Methodological considerations on selection of stable reference genes for RT-qPCR in the neonatal rat brain in hypoxia and hypothermia

Real-time reverse transcription PCR (qPCR) normalized to an internal reference gene (RG), is a frequently used method for quantifying gene expression changes in neuroscience. Although RG expression is assumed to be constantly independent of physiological or experimental conditions, several studies have shown that commonly used RGs are not expressed stably. The use of unstable RGs has a profound effect on the conclusions drawn from studies on gene expression, and almost universally results in spurious estimation of target gene expression. Approaches aimed at selecting and validating RGs often make use of different statistical methods, which may lead to conflicting results. The present study evaluates the expression of 5 candidate RGs (Actb, Pgk1, Sdha, Gapdh, Rnu6b) as a function of hypoxia exposure and hypothermic treatment in the neonatal rat cerebral cortex -in order to identify RGs that are stably expressed under these experimental conditions- and compares several statistical approaches that have been proposed to validate RGs. In doing so, we first analyzed the RG ranking stability proposed by several widely used statistical methods and related tools, i.e. the Coefficient of Variation (CV) analysis, GeNorm, NormFinder, BestKeeper, and the {Delta}Ct method. Subsequently, we compared RG expression patterns between the various experimental groups. We found that these statistical methods, next to producing different rankings per se, all ranked RGs displaying significant differences in expression levels between groups as the most stable RG. As a consequence, when assessing the impact of RG selection on target gene expression quantification, substantial differences in target gene expression profiles were observed. As such, by assessing mRNA expression profiles within the neonatal rat brain cortex in hypoxia and hypothermia as a showcase, this study underlines the importance of further validating RGs for each new experimental paradigm considering the limitations of each selection method.