Phenotypic variation of transcriptomic cell types in mouse motor cortex
Open accessPublished version of a preprint
Abstract
Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties 1,2 . Most existing neural taxonomies are based on either transcriptomic 3,4 or morpho-electric 5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells 7 . Here we used Patch-seq 8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip , Pvalb , Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.
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Written from the paper’s full text.
TL;DR
Patch-seq in adult mouse primary motor cortex linked transcriptomic cell types to morpho-electric phenotypes for 1,227 neurons, covering 77 of 90 neuronal t-types. Broad transcriptomic families were morpho-electrically distinct, but within families phenotypes varied continuously rather than forming discrete clusters.
Key findings
- Broad transcriptomic families (Pvalb, Sst, Vip, Lamp5, and excitatory classes) had largely distinct morpho-electric phenotypes; kNN classification was mostly diagonal, with only IT and ET subclasses strongly overlapping.
- Within families, morpho-electric phenotypes varied continuously: transcriptomic distance correlated with electrophysiological distance across 200 t-type pairs (r = 0.60), and IT transcriptomic distance correlated with soma depth (r = 0.70).
- Sparse reduced-rank regression predicted electrophysiological features from gene expression using 25 genes and a 5-dimensional latent space, with cross-validated R² = 0.38; the first three projection correlations were 0.90, 0.74, and 0.67. An ion-channel-only model performed similarly (R² = 0.33).
- Many individual t-types showed non-trivial phenotypic variability above k-means cluster baselines; for example, Vip Mybpc1_2 had high electrophysiological variability, whereas Pvalb Vipr2_2 (chandelier cells) appeared morpho-electrically homogeneous.
Methods
- Patch-seq combined patch-clamp recording, biocytin staining, and Smart-seq2 single-cell RNA sequencing in acute slices from adult mouse primary motor cortex (MOp); >2,000 cells were recorded, 1,329 passed initial QC, and 1,227 received final t-type assignments (817 inhibitory, 410 excitatory; 369 and 269 with morphological reconstructions, respectively).
- t-types were assigned by nearest-centroid/Pearson-correlation mapping to seven BICCN reference datasets, with consensus assignment; analyses extracted 29 electrophysiological and about 50 morphological features and used sparse reduced-rank regression, t-SNE, kNN classification, within-family distance correlations, and variance/Leiden clustering.
- Recordings were mostly at room temperature (25 °C), with follow-up experiments at 34 °C; layer boundaries were measured by Nissl staining, and morphologies were reconstructed and normalized by cortical thickness.
Limitations
- The paper states that some t-types were covered only sparsely or not at all, and that some very rare putative t-types might not be amenable to Patch-seq.
- The paper states that RNA extraction may have interfered with biocytin diffusion and that MOp thickness made complete morphology recovery difficult for some groups, such as deep L5 Martinotti cells with thin long axons.
- The paper states that technical challenges of Patch-seq or exact spatial location cannot be fully excluded as sources of within-type variability; evident from the Methods, experiments were not randomized or blinded, recordings were mostly at room temperature, and tissue shrinkage of about 10–20% was not compensated.
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- Findingown data
Patch-seq combines patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex.
“Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016). source
Not checked in the free run.
- Findingcitation
The study used Patch-seq to describe the morpho-electric phenotypes for most of the t-types in MOp.
“We used the Patch-seq technique8,17–19 to describe the morpho-electric phenotypes for most of the t-types in MOp20.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [8] Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016). source
Not checked in the free run.
- cites [17] Cadwell, C. R. et al. Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq. Nat. Protocols 12, 2531 (2017). source
Not checked in the free run.
- cites [18] Fuzik, J. et al. Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat. Biotechnol. 34, 175–183 (2016). source
Not checked in the free run.
- cites [19] Földy, C. et al. Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons. Proc. Natl Acad. Sci. USA 113, E5222–E5231 (2016). source
Not checked in the free run.
- cites [20] Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature https://doi.org/10.1038/s41586-021-03500-8 (2021). source
Not checked in the free run.
- Findingown data
Individual transcriptomic types within the same family were not well separated in the morpho-electric space.
“We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space.”
- Also stated in: § Continuous phenotypic variation · plain
“Within individual transcriptomic families, morpho-electric phenotypes rarely formed isolated clusters (Fig. 3).”
Quote in the paperSubstantial claim - Also stated in: § Continuous phenotypic variation · plain
- Findingown data
The study provides a morpho-electric annotation of almost all transcriptomically defined neural cell types.
“Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016). source
Not checked in the free run.
- Findingown data
Broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes.
“We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space.”
- Also stated in: § Distinct phenotypes of major families · hedged
“In summary, different transcriptomic families had largely distinct morpho-electric phenotypes, in agreement with them being well separated in the transcriptomic space4.”
- Also stated in: § Discussion · hedged
“Broad transcriptomic families were mostly well separated in their morpho-electric properties.”
Quote in the paperSubstantial claim - Also stated in: § Distinct phenotypes of major families · hedged
- Findingown data
There was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them.
“Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them.”
- Also stated in: § Continuous phenotypic variation · plain
“Moreover, we often found that morpho-electric phenotypes varied continuously from one t-type to another (Fig. 4).”
Quote in the paperSubstantial claim - Also stated in: § Continuous phenotypic variation · plain
- Findingown data
Patch-seq expression profiles were most similar to the single-nucleus Smart-seq2 data among the seven reference data sets.
“We found that Patch-seq expression profiles were most similar to the single-nucleus Smart-seq2 data (Extended Data Fig. 2g, h).”
Quote in the paperNot a substantial claim - Findingown data
There was good agreement between t-type assignments based on Smart-seq2 and those based on 10x reference data.
“At the same time, there was good agreement between t-type assignments based on Smart-seq2 and those based on 10x reference data (Extended Data Fig. 2i), so consensus t-type assignment over all seven reference data sets was used for all subsequent analysis.”
Quote in the paperSubstantial claim - Findingown data
Positioning all cells on reference maps made with t-distributed stochastic neighbour embedding (t-SNE) showed good overall coverage with only few uncovered regions.
“Positioning all cells on reference maps made with t-distributed stochastic neighbour embedding (t-SNE)22,23 also showed good overall coverage (Fig. 1c–e) with only few uncovered regions.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [22] van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Not checked in the free run.
- cites [23] Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 5416, 10 (2019).
Not checked in the free run.
- Findingcitation
The Sst Pvalb Calb2 type, which is transcriptomically in between the Sst and Pvalb subclasses, was also in between these subclasses in terms of its morpho-electric phenotype.
“Another finding was that the Sst Pvalb Calb2 type, which is transcriptomically in between the Sst and Pvalb subclasses, was also in between these subclasses in terms of its morpho-electric phenotype28.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] Scala, F. et al. Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas. Nat. Commun. 10, 4174 (2019). source
Not checked in the free run.
- Findingown data
Some genes selected by the model were prominent marker genes, such as Gad1, Slc6a1, Sst, Vip, Pvalb, Tac1, and Htr3a.
“Some of the genes selected by the model were prominent marker genes, such as the pan-inhibitory markers Gad1 and Slc6a1 (related to GABA (γ-aminobutyric acid) processing), or the more specific inhibitory markers Sst, Vip, Pvalb, Tac1, and Htr3a.”
Quote in the paperNot a substantial claim - Findingown data
Within major transcriptomic families, morpho-electric phenotypes and/or soma depth often varied smoothly across neighbouring t-types, indicating that transcriptomic neighbourhood relationships in many cases corresponded to similarities in other modalities.
“In summary, within major transcriptomic families, morpho-electric phenotypes and/or soma depth often varied smoothly across neighbouring t-types, indicating that transcriptomic neighbourhood relationships in many cases corresponded to similarities in other modalities.”
Quote in the paperSubstantial claim - Findingown data
The separability between inhibitory families was weaker with morphological features than with electrophysiological features.
“The separability between inhibitory families was weaker than with electrophysiological features (Fig. 3d).”
Quote in the paperNot a substantial claim - Findingown data
The transcriptomically isolated Lamp5 Lhx6 type consists of deep L5/L6 neurogliaform cells (NGCs).
“One interesting case was the transcriptomically isolated Lamp5 Lhx6 type, which consists of deep L5/L6 neurogliaform cells (NGCs).”
Quote in the paperSubstantial claim - Findingown data
The variation within families is structured across the transcriptomic landscape, such that the morpho-electric distance between t-types within a family is correlated with their transcriptomic distance.
“We found that this variation is structured across the transcriptomic landscape, such that the morpho-electric distance between t-types within a family is correlated with their transcriptomic distance.”
Quote in the paperSubstantial claim - Findingown data
Some other selected genes were more directly related to electrophysiological properties, such as the calcium channel subunit genes Cacna1e and Cacna2d3 or the potassium channel-interacting protein gene Kcnip2, which can modulate firing properties in individual families.
“Notably, some other selected genes were more directly related to electrophysiological properties, such as the calcium channel subunit genes Cacna1e and Cacna2d3 or the potassium channel-interacting protein gene Kcnip2, which can modulate firing properties in individual families.”
Quote in the paperNot a substantial claim - Findingown data
The variability in Vip Mybpc1_2 was not random: cells with low rebound were located close to the boundary with the low-rebound Vip Sncg type.
“This variability was not random: overlaying the rebound values on the t-SNE embedding (Fig. 5c) showed that cells with low rebound were located close to the boundary with the low-rebound Vip Sncg type.”
Quote in the paperSubstantial claim - Findingown data
Transcriptomic distances between IT t-types were strongly correlated with average soma depth differences (r = 0.70).
“Transcriptomic distances between t-types were strongly correlated with the average soma depth differences (r = 0.70; Fig. 4d).”
Quote in the paperSubstantial claim - Findingown data
The cross-validated correlations between the first three pairs of projections of gene expression and electrophysiological properties were 0.90, 0.74, and 0.67, respectively.
“The cross-validated correlations between the first three pairs of projections were 0.90, 0.74, and 0.67, respectively.”
Quote in the paperSubstantial claim - Findingown data
A confusion matrix for k-nearest neighbours classification of cells into families was mostly diagonal, with only the ET and IT subclasses strongly overlapping.
“We quantified this separation using a confusion matrix for k-nearest neighbours (kNN) classification of cells into families: it was mostly diagonal, with only the ET and IT subclasses strongly overlapping (Fig. 3d).”
Quote in the paperSubstantial claim - Findingown data
An alternative analysis using entropies of Leiden clustering often highlighted the same t-types as variable.
“We found that many t-types had total variance substantially above the variances of the k-means clusters (Fig. 5b) and an alternative analysis using entropies of Leiden clustering35 often highlighted the same t-types as variable (Extended Data Fig. 8).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019). source
Not checked in the free run.
- Findingown data
The first three components of the sparse reduced-rank regression clearly separated five major groups of neurons: the Pvalb, Sst, Vip, and Lamp5 interneuron subclasses, and the excitatory neuron class.
“These first three components clearly separated five major groups of neurons: the Pvalb, Sst, Vip, and Lamp5 interneuron subclasses, and the excitatory neuron class (Fig. 2).”
Quote in the paperSubstantial claim - Findingown data
A 2D t-SNE embedding of Patch-seq cells based on electrophysiological features showed that the major transcriptomic families have distinct electrophysiological properties: the Pvalb, Lamp5, Sst, Vip, CT, IT, and ET subclasses were mostly well separated from each other.
“Similarly, a 2D t-SNE embedding of Patch-seq cells based on the same electrophysiological features showed that the major transcriptomic families have distinct electrophysiological properties (Fig. 3a): the Pvalb, Lamp5, Sst, Vip, CT (corticothalamic), IT (intratelencephalic), and ET (extratelencephalic) subclasses were mostly well separated from each other.”
Quote in the paperSubstantial claim - Findingown data
Reduced-rank regression analysis using morphological features supported the separation of major families.
“Reduced-rank regression analysis using morphological features supported the separation of major families (Extended Data Fig. 5).”
Quote in the paperNot a substantial claim - Findingown data
Pooling all 200 pairs of t-types from the same family, transcriptomic distance and electrophysiological distance were correlated with r = 0.60.
“Pooling the pairs across all families, we found that these two distance measures were correlated, with r = 0.60 (Fig. 4c, n = 200 pairs; Extended Data Fig. 7).”
Quote in the paperSubstantial claim - Findingown data
Chandelier cells from both superficial and deep layers belonged to transcriptomically isolated Pvalb Vipr2 types.
“Furthermore, we confirmed that chandelier cells from both superficial and deep layers belonged to transcriptomically isolated Pvalb Vipr2 types.”
Quote in the paperSubstantial claim - Findingown data
Morpho-electric phenotypes often varied continuously from one t-type to another.
“Moreover, we often found that morpho-electric phenotypes varied continuously from one t-type to another (Fig. 4).”
Quote in the paperSubstantial claim - Findingown data
Within individual transcriptomic families, morpho-electric phenotypes rarely formed isolated clusters.
“Within individual transcriptomic families, morpho-electric phenotypes rarely formed isolated clusters (Fig. 3).”
Quote in the paperSubstantial claim - Findingown data
Many Sst Pvalb Calb2 neurons were classified as belonging to the Pvalb subclass on the basis of electrophysiology.
“For example, many Sst Pvalb Calb2 neurons were classified as belonging to the Pvalb subclass on the basis of electrophysiology.”
Quote in the paperNot a substantial claim - Findingown data
Some t-types, such as Pvalb Vipr2_2 (chandelier cells), appeared morpho-electrically homogeneous.
“Not all t-types showed high variability: some of them, such as Pvalb Vipr2_2 (chandelier cells), appeared morpho-electrically homogeneous.”
Quote in the paperNot a substantial claim - Findingown data
Similar examples of within-t-type variability were found in the morphological modality.
“We found similar examples in the morphological modality (Extended Data Fig. 8).”
Quote in the paperNot a substantial claim - Findingown data
The study used Patch-seq to provide the missing link between transcriptomic and morpho-electric descriptions of neurons in adult mouse motor cortex.
“We used Patch-seq to provide the missing link between transcriptomic and morpho-electric descriptions of neurons in adult mouse motor cortex.”
Quote in the paperSubstantial claim - Findingown data
Many t-types had total variance substantially above the variances of the k-means clusters.
“We found that many t-types had total variance substantially above the variances of the k-means clusters (Fig. 5b) and an alternative analysis using entropies of Leiden clustering35 often highlighted the same t-types as variable (Extended Data Fig. 8).”
Quote in the paperSubstantial claim - Findingown data
A reduced-rank regression model restricted to using only ion channel genes did not perform much worse than the full model (cross-validated R2 = 0.33 and correlations 0.86, 0.71, and 0.56, respectively, with regularization set to select 25 genes).
“A reduced-rank regression model restricted to using only ion channel genes (Extended Data Fig. 5) did not perform much worse than the full model (cross-validated R2 = 0.33 and correlations 0.86, 0.71, and 0.56, respectively, with regularization set to select 25 genes).”
Quote in the paperNot a substantial claim - Findingown data
A 2D t-SNE embedding based on morphological features, using only dendritic features for excitatory cells and both axonal and dendritic features for inhibitory cells, led to a strong separation between these two major classes.
“We also constructed a 2D t-SNE embedding based on the morphological features (Fig. 3b). We used only dendritic features for the excitatory cells, but both axonal and dendritic features for the inhibitory cells, leading to a strong separation between these two major classes.”
Quote in the paperSubstantial claim - Findingown data
There is non-trivial morpho-electric variability within multiple t-types.
“Furthermore, we found non-trivial morpho-electric variability within multiple t-types.”
Quote in the paperSubstantial claim - Findingown data
Across genes, there was a clear inverse relationship between μ and τ, that roughly followed the exponential law: τ ≈ exp(−1.5 × μ + a) for some horizontal offset a.
“Across genes, there was a clear inverse relationship between μ and τ, that roughly followed the exponential law: τ ≈ exp(−1.5 × μ + a) for some horizontal offset a.”
Quote in the paperNot a substantial claim - Findingreasoning
Four cells were assigned to an excitatory t-type, despite having clearly inhibitory firing, morphology, and/or soma depth location (such as L1).
“Four cells were assigned to an excitatory t-type, despite having clearly inhibitory firing, morphology, and/or soma depth location (such as L1).”
Quote in the paperNot a substantial claim - Premisecitation
Most existing neural taxonomies are based on either transcriptomic or morpho-electric criteria, because it has been technically challenging to study both aspects in the same set of cells.
“Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] 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.
- Interpretationown data
Neuronal types in the neocortex do not always form discrete entities.
“Our results suggest that neuronal types in the neocortex do not always form discrete entities.”
- Also stated in: § Discussion · plain
“This is at odds with the notion that t-types are discrete entities, an implicit assumption behind any cluster analysis.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Interpretationreasoning
The goal to assemble an exhaustive inventory of neural cell types might be unattainable if the types are not discrete entities.
“Thus, the goal to assemble an exhaustive inventory of neural cell types might be unattainable if the types, unlike the chemical elements in the periodic table, are not discrete entities.”
Quote in the paperNot a substantial claim - Study design and conductown data
The electrophysiological overlap between IT and ET neurons was confirmed in follow-up experiments at 34 °C.
“We confirmed the electrophysiological overlap between IT and ET neurons in follow-up experiments at 34 °C (Extended Data Fig. 6).”
Quote in the paperNot a substantial claim - Study design and conductnone given
Some t-types were covered only sparsely or not at all.
“First, some t-types were covered only sparsely or not at all.”
Quote in the paperNot a substantial claim - Interpretationown data
Within-t-type morpho-electric variability can in some cases be related to underlying transcriptomic variability.
“Together, these examples suggest that within-t-type morpho-electric variability can in some cases be related to the underlying transcriptomic variability.”
- Also stated in: § Discussion · hedged
“Although we cannot fully exclude the possibility that some of this variability can be attributed to technical challenges of Patch-seq or to factors such as the exact spatial location of the cell within motor cortex36, there are clear cases in our data that suggest that within-type morpho-electric variability is related to within-type transcriptomic variability.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The variance of k-means clusters (with k set to the number of t-types) reflects the minimal possible phenotypic variance obtainable in the data set.
- Also stated in: § Discussion · hedged
- Study design and conductown data
The continuous variation effect was confirmed in follow-up experiments at physiological temperature.
“We confirmed this effect in follow-up experiments at physiological temperature (Extended Data Fig. 6).”
Quote in the paperSubstantial claim - Interpretationown data
The most likely cause of the four cells being assigned to an excitatory t-type despite inhibitory features was RNA contamination from excitatory cells, which are much more abundant than inhibitory cells in the mouse cortex.
“The most likely cause of this was RNA contamination from excitatory cells, which are much more abundant than inhibitory cells in the mouse cortex (Extended Data Fig. 2).”
Quote in the paperNot a substantial claim
- May combine two claims. 48
- Classified as probably not a substantial claim. 49
May combine two claims.
Findingown dataThe resulting data set covered 77 out of the 90 neuronal t-types, with 73 t-types having at least one morphologically reconstructed neuron.
“The resulting data set covered 77 out of the 90 neuronal t-types (Fig. 1a), with 73 t-types having at least one morphologically reconstructed neuron.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCoverage was good for interneurons derived from the caudal and medial ganglionic eminences (CGE and MGE) and for excitatory neurons.
“The coverage was good for interneurons derived from the caudal and medial ganglionic eminences (CGE and MGE) and for excitatory neurons.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThree previously described morphological types of L5 Pvalb cells, as well as two morphological types of L5 Martinotti cells, corresponded to different t-types.
“We also showed that three previously described morphological types of L5 Pvalb cells5, as well as two morphological types of L5 Martinotti cells29,30, corresponded to different t-types.”
Quote in the paperSubstantial 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 [29] Muñoz, W., Tremblay, R., Levenstein, D. & Rudy, B. Layer-specific modulation of neocortical dendritic inhibition during active wakefulness. Science 355, 954–959 (2017). source
Not checked in the free run.
- cites [30] Nigro, M. J., Hashikawa-Yamasaki, Y. & Rudy, B. Diversity and connectivity of layer 5 somatostatin-expressing interneurons in the mouse barrel cortex. J. Neurosci. 38, 1622–1633 (2018). source
Not checked in the free run.
May combine two claims.
Findingown dataLamp5 Egln3_1 neurons had rather Vip- and Sst-like firing instead of the typical Lamp5 electrophysiology, and Vip Mybpc1 neurons often had Sst-like firing.
“Similarly, Lamp5 Egln3_1 neurons had rather Vip- and Sst-like firing instead of the typical Lamp5 electrophysiology, and Vip Mybpc1 neurons often had Sst-like firing.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe selected sparse reduced-rank regression model used 25 genes with a 5-dimensional latent space and achieved a cross-validated R2 of 0.38.
“The selected model used 25 genes with a 5-dimensional latent space and achieved a cross-validated R2 of 0.38.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] Kobak, D. et al. Sparse reduced-rank regression for exploratory visualization of paired multivariate data sets. R. Stat. Soc. 70, 980–1000 (2021). source
Not checked in the free run.
May combine two claims.
Findingown dataThe 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).”
Quote in the paperSubstantial 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 [24] Gouwens, N. W. et al. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat. Neurosci. 22, 1182–1195 (2019). source
Not checked in the free run.
May combine two claims.
Findingown dataElectrophysiological properties of the t-types within the Vip subclass varied continuously across the transcriptomic landscape; the membrane time constant had its largest values close to the Sncg subclass and gradually decreased towards Vip Gpc3.
“For example, electrophysiological properties of the t-types within the Vip subclass varied continuously across the transcriptomic landscape; the membrane time constant, for instance, had its largest values close to the Sncg subclass and gradually decreased towards Vip Gpc3 (Fig. 4a).”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationDifferent transcriptomic families had largely distinct morpho-electric phenotypes, in agreement with them being well separated in the transcriptomic space.
“In summary, different transcriptomic families had largely distinct morpho-electric phenotypes, in agreement with them being well separated in the transcriptomic space4.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] 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.
May combine two claims.
Findingown dataDifferent Pvalb t-types had different preferred morphologies but showed substantial overlap.
“While we found that different t-types had different preferred morphologies (Extended Data Table 1), they showed substantial overlap, in agreement with the L5 Pvalb t-types themselves not having clear boundaries4 (Fig. 1d).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] 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.
May combine two claims.
Findingown dataL4/5 and L5 IT t-types that were transcriptomically close to L2/3 IT t-types were located at the top of L5 close to the border between L2/3 and L5, whereas L5 IT t-types that were transcriptomically close to L6 IT t-types were located at the bottom of L5 close to the border with L6.
“For example, L4/5 and L5 IT t-types that were transcriptomically close to the L2/3 IT t-types were located at the top of L5 close to the border between L2/3 and L5, whereas L5 IT t-types that were transcriptomically close to L6 IT t-types were located at the bottom of L5 close to the border with L6.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBetween-family separability was strongest when electrophysiological and morphological features were combined into a joint representation, showing that these sets of properties are not redundant.
“The between-family separability was strongest when we had combined electrophysiological and morphological features into a joint representation (Fig. 3c, d), showing that these sets of properties are not redundant.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThese five major groups had distinct electrophysiological properties; for example, Pvalb neurons were characterized by high firing rates while Sst neurons had high values of the hyperpolarization sag and rebound.
“These groups had distinct electrophysiological properties: for example, as expected, Pvalb neurons were characterized by high firing rates while Sst neurons had high values of the hyperpolarization sag and rebound (Fig. 2, right).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIT t-types did not form distinct groups for each cortical layer; instead, soma depth and RNA expression varied continuously along a one-dimensional manifold.
“However, we found that IT t-types did not form distinct groups for each cortical layer; instead, the soma depth and RNA expression varied continuously along a one-dimensional manifold (Fig. 4d), in agreement with parallel findings based on a spatial transcriptomics approach34.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature https://doi.org/10.1038/s41586-021-03705-x (2021). source
Not checked in the free run.
May combine two claims.
FindingcitationA t-type, L4/5 IT_1, was located on the boundary between L2/3 and L5 and probably corresponds to the quasi-L4 neurons described previously in motor cortex.
“We were also able to identify a t-type, L4/5 IT_1, that was located on the boundary between L2/3 and L5 and probably corresponds to the quasi-L4 neurons described previously in motor cortex31.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] Yamawaki, N., Borges, K., Suter, B. A., Harris, K. D. & Shepherd, G. M. A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity. eLife 3, e05422 (2014). source
Not checked in the free run.
May combine two claims.
Findingown dataThe correlation between transcriptomic and electrophysiological distances was also observed within multiple individual families and for many individual electrophysiological features.
“The correlation was also observed within multiple individual families and for many individual electrophysiological features (Extended Data Fig. 7).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOur data sets are overall in good agreement and together offer an unprecedented view of cell type variability in the neocortex.
“Our data sets are overall in good agreement (Extended Data Fig. 9) and together offer an unprecedented view of cell type variability in the neocortex.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe shape of the axonal arbor of Pvalb cells showed continuous changes across the transcriptomic landscape: small shrub-like basket cells, horizontally elongated basket cells, and vertically elongated classical basket cells were located in different corners of the t-SNE embedding, with intermediate morphologies in between.
“The shape of the axonal arbor showed continuous changes across the transcriptomic landscape (Fig. 4e): small shrub-like basket cells, horizontally elongated basket cells, and vertically elongated classical basket cells were located in different corners of the t-SNE embedding, with intermediate morphologies in between.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMost t-types could be unambiguously placed into the correct transcriptomic family using electrophysiological features, but some t-types were in between two families.
“Most t-types could be unambiguously placed into the correct family (Fig. 5a), but some t-types were in between two families.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVip Mybpc1_2 was marked as having high electrophysiological variability and had high variance in input resistance, membrane time constant, and rebound.
“By contrast, Vip Mybpc1_2 was marked as having high electrophysiological variability and indeed had high variance in input resistance, membrane time constant, and rebound (Extended Data Fig. 4).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSst Pvalb Calb2 cells had high variability in maximum firing rate, but high-firing cells were mostly grouped in one part of the transcriptomic landscape.
“Similarly, Sst Pvalb Calb2 cells had high variability in terms of the maximum firing rate, but high-firing cells were mostly grouped in one part of the transcriptomic landscape (Fig. 5d).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the Sst subclass, morpho-electric properties varied continuously across the transcriptomic landscape, with neighbouring t-types consistently showing similar morphologies and similar rebound values.
“We observed the same in the Sst subclass, which is known to be transcriptomically4 and morpho-electrically29,30,33 diverse in L5. Here we also found that morpho-electric properties varied continuously across the transcriptomic landscape, with neighbouring t-types consistently showing similar morphologies and similar rebound values (Fig. 4b).”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [4] 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 [29] Muñoz, W., Tremblay, R., Levenstein, D. & Rudy, B. Layer-specific modulation of neocortical dendritic inhibition during active wakefulness. Science 355, 954–959 (2017). source
Not checked in the free run.
- cites [30] Nigro, M. J., Hashikawa-Yamasaki, Y. & Rudy, B. Diversity and connectivity of layer 5 somatostatin-expressing interneurons in the mouse barrel cortex. J. Neurosci. 38, 1622–1633 (2018). source
Not checked in the free run.
- cites [33] Naka, A. et al. Complementary networks of cortical somatostatin interneurons enforce layer specific control. eLife 8, e43696 (2019). source
Not checked in the free run.
May combine two claims.
Findingown dataWithin each class, cells were strongly segregated by soma depth, with excitatory cells forming mostly a one-dimensional manifold.
“Within each class, cells were strongly segregated by the soma depth, with excitatory cells forming mostly a one-dimensional manifold.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe resulting cDNA yield was not correlated with the hold time (Spearman correlation −0.01).
“The resulting cDNA yield was not correlated with the hold time (Spearman correlation −0.01).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationIn Gnb4-Cre mice, no labelled cells were found in MOp; all labelled cells were far outside MOp and close to the claustrum.
“We were unable to find any labelled cells in MOp in the Gnb4-Cre mice: all labelled cells were far outside of MOp and close to the claustrum46.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [46] Peng, H. et al. Morphological diversity of single neurons in molecularly defined cell types. Nature https://doi.org/10.1038/s41586-021-03941-1 (2021). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationThe addition of the RNase inhibitor resulted in an increase in osmolarity to the desired value of 315-320 mOSM without a further dilution.
“The addition of the inhibitor resulted in an increase in osmolarity to the desired value of 315–320 mOSM without a further dilution17.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Cadwell, C. R. et al. Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq. Nat. Protocols 12, 2531 (2017). source
Not checked in the free run.
May combine two claims.
Findingnone givenA substantial portion of the RNA extracted from the neurons was nascent and contained intronic sequences.
“A substantial portion of the RNA extracted from the neurons was nascent and contained intronic sequences.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataWe detected 42,466 genes, including pseudogenes and annotated non-coding segments, in at least one cell.
“We detected 42,466 genes, including pseudogenes and annotated non-coding segments, in at least one cell.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataOne hundred and ten cells mapped to the Sst subclass, 43 to IT, 12 to ET, 10 to Pvalb, and 2 to NP.
“One hundred and ten cells mapped to the Sst subclass, 43 to IT, 12 to ET, 10 to Pvalb, and 2 to NP.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data175 cells were assigned to L5 in the post hoc analysis.
“175 cells were assigned to L5 in the post hoc analysis (see below).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataNeurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.
“Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.”
- Also stated in: § Discussion · plain
“In this metaphor, neurons follow a hierarchy consisting of distinct, non-overlapping branches at the level of families (large leaves), but with a spectrum of cells forming continuous and correlated transcriptomic and morpho-electrical landscapes within each leaf.”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
May combine two claims.
Study design and conductown dataAfter excluding cells with poor mapping, 1,227 neurons remained for further analysis (817 inhibitory, 410 excitatory; 369 and 269 with morphological reconstructions, respectively).
“Cells that showed poor mapping (owing to a low read count or excessive RNA contamination) were excluded (Extended Data Fig. 2), leaving 1,227 neurons for further analysis (817 inhibitory, 410 excitatory; 369 and 269 with morphological reconstructions, respectively).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
PremisecitationTo understand the roles of t-types in cortical computations, it is necessary to know their anatomy, connectivity, and electrophysiology.
“However, to understand the roles of t-types in cortical computations, it is necessary to know their anatomy, connectivity, and electrophysiology7.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] 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.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe study performed whole-cell recordings from more than 2,000 cells, of which 1,329 passed initial quality control.
“In total, we performed whole-cell recordings from more than 2,000 cells, of which 1,329 cells (from 266 mice) passed initial quality control.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataThe mRNA of the 1,329 cells was sequenced, yielding on average 1.3 million exonic and 0.7 million intronic reads and 9,100 ± 3,500 detected genes per cell.
“The mRNA of these 1,329 cells was sequenced, yielding on average 1.3 million exonic and 0.7 million intronic reads (medians; mean ± s.d. on a log10 scale: 6.0 ± 0.6 and 5.6 ± 0.8, respectively) and 9,100 ± 3,500 (mean ± s.d.) detected genes per cell (Extended Data Fig. 2).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationLinking transcriptomically defined cell types to their phenotypes has remained a major challenge.
“High-throughput single-cell sequencing techniques have identified dozens of types of neuron on the basis of their transcriptional profiles3,4,14,15, but linking transcriptomically defined cell types (t-types) to their phenotypes has remained a major challenge16.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Huang, Z. J. & Paul, A. The diversity of GABAergic neurons and neural communication elements. Nat. Rev. Neurosci. 20, 563–572 (2019). source
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted).
PremisecitationThe current view is that a neuronal cell type is characterized by a common genetic profile that gives rise to distinct physiological and anatomical properties, including patterns of connectivity.
“The current view is that a neuronal cell type is characterized by a common genetic profile that gives rise to distinct physiological and anatomical properties, including patterns of connectivity7,9.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [7] 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.
- cites [9] Masland, R. H. Neuronal cell types. Curr. Biol. 14, R497–R500 (2004). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataOf the 1,329 neurons, 646 had sufficient staining for their morphologies to be reconstructed.
“Of these neurons, 646 had sufficient staining for their morphologies to be reconstructed.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationCortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties.
“Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1] Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015). source
Not checked in the free run.
- 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.
Classified as probably not a substantial claim.
PremisecitationHigh-throughput single-cell sequencing techniques have identified dozens of types of neuron on the basis of their transcriptional profiles.
“High-throughput single-cell sequencing techniques have identified dozens of types of neuron on the basis of their transcriptional profiles3,4,14,15, but linking transcriptomically defined cell types (t-types) to their phenotypes has remained a major challenge16.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [3] 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 [4] 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 [14] 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 [15] Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018). source
Not checked in the free run.
May combine two claims.
InterpretationcitationAlthough all deep NGCs belong to the Lamp5 subclass, some are derived from the CGE and some from the MGE, as in the hippocampus.
“Our results suggest that although all deep NGCs belong to the Lamp5 subclass, some are derived from the CGE and some from the MGE, as in the hippocampus25–27.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [25] Tricoire, L. et al. Common origins of hippocampal Ivy and nitric oxide synthase expressing neurogliaform cells. J. Neurosci. 30, 2165–2176 (2010). source
Not checked in the free run.
- cites [26] Pelkey, K. A. et al. Hippocampal GABAergic inhibitory interneurons. Physiol. Rev. 97, 1619–1747 (2017). source
Not checked in the free run.
- cites [27] Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol. 16, e2006387 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRecent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds.
“Consistent with our interpretation, recent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds27,37–39.”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [27] Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol. 16, e2006387 (2018). source
Not checked in the free run.
- cites [37] Muñoz-Manchado, A. B. et al. Diversity of interneurons in the dorsal striatum revealed by single-cell RNA sequencing and PatchSeq. Cell Rep. 24, 2179–2190.e7 (2018). source
Not checked in the free run.
- cites [38] Stanley, G. et al. Continuous and discrete neuron types of the adult murine striatum. Neuron 105, 688–699.e8 (2019). source
Not checked in the free run.
- cites [39] Wang, W. X. & Lefebvre, J. L. Morphological pseudotime ordering and fate mapping reveals diversification of cerebellar inhibitory interneurons. Preprint at https://doi.org/10.1101/2020.02.29.971366 (2020). source
Not checked in the free run.
May combine two claims.
Interpretationown dataAlthough some within-type morpho-electric variability may be attributed to technical challenges of Patch-seq or to factors such as the exact spatial location of the cell within motor cortex, there are clear cases in the data that suggest within-type morpho-electric variability is related to within-type transcriptomic variability.
“Although we cannot fully exclude the possibility that some of this variability can be attributed to technical challenges of Patch-seq or to factors such as the exact spatial location of the cell within motor cortex36, there are clear cases in our data that suggest that within-type morpho-electric variability is related to within-type transcriptomic variability.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [36] Cembrowski, M. S. et al. Spatial gene-expression gradients underlie prominent heterogeneity of ca1 pyramidal neurons. Neuron 89, 351–368 (2016). source
Not checked in the free run.
May combine two claims.
Interpretationown dataWhile overall transcriptomic family was highly predictive of the cell phenotype, some t-types exhibited properties similar to those of another transcriptomic family.
“Thus, while overall transcriptomic family was highly predictive of the cell phenotype, some t-types exhibited properties similar to those of another transcriptomic family.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrevious 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.”
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 [24] Gouwens, N. W. et al. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat. Neurosci. 22, 1182–1195 (2019). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationA parallel Patch-seq study of the inhibitory neurons in the mouse visual cortex focused on isolating multimodal neural types but also often observed continuous variation.
“A parallel Patch-seq study of the inhibitory neurons in the mouse visual cortex45 focused on isolating multimodal neural types (‘met-types’) but also often observed continuous variation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [45] Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183, 1–19 (2020). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenNo statistical methods were used to predetermine sample size.
“No statistical methods were used to predetermine sample size.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataWithin-type distributions of soma depths agreed well with previous data and with the layer-specific nomenclature of excitatory t-types, confirming the validity of t-type assignment.
“Within-type distributions of soma depths (Fig. 1b) agreed well with previous data4 and with the layer-specific nomenclature of excitatory t-types, confirming the validity of our t-type assignment.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] 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.
PremisecitationCortical studies have identified many intermediate cells with uncertain t-type assignments.
“Similarly, cortical studies have identified many intermediate cells with uncertain t-type assignments3,4.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [3] 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 [4] 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.
May combine two claims.
Study design and conductcitationThe RNA extraction process may have interfered with biocytin diffusion and MOp is quite thick, making it difficult to recover complete morphologies of some groups of neurons, such as deep L5 Martinotti cells.
“Second, as the RNA extraction process may have interfered with biocytin diffusion17 and as MOp is quite thick, it was difficult to recover complete morphologies of some groups of neurons, such as deep L5 Martinotti cells with thin long axons that reach all the way to L1.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Cadwell, C. R. et al. Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq. Nat. Protocols 12, 2531 (2017). 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 conductnone givenThe experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment, unless otherwise stated.
“The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment, unless otherwise stated.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningOn a fine within-family scale, both transcriptomic and morpho-electric landscapes are continuous rather than discrete.
“This is in agreement with the idea that on a fine within-family scale, both transcriptomic and morpho-electric landscapes are continuous rather than discrete.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataWe obtained high-quality electrophysiological recordings and extracted electrophysiological features of 184 cells.
“We obtained high-quality electrophysiological recordings and extracted electrophysiological features of 184 cells.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe investigators were blinded to the cell type of each sample during library construction and sequencing.
“The investigators were blinded to the cell type of each sample during library construction and sequencing.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataTwo inhibitory neurons were left out because some of the morphometric statistics could not be extracted owing to insufficient dendritic recovery; this left 367 inhibitory neurons (with 50 morphometric features) and 269 excitatory neurons (with 35 morphometric features).
“Two inhibitory neurons were left out because some of the morphometric statistics could not be extracted owing to insufficient dendritic recovery; this left 367 inhibitory neurons (with 50 morphometric features) and 269 excitatory neurons (with 35 morphometric features).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataWe obtained soma depth information for 1,284 neurons out of 1,329.
“We obtained soma depth information for 1,284 neurons out of 1,329 (45 neurons were mistakenly recorded using a solution with insufficient biocytin concentration, and we could measure soma depths for only 2 of those; for 2 other neurons the measurements could not be carried out because the slices were lost).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenSamples containing less than around 1 ng total cDNA (in the 15 μl final volume) or with an average size less than 1,500 bp were typically not sequenced (with some occasional exceptions).
“Samples containing less than around 1 ng total cDNA (in the 15 μl final volume) or with an average size less than 1,500 bp were typically not sequenced (with some occasional exceptions).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataWe did not use any axonal features for the excitatory cells because only a small part of the axon could typically be reconstructed.
“We did not use any axonal features for the excitatory cells because only a small part of the axon could typically be reconstructed.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataWe sequenced in total 185 neurons, obtained from 8 adult mice (7 Sst-Cre/Ai9 and 1 Pvalb-Cre/Ai9), of which 177 neurons passed the transcriptomic quality control and got a t-type assignment.
“We sequenced in total 185 neurons, obtained from 8 adult mice (7 Sst-Cre/Ai9 and 1 Pvalb-Cre/Ai9), of which 177 neurons passed the transcriptomic quality control and got a t-type assignment (see below).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenWe took special care to exclude reconstructions of all neurons that showed any signs of damage, lack of contrast, or poor overall staining.
“We took special care to exclude reconstructions of all neurons that showed any signs of damage, lack of contrast, or poor overall staining.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataOne cell was probably located outside MOp, based on the slice anatomy, and was excluded.
“In addition, one cell was probably located outside MOp, based on the slice anatomy, and was excluded as well.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataFor all statistical analysis, we used 17 of the 29 extracted electrophysiological features, excluding features that were zero for many cells, highly correlated with other features, highly skewed, or undefined for some cells.
“For all statistical analysis we used 17 features out of the extracted 29, excluding features that were equal to zero for many cells (afterdepolarization, burstiness, rebound number of APs, sag area, sag time, wildness), two Fano factor features that were highly correlated with the corresponding coefficient of variation features (AP Fano factor, ISI Fano factor) and another measure of latency that”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThis is effectively a leave-one-out cross-validation procedure.
“This is effectively a leave-one-out cross-validation procedure.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningTo prevent overlap between these two populations, we added a small constant value of 0.25 to the excitatory block-diagonal block, leading to the strong excitatory-inhibitory separation in Fig. 3b.
“To prevent overlap between these two populations, we added a small constant value of 0.25 to the excitatory block-diagonal block, leading to the strong excitatory–inhibitory separation in Fig. 3b.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataWe used the entropies of a Leiden clustering35 as an alternative way to approach the same question.
“We used the entropies of a Leiden clustering35 as an alternative way to approach the same question.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningWe did not use any features measuring morphological properties in the slice depth (y) direction because of possible slice shrinkage artefacts.
“We did not use any features measuring morphological properties in the slice depth (y) direction because of possible slice shrinkage artefacts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenForty-five sequenced cells were mistakenly recorded using a solution with a much smaller concentration of biocytin, and their morphologies could not be recovered.
“Forty-five sequenced cells were mistakenly recorded using a solution with a much smaller concentration of biocytin, and their morphologies could not be recovered.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenWe used bootstrapping over genes to assess the confidence of each t-type assignment.
“We used bootstrapping over genes to assess the confidence of each t-type assignment.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningTo provide a sensible baseline for the range of possible normalized total variances in a population of morpho-electrically homogeneous types, we used a clustering analysis.
“To provide a sensible baseline for the range of possible normalized total variances in a population of morpho-electrically homogeneous types, we used a clustering analysis.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The k-means clusters represent morpho-electrically homogeneous types.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe remaining 1,232 cells passed quality control and entered the second round.
“The remaining 1,232 cells passed quality control and entered the second round.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe distributions of the remaining features were visually checked for outliers and for meaningful variation between transcriptomic types, leading to a further exclusion of three features for the inhibitory cells.
“The distributions of the remaining features were visually checked for outliers and for meaningful variation between transcriptomic types, leading to a further exclusion of three features for the inhibitory cells.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenSpecial care was taken to ensure that the seal between the pipette and the cell membrane remained intact to reduce contamination from the extracellular environment.
“Special care was taken to ensure that the seal between the pipette and the cell membrane remained intact to reduce contamination from the extracellular environment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataWe did not use the Sncg and NP families owing to insufficient coverage in our data set (Fig. 1).
“We did not use the Sncg and NP families owing to insufficient coverage in our data set (Fig. 1).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenWe used the following quality control criteria: (1) seal resistance value >1 GΩ before achieving whole-cell configuration; (2) access resistance <30 MΩ.
“We used the following quality control criteria: (1) seal resistance value >1 GΩ before achieving whole-cell configuration; (2) access resistance <30 MΩ.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenIn each case, we excluded features with coefficient of variation below 0.25 (among the features with only positive values).
“In each case, we excluded features with coefficient of variation below 0.25 (among the features with only positive values).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe final number of cells with t-type assignment was 1,227.
“The final number of cells with t-type assignment was 1,227.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataWe reduced each set to five PCs, discarded PC1 (it was strongly correlated with the normalized soma depth and made the resulting embedding strongly influenced by the soma depth), and scaled the PCs by the standard deviation of the respective PC2.
“We reduced each set to five PCs, discarded PC1 (it was strongly correlated with the normalized soma depth and made the resulting embedding strongly influenced by the soma depth), and scaled the PCs by the standard deviation of the respective PC2.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe three large transcriptomic groups (CGE-derived interneurons, MGE-derived interneurons, and excitatory neurons) are perfectly separated from each other with no transcriptomically intermediate cells.
“the three large transcriptomic groups (CGE-derived interneurons, MGE-derived interneurons, and excitatory neurons) that are perfectly separated from each other with no transcriptomically intermediate cells4.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4] 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).
Study design and conductnone givenThis shrinkage was not compensated for in our analysis.
“This shrinkage was not compensated for in our analysis.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTissue shrinkage due to the fixation and staining procedures was about 10-20%.
“Consistently with previous studies, tissue shrinkage due to the fixation and staining procedures was about 10–20%5,28,49.”
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 [28] Scala, F. et al. Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas. Nat. Commun. 10, 4174 (2019). source
Not checked in the free run.
- cites [49] Markram, H., Lübke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. (Lond.) 500, 409–440 (1997). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Study design and conductnone givenThe measured electrophysiological properties of the 45 cells mistakenly recorded with a smaller biocytin concentration were not systematically different from those of the other sequenced cells.
“We made sure that the measured electrophysiological properties of these cells were not systematically different from those of the the other sequenced cells.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFour features were log-transformed to make their distribution more Gaussian-like: AP coefficient of variation, ISI coefficient of variation, ISI adaptation index, and latency.
“Four features were log-transformed to make their distribution more Gaussian-like: AP coefficient of variation, ISI coefficient of variation, ISI adaptation index, and latency.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataCells meeting any of the following exclusion criteria were declared low quality and did not get a t-type assignment.
“Cells meeting any of the following exclusion criteria were declared low quality and did not get a t-type assignment (Extended Data Fig. 2e): cells with the highest correlation below 0.4 (78 cells); cells that would be assigned to non-neural t-types, presumably owing to RNA contamination51 (14 cells; see also Extended Data Fig. 2j–n); cells with the highest correlation less than 0.02 above the max”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThese four cells were excluded from all analyses and visualizations.
“These four cells were excluded from all analyses and visualizations (as if they did not pass the transcriptomic quality control).”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataThe confidences obtained with seven reference data sets agreed well with each other and were averaged to obtain the consensus confidence.
“The confidences obtained with seven reference data sets agreed well with each other (Extended Data Fig. 2i) and were averaged to obtain the consensus confidence.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenSubsampling was done to eliminate a possible bias due to the t-type abundance.
“Subsampling was done to eliminate a possible bias due to the t-type abundance.”
Quote in the paperSubstantial claim
The authors state this as an assumption.
The variance of k-means clusters (with k set to the number of t-types) reflects the minimal possible phenotypic variance obtainable in the data set.
“The rationale here was that the variance of the k-means clusters would reflect the minimal possible variance obtainable in our data set.”
The authors state this as an assumption.
The k-means clusters represent morpho-electrically homogeneous types.
“reasoning that these clusters should be as homogeneous as possible given the variability in our data set.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Transcriptomically defined cell types have been shown to exhibit concordant morphological and physiological properties.
“Transcriptomically defined cell types have been shown to exhibit concordant morphological and physiological properties10,11.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [11]
In each mouse motor cortex subclass, morpho-electrical properties change continuously together with transcriptomic types.
“Within each subclass the morpho-electrical properties vary continuously along with the transcriptomic types.”
From What is a cell type and how to define it? (2022) · cites this paper as Scala et al., 2021
Some transcriptomic types show extra heterogeneity in morpho-electrical properties.
“There is also additional heterogeneity of morpho-electrical properties within some transcriptomic types, indicating a more complex picture.”
From What is a cell type and how to define it? (2022) · cites this paper as Scala et al., 2021
Patch-seq data integrating transcriptome, electrophysiology and morphology indicate that mouse motor cortex cell types have corresponding multimodal distinctions at the subclass level.
“Integration of transcriptomic, electrophysiological and morphological properties by Patch-seq reveals multimodal corresponding distinctions of mouse motor cortex cell types at subclass level (Scala et al., 2021).”
From What is a cell type and how to define it? (2022) · cites this paper as Scala et al., 2021
Excitatory t-types from IT subclasses with more similar transcriptomes were also located at adjacent cortical depths, suggesting that distance in t-space co-varied with anatomical distance, even within a layer.
“For example, excitatory t-types from the IT subclasses with more similar transcriptomes were also located at adjacent cortical depths, suggesting that distance in t-space co-varied with anatomical distance42, even within a layer (Fig. 3g), in line with the above MERFISH results (Fig. 2c).”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [42]
Electrophysiological properties of Sst interneurons varied continuously across the transcriptomic landscape.
“Similarly, electrophysiological properties of Sst interneurons varied continuously across the transcriptomic landscape42.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [42]
In Patch-seq, cells from L4/5 IT_1 type had no or minimal apical dendrites without tufts in L1, in contrast to cells from L2/3 IT, L4/5 IT_2 and L5 IT types, which had tufted apical dendrites.
“In Patch-seq42, cells from the L4/5 IT_1 type had no or minimal apical dendrites without tufts in L1, in contrast to cells from the L2/3 IT, L4/5 IT_2 and L5 IT types, which had tufted apical dendrites (Fig. 7f).”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [42]
Both groups of L5 ET cells had thick-tufted dendrites that were similar to each other.
“Both groups of cells had thick-tufted dendrites that were similar to each other (Fig. 8e), consistent with the Patch-seq study42.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [42]
Through integrative approaches, many t-types or subclasses were related to cortical neuron types traditionally defined by electrophysiological, morphological and connectional properties, bridging cell-type taxonomy with historical knowledge.
“Through integrative approaches such as Patch-seq42, Epi-retro-seq45 and axon projection mapping44,46, we related many t-types or subclasses to cortical neuron types traditionally defined by electrophysiological, morphological and connectional properties (Fig. 9a, b, f), thus bridging the cell-type taxonomy with historical knowledge.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Electrophysiological representation is described as both 17- and 16-dimensional
MinorThe electrophysiological representation is said to have 17 principal components, but the later combined analysis calls it 16-dimensional. No step is described that removes a component from the electrophysiological representation.
“All features were standardized across this set of cells and transformed with PCA into a set of 17 PCs.”
“For the t-SNE visualization of the morpho-electrical landscape, we stacked together the 48-dimensional morphological representation and the 16-dimensional electrophysiological representation obtained above, using only cells that had all morphological and all electrophysiologcal features (n = 628).”
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Self-citationAn integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types
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“Consistently with previous studies, tissue shrinkage due to the fixation and staining procedures was about 10–20% 5,28,49 .”
InfluentialSelf-citationAnatomy and physiology of the thick-tufted layer 5 pyramidal neuron
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“Large big-tufted cells with the apical dendrite often bifurcating close to the soma, suggesting that these were corticospinal cells [67, 68], They had I bigger hyperpolarization sag, lower Input resistance, and smaller AP width, compared to the L5 IT neurons (we confirmed these differences in…”
Four Unique Interneuron Populations Reside in Neocortical Layer 1
Schuman, B., Machold, R. P., Hashikawa, Y., Fuzik, J., Fishell, G. J., & Rudy, B. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.1613-18.2018 · Cited by 184 · see citing works
Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons
Földy, C., Darmanis, S., Aoto, J., Malenka, R. C., Quake, S. R., & Südhof, T. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610155113 · Cited by 179 · cited 1× · see citing works
“We used the Patch-seq technique 8,17–19 to describe the morpho-electric phenotypes for most of the t-types in MOp 20 .”
Efficient Algorithms for t-distributed Stochastic Neighborhood EmbeddingPreprint
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S., & Kluger, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.09005 · Cited by 172 · see citing works
Common Origins of Hippocampal Ivy and Nitric Oxide Synthase Expressing Neurogliaform Cells
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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
Continuous and Discrete Neuron Types of the Adult Murine Striatum
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C., & Quake, S. R. (2020) · Neuron · doi:10.1016/j.neuron.2019.11.004 · Cited by 158 · cited 1× · see citing works
“The selected model used 25 genes with a 5-dimensional latent space and achieved a cross-validated R 2 of 0.38.”
InfluentialDiversity of layer 5 projection neurons in the mouse motor cortex
Oswald, M. J., Tantirigama, M. L. S., Sonntag, I., Hughes, S. M., & Empson, R. M. (2013) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2013.00174 · Cited by 155 · cited 1× · see citing works
“Large big-tufted cells with the apical dendrite often bifurcating close to the soma, suggesting that these were corticospinal cells [67, 68], They had I bigger hyperpolarization sag, lower Input resistance, and smaller AP width, compared to the L5 IT neurons (we confirmed these differences in…”
Nigro, M. J., Hashikawa-Yamasaki, Y., & Rudy, B. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2415-17.2017 · Cited by 149 · see citing works
Cadwell, C. R., Scala, F., Li, S., Livrizzi, G., Shen, S., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · Nature Protocols · doi:10.1038/nprot.2017.120 · Cited by 147 · cited 7× · see citing works
“The addition of the inhibitor resulted in an increase in osmolarity to the desired value of 315–320 mOSM without a further dilution 17 .”
InfluentialSelf-citationA genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity
Yamawaki, N., Borges, K., Suter, B. A., Harris, K. D., & Shepherd, G. M. G. (2014) · eLife · doi:10.7554/elife.05422 · Cited by 141 · cited 1× · see citing works
“L4/5 IT_ 1 Located on the boundary between L2/3 and L5, likely corresponded to the quasi-L4 neurons described previously in motor cortex [31], Diverse morphologies with some pyramidal and some stellate cells.”
Complementary networks of cortical somatostatin interneurons enforce layer specific control
Naka, A., Veit, J., Shababo, B., Chance, R. K., Risso, D., Stafford, D., Snyder, B., Egladyous, A., Chu, D., Sridharan, S., et al. (2019) · eLife · doi:10.7554/elife.43696 · Cited by 117 · cited 1× · see citing works
“We observed the same in the Sst subclass, which is known to be transcriptomically 4 and morpho electrically 29,30,33 diverse in L5.”
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 · cited 1× · see citing works
“…near-projecting (NP) subclasses [4], Morphologically they have been classified Into big-tufted, small-tufted, untufted neurons, depending on the shape of the apical dendrite tuft, stellate neurons without an apical dendrite, and horizontal/inverted neurons in L6 [62, 63, 64, 65], Abundant in L2/3.”
Morphology and Physiology of Excitatory Neurons in Layer 6b of the Somatosensory Rat Barrel Cortex
Marx, M., & Feldmeyer, D. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs254 · Cited by 78 · cited 1× · see citing works
“…near-projecting (NP) subclasses [4], Morphologically they have been classified Into big-tufted, small-tufted, untufted neurons, depending on the shape of the apical dendrite tuft, stellate neurons without an apical dendrite, and horizontal/inverted neurons in L6 [62, 63, 64, 65], Abundant in L2/3.”
The Nissl Stain: A Stain for Cell Bodies in Brain SectionsPreprint
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Diversity of GABAergic Interneurons in Layer VIa and VIb of Mouse Barrel Cortex
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“L6, Mostly local axonal arborization within L6 [58].”
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · cited 1× · see citing works
“A comprehensive multi-modal taxonomy of neurons would resemble a ‘parts list’ of the brain, helping us to decipher its bewildering complexity 10,11 .”
Wang, Y., Ye, M., Kuang, X., Li, Y., & Hu, S. (2018) · IBRO Reports · doi:10.1016/j.ibror.2018.10.001 · Cited by 48 · cited 2× · see citing works
“…near-projecting (NP) subclasses [4], Morphologically they have been classified Into big-tufted, small-tufted, untufted neurons, depending on the shape of the apical dendrite tuft, stellate neurons without an apical dendrite, and horizontal/inverted neurons in L6 [62, 63, 64, 65], Abundant in L2/3.”
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 · cited 1× · see citing works
“2e): cells with the highest correlation below 0.4 (78 cells); cells that would be assigned to non-neural t-types, presumably owing to RNA contamination 51 (14 cells; see also Extended Data Fig.”
InfluentialA 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 33 · cited 1× · see citing works
“For further analysis we computed two different feature representations of each cell: the normalized z-profile and a set of morphometric statistics 24,28,55 .”
Self-citationWang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 32 · cited 1× · see citing works
“Consistent with our interpretation, recent transcriptomic and anatomical studies have argued that neurons in hippocampus, striatum, and cerebellum can be better described as forming partially continuous manifolds 27,37–39 .”
Sparse Reduced-Rank Regression for Exploratory Visualisation of Paired Multivariate Data
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P. (2021) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/rssc.12494 · Cited by 24 · see citing works
Self-citationMorphoPy: A python package for feature extraction of neural morphologies.
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02339 · Cited by 9 · cited 1× · see citing works
“Reconstructed morphologies were converted into the SWC format using NLMorphologyConverter 0.9.0 (http://neuronland.org) and further analysed using MorphoPy (https://github.com/berenslab/MorphoPy, version 0.6) 54 .”
InfluentialSelf-citationWang, W. X., & Lefebvre, J. L. (2020) · doi:10.1101/2020.02.29.971366
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A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065 · see citing works
Self-citationA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
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Self-citationA multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522 · see citing works
Self-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
What is a cell type and how to define it?
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Self-citationMorphological diversity of single neurons in molecularly defined cell types
Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03941-1 · Cited by 417 · see citing works
Self-citationLocal 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
“Could laminar differences in connectivity indicate cell type divisions within subclasses? Two recent studies investigated the correspondence among morphological, electrophysiological, and transcriptomic (MET) features of inhibitory neurons in primary visual cortex and motor cortex (63, 69).”
BackgroundSelf-citationUntangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle
Shepherd, G. M., & Yamawaki, N. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00459-3 · Cited by 255 · see citing works
“part a adapted with permission from reF.(197), CC BY 4.”
BackgroundSingle-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
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“It has become increasingly clear that different classification methods offer complementary information and that a comprehensive classification of cell types needs to unify all three modalities (BRAIN Initiative Cell Census Network, 2021; Scala et al., 2020; Zeng and Sanes, 2017).”
BackgroundSingle-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
“58 The integration of cell types defined by various approaches represents an important issue to be addressed, 58 as illustrated by recent studies in mice 51,54,59,60 and humans.”
Transcriptomic cytoarchitecture reveals principles of human neocortex organization
Jorstad, N. L., Close, J., Johansen, N., Yanny, A. M., Barkan, E. R., Travaglini, K. J., Bertagnolli, D., Campos, J., Casper, T., Crichton, K., et al. (2023) · Science · doi:10.1126/science.adf6812 · Cited by 209 · see citing works
“…and their relationships to transcriptomic astrocyte types. concordance of a transcriptomic cellular classification with other cellular properties in a way that integrates prior literature while identifying greater cellular diversity than previously appreciated ( 12 , 13 , 16 , 18 , 19 , 47 ).”
BackgroundA transcriptomic axis predicts state modulation of cortical interneurons
Bugeon, S., Duffield, J. A. M., Dipoppa, M., Ritoux, A., Prankerd, I., Nicoloutsopoulos, D., Orme, D., Shinn, M., Peng, H., Forrest, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04915-7 · Cited by 177 · see citing works
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
Cortical Somatostatin Interneuron Subtypes Form Cell-Type Specific Circuits
Wu, S. J., Sevier, E., Dwivedi, D., Saldi, G.-A., Hairston, A., Yu, S. Q., Abbott, L., Choi, D. H., Sherer, M., Qiu, Y., et al. (2022) · Neuron · doi:10.1016/j.neuron.2023.05.032 · Cited by 130 · see citing works
Direct neuronal reprogramming: Fast forward from new concepts toward therapeutic approaches
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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
“Mouse Patch-seq studies of cortical GABAergic neurons have shown discrete morphological and electrophysiological properties between the subclasses, yet a continuum within each subclass (16, 17).”
BackgroundResultSelf-citationKalmbach, B. E., Hodge, R. D., Jorstad, N. L., Owen, S. F., de Frates, R. A., Yanny, A. M., Dalley, R. A., Mallory, M., Graybuck, L. T., Radaelli, C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.030 · Cited by 104 · see citing works
Self-citationPatch-seq: Past, Present, and Future
Lipovsek, M., Bardy, C., Cadwell, C. R., Hadley, K., Kobak, D., & Tripathy, S. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1653-20.2020 · Cited by 98 · see citing works
“…to originate from the cell nucleus; Cadwell et al., 2017; Scala et al., 2020), similar to single-nucleus RNA-seq (Bakken et al., 2018), and so it can be beneficial to use both exonic and intronic read counts to quantify gene expression (Gouwens et al., 2020; Lee et al., 2020; Scala et al., 2020).”
BackgroundMethodInfluentialSelf-citationSingle-cell analysis of chromatin accessibility in the adult mouse brain
Zu, S., Li, Y., Wang, K., Armand, E. J., Mamde, S., Amaral, M. L., Wang, Y., Chu, A., Xie, Y., Miller, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06824-9 · Cited by 87 · see citing works
Self-citationSpatially resolved transcriptomics in neuroscience
Close, J. L., Long, B. R., & Zeng, H. (2021) · Nature Methods · doi:10.1038/s41592-020-01040-z · Cited by 94 · see citing works
Self-citationEstablishing a conceptual framework for holistic cell states and state transitions
Rafelski, S. M., & Theriot, J. A. (2024) · Cell · doi:10.1016/j.cell.2024.04.035 · Cited by 92 · see citing works
A transcriptional rheostat couples past activity to future sensory responses
Tsukahara, T., Brann, D. H., Pashkovski, S. L., Guitchounts, G., Bozza, T. C., & Datta, S. R. (2021) · Cell · doi:10.1016/j.cell.2021.11.022 · Cited by 77 · see citing works
“It remains unclear whether these transcriptional differences reflect an occult diversity of neural subtypes or whether they instead reflect the ongoing activity-dependent modulation of a single underlying cell type (Gouwens et al., 2020; Kim et al., 2020; Scala et al., 2021).”
BackgroundBakken, T. E., van Velthoven, C. T. J., Menon, V., Hodge, R. D., Yao, Z., Nguyen, T. N., Graybuck, L. T., Horwitz, G. D., Bertagnolli, D., Goldy, J., et al. (2021) · eLife · doi:10.7554/elife.64875 · Cited by 83 · see citing works
Self-citationNeural heterogeneity controls computations in spiking neural networks
Gast, R., Solla, S. A., & Kennedy, A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311885121 · Cited by 95 · see citing works
Neuronal parts list and wiring diagram for a visual system
Matsliah, A., Yu, S.-C., Kruk, K., Bland, D., Burke, A. T., Gager, J., Hebditch, J., Silverman, B., Willie, K. P., Willie, R., et al. (2024) · Nature · doi:10.1038/s41586-024-07981-1 · Cited by 85 · see citing works
Common 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
“…the retina, neurons with shared morphology also have consistentconnectivity (Jonas and Kording, 2015), spacing, arbor density, arbor stratification (Seung and Sümbül, 2014), and gene expression signatures (Macosko et al., 2015), often with one-to-one correspondences between phenotype and…”
BackgroundSelf-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
Joint variational autoencoders for multimodal imputation and embedding
Kalafut, N. C., Huang, X. L., & Wang, D. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00663-z · Cited by 77 · see citing works
“Raw Patch-seq datasets are avail508 able at [30,2].”
BackgroundMethodCell-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
“Recent surveys aimed at discovering excitatory cell types have used morphology alone 88 , morphology and electrophysiology 13 , transcriptomics 19,89 , or all three together using Patch-seq 16 .”
InfluentialSelf-citationWhat Does the Frontopolar Cortex Contribute to Goal-Directed Cognition and Action?
Hogeveen, J., Medalla, M., Ainsworth, M., Galeazzi, J. M., Hanlon, C. A., Mansouri, F. A., & Costa, V. D. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1143-22.2022 · Cited by 69 · see citing works
Inhibitory specificity from a connectomic census of mouse visual cortex
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2025) · Nature · doi:10.1038/s41586-024-07780-8 · Cited by 141 · see citing works
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“, 2017), which have now been resolved with increasing depth (Joglekar et al., 2021; Scala et al., 2021).”
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Identification of visual cortex cell types and species differences using single-cell RNA sequencing
Wei, J.-R., Hao, Z.-Z., Xu, C., Huang, M., Tang, L., Xu, N., Liu, R., Shen, Y., Teichmann, S. A., Miao, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34590-1 · Cited by 55 · see citing works
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“The brain slices containing the ACC were obtained following a previous protocol (82) with some modification.”
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
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A guide to the BRAIN Initiative Cell Census Network data ecosystem
Hawrylycz, M., Martone, M. E., Ascoli, G. A., Bjaalie, J. G., Dong, H., Ghosh, S., Gillis, J. A., Hertzano, R. P., Haynor, D. R., Hof, P. R., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002133 · Cited by 55 · see citing works
“…analyses of single-cell transcriptomes [8,9], chromatin accessibility [10], DNA methylomes [11], spatially resolved single-cell transcriptomes [12], anatomic characterization with morphological and electrophysiological properties [13,14], and cellular resolution input– output mapping [10,15].”
BackgroundMethodSelf-citationThe cell-type underpinnings of the human functional cortical connectome
Zhang, X.-H., Anderson, K. M., Dong, H.-M., Chopra, S., Dhamala, E., Emani, P. S., Gerstein, M., Margulies, D. S., & Holmes, A. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01812-2 · Cited by 62 · see citing works
“…IT in cluster 4 are preferentially distributed in the dorsal attention and frontoparietal networks involved in top–down attentional orienting and executive processes 56 53,59 , the regular-spiking Chandelier preferentially innervates the initial axon segment, strengthening output control 60,61 .”
Huang, W., Xu, Q., Su, J., Tang, L., Hao, Z.-Z., Xu, C., Liu, R., Shen, Y., Sang, X., Xu, N., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111322 · Cited by 48 · see citing works
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“The GABAergic cortical neurons can be classified into different classes depending on their morphology, intrinsic properties, and expression of specific transcription and molecular factors (Bouzas et al., 2008; Scala et al., 2021).”
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
“Moreover, mouse cortical neurons of the same t-type may vary strongly in their functional tuning 17 , as well as in their m-type, exhibiting divergent long range projections and local connectivity 18 .”
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Self-citationUnderstanding the molecular diversity of synapses
van Oostrum, M., & Schuman, E. M. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00888-w · Cited by 49 · see citing works
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
Self-citationAn update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo
Sánchez-Aguilera, A., Wheeler, D. W., Jurado-Parras, T., Valero, M., Nokia, M. S., Cid, E., Fernández‐Lamo, I., Sutton, N. M., García-Rincón, D., de la Prida, L. M., et al. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001213 · Cited by 35 · see citing works
“Even under this controlled condition, phenotypic variations in the morphoelectrical space are largely independent of transcriptomic variability within specific cell types, suggesting that the concept of cellular identity is more complex than originally thought [21].”
BackgroundMultimodal charting of molecular and functional cell states via in situ electro-sequencing
Li, Q., Lin, Z., Liu, R., Tang, X., Huang, J., He, Y., Sui, X., Tian, W., Shen, H., Zhou, H., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.023 · Cited by 52 · see citing works
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Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex
Bella, D. J. D., Domıńguez-Iturza, N., Brown, J. R., & Arlotta, P. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.021 · Cited by 37 · see citing works
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Self-citationConsistent 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
“…that the cell type diversity of inhibitory neurons is essentially conserved across brain areas 8 ; thus, although both the Scala et al. 2019 and the Scala et al. 2020 datasets profile neurons from mouse motor cortex, we can hope to use the mouse visual cortex Patch-seq dataset used in this study…”
BackgroundMethodSelf-citationSaunders, A. B., Huang, K. W., Vondrak, C., Hughes, C., Smolyar, K., Sen, H., Philson, A. C., Nemesh, J. C., Wysoker, A., Kashin, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34334-1 · Cited by 36 · see citing works
Predictive and robust gene selection for spatial transcriptomics
Covert, I., Gala, R., Wang, T., Svoboda, K., Sümbül, U., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37392-1 · Cited by 40 · see citing works
Functional specialization of hippocampal somatostatin-expressing interneurons
Chamberland, S., Grant, G., Machold, R. P., Nebet, E. R., Tian, G., Stich, J. L., Hanani, M., Kullander, K., & Tsien, R. W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306382121 · Cited by 38 · see citing works
“Large - scale efforts in the cortex have shown that morpho - electrophysiological and transcrip tomics (MET) parameters converge to describe interneuronal cat egories (51–53), with the advantage that each cell is individually sampled for MET features.”
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
How many markers are needed to robustly determine a cell's type?
Fischer, S., & Gillis, J. A. (2021) · iScience · doi:10.1016/j.isci.2021.103292 · Cited by 43 · see citing works
“Cell types and markers were defined based on transcriptional data, which only captures a facet of the true underlying biological types (Kim et al., 2020; Scala et al., 2020).”
BackgroundClonally heritable gene expression imparts a layer of diversity within cell types
Mold, J. E., Weissman, M. H., Ratz, M., Hagemann-Jensen, M., Hård, J., Eriksson, C., Toosi, H., Berghenstråhle, J., Ziegenhain, C., von Berlin, L., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.01.004 · Cited by 43 · see citing works
Self-citationParvalbumin neurons enhance temporal coding and reduce cortical noise in complex auditory scenes
Nocon, J. C., Gritton, H. J., James, N. M., Mount, R. A., Qu, Z., Han, X., & Sen, K. (2023) · Communications Biology · doi:10.1038/s42003-023-05126-0 · Cited by 31 · see citing works
“One of the most striking features of the cerebral cortex is the tremendous diversity of its cell types 37 .”
Wang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 32 · see citing works
“Developmental axonal reconstructions for the 732 MLIs were compiled from 32 mouse pups, which included the following injection to collection timelines: (1) P0 to P5; (2) P0 to P7; (3) P0 to P8; (4) P0 to P10; (5) P0 to P13; (6) P0 to P16; (7) P0 to P25; (8) P1 to P13; (9) P1 to P16; (10) P1 to P25; (11) P2 to P13; (12…”
NMDA receptor function in inhibitory neurons
Booker, S. A., & Wyllie, D. J. A. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108609 · Cited by 33 · see citing works
Jedlička, P., Bird, A. D., & Cuntz, H. (2022) · Open Biology · doi:10.1098/rsob.220073 · Cited by 28 · see citing works
Intrinsic neural diversity quenches the dynamic volatility of neural networks
Hutt, A., Rich, S., Valiante, T. A., & Lefebvre, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218841120 · Cited by 34 · see citing works
A Role for Vasoactive Intestinal Peptide Interneurons in Neurodevelopmental Disorders
Goff, K. M., & Goldberg, E. M. (2021) · Developmental Neuroscience · doi:10.1159/000515264 · Cited by 34 · see citing works
Advancements in the Quest to Map, Monitor, and Manipulate Neural Circuitry
Swanson, J., Chin, P.-S., Romero, J. M., Srivastava, S., Ortiz‐Guzman, J., Hunt, P. J., & Arenkiel, B. R. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.886302 · Cited by 32 · see citing works
“Such recombinases can be utilized to excise, invert, or conditionally express genes of interest via the “Lox-Stop-Lox” (LSL), “FRT-Stop-FRT” (Kuhlman and Huang, 2008), or “double-inverted orientation” [DIO; FLEx or FLEx (FRT)] methods (Atasoy et al., 2008; Schwarz et al., 2015).”
BackgroundA subpopulation of cortical VIP-expressing interneurons with highly dynamic spines
Georgiou, C., Kehayas, V., Lee, K. S., Brandalise, F., Sahlender, D. A., Blanc, J., Knott, G. W., & Holtmaat, A. J. D. G. (2022) · Communications Biology · doi:10.1038/s42003-022-03278-z · Cited by 23 · see citing works
Genetically Defined Subtypes of Somatostatin-Containing Cortical Interneurons
Hostetler, R. E., Hu, H., & Agmon, A. (2023) · eNeuro · doi:10.1523/eneuro.0204-23.2023 · Cited by 30 · see citing works
“A genetic and epigenetic basis for such a continuum was recently suggested by large-scale transcriptomic studies (Tasic et al., 2016; Gouwens et al., 2020; Scala et al., 2021; Yao et al., 2021b).”
Multimodal cortical neuronal cell type classification
Mao, X., & Staiger, J. F. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02923-2 · Cited by 34 · see citing works
“Results from Patch-seq indicate that properties can exhibit significant variations even within a specific electrophysiological type (e-type) or morphological type (m-type) [9, 26].”
BackgroundInfluentialNeuronal replacement: Concepts, achievements, and call for caution
Götz, M., & Bocchi, R. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.03.014 · Cited by 29 · see citing works
“…combining single-cell patching (i.e. electrophysiological properties), filling to examine morphology, and RNA sequencing came to the conclusion that only 3 major subtypes of projection neurons are discernible [6] d even fewer than sub-types classically associated with different cortical layers [27].”
Harris, N., Bates, S. G., Zhuang, Z., Bernstein, M., Stonemetz, J. M., Hill, T. J., Yu, Y. V., Calarco, J. A., & Sengupta, P. (2023) · Current Biology · doi:10.1016/j.cub.2023.02.073 · Cited by 29 · see citing works
CAJAL enables analysis and integration of single-cell morphological data using metric geometry
Govek, K. W., Nicodemus, P., Lin, Y., Crawford, J., Saturnino, A. B., Cui, H., Zoga, K., Hart, M. P., & Cámara, P. G. (2023) · Nature Communications · doi:10.1038/s41467-023-39424-2 · Cited by 27 · see citing works
“We used the same process described above (subheading “ Processing of Patch-seq and patch-clamp morphological reconstructions ” ) to compute the GW distance between the morphological reconstructions of the dendrites of 644 neurons pro fi led by Scala et al. 38 .”
InfluentialContinuous cell-type diversification in mouse visual cortex development
Gao, Y., van Velthoven, C. T. J., Lee, C., Thomas, E. D., Mathieu, R., Ayala, A. P., Barta, S., Bertagnolli, D., Campos, J., Cardenas, T., et al. (2025) · Nature · doi:10.1038/s41586-025-09644-1 · Cited by 43 · see citing works
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NEURD offers automated proofreading and feature extraction for connectomics
Celii, B., Papadopoulos, S., Ding, Z., Fahey, P. G., Wang, E. Y., Papadopoulos, C., Kunin, A. B., Patel, S. S., Bae, J. A., Bodor, Á. L., et al. (2025) · Nature · doi:10.1038/s41586-025-08660-5 · Cited by 40 · see citing works
“The relationship with molecularly defined cell classes can sometimes be inferred from extensive previous work relating morphological features to transcriptomic classes 33–35 , including from our consortium 36 .”
Self-citationSparse Reduced-Rank Regression for Exploratory Visualisation of Paired Multivariate Data
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P. (2021) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/rssc.12494 · Cited by 24 · see citing works
“To illustrate the structure of such datasets and motivate the development of sparse RRR for exploratory visualisation, we use principal component analysis (PCA) on the M1 dataset, one of the largest existing Patch-seq datasets (Scala et al., 2020).”
BackgroundMethodInfluentialSelf-citationTranscriptional regulation of neuronal identity
Sousa, E., & Flames, N. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15551 · Cited by 19 · see citing works
“In general, there is a good correlation between classical classifications of neuron types based on morphology or electrophysiological properties and their transcriptional profiles, both in mice and in C. elegans (Gouwens et al., 2020; Hobert et al., 2016; Scala et al., 2021).”
BackgroundOptimizing interneuron circuits for compartment-specific feedback inhibition
Keijser, J., & Sprekeler, H. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009933 · Cited by 26 · see citing works
Transcriptomic cell type structures in vivo neuronal activity across multiple timescales
Schneider, A. M., Azabou, M., McDougall-Vigier, L., Parks, D. F., Ensley, S., Bhaskaran‐Nair, K., Nowakowski, T. J., Dyer, E. L., & Hengen, K. B. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112318 · Cited by 25 · see citing works
“Currently, neural taxonomies rely on either transcriptomic or morpho-electric parameters (Ecker et al., 2017; Mukamel and Ngai, 2019; Scala et al., 2021; Gouwens et al., 2020).”
InfluentialKim, J. M., Saikia, J. M., Monte, K. M. A., Ha, E., Romaus‐Sanjurjo, D., Sanchez, J. J., Moore, A. X., Hernaiz-Llorens, M., Chavez‐Martinez, C. L., Agba, C. K., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.019 · Cited by 26 · see citing works
“17,18 To this end, we have applied patch-based scRNA-seq to inter-rogatethetranscriptomicprofilesofregeneratingvs.non-regener-ating CST neurons following PTEN and SOCS3 deletion.”
Simon, F., & Konstantinides, N. (2021) · Developmental Biology · doi:10.1016/j.ydbio.2021.08.001 · Cited by 23 · see citing works
Cellular and Synaptic Diversity of Layer 2-3 Pyramidal Neurons in Human IndividualsPreprint
Planert, H., Mittermaier, F. X., Grosser, S., Fidzinski, P., Schneider, U. C., Radbruch, H., Onken, J., Holtkamp, M., Schmitz, D., Alle, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467668 · Cited by 21 · see citing works
The vertebrate retina: a window into the evolution of computation in the brain
Baden, T. (2024) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2024.101391 · Cited by 20 · see citing works
“For example, in mouse, the retina houses in the order of 140 types of neurons [12,31–33] — compared with ∼ 90 neuron types that are thought to make up its primary visual cortex [34].”
A transcriptomic axis predicts state modulation of cortical interneuronsPreprint
Bugeon, S., Duffield, J. A. M., Dipoppa, M., Ritoux, A., Prankerd, I., Nicoloutsopoulos, D., Orme, D., Shinn, M., Peng, H., Forrest, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.24.465600 · Cited by 24 · see citing works
Perisomatic ultrastructure efficiently classifies cells in mouse cortex
Elabbady, L., Seshamani, S., Mu, S., Mahalingam, G., Schneider-Mizell, C. M., Bodor, Á. L., Bae, J. A., Brittain, D., Buchanan, J., Bumbarger, D. J., et al. (2025) · Nature · doi:10.1038/s41586-024-07765-7 · Cited by 23 · see citing works
“Many studies of anatomical diversity of cortical cells have focused on the diversity of dendritic and somatic morphologies, axonal projection patterns and synaptic connectivity patterns 3,4,21,43 .”
Juxtacellular opto-tagging of hippocampal CA1 neurons in freely moving mice
Ding, L., Balsamo, G., Chen, H., Blanco-Hernández, E., Zouridis, I. S., Naumann, R. K., Preston‐Ferrer, P., & Burgalossi, A. (2022) · eLife · doi:10.7554/elife.71720 · Cited by 20 · see citing works
“71720 2 of 19 advances have been made toward the systematic classification of neuronal cell types (Ascoli et al., 2008; Scala et al., 2020; Tasic et al., 2016; Zeisel et al., 2015).”
BackgroundSyConn2: dense synaptic connectivity inference for volume electron microscopy
Schubert, P. J., Dorkenwald, S., Januszewski, M., Klimesch, J., Svara, F., Mancu, A., Ahmad, H., Fee, M. S., Jain, V., & Kornfeld, J. M. R. (2022) · Nature Methods · doi:10.1038/s41592-022-01624-x · Cited by 20 · see citing works
“We next explored whether the point-based morphology neural network could also be used for unsupervised (without requiring handcrafted training data) cell type discovery 17 through dimensionality reduction of a learned latent space.”
BackgroundNewmaster, K. T., Kronman, F. N., Wu, Y.-T., & Kim, Y. (2022) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2021.787601 · Cited by 20 · see citing works
“…Zeng et al., 2012; Fuzik et al., 2016; Tasic et al., 2016, 2018; Tremblay et al., 2016; Zeng and Sanes, 2017; Lim et al., 2018; Saunders et al., 2018; Zeisel et al., 2018; Kanari et al., 2019; Winnubst et al., 2019; Gouwens et al., 2020; Ortiz et al., 2020; Scala et al., 2020; Yao et al., 2021).”
Elabbady, L., Seshamani, S., Mu, S., Mahalingam, G., Schneider-Mizell, C. M., Bodor, Á. L., Bae, J. A., Brittain, D., Buchanan, J., Bumbarger, D. J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.20.499976 · Cited by 19 · see citing works
Neuronal “parts list” and wiring diagram for a visual systemPreprint
Matsliah, A., Yu, S.-C., Kruk, K., Bland, D., Burke, A., Gager, J., Hebditch, J., Silverman, B., Willie, K. P., Willie, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.12.562119 · Cited by 21 · see citing works
“But proponents are coming around to a more nuanced view, because neurons of the same transcriptomic type can have highly variable morphological and electrical properties (Scala et al. 2020; Gouwens et al. 2020) .”
BackgroundMolecular logic for cellular specializations that initiate the auditory parallel processing pathways
Jing, J., Hu, M., Ngodup, T., Ma, Q., Lau, S.-N. N., Ljungberg, M. C., McGinley, M. J., Trussell, L. O., & Jiang, X. (2025) · Nature Communications · doi:10.1038/s41467-024-55257-z · Cited by 23 · see citing works
“Sparse reduced-rank regression (RRR) was performed as previously reported 96 .”
InfluentialSelf-citationMaking sense of mRNA landscapes: Translation control in neurodevelopment
Park, Y., Page, N. F., Salamon, I., Li, D., & Rašin, M. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1674 · Cited by 21 · see citing works
Weiler, S., Guggiana-Nilo, D., Bonhoeffer, T., Hübener, M., Rose, T., & Scheuß, V. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac303 · Cited by 22 · see citing works
Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortexPreprint
Lee, B., Dalley, R. A., Miller, J. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.08.515739 · Cited by 22 · see citing works
Self-citationZhang, S.-R., Wu, D., Luo, R., Wu, J., Chen, H., Li, Z.-M., Zhuang, J.-P., Hu, N.-Y., Li, X., Yang, J., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2103-23.2024 · Cited by 23 · see citing works
Neocortical somatostatin neuron diversity in cognition and learning
Park, E., Mosso, M. B., & Barth, A. L. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2024.12.004 · Cited by 20 · see citing works
“Furthermore, marked electrophysiological and morphological variability within some SST clusters challenge the idea that current, transcriptomically-de fi ned subclasses represent discrete entities [17,41,50].”
Integrating multimodal data to understand cortical circuit architecture and function
Arkhipov, A. S., da Costa, N. M., de Vries, S. E. J., Bakken, T. E., Bennett, C., Bernard, A., Berg, J., Buice, M. A., Collman, F., Daigle, T. L., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01904-7 · Cited by 22 · see citing works
“Scala et al. 38 profiled >1,300 neurons in the primary motor cortex.”
Self-citationYang, D., Qi, G., Ding, C., & Feldmeyer, D. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab340 · Cited by 18 · see citing works
Manuel, M. N., Tan, K. B., Kozić, Z., Molinek, M., Marcos, T. S., Razak, F. M. A., Dobolyi, D., Dobie, R., Henderson, B. E. P., Henderson, N. C., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001563 · Cited by 20 · see citing works
“7I) (Connors and Gutnick, 1990; Markram et al., 2004; Scala et al., 2021).”
Purkinje cell microzones mediate distinct kinematics of a single movement
Blot, F. G. C., White, J. J., van Hattem, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40111-5 · Cited by 21 · see citing works
“Such a continuum in protein levels and electro-physiological properties has also been observed in interneurons of cerebral cortex 62 , striatum 63 and hippocampus 64 .”
- Authors
- Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., Mulherkar, S., Tan, Z. H., Yao, Z., Zeng, H., Sandberg, R., Berens, P., & Tolias, A. S.
- Affiliations
- Baylor College of Medicine (US)
- University of Tübingen (DE)
- University of California, San Francisco (US)
- Karolinska Institutet (SE)
- Allen Institute for Brain Science (US)
- Bernstein Center for Computational Neuroscience Tübingen (DE)
- Corresponding author
- Berens, Philipp
- Tolias, Andreas S.
- ORCID
- Venue
- Nature
- Published
- 2020-11-12
- Volume
- 598
- Issue
- 7879
- Pages
- 144-150
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8113357
- OpenAlex
- W3106117005
- Semantic Scholar
- 2f9abad9f229707d2db8168508f8ddf4337690f9
- Cited by
- 398
- References
- 74
- Citations by year
- 2026: 52 · 2025: 83 · 2024: 62 · 2023: 74 · 2022: 63 · 2021: 47 · 2020: 14 · 2019: 1 · 2018: 1
- FWCI
- 15.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (EXC2064, (BE5601/4-1, 276693517, 390727645)
- Bundesministerium für Bildung und Forschung (FKZ 01GQ1601, FKZ 01GQ1601 and 01IS18039A, 01IS18039A, 01GQ1601)
- National Institutes of Health (EY002520)
- Intelligence Advanced Research Projects Activity (D16PC00003)
- Interior Business Center
- National Institute of Mental Health (R01MH109556, R01MH120404, R01MH122169, U19MH114830)
- National Eye Institute (P30EY002520, EY002520, T32EY007001, R01EY033492)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- neuronal cell types, primary motor cortex, Patch-seq, single-cell RNA sequencing, patch-clamp recording, neocortical neurons, neuronal heterogeneity, VIP, parvalbumin, SST
- MeSH terms
- Animals, Atlases as Topic, Female, Glutamates, Lysine, Male, Motor Cortex, Neurons, Organ Specificity, Phenotype, Staining and Labeling, Sequence Analysis, RNA, Patch-Clamp Techniques, Gene Expression Profiling, Mice, Single-Cell Analysis, GABAergic Neurons, Transcriptome
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