Letter | Published:

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

Nature Biotechnology volume 34, pages 199203 (2016) | Download Citation

Abstract

Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

ArrayExpress

References

  1. 1.

    , & Texture of the Nervous System of Man and the Vertebrates (Springer, 2002).

  2. 2.

    et al. Petilla Interneuron Nomenclature Group. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).

  3. 3.

    Many specialists for suppressing cortical excitation. Front. Neurosci. 2, 155–167 (2008).

  4. 4.

    et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).

  5. 5.

    & Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci. 13, 99–104 (1990).

  6. 6.

    & Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature 260, 799–802 (1976).

  7. 7.

    et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

  8. 8.

    Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22–24 (2014).

  9. 9.

    & The neuron identity problem: form meets function. Neuron 80, 602–612 (2013).

  10. 10.

    et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

  11. 11.

    et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).

  12. 12.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  13. 13.

    , , , & Genes and channels: patch/voltage-clamp analysis and single-cell RT-PCR. Cell Tissue Res. 302, 295–307 (2000).

  14. 14.

    & Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity. Methods Mol. Biol. 1183, 143–158 (2014).

  15. 15.

    , , & Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling. Front. Mol. Neurosci. 3, 10 (2010).

  16. 16.

    et al. Single-neuron RNA-Seq: technical feasibility and reproducibility. Front. Genet. 3, 124 (2012).

  17. 17.

    et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).

  18. 18.

    et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

  19. 19.

    et al. Emx1 is a marker for pyramidal neurons of the cerebral cortex. Cereb. Cortex 11, 1191–1198 (2001).

  20. 20.

    et al. The expression of vesicular glutamate transporters defines two classes of excitatory synapse. Neuron 31, 247–260 (2001).

  21. 21.

    S100 beta as a neurotrophic factor. Prog. Brain Res. 86, 169–181 (1990).

  22. 22.

    , , & Localization of the glial fibrillary acidic protein in astrocytes by immunofluorescence. Brain Res. 43, 429–435 (1972).

  23. 23.

    , , & Ectopic expression of the Dlx genes induces glutamic acid decarboxylase and Dlx expression. Development 129, 245–252 (2002).

  24. 24.

    et al. Regional and cellular patterns of reelin mRNA expression in the forebrain of the developing and adult mouse. J. Neurosci. 18, 7779–7799 (1998).

  25. 25.

    et al. Genetic fate mapping reveals that the caudal ganglionic eminence produces a large and diverse population of superficial cortical interneurons. J. Neurosci. 30, 1582–1594 (2010).

  26. 26.

    , , & Development of layer 1 neurons in the mouse neocortex. Cereb. Cortex 24, 2604–2618 (2014).

  27. 27.

    , & Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

  28. 28.

    , , , & The organization of two new cortical interneuronal circuits. Nat. Neurosci. 16, 210–218 (2013).

  29. 29.

    et al. Regulation of cortical microcircuits by unitary GABA-mediated volume transmission. Nature 461, 1278–1281 (2009).

  30. 30.

    et al. SynDIG1: an activity-regulated, AMPA- receptor-interacting transmembrane protein that regulates excitatory synapse development. Neuron 65, 80–93 (2010).

  31. 31.

    et al. Association of NPAS3 exonic variation with schizophrenia. Schizophr. Res. 120, 143–149 (2010).

  32. 32.

    et al. NPAS1 represses the generation of specific subtypes of cortical interneurons. Neuron 84, 940–953 (2014).

  33. 33.

    et al. DPP6 regulation of dendritic morphogenesis impacts hippocampal synaptic development. Nat. Commun. 4, 2270 (2013).

  34. 34.

    For better or for worse: complexins regulate SNARE function and vesicle fusion. Traffic 9, 1403–1413 (2008).

  35. 35.

    et al. Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes. Nature 468, 263–269 (2010).

  36. 36.

    , , & Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics. Methods Mol. Biol. 1183, 221–242 (2014).

  37. 37.

    & PCR and patch-clamp analysis of single neurons. Neuron 14, 1095–1100 (1995).

  38. 38.

    et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  39. 39.

    et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

  40. 40.

    et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  41. 41.

    , , & An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

  42. 42.

    et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

  43. 43.

    , & Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 33, 1–22 (2010).

  44. 44.

    & The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934).

Download references

Acknowledgements

We thank A. Morgan for technical assistance. This study was supported by grants DP1EY023176, P30EY002520, T32EY07001,and DP1OD008301 to A.S.T.; grants from the Swedish Research Council and the Swedish Foundation for Strategic Research (FFL4) to R.S.; grant R01MH103108 to A.S.T. and K.F.T.; grant R01NS062829 to K.F.T.; the McKnight Scholar Award to A.S.T.; and the Arnold and Mabel Beckman Foundation Young Investigator Award to A.S.T. C.R.C. was supported by grants F30MH095440, T32GM007330 and T32EB006350. M.B. and P.B. were supported by the Deutsche Forschungsgemeinschaft (DFG, EXC 307) and the German Federal Ministry of Education and Research (BMBF; BCCN Tübingen, FKZ 01GQ1002).

Author information

Author notes

    • Cathryn R Cadwell
    •  & Athanasia Palasantza

    These authors contributed equally to this work.

    • Rickard Sandberg
    •  & Andreas S Tolias

    These authors jointly supervised this work.

Affiliations

  1. Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.

    • Cathryn R Cadwell
    • , Xiaolong Jiang
    • , Philipp Berens
    • , Jacob Reimer
    • , Shan Shen
    • , Kimberley F Tolias
    •  & Andreas S Tolias
  2. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

    • Athanasia Palasantza
    • , Qiaolin Deng
    • , Marlene Yilmaz
    •  & Rickard Sandberg
  3. Ludwig Institute for Cancer Research, Stockholm, Sweden.

    • Athanasia Palasantza
    • , Qiaolin Deng
    • , Marlene Yilmaz
    •  & Rickard Sandberg
  4. Bernstein Center for Computational Neuroscience, Tübingen, Germany.

    • Philipp Berens
    • , Matthias Bethge
    •  & Andreas S Tolias
  5. Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.

    • Philipp Berens
  6. Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany.

    • Philipp Berens
    •  & Matthias Bethge
  7. Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

    • Matthias Bethge
  8. Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA.

    • Kimberley F Tolias

Authors

  1. Search for Cathryn R Cadwell in:

  2. Search for Athanasia Palasantza in:

  3. Search for Xiaolong Jiang in:

  4. Search for Philipp Berens in:

  5. Search for Qiaolin Deng in:

  6. Search for Marlene Yilmaz in:

  7. Search for Jacob Reimer in:

  8. Search for Shan Shen in:

  9. Search for Matthias Bethge in:

  10. Search for Kimberley F Tolias in:

  11. Search for Rickard Sandberg in:

  12. Search for Andreas S Tolias in:

Contributions

C.R.C. collected RNA samples, generated cDNA libraries, assisted with analysis and drafted the manuscript. A.P. performed the computational analyses of RNA-seq data. X.J. performed the ex vivo patch-clamp experiments, reconstructed neuronal morphologies and analyzed electrophysiological properties of the neurons. P.B. built the automated cell type classifier and generalized linear models. Q.D. and M.Y. generated cDNA and sequencing libraries. J.R. and S.S. performed the in vivo patch-clamp experiments. M.B. supervised the machine learning analysis. A.S.T., R.S. and K.F.T. supervised all experiments and analyses. All authors contributed to writing the paper.

Competing interests

R.S. has developed and patented Smart-seq2 and licensed that technology to Clontech, a Takara Bio Company.

Corresponding authors

Correspondence to Rickard Sandberg or Andreas S Tolias.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9

Excel files

  1. 1.

    Supplementary Table 1

    Mapping statistics of genes aligned uniquely, multimapping or unmapped.

  2. 2.

    Supplementary Table 2

    List with genes ranked according to biological variation.

  3. 3.

    Supplementary Table 3

    Genes used in regularized GLMs for predicting cell class and physiological properties

  4. 4.

    Supplementary Table 4

    Analyses of differential gene expression between interneurons of different cell type or electrophysiological properties.

  5. 5.

    Supplementary Table 5

    Results obtained using as background the genes expressed across L1 interneurons (~ 6,300) and comparing against the top 200 differentially expressed genes (up-regulated) within cluster B (SBCs)

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nbt.3445

Further reading