Principal Investigator: Prof. Peiying Hong

Poster Presenter: Yevhen Myshkevych


Utilizing flow cytometry for rapid virus-like particle quantification and pathogen prediction in different wastewater matrices




Water scarcity is considered one of the largest global risks that can bring detrimental impact on humanity development within the near future. In order to alleviate this issue and improve water security, alternative water sources like seawater, brackish water and treated wastewater (WW) are considered as water sources for potable and non-potable use. While desalination is considered an energy-intensive process, WW treatment and reuse are more energy efficient and accessible to landlocked countries. To ensure safe reuse, viral pathogens require additional attention due to their small particle size, high resistance to environmental stresses and high infectivity. The state-of-art technology allows viral pathogen concentration determination within 1-2 days from sampling, which in the case of WWTP failure, would meant inability to react swiftly and prevent massive community outbreak. Therefore, there is a need for a prompt and accurate method of viral pathogen concentration prediction. Flow cytometry (FCM) has the potential for small particle detection and quantification. This study aims to first optimize FCM for use in different wastewater matrices, and then establish correlations to determine if FCM readings are representative of pathogen concentration in wastewater matrices. To achieve that, the protocol established by Brussaard et el. was used as a basis for further optimization to be utilized on WW. Namely, we evaluated the need for use of glutaraldehyde fixative, Triton-X100 surfactant, and pre-processing steps (e.g. centrifugation; filtering through syringe filters with pore sizes of 5 μm, 0.45 μm, 0.2 μm and 0.02 um). Two dyes from SYBR family, which is reported to show the highest staining performance, were additionally examined. As a result, the original protocol has been optimized to capture the highest virus-like particle count ensuring the lowest background noise level possible. qPCR was then used as a tool for quantitative pathogen description with enterovirus, adenovirus, MS2 phage and PMMoV targeted. The qPCR data is correlated with FCM counts to determine if FCM data is predictive of the presence of enteric viral pathogens.