Quantification of differential gene expression by multiplexed targeted resequencing of cDNA

Peer Arts, Jori van der Raadt, Sebastianus H.C. van Gestel, Marloes Steehouwer, Jay Shendure, Alexander Hoischen and Cornelis A. Albers ()
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Peer Arts: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Jori van der Raadt: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Sebastianus H.C. van Gestel: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Marloes Steehouwer: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Jay Shendure: University of Washington
Alexander Hoischen: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Cornelis A. Albers: Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Whole-transcriptome or RNA sequencing (RNA-Seq) is a powerful and versatile tool for functional analysis of different types of RNA molecules, but sample reagent and sequencing cost can be prohibitive for hypothesis-driven studies where the aim is to quantify differential expression of a limited number of genes. Here we present an approach for quantification of differential mRNA expression by targeted resequencing of complementary DNA using single-molecule molecular inversion probes (cDNA-smMIPs) that enable highly multiplexed resequencing of cDNA target regions of ∼100 nucleotides and counting of individual molecules. We show that accurate estimates of differential expression can be obtained from molecule counts for hundreds of smMIPs per reaction and that smMIPs are also suitable for quantification of relative gene expression and allele-specific expression. Compared with low-coverage RNA-Seq and a hybridization-based targeted RNA-Seq method, cDNA-smMIPs are a cost-effective high-throughput tool for hypothesis-driven expression analysis in large numbers of genes (10 to 500) and samples (hundreds to thousands).

Date: 2017
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DOI: 10.1038/ncomms15190

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