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

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.

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Figure 1: Two morphologically and electrophysiologically distinct neuronal classes in neocortical layer 1.
Figure 2: Single-neuron transcriptome profiles predict cell type and electrophysiological properties.
Figure 3: Differential gene expression analysis reveals novel markers for L1 interneuron classes.

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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

Affiliations

Authors

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.

Corresponding authors

Correspondence to Rickard Sandberg or Andreas S Tolias.

Ethics declarations

Competing interests

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

Integrated supplementary information

Supplementary Figure 1 Optimization of Patch-seq protocol.

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).

Supplementary Figure 3 Quality control of cDNA libraries.

(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.

Supplementary Figure 4 Mapping statistics of Patch-seq and standard Smart-seq2 libraries.

(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.

Supplementary Figure 6 Marker gene expression overlaid onto t-SNE map.

Marker gene expression overlaid onto the two-dimensional t-SNE representation (from Fig. 2d,e). Colors according to log2- transformed gene expression values.

Supplementary Figure 7 Clustering of random samples of L1 interneurons.

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 information

Supplementary Text and Figures

Supplementary Figures 1–9 (PDF 2826 kb)

Supplementary Table 1

Mapping statistics of genes aligned uniquely, multimapping or unmapped. (XLSX 60 kb)

Supplementary Table 2

List with genes ranked according to biological variation. (XLSX 353 kb)

Supplementary Table 3

Genes used in regularized GLMs for predicting cell class and physiological properties (XLSX 39 kb)

Supplementary Table 4

Analyses of differential gene expression between interneurons of different cell type or electrophysiological properties. (XLSX 4558 kb)

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) (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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