There is no magic bullet: the importance of testing reference gene stability in RT-qPCR experiments across multiple closely related species

Reverse Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR) is the current gold standard tool for the study of gene expression. This technique is highly dependent on the validation of reference genes, which exhibit stable expression levels among experimental conditions. Often, reference genes are assumed to be stable a priori without a rigorous test of gene stability. However, such an oversight can easily lead to misinterpreting expression levels of target genes if the references genes are in fact not stable across experimental conditions. Even though most gene expression studies focus on just one species, comparative studies of gene expression among closely related species can be very informative from an evolutionary perspective. In our study, we have attempted to find stable reference genes for four closely related species of grasshoppers (Orthoptera: Acrididae) that together exhibit a spectrum of density-dependent phenotypic plasticity. Gene stability was assessed for eight reference genes in two tissues, two experimental conditions and all four species. We observed clear differences in the stability ranking of these reference genes, both between tissues and between species. Additionally, the choice of reference genes clearly influenced the results of a gene expression experiment. We offer suggestions for the use of reference genes in further studies using these four species, which should be taken as a cautionary tale for future studies involving RT-qPCR in a comparative framework.

Optimal use of statistical methods to validate reference gene stability in longitudinal studies

Multiple statistical approaches have been proposed to validate reference genes in qPCR assays. However, conflicting results from these statistical methods pose a major hurdle in the choice of the best reference genes. Indeed, as their respective approaches to calculating reference gene stability is different, their suitability has to be tested for a given experimental setting. In this study, the stability of 10 candidate reference genes (Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26) was assessed using four common statistical approaches (GeNorm, NormFinder, Coefficient of Variation analysis and Pairwise ΔCt method) in a longitudinal setting. We used the development of the cerebellum and the spinal cord of mice as a model to assess the suitability of these statistical methods for reference gene validation. GeNorm and the Pairwise ΔCt were found to be ill suited due to a fundamental assumption in their stability calculations. Whereas, NormFinder and Coefficient of Variation analysis fare better provided they are used complementarily. We therefore devised a workflow combining these two methods for validating reference genes in developmental studies. This workflow proves to be more robust than any of the methods used individually.