Quantifying gene expression by traditional DNA detection methods is unreliable. Detection of mRNA on a northern blot or PCR products on a gel or Southern blot does not allow precise quantification. For example, over the 20–40 cycles of a typical PCR, the amount of DNA product reaches a plateau that is not directly correlated with the amount of target DNA in the initial PCR.
Real-time PCR can be used to quantify nucleic acids by two common methods: relative quantification and absolute quantification. Absolute quantification gives the exact number of target DNA molecules by comparison with DNA standards using a calibration curve. It is therefore essential that the PCR of the sample and the standard have the same amplification efficiency.
Relative quantification is based on internal reference genes to determine fold-differences in expression of the target gene. The quantification is expressed as the change in expression levels of mRNA interpreted as complementary DNA (cDNA, generated by reverse transcription of mRNA). Relative quantification is easier to carry out as it does not require a calibration curve as the amount of the studied gene is compared to the amount of a control reference gene.
As the units used to express the results of relative quantification are unimportant the results can be compared across a number of different RTqPCR. The reason for using one or more housekeeping genes is to correct non-specific variation, such as the differences in the quantity and quality of RNA used, which can affect the efficiency of reverse transcription and therefore that of the whole PCR process. However, the most crucial aspect of the process is that the reference gene must be stable.
The selection of these reference genes was traditionally carried out in molecular biology using qualitative or semi-quantitative studies such as the visual examination of RNA gels, northern blot densitometry or semi-quantitative PCR (PCR mimics). Now, in the genome era, it is possible to carry out a more detailed estimate for many organisms using transcriptomic technologies. However, research has shown that amplification of the majority of reference genes used in quantifying the expression of mRNA varies according to experimental conditions. It is therefore necessary to carry out an initial statistically sound methodological study in order to select the most suitable reference gene.
A number of statistical algorithms have been developed that can detect which gene or genes are most suitable for use under given conditions. Those like geNORM or BestKeeper can compare pairs or geometric means for a matrix of different reference genes and tissues.