Easy-to-use strategy for reference gene selection in quantitative real-time PCR experiments

Quantitative studies are commonly realised in the biomedical research to compare RNA expression in different experimental or clinical conditions. These quantifications are performed through their comparison to the expression of the housekeeping gene transcripts like glyceraldehyde-3-phosphate dehydrogenase (G3PDH), albumin, actins, tubulins, cyclophilin, hypoxantine phsophoribosyltransferase (HRPT), L32. 28S, and 18S rRNAs are also used as internal standards. In this paper, it is recalled that the commonly used internal standards can quantitatively vary in response to various factors. Possible variations are illustrated using three experimental examples. Preferred types of internal standards are then proposed for each of these samples and thereafter the general procedure concerning the choice of an internal standard and the way to manage its uses are discussed.

This paper aims to discuss various aspects of the use of reference genes in qPCR technique used in the thousands of present studies. Most frequently, these are housekeeping genes and they must meet several criteria so that they can lay claim to the name. Lots of papers report that in different conditions, for different organisms and even tissues the basic assumption—the constant level of the expression is not maintained for many genes that seem to be perfect candidates. Moreover, their transcription can not be affected by experimental factors. Sounds simple and clear but a great number of designed protocols and lack of consistency among them brings confusion on how to perform experiment properly. Since during selection of the most stable normalizing gene we can not use any reference gene, different ways and algorithms for their selection were developed. Such methods, including examples of best normalizing genes in some specific cases and possible mistakes are presented based on available sources. Numerous examples of reference genes applications, which are usually in too few numbers in relevant articles not allowing to make a solid fundament for a reader, will be shown along with instructive compilations to make an evidence for presented statements and an arrangement of future qPCR experiments. To include all the pitfalls and problems associated with the normalization methods there is no way not to begin from sample preparation and its storage going through candidate gene selection, primer design and statistical analysis. This is important because numerous short reviews available cover the topic only in lesser extent at the same time giving the reader false conviction of complete topic recognition.

Analysis of RNA expression using techniques like real-time PCR has traditionally used reference or housekeeping genes to control for error between samples. This practice is being questioned as it becomes increasingly clear that some housekeeping genes may vary considerably in certain biological samples. We used real-time reverse transcription PCR (RT-PCR) to assess the levels of 13 housekeeping genes expressed in peripheral blood mononuclear cell culture and whole blood from healthy individuals and those with tuberculosis. Housekeeping genes were selected from conventionally used ones and from genes reported to be invariant in human T cell culture. None of the commonly used housekeeping genes [e.g., glyceraldehyde-phosphate-dehydrogenase (GAPDH)] were found to be suitable as internal references, as they were highly variable (>30-fold maximal variability). Furthermore, genes previously found to be invariant in human T cell culture also showed large variation in RNA expression (>34-fold maximal variability). Genes that were invariant in blood were highly variable in peripheral blood mononuclear cell culture. Our data show that RNA specifying human acidic ribosomal protein was the most suitable housekeeping gene for normalizing mRNA levels in human pulmonary tuberculosis. Validations of housekeeping genes are highly specific for a particular experimental model and are a crucial component in assessing any new model.