Accurate measurements of mRNA expression levels in tissues or cells are crucially dependent on the use of relevant reference genes for normalization of data. In this study we used quantitative real-time PCR and two Excel-based applets (geNorm and BestKeeper) to determine the best reference genes for quantification of target gene mRNA in a complex tissue organ such as the guinea pig cervix. Gene expression studies were conducted in cervical epithelium and stroma during pregnancy and parturition and in cultures of primary cells from this tissue. Among 15 reference gene candidates examined, both geNorm and BestKeeper found CLF1 and CLTC to be the most stable in cervical stroma and cervical epithelium, ACTB and PPIB in primary stroma cells, and CLTC and PPIB in primary epithelial cells. The order of stability among the remaining candidate genes was not in such an agreement. Commonly used reference such as GAPDH and B2M demonstrated lower stability. Determination of pairwise variation values for reference gene combinations using geNorm revealed that the geometric mean of the two most stable genes provides sufficient normalization in most cases. However, for cervical stroma tissue in which many reference gene candidates displayed low stability, inclusion of three reference genes in the geometric mean may improve accuracy of target gene expression level analyses. Using the top ranked reference genes we examined the expression levels of target gene PTGS2 in cervical tissue and cultured cervical cells. We compared the results with PTGS2 expression normalized to the least stable gene and found significant differences in gene expression, up to 10-fold in some samples, emphasizing the importance of appropriately selecting reference genes. We recommend using the geometric mean of CFL1 and CLTC for normalization of qPCR studies in guinea pig cervical tissue studies, ACTB and PPIB in primary stroma cells and CLTC and PPIB in primary epithelial cells from guinea pig.
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)