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Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes

Nature Biotechnology volume 34, pages 175–183 (2016)Cite this article

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Abstract

Traditionally, neuroscientists have defined the identity of neurons by the cells' location, morphology, connectivity and excitability. However, the direct relationship between these parameters and the molecular phenotypes has remained largely unexplored. Here, we present a method for obtaining full transcriptome data from single neocortical pyramidal cells and interneurons after whole-cell patch-clamp recordings in mouse brain slices. In our approach, termed Patch-seq, a patch-clamp stimulus protocol is followed by the aspiration of the entire somatic compartment into the recording pipette, reverse transcription of RNA including addition of unique molecular identifiers, cDNA amplification, Illumina library preparation and sequencing. We show that Patch-seq reveals a close link between electrophysiological characteristics, responses to acute chemical challenges and RNA expression of neurotransmitter receptors and channels. Moreover, it distinguishes neuronal subpopulations that correspond to both well-established and, to our knowledge, hitherto undescribed neuronal subtypes. Our findings demonstrate the ability of Patch-seq to precisely map neuronal subtypes and predict their network contributions in the brain.

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Figure 1: Neurophysiological diversity, distribution and representative molecular marks of CCK interneurons.
Figure 2: Workflow diagram of Patch-seq procedures.
Figure 3: Overview of Patch-seq methodology.
Figure 4: Molecular classification and validation of Patch-seq–sampled neurons using a large cortical data set.
Figure 5: Cell type–specific quantitative expression of ion channel and receptor genes in CCK+ interneurons and pyramidal cells.
Figure 6: Compliance of RNA-seq predictions with the neurophysiological phenotype of cortical interneurons.

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Acknowledgements

We thank A. Juréus for DNA sequencing, and the CLICK Imaging Facility at Karolinska Institutet for making the Imaris software package available for neuronal reconstructions, T. Klausberger and E. Borók for discussions and assistance with Neurolucida reconstructions. This work was supported by the European Research Council (BRAINCELL 261063, to S.L.), the Swedish Research Council (to S.L. and T.H.); Human Frontier Science Program (to A.Z.), the European Commission 7th Framework Program (PAINCAGE, to T.H.), Hjärnfonden (to T.H.) and the NovoNordisk Foundation (to T.H.).

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Author notes

  1. János Fuzik and Amit Zeisel: These authors contributed equally to this article.

  2. Sten Linnarsson and Tibor Harkany: These authors share senior authorship.

Authors and Affiliations

  1. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    János Fuzik, Amit Zeisel, Daniela Calvigioni, Sten Linnarsson & Tibor Harkany

  2. Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria

    János Fuzik, Daniela Calvigioni & Tibor Harkany

  3. Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary

    Zoltán Máté & Gábor Szabó

  4. Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan

    Yuchio Yanagawa

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Contributions

J.F., A.Z., S.L. and T.H. wrote the paper. J.F. performed electrophysiology and the electrophysiology-based cell classification, and drafted figures. A.Z. performed single-cell RNA-seq and the transcriptome-based cell classification, and drafted figures. D.C. performed post hoc morphological reconstruction of biocytin-filled neurons. Y.Y., Z.M. and G.S. provided unique reagents. S.L. and T.H. acquired funding and oversaw the research. All authors read and approved the manuscript for submission.

Corresponding authors

Correspondence to Sten Linnarsson or Tibor Harkany.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Distribution and molecular heterogeneity of dual-labeled interneurons in the postnatal mouse brain.

(a) Overview of CCKBAC/dsRed:GAD67gfp/+ mouse somatosensory cortex on postnatal day 20, revealing the distribution of CCK+ (magenta) and/or GAD67+ (green) cells in the different cortical layers (L; labeled from L1-L6a). Light pink/white color depicts co-localization. Overview image of the mouse forebrain (left) was reconstructed from tiled confocal photomicrographs using a Zeiss LSM780 laser-scanning microscope. Open rectangle indicates the location of the inset (right). Scale bars = 500 μm (left) and 200 μm (right). (b-d) Representative current-clamp recordings of CCK+/GAD67− pyramidal cells (Exc L2/3 (b), Exc L4 (c) and Exc L5 (d)). At the left of each panel, AP responses (top) to square current pulses (bottom) are shown. Phase-plane plots of the APs rising upon 2x rheobase current injection (top right) and rheobasic APs (bottom right) are depicted for each cell type. In phase-plane plots, the first AP is red, while subsequent APs shift from warm to cool blue color. For the rheobasic AP, the y-axis between -20 mV and +30 mV was omitted to emphasize AHP and ADP characteristics (scale bars = 200 pA (vertical), 25 ms (horizontal)). (e) Cell-type-specific expression of a voltage gated K+ channel interacting protein (Kcnip1), a GTPase-activating protein (Chn1), a protein kinase C substrate (Nrgn), a Ca2+ channel subunit (Cacna2d3), a Na+ channel subunit (Scn3a), Purkinje cell protein 4 (Pcp4), a G protein-signaling regulator (Rgs12), serotonin receptor subtype 3a (Htr3a), reelin (Reln), a superficial layer-specific marker, calbindin D28k (Calb1), a Ca2+-binding protein, vasoactive intestinal polypeptide (Vip) and neuropeptide Y (Npy) in sub-classified I-type CCK+ interneurons (magenta) and Exc-type pyramidal cells (green).

Supplementary Figure 2 Iterative development of Patch-seq procedures.

Step-wise improvements to the sampling, collection, ejection and analysis protocols are shown. Overall, 145 neurons were used to optimize recording and processing conditions, while ~120 neurons were processed to obtain reliable RNA-seq data. Axis labels on Bioanalyzer (Agilent) plots are: [FU], fluorescence unit; (bp) base pair. Magenta-colored “x” labels steps that had been omitted due to poorer outcomes.

Supplementary Figure 3 Linear regression of genes implicated in resting membrane potential and sub-threshold electrical events.

(a) Positive linear regression between Atp1a3 (subunit of the Na+/K+-ATPase) and Vrest. (b) Likewise, positive and close relationship between quantitative expression of Clcn3, a voltage-gated Cl− channel subunit, and Vrest. Each data point represents the two-dimensional population mean for the parameters indicated. Standard deviations were not plotted to retain maximum visual clarity.

Reference:

1Mindell, J.A. & Maduke, M. ClC chloride channels. Genome Biol. 2, REVIEWS 3003 (2001).

Supplementary Figure 4 Correlation of action potential (AP) parameters and mRNA expression for synaptic proteins, receptor subunits.

(a) Heat map of correlation coefficients (scaled, color-coded and filtered to <−0.4 or >0.4 and from >5 cells) of mRNA counts and characteristic electrophysiological properties of dual-labeled interneurons. Open rectangles denote significant examples in our predictive matrix, which are shown in (d,e). (b) Color matrix marks the relationship of individual parameters and particular time-locked phases of a single action AP or AP waveforms (in c). (d,e) Correlation between electrophysiology parameters and gene expression of Syt7 or Gria1. Neuronal subclass identity is shown by color-coding. (f-i) Correlation (f-h) and anti-correlation (i) of quantitative Kcnc1 expression and a subset of biophysical membrane parameters during the 1st AP.

Reference:

2Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 954 kb)

Supplementary Table 1

Electrophysiological parameters used to classify CCK+ interneurons in ex vivo brain slice preparations. (XLSX 31 kb)

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Fuzik, J., Zeisel, A., Máté, Z. et al. Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat Biotechnol 34, 175–183 (2016). https://doi.org/10.1038/nbt.3443

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