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.
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Cajal, S.R., Pasik, P. & Pasik, T. Texture of the Nervous System of Man and the Vertebrates (Springer, 2002).
Ascoli, G.A. 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).
Burkhalter, A. Many specialists for suppressing cortical excitation. Front. Neurosci. 2, 155–167 (2008).
Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).
Connors, B.W. & Gutnick, M.J. Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci. 13, 99–104 (1990).
Neher, E. & Sakmann, B. Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature 260, 799–802 (1976).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Sandberg, R. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22–24 (2014).
Fishell, G. & Heintz, N. The neuron identity problem: form meets function. Neuron 80, 602–612 (2013).
Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).
Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
Sucher, N.J., Deitcher, D.L., Baro, D.J., Warrick, R.M. & Guenther, E. Genes and channels: patch/voltage-clamp analysis and single-cell RT-PCR. Cell Tissue Res. 302, 295–307 (2000).
Toledo-Rodriguez, M. & Markram, H. Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity. Methods Mol. Biol. 1183, 143–158 (2014).
Subkhankulova, T., Yano, K., Robinson, H.P. & Livesey, F.J. Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling. Front. Mol. Neurosci. 3, 10 (2010).
Qiu, S. et al. Single-neuron RNA-Seq: technical feasibility and reproducibility. Front. Genet. 3, 124 (2012).
McGinley, M.J. et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Chan, C.H. et al. Emx1 is a marker for pyramidal neurons of the cerebral cortex. Cereb. Cortex 11, 1191–1198 (2001).
Fremeau, R.T. Jr. et al. The expression of vesicular glutamate transporters defines two classes of excitatory synapse. Neuron 31, 247–260 (2001).
Marshak, D.R. S100 beta as a neurotrophic factor. Prog. Brain Res. 86, 169–181 (1990).
Bignami, A., Eng, L.F., Dahl, D. & Uyeda, C.T. Localization of the glial fibrillary acidic protein in astrocytes by immunofluorescence. Brain Res. 43, 429–435 (1972).
Stühmer, T., Anderson, S.A., Ekker, M. & Rubenstein, J.L. Ectopic expression of the Dlx genes induces glutamic acid decarboxylase and Dlx expression. Development 129, 245–252 (2002).
Alcántara, S. 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).
Miyoshi, G. 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).
Ma, J., Yao, X.H., Fu, Y. & Yu, Y.C. Development of layer 1 neurons in the mouse neocortex. Cereb. Cortex 24, 2604–2618 (2014).
Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).
Jiang, X., Wang, G., Lee, A.J., Stornetta, R.L. & Zhu, J.J. The organization of two new cortical interneuronal circuits. Nat. Neurosci. 16, 210–218 (2013).
Oláh, S. et al. Regulation of cortical microcircuits by unitary GABA-mediated volume transmission. Nature 461, 1278–1281 (2009).
Kalashnikova, E. et al. SynDIG1: an activity-regulated, AMPA- receptor-interacting transmembrane protein that regulates excitatory synapse development. Neuron 65, 80–93 (2010).
Macintyre, G. et al. Association of NPAS3 exonic variation with schizophrenia. Schizophr. Res. 120, 143–149 (2010).
Stanco, A. et al. NPAS1 represses the generation of specific subtypes of cortical interneurons. Neuron 84, 940–953 (2014).
Lin, L. et al. DPP6 regulation of dendritic morphogenesis impacts hippocampal synaptic development. Nat. Commun. 4, 2270 (2013).
Brose, N. For better or for worse: complexins regulate SNARE function and vesicle fusion. Traffic 9, 1403–1413 (2008).
Chao, H.T. et al. Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes. Nature 468, 263–269 (2010).
Ting, J.T., Daigle, T.L., Chen, Q. & Feng, G. Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics. Methods Mol. Biol. 1183, 221–242 (2014).
Sucher, N.J. & Deitcher, D.L. PCR and patch-clamp analysis of single neurons. Neuron 14, 1095–1100 (1995).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).
Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 33, 1–22 (2010).
Clopper, C. & Pearson, E.S. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934).
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).
R.S. has developed and patented Smart-seq2 and licensed that technology to Clontech, a Takara Bio Company.
Integrated supplementary information
Pilot experiments were carried out to test the effect of various protocol modifications on cDNA yield, including addition of RNase inhibitor to the intracellular solution (a), silanization of glass capillaries (b), concentration of dNTPs in lysis buffer (c), extraction of cytoplasm only v. cytoplasm and nucleus (d), pipette tip size (e) (tip size inversely proportional to resistance, line represents linear regression), and volume of intracellular solution in patch pipette (f) (20 μl total reaction volume, RT-PCR performed on whole brain cDNA template).
Supplementary Figure 2 Modified RNase-free intracellular solution does not affect health of patch-clamp-recorded neurons.
Membrane potential of neurons patched with RNase-free modified intracellular solution for Patch-seq recordings. Data represent mean ± SE (n=3 neurons).
(a) Average size and concentration (from 300-9,000 bp) of all collected samples. Only samples with more than 200 pg/μl and an average size greater than 1,500 bp were sequenced (criteria denoted by dashed lines). Libraries generated from patched cells generally contained more (b) and higher quality, full-length (c) cDNA compared to negative controls in which no cell was patched. Distributions of concentrations (d) and average sizes (e) of cDNA libraries generated from ex vivo patched cells. Dashed lines in (b-e) indicate threshold criteria used for sequencing. (f) Pearson correlation between concentration of cDNA and number of genes detected for all sequenced ex vivo cells.
(a) Percentage of sequenced reads that aligned uniquely to the respective assembly for patched single neurons and dissociated HEK293T cells picked and sequenced according to standard Smart-seq2 protocol (Picelli et al. 2013). (b) Percentage of uniquely aligned reads that overlap annotated RefSeq exons, introns or between gene annotations. This comparison indicates that the libraries generated from patched single neurons have similar qualities as standard Smart-seq2 libraries generated from lysed whole cells.
Supplementary Figure 5 Dimension reductions and marker expression in interneurons, pyramidal neurons and astrocyte.
(a) Principal component analyses (PCA) of the expression profiles of 16,000 genes detected in any of the patched cells. Each cell was projected onto a two-dimensional space drawn by the first two principal components. (b) Two-dimensional representation of cells using t-SNE on the PCA space, as in (a) but considering the first 28 principal components. Note that single outlier cells cannot be well captured in t-SNE representations as a consequence of the neighborhood analyses of the closest 20 cells (perplexity parameter). Therefore, the distinct gene expression observed in the astrocyte in the PCA (a) is not well captured in the t-SNE map. (c) Boxplots that show marker gene expression for interneurons, pyramidal neurons and the astrocyte. The mean and median are represented by the circle and black line, respectively. The edges of the box represent the 25th and 75th percentiles. Outlier cells are marked with grey dots, and the whiskers extend to extreme data points not considered outliers.
Marker gene expression overlaid onto the two-dimensional t-SNE representation (from Fig. 2d,e). Colors according to log2- transformed gene expression values.
Investigating the smallest number of patched cells needed to identify the two L1 interneuron cell types with unbiased clustering of gene expression profiles. (a) Affinity propagation was used to cluster random subsets of L1 interneurons (ex vivo cells, n=46) at decreasing numbers of cells. After each subsampling and clustering, the cell assignments were compared to the assignment based on all cells (Fig. 2d) and the accuracy score calculated (i.e. the number of correctly assigned cells over all subsampled cells). Accuracy scores from the 250 iterations per step are shown as standard boxplots, with median and mean as a line and circle, respectively. Importantly, even with random samples of 31 cells we robustly captured the two cell types (median accuracy=90%). Sampling fewer cells, in particular below 26 cells, often failed to distinguish the two cell types in an unbiased manner. (b) Showing the percentage of iterations where the affinity propagation clustering resulted in one, or two or three clusters, with the subsampling of decreasing numbers of L1 interneurons. The decline in performance at lower cell numbers was mainly caused by an inability of the affinity propagation algorithm to find two clusters, and instead reporting only one cluster.
Supplementary Figure 8 Expression differences in interneurons patch-clamp-recorded ex vivo and in vivo.
(a) Coloring the t-SNE map (from Fig. 2d,e) according to ex vivo and in vivo patched neurons. (b) Heat map showing the top 17 genes that separate ex vivo and in vivo cells The genes were identified using SCDE and all have absolute values of Z-score above 1.96 (genes with asterisk have corrected absolute Z-score above 1.96). The heat map shows an increased expression of genes ex vivo relating to stress induction (including Fos, Fosb, Junb) and other genes with differential expression.
Supplementary Figure 9 Overlaying electrophysiological properties of cells onto the two-dimensional t-SNE map.
Coloring of cells in the t-SNE space according to their electrophysiological properties. (a) Identical to Fig. 2e, showing cell type assignments onto the t-SNE map, included to ease the comparison to panels b-l. (b-j) Cells colored according to continuous electrophysiological features, scaled by the highest and lowest measurement onto the indicated colorbar. (k-l) Cells colored based on their displayed spiking patterns during recordings. Grey squares show in vivo patched cells.
Supplementary Figures 1–9 (PDF 2826 kb)
Mapping statistics of genes aligned uniquely, multimapping or unmapped. (XLSX 60 kb)
List with genes ranked according to biological variation. (XLSX 353 kb)
Genes used in regularized GLMs for predicting cell class and physiological properties (XLSX 39 kb)
Analyses of differential gene expression between interneurons of different cell type or electrophysiological properties. (XLSX 4558 kb)
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) (XLSX 80 kb)
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Cadwell, C., Palasantza, A., Jiang, X. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat Biotechnol 34, 199–203 (2016). https://doi.org/10.1038/nbt.3445
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