Quantitative PCR (qPCR) technologies have exploded in the last decade. Commonly used for both diagnostics and basic research, new and exciting advances are being made in such areas as biomarker discovery and treatment monitoring. This rapid growth has also come with some problems, including what to call it. The technology is variously known as real-time PCR, real-time qPCR (RT-qPCR), qRT-PCR, and several other incarnations.
A new movement is under way that provides qPCR guidelines, not only for more consistent terminology, but also for more reliable experimental practice. Additionally, researchers are developing new ways to overcome obstacles in experimental designs and data analysis. Presenters at Select Biosciences’ “Advances in qPCR”, held recently in Berlin, described innovations as well as new strategies for overcoming bottlenecks.
The remarkable success and popularity of qPCR has also come with an assortment of challenges, according to Ivan Delgado, Ph.D., application scientist at Helixis. Dr. Delgado discussed a recent initiative designed to help ensure data relevance, accuracy, correct interpretation, and repeatability.
“In 2008 over 18,000 publications referenced qPCR. While most scientists demonstrate appropriate practice, some have failed to use this technique appropriately. A consortium of well-known researchers developed and recently published guidelines designed to provide a comprehensive toolkit for more uniformly performing and reporting qPCR data. As is often the case, the trick is in the details.”
The so-called MIQE (pronounced mykee) guidelines provide a recommended checklist for performing assays as well as documentation to accompany journal submissions (see August 2009 GEN, page 40).
“MIQE consists of 85 guidelines that can be quite daunting for scientists new to qPCR,” Dr. Delgado reported. “There is no question that many of these guidelines are definitely critical to generating reliable data, yet some are desirable best-practice. The next step for our community is to establish how to integrate these guidelines practically and seamlessly into everyday science.”
Two of the most critical guidelines address assay validation and template quality assessment. “It is important to assess the performance of the qPCR assay itself,” Dr. Delgado said. “For example, what is the efficiency of the reaction, the linear dynamic range, limit of detection, and the precision? qPCR reactions should first be validated by running a standard curve to evaluate the efficiency of the assay. Another important consideration is the need for good quality control of the nucleic acid samples, in particular RNA. Accurate quantification and quality assessments are needed to be sure of the input and integrity of the RNA in the sample.”
Judy Macemon, vp of marketing at Helixis, added that, although the guidelines are voluntary, many in the field believe that implementing the recommendations is good practice. “We are working closely with the guideline developers to make it easier for scientists to adopt them. Our new real-time PCR system software meets all of the MIQE guidelines. And the new Helixis instrument has been designated the first real-time PCR system to provide a MIQE-compliant solution.”
Where Not to Fail
“Junk in, junk out” goes the saying indicating the importance of good starting material. The same applies for data generated by qPCR, according to Ramon Goni, Ph.D., head of the qPCR division at Integromics.
“In our projects, we understood the critical need for checking datasets before they are analyzed,” Dr. Goni said. “Data analysis is only as good as the input data. So, even a correct analysis can produce wrong results if the input data has errors. Our strategy is to provide a workflow that minimizes discretional criteria and human input and instead interfaces directly with instrumentation and provides multilevel analyses.”
One of the company’s flagship products, RealTime StatMiner®, was designed with quality control in mind. It not only integrates filtering criteria, it automatically selects the best endogenous control. “We have designed this solution to allow great confidence in the analysis of experimental data because of built-in quality control measures,” Dr. Goni said.
“For example, the heatmap representation can immediately highlight sample outliers as well as find poor correlations with other samples under the same biological conditions. Also, the user can navigate gene by gene to assess the quality of replicates. It is compatible with all of Applied Biosystems’ RT-qPCR platforms and works with TaqMan® and SYBR® Green studies.”
Dr. Goni presented experimental data at the meeting that described the company’s completed studies utilizing tissue from patients with chronic obstructive pulmonary disease. “It is important to characterize the intactness of patient samples. We utilized RealTimeStatMiner to identify samples with mRNA degradation and outliers in measurements. This allowed us not only to validate the conclusions of the qPCR project, but, more importantly, to help optimize results. We’ve just submitted our data for publication.”
The company continues to expand and refine the software. “After every congress and meeting, we always come up with new ideas and new challenges. Methods continue to change, so it’s important to continually update your product.”