A two-part primer in quantitative PCR validation

If you’re a research scientist using quantitative PCR (qPCR), you must consider validation. Without validating your qPCR assay, you’ll never have confidence in your data. In this two-part article, we’ll discuss the most important terms used in discussions about qPCR assay validation.

Here, in part one, we address inclusivity, cross-reactivity, and linear dynamic range. In part two, we discuss the limit of detection, the limit of quantification, and assay precision and accuracy.

We also introduce our qPCR validation services. Rather than struggle with what can be a challenging process, please speak with our team about outsourcing these validation steps and achieving confidence in your PCR protocols.

What goes wrong when quantitative PCR isn’t validated

PCR is an incredibly powerful technique that amplifies DNA exponentially (the number of DNA copies theoretically doubles after each reaction cycle). Theoretically, starting from just a single double-strand DNA molecule, after 40 rounds of amplification, PCR could produce 1000 billion strands of the same sequence. But when misused, this powerful method can lead scientists to erroneous conclusions. Consider the researchers in the 1990s that believed to have extracted DNA from dinosaur bones and amber-embedded insects. It turned out that the researchers had actually amplified modern contaminating DNA.

This was unfortunate for the dino-DNA hunters, but consider what might happen when clinical and pre-clinical researchers misuse PCR. This might mean investing (wasting) millions on a drug candidate that (wrongly) seemed full of promise. In the clinic, misuse of qPCR might result in misdiagnosis, poor patient management, or failure to detect treatment toxicity.

Clearly, it’s vital that any research scientist or clinician using this awesome method must consider qPCR validation.

The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)

Fortunately, the research community has long been aware of the dangers of poorly validated qPCRs. To address this problem, multiple groups have published guidelines that can be used to avoid bad PCRs.

In 2009, Bustin et al. published the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) in the journal Clinical Chemistry. With these guidelines, the authors hoped to “target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency”. This study has been cited almost 11,000 times and will be an important read for those using qPCR.

Consensus guidelines for the validation of qPCR assays in clinical research by the CardioRNA consortium

But it seems not everyone got the memo. This year, de Gonzalo-Calvo et al. published their guidelines for validating qPCR assays in clinical research. These authors cite the “noticeable lack of technical standardization” and hope to raise the standard of qPCRs beyond research-use-only PCRs (e.g., PCRs for in vitro diagnostics).

Demystifying quantitative PCR validation

While we recommend the above-cited articles, discussions about qPCR validation can quickly get complex, and it’s easy to get lost among the various definitions and jargon. Do you know your inclusivity from your linearity? Can you tell the difference between the limit of detection and the limit of quantification? Don’t panic; below (and in Part II of this series), we hope to provide no-nonsense definitions to these terms and help demystify qPCR validation. And, even if you’re still confused, you can always outsource these validation steps to us.

What is qPCR inclusivity?

Inclusivity measures how well the qPCR detects (includes) all of the target strains/isolates we intend to capture. Suppose I’m developing a qPCR to detect influenza A. In this case, my PCR must amplify DNA from all of those variants I’d like to capture, for example, influenza A H1N1, H1N2, and H3N2. I don’t want to leave any of them out.

Now imagine a qPCR that hasn’t been validated for inclusivity. It doesn’t detect the H3N2 variant, but nobody realizes it because they didn’t validate it. In this case, it’s easy to see how a patient infected with the H3N2 variant of influenza A can be misdiagnosed.

Choosing the targets for inclusivity isn’t always straightforward and requires serious assessment. But broadly, the inclusivity targets should be chosen to reflect the genetic diversity of species on which the assay will be based. The number of strains and the complexity of the collection will depend on the intended use of the qPCR assay, but International standards recommend using up to 50 well-defined (certified) strains of the target organism, if possible.

What is qPCR exclusivity (or cross-reactivity)

Exclusivity assesses how well the qPCR excludes genetically similar non-targets (e.g., cross-reactive species that are not of interest). Suppose I’m developing a qPCR to detect influenza A. In this case, I’ll need to be confident my PCR doesn’t also amplify influenza B. We can only be sure about this by testing (validating) our PCR assay.

Now imagine a qPCR that hasn’t been validated for exclusivity. It (wrongly) detects influenza B when it’s intended to detect only influenza A. Any patient infected with influenza B would now be misdiagnosed as positive for influenza A when using this faulty PCR.

Both the exclusivity and inclusivity validation tests should be done in two parts: in silico and experimental. In the in silico part, using available genetic databases, the oligonucleotide, probe, and amplicon sequences are checked for sequence similarities/differences among targets/non-targets. When the in silico data looks good, we can move to the bench and test everything works as it should; we should include all the targets we want and exclude those we don’t.

Linear dynamic range

When validating a qPCR, it’s important to know the assay’s linear dynamic range. Remember that a qPCR starts with a template (input) and ends with a signal (usually fluorescence). The linear dynamic range is the range of templates (the fewest to the most DNA templates) over which the fluorescent emission (the signal) is directly proportional to the concentration DNA template (the input). So, we can be confident our qPCR results are quantitative when the starting DNA template (or RNA) amount is within this range.

The linear dynamic range should always be tested using a commercial standard or a sample of known concentration. This should be done for every target amplicon and every pair of primers that will be used.

To do this, a seven 10-fold dilution series of the DNA standard (in triplicate) is prepared and run in the assay (a well-optimized PCR test will have a linear range of 6–8 orders of magnitude). Each dilution will provide a threshold cycle value (Ct value). When plotting these data on a logarithmic graph, the Ct value and dilution factor should fit a straight line. The linear range of this plot is the linear dynamic range of the input template. Typically, linearity (R2) values of ≥ 0.980 are considered acceptable. The primer pair’s efficiency will also need to be between 90% and 110%. When the qPCR’s Ct values fall within the linear range of the assay, then it’s possible to report these as quantitative results.

Limit of detection, limit of quantification, assay precision, and assay accuracy

If you found the above useful, you can read more about qPCR validation in Part 2 of the series.

VRS qPCR services

Given the intricacies and nuances of qPCR, it can be a daunting task to understand, let alone complete a quality qPCR project. With a focus on viral detection and quantification, VRS offers a wide range of qPCR services. We can develop and fully validate your assay so that you can have confidence testing even your most precious samples. Get in touch with us and consider outsourcing your qPCR needs to VRS to save time and achieve full confidence in your results.

Edited by Reckon Better Scientific Editing