Gene expression studies: How to obtain accurate and reliable data by quantitative real-time RT PCR

Abstract

Real-time RT PCR has been recognized as an accurate, reliable and sensitive method for quantifying gene transcription. However, several steps preceding PCR represent critical points and source of inaccuracies. These steps include cell processing, RNA extraction, RNA storage, assessment of RNA concentration and cDNA synthesis. To compensate for potential variability introduced by the procedure, normalization of target gene expression has been established. Accurate normalization has become an absolute prerequisite for the correct quantification of gene expression. Several strategies are in use for the normalization of data, including normalization to sample size, to total RNA or to an internal reference. Among these, the use of housekeeping genes as an internal (endogenous) control is the most common approach. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper reference gene for normalization have become increasingly stringent. The aim of this paper is to discuss the concept of normalization in mRNA quantification, as well as to discuss several statistical algorithms developed to help the validation of potential reference genes. By showing that the use of inappropriate endogenous control might lead to incorrect results and misinterpretation of experimental data, we are joining the creators of Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) in an attempt to convince scientists that proper validation of potential reference genes is an absolute prerequisite for correct normalization and, therefore, for providing accurate and reliable data by quantitative real-time RT PCR gene expression analyses.

Keywords

• real-time pcr
• reference gene
• normalization
• validation
• genorm
• normfinder
• miqe