Application-focused validation of a viability-selective PMA-qPCR assay for selected skin-associated and...

Accurate quantification of viable bacteria is essential for ensuring product safety and quality in the food, pharmaceutical, and cosmetics industries. However, conventional plate culture methods are time-consuming and fail to detect viable but non-culturable (VBNC) cells, which frequently occur under environmental stress or preservation conditions and can influence product stability and safety. Accordingly, the need for a quantification method that accounts for VBNC cells has increased. In this study, we optimized and validated a viability-selective quantification method by combining propidium monoazide (PMA), a membrane-impermeable dye, with quantitative real-time polymerase chain reaction (qPCR). Staphylococcus aureus BF00309, Staphylococcus epidermidis CICARIA, and Lactobacillus jensenii KCTC 5194 were analyzed to validate the method. PMA treatment effectively suppressed DNA amplification in dead cells, resulting in significantly higher C _q values than in untreated controls. Strong linear correlations between C _q values and colony-forming units (CFU) obtained by plate counting were observed for all strains (R ² > 0.99), enabling the establishment of a C _q-CFU regression model for estimating viable cell numbers. The derived model successfully quantified membrane-intact viable cells, which may include potential VBNC-like populations, and showed high agreement with culture-based methods. Moreover, selective detection of target bacteria was achieved in mixed samples containing high concentrations of background microorganisms, with a stable detection limit of approximately 10³ CFU/mL. These results demonstrate that the optimized PMA-qPCR method provides a rapid complementary tool to conventional culture-based quantification and serves as a promising preliminary screening approach with strong potential for future applications in skin microbiome research and the evaluation of live bacteria-based products, pending further validation in complex product matrices.