Normalization

To achieve optimal relative expression results, appropriate normalization strategies are required to control for experimental error (Vandesompele et al., 2002; Pfaffl et al., 2004), and to ensure identical cycling performance during real-time PCR. These variations are introduced by various processes required to extract and process the RNA, during PCR set-up and by the cycling process. All the relative comparisons should be made on a constant basis of extracted RNA, on analyzed mass of tissue, or an identical amount of selected cells (e.g. microdissection, biopsy, cell culture or blood cells) (Skern et al., 2005). To ensure identical starting conditions, the relative expression data have to be equilibrated or normalized according to at least one of the following variables:

• sample size/mass or tissue volume

• total amount of extracted RNA

• total amount of genomic DNA

• reference ribosomal RNAs (e.g. 18S or 28S rRNA)

• reference messenger RNAs (mRNA)

• total amount of genomic DNA

• artificial RNA or DNA molecules (= standard material)

But the quality of normalized quantitative expression data cannot be better than the quality of the normalizer itself. Any variation in the normal-izer will obscure real changes and produce artefactual changes (Bustin, 2002; Bustin et al., 2005).

It cannot be emphasized enough that the choice of housekeeping or lineage specific genes is critical. For a number of commonly used reference genes, processed pseudogenes have been shown to exist, e.g. for P-actin or GAPDH (Dirnhofer et al., 1995; Ercodani et al., 1988). Pseudogenes may be responsible for specific amplification products in a fully mRNA independent fashion and result in specific amplification even in the absence of intact mRNA. It is vital to develop universal, artificial, stable, internal standard materials, that can be added prior to the RNA preparation, to monitor the efficiency of RT as well as the kinetic PCR respectively (Bustin, 2002). Usually more than one reference gene should be tested in a multiple pair-wise correlation analysis and a summary reference gene index be obtained (Pfaffl et al., 2004). This represents a weighted expression of at least three reference genes and a more reliable basis of normalization in relative quantification can be postulated.

There is increasing appreciation of these aspects of qRT-PCR software tools were established for the evaluation of reference gene expression levels. geNorm (Vandesompele et al., 2002) and BestKeeper (Pfaffl et al., 2004) allows for an accurate normalization of real-time qRT-PCR data by geometric averaging of multiple internal control genes (http://medgen.ugent.be/ ~jvdesomp/genorm). The geNorm Visual Basic applet for Microsoft Excel® determines the most stable reference genes from a set of 10 tested genes in a given cDNA sample panel, and calculates a gene expression normalization factor for each tissue sample based on the geometric mean of a user defined number of reference genes. The normalization strategy used in geNorm is a prerequisite for accurate kinetic RT-PCR expression profiling, which opens up the possibility of studying the biological relevance of small expression differences (Vandesompele et al., 2002). These normalizing strategies are summarized and described in detail elsewhere (Huggett et al., 2005; LightCycler® Relative Quantification Software, 2001).

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