Reliable gene expression analysis by reverse transcription-quantitative PCR: reporting and minimizing the uncertainty in data accuracy

Abstract

Reverse transcription-quantitative PCR (RT-qPCR) has been widely adopted to measure differences in mRNA levels; however, biological and technical variation strongly affects the accuracy of the reported differences. RT-qPCR specialists have warned that, unless researchers minimize this variability, they may report inaccurate differences and draw incorrect biological conclusions. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines describe procedures for conducting and reporting RT-qPCR experiments. The MIQE guidelines enable others to judge the reliability of reported results; however, a recent literature survey found low adherence to these guidelines. Additionally, even experiments that use appropriate procedures remain subject to individual variation that statistical methods cannot correct. For example, since ideal reference genes do not exist, the widely used method of normalizing RT-qPCR data to reference genes generates background noise that affects the accuracy of measured changes in mRNA levels. However, current RT-qPCR data reporting styles ignore this source of variation. In this commentary, we direct researchers to appropriate procedures, outline a method to present the remaining uncertainty in data accuracy, and propose an intuitive way to select reference genes to minimize uncertainty. Reporting the uncertainty in data accuracy also serves for quality assessment, enabling researchers and peer reviewers to confidently evaluate the reliability of gene expression data.

Figure 1.

Flow Chart for the Identification of Suitable Reference Genes in New Experimental Conditions and Subsequent Related Experiments. A starting pool of minimum 10 reference genes, which may or not include a minimum number of traditional housekeeping genes, can be selected from sources such as reference gene papers or from transcriptome data. This pool should be evaluated by a chosen algorithm and evaluation criteria to identify appropriate reference genes. For subsequent related experiments (closely related experimental conditions or repeated experiments), it may be sufficient to start with the evaluation of the small number of genes that were used for normalization in previous experiments, but when these genes fail the criteria, additional ones need to be incorporated. Strongly deviating experimental conditions may require reevaluation of the original starting pool to identify the best genes for normalization, or may even need the incorporation of additional candidate reference genes.

Figure 2.

Representation of RT-qPCR Data in Column Graphs Including the Uncertainty on the Accuracy. Relative expression data (RT-qPCR; average ± se, n = 4 biological replicates from one experiment) of CAT2 in the leaves ([A] to [C]) and RBOHF in the roots ([D] to [F]) of Arabidopsis exposed to excess Zn (100, 250, and 500 μM) and relative to the control (0 μM = 1.0). Fold up- or downregulations are represented on a log₂ scale y axis. (The control condition equals 1.0; hence, bars are not visible but standard errors are.) The normalized data are represented in white and the non-normalized data are in black. The difference between these is indicated by a gray bar and visualizes the uncertainty on the accuracy of the estimated GOI up- or downregulation, which varies according to the reference genes used for normalization. Normalization of data was performed with ACT2 only ([A] and [D]), four previously validated reference genes ([B] and [D]), or the gene(s) proposed by the GrayNorm algorithm yielding the lowest level of uncertainty ([C] and [F]). Statistics (one-way ANOVA and Dunnett comparison after testing normality with Shapiro-Wilk test and homoscedasticity with Bartlett test; *P < 0.05) were performed on both normalized and non-normalized data, and both data sets should yield significance to conclude a treatment effect. Uncertainty remains no matter the set of reference genes chosen, but the uncertainty is smaller in (C) and (F), allowing more accurate estimation of true GOI levels. This revealed, for example, that RBOHF is not upregulated in the roots at 500 μM Zn.

Figure 3.

Representation of RT-qPCR Data in Line Graphs Including the Uncertainty on the Accuracy. Representation of log₂ relative expression levels (RT-qPCR; average ± se, n = 3 to 8 biological replicates from one experiment) of NTRA in leaves of Arabidopsis exposed to Cd (5 and 10 μM) over a time period of 0, 2, 24, 48, and 72 h and relative to the control (0 μM, 0 h = 1.0). The normalized data are represented by full lines and the non-normalized data by dotted lines, which visualizes the uncertainty on the accuracy of estimated GOI up- or downregulation. The uncertainty level varies with the (set of) reference genes used for normalization. Normalization of data was performed with ACT2 (A), three previously validated reference genes (B), or the gene proposed by the GrayNorm algorithm yielding the lowest level of uncertainty (C). If statistical differences (one-way ANOVA and Tukey-Kramer adjustment; P < 0.05) were observed between treatments within a time point, they are indicated with different lowercase letters for normalized data and bold italic for non-normalized data (only indicated in [A], as they are the same for [B] and [C]). Both data sets should yield significance to conclude a concentration dependent effect. Uncertainty remains no matter which normalization chosen, but the uncertainty is smaller in (C), allowing more accurate estimation of true GOI expression levels.