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

Area of Science:

• Life sciences research, encompassing medicine, environmental science, and plant biology.
• Focus on gene expression profiling techniques.
• Application in both basic and applied scientific research.

Background:

• Quantitative polymerase chain reaction (qPCR) is a standard method for gene expression analysis.
• Accurate data normalization is critical for qPCR reliability.
• Current normalization relies on reference genes, often assumed to be stably expressed.

Purpose of the Study:

• To introduce a novel method for RT-qPCR data normalization using stable gene combinations.
• To demonstrate that a combination of genes outperforms individual reference genes.
• To show that optimal gene combinations can be identified computationally from RNA-sequencing data.

Main Methods:

• Utilizing comprehensive RNA-sequencing (RNA-Seq) databases for in silico analysis.
• Applying mathematical variance calculations to identify stable gene combinations.
• Developing and testing the method using the tomato (Solanum lycopersicum) model plant and the TomExpress database.

Main Results:

• A stable combination of genes, where individual expressions balance, provides superior normalization compared to single reference genes.
• The computational method successfully identified optimal gene combinations reflecting in vivo stability.
• The new method demonstrated significant improvements over commonly used housekeeping genes.

Conclusions:

• A stable combination of genes is more effective for RT-qPCR normalization than individual reference genes.
• Computational identification of gene combinations from RNA-Seq data is a viable and powerful approach.
• The method is applicable to various organisms with available RNA-Seq data, enhancing gene expression analysis accuracy.