Real-time quantitative PCR (qPCR) has become a definitive technique for quantification of differences in gene expression levels between samples. Over the past 10 years, the popularity of this method has grown exponentially, with the publication of well over 25,000 papers from diverse fields of science. Apart from the broad applicability of the technique, one of the central factors that have stimulated its impressive growth is the increased demand from journal review panels for the use of RT-qPCR to support phenotypic observations with quantitative, molecular data. Furthermore, gene expression analysis is now being used to support protein expression data from proteomics-based assays.
In this section we discuss MIQE guidelines that define the minimum information that needs to be provided when publishing qPCR experiments. We also describe RDML an XML-based markup language created for the consistent reporting of real-time PCR experiments.
Related Topics: qPCR Reagents, qPCR Instrumentation, Assay Design and Optimization, Real-Time PCR Data Analysis, Real-Time PCR Troubleshooting, High Resolution Melting, and PCR Primer and Probe Chemistries.
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A number of technical deficiencies can affect RT-qPCR assay performance, including improper experimental design, inadequate controls and replicates, lack of well-defined experimental conditions and sample handling techniques, poor quality of the RNA sample, suboptimal choice of primers for reverse transcription and qPCR reactions, lack of validation of reference genes, and inappropriate methods of data analysis. In an effort to assist the scientific community in producing consistent, high-quality data from qPCR experiments, the MIQE guidelines were published (Bustin et al. 2009) to provide directions for researchers to develop robust qPCR experiments and critically evaluate published qPCR experimental conclusions.
Given the highly dynamic nature of mRNA transcription and the potential variables introduced in sample handling and in the downstream processing steps (Garson et al. 2009), a standardized approach to each step of the RT-qPCR workflow is critical for reliable and reproducible results. The MIQE guidelines provide this framework with a checklist of experimental parameters to ensure high-quality results that will meet the acceptance criteria of any journal (Bustin et al. 2009).
Failure to properly account for individual quality control factors such as RNA integrity (see table below, Gingrich et al. 2008) can easily lead to erroneous conclusions, and in a worst case scenario, publication retraction (Bohlenius et al. 2007).
Impact of RNA degradation on real-time qPCR Cq*
| Incubation time, hr |
Cq | |||||
| RQI | 18S rRNA | β-Actin | GAPDH | HPRT | β-Tubulin | |
| 0 | 9.9 | 11.2 | 16.5 | 18.1 | 22.5 | 19.7 |
| 1 | 6.9 | 11.6 | 18.2 | 20.5 | 25.1 | 20.9 |
| 3 | 3.5 | 12.0 | 20.1 | 22.9 | 27.9 | 22.7 |
| 5 | 1.8 | 12.1 | 22.0 | 26.1 | 29.5 | 24.6 |
| 7 | 1.5 | 12.5 | 23.5 | 28.0 | 30.3 | 26.5 |
| ΔCq | — | 1.3 | 7.0 | 9.9 | 7.5 | 6.8 |
* Real-time qPCR was performed on liver carcinoma RNA samples that were degraded for different lengths of time by incubation at 90°C. Mean Cq values of five transcripts obtained from triplicate reactions were determined at a threshold of 100 relative fluorescence units (RFU). ΔCq indicates the change in Cq value over the 7 hr degradation period. Cq, quantification cycle; RQI, RNA quality indicator.
The publication of the MIQE guidelines was followed by an XML-based RDML for the consistent reporting of real-time PCR data, developed by the RDML consortium (Lefever et al. 2009). This consortium is active in the development of appropriate and standardized terminology, guidelines on minimum information for biological and biomedical investigations, and a flexible and universal data file structure with tools to create, process, and validate RDML files. Additional detailed information about the RDML project is available at www.rdml.org.
The ultimate goal of RDML and MIQE is to establish a clear framework within which to conduct RT-qPCR experiments and to provide an established yardstick for reviewers and editors to use in the evaluation of the technical quality of submitted manuscripts. As a consequence, investigations that use this widely applied methodology will produce data that are more consistent, more comparable, and ultimately more reliable.
For over a decade now, Bio-Rad has provided researchers with tools and training for real-time PCR assay optimization and validation that directly support the MIQE guidelines objectives. Most recently, Bio-Rad updated the CFX Maestro™ Software to enable RDML data export and entered into an arrangement with Biogazelle to distribute qbase+ software with our real-time PCR detection systems. This program enables users to annotate their experiments with MIQE-compliant experimental details and analyze their qPCR data using correct mathematical and statistical tools.
Practical step-by-step guidelines on how to implement the MIQE guidelines can be found in our technical note literature, downloadable from the documents tab below.
Böhlenius H et al. (2007). Retraction, Science 316, 367. PMID: 17446370
Bustin SA et al. (2009). The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55, 611–622. PMID: 19246619
Garson JA et al. (2009). Unreliable real-time PCR analysis of human endogenous retrovirus-W (HERV-W) RNA expression and DNA copy number in multiple sclerosis. AIDS Res Hum Retroviruses 25, 377–378; author reply 379–381. PMID: 19292592
Gingrich J et al. (2008). Effect of RNA degradation on data quality in quantitative PCR and microarray experiments. Bio-Rad Bulletin 5452.
Lefever S et al. (2009). RDML: Structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res 37, 2065–2069. PMID: 19223324
Fliege F and Pfaffl MW (2006). RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med 27, 126–139. PMID: 16469371
Gutierrez L et al. (2008). The lack of a systematic validation of reference genes: A serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J 6, 609–618. PMID: 18433420
Hellemans J et al. (2007). qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8, R19. PMID: 17291332
Vandesompele J et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3, research0034.1– research34.11. PMID: 12184808