Realtime qPCR amplification efficiency

Each analyzed sample generates an individual amplification history during real-time fluorescence analysis. As we know from laboratory practice, biological replicates, even technical replicates, result in significantly different fluorescence curves as a result of sample-to-sample variations (Figure 3.2). Changing PCR efficiencies are caused by RT and PCR inhibitors or enhancers, and by variations in the RNA pattern extracted. Thus the shapes of fluorescence amplification curves differ in the background level (noisy, constant or increasing), the take-off point (early or late), the steepness (good

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Cycles

Figure 3.2

Variation of fluorescence amplification plot of three different genes run in quadruplicates.

or bad efficiency), the change-over to the plateau phase (quick or steady), and in the appearance of the PCR plateau (constant, in or decreasing trend) (Tichopad et al., 2003; Tichopad et al., 2004). The PCR amplification efficiency bears the biggest impact on amplification kinetics and is critically influenced by PCR reaction components. Therefore CP determination of the threshold level and in consequence the accuracy of the quantification results are influenced by the amplification efficiency. The efficiency evaluation is an essential marker and the correction is necessary in real-time gene quantification (Rasmussen, 2001; Liu and Saint, 2002a; Liu and Saint, 2002b; Tichopad et al., 2003; Meijerink et al., 2001).

A constant amplification efficiency in all compared samples is one important criterion for reliable comparison between samples. This becomes crucially important when analyzing the relationship between an unknown and a reference sequence, which is performed in all relative quantification models. In experimental designs employing standardization with reference genes, the demand for invariable amplification efficiency between target and standard is often ignored, despite the fact that corrections have been suggested in the recent literature (Pfaffl, 2001; Pfaffl et al., 2002; Liu and Saint, 2002a; Liu and Saint, 2002b; Soong et al., 2000; Wilhelm et al., 2003). A correction for efficiency, as performed in efficiency corrected mathematical models (eqs. 5-8), is strongly recommended and results in a more reliable estimation of the 'real' expression changes compared with NO efficiency correction. Even small efficiency differences between target and reference generate false expression ratio, and the researcher over- or underestimates the initial mRNA amount. A theoretic difference in qPCR efficiency (AE) of 3% (AE = 0.03) between a low copy target gene and medium copy reference gene generate falsely calculated differences in expression ratio of 242% in case of Etarget > Eref after 30 performed cycles. This gap will increase dramatically by higher efficiency differences AE = 0.05 (432%) and AE = 0.10 (1,744%). The assessment of the sample specific efficiencies must be carried out before any relative calculation is done. Some tools are available to correct for efficiency differences. The LightCycler® Relative Expression Software (2001), Q-Gene (Muller et al., 2002), qBase (Hellmans et al., 2006), SoFar (Wilhelm et al., 2003), and various REST software applications (LightCycler® Relative Quantification Software, 2001; Pfaffl et al., 2002; Pfaffl and Horgan, 2002; Pfaffl and Horgan, 2005) allow the evaluation of amplification efficiency plots. In most of the applications a triplicate determination of real-time PCR efficiency for every sample is recommended. Therefore efficiency corrections should be included in the relative quantification procedure and the future software applications should calculate automatically the qPCR efficiency (Pfaffl, 2004).

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