Comparison of qRT-PCR and ddPCR for multi-strain probiotic detection after a randomized human clinical trial

The ability to detect probiotic consumption during a human clinical trial is crucial to verify and validate placebo and verum groups in post hoc analysis. While bacterial plating is still a common method for detecting and counting bacteria, when dealing with complex matrices like fecal samples, and given that most probiotics share genera or even species with commensal bacteria, plate counting is not a precise or accurate enough method. Species-specific quantitative real-time polymerase chain reaction (qRT-PCR) has been the most cited method in the literature and when properly validated and optimized remains the high watermark for detecting probiotics from fecal samples. Recent advancements in PCR technology have given rise to a parallel platform, droplet digital PCR (ddPCR). In this work we aimed to detect the components of a multi-strain probiotic product from a human clinical trial and compare both methods. This work dually demonstrates a process for determining multi-strain detection criteria as well as directly comparing the methods through the lens of sensitivity and specificity or the ability to properly discern true positives and true negatives. We described the optimization and validation of three assays for use in our detection panel and observed that, between qRT-PCR and ddPCR. The two methods were found to be quite congruent with ddPCR demonstrating a 10-100 fold lower limit of detection. Moreover, we discovered that most of the sensitivity and specificity had come from a single assay alone (Bifidobacterium animalis subsp. lactis Bl-04). This is despite all three assays performing well in optimization and validation. This suggests that more work needs to be done in the validation stage when developing novel probiotic detection assays. Taken together we can recommend ddPCR as a method for detecting probiotics from human clinical trials, but that qRT-PCR still performs well and comparably to ddPCR, when properly optimized and validated. However, when novel assays or those with unknown performance in a given biological matrix are needed, employing a strategy that combines multiple assays in a layered discrimination approach can help mitigate the potential underperformance of any single assay.