Comparison of relative mRNA quantification models and the impact of RNA integrity in quantitative real-time RT-PCR

• Published in: Biotechnology letters 2006-10, Vol.28 (19), p.1601-1613
• Main Authors: Fleige, Simone, Walf, Vanessa, Huch, Silvia, Prgomet, Christian, Sehm, Julia, Pfaffl, Michael W
• Format: Article
• Language: English
• Subjects: Algorithms; Base Sequence; Biological and medical sciences; Biotechnology; Efficiency; Fundamental and applied biological sciences. Psychology; Gene Expression; Gene expression study; messenger RNA; Models, Biological; Molecular Sequence Data; qRT-PCR; real-time RT-PCR; Reproducibility of Results; Reverse Transcriptase Polymerase Chain Reaction - methods; RNA integrity; RNA integrity number (RIN); RNA Stability; RNA, Messenger - analysis; RNA, Ribosomal, 18S - analysis; RNA, Ribosomal, 28S - analysis
• ISSN: 0141-5492; 1573-6776
• DOI: 10.1007/s10529-006-9127-2

Relative quantification in quantitative real-time RT-PCR is increasingly used to quantify gene expression changes. In general, two different relative mRNA quantification models exist: the delta-delta Ct and the efficiency-corrected Ct model. Both models have their advantages and disadvantages in terms of simplification on the one hand and efficiency correction on the other. The particular problem of RNA integrity and its effect on relative quantification in qRT-PCR performance was tested in different bovine tissues and cell lines (n = 11). Therefore different artificial and standardized RNA degradation levels were used. Currently fully automated capillary electrophoresis systems have become the new standard in RNA quality assessment. RNA quality was rated according the RNA integrity number (RIN). Furthermore, the effect of different length of amplified products and RNA integrity on expression analyses was investigated. We found significant impact of RNA integrity on relative expression results, mainly on cycle threshold (Ct) values and a minor effect on PCR efficiency. To minimize the interference of RNA integrity on relative quantification models, we can recommend to normalize gene expression by an internal reference gene and to perform an efficiency correction. Results demonstrate that innovative new quantification methods and normalization models can improve future mRNA quantification.