Absolute quantification of viable bacteria abundances in food by next-generation sequencing Quantitative NGS of viable microbes

Next-generation sequencing (NGS) is an important tool for taxonomical bacteria identification. Recent techno-logical developments have led to its improvement and availability. Despite the undeniable advantages of this approach, it has several limitations and shortcomings. The usual outcome of microbiota sequencing is a relative abundance of bacterial taxa. The information about bacteria viability or enumeration is missing. However, this knowledge is crucial for many applications. In the current study, we elaborated the complete workflow for the absolute quantification of living bacteria based on 16S rRNA gene amplicon sequencing. A fluorescent PMAxx reagent penetrating a damaged cell membrane was used to discriminate between the total and viable bacterial population. Bacteria enumeration was estimated by the spike-in technique or qPCR quantification. For method optimization, twenty bacterial species were taken, and the results of the workflow were validated by widely accepted methodologies: flow cytometry, microbiological plating, and viability-qPCR. Despite the minor discrepancy between all methods used, they all showed compatible results. Finally, we tested the workflow with actual food samples and received a good correlation between the methods regarding the estimation of the number of viable bacteria. Overall, the elaborated and integrated NGS approach could be the next step in perceiving a holistic picture of a sample microbiota.

Automated summary

Next-generation sequencing (NGS) is an important tool for bacteria identification, but it has limitations. This study presents a complete workflow for absolute quantification of living bacteria using 16S rRNA gene amplicon sequencing. The workflow utilizes a fluorescent PMAxx reagent to distinguish between total and viable bacteria populations. The results were validated using flow cytometry, microbiological plating, and viability-qPCR. The workflow was also tested with actual food samples and showed good correlation with other methods.