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Front. Mol. Neurosci. | doi: 10.3389/fnmol.2018.00363

Assessing transcriptome quality in patch-seq datasets

 Shreejoy J. Tripathy1, 2*, Lilah Toker1, 2,  Claire Bomkamp2, B. O. Mancarci1, 2,  Manuel Belmadani1, 2 and  Paul Pavlidis1, 2*
  • 1Michael Smith Laboratories, University of British Columbia, Canada
  • 2Michael Smith Laboratories, University of British Columbia, Canada

Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented single-cell 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 single-cell 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: Gene Expression Profiling, Patch-Clamp Techniques, Sequencing Data Analysis, Neurophysiology, Meta-analysis, Ion Channels, cell types

Received: 11 Jun 2018; Accepted: 13 Sep 2018.

Edited by:

Bruno Cauli, Centre national de la recherche scientifique (CNRS), France

Reviewed by:

Ludovic Tricoire, Université Pierre et Marie Curie, France
Elisa L. Hill-Yardin, RMIT University, Australia  

Copyright: © 2018 Tripathy, Toker, Bomkamp, Mancarci, Belmadani and Pavlidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Shreejoy J. Tripathy, University of British Columbia, Michael Smith Laboratories, Vancouver, BC, Canada, stripat3@gmail.com
Dr. Paul Pavlidis, University of British Columbia, Michael Smith Laboratories, Vancouver, BC, Canada, paul@msl.ubc.ca