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, Jacqueline C. Mitchell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Article numbere1009868
JournalPLoS Computational Biology
Volume18
Issue number2
DOIs
Publication statusPublished - Feb 2022

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