Precise, reliable data are foundational to scientific discovery. Without quality reagents and robust, well-maintained instruments, researchers cannot expect their experiments to yield “good” data that lead to valid conclusions. Trustworthy and consistent data are necessary to draw conclusions that move research forward, so it’s important that scientists can evaluate the data generated by every technique they employ.
Digital PCR (dPCR) has introduced unprecedented levels of precision and accuracy in nucleic acid quantification by enabling absolute quantification of nucleic acid targets without the requirement to normalize to a standard curve. dPCR involves diluting and partitioning a sample into thousands of discrete subunits in which individual PCR amplification reactions take place. Each partition ideally contains either zero or up to several template molecules. When fluorescent probes are used to identify amplified target DNA, the individual partitions can be analyzed in a binary manner as positive or negative based on the presence of fluorescence. Researchers can use the ratio of positive to negative droplets to quantify the initial amount of target sequence present in the sample with Poisson statistical analysis.
However, not all dPCR is created equal. As with any research method, the quality of the instrument and reagents as well as user ability can impact the outcome and reliability of dPCR experiments. Here, we’ll discuss what defines quality dPCR data and how users can maximize their success with robust dPCR platforms and good experimental practice to drive accurate scientific discovery.
What defines data quality in dPCR experiments?
As described above, dPCR analysis relies on a straightforward binary between “positive” partitions that contain the target sequence and “negative” partitions that do not. Clear threshold placement for defining positive versus negative partitions is thus paramount to achieving quality dPCR data. Optimal dPCR data has tight and consistent amplitudes for both positive and negative partitions, enabling researchers to set the threshold easily. This threshold should be above the uppermost limit of the negative population to minimize the probability of misclassifying negative partitions. Strong amplitude separation between populations provides greater confidence in experimental results, but inconsistency or jitter in analyses can compromise this distinction. Conversely, inconsistent amplitudes within each partition population can make it difficult for researchers to confidently set thresholds. If partitions are poorly separated, slight threshold changes can alter results and ultimately prevent accurate quantification.
Quality tools, quality results
One of the best ways to ensure success in dPCR experiments is to invest in validated assays and high-quality tools. Assays with inadequate target sequence specificity can lead to results with random positive data points reflective of false positives or weak and unclear results. Researchers need assays that are highly specific for the sequence of interest. Additionally, assays should have high amplification efficiency and a strong signal when amplifying the target sequence.
However, even high-quality assays can fail to yield good results without a robust dPCR platform. An ideal dPCR instrument should produce consistent partitions of defined size to ensure accurate quantification of nucleic acid targets. Additionally, high-quality optics and a stable optical bench are essential to minimize noise, maximize sensitivity, and prevent false positive and negative data points. An instrument with a large dynamic range for each detection channel can also ensure accurate detection and quantification of the target sequence in a sample at both very low and high concentrations, enabling use of this technique to its full potential.
Overcoming environmental factors and human error
Data quality can also be influenced by factors outside the dPCR platform. Unexpected positive partitions can be a commonly observed issue in dPCR data for a variety of reasons. Poor sample partitioning, which can produce unexpected positive partitions, may result from instrument dispersion issues or poor sample preparation. Random positive partitions can impact researchers’ ability to confidently set thresholds, reducing sensitivity and lowering the limit of detection. User error or environmental factors can contribute to cross-contamination of samples, producing inconsistencies and reducing sensitivity for samples with low copy numbers.
Finally, data analysis procedures can introduce inefficiency and room for error, particularly for inexperienced researchers. Selecting a dPCR platform with a low risk of cross-contamination and user-friendly analysis software can give researchers clarity and confidence in visualizing and analyzing data.
The cost of bad dPCR data
When dPCR data are compromised, a platform can become a burden to researchers rather than a powerful tool. Low-quality data introduce unnecessary uncertainty, placing responsibility on the researcher to make decisions about threshold setting and other factors on a case-by-case basis.
Unreliable and inconsistent data can also limit users’ ability to draw conclusions and use results to guide further experiments. This requires researchers to repeat experiments, costing valuable time and money. Whether a lab is focused on basic exploratory research or analyzing circulating tumor DNA in liquid biopsy samples, the ability to generate clear and reliable dPCR data is absolutely essential. By employing high-quality tools, instruments, and methods, researchers can work confidently and maximize every experiment's success and impact.