Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
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Abstract
Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.
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- Findingown data
For most t-types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties.
“for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties”
Quote in the paperSubstantial claim - Findingown data
Most transcriptomic types (t-types) occupy specific laminar positions within visual cortex.
“We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex”
Quote in the paperSubstantial claim - Findingcitation
mRNA collected from one neuron via Patch-seq often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.
“we (Figure S2B) and others (Tripathy et al., 2018) found that the mRNA collected from one neuron via a Patch-seq recording often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Tripathy et al., 2018 source
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- Findingown data
The scRNAseq transcriptomes from Patch-seq recordings exhibited a similar number of total genes as those from dissociated neurons.
“The scRNAseq transcriptomes from cDNA obtained from Patch-seq recordings of individual GABAergic interneurons exhibited a similar number of total genes as the scRNAseq transcriptomes obtained from individual dissociated neurons”
Quote in the paperSubstantial claim - Findingown data
The two transcriptomic datasets had similar structure when visualized together via UMAP.
“the two transcriptomic datasets had similar structure when visualized together via a Uniform Manifold Approximation and Projection (UMAP, (Becht et al., 2018)) method”
Quote in the paperSubstantial claim
- Findingown data
Layer-specific axon innervation pattern is a defining feature distinguishing different met-types.
“We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types.”
Quote in the paperSubstantial claim - Findingown data
3,855 of 4,270 cells had consistent t-type mapping (2,955 highly consistent, 900 moderately consistent).
“We found that 3,855 of the 4,270 cells had consistent t-type mapping (2,955 “highly consistent” and 900 “moderately consistent””
Quote in the paperSubstantial claim - Findingown data
These properties display both discrete and continuous variation among t-types.
“These properties display both discrete and continuous variation among t-types.”
Quote in the paperNot a substantial claim - Findingown data
Through multimodal integrated analysis, 28 met-types were defined that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability.
“Through multimodal integrated analysis we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability.”
Quote in the paperSubstantial claim - Findingown data
Transcriptomes from Patch-seq recordings more frequently exhibit dropout compared to dissociated cells.
“compared to those of equivalent dissociated cells, the transcriptomes of cells obtained by Patch-seq recordings more frequently exhibit “dropout””
Quote in the paperNot a substantial claim - Findingown data
Additional anatomical precision revealed further layer specificity of t-types.
“the additional anatomical precision gained from targeting individual cells with recording electrodes in brain slices revealed further layer specificity”
Quote in the paperSubstantial claim - Findingcitation
The Vip subclass contained five met-types, all of which resembled bipolar or bitufted cells.
“We identified five Vip met-types (Figure 7I–L), all resembling bipolar or bitufted cells (Peters and Kara, 1985; Prönneke et al., 2015).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Peters and Kara, 1985
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- cites Prönneke et al., 2015 source
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- Findingown data
For the first four Pvalb met-types, the delay to action potential onset at rheobase varied with cortical depth and tended to become shorter as distance from the pia increased.
“Among these first four Pvalb met-types, there appeared to be depth-dependent delays in the onset of AP generation at rheobase that tended to decrease with increasing depth from pia (Figure 6H).”
Quote in the paperNot a substantial claim - Findingown data
Thirteen met-types were identified within the Sst subclass.
“We identified thirteen met-types within the Sst subclass”
Quote in the paperSubstantial claim - Findingown data
Many t-types were distinguished by layer-selective axonal projection.
“many t-types were distinguished by a layer-selective axonal projection.”
Quote in the paperSubstantial claim - Findingown data
Lamp5-MET-1 was heterogeneous, showing variation in gene expression, morphology, and electrophysiology.
“This met-type was fairly heterogenous, with variation in gene expression (as cells came from the Lamp5 Lsp1, Lamp5 Plch2 Dock5, and Lamp5 Ntn1 Npy2r t-types), morphology (ranging from L1 cells with horizontally-extending axon to deeper cells with round, dense axon), and electrophysiology (such as differences in the late-spiking phenotype, as measured by the latency to the first action potential at”
Quote in the paperNot a substantial claim - Findingown data
Pvalb-MET-1 showed greater hyperpolarization-induced sag than the other Pvalb met-types.
“and also exhibited more hyperpolarization-induced sag than the other Pvalb met-types (Figure 6H)”
Quote in the paperNot a substantial claim - Findingcitation
Pvalb-MET-5, which contained Pvalb Vipr2 cells, corresponded to chandelier cells as previously identified.
“Pvalb-MET-5 (containing Pvalb Vipr2 cells) represented chandelier cells as previously identified (Woodruff and Yuste, 2008; Taniguchi et al., 2013; Inan and Anderson, 2014; Tasic et al., 2018; Gouwens et al., 2019)”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Woodruff and Yuste, 2008
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- cites Taniguchi et al., 2013 source
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- cites Inan and Anderson, 2014
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- Findingown data
Integrated analysis produced a classification of 28 cortical interneuron met-types with congruent transcriptomic and morpho-electrical characteristics.
“3) Integrated analysis results in a classification of 28 met-types of cortical interneurons with congruent transcriptomic and morpho-electrical characteristics.”
Quote in the paperSubstantial claim - Findingown data
The Lamp5 subclass contained two met-types.
“We identified two met-types among the Lamp5 cells (Figure 7A–C).”
Quote in the paperNot a substantial claim - Findingown data
Community detection identified 28 met-types from the graph.
“We then used a community detection algorithm (Traag et al., 2019) to identify 28 met-types from the graph.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Traag et al., 2019 source
Not checked in the free run.
- Findingown data
In some cases, scRNAseq data from Patch-seq recordings mapped more often to one t-type, while scRNAseq data from dissociated cells mapped more often to a related t-type.
“Also, we note cases in which scRNAseq data from Patch-seq recordings mapped more frequently to one t-type (Sst Hpse Cbln4, Pvalb Reln Itm2a) while scRNAseq data from dissociated cells mapped more frequently to another, related t-type (Sst Hpse Sema3c, Pvalb Reln Tac1, respectively).”
Quote in the paperNot a substantial claim - Findingown data
The Pvalb subclass contains five met-types whose identities largely correspond to differences in cortical layer.
“Five met-types were identified in the Pvalb subclass and mostly reflected different laminar positions (Figure 6E–H).”
Quote in the paperSubstantial claim - Findingown data
The third principal component varied systematically across the transcriptomic types within Lamp5-MET-1.
“The third principal component (PC-3) varied systematically across the t-types found in Lamp5-MET-1.”
Quote in the paperSubstantial claim - Findingown data
Sncg-MET-1 was present in most cortical layers.
“Sncg-MET-1 was found in most cortical layers.”
Quote in the paperNot a substantial claim - Findingown data
Cells in Pvalb-MET-1 mapped to the transcriptomic type Pvalb Gabrg1.
“Pvalb-MET-1 contained cells mapped to the transcriptomically-distinct Pvalb Gabrg1 t-type”
Quote in the paperNot a substantial claim - Findingown data
The correspondence between independently derived transcriptomic types and morpho-electrical types is mostly consistent but not strictly one-to-one.
“2) Correspondence between the independently derived t- and me-types is mostly consistent but not strictly one-to-one.”
Quote in the paperSubstantial claim - Findingown data
Pvalb-MET-4 included cells that mapped to both Pvalb Reln Itm2a and Pvalb Tpbg.
“Pvalb-MET-4 contained cells that mapped to both Pvalb Reln Itm2a and Pvalb Tpbg.”
Quote in the paperNot a substantial claim - Findingown data
Pvalb-MET-5 had higher input resistance than Pvalb-MET-4.
“and had higher input resistances compared to Pvalb-MET-4 (Figure 6H).”
Quote in the paperNot a substantial claim - Findingown data
Pvalb-MET-2 comprised four Pvalb transcriptomic types.
“Pvalb-MET-2 was composed of four Pvalb t-types”
Quote in the paperNot a substantial claim - Findingown data
Sncg-MET-1 cells showed regular, adapting firing patterns.
“They exhibited regular, adapting firing patterns.”
Quote in the paperNot a substantial claim - Findingown data
Several other properties of Lamp5-MET-1 cells also varied along the PC-3 dimension.
“Interestingly, several other properties of these cells also varied along this dimension (Figure 7D).”
Quote in the paperSubstantial claim - Findingown data
Vip-MET-3 expressed the marker genes Sfrp2 and Lmo1.
“Vip-MET-3 expressed the marker genes Sfrp2 and Lmo1 (Figure 7K).”
Quote in the paperNot a substantial claim - Findingown data
Some Sst t-types in L5 were preferentially located in upper part, while others occupied lower part of L5.
“we found that some Sst t-types in L5 were preferentially located in the upper part (e.g., Sst Calb2 Pdlim5, Sst Hpse Cbln4, Sst Hpse Sema3c), while others occupied the lower part of L5 (e.g., Sst Esm1, Sst Rxfp1 Eya1, Sst Crhr2 Efemp1).”
Quote in the paperSubstantial claim - Findingown data
Vip-MET-2 had more distributed axon innervation that spanned layers 2/3 to 6 but mostly avoided layer 4.
“Vip-MET-2 also exhibited more distributed axon innervation, spanning L2/3 to L6, but mostly avoiding L4.”
Quote in the paperNot a substantial claim - Findingown data
The Sncg subclass contained three met-types.
“Three met-types were identified in the Sncg subclass (Figure 7E–H).”
Quote in the paperNot a substantial claim - Findingown data
A smaller number of t-types were found across multiple layers.
“A smaller number of t-types were notably found across multiple layers (e.g., Lamp5 Lsp1, Sncg Vip Itih5, and Sst Calb2 Pdlim5).”
Quote in the paperNot a substantial claim - Findingown data
Vip-MET-1 and Vip-MET-2 cells differed in gene expression.
“Vip-MET-1 and −2 cells also differed in their gene expression.”
Quote in the paperSubstantial claim - Findingown data
These met-types consistently target one or two other layers more than layer 1.
“In any case, we find that these met-types consistently target one or two other layers to a greater extent than L1.”
Quote in the paperNot a substantial claim - Findingown data
The results support the existence of at least 28 GABAergic interneuron met-types with congruent transcriptomic, electrophysiological, and morphological properties.
“Altogether, our results support the presence of at least 28 GABAergic interneuron met-types that have congruent transcriptomic, electrophysiological and morphological properties (Figures 6 and 7).”
Quote in the paperSubstantial claim - Findingown data
Two met-types corresponding to Sst Calb2 Pdlim5, Sst-MET-3 in layer 2/3 and Sst-MET-4 in layer 5 (fanning-out Martinotti cells), have extensive axon elaborations in layer 1 relative to other layers, as described for classically defined Martinotti cells.
“Our data show that two met-types corresponding to t-type Sst Calb2 Pdlim5, Sst-MET-3 (in L2/3) and Sst-MET-4 (in L5, the fanning-out Martinotti cells), exhibit extensive axon elaborations in L1 relative to other layers as described for classically-defined Martinotti cells.”
Quote in the paperSubstantial claim - Findingown data
Axons of neurons in all other Sst met-types preferentially arborize outside layer 1.
“The axons of neurons mapping to all other Sst met-types, by comparison, preferentially arborize outside of L1.”
Quote in the paperSubstantial claim - Findingown data
Sst met-types located mainly in the deeper part of layer 5 and in layer 6 have predominantly layer 5 and layer 6 axonal projections.
“Sst met-types found mainly in the deeper part of L5 and in L6 have predominantly L5 and L6 axonal projections.”
Quote in the paperSubstantial claim - Findingnone given
Extracting the nucleus at the end of the electrophysiology experiment substantially increased transcriptomic data quality.
“Extracting the nucleus at the conclusion of the electrophysiology experiment led to a substantial increase in transcriptomic data quality.”
Quote in the paperNot a substantial claim - Premisecitation
Evidence suggests four major subclasses of cortical GABAergic neurons: Pvalb, Sst, Vip, and Htr3a+/Vip-.
“Evidence collected to date suggests the presence of four major subclasses of cortical GABAergic neurons: parvalbumin (Pvalb)-positive, somatostatin (Sst)-positive, vasoactive intestinal peptide (Vip)-positive cells, and cells that express 5-hydroxytryptamine receptor 3A but lack Vip (Htr3a+/Vip-)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Tremblay et al., 2016 source
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- Premisecitation
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”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Studies applying anatomical, physiological, and neurochemical approaches in other cortical areas have arrived at varying numbers of GABAergic neuronal types.
“studies applying anatomical, physiological, and neurochemical approaches in other cortical areas have also arrived at varying numbers of GABAergic neuronal types”
Quote in the paperSubstantial claim0 of 3 sources checked - Study design and conductown data
Both transcriptomic and electrophysiological data from 4,270 cortical GABAergic interneurons passed QC criteria.
“Both the transcriptomic and electrophysiological data from 4,270 cortical GABAergic interneurons passed our QC criteria”
Quote in the paperSubstantial claim - Premisecitation
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”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Tasic et al., 2018 source
Not checked in the free run.
- Study design and conductown data
Relative expression of key marker genes was similar in cells from the two datasets assigned to the same t-type.
“the relative expression level of key marker genes was similar in cells from the two data sets assigned to the same t-type”
Quote in the paperSubstantial claim - Study design and conductown data
Cells from transgenic driver mice map to similar t-types regardless of whether transcriptomes originated from dissociated cells or Patch-seq.
“cells obtained from transgenic driver mice that label subclasses or specific types of GABAergic interneurons map to similar t-types regardless of whether the examined transcriptomes originated from dissociated cells or Patch-seq recordings”
Quote in the paperSubstantial claim - Study design and conductown data
Layer distribution of Patch-seq cells mapped with high confidence to a t-type closely resembled that of dissociated cells mapping to the same t-type.
“the layer distribution of cells from Patch-seq recordings that mapped with high confidence to a given t-type closely resembled the layer distribution of dissociated cells mapping to the same t-type”
Quote in the paperSubstantial claim - Study design and conductown data
An independent approach assigned 79% of highly-consistent cells to the same t-type.
“an independent approach (Figure S1E, Methods) that did not rely on the hierarchical structure of the transcriptomic tree assigned cells to similar t-types (79% of highly-consistent cells were assigned to the same t-type by both methods).”
Quote in the paperSubstantial claim - Interpretationcitation
Pvalb-MET-2 included fast-spiking cells that resembled locally projecting layer 6 cells and deep basket cells.
“and contained fast-spiking cells resembling locally-projecting L6 cells (Bortone et al., 2014) and deep basket cells (Markram et al., 2015; Jiang et al., 2015).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
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).”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationnone given
Contamination and dropout in Patch-seq transcriptomic data can introduce ambiguity when mapping cells to transcriptomic types.
“Contamination and dropout in the Patch-seq transcriptomic data can also introduce ambiguity in t-type mapping.”
Quote in the paperNot a substantial claim - Interpretationown data
Lamp5-MET-1 therefore shows continuous variation across multiple data modalities in a coordinated manner.
“Therefore, this met-type exhibits continuous variation in multiple data modalities in a concerted way.”
Quote in the paperSubstantial claim - Interpretationcitation
The interlaminar or translaminar projections of these Pvalb met-types resemble projections previously reported for layer 6 fast-spiking neurons.
“These interlaminar or translaminar projections resemble those described previously for fast-spiking neurons in L6 (Frandolig et al., 2019; Bortone et al., 2014).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationown data
The specific laminar locations of many GABAergic t-types provide a strong anatomical correlate of transcriptomic groupings.
“The specific laminar (or sub-laminar) locations of many GABAergic t-types across subclasses provides a strong anatomical correlate of the cell groupings defined based on transcriptomic data alone.”
Quote in the paperSubstantial claim - Interpretationreasoning
The subdivision of continuous variation into separate transcriptomic types depends on the statistical criterion chosen, which can be arbitrary without other supporting evidence.
“How such continuous variation is subdivided into separate t-types depends on the specific statistical criterion chosen, which can be arbitrary in the absence of other supporting evidence.”
Quote in the paperSubstantial claim - Interpretationreasoning
The inherent variation in the data and the lower granularity of met-types compared with t-types (28 versus 60) may have both technical and biological causes.
“The inherent variations displayed in our data – sometimes consistent and sometimes differential across the modalities – and the resulting lower granularity of met-types compared to the original t-types (28 vs 60) may be due to both technical and biological reasons.”
Quote in the paperNot a substantial claim - Interpretationown data
The findings reveal patterns and some underlying rules of cortical GABAergic cellular diversity that may generalize to other brain cell types.
“These findings reveal patterns and some underlying rules of the complex landscape of cortical GABAergic cellular diversity, which may be generalizable to other brain cell types.”
Quote in the paperNot a substantial claim - Interpretationcitation
Some of the ambiguity caused by continuous and sometimes differential variation across modalities could also reflect biological reality.
“However, some of the ambiguity in types due to the existence of continuous and sometimes differential variations along the three modalities could also reflect the biological reality.”
Quote in the paperNot a substantial claim - Interpretationown data
These discrepancies may partly reflect differences in gene expression between slice and dissociated cell preparations.
“Such discrepancies may in part reflect gene expression differences in slice versus dissociated cell preparations.”
Quote in the paperNot a substantial claim - Interpretationcitation
A dominant layer 4 projection appears to be a common feature of Sst Hpse Cbln4-type neurons across cortical areas.
“A dominant L4 projection appears to be a common feature of Sst Hpse Cbln4-type neurons across cortical areas (Naka et al., 2019), while the L1 projection is not always present.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Naka et al., 2019 source
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- May combine two claims. 52
- The quote only partly states this claim. 2
- Classified as probably not a substantial claim. 35
May combine two claims.
Findingown dataThe other two Sncg met-types had few cells (n=3 and n=2) but had fairly distinctive properties.
“The other two Sncg met-types had few cells (n=3 and n=2) but fairly distinctive properties.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataNearly all t- and met-types in each subclass are arranged along cortical depth, with soma locations often centered in a layer or sublayer and continuous variation within and between types.
“5) Unexpectedly, we found that nearly all t- and met-types in each subclass are arranged along the cortical depth; the soma locations of each type are often centered in a layer or sublayer, with continuous variations both within and between types.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVip-MET-1 typically showed regular, sustained firing, while Vip-MET-2 firing was usually irregular.
“Vip-MET-1 also typically had regular, sustained firing while Vip-MET-2 firing was usually irregular (Figure 7L).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVip-MET-3 was located in layer 5 and had short bitufted dendrites and complex axon elaboration at layer borders.
“Vip-MET-3 was found in L5 (Figure 7I) and had short, bitfuted dendrites and complex axon elaboration at layer borders.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThese met-types were fairly cohesive in low-dimensional projections and could be identified by marker gene combinations.
“These met-types were fairly cohesive in low-dimensional projections of transcriptomic and electrophysiological space (Figure 6B) and could be identified by particular combinations of marker genes”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSome t-types were found at L1-L2/3 border, while others were distributed across L2/3.
“some t-types were found right at the L1-L2/3 border (Vip Col15a1 Pde1a, Pvalb Vipr2), while others were distributed across L2/3 (Vip Chat Htr1f, Pvalb Tpbg)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost t-types were strongly associated with a single met-type, and most met-types were composed of several t-types.
“Most t-types were strongly associated with a single met-type, and most met-types were composed of several t-types”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPvalb-MET-4 was made up mainly of layer 4 and layer 2/3 fast-spiking cells, with some deeper cells, and had a layer 2/3-dominant axonal projection with sparse projections to deeper layers.
“These consisted of mainly L4 and L2/3 fast-spiking cells (but included some deeper cells as well) with a L2/3-dominant axonal projection and sparse projections to deeper layers.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVip-MET-1 cells expressed Npy, whereas Vip-MET-2 cells expressed Ptprt and Rspo1.
“Vip-MET-1 cells expressed Npy while Vip-MET-2 cells expressed Ptprt and Rspo1 (Figure 7K).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMost Lamp5 cells other than Lamp5 Lhx6 cells belonged to Lamp5-MET-1; the Lamp5 Lhx6 cells formed Lamp5-MET-2.
“Most of the other Lamp5 cells were in the Lamp5-MET-1 type, except for the Lamp5 Lhx6 cells which formed Lamp5-MET-2.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLamp5-MET-2 could be distinguished by the presence or absence of several marker genes, was located in deep layers, and had electrophysiological properties similar to Lamp5-MET-1, including high upstroke-to-downstroke ratios and late-spiking.
“Lamp5-MET-2 was distinguishable by the presence or absence of several marker genes (Figure 7B) and was found in deep layers; the electrophysiological properties resembled Lamp5-MET-1, with high-upstroke downstroke ratios and late-spiking.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVip-MET-1 cells were mainly in layers 2/3 and 4, had bitufted dendrites extending into layer 1, and had fairly concentrated axon innervation of lower layer 2/3.
“Vip-MET-1 had cells primarily in L2/3 and L4, with bitufted dendrites extending into L1, and a fairly concentrated axon innervation of lower L2/3.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSncg-MET-3 cells were located deeply and belonged to the Sncg Slc17a8 transcriptomic type.
“Sncg-MET-3 cells were deep and belonged to the Sncg Slc17a8 t-type.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSome t-types had overlapping morpho-electric features, while others exhibited electrophysiological or morphological heterogeneity.
“some t-types had overlapping morpho-electric features, while others exhibited electrophysiological (e.g., Sst Mme Fam114a1, Sst Tac1 Htr1d) or morphological (e.g., Sst Calb2 Pdlim5, Pvalb Reln Itm2a) heterogeneity.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAction potential waveforms were similar among Pvalb met-types and had the fast-spiking shape.
“AP shapes were quite consistent across the Pvalb met-types and were typical of a fast-spiking phenotype.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPvalb-MET-1 and Pvalb-MET-2 were located mainly in layers 5 and 6 and had basket-like morphology with axons and dendrites confined to the deep parts of those layers.
“The first two met-types (Pvalb-MET-1 and Pvalb-MET-2) were found predominantly in L5 and L6 and had basket-like morphologies with axon and dendrites restricted to the deep part of L5 and L6.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSncg-MET-1 cells had bitufted or multipolar dendrites and a wide translaminar axon that mostly avoided layer 1.
“These cells had bitufted or multipolar dendrites with wide, translaminar axon that mostly avoided L1.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataDeep Pvalb met-types could be separated by marker gene expression: Pvalb-MET-1 expressed Gabrg1, Bche, and Th, whereas Pvalb-MET-2 expressed Sema3e.
“Marker genes also could distinguish these deep Pvalb met-types, as Pvalb-MET-1 cells expressed Gabrg1, Bche, and Th, while Pvalb-MET-2 cells expressed Sema3e (Figure 6G).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMorpho-electrical properties of GABAergic interneurons largely co-vary with transcriptomic types, and discrete and continuous variation coexist across the three modalities.
“1) Morpho-electrical features of GABAergic interneurons largely co-vary with transcriptomic types, with discrete and continuous variations co-existing along the three modalities.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVip-MET-4 was located in lower layer 2/3 and innervated layers 2/3 through 6, similar to Vip-MET-2.
“Vip-MET-4 was found in lower L2/3 and innervated L2/3 through L6 similar to Vip-MET-2.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAxons of many types target specific layers, which is a hallmark distinction between met-types; differential axon targeting specificity redefines the traditional concept of Sst interneuron types.
“6) The axons of many types also target specific layers, and this is a hallmark distinction between met-types; in particular, differential axon targeting specificity redefines the traditional concept of Sst interneuron types.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAt one end of the PC-3 spectrum, cells had low Npy expression, wide axons, and fewer late-spiking cells; at the other end, cells expressed more Npy, had rounder axons, and were consistently late-spiking.
“One end of the spectrum defined by PC-3 had cells with low Npy expression, wide axons, and fewer cells with a late-spiking phenotype, while the other end had cells expressing more Npy with rounder axons that were consistently late-spiking.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSncg-MET-2 cells expressed Car10 and Nptx2, had very high input resistance, and were located at the layer 1-layer 2/3 border.
“Sncg-MET-2 cells expressed Car10 and Nptx2 (Figure 7G), had very high input resistances (Figure 7H), and were found at the L1-L2/3 border (Figure 7E).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationLamp5-MET-1 cells had action potentials with high upstroke-to-downstroke ratios and neurogliaform-like morphology, and were found mainly in layers 1 and 2/3 but also in deeper layers.
“Lamp5-MET-1 had APs with high upstroke/downstroke ratios and a neurogliaform-like morphology (Jiang et al., 2013; Jiang et al., 2015; Schuman et al., 2019); these cells were found primarily in L1 and L2/3 but also appeared in deeper cortical layers.”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Findingown dataVip-MET-4 had enriched layer 4 axonal innervation, showed irregular and/or transient firing, and had more hyperpolarization-evoked sag than the first three Vip met-types.
“However, these cells had enriched L4 axonal innervation; they also exhibited irregular and/or transient firing and had more hyperpolarization-evoked sag than the first three Vip met-types (Figure 7L).”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataVip-MET-5 cells also frequently fired bursts near rheobase.
“These cells also frequently fired bursts near rheobase.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPvalb-MET-3 cells mapped to Pvalb Reln Tac1 and were mostly layer 5 fast-spiking cells with dominant layer 5 axon innervation and a few collaterals extending superficially.
“Pvalb-MET-3 had cells that mapped to Pvalb Reln Tac1 and contained mostly L5 fast-spiking cells with L5-dominant axon innervation and a few collaterals extending into superficial layers.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVip-MET-2 had a laminar position similar to Vip-MET-1 but longer dendrites, a narrower profile, and wider axonal branching only in deep layers.
“Vip-MET-2 had a similar laminar position to Vip-MET-1 but with longer dendrites and a narrower profile with wider axonal branching only in deep layers.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe study identifies 13 Sst met-types, substantially more than reported in previous morphological and/or electrophysiological studies.
“We identify 13 Sst met-types (Figure 6), which is substantially more than described in previous morphological and/or electrophysiological studies (Kawaguchi and Kubota, 1996; McGarry et al., 2010; Markram et al., 2015; Jiang et al., 2015; Munõz et al., 2017).”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Findingown dataA major insight is that the somas of many GABAergic t- or met-types are restricted to a single cortical layer or sublayer, and Sst interneurons elaborate axons in a dominant cortical layer.
“A major insight gained from our study is that the somas of many GABAergic t- or met-types are restricted to a single cortical layer or sublayer (Figure 2) and, in the case of Sst interneurons, elaborate their axons in a dominant cortical layer (Figure 4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAll Sst met-types contain at least one cell with some layer 1 projection and could technically be considered a Martinotti cell, usually the T-shaped type.
“However, all these Sst met-types contain at least one cell that had some degree of L1 projection and could therefore technically be considered a Martinotti cell (usually the T-shaped) as well.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSst-MET-2 densely innervates layer 2/3 and largely avoids layer 1.
“For example, Sst-MET-2 densely innervates L2/3 and largely avoids L1.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSst-MET-8 neurons, which map to Sst Hpse Cbln4 and are one of two major Sst met-types in layer 4, extensively innervate layer 4 and innervate layer 1 less.
“The axons of Sst-MET-8 neurons (mapping to the Sst Hpse Cbln4 t-type) — one of two major Sst met-types found in L4 — extensively innervate L4 and innervate L1 to a lesser degree.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisereasoningscRNAseq 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”
Quote in the paperSubstantial claim0 of 3 sources checkedClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe mapping method compared marker gene expression at each branch point and robustness was assessed by bootstrap.
“The mapping method was based on comparing a cell’s marker genes expression at each branch point of the reference hierarchical transcriptomic tree with the marker genes expression of the reference cell types (Methods, Figure S1D); robustness was assessed by a bootstrap approach (Methods).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe mapping procedure was designed to minimize contamination from nearby non-neuronal cells and glutamatergic neurons.
“Our mapping procedure for GABAergic neurons was designed to minimize the contribution of contamination from nearby non-neuronal cells (such as glia) and glutamatergic neurons.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationThe field has not readily converged on a consensus number or definition of neuronal cell types.
“the field has not readily converged on a consensus number, or even definition, of neuronal cell types”
Quote in the paperSubstantial claim0 of 3 sources checkedClassified as probably not a substantial claim.
Study design and conductreasoningAnalyses by t-type were restricted to the 2,955 highly-consistent cells to limit mapping ambiguities.
“When we performed analyses by t-type, we restricted the data set to the 2,955 cells with highly-consistent mapping to limit the effect of mapping ambiguities due to technical issues on our results”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrior 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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Tasic et al., 2018 source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationScala et al. reported a continuum of morphological and electrophysiological variability in mouse primary motor cortex glutamatergic and GABAergic neurons, where similar transcriptomic types often have similar morpho-electric features without clear boundaries.
“Consistent with our results, (Scala et al., 2020) describe a continuum of variability in the morphology and electrophysiology of glutamatergic and GABAergic neurons in primary motor cortex of the mouse — similar transcriptomic cell types frequently exhibit similar morpho-electric features, often without clear boundaries between them.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Scala et al., 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationQue et al. reported that neurons mapping to distinct Pvalb transcriptomic types in mouse hippocampus have qualitatively similar morphological characteristics.
“Also, (Que et al., 2020) report that neurons mapping to distinct Pvalb t-types in the mouse hippocampus exhibit qualitatively similar morphological characteristics.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Que et al., 2020
Not checked in the free run.
May combine two claims.
Interpretationown dataThe 28 met-types are labeled by salient gene markers and offer a systematic framework that encapsulates historical knowledge of cortical interneuron types.
“4) The 28 met-types are labeled by salient gene markers and provide a systematic framework to encapsulate historical knowledge of cortical interneuron types.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationMarker 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).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Zhang et al., 2020
Not checked in the free run.
- cites Tasic et al., 2018 source
Not checked in the free run.
May combine two claims.
InterpretationcitationThe two ends of the PC-3 spectrum resembled layer 1 interneuron populations from a recent barrel cortex study: cells with higher Npy expression matched Ndnf+/Npy+ neurogliaform cells, and the opposite end resembled canopy cells.
“We noted that each end of the spectrum resembled populations of L1 interneurons described in a recent study in barrel cortex (Schuman et al., 2019): the cells here with higher Npy expression have electrophysiology and morphology consistent with Ndnf+/Npy+ neurogliaform cells, and those at the other end have properties more similar to the “canopy cells” described in that study.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Schuman et al., 2019 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe limited set of stimuli in the patch-clamp protocol, the relatively small number of morphological reconstructions, and the lower dimensionality of electrophysiological and morphological data may all contribute to the difference in granularity.
“The limited number of stimuli in our standardized patch clamp protocol, the relatively small number of morphological reconstructions, and the much lower dimensionality of electrophysiological and morphological data compared to transcriptomic data may all contribute to this difference.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRecent evidence suggests that different morphological phenotypes correspond to different synaptic connectivity profiles.
“Recent evidence also suggests that different morphological phenotypes correspond to differential synaptic connectivity profiles (Nigro et al., 2018; Naka et al., 2019; Scala et al., 2019).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThere is no absolute definition of a Martinotti cell based on the total amount of axon in layer 1, although it is generally described as significant.
“We are not aware of an absolute definition of a Martinotti cell in terms of total amount of axon in L1, though it is generally described as “significant” (Tremblay et al., 2016; Zhou et al., 2020).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe authors made every effort to produce a fully connected neuronal structure while staying faithful to the image data.
“Every attempt was made to generate a completely connected neuronal structure while remaining faithful to image data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningNo randomization was used during data collection because there was a single experimental condition for all acquired data.
“No randomization was used during data collection as there was a single experimental condition for all acquired data.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenA cell was labeled highly consistent if the sum of its two highest mapping probabilities exceeded 70% and the ratio of the top probability to the next was greater than 2; otherwise it was moderately consistent.
“If the sum of the first two highest mapping probabilities for a cell was more than 70% and the ratio of the mapping probability of the first type to the next highest was more than 2, then the cell was labeled as “highly consistent.” Otherwise the cell was labeled “moderately consistent” (Figure S1G).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenIndividual sweeps were included only if bridge balance was below 20 MΩ and below 15% of input resistance, bias current was within 0 ± 100 pA, and root mean square noise was below 0.07 mV in a short window and below 0.5 mV in a longer window.
“For an individual sweep to be included, the following criteria were applied: (1) the bridge balance was <20 MΩ and <15% of the Rinput; (2) bias (leak) current 0 ± 100 pA; and (3) root mean square noise measurements in a short window (1.5 ms, to gauge high frequency noise) and longer window (500 ms, to measure patch instability) <0.07 mV and 0.5 mV, respectively.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationThis result suggests that transcriptomic types may need revision by combining another modality, such as connectivity, through supervised clustering to improve consistency.
“This intriguing result suggests that transcriptomic types may be revised based on the combination with another modality (e.g., connectivity) via approaches such as supervised clustering to achieve better consistency.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataTwo principal components that correlated with collection method were removed from the UMAP visualization to reduce technical bias.
“two PCs that exhibited high correlation with collection method (Pearson’s r = 0.94 and 0.22) were removed to reduce the effects of technical bias in the plots.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe bath and internal solutions and preparation methods were chosen to maximize tissue quality, match common field solutions, reduce RNAse activity, and maximize mRNA recovery.
“The composition of bath and internal solution as well as preparation methods were made to maximize the tissue quality of slices from adult mice, to align with solution compositions typically used in the field (to maximize the chance of comparison to previous studies), modified to reduce RNAse activity and ensure maximal gain of mRNA content.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll procedures were carried out in accordance with the Institutional Animal Care and Use Committee at the Allen Institute for Brain Science.
“all procedures were carried out in accordance with the Institutional Animal Care and Use Committee at the Allen Institute for Brain Science.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataPatch-seq transcriptomes were mapped top-down on the reference taxonomy tree by correlation with branch-point markers, with 100 bootstrap iterations sampling 70% of reference cells and 70% of markers to assess mapping robustness.
“The Patch-seq transcriptomes were mapped to the reference taxonomy tree in the top down manner, trying to resolve broad classes first, followed by subclasses and types (Figure S1C and D). For each single cell transcriptome, starting from the root and at each branch point, we computed its correlation with the reference cell types using the markers associated with the given branch point (i.e., the”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenFor inclusion in analysis, a cell had to have a seal greater than 1 GΩ before break-in and an initial access resistance below 20 MΩ and below 15% of input resistance.
“To be included in the analysis, a cell needed to have a >1 GΩ seal recorded before break-in and an initial access resistance <20 MΩ and <15% of the Rinput.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenBrains were sliced either coronally or at a 17° angle from the coronal plane to preserve the intactness of neuronal processes in visual cortex.
“Brains were mounted for slicing either in the coronal plane or at a 17° angle from the coronal plane to preserve the intactness of neuronal processes in the visual cortex.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Premisenone givenMultiple populations can be distinct yet still overlap substantially, which makes it hard to determine the origin of a given cell.
“Even if multiple populations are different, they may heavily overlap, making it difficult to tell where a given cell came from; we used supervised classifiers to estimate how reliable predictions about type could be using different modalities.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisereasoningA two-component model would yield a higher likelihood from overfitting even when the underlying distributions are identical, so the significance of the observed LLR was evaluated using parametric bootstrap.
“Since the two-component model would produce a higher likelihood due to overfitting even if the underlying distributions were the same, the statistical significance of the observed LLR was assessed by parametric boostrap.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe study did not blind data collection or analysis to the experimental conditions, as there was only one condition for all data.
“Data collection and analyses were not performed blind to the conditions of the experiments as there was a single experimental condition for all acquired data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe various stimulus protocols were not given in a randomized sequence.
“The different stimulus protocols were not presented in a randomized order.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNBLAST compares two morphologies by examining the distance and local direction of nearby points between them.
“This method compares a pair of morphologies by analyzing the distance and local direction of nearby points between the two structures.”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationKim et al. found that long-range connections from visual cortex layer 2/3 neurons correlate not with transcriptomic types but with gene expression differences within types.
“A recent study showed that long-range connections from visual cortex layer 2/3 neurons do not correlate with transcriptomic types but rather with within-type gene expression differences (Kim et al., 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kim et al., 2020 source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.
Study design and conductown dataA feedforward neural network classifier was trained on reference cells split into 80% training and 20% test sets, and the training and prediction were repeated 100 times with new random training sets.
“We built a feedforward neural network classifier which receives gene expression data as input to predict the cell type for each individual cell. To train the classifier, we used the reference cells and their corresponding types. The reference cells were divided into two groups of training (80%) and test (20%) sets.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study did not use statistical methods to predetermine sample sizes, but its sample sizes are similar to those in previous publications.
“No statistical methods were used to predetermine sample sizes, but the sample sizes here are similar to those reported in previous publications.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThese met-types represent a unified definition of cortical GABAergic interneuron types based on multimodal experimental data and enable cross-modal feature prediction.
“These met-types represent a version of a unified definition of cortical GABAergic interneuron types established with multimodal experimental data and enabling cross-modal feature prediction.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenCells with inconsistent mapping were excluded from analyses that used transcriptomic type assignments.
“Cells with “inconsistent” mapping were not used in analyses that incorporated t-type assignments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningFeatures that were mainly determined by z-dimension differences were excluded from analysis to reduce technical artifacts from z-compression after slice processing.
“While slice angle tilt and shrinkage correction were performed (see above), features predominantly determined by differences in the z-dimension were not analyzed to minimize technical artifacts due to z-compression of the slice after processing.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMultiplex FISH is already used to measure the proportions of transcriptomic types in brain tissue.
“In addition, multiplex FISH is already being used to measure the proportions of transcriptomic types within a given brain tissue (Hodge et al., 2019; Moffitt et al., 2018; Zhang et al., 2020).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
Interpretationown dataThe results show that Sst neurons have clear laminar innervation preferences that vary across met-types, so dominant laminar targets may be a more reliable distinguishing feature than whether axon is present in layer 1.
“These results reveal that Sst neurons have clear laminar innervation preferences that vary across met-types; consequently, their dominant laminar targets may be a more reliable distinguishing feature than the presence or absence of axon in layer 1.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductcitationElectrophysiological features were measured from short and long current pulses using methods previously described by Gouwens et al.
“Electrophysiological features were measured from responses elicited by short (3 ms) current pulses and long (1 s) current steps as previously described (Gouwens et al., 2019).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gouwens et al., 2019 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningNdnf-IRES2-dgCre mice were not given trimethoprim because baseline dgCre activity was sufficient to label cells with the Ai14 reporter.
“Ndnf-IRES2-dgCre animals did not receive trimethoprim induction, since the baseline dgCre activity (without trimethoprim) was sufficient to label the cells with the Ai14 reporter.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
GABAergic interneuron morphology is better preserved in ~350 um thick in vitro slice preparations than glutamatergic pyramidal neuron morphology.
“the morphology of GABAergic interneurons is better preserved in ~350 um thick in vitro slice preparations than that of glutamatergic pyramidal neurons with known long-range projections”
The paper relies on this without support.
The morpho-electric profiles of most t-types are largely unknown and their correspondence to historically defined GABAergic types has not been established.
“The morpho-electric profiles of most t-types, however, are largely unknown and their correspondence to historically defined GABAergic types has not been established.”
The paper relies on this without support.
The mapping algorithms and quality thresholds reliably assign Patch-seq transcriptomes to the correct reference transcriptomic types.
“For the reasons explained above, we used transcriptomes of dissociated cells from (Tasic et al., 2018) as reference dataset and mapped Patch-seq transcriptomes to the reference data to identify their cell types.”
The paper relies on this without support.
The standardized patch-clamp protocol and morphological reconstructions capture the relevant electrophysiological and morphological properties that distinguish cell types.
“The limited number of stimuli in our standardized patch clamp protocol, the relatively small number of morphological reconstructions, and the much lower dimensionality of electrophysiological and morphological data compared to transcriptomic data may all contribute to this difference.”
The paper relies on this without support.
The marker genes used to label met-types are selective for those met-types.
“Marker genes also could distinguish these deep Pvalb met-types, as Pvalb-MET-1 cells expressed Gabrg1, Bche, and Th, while Pvalb-MET-2 cells expressed Sema3e (Figure 6G).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Transcriptomically defined cell types have been shown to exhibit concordant morphological and physiological properties.
“Transcriptomically defined cell types have been shown to exhibit concordant morphological and physiological properties10,11.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [10]
A parallel Patch-seq study of the inhibitory neurons in the mouse visual cortex focused on isolating multimodal neural types but also often observed continuous variation.
“A parallel Patch-seq study of the inhibitory neurons in the mouse visual cortex45 focused on isolating multimodal neural types (‘met-types’) but also often observed continuous variation.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [45]
These visual cortex GABAergic MET types show strong cross-modality concordance and mutual predictability.
“These visual cortex GABAergic MET types show robust cross-modality concordance and mutual predictability.”
From What is a cell type and how to define it? (2022) · cites this paper as Gouwens et al., 2020
To reduce heterogeneity at the individual type level, triple-modality MET types were defined at a granularity between transcriptomic subclasses and types.
“To overcome heterogeneity at individual type level, a set of triple-modality MET types are defined at an intermediate level of granularity (between transcriptomic subclasses and types).”
From What is a cell type and how to define it? (2022) · cites this paper as Gouwens et al., 2020
A Patch-seq study of mouse visual cortical GABAergic neurons found relatively high corresponding continuous variation of transcriptomic types with cortical-depth anatomical distribution and morpho-electrical properties.
“Another Patch-seq study of mouse visual cortical GABAergic neurons also reveals a relatively high degree of corresponding continuous variations of transcriptomic types with their anatomical distribution along the cortical depth and the variations of their morpho-electrical properties (Fig. 3B) (Gouwens et al., 2020).”
From What is a cell type and how to define it? (2022) · cites this paper as Gouwens et al., 2020
A Patch-seq study simultaneously profiled transcriptomic, electrophysiological, and morphological properties of a large set of GABAergic interneurons from mouse visual cortex and found excellent correspondence among the three modalities.
“using the Patch-seq approach, we simultaneously profiled the transcriptomic, electrophysiological, and morphological properties of a large set of GABAergic interneurons from mouse visual cortex and found excellent correspondence among the three modalities”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Gouwens et al., 2020
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 Gouwens et al., 2020
Since isocortical Sst Myh8 and Etv1 cells are L5 Martinotti cells, the HPF Myh8 and Etv1 cells are likely the Martinotti-like oriens lacunosum-moleculare (OLM) cells.
“Since isocortical Sst Myh8 and Etv1 cells are L5 Martinotti cells (Gouwens et al., 2020) (Fig. S1B), the HPF Myh8 and Etv1 cells are likely the Martinotti-like oriens lacunosum-moleculare (OLM) cells (Leao et al., 2012).”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Gouwens et al., 2020
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InfluentialSelf-citationMolecular diversity and specializations among the cells of the adult mouse brain
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“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 (Saunders et al., 2018; Tasic et al., 2018; Zeisel et al., 2018; Zeisel et al., 2015; Tasic et al., 2016; Shekhar et al., 2016).”
A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex
Taniguchi, H., He, M., Wu, P., Kim, S., Paik, R., Sugino, K., Kvitsani, D., Fu, Y., Lu, J., Lin, Y., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.07.026 · Cited by 1,828 · see citing works
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · see citing works
Self-citationShared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746 · see citing works
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · cited 2× · see citing works
“Our previous efforts have systematically defined neuronal cell types in mouse visual cortex based on either transcriptomic characteristics (Tasic et al., 2018; Tasic et al., 2016) or electrophysiological and morphological characteristics (Gouwens et al.”
Self-citationReconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,705 · cited 3× · see citing works
“We identify 13 Sst met-types (Figure 6), which is substantially more than described in previous morphological and/or electrophysiological studies (Kawaguchi and Kubota, 1996; McGarry et al., 2010; Markram et al., 2015; Jiang et al., 2015; Munõz et al., 2017).”
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491 · cited 1× · see citing works
“Despite the diversity and amount of data collected and analyzed to this point, the field has not readily converged on a consensus number, or even definition, of neuronal cell types; indeed, efforts to determine a consensus number of cell types have been limited by well-documented challenges (Ascoli et al., 2008; DeFel…”
The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas
Wang, Q., Ding, S., Li, Y., Royall, J. J., Feng, D. H., Lesnar, P., Graddis, N., Naeemi, M., Facer, B. A. C., Ho, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.007 · Cited by 1,410 · cited 5× · see citing works
“A tilt angle correction was also performed based on the estimated difference (via CCF registration) between the slicing angle and the direct pia-white matter direction at the cell’s location (Gouwens et al., 2019).”
InfluentialSelf-citationMolecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365 · cited 1× · see citing works
“In addition, multiplex FISH is already being used to measure the proportions of transcriptomic types within a given brain tissue (Hodge et al., 2019; Moffitt et al., 2018; Zhang et al., 2020).”
Leptin Action on GABAergic Neurons Prevents Obesity and Reduces Inhibitory Tone to POMC Neurons
Vong, L., Ye, C., Yang, Z., Choi, B. J., Chua, S. C., & Lowell, B. B. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.028 · Cited by 1,238 · see citing works
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · cited 1× · see citing works
“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 (Saunders et al., 2018; Tasic et al., 2018; Zeisel et al., 2018; Zeisel et al., 2015; Tasic et al., 2016; Shekhar et al., 2016).”
[6] Correction for liquid junction potentials in patch clamp experiments
Neher, E. (1992) · Methods in Enzymology · doi:10.1016/0076-6879(92)07008-c · Cited by 1,132 · cited 1× · see citing works
“Data were reported uncorrected for the measured (Neher, 1992) –14 mV liquid junction potential between the electrode and bath solutions.”
Madisen, L., Garner, A. R., Shimaoka, D., Chuong, A. S., Klapoetke, N. C., Li, L., van der Bourg, A., Niino, Y., Egolf, L., Monetti, C., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.022 · Cited by 1,114 · see citing works
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
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“This was also the case when cells were mapped to a different set of t-types based on a larger dissociated cell data set from all of cortex and the hippocampus (Figure S8, (Yao et al., 2020)).”
Self-citationA taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065 · see citing works
Self-citationA Developmental Switch in the Response of DRG Neurons to ETS Transcription Factor Signaling
Hippenmeyer, S., Pecho‐Vrieseling, E., Sigrist, M., Portmann, T., Laengle, C., Ladle, D. R., & Arber, S. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030159 · Cited by 1,059 · see citing works
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Highly Multiplexed Subcellular RNA Sequencing in Situ
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973 · cited 2× · see citing works
“org, facilitates answers to a variety of critical questions, including: (1) to what extent do neurons categorized into the same transcriptomic type (t-type) on the basis of scRNAseq data exhibit similar morphological and/or electrophysiological features; (2) to what extent do neurons belonging to different t-types exh…”
BackgroundPrinciples of connectivity among morphologically defined cell types in adult neocortex
Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A. S., Patel, S., & Tolias, A. S. (2015) · Science · doi:10.1126/science.aac9462 · Cited by 908 · cited 3× · see citing works
“We identify 13 Sst met-types (Figure 6), which is substantially more than described in previous morphological and/or electrophysiological studies (Kawaguchi and Kubota, 1996; McGarry et al., 2010; Markram et al., 2015; Jiang et al., 2015; Munõz et al., 2017).”
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841 · cited 1× · see citing works
“Electrophysiological, biochemical, immunohistochemical, and genetic methods have subsequently helped define and distinguish increasingly more specific sets of neurons (reviewed recently in (Klausberger and Somogyi, 2008; Huang and Paul, 2019; Tremblay et al., 2016; Zeng and Sanes, 2017; Cembrowski and Spruston, 2019)).”
New insights into the classification and nomenclature of cortical GABAergic interneurons
DeFelipe, J., López-Cruz, P. L., Benavides-Piccione, R., Bielza, C., Larrañaga, P., Anderson, S., Burkhalter, A., Cauli, B., Fairén, A., Feldmeyer, D., et al. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3444 · Cited by 833 · cited 2× · see citing works
“example, in the Sst subclass, the Sst Calb2 Pdlim5 type had a L1-dominant (Martinotti-type) axon (DeFelipe et al., 2013), while Sst Tac1 Htr1d had a L2/3-dominant axon, Sst Hpse Cbln4 had a L4-dominant axon, and Sst Rxfp1 Prdm8 had a L5/L6-dominant axon.”
Daigle, T. L., Madisen, L., Hage, T. A., Valley, M. T., Knoblich, U., Larsen, R. S., Takeno, M. M., Huang, L., Gu, H., Larsen, R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.035 · Cited by 817 · see citing works
Self-citationDhillon, H., Zigman, J. M., Ye, C., Lee, C. E., McGovern, R. A., Tang, V., Kenny, C. D., Christiansen, L. M., White, R. D., Edelstein, E. A., et al. (2006) · Neuron · doi:10.1016/j.neuron.2005.12.021 · Cited by 791 · see citing works
Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010) · Nature Biotechnology · doi:10.1038/nbt.1612 · Cited by 707 · cited 1× · see citing works
“Reconstructions were generated based on a 3D image stack that was run through a Vaa3D-based image processing and reconstruction pipeline (Peng et al., 2010).”
Synaptic release of GABA by AgRP neurons is required for normal regulation of energy balance
Tong, Q., Ye, C.-P., Jones, J. E., Elmquist, J. K., & Lowell, B. B. (2008) · Nature Neuroscience · doi:10.1038/nn.2167 · Cited by 689 · see citing works
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Self-citationSpatial organization of the somatosensory cortex revealed by osmFISH
Codeluppi, S., Borm, L. E., Zeisel, A., Manno, G. L., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018) · Nature Methods · doi:10.1038/s41592-018-0175-z · Cited by 649 · cited 1× · see citing works
“org, facilitates answers to a variety of critical questions, including: (1) to what extent do neurons categorized into the same transcriptomic type (t-type) on the basis of scRNAseq data exhibit similar morphological and/or electrophysiological features; (2) to what extent do neurons belonging to different t-types exh…”
Gerfen, C. R., Paletzki, R., & Heintz, N. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.016 · Cited by 647 · see citing works
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3445 · Cited by 581 · cited 2× · see citing works
“In particular, using a modified version of the Patch-seq technique (Cadwell et al., 2016; Fuzik et al., 2016; Földy et al., 2016), we characterized electrophysiological responses to a series of hyperpolarizing and depolarizing current injections, extracted and reverse transcribed nuclear and cytosolic mRNA, sequenced…”
Wang, Y., Toledo‐Rodriguez, M., Gupta, A., Wu, C., Silberberg, G., Luo, J., & Markram, H. (2004) · The Journal of Physiology · doi:10.1113/jphysiol.2004.073353 · Cited by 491 · cited 1× · see citing works
“As mentioned, this met-type reflects the well-described Martinotti-type neurons (Wang et al., 2004) and includes cells that resemble the recently”
Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation
Harris, J. A., Hirokawa, K. E., Sorensen, S. A., Gu, H., Mills, M., Ng, L. L., Bohn, P., Mortrud, M., Ouellette, B., Kidney, J., et al. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00076 · Cited by 485 · see citing works
Self-citationClassification of electrophysiological and morphological neuron types in the mouse visual cortex
Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0417-0 · Cited by 456 · cited 12× · see citing works
“Applying a sparse principal components analysis (sPCA) to each feature vector revealed the most informative sparse principal components (sPCs, see Methods, (Gouwens et al., 2019)), which were aggregated to form a set of 44 sPCs that characterized the electrophysiological properties of the cells in the data set (Method…”
InfluentialSelf-citationDistinct Subtypes of Somatostatin-Containing Neocortical Interneurons Revealed in Transgenic Mice
Ma, Y., Hu, H., Berrebi, A. S., Mathers, P. H., & Agmon, A. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.0661-06.2006 · Cited by 432 · see citing works
Poulin, J.-F., Caronia, G., Hofer, C., Cui, Q., Helm, B., Ramakrishnan, C., Chan, C. S., Dombeck, D. A., Deisseroth, K., & Awatramani, R. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0203-4 · Cited by 423 · see citing works
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · cited 1× · see citing works
“Sst-MET-1 mapped to the Sst Chodl t-type, generally occupied deeper layers, and corresponded to non-Martinotti, long-range projecting inhibitory neurons (Tasic et al., 2018; Paul et al., 2017; He et al., 2016).”
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
The formal definition of reference priors
Berger, J. O., Bernardo, J. M., & Sun, D. (2009) · The Annals of Statistics · doi:10.1214/07-aos587 · Cited by 394 · cited 1× · see citing works
“whether the ambiguity in the mapping of Patch-seq transcriptomes is similar to this reference cells ambiguity by computing the Kullback-Leibler (KL) divergence (Berger et al., 2009) between the mapping probability distributions of cells with Patch-seq recordings and the reference mapping probability distribution.”
Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3443 · Cited by 386 · cited 2× · see citing works
“In particular, using a modified version of the Patch-seq technique (Cadwell et al., 2016; Fuzik et al., 2016; Földy et al., 2016), we characterized electrophysiological responses to a series of hyperpolarizing and depolarizing current injections, extracted and reverse transcribed nuclear and cytosolic mRNA, sequenced…”
Kawaguchi, Y., & Kubota, Y. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-08-02701.1996 · Cited by 380 · see citing works
High-throughput mapping of single-neuron projections by sequencing of barcoded RNA
Kebschull, J. M., Garcia da Silva, P., Reid, A. P., Peikon, I. D., Albeanu, D. F., & Zador, A. M. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.036 · Cited by 376 · see citing works
Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008) · Nature Protocols · doi:10.1038/nprot.2008.51 · Cited by 354 · cited 1× · see citing works
“Briefly, feature definitions were collected from prior studies (Scorcioni et al., 2008; Markram et al., 2015).”
Strategies and Tools for Combinatorial Targeting of GABAergic Neurons in Mouse Cerebral Cortex
He, M., Tucciarone, J., Lee, S., Nigro, M. J., Kim, Y., Levine, J. M., Kelly, S. M., Krugikov, I., Wu, P., Chen, Y., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.08.021 · Cited by 339 · see citing works
Self-citationThe organization of two new cortical interneuronal circuits
Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L., & Zhu, J. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3305 · Cited by 338 · see citing works
Extensible visualization and analysis for multidimensional images using Vaa3D
Peng, H., Bria, A., Zhou, Z., Iannello, G., & Long, F. (2014) · Nature Protocols · doi:10.1038/nprot.2014.011 · Cited by 315 · cited 1× · see citing works
“, virtual finger, polyline) in the Mozak extension (Zoran Popović, Center for Game Science, University of Washington) of Terafly tools (Peng et al., 2014; Bria et al., 2016) in Vaa3D.”
Self-citationLayer-specific modulation of neocortical dendritic inhibition during active wakefulness
Muñoz, W., Tremblay, R., Levenstein, D., & Rudy, B. (2017) · Science · doi:10.1126/science.aag2599 · Cited by 305 · see citing works
Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex
Bortone, D. S., Olsen, S. R., & Scanziani, M. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.021 · Cited by 300 · cited 2× · see citing works
“These interlaminar or translaminar projections resemble those described previously for fast-spiking neurons in L6 (Frandolig et al., 2019; Bortone et al., 2014).”
Costa, M., Manton, J. D., Ostrovsky, A. D., Prohaska, S., & Jefferis, G. S. X. E. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.012 · Cited by 294 · see citing works
The spatial and temporal origin of chandelier cells in mouse neocortex
Taniguchi, H., Lu, J., & Huang, Z. J. (2013) · Science · doi:10.1126/science.1227622 · Cited by 278 · cited 1× · see citing works
“Pvalb-MET-5 (containing Pvalb Vipr2 cells) represented chandelier cells as previously identified (Woodruff and Yuste, 2008; Taniguchi et al., 2013; Inan and Anderson, 2014; Tasic et al., 2018; Gouwens et al., 2019) and had higher input resistances compared to Pvalb-MET-4 (Figure 6H).”
Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics
Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006387 · Cited by 276 · see citing works
Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons
Cembrowski, M. S., & Spruston, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0125-5 · Cited by 258 · cited 1× · see citing works
“Electrophysiological, biochemical, immunohistochemical, and genetic methods have subsequently helped define and distinguish increasingly more specific sets of neurons (reviewed recently in (Klausberger and Somogyi, 2008; Huang and Paul, 2019; Tremblay et al., 2016; Zeng and Sanes, 2017; Cembrowski and Spruston, 2019)).”
Prönneke, A., Scheuer, B., Wagener, R. J., Möck, M., Witte, M., & Staiger, J. F. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv202 · Cited by 204 · cited 1× · see citing works
“We identified five Vip met-types (Figure 7I–L), all resembling bipolar or bitufted cells (Peters and Kara, 1985; Prönneke et al., 2015).”
The diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · cited 1× · see citing works
“Electrophysiological, biochemical, immunohistochemical, and genetic methods have subsequently helped define and distinguish increasingly more specific sets of neurons (reviewed recently in (Klausberger and Somogyi, 2008; Huang and Paul, 2019; Tremblay et al., 2016; Zeng and Sanes, 2017; Cembrowski and Spruston, 2019)).”
Neuroinformatics of the Allen Mouse Brain Connectivity Atlas
Kuan, L., Li, Y., Lau, C., Feng, D. H., Bernard, A., Sunkin, S. M., Zeng, H., Dang, C., Hawrylycz, M., & Ng, L. (2014) · Methods · doi:10.1016/j.ymeth.2014.12.013 · Cited by 195 · cited 5× · see citing works
“A tilt angle correction was also performed based on the estimated difference (via CCF registration) between the slicing angle and the direct pia-white matter direction at the cell’s location (Gouwens et al., 2019).”
InfluentialLayer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas
Scala, F., Kobak, D., Shan, S., Bernaerts, Y., Laturnus, S., Cadwell, C. R., Hartmanis, L., Froudarakis, E., Castro, J. R., Tan, Z. H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12058-z · Cited by 193 · cited 3× · see citing works
“A previous study also observed that cells of this t-type had fast-spiking-like firing properties (Scala et al., 2019).”
A bidirectional circuit switch reroutes pheromone signals in male and female brains
Kohl, J., Ostrovsky, A. D., Frechter, S., & Jefferis, G. S. X. E. (2013) · Cell · doi:10.1016/j.cell.2013.11.025 · Cited by 188 · cited 1× · see citing works
“Example neurons were chosen by calculating a pairwise similarity score for axonal branch structure and location (NBLAST score) (Costa et al., 2016; Kohl et al., 2013) for all reconstructed neurons in a t-type.”
The neuronal composition of area 17 of rat visual cortex. II. The nonpyramidal cells
Peters, A., & Kara, D. A. (1985) · The Journal of Comparative Neurology · doi:10.1002/cne.902340209 · Cited by 185 · cited 1× · see citing works
“We identified five Vip met-types (Figure 7I–L), all resembling bipolar or bitufted cells (Peters and Kara, 1985; Prönneke et al., 2015).”
Four Unique Interneuron Populations Reside in Neocortical Layer 1
Schuman, B., Machold, R. P., Hashikawa, Y., Fuzik, J., Fishell, G. J., & Rudy, B. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.1613-18.2018 · Cited by 184 · cited 2× · see citing works
“We noted that each end of the spectrum resembled populations of L1 interneurons described in a recent study in barrel cortex (Schuman et al., 2019): the cells here with higher Npy expression have electrophysiology and morphology consistent with Ndnf+/Npy+ neurogliaform cells, and those at the other end have properties…”
Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons
Földy, C., Darmanis, S., Aoto, J., Malenka, R. C., Quake, S. R., & Südhof, T. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610155113 · Cited by 179 · cited 2× · see citing works
“In particular, using a modified version of the Patch-seq technique (Cadwell et al., 2016; Fuzik et al., 2016; Földy et al., 2016), we characterized electrophysiological responses to a series of hyperpolarizing and depolarizing current injections, extracted and reverse transcribed nuclear and cytosolic mRNA, sequenced…”
McGarry, L. M. (2010) · Frontiers in Neural Circuits · doi:10.3389/fncir.2010.00012 · Cited by 166 · see citing works
Continuous and Discrete Neuron Types of the Adult Murine Striatum
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C., & Quake, S. R. (2020) · Neuron · doi:10.1016/j.neuron.2019.11.004 · Cited by 158 · cited 1× · see citing works
“Even in transcriptomic data from isolated cells, continuous variations were frequently observed (Stanley et al., 2020; Harris et al., 2018; Tasic et al., 2018).”
Nigro, M. J., Hashikawa-Yamasaki, Y., & Rudy, B. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2415-17.2017 · Cited by 149 · cited 2× · see citing works
“described L2/3 Martinotti and L5/6 fanning-out Martinotti cells (Nigro et al., 2018; Munõz et al., 2017).”
Chrna2-Martinotti cells synchronize layer 5 type A pyramidal cells via rebound excitation
Hilscher, M. M., Leão, R. N., Edwards, S. J., Leão, K. E., & Kullander, K. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001392 · Cited by 118 · cited 1× · see citing works
“, 2017) and alpha-2 Martinotti cells (Hilscher et al., 2017).”
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 · cited 3× · see citing works
“Recent evidence also suggests that different morphological phenotypes correspond to differential synaptic connectivity profiles (Nigro et al., 2018; Naka et al., 2019; Scala et al., 2019).”
Deitcher, Y., Eyal, G., Kanari, L., Verhoog, M. B., Kahou, G. A. A., Mansvelder, H. D., de Kock, C. P. J., & Segev, I. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx226 · Cited by 114 · cited 1× · see citing works
“in the dimension perpendicular to the cut surface to correct for shrinkage (Deitcher et al., 2017; Egger et al., 2008) after tissue processing.”
Genetic targeting and physiological features of VGLUT3+ amacrine cells
Grimes, W. N., Seal, R. P., Oesch, N. W., Edwards, R. H., & Diamond, J. S. (2011) · Visual Neuroscience · doi:10.1017/s0952523811000290 · Cited by 110 · see citing works
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 · cited 1× · see citing works
“A recent study showed that long-range connections from visual cortex layer 2/3 neurons do not correlate with transcriptomic types but rather with within-type gene expression differences (Kim et al., 2020).”
Egger, V., Nevian, T., & Bruno, R. M. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm126 · Cited by 97 · cited 1× · see citing works
“in the dimension perpendicular to the cut surface to correct for shrinkage (Deitcher et al., 2017; Egger et al., 2008) after tissue processing.”
Bria, A., Iannello, G., Onofri, L., & Peng, H. (2016) · Nature Methods · doi:10.1038/nmeth.3767 · Cited by 82 · cited 1× · see citing works
“, virtual finger, polyline) in the Mozak extension (Zoran Popović, Center for Game Science, University of Washington) of Terafly tools (Peng et al., 2014; Bria et al., 2016) in Vaa3D.”
Frandolig, J. E., Matney, C. J., Lee, K., Kim, J., Chevée, M., Kim, S.-J., Bickert, A. A., & Brown, S. P. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.048 · Cited by 78 · cited 1× · see citing works
“These interlaminar or translaminar projections resemble those described previously for fast-spiking neurons in L6 (Frandolig et al., 2019; Bortone et al., 2014).”
The chandelier cell, form and function
Inan, M., & Anderson, S. A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.009 · Cited by 75 · cited 1× · see citing works
“Pvalb-MET-5 (containing Pvalb Vipr2 cells) represented chandelier cells as previously identified (Woodruff and Yuste, 2008; Taniguchi et al., 2013; Inan and Anderson, 2014; Tasic et al., 2018; Gouwens et al., 2019) and had higher input resistances compared to Pvalb-MET-4 (Figure 6H).”
Dense transcript profiling in single cells by image correlation decoding
Coskun, A. F., & Cai, L. (2016) · Nature Methods · doi:10.1038/nmeth.3895 · Cited by 74 · cited 1× · see citing works
“org, facilitates answers to a variety of critical questions, including: (1) to what extent do neurons categorized into the same transcriptomic type (t-type) on the basis of scRNAseq data exhibit similar morphological and/or electrophysiological features; (2) to what extent do neurons belonging to different t-types exh…”
Strategies and Tools for Combinatorial Targeting of GABAergic Neurons in Mouse Cerebral Cortex
He, M., Tucciarone, J., Lee, S., Nigro, M. J., Kim, Y., Levine, J. M., Kelly, S. M., Krugikov, I., Wu, P., Chen, Y., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.009 · Cited by 71 · see citing works
Self-citationZhou, Z., Liu, X., Long, B., & Peng, H. (2015) · Neuroinformatics · doi:10.1007/s12021-015-9278-1 · Cited by 63 · cited 2× · see citing works
“Images were used to generate an automated reconstruction of the neuron using TReMAP (Zhou et al., 2016).”
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44 · cited 5× · see citing works
“…these and other related questions will facilitate the interpretation of high-throughput in situ molecular surveys of cell types in the brain currently being undertaken (Lee et al., 2014; Chen et al., 2015; Coskun and Cai, 2016; Kebschull et al., 2016; Codeluppi et al., 2018; Zhang et al., 2020).”
BackgroundMethodInfluentialAssessing Transcriptome Quality in Patch-Seq Datasets
Tripathy, S. J., Toker, L., Bomkamp, C., Mancarci, B. O., Belmadani, M., & Pavlidis, P. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00363 · Cited by 39 · cited 1× · see citing works
“However, we (Figure S2B) and others (Tripathy et al., 2018) found that the mRNA collected from one neuron via a Patch-seq recording often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.”
Roskams, J., & Popović, Z. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.045 · Cited by 34 · see citing works
Of Mice and Men, and Chandeliers
Woodruff, A. R., & Yuste, R. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060243 · Cited by 27 · cited 1× · see citing works
“Pvalb-MET-5 (containing Pvalb Vipr2 cells) represented chandelier cells as previously identified (Woodruff and Yuste, 2008; Taniguchi et al., 2013; Inan and Anderson, 2014; Tasic et al., 2018; Gouwens et al., 2019) and had higher input resistances compared to Pvalb-MET-4 (Figure 6H).”
Zhou, X., Mansori, I., Fischer, T., Witte, M., & Staiger, J. F. (2020) · Journal of Comparative Neurology · doi:10.1002/cne.24756 · Cited by 18 · cited 1× · see citing works
“We are not aware of an absolute definition of a Martinotti cell in terms of total amount of axon in L1, though it is generally described as “significant” (Tremblay et al., 2016; Zhou et al., 2020).”
Tebaykin, D., Tripathy, S. J., Binnion, N., Li, B., Gerkin, R. C., & Pavlidis, P. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00604.2017 · Cited by 15 · see citing works
Que, L., Lukacsovich, D., & Földy, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.21.913103 · Cited by 6 · cited 1× · see citing works
“Also, (Que et al., 2020) report that neurons mapping to distinct Pvalb t-types in the mouse hippocampus exhibit qualitatively similar morphological characteristics.”
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A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065 · see citing works
Self-citationA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
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Self-citationComparative cellular analysis of motor cortex in human, marmoset and mouse
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Self-citationA multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522 · see citing works
Self-citationWhat is a cell type and how to define it?
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Self-citationMorphological diversity of single neurons in molecularly defined cell types
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Self-citationPhenotypic variation of transcriptomic cell types in mouse motor cortex
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Local connectivity and synaptic dynamics in mouse and human neocortex
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Self-citationSingle-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Specialized astrocytes mediate glutamatergic gliotransmission in the CNS
Ceglia, R. D., Ledonne, A., Litvin, D. G., Lind, B. L., Carriero, G., Latagliata, E. C., Bindocci, E., Castro, M. A. D., Savtchouk, I., Vitali, I., et al. (2023) · Nature · doi:10.1038/s41586-023-06502-w · Cited by 279 · see citing works
“8a–h) was conducted according to published protocols 43,44,58,59 with minor modifications.”
MethodIdentification of neural oscillations and epileptiform changes in human brain organoids
Samarasinghe, R. A., Miranda, O. A., Buth, J. E., Mitchell, S. A., Ferando, I., Watanabe, M., Allison, T. F., Kurdian, A., Fotion, N. N., Gandal, M. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00906-5 · Cited by 278 · see citing works
Human neocortical expansion involves glutamatergic neuron diversification
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285 · see citing works
Self-citationSingle-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works
Cortical interneurons in autism
Contractor, A., Ethell, I. M., & Portera‐Cailliau, C. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00967-6 · Cited by 180 · see citing works
A transcriptomic axis predicts state modulation of cortical interneurons
Bugeon, S., Duffield, J. A. M., Dipoppa, M., Ritoux, A., Prankerd, I., Nicoloutsopoulos, D., Orme, D., Shinn, M., Peng, H., Forrest, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04915-7 · Cited by 177 · see citing works
The Neurodata Without Borders ecosystem for neurophysiological data science
Rübel, O., Tritt, A., Ly, R., Dichter, B., Ghosh, S., Niu, L., Baker, P. M., Soltész, I., Ng, L., Svoboda, K., et al. (2022) · eLife · doi:10.7554/elife.78362 · Cited by 182 · see citing works
The quest for multiscale brain modeling
D'Angelo, E. U., & Jirsa, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.06.007 · Cited by 155 · see citing works
Alternating sources of perisomatic inhibition during behavior
Dudok, B., Klein, P., Hwaun, E., Lee, B. R., Yao, Z., Fong, O., Bowler, J. C., Terada, S., Sparks, F. T., Szabo, G. G., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.003 · Cited by 161 · see citing works
“Cells were mapped to a consensus taxonomy previously constructed from transcriptomic data of dissociated corticohippocampal INs using a bootstrapping approach (Gouwens et al., 2020; Yao et al., 2020).”
Self-citationGoshisht, M. K. (2024) · ACS Omega · doi:10.1021/acsomega.3c05913 · Cited by 169 · see citing works
Hippocampal GABAergic interneurons and memory
Tzilivaki, A., Tukker, J. J., Maier, N., Poirazi, P., Sammons, R. P., & Schmitz, D. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.016 · Cited by 145 · see citing works
“176 Importantly, the link between transcriptomic and morphoelectric subtypes was not 1:1.”
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 130 · see citing works
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 · see citing works
“Proximity in the UMAPs suggest that quantitative features from all three modalities canbeused todistinguishGABAergic subclasses.”
BackgroundMethodResultInfluentialSelf-citationCell-type profiling in salamanders identifies innovations in vertebrate forebrain evolution
Woych, J., Gurrola, A. O., Deryckere, A., Jaeger, E. C. B., Gumnit, E., Merello, G., Gu, J., Joven, A., Leigh, N. D., Yun, M. H., et al. (2022) · Science · doi:10.1126/science.abp9186 · Cited by 112 · see citing works
The proteomic landscape of synaptic diversity across brain regions and cell types
van Oostrum, M., Blok, T. M., Giandomenico, S. L., tom Dieck, S., Tushev, G., Fürst, N., Langer, J. D., & Schuman, E. M. (2023) · Cell · doi:10.1016/j.cell.2023.09.028 · Cited by 135 · see citing works
The Parvalbumin Hypothesis of Autism Spectrum Disorder
Filice, F., Janickova, L., Henzi, T., Bilella, A., & Schwaller, B. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.577525 · Cited by 114 · see citing works
“, 2019), patch sequencing (Patch-seq) yielding morphoelectric (met-type) and transcriptomic (t-type) datasets (Gouwens et al., 2020), or machine learning applied to a large patch-seq dataset (Gala et al.”
BackgroundKalmbach, 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 104 · see citing works
Self-citationPatch-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 98 · see citing works
“…to originate from the cell nucleus; Cadwell et al., 2017; Scala et al., 2020), similar to single-nucleus RNA-seq (Bakken et al., 2018), and so it can be beneficial to use both exonic and intronic read counts to quantify gene expression (Gouwens et al., 2020; Lee et al., 2020; Scala et al., 2020).”
BackgroundMethodInfluentialExplainable multi-task learning for multi-modality biological data analysis
Tang, X., Zhang, J., He, Y., Zhang, X., Lin, Z., Partarrieu, S., Hanna, E. B., Ren, Z., Shen, H., Yang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37477-x · Cited by 112 · see citing works
“We fi rst benchmarked the unsupervised group identi fi cation performance of UnitedNet by combining cell electrophysiological and morphological features to identify transcriptomic cell types 29,38 .”
InfluentialThe Epigenome in Neurodevelopmental Disorders
Reichard, J., & Zimmer‐Bensch, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.776809 · Cited by 112 · see citing works
Lee, E., Balasubramanian, H., Tsolias, A., Anakwe, S. U., Medalla, M., Shenoy, K. V., & Chandrasekaran, C. (2021) · eLife · doi:10.7554/elife.67490 · Cited by 93 · see citing works
Spatially resolved transcriptomics in neuroscience
Close, J. L., Long, B. R., & Zeng, H. (2021) · Nature Methods · doi:10.1038/s41592-020-01040-z · Cited by 94 · see citing works
A transcriptional rheostat couples past activity to future sensory responses
Tsukahara, T., Brann, D. H., Pashkovski, S. L., Guitchounts, G., Bozza, T. C., & Datta, S. R. (2021) · Cell · doi:10.1016/j.cell.2021.11.022 · Cited by 77 · see citing works
“It remains unclear whether these transcriptional differences reflect an occult diversity of neural subtypes or whether they instead reflect the ongoing activity-dependent modulation of a single underlying cell type (Gouwens et al., 2020; Kim et al., 2020; Scala et al., 2021).”
BackgroundChartrand, T., Dalley, R., Close, J., Goriounova, N. A., Lee, B. R., Mann, R., Miller, J. A., Molnar, G., Mukora, A., Alfiler, L., et al. (2023) · Science · doi:10.1126/science.adf0805 · Cited by 88 · see citing works
“A continuous transition between Ndnf/Npy NGFC cells and Ndnf/Npy canopy cells was previously noted (24), corresponding to similarity between the Lamp5 Ntn1 Npy2r t-type (LAMP subclass) and Lamp5 Fam19a1 Tmem182 (MC4R subclass).”
BackgroundMethodInfluentialSelf-citationNeuronal parts list and wiring diagram for a visual system
Matsliah, A., Yu, S.-C., Kruk, K., Bland, D., Burke, A. T., Gager, J., Hebditch, J., Silverman, B., Willie, K. P., Willie, R., et al. (2024) · Nature · doi:10.1038/s41586-024-07981-1 · Cited by 85 · see citing works
Cortical disinhibitory circuits: cell types, connectivity and function
Kullander, K., & Topolnik, L. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.009 · Cited by 83 · see citing works
Long-term stability of cortical ensembles
Pérez-Ortega, J., Alejandre-García, T., & Yuste, R. (2021) · eLife · doi:10.7554/elife.64449 · Cited by 82 · see citing works
“One limitation of this study is that we did not image the activity of GABAergic interneurons, which are at least 28 different types based on the morphoelectric and transcriptomic classifications (Yuste et al., 2020; Gouwens et al., 2020; Yao et al., 2021).”
MethodHage, T. A., Bosma-Moody, A., Baker, C. A., Kratz, M. B., Campagnola, L., Jarsky, T., Zeng, H., & Murphy, G. J. (2022) · eLife · doi:10.7554/elife.71103 · Cited by 84 · see citing works
Self-citationCommon cell type nomenclature for the mammalian brain
Miller, J. A., Gouwens, N. W., Tasic, B., Collman, F., van Velthoven, C. T. J., Bakken, T. E., Hawrylycz, M., Zeng, H., Lein, E. S., & Bernard, A. (2020) · eLife · doi:10.7554/elife.59928 · Cited by 73 · see citing works
“…such as neurogliaform, chandelier, Martinotti, and pyramidal cells, have been identified based on highly distinct shape, location, or electrical properties, providing robust and consistent classifications of neuronal cell types and a common vocabulary (Greig et al., 2013; Markram et al., 2004).”
BackgroundSelf-citationTossell, K., Yu, X., Giannos, P., Soto, B. A., Nollet, M., Yustos, R., Miracca, G., Vicente, M., Miao, A., Hsieh, B., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01430-4 · Cited by 82 · see citing works
“Many neocortical Sst -expressing GABA cells are slow spiking and primarily innervate dendrites of pyramidal cells 42,45 .”
BackgroundInfluentialThe plasticitome of cortical interneurons
McFarlan, A. R., Chou, C. Y. C., Watanabe, A., Cherepacha, N., Haddad, M. G., Owens, H., & Sjöström, P. J. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00663-9 · Cited by 67 · see citing works
“For example, L2/3 PCs in mouse developing primary visual cortex (postnatal day 14–23) receive inhibition proportional to excitation 52 , but how is such balance achieved?”
BackgroundJoint variational autoencoders for multimodal imputation and embedding
Kalafut, N. C., Huang, X. L., & Wang, D. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00663-z · Cited by 77 · see citing works
“Raw Patch-seq datasets are avail508 able at [30,2].”
BackgroundMethodInfluentialChang, C.-W., Evans, M. D., Yu, X., Yu, G.-Q., & Mucke, L. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109855 · Cited by 81 · see citing works
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortexPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525290 · Cited by 70 · see citing works
Inhibitory specificity from a connectomic census of mouse visual cortex
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2025) · Nature · doi:10.1038/s41586-024-07780-8 · Cited by 141 · see citing works
“16.”
Dissociable Roles of Pallidal Neuron Subtypes in Regulating Motor Patterns
Cui, Q., Pamukcu, A., Cherian, S., Chang, I. Y. M., Berceau, B. L., Xenias, H. S., Higgs, M. H., Rajamanickam, S., Chen, Y., Du, X., et al. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2210-20.2021 · Cited by 61 · see citing works
Applications of deep learning in understanding gene regulation
Li, Z., Gao, E., Zhou, J., Han, W., Xu, X., & Gao, X. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100384 · Cited by 68 · see citing works
A marmoset brain cell census reveals regional specialization of cellular identities
Krienen, F. M., Levandowski, K. M., Zaniewski, H., del Rosario, R. C. H., Schroeder, M. E., Goldman, M., Wienisch, M., Lutservitz, A., Beja-Glasser, V. F., Chen, C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adk3986 · Cited by 66 · see citing works
Bi, Q., Wang, C., Cheng, G., Chen, N., Wei, B., Liu, X., Li, L., Lu, C. C., He, J., Weng, Y., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.06.018 · Cited by 65 · see citing works
Identification of visual cortex cell types and species differences using single-cell RNA sequencing
Wei, J.-R., Hao, Z.-Z., Xu, C., Huang, M., Tang, L., Xu, N., Liu, R., Shen, Y., Teichmann, S. A., Miao, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34590-1 · Cited by 55 · see citing works
Chen, S., Xu, H., Dong, S., & Xiao, L. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.2494-21.2022 · Cited by 58 · see citing works
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization
Lee, B., Budzillo, A., Hadley, K., Miller, J. A., Jarsky, T., Baker, K. S., Hill, D. D., Kim, L., Mann, R., Ng, L., et al. (2021) · eLife · doi:10.7554/elife.65482 · Cited by 53 · see citing works
Self-citationGao, L., Liu, S., Wang, Y., Wu, Q., Gou, L., & Yan, J. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01339-y · Cited by 53 · see citing works
A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types
Ben‐Simon, Y., Hooper, M. J., Narayan, S., Daigle, T. L., Dwivedi, D., Way, S. W., Oster, A., Stafford, D. A., Mich, J. K., Taormina, M. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.002 · Cited by 81 · see citing works
“Since cell-type distribution and cell shapes across neocortical layers for all TCPs are known, 9,51,52 we visually matched each observed LCP with the expected pattern of the TCP to derive initial evaluation of target specificity.”
Self-citationGenerating and Using Transcriptomically Based Retinal Cell Atlases
Shekhar, K., & Sanes, J. R. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-032621-075200 · Cited by 50 · see citing works
The cell-type underpinnings of the human functional cortical connectome
Zhang, X.-H., Anderson, K. M., Dong, H.-M., Chopra, S., Dhamala, E., Emani, P. S., Gerstein, M., Margulies, D. S., & Holmes, A. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01812-2 · Cited by 62 · see citing works
“…IT in cluster 4 are preferentially distributed in the dorsal attention and frontoparietal networks involved in top–down attentional orienting and executive processes 56 53,59 , the regular-spiking Chandelier preferentially innervates the initial axon segment, strengthening output control 60,61 .”
Bryson, A., Reid, C. A., & Petrou, S. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15769 · Cited by 63 · see citing works
Somatostatin interneurons control the timing of developmental desynchronization in cortical networks
Mòdol, L., Moissidis, M., Selten, M. M., Oozeer, F., & Marín, Ó. (2024) · Neuron · doi:10.1016/j.neuron.2024.03.014 · Cited by 59 · see citing works
Inhibition in the auditory cortex
Studer, F., & Barkat, T. R. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.11.021 · Cited by 52 · see citing works
Huang, W., Xu, Q., Su, J., Tang, L., Hao, Z.-Z., Xu, C., Liu, R., Shen, Y., Sang, X., Xu, N., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111322 · Cited by 48 · see citing works
From Cell Types to an Integrated Understanding of Brain Evolution: The Case of the Cerebral Cortex
Tosches, M. A. (2021) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-120319-112654 · Cited by 44 · see citing works
de Kock, C. P. J., Pie, J. L., Pieneman, A., Mease, R. A., Bast, A., Guest, J. M., Oberlaender, M., Mansvelder, H., & Sakmann, B. (2021) · Communications Biology · doi:10.1038/s42003-021-02241-8 · Cited by 35 · see citing works
Interneuron Dysfunction and Inhibitory Deficits in Autism and Fragile X Syndrome
Nomura, T. (2021) · Cells · doi:10.3390/cells10102610 · Cited by 48 · see citing works
Transcriptomic neuron types vary topographically in function and morphology
Shainer, I., Kappel, J. M., Laurell, E., Donovan, J. C., Schneider, M., Kuehn, E., Arnold-Ammer, I., Stemmer, M., Larsch, J., & Baier, H. (2025) · Nature · doi:10.1038/s41586-024-08518-2 · Cited by 54 · see citing works
“Similarly, in the mouse visual cortex, morpho-electric properties measured in tissue slices were found to be relatively homogeneous within individual t-types 12,13 .”
An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes
Furlanis, E., Dai, M., Garcia, B. L., Tran, T. K., Vergara, J., Pereira, A., Gorissen, B. L., Wills, S., Vlachos, A., Hairston, A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2025.05.002 · Cited by 66 · see citing works
Self-citationSingle-cell transcriptomic classification of rabies-infected cortical neurons
Patiño, M., Lagos, W. N., Patne, N. S., Tasic, B., Zeng, H., & Callaway, E. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2203677119 · Cited by 40 · see citing works
Consens, M. E., Chen, Y., Menon, V., Wang, Y., Schneider, J. A., De Jager, P. L., Bennett, D. A., Tripathy, S. J., & Felsky, D. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.903175 · Cited by 44 · see citing works
Amunts, K., Axer, M., Banerjee, S., Bitsch, L., Bjaalie, J. G., Brauner, P., Brovelli, A., Calarco, N., Carrère, M., Caspers, S., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00137 · Cited by 53 · see citing works
“Therefore, it has become clear that defining and achieving ambitious scientific goals will require close collaboration between laboratories with expertise in different brain regions and techniques, for example, specialists in image analysis, neuroanatomy, data analysis, computation and physiology.”
BackgroundUnderstanding the molecular diversity of synapses
van Oostrum, M., & Schuman, E. M. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00888-w · Cited by 49 · see citing works
A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data
Nguyen, N. D., Huang, J., & Wang, D. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00185-x · Cited by 42 · see citing works
A cell-type-specific error-correction signal in the posterior parietal cortex
Green, J., Bruno, C. A., Traunmüller, L., Ding, J., Hrvatin, S., Wilson, D. E., Khodadad, T., Samuels, J., Greenberg, M. E., & Harvey, C. D. (2023) · Nature · doi:10.1038/s41586-023-06357-1 · Cited by 43 · see citing works
An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo
Sánchez-Aguilera, A., Wheeler, D. W., Jurado-Parras, T., Valero, M., Nokia, M. S., Cid, E., Fernández‐Lamo, I., Sutton, N. M., García-Rincón, D., de la Prida, L. M., et al. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001213 · Cited by 35 · see citing works
“Although in vivo approaches are the goal, the community efforts to build a systematic cellular taxonomy integrating a wealth of transcriptomic, morphological, and electrophysiological single-cell data rely on in vitro strategies [20].”
BackgroundMultimodal charting of molecular and functional cell states via in situ electro-sequencing
Li, Q., Lin, Z., Liu, R., Tang, X., Huang, J., He, Y., Sui, X., Tian, W., Shen, H., Zhou, H., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.023 · Cited by 52 · see citing works
Synapse-specific burst coding sustained by local axonal translation
Wong, H. H., Watt, A. J., & Sjöström, P. J. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.011 · Cited by 40 · see citing works
Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex
Bella, D. J. D., Domıńguez-Iturza, N., Brown, J. R., & Arlotta, P. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.021 · Cited by 37 · see citing works
Consistent cross-modal identification of cortical neurons with coupled autoencoders
Gala, R., Budzillo, A., Baftizadeh, F., Miller, J. A., Gouwens, N. W., Arkhipov, A. S., Murphy, G. J., Tasic, B., Zeng, H., Hawrylycz, M., et al. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00030-1 · Cited by 40 · see citing works
Self-citationKim, M.-H., Radaelli, C., Thomsen, E. R., Monet, D., Chartrand, T., Jorstad, N. L., Mahoney, J. T., Taormina, M. J., Long, B., Baker, K. S., et al. (2023) · eLife · doi:10.7554/elife.81863 · Cited by 43 · see citing works
“Patch-seq studies in mouse ( Gouwens et al., 2019 ; Gouwens et al., 2020 ), monkey ( Bakken et al., 2021 ), and human ( Berg et al., 2021 ; Lee et al., 2022 ) suggest that there is a strong correlation of intrinsic and morphological features to highly granular transcriptomically defined cell types…”
Self-citationOrigin, Development, and Synaptogenesis of Cortical Interneurons
Llorca, A., & Deogracias, R. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.929469 · Cited by 40 · see citing works
“Combining transcriptomics with more classic criteria has led to the identification of 28 IN types with discernible morphology, physiological properties and gene expression profiles in this same area (Gouwens et al., 2020).”
BackgroundMethodSaunders, A. B., Huang, K. W., Vondrak, C. J., Hughes, C., Smolyar, K., Sen, H., Philson, A. C., Nemesh, J. C., Wysoker, A., Kashin, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34334-1 · Cited by 36 · see citing works
Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37 · see citing works
“Patch-seq dataset includes transcriptomics and electrophysiological (ephys) data for 4435 neuronal cells in mouse visual cortex [81].”
MethodInfluentialMaksymetz, J., Byun, N. E., Luessen, D. J., Li, B., Barry, R. L., Gore, J. C., Niswender, C. M., Lindsley, C. W., Joffe, M. E., & Conn, P. J. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109950 · Cited by 38 · see citing works
“Importantly, SST-INs are a heterogenous population with subtypes exhibiting different electrophysiological and morphological characteristics and distinct functions across cortical layers (Gouwens et al., 2020; Yavorska and Wehr, 2016).”
Loss of LAMP5 interneurons drives neuronal network dysfunction in Alzheimer’s disease
Deng, Y., Bi, M., Delerue, F., Forrest, S. L., Chan, G., van der Hoven, J., van Hummel, A., Feiten, A., Lee, S., Martínez-Valbuena, I., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-022-02457-w · Cited by 39 · see citing works
“Recent single-cell sorting studies of mouse brains have identified a non-overlapping subpopulation of inhibitory interneurons expressing LAMP5 but its function(s) is yet-to-be-defined [11, 13, 37].”
BackgroundSelf-citationPredictive and robust gene selection for spatial transcriptomics
Covert, I., Gala, R., Wang, T., Svoboda, K., Sümbül, U., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37392-1 · Cited by 40 · see citing works
“, morphological, connectivity, or electrophysiological studies), FISH can also link transcriptomic identity with other data to provide a better understanding of the functional role of individual cell types [9, 13, 14, 15, 16].”
InfluentialSelf-citationFisher, J., Verhagen, M. G., Long, Z., Moissidis, M., Yan, Y., He, C., Wang, J., Micoli, E., Alastruey, C. M., Moors, R., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.11.013 · Cited by 36 · see citing works
“13.”
Functional specialization of hippocampal somatostatin-expressing interneurons
Chamberland, S., Grant, G., Machold, R. P., Nebet, E. R., Tian, G., Stich, J. L., Hanani, M., Kullander, K., & Tsien, R. W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306382121 · Cited by 38 · see citing works
“Large - scale efforts in the cortex have shown that morpho - electrophysiological and transcrip tomics (MET) parameters converge to describe interneuronal cat egories (51–53), with the advantage that each cell is individually sampled for MET features.”
An epigenome atlas of neural progenitors within the embryonic mouse forebrain
Rhodes, C. T., Thompson, J. J., Mitra, A., Asokumar, D., Lee, D. R., Lee, D. J., Zhang, Y., Jason, E., Dale, R., Rocha, P. P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31793-4 · Cited by 33 · see citing works
“7a, b) and (2) early and late expressing genes visualized in heatmaps (Fig.”
BackgroundCombining hypothesis- and data-driven neuroscience modeling in FAIR workflows
Eriksson, O., Bhalla, U. S., Blackwell, K. T., Crook, S. M., Keller, D. X., Kramer, A., Linne, M., Saudargienė, A., Wade, R. C., & Kotaleski, J. H. (2022) · eLife · doi:10.7554/elife.69013 · Cited by 35 · see citing works
Goff, K. M., Liebergall, S. R., Jiang, E., Somarowthu, A., & Goldberg, E. M. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112628 · Cited by 38 · see citing works
The impact of (ab)normal maternal environment on cortical development
Vasistha, N. A., & Khodosevich, K. V. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102054 · Cited by 36 · see citing works
Stimulus novelty uncovers coding diversity in survey of visual cortexPreprint
Garrett, M., Groblewski, P. A., Piet, A. T., Ollerenshaw, D. R., Najafi, F., Yavorska, I., Amster, A., Bennett, C., Buice, M. A., Caldejon, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.14.528085 · Cited by 29 · see citing works
Layer 1 NDNF interneurons are specialized top-down master regulators of cortical circuits
Hartung, J., Schroeder, A., Vázquez, R. A. P., Poorthuis, R. B., & Letzkus, J. J. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114212 · Cited by 33 · see citing works
NMDA receptor function in inhibitory neurons
Booker, S. A., & Wyllie, D. J. A. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108609 · Cited by 33 · see citing works
Oláh, V. J., Goettemoeller, A. M., Rayaprolu, S., Dammer, E. B., Seyfried, N. T., Rangaraju, S., Dimidschstein, J., & Rowan, M. J. (2022) · eLife · doi:10.7554/elife.75316 · Cited by 32 · see citing works
Micheva, K. D., Király, M., Perez, M. M., & Madison, D. V. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab018 · Cited by 32 · see citing works
Long-Range GABAergic Projections of Cortical Origin in Brain Function
Urrutia-Piñones, J., Morales-Moraga, C., Sanguinetti, N. K., Escobar, A. P., & Chiu, C. Q. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.841869 · Cited by 33 · see citing works
Candelabrum cells are ubiquitous cerebellar cortex interneurons with specialized circuit properties
Osorno, T., Rudolph, S., Nguyen, T., Kozareva, V., Nadaf, N. M., Norton, A., Macosko, E. Z., Lee, W.-C. A., & Regehr, W. G. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01057-x · Cited by 33 · see citing works
“A summary of the expression of key genes showed that it is not always possible to delineate a cell type with a single gene 10, 16 .”
SCN1A channelopathies: Navigating from genotype to neural circuit dysfunction
Bryson, A., & Petrou, S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1173460 · Cited by 35 · see citing works
“Techniques involving conditional deletion of Scn1a within fine-grained cellular populations, precisely interrogating network function with all-optical electrophysiology, and the development of SCN1A-associated EIDEE gain-of-function mouse models will be invaluable for tackling these questions (94, 95).”
BackgroundToward Understanding the Diverse Roles of Perisomatic Interneurons in Epilepsy
Dudok, B., Klein, P., & Soltész, I. (2021) · Epiliepsy currents/Epilepsy currents · doi:10.1177/15357597211053687 · Cited by 28 · see citing works
“, by clustering cells considering the quantitative expression levels of all genes) are more likely to correspond to distinct morpho-functional cell types representing circuit elements with unique roles in brain function.(26) Importantly, such advances in transcriptomics allow researchers to computationally identify di…”
BackgroundAdvancements in the Quest to Map, Monitor, and Manipulate Neural Circuitry
Swanson, J., Chin, P.-S., Romero, J. M., Srivastava, S., Ortiz‐Guzman, J., Hunt, P. J., & Arenkiel, B. R. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.886302 · Cited by 32 · see citing works
A subpopulation of cortical VIP-expressing interneurons with highly dynamic spines
Georgiou, C., Kehayas, V., Lee, K. S., Brandalise, F., Sahlender, D. A., Blanc, J., Knott, G. W., & Holtmaat, A. J. D. G. (2022) · Communications Biology · doi:10.1038/s42003-022-03278-z · Cited by 23 · see citing works
- Authors
- Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., Bertagnolli, D., Bickley, K., Bomben, J., Braun, T., Brouner, K., Casper, T., Crichton, K., Daigle, T. L., Dalley, R., de Frates, R. A., Dee, N., Desta, T., Lee, S. D., Dotson, N., Egdorf, T., Ellingwood, L., Enstrom, R., Esposito, L., Farrell, C., Feng, D., Fong, O., Gala, R., Gamlin, C., Gary, A., Glandon, A., Goldy, J., Gorham, M., Graybuck, L., Gu, H., Hadley, K., Hawrylycz, M. J., Henry, A. M., Hill, D., Hupp, M., Kebede, S., Kim, T. K., Kim, L., Kroll, M., Lee, C., Link, K. E., Mallory, M., Mann, R., Maxwell, M., McGraw, M., McMillen, D., Mukora, A., Ng, L., Ng, L., Ngo, K., Nicovich, P. R., Oldre, A., Park, D., Peng, H., Penn, O., Pham, T., Pom, A., Popović, Z., Potekhina, L., Rajanbabu, R., Ransford, S., Reid, D., Rimorin, C., Robertson, M., Ronellenfitch, K., Ruiz, A., Sandman, D., Smith, K., Sulc, J., Sunkin, S. M., Szafer, A., Tieu, M., Torkelson, A., Trinh, J., Tung, H., Wakeman, W., Ward, K., Williams, G., Zhou, Z., Ting, J. T., Arkhipov, A., Sümbül, U., Lein, E. S., Koch, C., Yao, Z., Tasic, B., Berg, J., Murphy, G. J., & Zeng, H.
- Affiliations
- Allen Institute for Brain Science (US)
- University of Washington (US)
- Corresponding author
- Gouwens, Nathan W.
- Sorensen, Staci A.
- Murphy, Gabe J.
- ORCID
- Gouwens, Nathan W.
- Sorensen, Staci A.
- Baftizadeh, Fahimeh
- Budzillo, Agata
- Lee, Brian R.
- Jarsky, Tim
- Alfiler, Lauren
- Baker, Katherine
- Barkan, Eliza
- Berry, Kyla
- Bertagnolli, Darren
- Bickley, Kris
- Bomben, Jasmine
- Braun, Thomas
- Brouner, Krissy
- Casper, Tamara
- Crichton, Kirsten
- Daigle, Tanya L.
- Dalley, Rachel
- Dee, Nick
- Desta, Tsega
- Lee, Samuel Dingman
- Dotson, Nadezhda
- Egdorf, Tom
- Ellingwood, Lauren
- Enstrom, Rachel
- Esposito, Luke
- Farrell, Colin
- Feng, David
- Fong, Olivia
- Gala, Rohan
- Gamlin, Clare
- Gary, Amanda
- Glandon, Alexandra
- Goldy, Jeff
- Gorham, Melissa
- Graybuck, Lucas
- Hadley, Kristen
- Hawrylycz, Michael J.
- Henry, Alex M.
- Hill, DiJon
- Hupp, Madie
- Kebede, Sara
- Kim, Tae Kyung
- Kim, Lisa
- Kroll, Matthew
- Lee, Changkyu
- Link, Katherine E.
- Mallory, Matthew
- Mann, Rusty
- Maxwell, Michelle
- McMillen, Delissa
- Mukora, Alice
- Ng, Lindsay
- Ng, Lydia
- Nicovich, Philip R.
- Oldre, Aaron
- Popović, Zoran
- Potekhina, Lydia
- Rajanbabu, Ramkumar
- Ransford, Shea
- Reid, David
- Rimorin, Christine
- Ronellenfitch, Kara
- Ruiz, Augustin
- Smith, Kimberly
- Sunkin, Susan M.
- Szafer, Aaron
- Tieu, Michael
- Torkelson, Amy
- Trinh, Jessica
- Tung, Herman
- Wakeman, Wayne
- Ward, Katelyn
- Williams, Grace
- Zhou, Zhi
- Ting, Jonathan T.
- Arkhipov, Anton
- Sümbül, Uygar
- Lein, Ed S.
- Yao, Zizhen
- Tasic, Bosiljka
- Berg, Jim
- Murphy, Gabe J.
- Zeng, Hongkui
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 183
- Issue
- 4
- Pages
- 935-953.e19
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7781065
- OpenAlex
- W3101846945
- Semantic Scholar
- 82788e871368c8e83c596996c4db302dd3f44074
- Cited by
- 538
- References
- 99
- Citations by year
- 2026: 59 · 2025: 98 · 2024: 105 · 2023: 109 · 2022: 80 · 2021: 68 · 2020: 18
- FWCI
- 31.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 376 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- GABAergic interneurons, visual cortex, neuronal cell types, neuronal morphology, electrophysiological properties, multimodal integration, mice
- MeSH terms
- Animals, Cerebral Cortex, Female, Hippocampus, Ion Channels, Male, Mice, Inbred C57BL, Nerve Tissue Proteins, Gene Expression Profiling, Electrophysiological Phenomena, GABAergic Neurons, Transcriptome
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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