Increased Confidence in Results
Increased experimental transparency constitutes a key advantage of adopting a relevant and comprehensive set of standards for qPCR. The MIQE guidelines stipulate full disclosure of reagents, protocols, and analysis methods, thus establishing that qPCR data meets a minimal set of standards. This increases confidence in its validity by ensuring that data meets a uniform quality benchmark before it is submitted for publication, rather than discovering flaws in the data after it is published.
The guidelines could eventually become a “quality label” denoting the data in a conforming publication as being of high quality. In addition, once data is provided in the RDML format, it can be carefully analyzed by the research community to assure that it supports the conclusions of the study.
The MIQE guidelines are in their initial formulation and are expected to be in continual evolution. The consortium is asking for feedback from the research community, commercial suppliers of qPCR products, and scientific journals to assure that they have optimal utility.
While a first look at the MIQE guidelines might lead researchers to assume that they will require significant additional effort and slow down the publication process, it is important to note that many of them are discretionary, with only those having the most impact on data quality being mandatory.
Most can be adopted quickly, because they are obvious—for example reporting the quantification cycle (Cq), previously variously referred to as Ct, Cp, or TOP, noting gene accession numbers, defining amplicon locations, and calculating qPCR efficiency. Adoption of the mandatory guidelines as a first strategy assures that key parameters affecting data quality are being addressed immediately and will have a swift impact on confidence levels in the data and the conclusions drawn from it.
Focus on pre-PCR
For RT-qPCR experiments, an additional focus on the pre-PCR steps is essential as these can be a major source of error. The guidelines address sampling, RNA stabilization, storage, and quality-assurance procedures. For example, degraded RNA has a substantial impact on qPCR results and the conclusions drawn from them. Significant degradation of target RNA can result in a Cq that is artificially high, leading to an underestimation of its concentration and copy number (Figure 1).
This effect of RNA quality on RT-qPCR results can be dramatic, with a difference of more than six cycles in the Cq values of intact and heavily degraded and intact RNA samples (Figure 2). Differential sensitivity of mRNA to degradation can have an enormous impact on the interpretation of qPCR results, as different reference genes appear to be suitable for normalization in degraded versus intact RNA samples (Table).
Normalizing RT-qPCR results to reference genes without knowledge of the degradation status of the RNA could thus lead to incorrect conclusions6. In addition, the degree of dependence can vary from one sample to another, emphasizing the need to understand the relationship between RNA quality and the results in any given study (Figure 3).