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

Abstract

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