Detection of biological loads in sewage using the automated robot-driven photoelectrochemical biosensing platform

The current monitoring technologies for biological loads in sewage exhibit drawbacks, including low detection efficiency and insufficient accuracy. The authors’ research team has developed an innovative approach that harnesses the power of the CRISPR/Cas12a system within an automated robot-driven photoelectrochemical biosensing platform. This platform allows early detection and tracking of infectious disease outbreaks, providing timely data support for public health institutions to take appropriate prevention and control measures.

Abstract:

Real-time polymerase chain reaction (RT-PCR) remains the most prevalent molecular detection technology for sewage analysis but is plagued with numerous disadvantages, such as time consumption, high manpower requirements, and susceptibility to false negatives. In this study, an automated robot-driven photoelectrochemical (PEC) biosensing platform is constructed, that utilizes the CRISPR/Cas12a system to achieve fast, ultrasensitive, high specificity detection of biological loads in sewage. The Shennong-1 robot integrates several functional modules, involving sewage sampling and pretreatment to streamline the sewage monitoring. A screen-printed electrode is employed with a vertical graphene-based working electrode and enhanced with surface-deposited Au nanoparticles (NPs). CdTe/ZnS quantum dots (QDs) are further fabricated through the double-stranded DNA (dsDNA) anchored on Au NPs. Using the cDNA template of Omicron BA.5 spike gene as a model, the PEC biosensor demonstrates excellent analytical performance, with a lower detection limit of 2.93 × 102 zm and an outstanding selectivity at the level of single-base mutation recognition. Furthermore, the rapid, accurate detection of BA.5 in sewage demonstrates the feasibility of the PEC platform for sewage monitoring. In conclusion, this platform allows early detection and tracking of infectious disease outbreaks, providing timely data support for public health institutions to take appropriate prevention and control measures.

Author list:

Yiming Zhang†, Zhi Chen†, Songrui Wei†, Yujun Zhang, Hai Fu, Han Zhang*, Defa Li*, Zhongjian Xie*

How to cite:

Y. Zhang, Z. Chen, S. Wei, Y. Zhang, H. Fu, H. Zhang, D. Li, Z. Xie, Exploration 2024, 20230128.
https://doi.org/10.1002/EXP.20230128