Cited by 524Neuroscience and Neuropharmacology ResearchSingle-cell and spatial transcriptomicsNeural dynamics and brain function

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index; 5 of the 66 sources its claims cite were checked for support.

Classification of electrophysiological and morphological neuron types in the mouse visual cortex

Open access
Gouwens, Nathan W., Sorensen, Staci A., Berg, Jim, Lee, Changkyu, Jarsky, Tim, Ting, Jonathan, Sunkin, Susan M., Feng, David, Anastassiou, Costas A., Barkan, Eliza, Bickley, Kris, Blesie, Nicole, Braun, Thomas, Brouner, Krissy, Budzillo, Agata, Caldejon, Shiella, Casper, Tamara, Castelli, Dan, Chong, Peter, Crichton, Kirsten, Cuhaciyan, Christine, Daigle, Tanya L., Dalley, Rachel, Dee, Nick, Desta, Tsega, Ding, Song-Lin, Dingman, Samuel, Doperalski, Alyse, Dotson, Nadezhda, Egdorf, Tom, Fisher, Michael, de Frates, Rebecca A., Garren, Emma, Garwood, Marissa, Gary, Amanda, Gaudreault, Nathalie, Godfrey, Keith, Gorham, Melissa, Gu, Hong, Habel, Caroline, Hadley, Kristen, Harrington, James, Harris, Julie A., Henry, Alex, Hill, DiJon, Josephsen, Sam, Kebede, Sara, Kim, Lisa, Kroll, Matthew, Lee, Brian, Lemon, Tracy, Link, Katherine E., Liu, Xiaoxiao, Long, Brian, Mann, Rusty, McGraw, Medea, Mihalas, Stefan, Mukora, Alice, Murphy, Gabe J., Ng, Lindsay, Ngo, Kiet, Nguyen, Thuc Nghi, Nicovich, Philip R., Oldre, Aaron, Park, Daniel, Parry, Sheana, Perkins, Jed, Potekhina, Lydia, Reid, David, Robertson, Miranda, Sandman, David, Schroedter, Martin, Slaughterbeck, Cliff, Soler-Llavina, Gilberto, Sulc, Josef, Szafer, Aaron, Tasic, Bosiljka, Taskin, Naz, Teeter, Corinne, Thatra, Nivretta, Tung, Herman, Wakeman, Wayne, Williams, Grace, Young, Rob, Zhou, Zhi, Farrell, Colin, Peng, Hanchuan, Hawrylycz, Michael J., Lein, Ed, Ng, Lydia, Arkhipov, Anton, Bernard, Amy, Phillips, John W., Zeng, Hongkui, Koch, Christof·2019·Nature Neuroscience

Published version of a preprint

Abstract

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines. Gouwens et al. established a morpho-electrical taxonomy of cell types for the mouse visual cortex via unsupervised clustering analysis of multiple quantitative features from 1,938 neurons available online at the Allen Cell Types Database.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This paper presents a standardized patch-clamp and biocytin-reconstruction pipeline to classify neurons in adult mouse visual cortex, yielding 17 electrophysiological types, 38 morphological types, and 46 joint morpho-electric types. The joint types align better with transcriptomic subclasses than either modality alone, and the data and tools are publicly released.

Key findings

  • From 1,938 neurons passing electrophysiology QC (928 spiny/excitatory, 1,010 aspiny or sparsely spiny/inhibitory), 461 were reconstructed (254 spiny, 207 aspiny/sparsely spiny), and 452 had both modalities; unsupervised clustering identified 17 e-types (4 excitatory, 13 inhibitory), 38 m-types (19 spiny, 19 aspiny), and 46 me-types (20 excitatory, 26 inhibitory).
  • Joint clustering produced 46 me-types rather than the 120 simple intersections of e-types and m-types, and these me-types showed stronger consistency with transcriptomic subclasses than e-types or m-types alone.
  • Morphological features were more informative for excitatory me-types, while both electrophysiological and morphological features predicted many inhibitory me-types; selective transgenic line/layer combinations linked some me-types to specific transcriptomic types.
  • The t-SNE projection separated excitatory and inhibitory cells and showed coherence by transgenic driver, with Pvalb, Sst, Vip, and Chat populations occupying related but distinct regions.

Methods

  • Standardized in vitro pipeline: whole-cell current-clamp recordings from adult mouse visual cortex slices with biocytin-filled pipettes, fixed stimulus protocols (short and long current steps, ramps, noisy injections), quality-control criteria, transgenic Cre driver lines, and atlas mapping; most cells came from primary visual cortex (n=1,599).
  • Electrophysiology features were reduced with sparse PCA and clustered using Gaussian mixture models with entropy-based merging and subsampling stability; morphology features from reconstructed dendrites and axons were clustered hierarchically with co-clustering; joint clustering combined both feature sets across multiple methods and weightings to derive consensus clusters.
  • Transcriptomic correspondence was inferred using transgenic line/layer labels and prior FACS data from Tasic et al., with binary consistency scores for subclasses and selective line/layer combinations for transcriptomic types.

Limitations

  • The authors state that electrophysiological results depend on their specific recording protocols and solutions; other protocols could reveal additional distinguishing features and require revising the taxonomy. They also note that their transgenic sampling strategy does not allow estimating relative proportions of cell types in the circuit, and they could not clearly link to the Sncg subclass, which they suspect was underrepresented.
  • The authors acknowledge biases from chosen stimuli and quantified morphological features, and note that z-dimension-dependent morphological features were excluded because of slice z-compression, though they report that re-running clustering after volume restoration and alignment showed good agreement.
  • Evident from the text: transgenic lines are imperfect proxies for transcriptomic identity, some me-types showed heterogeneity even among cells putatively from a single transcriptomic type, and morphological reconstructions were a selected subset rather than all recorded cells.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41593-019-0417-0

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

79 Own result · 33 Own method · 29 Not checked yet · 18 Own interpretation · 7 No basis given · 1 Supported · 1 Supported so far

Verify all 61 references — free account
  • Probably not a substantial claim. 47
  • It may state two separate claims as one. 55
  • The quote only partly states this claim. 1
86 of 86
  • The authors state this as an assumption.

    Spiny neurons are excitatory.

    “928 were spiny neurons, which we assume to be excitatory”
  • The authors state this as an assumption.

    Aspiny or sparsely spiny neurons are inhibitory.

    “1,010 were aspiny or sparsely spiny, which we assume to be inhibitory”
  • The paper relies on this without support.

    Smaller studies could not be directly compared for morpho-electric properties because of differing protocols, analysis methods, and transgenic line availability, which hindered building a systematic classification.

    “Historically, though, direct comparisons of morpho-electric properties across smaller studies have been constrained by differences in experimental protocols, analysis methods, and access to specific transgenic lines, limiting the construction of a systematic classification scheme.”
  • The paper relies on this without support.

    The features that separate types may be particular to the studied population and may not generalize to all cortical neurons.

    “features used to distinguish types can be specific to the population being studied and may not be broadly applicable (or distinguishing) across all cortical neurons.”
  • The authors state this as an assumption.

    Sncg inhibitory subclass cells are underrepresented in the dataset.

    “we presume that these cells are underrepresented in our data set”
  • The paper relies on this without support.

    Transgenic lines are imperfect labels of transcriptomic cell types.

    “While transgenic lines are imperfect labels of transcriptomic cell types”
  • The authors state this as an assumption.

    Morphological feature distributions were assumed to be normal for the t-test, but this was not formally tested.

    “Morphological feature distributions were assumed to be normal for this method, but this was not formally tested (see Supplementary Figs. 19 and 21 for plots of distributions).”
7 of 7

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Citations

Cites

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Research works printed but not linked: 1

  1. Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018). · DOI · Paper page

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Metadata

Authors
Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., Bickley, K., Blesie, N., Braun, T., Brouner, K., Budzillo, A., Caldejon, S., Casper, T., Castelli, D., Chong, P., Crichton, K., Cuhaciyan, C., Daigle, T. L., Dalley, R., Dee, N., Desta, T., Ding, S.-L., Dingman, S., Doperalski, A., Dotson, N., Egdorf, T., Fisher, M., de Frates, R. A., Garren, E., Garwood, M., Gary, A., Gaudreault, N., Godfrey, K., Gorham, M., Gu, H., Habel, C., Hadley, K., Harrington, J., Harris, J. A., Henry, A., Hill, D., Josephsen, S., Kebede, S., Kim, L., Kroll, M., Lee, B., Lemon, T., Link, K. E., Liu, X., Long, B., Mann, R., McGraw, M., Mihalas, S., Mukora, A., Murphy, G. J., Ng, L., Ngo, K., Nguyen, T. N., Nicovich, P. R., Oldre, A., Park, D., Parry, S., Perkins, J., Potekhina, L., Reid, D., Robertson, M., Sandman, D., Schroedter, M., Slaughterbeck, C., Soler-Llavina, G., Sulc, J., Szafer, A., Tasic, B., Taskin, N., Teeter, C., Thatra, N., Tung, H., Wakeman, W., Williams, G., Young, R., Zhou, Z., Farrell, C., Peng, H., Hawrylycz, M. J., Lein, E., Ng, L., Arkhipov, A., Bernard, A., Phillips, J. W., Zeng, H., & Koch, C.
Affiliations
Allen Institute for Brain Science (US)
Corresponding author
Zeng, Hongkui
Venue
Nature Neuroscience
Published
2019-06-17
Volume
22
Issue
7
Pages
1182-1195
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2949311293
Cited by
456
References
59
Citations by year
2026: 44 · 2025: 69 · 2024: 54 · 2023: 64 · 2022: 63 · 2021: 66 · 2020: 62 · 2019: 30
FWCI
26.25
Type
journal article
Language
en
Open access
Locations
  • Nature Neuroscience (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health
  • National Institute of Mental Health (U01MH105982, U19MH114830)
  • National Eye Institute (R01EY023173)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
neuron classification, mouse visual cortex, neuronal morphology, electrophysiological properties, single-cell characterization, unsupervised clustering
MeSH terms
Action Potentials, Animals, Mice, Transgenic, Neurons, Visual Cortex, Databases, Factual, Genes, Reporter, Patch-Clamp Techniques, Cell Shape, Mice, Transcriptome, Datasets as Topic

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