The increasing demand for water necessitates water reuse strategies, with direct potable reuse (DPR) representing a significant challenge. Concerns regarding DPR's safety and societal acceptance highlight the need for improved waterborne microorganism monitoring. Flow cytometry (FCM), particularly flow virometry (FVM), is emerging as a promising method. However, current FVM protocols lack standardization, hindering data validation and interlaboratory comparison. Researchers face difficulties differentiating viruses from virus-like particles (VLPs), and absolute quantification remains a challenge. Existing protocol optimization methods, typically pipeline-style approaches, overlook potential interaction effects between factors. Manual gating of FVM data, currently the standard, is time-consuming, subjective, and introduces significant interlaboratory variability. This study addresses these challenges by optimizing a sample preparation protocol for T4 bacteriophage—a widely accepted viral surrogate—using a fractional factorial experimental design, a more rigorous approach than traditional pipeline optimization. Additionally, it investigates the application of density-based clustering, specifically the OPTICS algorithm, as an objective and automated alternative to manual gating for improved data analysis.

Several studies have explored FVM for water quality assessment. Brussaard et al. developed a staining protocol for FVM, but Huang et al. noted its limitations in reclaimed water samples. Most protocol optimizations have used sequential, pipeline approaches, neglecting potential interaction effects. Manual gating, while standard, introduces substantial inter-laboratory variability. Lippé highlighted the need for standardized protocols and well-characterized viruses for FVM. Dlusskaya et al. demonstrated that FVM's sensitivity and accuracy need improvement for quantifying most natural viral populations. The need for a more efficient and less subjective approach to FVM data analysis is clear.

This study employed a two-pronged approach. First, a fractional factorial experimental design (2⁶⁻²) was used to optimize the FVM detection of T4 bacteriophage. Six factors were considered: stain type (SYBR Green I or SYBR Gold), diluent (Milli-Q water or Tris-EDTA buffer), dye concentration, staining temperature, staining time, and glutaraldehyde addition. The experiment was replicated four times with randomized run order. T4 stock was prepared using protocols adapted from Bonilla et al. (2016), and titers were determined by plate-based culturing and qPCR. FVM analysis was performed using an Agilent NovoCyte 2070V flow cytometer. Data were analyzed using FlowJo 10 software and RStudio, focusing on event count, mean fluorescence intensity (MFI), and coefficient of variation (CV). Second, the study compared manual gating to density-based clustering using the OPTICS algorithm for analyzing viral surrogates in complex matrices. A mixed-target solution containing T4, φ6 bacteriophages, and fluorescent polystyrene beads of different sizes was prepared. Ten replicates of this solution, along with 2x, 4x, 8x, and 16x dilutions were analyzed. Data were preprocessed by applying a log transformation and standardizing the features. OPTICS was implemented in R using the dbscan package, comparing manual and automated cluster extraction. A similar experiment was performed using spiked wastewater samples. Data were analyzed using manual gating, OPTICS ordering with manual extraction, and OPTICS ordering with automated extraction (opticskxi package).

The fractional factorial design revealed that glutaraldehyde addition significantly improved T4 detection, increasing event count, MFI, and decreasing CV. Optimizing stain temperature and diluent (TE buffer) further enhanced signal tightness. While several main effects were significant, few two-way interactions were observed. The optimized protocol involves diluting the sample in TE buffer, adding 0.5% glutaraldehyde, and staining with SYBR Green I at 5 x 10⁻⁵ times the sample volume at 50 °C for at least 1 min. In the comparison of data analysis methods, density-based clustering performed as well as or better than manual gating for engineered beads. However, results for viral targets were more mixed. Manual gating reliably identified separate T4 and φ6 populations, while OPTICS-based methods sometimes grouped them together or with background noise, particularly in complex matrices. Manual cluster extraction performed better than opticskxi at identifying T4 in the environmental-spike experiment. The inclusion of FSC in the OPTICS ordering did not improve the ability to discriminate T4 from background.

This study successfully optimized a FVM protocol for T4 detection using a fractional factorial design, offering a more efficient and rigorous approach compared to traditional methods. Density-based clustering showed promise as an automated alternative to manual gating, particularly for well-defined clusters like those produced by beads. However, the challenges encountered with viral targets in complex matrices highlight the need for further refinement of clustering methods, including strategies for optimal parameter selection and weighting of FVM data dimensions. The inconsistencies between manual gating and clustering results for viral targets emphasize the complexity of analyzing real-world samples. The optimized protocol is recommended as a validation step for FVM experiments, facilitating instrument performance assessment and interlaboratory comparisons. The use of both biological and non-biological standards is vital for validating FVM data.

This research demonstrates the effectiveness of fractional factorial designs in optimizing FVM sample preparation protocols and the potential of density-based clustering for automated data analysis. While the optimized T4 protocol and OPTICS clustering improved FVM's efficiency and objectivity, further research is needed to enhance the performance of clustering for complex samples. Future work should focus on refining OPTICS parameter selection, improving automated cluster extraction, and optimizing weighting of FVM data dimensions to better handle the challenges of real-world sample analysis. The proposed approach offers a significant step toward enhancing FVM's reliability and applicability in advanced water treatment and reuse.

The optimized protocol was developed for T4 in a controlled laboratory setting, not necessarily generalizable to all waterborne viruses. The density-based clustering methods were not universally superior to manual gating for viral targets, especially in complex matrices like wastewater. Further refinement of clustering parameters and strategies are needed to enhance accuracy and reliability. The study used a single wastewater sample, limiting generalizability to other wastewater sources.