Classification of electrophysiological and morphological neuron types in the mouse visual cortex
Open accessPublished version of a preprint
Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines. Gouwens et al. established a morpho-electrical taxonomy of cell types for the mouse visual cortex via unsupervised clustering analysis of multiple quantitative features from 1,938 neurons available online at the Allen Cell Types Database.
AI snapshot
Written from the paper’s full text.
TL;DR
This paper presents a standardized patch-clamp and biocytin-reconstruction pipeline to classify neurons in adult mouse visual cortex, yielding 17 electrophysiological types, 38 morphological types, and 46 joint morpho-electric types. The joint types align better with transcriptomic subclasses than either modality alone, and the data and tools are publicly released.
Key findings
- From 1,938 neurons passing electrophysiology QC (928 spiny/excitatory, 1,010 aspiny or sparsely spiny/inhibitory), 461 were reconstructed (254 spiny, 207 aspiny/sparsely spiny), and 452 had both modalities; unsupervised clustering identified 17 e-types (4 excitatory, 13 inhibitory), 38 m-types (19 spiny, 19 aspiny), and 46 me-types (20 excitatory, 26 inhibitory).
- Joint clustering produced 46 me-types rather than the 120 simple intersections of e-types and m-types, and these me-types showed stronger consistency with transcriptomic subclasses than e-types or m-types alone.
- Morphological features were more informative for excitatory me-types, while both electrophysiological and morphological features predicted many inhibitory me-types; selective transgenic line/layer combinations linked some me-types to specific transcriptomic types.
- The t-SNE projection separated excitatory and inhibitory cells and showed coherence by transgenic driver, with Pvalb, Sst, Vip, and Chat populations occupying related but distinct regions.
Methods
- Standardized in vitro pipeline: whole-cell current-clamp recordings from adult mouse visual cortex slices with biocytin-filled pipettes, fixed stimulus protocols (short and long current steps, ramps, noisy injections), quality-control criteria, transgenic Cre driver lines, and atlas mapping; most cells came from primary visual cortex (n=1,599).
- Electrophysiology features were reduced with sparse PCA and clustered using Gaussian mixture models with entropy-based merging and subsampling stability; morphology features from reconstructed dendrites and axons were clustered hierarchically with co-clustering; joint clustering combined both feature sets across multiple methods and weightings to derive consensus clusters.
- Transcriptomic correspondence was inferred using transgenic line/layer labels and prior FACS data from Tasic et al., with binary consistency scores for subclasses and selective line/layer combinations for transcriptomic types.
Limitations
- The authors state that electrophysiological results depend on their specific recording protocols and solutions; other protocols could reveal additional distinguishing features and require revising the taxonomy. They also note that their transgenic sampling strategy does not allow estimating relative proportions of cell types in the circuit, and they could not clearly link to the Sncg subclass, which they suspect was underrepresented.
- The authors acknowledge biases from chosen stimuli and quantified morphological features, and note that z-dimension-dependent morphological features were excluded because of slice z-compression, though they report that re-running clustering after volume restoration and alignment showed good agreement.
- Evident from the text: transgenic lines are imperfect proxies for transcriptomic identity, some me-types showed heterogeneity even among cells putatively from a single transcriptomic type, and morphological reconstructions were a selected subset rather than all recorded cells.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 66 cited sources checked · 0 supported · 0 flagged
- Findingown data
Combining electrophysiological and morphological data in joint clustering produced 46 me-types.
“Joint clustering based on combined electrophysiological and morphological data resulted in 46 me-types.”
Quote in the paperSubstantial claim - Findingown data
Morphological properties were measured from 461 reconstructed neurons.
“morphological properties were measured from 461 reconstructed neurons”
Quote in the paperSubstantial claim - Findingown data
Unsupervised clustering assigned adult mouse visual cortex neurons to 17 electrophysiological types and 38 morphological types.
“Unsupervised clustering methods mapped neurons of the adult mouse visual cortex to 17 distinct electrophysiological or e-types and 38 morphological or m-types.”
Quote in the paperSubstantial claim - Findingown data
The study measured intrinsic physiological properties in 1,938 neurons from adult laboratory mouse visual cortex.
“Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex”
Quote in the paperSubstantial claim - Findingown data
452 neurons had both physiological and morphological measurements available.
“452 neurons had both measurements available”
Quote in the paperSubstantial claim
- Findingnone given
Transgenic labels provided initial correspondences with major transcriptomic subclasses and specific transcriptomic types.
“The use of transgenic labels established preliminary correspondences with major transcriptomic subclasses and specific transcriptomic types.”
Quote in the paperNot a substantial claim - Findingown data
The authors reported that their types corresponded well with previously defined transcriptomic types and subclasses when using the same transgenic lines.
“found good correspondence with previously-defined transcriptomic cell types and subclasses using the same transgenic mouse lines”
Quote in the paperSubstantial claim - Findingown data
For classifying excitatory me-types, morphological features were more distinguishing than electrophysiological features.
“This confirmed that morphological features were more distinguishing for excitatory me-type classification, while both electrophysiological and morphological features could accurately predict many inhibitory me-types.”
Quote in the paperSubstantial claim - Findingown data
Some inhibitory m-types, such as Aspiny_4 and Aspiny_8, spread across several me-types because of electrophysiological differences.
“Certain inhibitory m-types (e.g. Aspiny_4, Aspiny_8) were distributed across multiple me-types due to differences in electrophysiological properties.”
Quote in the paperNot a substantial claim - Findingown data
Each e-type was not entirely homogeneous.
“each one was not entirely homogeneous”
Quote in the paperNot a substantial claim - Findingown data
In some cases, there appeared to be a one-to-several correspondence between t-types and me-types and vice versa.
“In some cases, there appeared to be a one-to-several correspondence between t-types and me-types (and vice versa).”
Quote in the paperSubstantial claim - Findingown data
Most me-types looked fairly homogeneous when described by their constituent unimodal e- or m-types.
“When we examined the composition of these me-types in terms of unimodal e- or m-types, most me-types appeared relatively homogeneous.”
Quote in the paperSubstantial claim - Findingown data
Membrane potential trajectories during the interspike interval differed substantially between cells.
“The shape of the membrane potential trajectory during the interspike interval (ISI) also varied significantly across cells.”
Quote in the paperSubstantial claim - Findingcitation
The authors' results broadly matched earlier slice-electrophysiology classifications from the same area, related areas, and other species.
“Our results here were largely in agreement with cell-type classifications based on slice electrophysiology presented in previous studies in the same area and related areas and species6,8,29”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [6] Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015). source
Not checked in the free run.
- cites [8] Druckmann, S., Hill, S., Schürmann, F., Markram, H. & Segev, I. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. Cereb. Cortex 23, 2994–3006 (2013). source
Not checked in the free run.
- cites [29] Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004). source
Not checked in the free run.
- Findingown data
The t-SNE embedding separated spiny excitatory cells from aspiny or sparsely spiny inhibitory cells fairly well.
“The projection separated the spiny excitatory neurons from the aspiny/sparsely spiny inhibitory neurons reasonably well”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Spiny neurons are excitatory.
- The authors state this as an assumption: Aspiny or sparsely spiny neurons are inhibitory.
- Findingown data
The 17 e-types from the GMM in higher-dimensional sPCA space also appeared largely coherent in the t-SNE projection.
“The 17 e-types identified by the GMM in higher-dimensional sPCA spaces were largely coherent in the t-SNE projection as well”
Quote in the paperNot a substantial claim - Findingown data
The study identified a subset of transgenic line and layer combinations that were each selective for a small number of transcriptomic types.
“we were nevertheless able to identify a subset of transgenic line and layer combinations that were each selective for a small number of transcriptomic types (t-types, Fig. 7).”
Quote in the paperSubstantial claim - Findingown data
The me-types, and to a lesser extent the m-types, occupied consistent locations in the electrophysiological t-SNE projection.
“The me-types and, to a lesser extent, m-types were found in consistent locations in the electrophysiological projection (Fig. 6d-e, Supplementary Figs. 23 and 31).”
Quote in the paperNot a substantial claim - Findingown data
Morphologies were reconstructed for 461 cells: 254 spiny and 207 aspiny or sparsely spiny.
“We reconstructed the morphologies (Methods) of 461 cells (254 spiny and 207 aspiny/sparsely spiny).”
Quote in the paperNot a substantial claim - Findingown data
The supervised classifiers generally made fewer errors when predicting me-types associated with different transcriptomic subclasses.
“In addition, the classifiers generally made fewer errors predicting me-types associated with different transcriptomic subclasses, further supporting the proposed organization of me-types.”
Quote in the paperSubstantial claim - Findingown data
Inhibitory e-types showed greater diversity than excitatory e-types.
“Inhibitory e-types were more diverse than their excitatory counterparts.”
Quote in the paperSubstantial claim - Findingown data
Most spiny m-types were separated by soma depth, apical dendrite size and extent (especially the apical tuft), and basal dendrite distribution.
“The majority of spiny m-types were distinguished based on their relative soma depth, the size and extent of their apical dendrite, particularly the apical tuft, and the relative distribution of the basal dendrites”
Quote in the paperSubstantial claim - Findingown data
Cells from certain transgenic driver lines formed relatively coherent groups in the t-SNE projection.
“populations of cells defined by certain transgenic drivers were also relatively coherent in this projection”
Quote in the paperNot a substantial claim - Findingown data
This analysis produced 46 me-types, far fewer than the 120 e-type/m-type combinations from simply intersecting the two unimodal classifications.
“The number of me-types from this analysis (46 total) was substantially smaller than the number of e-type/m-type combinations observed by a simple intersection of both unimodal classifications (120 total”
Quote in the paperSubstantial claim - Findingown data
Within a single e-type, cells from one transgenic line sometimes clustered near each other yet apart from cells of other lines.
“sets of cells labeled by a given transgenic lines in some cases appear near each other but separated from cells labeled by other lines, even within the same e-type”
Quote in the paperNot a substantial claim - Findingown data
Chat cells, which form a subset of Vip cells, appeared inside the larger Vip region near its boundary with excitatory cells.
“Chat cells, a subset of Vip cells12,25,26, were located within the broader region populated by the Vip cells near its border with excitatory cells.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- cites [25] Gonchar, Y., Wang, Q. & Burkhalter, A. Multiple distinct subtypes of GABAergic neurons in mouse visual cortex identified by triple immunostaining. Front. Neuroanat. 1, 3 (2008). source
Not checked in the free run.
- cites [26] von Engelhardt, J., Eliava, M., Meyer, A. H., Rozov, A. & Monyer, H. Functional characterization of intrinsic cholinergic interneurons in the cortex. J. Neurosci. 27, 5633–5642 (2007). source
Not checked in the free run.
- Findingown data
Markers for largely non-overlapping interneuron subclasses showed little overlap in the t-SNE projection.
“These markers label largely mutually-exclusive subclasses of interneurons2,12, and we likewise saw relatively little overlap in the t-SNE projection.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [2] Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016). source
Not checked in the free run.
- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- Findingown data
A random-forest classifier indicated that components from action potential width and instantaneous firing rate were more important for e-type.
“which found that components derived from AP width and instantaneous firing rate had higher relative feature importances”
Quote in the paperNot a substantial claim - Findingown data
Joint clustering yielded 20 excitatory and 26 inhibitory me-types.
“We found 20 excitatory clusters and 26 inhibitory clusters, or me-types”
Quote in the paperSubstantial claim - Findingown data
Cells in L4 labeled by the Nr5a1-Cre line are predominantly of a single t-type, with 96% in one type.
“For example, the cells in L4 labeled by the Nr5a1-Cre line are predominantly of a single t-type (Fig 7a, 96% in one type)”
Quote in the paperSubstantial claim - Findingown data
The 19 spiny excitatory m-types spanned layers L2/3 through L6b.
“The 19 spiny, excitatory m-types (Fig. 3, Supplementary Figs. 18-19, Supplementary Spreadsheet 1) were distributed across layers (L) 2/3-L6b”
Quote in the paperNot a substantial claim - Findingown data
Exc_3, a large excitatory e-type, split across numerous excitatory me-types.
“the large excitatory e-type Exc_3 was divided across many excitatory me-types”
Quote in the paperNot a substantial claim - Findingown data
Both electrophysiological and morphological features could accurately predict many inhibitory me-types.
“This confirmed that morphological features were more distinguishing for excitatory me-type classification, while both electrophysiological and morphological features could accurately predict many inhibitory me-types.”
Quote in the paperSubstantial claim - Findingown data
Individual e-types varied in many features, such as firing pattern, action potential shape, subthreshold properties, and f-I curve position and form.
“Specific e-types differed from each other across a variety of characteristics, including firing patterns (e.g., adapting, irregular, transient), AP shape, subthreshold properties (membrane time constant, sag), and f-I curve location and shape.”
Quote in the paperSubstantial claim - Findingown data
Several me-types were observed among L4 Nr5a1-labeled neurons.
“For example, several me-types were observed among L4 Nr5a1-labeled neurons, suggesting some heterogeneity within the single t-type L4-IT-Rspo1 (Fig. 7a).”
Quote in the paperSubstantial claim - Findingown data
Firing rates at 100 pA above rheobase seldom went above 30 spikes per second across cells.
“Across cells, firing rates at 100 pA above rheobase rarely exceeded 30 spikes/s.”
Quote in the paperNot a substantial claim - Findingown data
The t-SNE projection was fairly robust to feature removal.
“the t-SNE projection was fairly robust to feature removal”
Quote in the paperNot a substantial claim - Findingown data
The Nkx2-1 line labeled several relatively specific populations.
“The Nkx2-1 line labeled several relatively specific populations.”
Quote in the paperNot a substantial claim - Findingreasoning
This data production enabled the combined analysis of more than 1,900 patch-clamp recordings.
“this data production enabled the combined analysis of over 1,900 patch-clamp recordings.”
Quote in the paperSubstantial claim - Findingown data
Morphological features capture the diversity of most excitatory cells more clearly than electrophysiological features.
“morphological features capture the diversity of most excitatory cells more clearly than electrophysiological ones”
Quote in the paperNot a substantial claim - Findingown data
The coefficient of variation of instantaneous frequency across sets of five consecutive ISIs during long current steps had a bimodal distribution, with the minimum between its peaks at CV = 0.18.
“The distribution of these CVs was bimodal, with a large, narrow peak at low CV values (considered to represent firing at a relatively constant rate) and a wide peak at higher CV values. The minimum value of between these peaks was found at CV = 0.18.”
Quote in the paperSubstantial claim - Findingown data
The ability of each modality to predict me-types varies across subclasses.
“We find that the ability of each modality to predict me-types varies across subclasses (Supplementary Fig. 31)”
Quote in the paperSubstantial claim - Findingown data
The cells labeled by those two lines were in neighboring yet offset locations in the t-SNE projection.
“the cells labeled were in neighboring yet offset locations in the t-SNE projection (see also Supplementary Fig. 31)”
Quote in the paperNot a substantial claim - Findingown data
The study was not yet able to establish clear links to the recently described Sncg inhibitory subclass.
“We note that we are not yet able to establish clear links to the recently described Sncg inhibitory subclass14”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
- Findingown data
The study observed some degree of heterogeneity in me-types even among cells putatively from a single t-type.
“Interestingly, we also observe some degree of heterogeneity in me-types even among cells putatively from a single t-type (see, for example, Fig. 7a).”
Quote in the paperNot a substantial claim - Findingown data
For the small number of L5-L6 Nkx2-1-labeled cells with both morphology and electrophysiological data, the t-SNE location was tight and consistent with the observed me-type.
“while we had a small number of such cells with both morphology and electrophysiological data, the location in the t-SNE projection was tight and consistent with the me-type observed (Fig. 7j).”
Quote in the paperNot a substantial claim - Findingown data
For most of the limited set of t-types analyzed, the study could make relatively specific correspondences with me-types.
“While we were only able to perform this analysis for a limited set of t-types, we found that we could make relatively specific correspondences with me-types in most of those cases.”
Quote in the paperSubstantial claim - Findingown data
These identified types showed strong correlation with transcriptomically defined neuronal subclasses and types.
“that showed strong correlation with transcriptomically defined neuronal subclasses and types.”
Quote in the paperSubstantial claim - Findingown data
Electrophysiological features clearly identify different L6 interneurons from Lamp5, Pvalb, and Sst subclasses that have similar dendritic and axonal arbors.
“while electrophysiological features clearly identify different L6 interneurons from Lamp5, Pvalb, and Sst subclasses that have similar dendritic and axonal arbors.”
Quote in the paperSubstantial claim - Findingown data
In L1-L4, the Nkx2-1 line is associated mostly with the Pvalb-Vipr2 type, expected to be chandelier cells, and the Pvalb-Tpbg type.
“In L1-L4, the line is associated mostly with the Pvalb-Vipr2 type (expected to be chandelier cells14) and the Pvalb-Tpbg type (Fig. 7i).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
- Findingown data
In L5-L6, Nkx2-1-labeled cells are mostly associated with the Lamp5-Lhx6 t-type.
“In L5-L6, the Nkx2-1-labeled cells are mostly associated with the Lamp5-Lhx6 t-type”
Quote in the paperNot a substantial claim - Findingown data
The identified me-types are better correlated with excitatory and inhibitory subclasses and types defined conventionally or by molecular identities than each modality separately.
“The me-types we identified are better correlated with excitatory and inhibitory subclasses and types defined conventionally or by molecular identities than each modality separately (Figs. 5-7)”
Quote in the paperSubstantial claim - Findingown data
Neurons labeled by Ndnf in L1, associated with a handful of Lamp5 t-types, had one of two me-types, ME_Inh_17 and 18.
“Among inhibitory neurons, neurons labeled by Ndnf in L1, associated with a handful of Lamp5 t-types, had one of two me-types (ME_Inh_17 and 18, Fig. 7f).”
Quote in the paperNot a substantial claim - Findingown data
L2/3 Chat-labeled neurons, mostly associated with the Vip-Ptprt-Pkp2 t-type, were in a few similar me-types.
“The L2/3 Chat-labeled neurons, mostly associated with the Vip-Ptprt-Pkp2 t-type, were in a few similar me-types (Fig. 7g).”
Quote in the paperNot a substantial claim - Findingown data
L6 Ntsr1-labeled neurons, linked to four related L6 CT t-types, labeled a set of me-types with similar electrophysiological features.
“L6 Ntsr1-labeled neurons (linked to four related L6 CT t-types) labeled a set of me-types with similar electrophysiological features (Fig. 7e).”
Quote in the paperSubstantial claim - Study design and conductown data
The authors used quantitative features and unsupervised methods to classify neurons into distinct types.
“Quantitative features were used to classify neurons into distinct types using unsupervised methods.”
Quote in the paperSubstantial claim - Study design and conductown data
The authors created a pipeline for single-cell characterization that relied on standardized patch-clamp recordings in brain slices and biocytin-based reconstructions of neuronal morphology.
“we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions”
Quote in the paperSubstantial claim - Premisecitation
Descriptions of cell types based on morphology and physiology have extracted quantitative features yet often depended on expert annotation and categorization.
“morphological and physiological descriptions of cell types (including in these large-scale studies) have used quantitative feature extraction but often ultimately relied on expert annotation and categorization7”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Ascoli, G. A. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008). source
Not checked in the free run.
- Interpretationown data
In sum, joint clustering yielded me-types that reflected both measured modalities without either one dominating.
“Overall, we found that our joint clustering produced a set of me-types that reflected both experimental modalities measured without either dominating the classification.”
Quote in the paperSubstantial claim - Interpretationown data
The presence of several me-types among L4 Nr5a1-labeled neurons suggests heterogeneity within the single t-type L4-IT-Rspo1.
“For example, several me-types were observed among L4 Nr5a1-labeled neurons, suggesting some heterogeneity within the single t-type L4-IT-Rspo1 (Fig. 7a).”
Quote in the paperSubstantial claim - Interpretationown data
Multiple electrophysiological features could support at least a coarse identification of e-type.
“suggesting that at least coarse e-type identity could be inferred from multiple electrophysiological features.”
Quote in the paperSubstantial claim - Study design and conductreasoning
These 13 data sets captured several aspects of electrophysiological responses that were robustly extractable and comparable across all cells.
“Together, they represented multiple aspects of the electrophysiological responses that could be robustly extracted and compared across all the cells.”
Quote in the paperSubstantial claim - Study design and conductown data
Cross-validation with support vector machine and random forest classifiers showed good predictability for clusters in both aspiny and spiny datasets.
“For aspiny and spiny datasets, clusters showed good predictability by cross-validation with two supervised classifiers (support vector machine and random forest, see Methods).”
Quote in the paperSubstantial claim - Interpretationown data
The findings imply that combining morphology and electrophysiology yields me-types more strongly correlated with neuronal subclasses than either modality alone.
“These results suggest that the joint morpho-electric clustering identifies a set of me-types that is better correlated with neuronal subclasses than each modality separately.”
Quote in the paperSubstantial claim - Interpretationown data
The lower error rate for me-types associated with different transcriptomic subclasses supports the proposed organization of me-types.
“In addition, the classifiers generally made fewer errors predicting me-types associated with different transcriptomic subclasses, further supporting the proposed organization of me-types.”
Quote in the paperSubstantial claim - Interpretationcitation
Different recording conditions and methods could amplify or diminish differences in particular features across neurons.
“and could amplify or diminish differences in particular features across neurons.”
Quote in the paperNot a substantial claim - Study design and conductnone given
The study developed unsupervised classification methods that were consistently applied across all recorded cells.
“We developed unsupervised classification methods that were consistently applied across all recorded cells”
Quote in the paperSubstantial claim - Interpretationown data
The me-types provide a more robust description of cortical cell types than single-modality data alone.
“and thus provide a more robust description of cortical cell types than single-modality data alone.”
Quote in the paperSubstantial claim - Study design and conductcitation
An advantage of the present study is that the same protocols and conditions were used for each cell type targeted, making it an ideal dataset to bridge data collected in different laboratories targeting different neurons.
“An advantage of the present study is that the same protocols / conditions were used for each cell type targeted, making it an ideal dataset to bridge data collected in different laboratories, targeting different neurons38.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [38] Toledo-Rodriguez, M. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb. Cortex 14, 1310–1327 (2004). source
Not checked in the free run.
- Interpretationreasoning
A novel aspect of the study's approach is the dual-modality, joint clustering analysis combining electrophysiological and morphological features to define me-types across a diverse array of cortical neurons.
“A novel aspect of our approach is the dual-modality, joint clustering analysis combining electrophysiological and morphological features to define me-types across a diverse array of cortical neurons.”
Quote in the paperSubstantial claim - Interpretationown data
The joint clustering analysis is a substantial improvement over previous attempts that derived me-types from simple intersectional overlay of m- and e-types.
“A substantial improvement over previous attempts in deriving me-types from the simple intersectional overlay of m- and e-types”
Quote in the paperSubstantial claim - Study design and conductcitation
The use of standardized protocols and equipment throughout yields a classification that does not need adjustment for interlaboratory variability.
“our use of standardized protocols and equipment throughout yields a classification that does not need to be adjusted for interlaboratory variability38.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [38] Toledo-Rodriguez, M. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb. Cortex 14, 1310–1327 (2004). source
Not checked in the free run.
- Premisecitation
These Nos1/Sst-labeled cells are expected to have the Sst-Chodl t-type, which is linked to deep long-range projecting interneurons.
“these cells are expected to have the Sst-Chodl t-type, linked to deep long-range projecting interneurons12,14,36,37 (Fig. 7h).”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
- cites [36] Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018). source
Not checked in the free run.
- cites [37] Kanari, L. et al. Objective classification of neocortical pyramidal cells. Cereb. Cortex 29, 1719–1735 (2019). source
Not checked in the free run.
- Premisecitation
Different recording conditions and methods affect electrophysiological responses.
“different recording conditions and methods affect electrophysiological responses39–41”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [39] He, M. et al. Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex. Neuron 92, 555 (2016). source
Not checked in the free run.
- cites [40] Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539.e20 (2017). source
Not checked in the free run.
- cites [41] Tebaykin, D. et al. Modeling sources of interlaboratory variability in electrophysiological properties of mammalian neurons. J. Neurophysiol. 119, 1329–1339 (2018). source
Not checked in the free run.
- Study design and conductnone given
The study acquired data using a standardized pipeline and uniform quality control checks so that each cell underwent an identical process.
“We acquired data using a standardized pipeline and uniform quality control checks such that each cell was subject to an identical process”
Quote in the paperSubstantial claim - Interpretationreasoning
The joint clustering analysis is a more principled approach to extract co-varying features from each modality out of their respective diverse ranges of heterogeneity, resulting in more coherent and meaningful grouping of cells.
“the joint clustering analysis is a more principled approach to extract co-varying features from each modality out of their respective diverse ranges of heterogeneity, resulting in more coherent and meaningful grouping of cells.”
Quote in the paperSubstantial claim - Interpretationown data
The neighboring yet offset t-SNE locations suggest some graded electrophysiological differences between those cells.
“suggesting some graded electrophysiological differences between those cells.”
Quote in the paperNot a substantial claim - Premisecitation
Conventional electrophysiological and morphological classification often relies on predetermined, cell-specific feature selection and/or qualitative assessments of firing pattern or cell shape.
“Conventional electrophysiological and morphological classification often relies on predetermined, cell-specific feature selection and/or qualitative assessments of firing pattern or cell shape5,7.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5] Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015). source
Not checked in the free run.
- cites [7] Ascoli, G. A. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008). source
Not checked in the free run.
- Interpretationown data
The preliminary correspondences found in this study support nearly all of the major transcriptomic subclasses identified in the mouse visual cortex.
“The preliminary correspondences we find here (Fig. 6) support nearly all of the major transcriptomic subclasses identified in the mouse visual cortex.”
Quote in the paperSubstantial claim - Interpretationreasoning
Clustering with both modalities allows the most distinct features to be used across different types.
“Therefore, clustering with both modalities allows the most distinct features to be used across different types.”
Quote in the paperSubstantial claim - Study design and conductcitation
Features mainly determined by z-dimension differences were excluded from morphological classification because of z-compression in processed acute slices and variable tilt angles of sampled visual cortical neurons.
“Features predominantly determined by differences in the z-dimension were not included in our morphological classification due to z-compression in the acute slice after processing32,33, and the variable tilt angles of sampled visual cortical neurons.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [32] Schubert, D., Kötter, R., Zilles, K., Luhmann, H. J. & StaigerJ. F.. Cell type-specific circuits of cortical layer IV spiny neurons. J. Neurosci. 23, 2961–2970 (2003). source
Not checked in the free run.
- cites [33] Scorcioni, R., Polavaram, S. & Ascoli, G. A. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 3, 866–876 (2008). source
Not checked in the free run.
- Premisecitation
These dendritic types approximately correspond to interneurons (aspiny and sparsely spiny) and pyramidal or spiny stellate neurons (spiny).
“These different dendritic types roughly equate to interneurons (aspiny and sparsely spiny) and pyramidal or spiny stellate neurons (spiny).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Tripathy, S. J. et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol. 13, e1005814 (2017). source
Not checked in the free run.
- Classified as probably not a substantial claim. 47
- May combine two claims. 55
- The quote only partly states this claim. 1
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe majority of cells (1,599) came from primary visual cortex (VISp), with the rest from nearby higher visual areas.
“Most cells (n=1,599) were from primary visual cortex (VISp); the remainder were in nearby higher visual areas.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe authors released the experimental data and analysis tools publicly through the Allen Cell Types Database and the Allen Software Development Kit.
“The experimental data and analysis tools are made available as a public resource as part of the Allen Cell Types Database and the Allen Software Development Kit.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe authors created a public online resource, the Allen Cell Types Database, for displaying the data sets.
“We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe authors produced a taxonomy for mouse visual cortex containing 17 electrophysiological types, 38 morphological types, and 46 joint types.
“We established a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types, 38 m-types, and 46 me-types”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFor each data subset, sparse principal components analysis usually yielded 1 to 8 components above an adjusted explained-variance cutoff.
“We applied a sparse principal components analysis20,21 (sPCA) procedure to each collected subset of the data, which typically identified 1-8 components that exceeded an adjusted explained variance threshold per subset”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [20] Zou, H., Hastie, T. & Tibshirani, R. Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265–286 (2006). source
Not checked in the free run.
- cites [21] Baden, T. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataAt the time of writing, the website held electrophysiological data for 1,861 of 1,938 cells and 431 of 461 reconstructions.
“As of this writing, 1,861 out of 1,938 cells had electrophysiological data on the website, and 431 out of 461 reconstructions were on the website.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataApplying their approach to juvenile rat somatosensory cortex morphologies yielded spiny clusters that either split BBP qualitative m-types (the single L2/3 PC type became six) or merged them (L5 thick tufted 1 and 2).
“we identified spiny clusters that subdivided the BBP qualitative m-types (e.g., the single L2/3 PC m-type was split into 6 m-types), and those that merged m-types (e.g., L5 thick tufted 1 and 2”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWhen excitatory and inhibitory cells were clustered together, the result was a similar but not identical count of 14 clusters, including one mixed cluster.
“clustering them together resulted in a similar, though not identical, number of clusters (14 total), but it also produced one cluster that contained both excitatory and inhibitory neurons”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataExcitatory e-types resembled one another, having broad action potentials and comparable membrane capacitance and input resistance distributions.
“The excitatory e-types were relatively similar to each other, with wide APs and similar distributions of membrane capacitance and input resistance”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataInhibitory me-types aligned with the various inhibitory e-types, with some mixing among fast-spiking e-types Inh_8 through Inh_13.
“Inhibitory me-types were consistent with the different inhibitory e-types, although there was some mixing between, for example, the different fast-spiking e-types (Inh_8 through Inh_13”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataClustering produced 17 e-types overall: four for the 928 excitatory neurons and thirteen for the 1,010 inhibitory neurons.
“This procedure yielded 17 total clusters, or e-types: four e-types for the 928 excitatory neurons and thirteen for the 1,010 inhibitory neurons.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Spiny neurons are excitatory.
- The authors state this as an assumption: Aspiny or sparsely spiny neurons are inhibitory.
May combine two claims.
Findingown dataFor inhibitory cells, e-types were relatively consistent, m-types scored lower, and most me-types were highly consistent with transcriptomic subclasses; often one subclass scored high and the others low.
“On the inhibitory side, e-types showed relatively high consistency, m-types had lower scores, and most me-types were again highly consistent with transcriptomic subclasses—for many me-types, a single subclass had a high score while the rest were low”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSpiny m-types were mostly confined to one layer but not exclusively so, except for Spiny_1, 3, 10, 14, and 19.
“Spiny m-types were predominantly found in a single layer, but were not exclusive to that layer, although see Spiny_1, 3, 10, 14, and 19”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe 19 inhibitory interneuron m-types spanned all cortical layers L1-L6b; most occurred in multiple layers, except for a few L2/3-specific types.
“The 19 m-types for inhibitory interneurons were distributed across all cortical layers (L1-L6b), with most m-types found in multiple layers with the exception of several L2/3-specific m-types”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThese me-types usually matched an excitatory m-type, although sometimes several m-types were collapsed into fewer me-types.
“Each of these me-types was usually consistent with an excitatory m-type (Fig. 5c), though in some cases multiple m-types were collapsed into fewer me-types.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningAlmost all inhibitory e-types had lower membrane capacitance than excitatory e-types, with Inh_4 as a clear exception.
“membrane capacitance values were nearly all lower than those of the excitatory e-types (though Inh_4 was a notable exception)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataM-type distributions differed across transgenic lines, and many m-types occupied fairly consistent positions on the electrophysiology-based t-SNE projection, though not all did.
“The distribution of m-types varied across transgenic lines (Supplementary Fig. 22), and many, but not all, m-types were found in relatively consistent locations on the electrophysiology-based t-SNE projection”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSoma depth, dendrite and axon size, and their distributions were all important for separating inhibitory m-types.
“Relative soma depth, dendrite and axon size and distribution were all important for distinguishing among inhibitory m-types”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe analysis identified 38 m-types in total: 19 spiny and 19 aspiny.
“In total, 38 clusters or m-types were identified, 19 for spiny neurons (Fig. 3) and 19 for aspiny neurons (Fig. 4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOf the 461 reconstructed cells, 452 underwent electrophysiological classification; the nine excluded mainly failed to fire an action potential during the 25 pA/s ramp.
“All but 9 of those 461 cells underwent electrophysiological classification; the primary reason for not including these nine was failure to elicit an action potential with the 25 pA/s ramp stimulus.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataWithin a layer, aspiny m-types from the same subclass (such as nest and small BCs) often merged, yet Martinotti, double-bouquet, and chandelier cells formed fairly distinct groups.
“Aspiny m-types within a subclass (e.g., nest and small BCs) were frequently merged within a layer, but we also identified fairly distinct groupings of Martinotti cells, double-bouquet cells, and chandelier cells”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSome e-types contained more neurons with bursting or delayed firing (Exc_4 and Inh_1, respectively), yet these patterns did not strictly define the types.
“here certain e-types had higher proportions of neurons that exhibited these patterns (e.g., Exc_4 for bursting, Inh_1 for delayed firing) but were not strictly demarcated by these patterns.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor excitatory cells, most e-types had low consistency scores except Exc_4; m-types scored higher, and me-types higher still.
“Among excitatory cells, we found that most excitatory e-types had low consistency scores (except Exc_4), while more m-types had higher scores, and even more me-types had high scores”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe 1,938 cells included 928 spiny neurons and 1,010 aspiny or sparsely spiny neurons.
“Of these, 928 were spiny neurons, which we assume to be excitatory, and 1,010 were aspiny or sparsely spiny, which we assume to be inhibitory”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Spiny neurons are excitatory.
- The authors state this as an assumption: Aspiny or sparsely spiny neurons are inhibitory.
May combine two claims.
Findingown dataPvalb-labeled cells sat next to Sst cells, while Vip cells occupied the opposite side of the Sst cells.
“Pvalb-labeled cells were adjacent to Sst cells, and Vip cells were found on the other side of Sst cells.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe study identified 17 e-types, 38 m-types, and 46 me-types.
“This led to the identification of 17 e-, 38 m-, and 46 me-types”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe study identified 46 me-types by joint clustering, in contrast to 120 observed combinations by intersecting e- and m-types.
“For example, we identify 46 me-types by joint clustering, in contrast to the 120 observed combinations by just intersecting e- and m-types (Supplementary Fig. 29).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPenk-IRES2-Cre-neo in L5-L6 and Oxtr-T2A-Cre in L6, which labeled overlapping sets of t-types, both identified the same me-type ME_Exc_6.
“Interestingly, Penk-IRES2-Cre-neo in L5-L6 and Oxtr-T2A-Cre in L6, which labeled overlapping sets of t-types, both identified the same me-type (ME_Exc_6)”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataSparse PCA yielded 56 components for inhibitory neurons, 54 for excitatory neurons, and 54 for all neurons combined.
“Analysis of inhibitory neurons yielded 56 total components, excitatory neurons yielded 54 components, and all neurons combined yielded 54 components.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataL5-L6 neurons labeled by a Nos1/Sst intersectional strategy were quite coherent in the electrophysiological t-SNE projection and had mostly one of two similar me-types.
“The L5-L6 neurons labeled by a Nos1/Sst intersectional strategy were quite coherent in the electrophysiological t-SNE projection and had mostly one of two similar me-types”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOther transgenic line/layer combinations labeling between one and three t-types predominantly had a single me-type, and those cells were tightly clustered in the electrophysiological t-SNE projection.
“However, other combinations labeling between one and three t-types predominantly had a single me-type (Fig. 7b-d), which were also tightly clustered in the electrophysiological t-SNE projection.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThese L1-L4 Nkx2-1-labeled neurons had me-types that contained fast-spiking cells and had basket-cell and chandelier-cell morphologies.
“Accordingly, we found that these neurons had me-types that contained fast-spiking cells and had basket-cell and chandelier-cell morphologies (see Figs. 5d and 6b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenThe mouse morpho-electric visual cortical dataset is large and diverse.
“This mouse morpho-electric visual cortical dataset is large and diverse”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataSome cells exhibited a sag in membrane potential during hyperpolarizing step responses, attributable to Ih activation.
“This emphasized the “sag” in the membrane potential due to the activation of Ih observed in some cells.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataRandom forest achieved 79/70% out-of-bag prediction accuracy and SVM achieved 89/79% leave-one-out cross-validation accuracy for spiny/aspiny neurons.
“Random forest classifier gave 79/70% out-of-bag prediction accuracy and support vector machine showed 89/79% prediction accuracy in leave-one-out cross validation for spiny/aspiny neurons.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
FindingcitationThe dendritic morphology of 1938 neurons was classified as aspiny, sparsely spiny, or spiny based on qualitative microscopic assessment.
“The dendritic morphology of each neuron (N=1938) was identified as either aspiny, sparsely spiny or spiny49 based on a qualitative assessment of the neuron’s dendrites by viewing the slides under the microscope and/or in the 63X image.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Tripathy, S. J. et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol. 13, e1005814 (2017). source
Not checked in the free run.
May combine two claims.
PremisecitationSome cell types have been quantified, but those studies often relied on extra labels such as individual gene markers to define the types beforehand.
“Specific cell types have been characterized quantitatively9–11, but these studies frequently have used additional labels (individual gene markers) to pre-define cell types.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [9] Bortone, D. S., Olsen, S. R. & Scanziani, M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex. Neuron 82, 474–485 (2014). source
Not checked in the free run.
- cites [10] Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015). source
Not checked in the free run.
- cites [11] Dehorter, N. et al. Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch. Science 349, 1216–1220 (2015). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataThe authors used many transgenic mouse lines to cover excitatory and inhibitory classes across cortical layers, target rare populations, and connect with other approaches like transcriptomic characterization.
“A diverse set of transgenic mouse lines14–18 were used to ensure broad coverage of excitatory and inhibitory classes across all cortical layers, to enable selective targeting of rare cell populations, and to link our study with other experimental approaches, such as transcriptomic characterization19.”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
- cites [15] Peng, H., Ruan, Z., Long, F., Simpson, J. H. & Myers, E. W. V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat. Biotechnol. 28, 348–353 (2010). source
Not checked in the free run.
- cites [16] Harris, J. A. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits 8, 76 (2014). source
Not checked in the free run.
- cites [17] Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010). source
Not checked in the free run.
- cites [18] Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480.e22 (2018). source
Not checked in the free run.
- cites [19] Bernard, A., Sorensen, S. A. & Lein, E. S. Shifting the paradigm: new approaches for characterizing and classifying neurons. Curr. Opin. Neurobiol. 19, 530–536 (2009). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEarlier work described excitatory neuron groups whose properties are often tied to cortical layer and axonal projection targets.
“excitatory neuron populations with features often linked to their laminar locations as well as the locations to which they project their axons2–4”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [2] Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016). source
Not checked in the free run.
- cites [3] Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015). source
Not checked in the free run.
- cites [4] Lodato, S. & Arlotta, P. Generating neuronal diversity in the mammalian cerebral cortex. Annu. Rev. Cell Dev. Biol. 31, 699–720 (2015). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenThe entire pipeline-including slice preparation, recording, stimulation, staining, imaging, reconstruction, and atlas mapping-used highly standardized and quality-controlled methods.
“Every aspect of the pipeline, from slice preparation, recording, and stimulation, to staining, imaging, reconstruction, and mapping of cells to a reference atlas, employed highly standardized and quality-controlled methodology.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationMethods that use extra labels can reveal heterogeneity between chosen populations, yet those differences might not co-vary with other features or show discontinuities across a wider set of cortical cells.
“these approaches may successfully identify heterogeneity in features between specific populations under investigation, but these differences may not necessarily co-vary with other features or display discontinuities when examined in the context of a broader set of cortical cells—significant criteria for cell-type classification1.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisereasoningThe features that separate types may be particular to the studied population and may not generalize to all cortical neurons.
“features used to distinguish types can be specific to the population being studied and may not be broadly applicable (or distinguishing) across all cortical neurons.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe authors used a uniform protocol to characterize mouse visual cortex neurons and built an unsupervised classification of cell types from electrophysiology and morphology.
“We characterized mouse visual cortex neurons with a uniform experimental protocol and developed an unsupervised classification scheme of cell types based on electrophysiology and morphology.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationQualitative descriptions may agree with statistical feature analyses, but they are constrained by how many criteria can be used at once to separate types.
“Such qualitative descriptions are valuable and can be consistent with statistical analyses of features8, but they can be limited by the number of criteria that can be simultaneously used to differentiate types.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Druckmann, S., Hill, S., Schürmann, F., Markram, H. & Segev, I. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. Cereb. Cortex 23, 2994–3006 (2013). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRecent transcriptomic work with single-cell RNA sequencing used unsupervised classification to build a taxonomy of transcriptomic types in mouse neocortex.
“Recent studies using transcriptomic characterization based on single-cell RNA-sequencing techniques performed unsupervised classification to generate a taxonomy of transcriptomic types in the mouse neocortex12–14.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- cites [13] Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). source
Not checked in the free run.
- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLarge-scale efforts, such as the Blue Brain Project's work on juvenile rat somatosensory cortex, addressed these limitations by using large coherent data sets to uncover principles of local connectivity and computation in a cortical column.
“Recent studies performed at larger scales5,6, including the Blue Brain Project’s thorough classification of morpho-electric diversity in juvenile rat somatosensory cortex, have overcome many of these issues, using large, coherent data sets to identify principles of local connectivity and computation within a cortical column.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5] Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015). source
Not checked in the free run.
- cites [6] Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenSuch transcriptomic methods depend on data from thousands of neurons produced under standardized conditions.
“These approaches rely on data from thousands of neurons generated in a standardized manner.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEarlier studies identified three main interneuron groups in neocortex-Pvalb+, Sst+, and Htr3a+-each containing distinct types.
“leading to the characterization of three major populations (i.e., Pvalb+, Sst+, and Htr3a+) of neocortical interneurons, each with distinct types”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [2] Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016). source
Not checked in the free run.
- cites [3] Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015). source
Not checked in the free run.
- cites [4] Lodato, S. & Arlotta, P. Generating neuronal diversity in the mammalian cerebral cortex. Annu. Rev. Cell Dev. Biol. 31, 699–720 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThis finding matches earlier descriptions of excitatory m-type distribution in other brain regions.
“This agrees with previous descriptions of excitatory m-type distribution in other brain regions8,21.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [8] Druckmann, S., Hill, S., Schürmann, F., Markram, H. & Segev, I. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. Cereb. Cortex 23, 2994–3006 (2013). source
Not checked in the free run.
- cites [21] Baden, T. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataEvery recorded cell followed the same workflow, and only those meeting predefined quality-control criteria entered the final set of 1,938 cells that passed electrophysiology QC.
“All recorded cells underwent the same workflow (Fig. 1a), and only cells that met pre-defined quality control (QC) standards were included in the final data set, which consisted of 1,938 cells passing electrophysiology QC.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataIntrinsic electrophysiological properties were measured using a standardized current-clamp protocol with diverse stimuli such as square pulses, ramps, and noisy current injections.
“We characterized the intrinsic electrophysiological properties with a standardized current-clamp protocol that contained a variety of stimuli, including square pulses, ramps, and noisy current injections”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningWithin a type, properties can vary widely if the variation is gradual; such intermediates leave no clear boundary for splitting the cluster.
“Properties may vary considerably within a type as long as gradual variation is present; the existence of these intermediates means that there is no identifiable place to subdivide the cluster.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe coherence of transgenic groups implies that electrophysiological features are similar within genetically defined cortical populations and distinct between non-overlapping ones.
“This cohesiveness suggests that there is similarity in e-features within genetically-defined populations of cortical cells, as well as distinctiveness between non-overlapping populations.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrior single-cell RNA-seq studies have defined taxonomies of mouse neocortical cell types from isolated neurons and glia.
“Recent studies have defined taxonomies of mouse neocortical cell types by single-cell RNA-seq transcriptomic profiling of isolated neurons and glia12–14.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- cites [13] Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). source
Not checked in the free run.
- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataImage quality control for 63X Z-stacks included a z-stack plane count, a visual check for proper stitching alignment and even illumination, and evaluation based on quality metrics that would impact cell reconstruction.
“Image quality control included a z-stack plane count, a visual check for proper stitching alignment and even illumination throughout the images. 63X Z-stacks were evaluated based on quality metrics that would impact cell reconstruction, as opposed to aesthetic quality.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe study's approach is quantitative, reproducible, transferable to new data, and robust across the varied characteristics of the entire population of neurons studied.
“Our approach, while certainly not free of biases (e.g., choice of stimuli delivered to the cells, morphological features quantified), has the advantage of being quantitative, reproducible, transferable to new data (e.g., Supplementary Fig. 24-26), and robust across the varied characteristics of the entire population of neurons studied.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe study's methods and data are publicly accessible as part of the Allen Cell Types Database.
“Our methods and data are publicly accessible as part of the Allen Cell Types Database at celltypes.brain-map.org”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe study used both electrophysiological and morphological features measured from the same cell together when possible.
“we used both electrophysiological and morphological features measured from the same cell together when possible.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataElectrophysiologically, truncated neurons were distributed in each type and found throughout the t-SNE representation of electrophysiology space.
“Electrophysiologically, truncated neurons were distributed in each type and found throughout the t-SNE representation of electrophysiology space.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataRepeating the entire procedure, including robustness evaluation, on 90% subsets generated a cellwise co-clustering matrix that supported cluster stability assessment.
“The entire procedure (including the robustness evaluation) was repeated on 90% subsets of the dataset to generate a cellwise co-clustering matrix, which supported the assessment of cluster stability.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataExc_4 has the lowest fraction of neurons with a truncated apical dendrite, and the region of the t-SNE projection where Exc_4 is primarily represented appears to have fewer truncated neurons.
“However, Exc_4 has the lowest fraction of neurons with a truncated apical dendrite, and the region of the t-SNE projection where Exc_4 is primarily represented appears to have fewer truncated neurons (Supplementary Fig. 32).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationRecent work found shared t-types among inhibitory cells but different t-types among excitatory cells from separate cortical areas.
“The recent finding of shared t-types among inhibitory cells but different t-types among excitatory cells from separate cortical areas14”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenOnly spiny neurons with an intact apical dendrite were included in morphological analysis, while all neurons were included in electrophysiology analysis, with apical dendrite truncation annotated.
“Although only spiny neurons with an intact apical dendrite were included in morphological analysis, all neurons were included in electrophysiology analysis, though the truncation of the apical dendrite was annotated.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenImage quality evaluation included qualitative evaluation of signal to noise, in-focus cell body, absent or negligible tessellation, and overall evenness of section illumination and bounding box region.
“Image quality evaluation included a qualitative evaluation of signal to noise for the imaged object (with high signal apparent in the cell body and dendrites, as opposed to background stain in the surrounding tissue, which can occur when cell filling leaks), in-focus cell body, and absent or negligible tessellation (tiling and stitching edge artifact). Overall evenness of section illumination and”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe study's electrophysiological results reflect the specific experimental protocols used here.
“we note that our electrophysiological results reflect the specific experimental protocols used here”
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 Institutional Animal Care and Use Committee at the Allen Institute for Brain Science.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIndividual t-types can exhibit substantial continuous variation within a type.
“It is known that individual t-types can exhibit substantial continuous variation within a type14”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). 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 conductcitationCluster stability was assessed by generating 100 random 90% subsamples, fitting them with the best GMM, merging as before, and dissolving clusters whose average Jaccard similarity was below 0.5.
“Cluster stability was then evaluated by subsampling analysis23.”
- Also stated in: § Electrophysiological classification. · plain
“clusters that had average Jaccard similarities below 0.5 were dissolved by merging into other clusters (using the entropy criterion above), resulting in a final set of clusters, or e-types.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Hennig, C. Cluster-wise assessment of cluster stability. Comput. Stat. Data Anal. 52, 258–271 (2007). source
Not checked in the free run.
- Also stated in: § Electrophysiological classification. · plain
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenTo be included in analysis, a >1 GΩ seal was recorded prior to break-in and the initial access resistance was <20 MΩ and <15% of the Rinput.
“To be included in analysis, a > 1 GΩ seal was recorded prior to break-in and the 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). May combine two claims.
Study design and conductnone givenThe composition of bath and internal solution and preparation methods were designed to maximize tissue quality of slices from adult mice and to align with solution compositions typically used in the field.
“The composition of bath and internal solution as well as preparation methods were made to a) maximize the tissue quality of slices from adult mice, and b) align with solution compositions typically used in the field (to maximize the chance of comparison to previous studies).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenFor an individual sweep to be included, the bridge balance was <20 MΩ and <15% of the Rinput, bias current was 0 +/- 100 pA, RMS noise in short and long windows was <0.07 mV and 0.5 mV respectively, and the voltage difference between the end of the sweep and immediately prior to stimulus onset was <1 mV.
“For an individual sweep to be included: (1) the bridge balance was < 20 MΩ and < 15% of the Rinput, (2) bias (leak) current 0 +/- 100 pA”
- Also stated in: § Patch clamp recording. · plain
“(3) root mean square (RMS) 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 and (4) the difference in the voltage at the end of the data sweep (measured over 500 ms of rest) and the voltage measured immediately prior to the stimulus onset < 1 mV.”
Quote in the paperSubstantial claim- Also stated in: § Patch clamp recording. · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataAnalyzing putatively excitatory and inhibitory cells separately produced results similar to analyzing all cells together.
“Data from putatively excitatory cells and inhibitory cells (determined by the presence and type of dendritic spines) were analyzed separately, though similar results were observed when all cells were analyzed together (see Supplementary Fig. 6).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationApplying the methods of this study to a different cortical area may identify the same inhibitory me-types.
“suggests that applying the methods of this study to a different cortical area may identify the same inhibitory me-types.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe procedure was also run on 20%, 40%, 60%, and 80% random subsamples (10 draws each) to evaluate how the number of clusters varied with sample size.
“In addition, the procedure was used on 20%, 40%, 60%, and 80% random subsamples of the data (each drawn 10 times) to evaluate how the number of clusters identified varied with the number of samples used in the analysis.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe code for electrophysiological feature analysis is publicly available as part of the Allen SDK.
“The code for feature analysis is publicly available as part of the Allen SDK.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Study design and conductreasoningDirect comparisons with previous studies should take into account that specific protocols and solution composition vary within the literature.
“direct comparisons with previous studies should take into account the fact that specific protocols and solution composition vary within the literature.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationLarge-scale transcriptomic studies have provided informative taxonomies of cortical cell types.
“Large-scale transcriptomic studies have provided informative taxonomies of cortical cell types12–14.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
- cites [13] Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). source
Not checked in the free run.
- cites [14] Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataClusters with average Jaccard similarity below 0.5 were deemed unstable and merged with another cluster having the highest between-cluster rate if that rate exceeded 35%, a threshold chosen to match the 5th percentile of within-cluster rates in stable clusters.
“Clusters with average Jaccard similarities of below 0.5 were considered unstable and were merged with another cluster with the highest between-cluster rate if it exceeded 35% (chosen because it was similar to the 5th percentile of the within-cluster rates of all cells in stable clusters).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAn alternate analyst checked for missed branches or inappropriate connections as a final step in manual correction and curation.
“As a final step in the manual correction and curation process, an alternate analyst checked for missed branches or inappropriate connections.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe different stimulus protocols were not presented in randomized order.
“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). May combine two claims.
Study design and conductreasoningData collection and analysis were not blinded to experimental conditions because there was a single experimental condition for all acquired data.
“Data collection and analysis 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). May combine two claims.
Study design and conductreasoningNo randomization was used during data collection because all acquired data came from a single experimental condition.
“No randomization was used during data collection 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 conductcitationAutomated reconstructions were extensively manually corrected and curated using tools in the Mozak extension of Terafly tools in Vaa3D.
“Automated reconstructions were then extensively manually corrected and curated using a range of tools (e.g., virtual finger, polyline) in the Mozak extension (Zoran Popovic, Center for Game Science, University of Washington) of Terafly tools50,53 in Vaa3D.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [50] Neher, E. Correction for liquid junction potentials in patch clamp experiments. Methods Enzymol. 207, 123–131 (1992). source
Not checked in the free run.
- cites [53] Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataRestoring reconstructions to original volumes, aligning them to the perpendicular cortical axis, and re-running clustering showed good agreement with the original clustering results.
“However, to test the effects of z-shrinkage and tilt angle on the classification results, we restored reconstructions to their original volumes and aligned them to the perpendicular cortical axis (based on registration to the common mouse reference atlas), and re-ran our clustering analysis (Supplementary Fig. 33). We found good agreement between the clustering results before and after adjustment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationNo statistical methods pre-determined sample sizes, but the sample sizes were similar to those in previous publications.
“No statistical methods were used to pre-determine sample sizes, but the sample sizes here are similar to those reported in previous publications5,6,12.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015). source
Not checked in the free run.
- cites [6] Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015). source
Not checked in the free run.
- cites [12] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenp-values were adjusted for multiple testing using the two-stage Benjamini-Hochberg step-up procedure to control the false discovery rate.
“The p-values were adjusted for multiple testing using the two-stage Benjamini and Hochberge step-up procedure to control the false discovery rate.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Morphological feature distributions were assumed to be normal for the t-test, but this was not formally tested.
The authors state this as an assumption.
Spiny neurons are excitatory.
“928 were spiny neurons, which we assume to be excitatory”
The authors state this as an assumption.
Aspiny or sparsely spiny neurons are inhibitory.
“1,010 were aspiny or sparsely spiny, which we assume to be inhibitory”
The paper relies on this without support.
Smaller studies could not be directly compared for morpho-electric properties because of differing protocols, analysis methods, and transgenic line availability, which hindered building a systematic classification.
“Historically, though, direct comparisons of morpho-electric properties across smaller studies have been constrained by differences in experimental protocols, analysis methods, and access to specific transgenic lines, limiting the construction of a systematic classification scheme.”
The paper relies on this without support.
The features that separate types may be particular to the studied population and may not generalize to all cortical neurons.
“features used to distinguish types can be specific to the population being studied and may not be broadly applicable (or distinguishing) across all cortical neurons.”
The authors state this as an assumption.
Sncg inhibitory subclass cells are underrepresented in the dataset.
“we presume that these cells are underrepresented in our data set”
The paper relies on this without support.
Transgenic lines are imperfect labels of transcriptomic cell types.
“While transgenic lines are imperfect labels of transcriptomic cell types”
The authors state this as an assumption.
Morphological feature distributions were assumed to be normal for the t-test, but this was not formally tested.
“Morphological feature distributions were assumed to be normal for this method, but this was not formally tested (see Supplementary Figs. 19 and 21 for plots of distributions).”
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?
The observed phenotypes included most of the morphological and electrophysiological types of cortical neurons that have been described previously in mice and rats, allowing linking of transcriptomic and morpho-electric descriptions.
“The observed phenotypes included most of the morphological and electrophysiological types of cortical neurons that have been described previously in mice and rats5,6,24, allowing us to link transcriptomic and morpho-electric descriptions (Extended Data Fig. 3, Supplementary File 1).”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [24]
Previous studies using transgenic lines had shown that morpho-electric properties within these families can be highly variable.
“Previous studies using transgenic lines had shown that morpho-electric properties within these families can be highly variable5,24.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [24]
The number of VISp GABAergic t-types (60) and me-types (26) differs by more than a factor of two.
“the number of VISp GABAergic “t-types” (60) and “me-types” (26) - differs by more than a factor of two”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Gouwens et al., 2019
Electrophysiological 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).”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Gouwens et al., 2019
Gowens et al. identified twice as many morphological types (m-types) as electrophysiological ones (e-types) in a parallel work based on unsupervised clustering.
“This is in qualitative agreement with the findings of Gowens et al.76, who, in a parallel work based on unsupervised clustering, identified twice as many morphological types (m-types) as electrophysiological ones (e-types).”
From Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas (2019) · cites this paper as [76]
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106 · see citing works
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907 · see citing works
Self-citationSparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954 · see citing works
Optimal Detection of Changepoints With a Linear Computational Cost
Killick, R., Fearnhead, P., & Eckley, I. A. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.737745 · Cited by 2,586 · see citing works
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033 · see citing works
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892 · see citing works
The action potential in mammalian central neurons
Bean, B. P. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2148 · Cited by 1,785 · see citing works
Shared 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 · see citing works
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 · see citing works
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 · see citing works
The neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215 · see citing works
Cortex: Statistics and Geometry of Neuronal Connectivity
Braitenberg, V., & Schüz, A. (1998) · doi:10.1007/978-3-662-03733-1 · Cited by 1,178 · see citing works
[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 · see citing works
The functional diversity of retinal ganglion cells in the mouse
Baden, T., Berens, P., Franke, K., Román Rosón, M., Bethge, M., & Euler, T. (2016) · Nature · doi:10.1038/nature16468 · Cited by 999 · see citing works
Cluster-wise assessment of cluster stability
Hennig, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.11.025 · Cited by 930 · see citing works
Principles of connectivity among morphologically defined cell types in adult neocortex
Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A. S., Patel, S., & Tolias, A. S. (2015) · Science · doi:10.1126/science.aac9462 · Cited by 908 · see citing works
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 · see citing works
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 · see citing works
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-citationPeng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010) · Nature Biotechnology · doi:10.1038/nbt.1612 · Cited by 707 · see citing works
NeuroMorpho.Org: A Central Resource for Neuronal Morphologies
Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2055-07.2007 · Cited by 664 · see citing works
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-citationTranscriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · see citing works
Generating Neuronal Diversity in the Mammalian Cerebral Cortex
Lodato, S., & Arlotta, P. (2015) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100814-125353 · Cited by 389 · see citing works
Gonchar, Y. (2008) · Frontiers in Neuroanatomy · doi:10.3389/neuro.05.003.2007 · Cited by 369 · see citing works
Combining Mixture Components for Clustering
Baudry, J.-P., Raftery, A. E., Celeux, G., et al. (2010) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2010.08111 · Cited by 356 · see citing works
Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008) · Nature Protocols · doi:10.1038/nprot.2008.51 · Cited by 354 · see citing works
Hattox, A. M., & Nelson, S. B. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00397.2007 · Cited by 344 · see citing works
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
The organization of two new cortical interneuronal circuits
Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L., & Zhu, J. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3305 · Cited by 338 · see citing works
Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W., & Callaway, E. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.002 · Cited by 323 · 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 · see citing works
Self-citationOberlaender, M., de Kock, C. P. J., Bruno, R. M., Ramirez, A., Meyer, H. S., Dercksen, V. J., Helmstaedter, M. N., & Sakmann, B. J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr317 · Cited by 304 · 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 · see citing works
Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type
Boldog, E., Bakken, T. E., Hodge, R. D., Novotny, M., Aevermann, B. D., Baka, J., Bordé, S., Close, J. L., Diez-Fuertes, F., Ding, S.-L., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0205-2 · Cited by 291 · see citing works
Self-citationGeneralized leaky integrate-and-fire models classify multiple neuron types
Teeter, C., Iyer, R., Menon, V., Gouwens, N. W., Feng, D. H., Berg, J., Szafer, A., Cain, N., Zeng, H., Hawrylycz, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02717-4 · Cited by 277 · see citing works
Self-citationToledo-Rodriguez, M. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh092 · Cited by 241 · see citing works
Staiger, J. F. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh029 · Cited by 226 · see citing works
Cell Type-Specific Circuits of Cortical Layer IV Spiny Neurons
Schubert, D., Kötter, R., Zilles, K., Luhmann, H. J., & Staiger, J. F. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-07-02961.2003 · Cited by 207 · see citing works
Functional Characterization of Intrinsic Cholinergic Interneurons in the Cortex
von Engelhardt, J., Eliava, M., Meyer, A. H., Rozov, A., & Monyer, H. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.4647-06.2007 · Cited by 177 · see citing works
Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch
Dehorter, N., Ciceri, G., Bartolini, G., Lim, L., del Pino, I., & Marín, O. (2015) · Science · doi:10.1126/science.aab3415 · Cited by 169 · see citing works
Systematic generation of biophysically detailed models for diverse cortical neuron types
Gouwens, N. W., Berg, J., Feng, D. H., Sorensen, S. A., Zeng, H., Hawrylycz, M., Koch, C., & Arkhipov, A. S. (2018) · Nature Communications · doi:10.1038/s41467-017-02718-3 · Cited by 132 · see citing works
Self-citationZhang, L., Weiner, J. L., Valiante, T. A., Velumian, A. A., Watson, P. L., Jahromi, S. S., Schertzer, S., Pennefather, P., & Carlen, P. L. (1994) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/bf00374778 · Cited by 120 · see citing works
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 · see citing works
Egger, V., Nevian, T., & Bruno, R. M. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm126 · Cited by 97 · see citing works
Objective Morphological Classification of Neocortical Pyramidal Cells
Kanari, L., Ramaswamy, S., Shi, Y., Morand, S., Meystre, J., Perin, R., Abdellah, M., Wang, Y., Hess, K., & Markram, H. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy339 · Cited by 94 · see citing works
Bria, A., Iannello, G., Onofri, L., & Peng, H. (2016) · Nature Methods · doi:10.1038/nmeth.3767 · Cited by 82 · see citing works
Visual physiology of the layer 4 cortical circuit in silico
Arkhipov, A. S., Gouwens, N. W., Billeh, Y. N., Gratiy, S. L., Iyer, R., Wei, Z., Xu, Z., Abbasi-Asl, R., Berg, J., Buice, M. A., et al. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006535 · Cited by 77 · see citing works
Self-citationKaczorowski, C. C., Disterhoft, J. F., & Spruston, N. (2006) · The Journal of Physiology · doi:10.1113/jphysiol.2006.124586 · Cited by 73 · see citing works
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
Transcriptomic correlates of neuron electrophysiological diversity
Tripathy, S. J., Toker, L., Li, B., Crichlow, C.-L., Tebaykin, D., Mancarci, B. O., & Pavlidis, P. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005814 · Cited by 69 · see citing works
Druckmann, S., Hill, S., Schürmann, F., Markram, H., & Segev, I. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs290 · Cited by 66 · see citing works
Zhou, Z., Liu, X., Long, B., & Peng, H. (2015) · Neuroinformatics · doi:10.1007/s12021-015-9278-1 · Cited by 63 · see citing works
Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures
Zhou, Z., Sorensen, S. A., Zeng, H., Hawrylycz, M., & Peng, H. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9249-y · Cited by 47 · see citing works
Self-citationShifting the paradigm: new approaches for characterizing and classifying neurons
Bernard, A., Sorensen, S. A., & Lein, E. S. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.09.010 · Cited by 26 · see citing works
Self-citationTebaykin, 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
Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow
Stockton, D. B., & Santamarı́a, F. (2017) · Neuroinformatics · doi:10.1007/s12021-017-9337-x · Cited by 13 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018). · DOI · Paper page
Cited by
Sorted by citations
Comparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812 · see citing works
Self-citationIntegrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538 · see citing works
“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-citationDevelopment and arealization of the cerebral cortex
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408 · see citing works
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
“Note: we did not encounter double-bouquet basket cells, previously described in L2/3 of mouse V1 [5], or L6 FS cells with translaminar axons reaching up to L1, that were also reported In V1 [24][60].”
InfluentialLocal connectivity and synaptic dynamics in mouse and human neocortex
Campagnola, L., Seeman, S. C., Chartrand, T., Kim, L., Hoggarth, A., Gamlin, C., Ito, S., Trinh, J., Davoudian, P., Radaelli, C., et al. (2022) · Science · doi:10.1126/science.abj5861 · Cited by 363 · see citing works
Self-citationBilleh, Y. N., Cai, B., Gratiy, S. L., Dai, K., Iyer, R., Gouwens, N. W., Abbasi-Asl, R., Jia, X., Siegle, J. H., Olsen, S. R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.040 · Cited by 300 · see citing works
Self-citationSenzai, Y., Fernández‐Ruiz, A., & Buzsáki, G. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.009 · Cited by 263 · 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-citationThe diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · see citing works
Single-cell 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
Self-citationSparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex
Seeman, S. C., Campagnola, L., Davoudian, P. A., Hoggarth, A., Hage, T. A., Bosma-Moody, A., Baker, C. A., Lee, J. H., Mihalas, S., Teeter, C., et al. (2018) · eLife · doi:10.7554/elife.37349 · Cited by 183 · see citing works
Self-citationLayer 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 · see citing works
Synaptic Specificity and Application of Anterograde Transsynaptic AAV for Probing Neural Circuitry
Zingg, B., Peng, B., Huang, J., Tao, H. W., & Zhang, L. I. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2158-19.2020 · Cited by 189 · see citing works
“Last, we demonstrate that AAV1 transsynaptic tagging may be used to catalog cell types based on their input, morphology, and gene expression by incorporating the use of transgenic mice and techniques for achieving sparse labeling of transsynaptically tagged neurons (Kim et al., 2017; Gouwens et al., 2019; Li et al., 2…”
BackgroundCircuitry Underlying Experience-Dependent Plasticity in the Mouse Visual System
Hooks, B. M., & Chen, C. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.031 · Cited by 166 · 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
“: Tissue was stained, then mounted sections were imaged as described previously (Gouwens et al., 2019).”
Self-citationRegional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network
Whitesell, J. D., Liska, A., Coletta, L., Hirokawa, K. E., Bohn, P., Williford, A., Groblewski, P. A., Graddis, N., Kuan, L., Knox, J. E., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.011 · Cited by 134 · see citing works
“Preferential Connectivity between DMN Regions by Target-Defined Neuron Type Cre lines are useful for labeling broad cell classes, but many contain a mix of cell types based on transcriptomic, morphological, and physiological properties (Gouwens et al., 2019; Tasic et al., 2016, 2018).”
Self-citationCellExplorer: A framework for visualizing and characterizing single neurons
Petersen, P., Siegle, J. H., Steinmetz, N. A., Mahallati, S., & Buzsáki, G. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.002 · Cited by 146 · see citing works
“However, currently, a gap exists between neuron classification schemes based on molecular and physiological methods (Gouwens et al., 2019; Jia et al., 2019; Kepecs and Fishell, 2014; Klausberger and Somogyi, 2008; McBain and Fisahn, 2001; Roux and Buzsáki, 2015; Rudy et al., 2011), largely because of vast differences…”
BackgroundStöckl, C., & Maass, W. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00311-4 · Cited by 155 · see citing works
Vision-dependent specification of cell types and function in the developing cortex
Cheng, S., Butrus, S., Tan, L., Xu, R., Sagireddy, S., Trachtenberg, J. T., Shekhar, K., & Zipursky, S. L. (2022) · Cell · doi:10.1016/j.cell.2021.12.022 · Cited by 128 · 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.”
BackgroundMethodInfluentialSelf-citationHow Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model
Niell, C. M., & Scanziani, M. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102320-085825 · Cited by 109 · see citing works
Mossink, B., Negwer, M., Schubert, D., & Nadif Kasri, N. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03714-5 · Cited by 114 · see citing works
“Furthermore, studies measuring multiple modalities on the same neurons promise a unification of classifications from single-cell electrophysiology, morphology and RNA sequencing (Patch-Seq), and have delivered insights in glutamatergic [436] and GABAergic neuron populations [333].”
BackgroundChameh, H. M., Rich, S., Wang, L., Chen, F., Zhang, L., Carlen, P. L., Tripathy, S. J., & Valiante, T. A. (2021) · Nature Communications · doi:10.1038/s41467-021-22741-9 · Cited by 110 · see citing works
Kalmbach, B. E., Hodge, R. D., Jorstad, N. L., Owen, S. F., de Frates, R. A., Yanny, A. M., Dalley, R. A., Mallory, M., Graybuck, L. T., Radaelli, C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.030 · Cited by 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
“…morphologic features and electrophysiological properties in the same individual neurons have been fundamental for improving our understanding of the cell types that are the building blocks of neuronal circuits (Jiang et al., 2015; Markram et al., 2015; Zeng and Sanes, 2017; Gouwens et al., 2019).”
BackgroundMethodNpas1 + -Nkx2.1 + Neurons Are an Integral Part of the Cortico-pallido-cortical Loop
Abecassis, Z. A., Berceau, B. L., Win, P. H., García, D., Xenias, H. S., Cui, Q., Pamukcu, A., Cherian, S., Hernández, V. M., Chon, U., et al. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1199-19.2019 · Cited by 93 · see citing works
Hodge, R. D., Miller, J. A., Novotny, M. A., Kalmbach, B. E., Ting, J. T., Bakken, T. E., Aevermann, B. D., Barkan, E. R., Berkowitz-Cerasano, M. L., Cobbs, C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14952-3 · Cited by 92 · see citing works
Self-citationColombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01167-6 · Cited by 100 · see citing works
Microcircuits for spatial coding in the medial entorhinal cortex
Tukker, J. J., Beed, P. S., Brecht, M., Kempter, R., Moser, E. I., & Schmitz, D. (2021) · Physiological Reviews · doi:10.1152/physrev.00042.2020 · Cited by 89 · see citing works
Chartrand, T., Dalley, R., Close, J., Goriounova, N. A., Lee, B. R., Mann, R., Miller, J. A., Molnar, G., Mukora, A., Alfiler, L., et al. (2023) · Science · doi:10.1126/science.adf0805 · Cited by 88 · see citing works
“For mouse cells, 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 (79).”
MethodInfluentialSelf-citationGoff, K. M., & Goldberg, E. M. (2019) · eLife · doi:10.7554/elife.46846 · Cited by 93 · see citing works
“This distinction may be relevant for ongoing efforts to create a comprehensive classification of VIP-INs (Gouwens et al., 2019; He et al., 2016; Prönneke et al., 2015; Prönneke et al., 2018).”
BackgroundStructure and function of axo-axonic inhibition
Schneider-Mizell, C. M., Bodor, A. L., Collman, F., Brittain, D., Bleckert, A. A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2021) · eLife · doi:10.7554/elife.73783 · Cited by 111 · see citing works
Self-citationHage, 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
Self-citationEvolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2020) · doi:10.1101/2020.03.31.016972 · Cited by 809 · see citing works
Self-citationMicrofluidics for Neuronal Cell and Circuit Engineering
Habibey, R., Arias, J. E. R., Striebel, J., & Busskamp, V. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00212 · Cited by 86 · see citing works
Defining the Homo- and Heterodimerization Propensities of Metabotropic Glutamate Receptors
Lee, J., Munguba, H., Gutzeit, V. A., Singh, D. R., Kristt, M., Dittman, J. S., & Levitz, J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107605 · Cited by 76 · see citing works
“…and electrophysiological identities of molecularly defined neuronal subtypes within the cortex (Cadwell et al., 2016; Fuzik et al., 2016; Gouwens et al., 2019; Muñoz-Manchado et al., 2018; Naka et al., 2019), but a precise understanding of how specific signaling molecules segregate into…”
Chang, 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
“FS cells exhibiting AP adaption upon stimulation, as indicated by increasing inter-spike intervals, were excluded from analysis because their firing patterns were inconsistent with those of typical PV cells (Gouwens et al., 2019, 2020).”
The SONATA data format for efficient description of large-scale network models
Dai, K., Hernando, J., Billeh, Y. N., Gratiy, S. L., Planas, J., Davison, A. P., Durá-Bernal, S., Gleeson, P., Devresse, A., Dichter, B., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007696 · Cited by 84 · see citing works
Veldman, M. B., Park, C. S., Eyermann, C. M., Zhang, J. Y., Zuniga-Sanchez, E., Hirano, A. A., Daigle, T. L., Foster, N. N., Zhu, M., Langfelder, P., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.019 · Cited by 66 · see citing works
“, 2008; Jefferis and Livet, 2012), but they often require specialized skills and are labor intensive or invasive (Gouwens et al., 2019; Economo et al., 2016; Chan et al., 2017) or have cellular tropism of certain viruses (Chan et al.”
BackgroundSelf-citationDiscovering the Computational Relevance of Brain Network Organization
Ito, T., Hearne, L. J., Mill, R. D., Cocuzza, C. V., & Cole, M. W. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.10.005 · Cited by 65 · see citing works
“For instance, relevant local neural population properties include population firing thresholds [43,51], neuron types [52,53], excitatory–inhibitory balance [54,55], local glial cell properties [56], and local recurrent connectivity [57,58].”
BackgroundHafner, G., Witte, M., Guy, J., Subhashini, N., Fenno, L. E., Ramakrishnan, C., Kim, Y. S., Deisseroth, K. A., Callaway, E. M., Oberhuber, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.064 · Cited by 62 · see citing works
“LIV might have a unique role as a projection layer in the visual cortex because its composition of excitatory cells is dominated by neurons with a pronounced apical dendritic tuft, mirroring the morphology of typical projection neurons (Gouwens et al., 2019).”
BackgroundCell-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
Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits
Dai, K., Gratiy, S. L., Billeh, Y. N., Xu, R., Cai, B., Cain, N., Rimehaug, A. E., Stasik, A. J., Einevoll, G. T., Mihalaş, Ş., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008386 · Cited by 72 · see citing works
Leveraging the Cell Ontology to classify unseen cell types
Wang, S., Pisco, A. O., McGeever, A., Brbić, M., Žitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., & Altman, R. B. (2021) · Nature Communications · doi:10.1038/s41467-021-25725-x · Cited by 79 · see citing works
Guet-McCreight, A., Skinner, F. K., & Topolnik, L. (2020) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.00032 · Cited by 64 · see citing works
“It is important to note that VIP+ cells exhibit a large diversity in transcriptomic, morphological and physiological properties (Prönneke et al., 2015; Harris et al., 2018; Gouwens et al., 2019; Hodge et al., 2019), which so far made the definition of cell types largely impossible.”
BackgroundInhibitory 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
Toledo, A. P., Reyna‐Jeldes, M., Ortega, L., Coddou, C., & Yévenes, G. E. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23105390 · Cited by 71 · see citing works
“These parameters, among others, are frequently associated with functional and genetic data, allowing the morpho-functional classification of specific neuron subtypes [87,91,92].”
BackgroundSingle-cell biology: beyond the sum of its parts
Schier, A. F. (2020) · Nature Methods · doi:10.1038/s41592-019-0693-3 · Cited by 56 · see citing works
Vision Changes the Cellular Composition of Binocular Circuitry during the Critical Period
Tan, L., Tring, E., Ringach, D. L., Zipursky, S. L., & Trachtenberg, J. T. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.022 · Cited by 56 · see citing works
“The cortex is composed of many different neuron types and each of these forms a dense complex network of synaptic connections (Gouwens et al., 2019; Tasic et al., 2018).”
BackgroundKim, Y. S., Baek, J. H., Im, I. H., Lee, D. H., Park, M. H., & Jang, H. W. (2024) · ACS Nano · doi:10.1021/acsnano.4c12884 · Cited by 70 · see citing works
Corticothalamic feedback sculpts visual spatial integration in mouse thalamus
Born, G., Schneider-Soupiadis, F. A., Erisken, S., Vaičeliūnaitė, A., Lao, C. L., Mobarhan, M. H., Spacek, M. A., Einevoll, G. T., & Busse, L. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00943-0 · Cited by 58 · see citing works
Que, L., Lukacsovich, D., Luo, W., & Földy, C. (2021) · Nature Communications · doi:10.1038/s41467-020-20328-4 · Cited by 58 · see citing works
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
“Many studies have shown that patch duration must range from 15 mins up to 60 mins to obtain optimal filling of neuronal processes with biocytin (Gouwens et al. 2019; Marx et al. 2012; Cadwell et al. 2017).”
BackgroundMethodInfluentialSelf-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
Generating 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
Light microscopy based approach for mapping connectivity with molecular specificity
Shen, F. Y., Harrington, M. M., Walker, L. A., Cheng, H. P. J., Boyden, E. S., & Cai, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18422-8 · Cited by 57 · see citing works
Efficient and robust coding in heterogeneous recurrent networks
Zeldenrust, F., Gutkin, B. S., & Denève, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008673 · Cited by 52 · see citing works
“The second term of equation (5) denotes the lateral (j 6=m) and output (j =m) filters, that are substracted from the filtered input as a result of the spiking activity of any of the neurons.”
BackgroundControl the collective behaviors in a functional neural network
Yao, Z., & Wang, C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111361 · Cited by 58 · see citing works
Identification of Mouse Claustral Neuron Types Based on Their Intrinsic Electrical Properties
Graf, M. R., Nair, A., Wong, K. L. L., Tang, Y., & Augustine, G. J. (2020) · eNeuro · doi:10.1523/eneuro.0216-20.2020 · Cited by 47 · see citing works
“Comparison of claustral and cortical neurons In cerebral cortex, INs differ fundamentally from PNs (McCormick et al., 1985; Gouwens et al., 2019) and can be grouped into three main subtypes: PV-INs, SST-INs, and 5Ht3aR-INs, with the latter further subdivided into VIP-expressing and non-expressing INs (Lee et al.”
BackgroundBomkamp, C., Tripathy, S. J., Gonzales, C. B., Hjerling‐Leffler, J., Craig, A. M., & Pavlidis, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007113 · Cited by 42 · see citing works
Automatic deep learning-driven label-free image-guided patch clamp system
Koos, K., Oláh, G., Balassa, T., Mihut, N., Rózsa, M., Ozsvár, A., Tasnádi, E. A., Barzó, P., Faragó, N., Puskás, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21291-4 · Cited by 46 · see citing works
Mosher, C. P., Wei, Y., Kamiński, J., Nandi, A., Mamelak, A. N., Anastassiou, C. A., & Rutishauser, U. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.02.027 · Cited by 44 · see citing works
“The narrowwidth EAP waveform and decreased spike rate variability of NS units (robustly classified in our analysis as AP neurons) is strongly reminiscent of inhibitory interneurons and basket cells (e.g., Barthó et al., 2004, Joshi and Hawken, 2006; Krimer et al., 2005; McCormick et al., 1985; Gouwens et al., 2019; An…”
Self-citationHow is flexible electronics advancing neuroscience research?
Chen, Y., Rommelfanger, N. J., Mahdi, A. I., Wu, X., Keene, S. T., Obaid, A., Salleo, A., Wang, H., & Hong, G. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.120559 · Cited by 52 · 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 .”
MicroRNAs Instruct and Maintain Cell Type Diversity in the Nervous System
Zolboot, N., Du, J. X., Zampa, F., & Lippi, G. (2021) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.646072 · Cited by 48 · see citing works
“Consistent with this finding, there are numerous miRNAs known to inhibit microglia activation, which are described in detail here (Guo et al., 2019).”
BackgroundPrimate Neuronal Connections are Sparse as Compared to Mouse
Wildenberg, G. A., Rosen, M. C., Lundell, J., Paukner, D., Freedman, D. J., & Kasthuri, N. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.109709 · Cited by 46 · see citing works
Single-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
Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2019) · doi:10.1101/675280 · Cited by 47 · see citing works
Self-citationPhenotypic variation within and across transcriptomic cell types in mouse motor cortex
(2020) · doi:10.1101/2020.02.03.929158 · Cited by 397 · see citing works
“…cortices where most studies providing anatomical descriptions of neural types have been performed (Jiang et al., 2015; Markram et al., 2015; Gouwens et al., 2019), and partially due to the RNA extraction process requiring the aspiration of the cell contents and potentially interfering with…”
BackgroundMethodResultInfluentialNandi, A., Chartrand, T., Geit, W. A. H. V., Buchin, A., Yao, Z., Lee, S. Y., Wei, Y., Kalmbach, B. E., Lee, B., Lein, E. S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111176 · Cited by 48 · see citing works
“1 g Pvalb > g Sst > g Vip supported by transcriptomics.”
InfluentialSelf-citationVasques, X., Paik, H., & Cif, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-38558-z · Cited by 50 · see citing works
Robust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGN
Spacek, M. A., Crombie, D., Bauer, Y., Born, G., Liu, X., Katzner, S., & Busse, L. (2022) · eLife · doi:10.7554/elife.70469 · Cited by 36 · see citing works
Cellular composition and circuit organization of the locus coeruleus of adult mice
McKinney, A., Hu, M., Hoskins, A., Mohammadyar, A., Naeem, N., Jing, J., Patel, S. S., Sheth, B. R., & Jiang, X. (2023) · eLife · doi:10.7554/elife.80100 · Cited by 53 · see citing works
Assessing 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 · see citing works
Casingal, C. R., Descant, K. D., & Anton, E. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.034 · Cited by 42 · see citing works
Functional cell types in the mouse superior colliculus
Li, Y.-T., & Meister, M. (2023) · eLife · doi:10.7554/elife.82367 · Cited by 51 · see citing works
Augustinaite, S., & Kühn, B. (2020) · Current Biology · doi:10.1016/j.cub.2020.07.069 · Cited by 36 · 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-citationActive Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations
Sinha, M., & Narayanan, R. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.08.035 · Cited by 42 · see citing works
Kim, 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
Self-citation(2020) · doi:10.1101/2020.02.03.932244 · Cited by 537 · see citing works
“…neuronal cell types in mouse visual cortex based on either transcriptomic characteristics using a high-throughput scRNAseq pipeline (Tasic et al., 2018; Tasic et al., 2016) or, from a separate standardized pipeline, electrophysiological and morphological characteristics (Gouwens et al., 2019).”
BackgroundMethodInfluentialSelf-citationZhang, T., Zeng, Y., Zhang, Y., Zhang, X., Shi, M., Tang, L., Zhang, D., & Xu, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-86780-4 · Cited by 40 · see citing works
Embryonic progenitor pools generate diversity in fine-scale excitatory cortical subnetworks
Ellender, T. J., Avery, S. V., Mahfooz, K., Scaber, J., von Klemperer, A., Nixon, S. L., Buchan, M. J., van Rheede, J. J., Gatti, A., Waites, C. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13206-1 · Cited by 38 · see citing works
Sabbagh, U., Govindaiah, G., Somaiya, R. D., Ha, R. V., Wei, J., Guido, W., & Fox, M. A. (2020) · Journal of Neurochemistry · doi:10.1111/jnc.15101 · Cited by 35 · see citing works
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018820 · Cited by 35 · see citing works
“Electrophysiology feature analysis 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).”
MethodSelf-citationChandelier cell anatomy and function reveal a variably distributed but common signalPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Collman, F., Brittain, D., Bleckert, A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018952 · Cited by 37 · see citing works
Self-citationTrojanowski, N. F., Bottorff, J., & Turrigiano, G. G. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.027 · Cited by 38 · see citing works
“Most popular have been techniques that sort cells based on their transcriptional profile or projection targets, though efforts are ongoing to incorporate this anatomical and transcriptional data with electrophysiological data (Gouwens et al., 2019).”
BackgroundA Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination
Laturnus, S., Kobak, D., & Berens, P. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09461-z · Cited by 30 · see citing works
“…have been used in a wide variety of studies across species and brain areas (Yanbin et al. 2013; Polavaram et al. 2014; Yanbin et al. 2015; Gouwens et al. 2019) and have been shown to perform well in a one-vs-rest classification of cortical neurons (Mihaljevi´c et al. 2015; Mihaljevi´c et…”
BackgroundMethodSharing neurophysiology data from the Allen Brain Observatory
de Vries, S. E. J., Siegle, J. H., & Koch, C. (2023) · eLife · doi:10.7554/elife.85550 · Cited by 43 · see citing works
“…et al., 2007 ) and subsequent surveys of gene expression ( Bakken et al., 2016 ; Hawrylycz et al., 2012 ; Miller et al., 2014 ), mesoscale connectivity ( Harris et al., 2019 ; Oh et al., 2014 ), and in vitro firing patterns ( Gouwens et al., 2019 ) have become essential resources across the field.”
BackgroundCell-class-specific electric field entrainment of neural activity
Lee, S. Y., Kozalakis, K., Baftizadeh, F., Campagnola, L., Jarsky, T., Koch, C., & Anastassiou, C. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.05.009 · Cited by 39 · see citing works
“Consistent with other studies, 36 the instantaneous firing-rate (1/ISI) distributions show the typical slow and irregular firing that pyramidal neurons are known for (spike rate: 8.5 ± 3.0 Hz; Figure 4B) as well as the fast, more stereotyped firing of Pvalb (133.4 ± 35.7 Hz; Figure 4B).”
InfluentialSelf-citationLayer-specific parameters of intracortical microstimulation of the somatosensory cortex
Urdaneta, M. E., Kunigk, N. G., Delgado, F., Fried, S. I., & Otto, K. J. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abedde · Cited by 31 · see citing works
“Compared to other layers, neurons in L5 have a larger soma surface area and an increased volume [46, 47].”
Weiler, S., Guggiana-Nilo, D., Bonhoeffer, T., Hübener, M., Rose, T., & Scheuß, V. (2022) · Current Biology · doi:10.1016/j.cub.2022.02.048 · Cited by 34 · see citing works
“The dendritic morphology of L2/3 PCs in primary visual cortex (V1) varies in terms of both complexity and spatial extent.(8) In principle, specialized dendritic architectures can give rise to biased sampling of inputs across the representation of visual space in V1, resulting in specific tuning properties.”
The impact of (ab)normal maternal environment on cortical development
Vasistha, N. A., & Khodosevich, K. (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
Self-citationHeterogeneous somatostatin-expressing neuron population in mouse ventral tegmental area
Nagaeva, E. I., Zubarev, I., Gonzales, C. B., Forss, M., Nikouei, K., de Miguel, E., Elsilä, L. V., Linden, A.-M., Hjerling‐Leffler, J., Augustine, G. J., et al. (2020) · eLife · doi:10.7554/elife.59328 · Cited by 32 · see citing works
Wang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 32 · see citing works
Constraining computational models using electron microscopy wiring diagrams
Litwin-Kumar, A., & Turaga, S. C. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.07.007 · Cited by 29 · see citing works
Neuronal spike-rate adaptation supports working memory in language processing
Fitz, H., Uhlmann, M., den Broek, D. V., Duarte, R., Peter Hagoort, A., & Petersson, K. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2000222117 · Cited by 35 · see citing works
“Results Within the range of observed intrinsic plasticity (18, 19), we focus on the decrease in excitability due to spike-rate adaptation (SRA) which has been found in many types of excitatory cortical neurons (20, 31).”
BackgroundOláh, 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
- Authors
- Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., Bickley, K., Blesie, N., Braun, T., Brouner, K., Budzillo, A., Caldejon, S., Casper, T., Castelli, D., Chong, P., Crichton, K., Cuhaciyan, C., Daigle, T. L., Dalley, R., Dee, N., Desta, T., Ding, S.-L., Dingman, S., Doperalski, A., Dotson, N., Egdorf, T., Fisher, M., de Frates, R. A., Garren, E., Garwood, M., Gary, A., Gaudreault, N., Godfrey, K., Gorham, M., Gu, H., Habel, C., Hadley, K., Harrington, J., Harris, J. A., Henry, A., Hill, D., Josephsen, S., Kebede, S., Kim, L., Kroll, M., Lee, B., Lemon, T., Link, K. E., Liu, X., Long, B., Mann, R., McGraw, M., Mihalas, S., Mukora, A., Murphy, G. J., Ng, L., Ngo, K., Nguyen, T. N., Nicovich, P. R., Oldre, A., Park, D., Parry, S., Perkins, J., Potekhina, L., Reid, D., Robertson, M., Sandman, D., Schroedter, M., Slaughterbeck, C., Soler-Llavina, G., Sulc, J., Szafer, A., Tasic, B., Taskin, N., Teeter, C., Thatra, N., Tung, H., Wakeman, W., Williams, G., Young, R., Zhou, Z., Farrell, C., Peng, H., Hawrylycz, M. J., Lein, E., Ng, L., Arkhipov, A., Bernard, A., Phillips, J. W., Zeng, H., & Koch, C.
- Affiliations
- Allen Institute for Brain Science (US)
- Corresponding author
- Zeng, Hongkui
- ORCID
- Gouwens, Nathan W.
- Sorensen, Staci A.
- Berg, Jim
- Lee, Changkyu
- Jarsky, Tim
- Ting, Jonathan
- Sunkin, Susan M.
- Feng, David
- Anastassiou, Costas A.
- Barkan, Eliza
- Bickley, Kris
- Braun, Thomas
- Brouner, Krissy
- Budzillo, Agata
- Caldejon, Shiella
- Casper, Tamara
- Chong, Peter
- Crichton, Kirsten
- Daigle, Tanya L.
- Dalley, Rachel
- Dee, Nick
- Desta, Tsega
- Ding, Song-Lin
- Dotson, Nadezhda
- Egdorf, Tom
- Fisher, Michael
- Gary, Amanda
- Gorham, Melissa
- Hadley, Kristen
- Harris, Julie A.
- Henry, Alex
- Hill, DiJon
- Kebede, Sara
- Kim, Lisa
- Kroll, Matthew
- Lee, Brian
- Link, Katherine E.
- Liu, Xiaoxiao
- Long, Brian
- Mann, Rusty
- Mihalas, Stefan
- Mukora, Alice
- Murphy, Gabe J.
- Ng, Lindsay
- Nguyen, Thuc Nghi
- Nicovich, Philip R.
- Oldre, Aaron
- Potekhina, Lydia
- Reid, David
- Schroedter, Martin
- Slaughterbeck, Cliff
- Soler-Llavina, Gilberto
- Szafer, Aaron
- Tasic, Bosiljka
- Taskin, Naz
- Teeter, Corinne
- Tung, Herman
- Wakeman, Wayne
- Williams, Grace
- Zhou, Zhi
- Farrell, Colin
- Hawrylycz, Michael J.
- Lein, Ed
- Ng, Lydia
- Arkhipov, Anton
- Bernard, Amy
- Phillips, John W.
- Zeng, Hongkui
- Koch, Christof
- Venue
- Nature Neuroscience
- Published
- 2019-06-17
- Volume
- 22
- Issue
- 7
- Pages
- 1182-1195
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC8078853
- OpenAlex
- W2949311293
- Semantic Scholar
- fccd157b961e9e3bfa5a0d5616b3a3ecb381ce85
- Cited by
- 456
- References
- 59
- Citations by year
- 2026: 44 · 2025: 69 · 2024: 54 · 2023: 64 · 2022: 63 · 2021: 66 · 2020: 62 · 2019: 30 · 2018: 4
- FWCI
- 26.25
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (U01MH105982, U19MH114830)
- National Eye Institute (R01EY023173)
- 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
- neuron classification, mouse visual cortex, neuronal morphology, electrophysiological properties, single-cell characterization, unsupervised clustering
- MeSH terms
- Action Potentials, Animals, Mice, Transgenic, Neurons, Visual Cortex, Databases, Factual, Genes, Reporter, Patch-Clamp Techniques, Cell Shape, Mice, Transcriptome, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review