The polymerase chain reaction (PCR) turns 34 this year, and life scientists use it as much as ever—especially those technologies that quantify the DNA, such as real-time or quantitative PCR (qPCR) and digital PCR (dPCR). When the gloves come off, though, which of these techniques packs the most punch?
To quantify the DNA in samples with qPCR, scientists must create a standard curve and compare their sample unknowns against it. With dPCR, a sample is split into many sub-reactions that either contain the target molecule or does not; that binary approach (0 or 1) is why it’s called digital PCR.
To help scientists quickly assess the differences, William Taylor, director of the Molecular Resource Center at The University of Tennessee Health Science Center, points out that qPCR won’t give you the exact number of molecules, but it’s simpler and less expensive. He adds that qPCR “is just fine for most quantifications, even without a standard curve, since most folks want relative quantification—for example, control vs. treated.” On the other hand, he notes that qPCR gives you the number of molecules, but it costs more and requires specialized equipment.
If we add a little spread for those betting on these approaches, either technique could come out on top—depending on when and where they face off.
qPCR’s punching power
To detect, quantify and characterize nucleic acids for various applications, scientists can use qPCR. According to Rod Pennington, senior research scientist at Promega, “qPCR can be sensitive enough to detect vanishingly small quantities of a target nucleic acid in a very short period of time.”
This technique also provides flexibility because of the availability of many methods and chemistries for qPCR. Some methods even provide very high throughout. “With the right instrumentation, reagents and experimental design, dozens or even hundreds of samples can be analyzed simultaneously, with multiple targets detected or characterized in each sample,” Pennington says.
Some other experts agree. According to Nick Heredia, R&D manager at Bio-Rad, “qPCR has benefits in a wide dynamic range and slightly faster time to answer compared to dPCR.”
Despite those powerful punches from qPCR, everything comes with some weaknesses. Getting the method right, for example, can be challenging, and it requires optimization. “For ambitious multiplex designs, the optimization matrix can be complex, and you may go through several rounds of primer and probe design,” Pennington explains.
The most commonly noted challenge with qPCR is the need for a standard curve.
“The standard curve can introduce significant error into qPCR’s quantification, which leads to poor reproducibility and affects low-end sensitivity,” Heredia says. “Poor sensitivity means that qPCR cannot easily detect less than two-fold differences between samples without many replicates and rigorous statistical analysis.”
Regarding the best applications for qPCR, Pennington says: “qPCR is probably the best fit with applications designed to detect well-characterized target sequences, preferably in high-quality samples free of PCR inhibitors, where the overall nucleic acid background is controlled and well understood.” As examples, he mentions pathogen detection, single nucleotide polymorphism (SNP) detection and gene-expression analysis.
Straight dope on dPCR
Heredia says dPCR provides five benefits: accuracy, sensitivity, reproducibility, direct quantification and multiplexing. A single dPCR reaction, he says, “has the sensitivity for detecting small-fold differences, and partitioning further increases sensitivity for detection of rare targets common in cancer detection.” For sensitivity, everyone agrees that dPCR comes out on top. Compared with qPCR, says Heredia, “dPCR can be a couple of orders of magnitude more sensitive in detecting rare targets in a complex background.”
That high level of quantitation from dPCR comes without the need for a standard curve—the quantification is direct. This technique also provides day-to-day and lab-to-lab reproducibility, says Heredia, and the partitioning in dPCR “allows for higher-level multiplexing than qPCR.”
But like qPCR, dPCR isn’t perfect. It provides less dynamic range and takes longer than qPCR.
The benefits of dPCR, though, make it applicable to many areas of research. Heredia points out several, including “emerging applications, such as genome-editing assessments, single-cell gene expression, micro RNA and higher-level multiplexing.”
One team’s experience
Matthew Breen—Oscar J. Fletcher Distinguished Professor of Comparative Oncology Genetics at the North Carolina State University (NCSU) College of Veterinary Medicine, along with two of his lab members, Rachael Thomas and Katherine Kennedy, teamed up to provide the breadth of their experience with qPCR and dPCR. This team is also part of the CADET (CAncer DETection) program at Sentinel Biomedical.
In describing the benefits of dPCR, the NCSU/Sentinel Biomedical team writes: “PCR-related reagents needed for dPCR are comparable to qPCR, and so assays developed/used for qPCR may generally be redeployed for use in dPCR with little need for re-optimization.” As they note: “This applies to both hydrolysis-based assays—for example, TaqMan—as well as SYBR/EvaGreen-based assays.” The researchers have also found that dPCR can tolerate PCR inhibitors better than qPCR. Breen says, “Our side-by-side data of the same specimens run by qPCR and dPCR revealed that where qPCR failed, dPCR succeeded.”
With dPCR, these scientists do not always need replicates, which is very helpful when sample size is limited. They add that this “offers a cost saving over qPCR.” For samples that are very rare, replicates can be used to increase the sensitivity of dPCR. The NCSU/Sentinel Biomedical team summarizes: “In our hands, we have shown that dPCR can provide robust data from a much broader range of input DNA amount than for qPCR, with input amounts down to the picogram level.”
Still, the NCSU/Sentinel Biomedical researchers agree with the dPCR challenges mentioned by others, including cost and time. They add that dPCR “does not allow for melt-curve analysis to validate the nature of the amplicon.” Furthermore, “since amplicons cannot be recovered from the dPCR, the amplicons cannot be used for post-PCR analysis.”
Overall, looking for elements that are rare in a complex mixture plays to dPCR’s strong points.
“For example, in our hands, dPCR has proved to be far superior to qPCR in detecting the presence of known DNA mutations in the background of a high abundance of wild type sequence,” the NCSU/Sentinel Biomedical team points out. “This has been especially important for the early detection of cancers in liquid biopsies, and subsequent monitoring of residual disease during therapy.”
An oligonucleotide uppercut
At Eurofins MWG Operon, Philipp Wenter, vice president of manufacturing and R&D, and his colleagues create high-quality oligonucleotide reagents that can be used in PCR assays. These reagents include primers, probes and gene strands or genes that provide synthetic templates for positive controls.
In developing oligonucleotide reagents, says Wenter, “the main challenges are making high-quality primers and probes with consistent lot-to-lot reproducibility.” To produce pure primers and probes, manufacturers must even consider very small amounts of amplifiable template, which can contaminate the products. “This includes any trace amounts of genomic DNA that may be introduced during the manufacturing process of these reagents,” Wenter explains. “Contamination with trace amounts of human DNA and microbial DNA are naturally the most common occurrences here and need to be tightly controlled in the lab environment.”
As qPCR runs more cycles, any contaminate grows. “We have developed high-quality probes and primers that specifically minimize any type of genomic-DNA contamination,” Wenter explains.
Wenter and his colleagues are also working on tools for dPCR. “For synthetic DNA templates that are used for the positive control in digital PCR, we have developed high-quality gene fragments with very low error rate that can be used as positive controls, for example, in SNP analysis,” he says. Successful pilot studies with these fragments show that “the error rates are low enough that single-molecule sensitivity of digital PCR is not negatively impacted,” Wenter adds.
After a few rounds of reviews and recommendations all around, both qPCR and dPCR offer advantages and challenges. At the final bell, the winner will depend on the expertise and economics of a lab, as well as what must be accomplished.
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