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

Nirmal Kumar Sampathkumar, Venkat Krishnan Sundaram, Prakroothi S Danthi, Rasha Barakat, Shiden Solomon, Mrityunjoy Mondal, Ivo Carre, Tatiana El Jalkh, Aïda Padilla-Ferrer, Julien Grenier, Charbel Massaad and Jacqueline C Mitchell

PLOS Computational Biology, 2022, vol. 18, issue 2, 1-24

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, however, we demonstrate that the statistical approach to determine the best reference genes from commonly used conventional 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 conventional reference genes render the same results as stable reference genes selected from RNA-Seq data. 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.Author summary: RTqPCR is a powerful technique that is widely used to quantify gene expression in research and diagnostics of different diseases. The technique involves making multiple copies (amplification) of a specific target DNA. The amplified target DNA binds to a molecule that emits fluorescence upon binding. The extent of fluorescence correlates to the amount of DNA present. To precisely quantify this fluorescence (and thus the quantities of target DNA), internal control genes also called as reference genes need to be determined. Such genes, in principle, do not have varied expression across samples would exhibit the same fluorescence in all samples. They can thus be used normalize the expression of the Target DNA. Unfortunately, choosing the right reference gene is very tricky and poor choice of reference genes results in unreliable data both in research and in diagnostics. In this study, we validate a statistical approach to find stably expressed reference genes for any experimental setting using a given set of candidates. We compare our approach to RNA sequencing which quantifies the expression of thousands of genes at the same time. We highlight the advantageous of our approach which is cost effective and saves a lot of time when compared to sequencing.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009868

DOI: 10.1371/journal.pcbi.1009868

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