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Correct identification of reference genes in cell lines used in QRT PCR gene expression experiments 

QRT PCR is a powerful tool to determine relative gene expression in cells that have been exposed to two or more experimental conditions.  RNA extracted from the cells is reverse transcribed to cDNA before QRT-PCR amplification with primers specific to the genes of interest (GOI).  Proper use of this method requires a step to normalise results obtained for the target GOIs to the values obtained from reference genes whose expression is assumed to be unaffected by experimental conditions.  But how do you know which are the most appropriate reference genes to use in your experiments? 

Literature review shows that GAPDH and Beta Actin (ACTB) are amongst the most popular genes selected but often, researchers do not check to see if these genes are stably expressed under the conditions of the experiment.  One strategy to determine optimal reference genes is to perform a GeNorm analysis (Vandesompele et al (2002)1).  QRT-PCR, using a panel of primers for a candidate set of reference genes is performed on a representative set of cDNA samples from the study.  Analysis of the data using the algorithms developed by Vandesompele et al identifies not only the most stable reference genes but also the minimum number required.  If more than one reference gene is used, gene expression of the GOIs should be normalised to the geometric means of Ct values of the reference genes. 

In ECACC, as part of a recent study using three dimensional spheroid cultures of A549 lung cancer cells (ECACC 86012804) we carried out a GeNorm analysis to find the optimum reference genes for our experiments. The results are shown in the graph below; the lower the GeNorm M value, the more stable the gene expression.




In conclusion, we showed that the optimum reference genes were TOP1 and ATP5B and that we needed to normalise the Ct values from our GOIs to the geometric means of those obtained for these two genes.  Interestingly, GAPDH and ACTB, two commonly used reference genes, were significantly less stable and not appropriate for our study.



1. Vandesompele, J. et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol.3 (2002)



October 2016




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