Flow virometry for water-quality assessment: protocol optimization for a model virus and automation of data analysis

Abstract Flow virometry (FVM) can support advanced water treatment and reuse by delivering near-real-time information about viral quality. But maximizing the potential of FVM in applications requires protocols to facilitate data validation interlaboratory comparison—as well as approaches protocol design extend suite viruses that feasibly efficiently monitor. We address these needs herein. First, we optimize a sample-preparation for model virus using fractional factorial experimental design. The final FVM-based detection T4—an environmentally relevant surrogate—blends improves on existing developed traditional pipeline-style optimization approach. Second, test whether density-based clustering aid improve analysis surrogates complex matrices relative manual gating. compare gating with results obtained through algorithmic clustering: specifically, leveraging OPTICS (Ordering Points Identify Cluster Structure) ordering algorithm. demonstrate OPTICS-assisted work or better than data, identify features difficult detect our combined automated data-analysis pipeline wastewater samples augmented surrogates. recommend use this validate instrument performance prior alongside application environmental samples. Adoption consistent, optimized analytical approach (i) centers widely available, easy-to-use target, (ii) includes will bolster confidence microbial water-quality monitoring.