Mastering dMIQE Guidelines for the Publication of dPCR Data

The Poisson distribution model assumes that target molecules are randomly and independently distributed among the microreaction. Poisson distribution becomes a valid approximation of the true distribution of target molecules with the more microreactions per sample that occur (The dMIQE Group, 2020). In this context the "Law of Large Numbers" comes into play, which states that as the number of reactions increases, the observed frequencies (number of positive microchambers with the target) converge to the true probabilities (probability of a reaction containing a target molecule). Having a large number of reactions allows researchers to achieve the following benefits:

1. Increased statistical confidence: With more microreactions, the likelihood of having more microreactions positive for the target increases. This leads to higher statistical confidence in the quantification of the target concentration.
2. Better representation of the sample: A larger number of microreactions paired with improved sample volume testing (see digital pooling tech note) can help improve the random sampling of the target molecules in the sample, making the results more representative of the actual target concentration. It can also reduce sampling errors ensuring that the quantification is less affected by outliers.
3. Enhanced sensitivity and precision: The statistical power of dPCR increases with the number of microreactions, enabling accurate quantification even at low target concentrations

IMPORTANT: Having more microreactions helps bring you “closer to the truth” by helping to minimize the impact of uncertainty.

Furthermore, a figure from the dMIQE guidelines (Figure 1) provides a fascinating insight. Based on modeling the 95% confidence limit of λ, they see drastic improvement of the relative uncertainty experiences at the 10K microreaction point. Beyond this point, while the enhancements in relative uncertainty are still evident, a more gradual trend is seen. Understanding this transition can help researchers optimize their dPCR experiments based on the dynamic range needs for their work.

The requirement for dPCR instruments to provide equal volume in each microreaction is critical for ensuring the accuracy and precision of dPCR quantification (The dMIQE Group, 2020). Maintaining equal volume in microreactions is essential for several reasons:

1. Poisson statistical model: The quantification of target molecules in dPCR relies on the Poisson statistical model. For this model to hold true, each reaction must have an equal chance of containing a single target molecule or being empty. Deviations in reaction volumes could introduce biases in the distribution of target molecules, leading to inaccurate quantification results.
2. Overall precision: Equal reaction volumes contribute to the precision of dPCR quantification. The Poisson statistical model assumes that the variance in the number of target molecules per microreaction is equal to the mean. Deviations in volumes across all microreactions could lead to increased variance and decreased precision in target quantification.
3. Consistency in assay performance: When using dPCR for clinical research purposes, consistent assay performance is crucial. Equal volume in each reaction contributes to the reproducibility and robustness of the assay, enhancing the reliability of results over time and between different instruments.

The requirement for dPCR instruments to provide clear discrimination between positive and negative microreactions is critical for accurate target quantification and reliable data interpretation (The dMIQE Group, 2020). Clear discrimination between positive and negative microreactions is essential for several reasons:

1. Accurate target detection: The primary purpose of dPCR is to accurately detect and quantify the presence of target molecules in a sample. Clear discrimination between positive (containing the target) and negative (not containing the target) microreactions is crucial to ensure that the correct number of target molecules is identified and measured.
2. Avoiding false positives and negatives: Clear discrimination helps to minimize the risk of false positives (erroneously identifying a microreaction as positive) and false negatives (failing to detect a positive microreaction). False results can lead to inaccurate conclusions and misinterpretation of experimental data.
3. Reducing the amount of “rain” in scatter plots: Microchambers with intermediate levels of fluorescence result in what is considered “rain”. dPCR instruments should have reliable and adjustable thresholding capabilities to accurately define the limit between positive and negative microchambers to account for possible sub-optimal amplification efficiencies.
4. Improving translational research applications: In clinical research and translational research settings, accurate target detection is crucial for identifying disease-related markers and monitoring with quick turnaround times. Clear discrimination enables the sensitive and specific detection of target molecules, enhancing the translational research capabilities of dPCR.

The dMIQE Group. (2020). The digital MIQE guidelines update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clinical Chemistry, 66(8), 1012-1029. https://academic.oup.com/clinchem/article/66/8/1012/5880117