Arguably, no technique has had greater impact on the progress of biomedical research in recent years than quantitative real-time PCR. It has accelerated the pace of research and opened up exciting possibilities for detection and treatment of disease. The widepread adoption of qPCR as a standard technique is evident even in the most cursory literature search; the term “real-time PCR” returns over 14,000 papers published in 2009 alone. However, many scientists are concerned about the lack of standardization of qPCR experiments.
Quality assessment is a big fat elephant sitting in the room: everyone knows what needs to be done, but most researchers do not follow basic quality control guidelines. This serves to undermine the integrity of the scientific literature to such an extent, that a high proportion of publications are reporting technical or analytic artifacts”. Prof. Stephen Bustin, April 2009 SciTopics article.
The publication of a comprehensive set of guidelines for quantitative, real-time PCR highlights a need for greater consistency and standardization in reporting the results of qPCR and RT-qPCR analyses. The MIQE (Minimum Information for Quantitative Real-Time PCR) guidelines, published in the April 2009 edition of Clinical Chemistry, seek to create global consensus on how to best perform qPCR experiments and how to report qPCR results.
The paper, published by an international group of scientists from institutions throughout Europe and the United States, seeks to address issues from basic nomenclature (Cq, vs Ct, Cp, or TOP) to quality control of nucleic acids to oligo design and assay normalization. Some of the topics covered in detail are:
- Sample processing
- DNA and RNA quality and integrity
- Appropriate controls
- Comprehensive reporting of reagents, plasticware and protocols
- Inclusion of oligo sequences and accession numbers of target genes
- Publication of primer sequences
- Considerations of target secondary structure and specificity of oligos for target
- Inclusion of information on the validity of the reference genes for the sample type used
- Comprehensive reporting of data analysis methods
The stated aim of the guidelines is to support the integrity of the scientific literature, promote consistency between labs, and increase experimental transparency. The MIQE checklist contains a list of mandatory and required elements that at first glance is somewhat daunting. However, the required elements are clearly there to help consistency, transparency, accuracy and reproducibility, and many of them, such as reporting the accession number of the target gene, and accurately identifying the reagents and protocols used, do not appear to be cumbersome. Most address the need for commonsense controls, accurate descriptions of sample handling processes, and consistency in nomenclature and normalization of results.
A related GEN article, published in August states:
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”
The authors are actively seeking feedback from the research community on these guidelines and consider them to be a constantly evolving document that is very much a work in progress.
What do you think?
It is imperative that researchers clearly outline methods in publications for accurate interpretation of data between labs and around the world. Adopting standard experimental and reporting techniques will aid advancement in a particular field by reducing ambiguity in conclusions reached in published research.
Using consistent terminology is a good start, but the suggestions for appropriate controls and integrity of starting template provide quality control.
Because there are so many different instrument platforms, software packages and consumables, it is very important to clearly specify these things in publications. When studies are published with conflicting results, this information will allow researchers in the field to be sure the results are real and not simply due to differences in software algorithms, for example.
I hope that scientists will be open to employing the MIQE guidelines in future publications. In this world of blogs and forums, researchers can discuss and hone these suggestions as they are implemented in specific disciplines.