Quantification of microRNAs – MIQE challenges and solutions

Abstract
According to the MIQE guidelines, the assessment of total RNA integrity and the normalisation strategy are essential steps to verify meaningful microRNA expression results in real-time RT-PCR, RT-qPCR arrays, and hybridisation microarrays. It was previously shown (Fleige et al., 2006) that degraded total RNA strongly compromises the experimental expression data of downstream applications. Integrity can be tested by commercially available automated capillary-electrophoresis systems, e.g. Agilent Bioanalyzer 2100 or Bio-Rad Experion, which are on the way to become the standard in RNA quality assessment. Herein, the importance of overall RNA integrity was analyzed by determining the RNA quality of different tissues, after various extraction procedures, and degradation levels. The significant correlation of total RNA integrity on RT-qPCR performance on mRNA and microRNA expression profiling is discussed. On the basis of the derived results we can argue that real-time RT-PCR performance on microRNA level is highly affected by the total RNA integrity (Becker et al., 2010). We can recommend a RIN/RQI higher than five as good RNA quality and suitable for downstream mRNA and microRNA quantification. Comparative studies on microRNA profiling have been limited to different profiling platforms or the comparison of normalization techniques within one platform. The impact of seven different normalization methods (see below) on intra- and inter-platform performance was investigated: 1) one-colour hybridization-based array (Agilent Technologies); 2) multiplex RT followed by qPCR low-density array (TLDA, Applied Biosystems); 3) singleplex RT-qPCR assays (Applied Biosystems). Intra-platform comparisons revealed highest trueness in identifying differential expression for loess, loessM, INV, and GPA normalized data across platforms and biological effects. This indicates the successful adoption of loessM and GPA to one-colour microRNA profiling experiments. Inter-platform comparisons of the two profiling systems suggested quantile normalization of Agilent Technology array and geometric mean normalization of TLDA maximized the number of inter-platform validated differentially expressed microRNAs most reliably. Thus, when using one profiling platform we recommend applying loess, loessM, INV or GPA, however, for inter-platform validations normalization methods which identify rather high numbers of differentially expressed microRNAs such as quantile for hybridization platforms or geometric mean normalization for RT-qPCR systems can be recommended.