RNA-Seq is not required to determine stable reference genes for qPCR normalization

Abstract

Assessment of differential gene expression by qPCR is heavily influenced by the choice of reference genes. Although numerous statistical approaches have been proposed to determine the best reference genes, they can give rise to conflicting results depending on experimental conditions. Hence, recent studies propose the use of RNA-Seq to identify stable genes followed by the application of different statistical approaches to determine the best set of reference genes for qPCR data normalization. In this study, we demonstrate that the statistical approach to determine the best reference genes from randomly selected candidates is more important than the preselection of stable candidates from RNA-Seq data. Using a qPCR data normalization workflow that we have previously established; we show that qPCR data normalization using randomly chosen conventional reference genes renders the same results as stable reference genes selected from RNA-Seq data. Furthermore, the differential expression of target genes assessed by qPCR using our normalization strategy is comparable to RNA-Seq results. We validated these observations in two distinct cross-sectional experimental conditions involving human iPSC derived microglial cells and mouse sciatic nerves. These results taken together show that given a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data do not offer any significant advantage over commonly used reference genes for normalizing qPCR assays.

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“…This validation was deemed valuable, particularly for genes susceptible to normalization biases based on factors such as transcript length, expression levels, and other unidentified parameters. 55 Therefore, validation of the genes of interest would be confirmed through RT-qPCR. The expression levels of genes associated with the protein quality control system were previously demonstrated in Section 2.7.…”