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Front Mol Neurosci. 2018 Oct 8;11:363. doi: 10.3389/fnmol.2018.00363. eCollection 2018.

Assessing Transcriptome Quality in Patch-Seq Datasets.

Author information

1
Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
2
Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.

Abstract

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.

KEYWORDS:

cell types; gene expression profiling; ion channels; meta-analysis; neurophysiology; patch-clamp techniques; sequencing data analysis

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