Abstract

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type–specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

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Acknowledgements

We would like to thank M. Chillon Rodrigues (Universitat Autònoma de Barcelona) for providing CAV-Cre, S. Mihalas for advice on data analysis, H. Gu, M. Mills, H. Gill and K. Hadley for technical assistance, C. Ye and A. Kaykas for help with the next generation sequencing, and the Department of In vivo Sciences, especially R. Larsen, L. Pearson and J. Harrington for mouse husbandry. We thank J. Waters and E. Lein for comments on the manuscript. The authors thank the Allen Institute founders, Paul G. Allen and Jody Allen, for their vision, encouragement and support. This work was funded by the Allen Institute for Brain Science, and by US National Institutes of Health grants R01EY023173 and U01MH105982 to H.Z.

Author information

Author notes

    • Bosiljka Tasic
    •  & Vilas Menon

    These authors contributed equally to this work.

Affiliations

  1. Allen Institute for Brain Science, Seattle, Washington, USA.

    • Bosiljka Tasic
    • , Vilas Menon
    • , Thuc Nghi Nguyen
    • , Tae Kyung Kim
    • , Tim Jarsky
    • , Zizhen Yao
    • , Boaz Levi
    • , Lucas T Gray
    • , Staci A Sorensen
    • , Tim Dolbeare
    • , Darren Bertagnolli
    • , Jeff Goldy
    • , Nadiya Shapovalova
    • , Sheana Parry
    • , Changkyu Lee
    • , Kimberly Smith
    • , Amy Bernard
    • , Linda Madisen
    • , Susan M Sunkin
    • , Michael Hawrylycz
    • , Christof Koch
    •  & Hongkui Zeng

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Contributions

B.T. and H.Z. designed and supervised the study. T.N.N., T.K.K. and B.T. performed single-cell RNA-seq. V.M. and Z.Y. performed transcriptome data analysis with contributions from L.T.G., T.N.N., B.T., T.K.K., C.L. and M.H. T.N.N. performed stereotaxic injections. T.J. performed electrophysiology and associated data analysis. T.N.N., T.K.K. and B.T. performed single-cell isolation with contributions from B.L., N.S. and S.P. S.A.S. performed imaging of biocytin-filled cells and morphological reconstructions. D.B., J.G., K.S. and A.B. performed qRT-PCR and RNA DFISH in collaboration with T.N.N. and B.T. L.M. generated transgenic mice. T.D. designed the online scientific vignette in collaboration with B.T. and V.M. S.M.S. provided program management support. L.G., T.N., V.M. and B.T. prepared the figures. B.T., V.M., H.Z. and C.K. wrote the manuscript in consultation with all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Bosiljka Tasic.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–17

  2. 2.

    Supplementary Methods Checklist

Excel files

  1. 1.

    Supplementary Table 1: Transgenic driver lines.

  2. 2.

    Supplementary Table 2: Transgenic reporter lines.

  3. 3.

    Supplementary Table 3: Single cell samples.

  4. 4.

    Supplementary Table 4: Cre line and cell type relationships.

    The percentage of cell types detected in each Cre line/dissection combination separated by core and intermediate cells. These data were used to generate graphical representation in Fig. 2b

  5. 5.

    Supplementary Table 5: Evaluation of enrichment of interneuron types in upper or lower cortical layers using the hypergeometric test.

    For details on statistics methodology see Methods. Note that lack of statistically significant enrichment does not necessarily indicate that there is no enrichment, as our sampling did not allow comprehensive evaluation of spatial enrichment for all types. We do not claim lower layer-enrichment for the Pvalb-Wt1 type because we obtained statistical significance only in one of the two examined recombinase lines. Additional information on spatial enrichment for some of these types can be obtained by examination of cell-type-specific markers by RNA ISH.

  6. 6.

    Supplementary Table 6: Marker genes for transcriptomic cell types.

    The table also contains an earlier version of the cell type nomenclature used in the original release of the online scientific vignette.

  7. 7.

    Supplementary Table 7: Transcriptomic cell types: correspondence to literature

  8. 8.

    Supplementary Table 8: Differentially processed exons among cell types.

  9. 9.

    Supplementary Table 9: Evaluation of correspondence between RNA-seq and Allen Brain Atlas chromogenic RNA ISH data.

    Out of 228 genes examined, 72% show agreement between single cell RNA-seq and Allen Brain Atlas data. For most of the other genes, the disagreement is due to the absence of signal in the Allen Brain Atlas ISH (17%). Small numbers of genes display apparently ubiquitous signal in VISp by ISH (2%), specificity of the signal that is difficult to interpret (2%), or the ISH pattern that, in fact, disagrees with RNA-seq (2%). For about 4% of the genes, the data is not available in the Allen Brain Atlas

  10. 10.

    Supplementary Table 10: Cluster identities after subsampling of single cell data.

    Cluster identities obtained using the full depth sequencing data (median of ~4.4 million mapped reads or ~8.7 million total reads) are compared to cluster identities obtained when data from each cell were subsampled to 100,000 and 1,000,000 mapped reads per cell. We detect fewer clusters with decreasing single cell sequencing sample depth.

  11. 11.

    Supplementary Table 11: Genetic background estimate for all animals used in the study.

    In our experimental animals, the percent of C57BL/6J genetic background ranges from 75% to 100% with the average of ~96%. In cases where the original ancestor we obtained was on mixed background, we adopted a conservative estimate of 0% C57BL/6J in that ancestor.

  12. 12.

    Supplementary Table 12: Consequences of cluster validation parameter change on single cell classification.

    Cluster identity assignment for each cell is listed for our default parameters (20 genes, p < 0.01), and after changes in these parameters: decrease or increase in the number of genes to 10, and 50, respectively, and change in the p value to p < 0.05. With parameter change, on average, ~3% of the cells change cluster identity (from one core to another core, from one core to intermediate connecting two different cores, or from intermediate connecting two cores to an intermediate connecting two different cores or becoming a third core), while ~18% change from core to intermediate and vice versa (but stay within same core/intermediate identity combination). However, the total number of core clusters is preserved for all parameter changes.

  13. 13.

    Supplementary Table 13: Probes for DFISH.

  14. 14.

    Supplementary Table 14: Quantitative RT-PCR assays.

Zip files

  1. 1.

    Supplementary Software

    Contains the code for an iteration of the PCA and WGCNA-based clustering methods, the cluster membership validation algorithm, as well as the differential gene expression algorithm.

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https://doi.org/10.1038/nn.4216

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