Optimization of TaqMan-based quantitative PCR diagnosis for Entamoeba histolytica using Droplet Digital PCR

BackgroundTaqMan-probed quantitative PCR (qPCR) is highly valued for diagnosing Entamoeba histolytica infections (amebiasis). However, unclear cycle threshold (Ct) values often yield low-titer positive results, complicating interpretation. This study aimed to optimize qPCR primer-probe sets with logically determined cut-off Ct value using droplet digital PCR (ddPCR). Methodology/Principal FindingsAmplification efficacy was evaluated using ddPCR by measuring absolute positive droplet counts (APD) and mean fluorescence intensity at different PCR cycles and annealing temperatures (AT). A primer-probe specific cut-off Ct value was determined from a standard curve by correlating Ct values with APD. Twenty primer-probe sets targeting small subunit rRNA gene regions (X64142) were designed from previous papers. Amplification efficacy remained consistent at high PCR cycles (50 cycles), but differed at lower PCR cycles (30 cycles), identifying five sets with higher amplification efficiency than other candidates. Of these, only two sets maintained efficiency at higher AT (62{degrees}C). Ct value was inversely proportional to the square of APD, defining the specific cut-off Ct value as 36 cycles. Selected primer-probe set with a cut-off effectively differentiated E. histolytica infection in clinical specimens. However, discordant results between Ct value and APD were seen in some cases with high Ct value. Shotgun metagenomic sequencing suggested microbial-independent false-positive reactions contributed to these discrepancies, although specific reactants were unidentified. Conclusions/SignificanceThe combination use of ddPCR with qPCR revealed that false positive reactions of qPCR and/or ddPCR commonly happen in stool specimens. Also, this study emphasizes the value of ddPCR for establishing accurate cut-off values with efficient primer-probes. Author SummaryAccurate diagnosis of Entamoeba histolytica infection is critical for both clinical management and epidemiological monitoring. However, current diagnostic methods on TaqMan-qPCR for detecting this pathogen is hampered by inconsistent methodologies and ambiguous results, particularly in low-titer stool samples with high cycle threshold (Ct) values. To address this issue, this study introduces a novel approach using droplet digital PCR (ddPCR) to evaluate qPCR amplification efficacy and establish a robust theoretical cut-off Ct value for improved diagnostic accuracy. By evaluating different primer-probe sets, we identified optimal candidates and determined a Ct cut-off of 36 cycles, allowing for more reliable differentiation between true infections and false positives. Our findings enhance the diagnostic specificity and reliability, providing a practical and scalable framework for improving pathogen detection protocols, particularly in resource-limited settings. The methodology represents a significant advancement in molecular diagnostics, addressing a critical gap in accurate detection of E. histolytica in clinical stool samples and potentially other clinical specimens in clinical and research settings.