Assessing Transcriptome Quality in Patch-Seq Datasets

Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented access to a neuron's transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples

Identification of cell type marker genes of the brain and their use in estimation of cell type proportions

Establishing the molecular diversity of cell types is crucial for the study of the nervous system. I compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. I used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. By examining datasets with known cell type proportion differences, I further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. Using this approach, I show that majority of genes previously reported as differentially expressed in Parkinson’s disease can be attributed to the reduction in dopaminergic cell number rather than regulatory events. To facilitate use of this expanding resource, I provide a user-friendly web interface at www.neuroexpresso.org.Science, Faculty ofGraduat

Assessing Transcriptome Quality in Patch-Seq Datasets

<p>Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented access to a neuron's transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples.</p

Transcriptomic correlates of neuron electrophysiological diversity

<div><p>How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like <i>Gabrd</i> and <i>Scn1a</i> (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.</p></div

Descriptions for neuron types composing the Allen Institutes for Brain Sciences cell types validation dataset.

<p>Mouse line indicates cre-driver lines used to label specific populations of cells in the adult mouse visual cortex. N cells indicates number of cells assayed per cre-line via single-cell RNAseq or patch-clamp electrophysiology. Color indicates cell type color used within this manuscript.</p

Consistency of gene-electrophysiological property correlations between NeuroExpresso/NeuroElectro discovery and AIBS validation datasets.

<p>Overall AIBS consistency indicates overall Spearman rank correlation between the full set of gene-electrophysiological correlations calculated in both the discovery and validation datasets, as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005814#pcbi.1005814.g002" target="_blank"><i>Fig 2</i>B</a>. P-values based on 1000 random reshuffles of cell type labels in the AIBS validation dataset. Discovered genes, <i>p</i>_<i>adj</i> < 0.05 reflects count of genes significantly correlated with each ephys property with in discovery dataset (only includes genes that are also present in AIBS scRNAseq dataset). AIBS consistency, |r_s|> 0.3 reflects count and percentage of discovered genes that further show a consistent relationship in the AIBS validation dataset. P-value also based on 1000 shuffled samples of cell type labels in the validation dataset.</p

Descriptions for neuron types composing the NeuroExpresso/NeuroElectro discovery dataset.

<p>References for individual transcriptomic and electrophysiological samples are available in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005814#pcbi.1005814.s008" target="_blank">S2 Table</a></b>.</p

Multivariate gene expression can predict cell type-specific electrophysiological parameters.

<p>A) Comparison of observed action potential amplitudes (AP_amp; x-axis) to predicted values (y-axis) using gene expression-based statistical models trained using the NeuroExpresso/NeuroElectro discovery dataset. The y-value of each point (a cell type) is based on leave-one-out cross-validation (LOOCV). R²_LOOCV indicates the calculated R² across the set of cell type predictions and grey line indicates the unity line. B) Same as A, but observed and predicted values are based on the AIBS validation dataset. Ephys predictions on y-axis are made by applying the discovery dataset-based models (as in A) to the AIBS-dataset multivariate gene expression profiles. R²_AIBS is calculated across the set of predictions made for the AIBS cell types and grey line indicates best linear fit. C,D) Same as A and B, but for maximum firing rate (FR_max). E) Summarized performance of gene expression-based statistical models for predicting ephys parameters. Large dots indicate the R²_LOOCV from the NeuExp/NeuElec discovery dataset (pink), R²_AIBS values from the validation dataset (green), and R²_LOOCV values on a version of the NeuExp/NeuElec discovery dataset where cell type labels were randomly shuffled (blue). Boxplots are based on 100 bootstrap resamples of the discovery dataset and small dots indicate boxplot outliers.</p

Ion channel specific gene-electrophysiological correlations and literature evidence for causal regulation.

<p>A) Heatmap showing NeuExp/NeuElec dataset gene-ephys correlations for ion channel genes. Genes filtered for those with at least one significant ephys correlation (p_adj < 0.05) and with validation supported in AIBS dataset. Gene names in bold indicate those we found to be previously studied for specific predicted ephys properties, based on our literature search. Symbols within heatmap: ·, p_adj <0.1; *, p_adj <0.05; **, p_adj <0.01; /, indicates inconsistency between discovery and AIBS validation dataset. B) Correlation between cell type-specific <i>Scn1a</i> (Na_v1.1) gene expression and maximum firing rate (FR_max) from discovery dataset (NeuExp/NeuElec, left) and Allen Institute dataset (AIBS, right). Grey trend lines indicate linear fit. C) Replotted data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005814#pcbi.1005814.ref033" target="_blank">33</a>], showing evoked firing rates at 300 pA current injection for parvalbumin positive interneurons in control and Scn1a heterozygous mice (Scn1a +/-). Data plotted as mean +/- SEM. D) Same as B, but for <i>Hcn3</i> and resting membrane potential (V_rest). E) Replotted data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005814#pcbi.1005814.ref034" target="_blank">34</a>], where V_rest from CA1 OLM interneurons was measured before and after the application of ZD7288, a selective antagonist of HCN channels. F) Same as B, but for <i>Gabrd</i> and V_rest. G) Replotted data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005814#pcbi.1005814.ref035" target="_blank">35</a>], showing V_rest recorded from dorsal motor nucleus of vagus neurons after application of THIP, a selective agonist of <i>Gabrd</i>-mediated tonic inhibition.</p