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.
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Data availability
Electrophysiological and morphological data supporting the findings of this study are available in the Allen Cell Types Database, celltypes.brain-map.org. Morphological data are also available through the NeuroMorpho.org repository59 at neuromorpho.org.
Code availability
The custom electrophysiology data acquisition software (MIES) is available at https://github.com/alleninstitute/mies. The Vaa3D morphological reconstruction software, including the Mozak extension, is freely available at www.vaa3d.org and its code is available at https://github.com/Vaa3D. The code for electrophysiological and morphological feature analysis is available as part of the open-source Allen SDK repository (github.com/alleninstitute/allensdk) and IPFX repository (github.com/alleninstitute/ipfx). The clustering analysis code will be made available at github.com/alleninstitute/drcme.
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Acknowledgements
The authors thank Z. Popovich for creating the Mozak custom user interface for the 3D reconstruction software Vaa3D and the Mozak citizen-scientists who contributed to the reconstruction work. They also thank B. Berg, S. Coulter, C. Dang, and A. Jones for leadership and guidance. This work was funded by the Allen Institute for Brain Science. The study was also supported by award number U01MH105982 to H.Z. from the National Institute of Mental Health and Eunice Kennedy Shriver National Institute of Child Health & Human Development, parts of the National Institutes of Health. The content in this story and the associated study do not necessarily represent the official views of the NIH. We dedicate this paper to the vision, encouragement, and long-term support of our founder, Paul G. Allen.
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Contributions
H.Z. and C.K. conceptualized the study. T.L.D., B.T., T.N.N., and E.G. contributed to the generation and/or characterization of specific transgenic mouse lines. J.H., M. Garwood, M.R., and N.B provided mouse colony management. N. Dee, S.P., N. Taskin, T.C., M.K., J.S., K.C., H.T., and E.B. prepared tissue slices. A.O., D.H., K.H., S.J., Li. N., L.K., and R.M. performed electrophysiology experiments. T.L., M.M., K. Brouner, A.D., C.H., D.P., A.G., T.E., H.G., and K. Bickley processed slices for biocytin staining. S.C., C.C., M. Gorham, S.D., N. Dotson, K.N., and L.P. imaged biocytin-stained slices and cells. S.A.S., T.D., M.F., A.H., D.S., N. Thatra, R.D., G.W., A.M., R.A.d.F., S.-L.D., and S.K. reconstructed neurons and/or provided anatomical annotations. N.W.G., X.L., C.L., A. Budzillo, J.B., S.A.S., K.G. and K.E.L. performed analyses. J.B., A.O., J.T., B. Lee, P.C., S.A.S., and N. Dee contributed to method development studies. H.P., Z.Z., B. Long, C.F., J.P., C.S., M.S., D.R., T.B., A. Budzillo, D.C., K.E.L., and T.J. designed, wrote, or built tools for pipeline data generation. S.M.S. provided program management support. J.W.P., C.K., H.Z., A. Bernard, J.B., T.L., M.M., N.G., P.R.N., L.P., S.A.S., N. Dee, and S.P. organized and managed pipeline data generation. N.W.G., K.G., Ly. N., W.W., R.Y., D.F., and A.S. organized and managed pipeline data storage and processing. N.W.G., C.A.A., A.A., S.M., H.P., C.T., M.J.H., J.B., T.J., G.S.-L., J.T., B. Lee, G.J.M., E.L., J.W.P., C.K., H.Z., A. Bernard, S.A.S., J.A.H., and B.T. provided scientific direction. N.W.G., C.L., J.B., and S.A.S. prepared the figures. N.W.G., J.B., and S.A.S. wrote the manuscript in consultation with all authors. H.Z., C.K., and A.A. provided substantial review and edits to the manuscript.
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Journal peer review information: Nature Neuroscience thanks Ruben Armananzas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Supplementary information
Supplementary Dataset 1
Feature comparisons between m-types. Morphological features were assessed to identify statistically significant differences between groups using the two-tailed Student’s t-test. The P values were then adjusted for multiple testing using the two-stage Benjamini–Hochberg step-up procedure to control the false discovery rate. The sample sizes for each group are given in the attached spreadsheet.
Supplementary Dataset 2
Transgenic lines used in the study. Inventory of transgenic lines used in this study with additional information including their sources and repository details.
Supplementary Dataset 3
Type classifications and morphological adjustment parameters by cell. The e-types, m-types, and me-types are provided for each cell (when applicable). Cells are listed by ‘specimen ID’, which are the identifiers used in the Allen Cell Types Database (celltypes.brain-map.org). The morphological parameters listed can be used to adjust the original reconstructions (provided online) for z-shrinkage and slice angle as described in Methods and Supplementary Fig. 33.
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Gouwens, N.W., Sorensen, S.A., Berg, J. et al. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 22, 1182–1195 (2019). https://doi.org/10.1038/s41593-019-0417-0
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DOI: https://doi.org/10.1038/s41593-019-0417-0
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