A Whole Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies

While qPCR is widely recognized as among the most accurate of methods for quantifying gene expression, it is highly dependent on the use of reliable, stably expressed reference genes. With the increased availability of high-throughput methods for measuring gene expression, whole transcriptome approaches may be increasingly utilized for reference gene selection and validation. In this study, RNA-seq was used to identify a set of novel qPCR reference genes and also to evaluate a panel of traditional “housekeeping” reference genes in two species of the evolutionary model plant genus Mimulus More broadly, the methods proposed in this study can be used to harness the power of transcriptomes to identify appropriate reference genes for qPCR in any study organism, including emerging and non-model systems. Researchers at Whitman College found that RNA-seq accurately estimates gene expression means in comparison to qPCR, and that expression means are robust to moderate environmental and genetic variation. However, measures of expression variability were only in agreement with qPCR for samples obtained from a shared environment. This result, along with transcriptome-wide comparisons, suggests that environmental changes have greater impacts on expression variability than on expression means. The researchers discuss how this issue can be addressed through experimental design, and suggest that the ever-expanding pool of published transcriptomes represents a rich and low-cost resource for developing better reference genes for qPCR.

Expression variability estimates for selected traditional reference genes, based on CV

Expression variability in M. guttatus (A) and M. l. luteus (B) measured via RNA-seq on both T1 and T2 (left column) or via qPCR on T2 samples only (right column). Grey bars show the calculated CV when all tissue types are included and black bars show the calculated CV when petal tissue is excluded.