Background: The elucidation of gene expression patterns leads to a better understanding of biological processes. Real-time quantitative RT-PCR has become the standard method for in-depth studies of gene expression. A biologically meaningful reporting of target mRNA quantities requires accurate and reliable normalization in order to identify real gene-specific variation. The purpose of normalization is to control several variables such as different amounts and quality of starting material, variable enzymatic efficiencies of retrotranscription from RNA to cDNA, or differences between tissues or cells in overall transcriptional activity. The validity of a housekeeping gene as endogenous control relies on the stability of its expression level across the sample panel being analysed. In the present report we describe the first systematic evaluation of potential internal controls during tomato development process to identify which are the most reliable for transcript quantification by real-time RT-PCR.
Results: In this study, we assess the expression stability of 7 traditional and 4 novel housekeeping genes in a set of 27 samples representing different tissues and organs of tomato plants at different developmental stages. First, we designed, tested and optimized amplification primers for real-time RT-PCR. Then, expression data from each candidate gene were evaluated with three complementary approaches based on different statistical procedures. Our analysis suggests that SGN-U314153 (CAC), SGN-U321250 (TIP41), SGN-U346908 ("Expressed") and SGN-U316474 (SAND) genes provide superior transcript normalization in tomato development studies. We recommend different combinations of these exceptionally stable housekeeping genes for suited normalization of different developmental series, including the complete tomato development process.
Conclusion: This work constitutes the first effort for the selection of optimal endogenous controls for quantitative real-time RT-PCR studies of gene expression during tomato development process. From our study a tool-kit of control genes emerges that outperform the traditional genes in terms of expression stability.