suggests that a general principle of cortical cell-type organization is its hierarchical relationship, whereby high-level classes linked by major branches comprise progressively finer subpopulations connected by minor branches.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [9]
Recent scRNA-seq studies sampled >20,000-750,000 cells in individual mouse brain regions.
“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
A recurring theme from single-cell studies is greater diversity of glutamatergic vs GABAergic neurons across brain regions or species.
“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
Some cells, such as cortical layer 5 pyramidal tract neurons, may be more likely to die during cell isolation for scRNA-seq.
“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
Retro-seq uses retrograde virus infection of axon terminals in a chosen region to label and sort the projecting source neurons' cell bodies, enabling sc/snRNA-seq of their transcriptomes.
“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 mouse cerebral cortex, high-throughput multi-omics detected only 9-22 cell types, whereas scRNA-seq detected more than 100.
“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 marmosets, gene-expression patterns of conserved interneuron types varied by cortical region, unlike in mice.
“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]
These four marker genes stably define a core set of neocortical interneuron types in ferrets, mice and primates.
“which suggests that the genes that encode these markers stably delineate a core repertoire of neocortical interneuron types in these species11,15,18”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [18]
Somatostatin-expressing cortical interneurons comprise several anatomically, electrophysiologically, and molecularly defined cell types whose dysfunction is associated with neuropsychiatric and neurological disorders.
“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
We chose to focus on SST neurons because this population is known to be diverse and to be composed of several relatively rare subpopulations, and thus might serve as a good test case.
“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
Subclasses of excitatory neurons in different layers of cortex (ITL23GL, ITL4GL, ITL5GL, PTGL, NPGL, ITL6GL, CTGL and L6bGL) were clearly identified.
“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]
Single-cell sequencing studies in the brain have shown that neuron types with the genetic constituents required for releasing more than one neurotransmitter are ubiquitous.
“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]
Most analyses in these single-cell sequencing studies are performed on populations of neurons.
“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]
These four inhibitory subpopulations are likely the remaining non-MGE origin cells (arising from CGE and/or POA).
“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]
Prior studies indicate that inhibitory neurons show uniform molecular features even between distant cortical areas.
“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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- 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 · 2017: 1 · 2014: 1
- 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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