Relative quantification or comparative quantification measures the relative change in mRNA expression levels. It determines the changes in steady-state mRNA levels of a gene across multiple samples and expresses it relative to the levels of another RNA. Relative quantification does not require a calibration curve or standards with known concentrations and the reference can be any transcript, as long as its sequence is known (Bustin, 2002). The units used to express relative quantities are irrelevant, and the relative quantities can be compared across multiple real-time RT-PCR experiments (Orlando et al., 1998; Vandesompele et al., 2002; Hellemans et al., 2006). It is the adequate tool to investigate small physiological changes in gene expression levels. Often constant expressed reference genes are chosen as reference genes, which can be co-amplified in the same tube in a multiplex assay (as endogenous controls) or can be amplified in a separate tube (as exogenous controls) (Wittwer et al., 2001; Livak, 1997, 2001; Morse et al., 2005). Multiple possibilities are obvious to compare a gene of interest (GOI) mRNA expression to one of the following parameters. A gene expression can be relative to:
• an endogenous control, e.g. a constant expressed reference gene or another GOI
• an exogenous control, e.g. an universal and/or artificial control RNA or DNA
• an reference gene index, e.g. consisting of multiple averaged endogenous controls
• a target gene index, e.g. consisting of averaged GOIs analyzed in the study
To determine the level of expression, the differences (A) between the threshold cycle (Ct) or crossing points (CP) are measured. Thus the mentioned methods can be summarized as the ACP methods (Morse et al., 2005; Livak and Schmittgen, 2001). But the complexity of the relative quantification procedure can be increased. In a further step a second relative parameter can be added, e.g. comparing the GOI expression level relative to:
• a nontreated control
• healthy individuals
These more complex relative quantification methods can be summarized as AACP methods (Livak and Schmittgen, 2001).
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