Flow cytometry-based method facilitates optimization of PMA treatment condition for PMA-qPCR method

Assessing probiotic viability in mixed species yogurt using a novel propidium monoazide (PMAxx)-quantitative PCR method

Viability is a prerequisite for any therapeutic benefits associated with the ingestion of probiotic bacteria. Current culture-based techniques are inadequate for the enumeration of probiotics in mixed-species food products. This study utilized a quantitative PCR (qPCR) method coupled with propidium monoazide (PMAxx), and novel species-specific tuf gene primers to selectively enumerate Lacticaseibacillus rhamnosus, Bifidobacterium spp., and yogurt starter cultures in mixed-species probiotic yogurt. The method was optimized for PMAxx concentration and specificity and evaluated for efficiency and applicability. PMAxx-qPCR showed high specificity to the target organisms in mixed-species yogurt, quantifying only viable cells. The linear dynamic ranges were established over five to seven orders of magnitude. The assay was reliable with an efficiency of 91-99%, R2 values > 0.99, and a good correlation to the plate count method (r = 0.882). The results of this study demonstrate the high selectivity, improved lead time, and reliability of PMAxx-qPCR over the culture-dependent method, making it a valuable tool for inline viability verification during processing and improving probiotic quality assurance for processors and consumers.

Machine learning-based automated fungal cell counting under a complicated background with ilastik and ImageJ

Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high-throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy-to-use fungal cell counting pipeline that combined the machine learning-based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro 'Fungal Cell Counter'. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter-operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low-cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.