Development of a quantitative PMA-16S rRNA gene sequencing workflow for absolute abundance measurements of seawater microbial communities

Ecological risk assessments rarely consider the impacts of environmental stress on microbial communities. Incorporating microbial community responses into these evaluations requires establishing sensitivity thresholds based on the absolute abundance of viable taxa. While essential for describing microbial community dynamics, sequencing-based analyses are typically limited to relative proportions and fail to reveal the magnitude or directionality of abundance shifts. This study presents a workflow that combines propidium monoazide (PMA) treatment and microbial load estimates with 16S rRNA gene amplicon sequencing and quantitative microbiome profiling (QMP) to assess the absolute abundance of viable taxa in seawater microbiomes. Using natural seawater, microbial load estimates from droplet digital PCR (ddPCR) and flow cytometry (FC) correlated strongly for total and intact cell counts, confirming the suitability of both methods for normalising 16S rRNA gene amplicon sequencing data. We demonstrated that PMA at concentrations of 2.5–15 µM effectively inhibited PCR amplification of DNA from membrane-compromised cells, reducing 16S RNA gene copies by 24–44% relative to untreated samples. Samples with known proportions of intact cells were generated by mixing heat-killed and natural seawater, enabling absolute abundance assessments by normalising 16S rRNA gene amplicon sequencing data to intact cell loads estimated via ddPCR and FC. This approach facilitated detailed comparisons of the effects of QMP versus relative microbiome profiling (RMP) on alpha and beta diversity metrics and on relative and absolute amplicon sequence variant (ASV) abundance profiles. Unlike RMP, QMP captured significant shifts in the microbial community composition across samples with decreasing proportions of intact cells. While RMP failed to detect abundance changes at ASV-level, QMP revealed consistent abundance declines. This workflow enhanced the accuracy in representing microbial community dynamics by addressing key limitations of RMP such as the inclusion of damaged cells or extracellular DNA and the misleading proportions of identified taxa. It is particularly suited for quantifying the magnitude and direction of changes in taxa abundance following stress exposure, making it directly applicable to microbial stress-response modelling.