A stable combination of non-stable genes outperforms standard reference genes for RT-qPCR data normalization

Authors: DJARI, ANIS - University Of Toulouse; MADIGNIER, GUILLAUME - University Of Toulouse; CHEVRIN, CHRISTIAN - University Of Toulouse; VAN DER REST, BENOIT - University Of Toulouse; Giovannoni, James; BOUZAYEN, MONDHER - University Of Toulouse; PIRELLO, JULIEN - University Of Toulouse; MAZA, ELLE - University Of Toulouse

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/6/2024
Publication Date: 12/28/2024
Citation: Djari, A., Madignier, G., Chevrin, C., Van Der Rest, B., Giovannoni, J.J., Bouzayen, M., Pirello, J., Maza, E. 2024. A stable combination of non-stable genes outperforms standard reference genes for RT-qPCR data normalization. Scientific Reports. 14. Article 31278. https://doi.org/10.1038/s41598-024-82651-w.
DOI: https://doi.org/10.1038/s41598-024-82651-w

Interpretive Summary: The reverse transcription quantitative polymerase chain reaction (denoted qPCR) is a common and widespread technique of molecular biology for detecting and quantifying gene expression. qPCR has become the gold standard technique for nucleic acids quantification in many domains of life sciences, as medicine, environment, and plants, for both basic and applied research. In this study, we show that a suitable combination of non-stable genes outperforms the common use of stable genes for qPCR data normalization. Moreover, we show that such a reliable combination of genes can be found in silico using a comprehensive database of publicly available RNA-Seq data. As a case study, we developed our methodology on the tomato model plant (Solanum lycopersicum) using the RNA-Seq data from TomExpress.

Technical Abstract: Gene expression profiling is of key importance in all domains of life sciences, as medicine, environment, and plants, for both basic and applied research. qPCR remains a standard method for gene expression analyses, with its data normalization step being crucial for ensuring accuracy. Currently, the most widely used normalization method is based on the use of reference genes, assumed to be stably expressed across all experimental conditions. In the present study, we show that finding a stable combination of genes, regardless of their individual stability, outperforms standard reference genes for RT-qPCR data normalization. A stable combination of genes consists of a fixed number of genes whose individual expression balance each other all along experimental conditions of interest. Moreover, the present study shows that such an optimal combination of genes can be found using a comprehensive database of RNA-Seq data. As a case study, this new method was developed using the tomato model plant, with corresponding RNA-Seq data from the TomExpress database. However, the method is potentially applicable to other organisms with available RNA-seq data. Our results demonstrate the superiority of the reported method over commonly used housekeeping genes or other stably expressed genes.