ABSTRACT
BACKGROUND: Harmful algal blooms (HABs), caused by the rapid proliferation or aggregation of microorganisms, are catastrophic for the environment. The Prymnesium parvum is a haptophyte algal species that is found worldwide and is responsible for extensive blooms and death of larval amphibians and bivalves, causing serious negative impacts on the ecological environment. For the prevention and management of environmental pollution, it is crucial to explore and develop early detection strategies for HABs on-site using simple methods. The major challenge related to early detection is the accurate and sensitive detection of algae present in low abundance. RESULTS: Herein, recombinase polymerase amplification (RPA) was combined with clustered regularly interspaced short palindromic repeats and Cas12a protein (CRISPR-LbaCas12a) systems, and the lateral flow dipstick (LFD) was used for the first time for early detection of P. parvum. The internal transcribed spacer (ITS) of P. parvum was selected as the target sequence, and the concentration of single-strand DNA reporters, buffer liquid system, reaction time, and amount of gold particles were optimized. The RPA-CRISPR-LbaCas12a-LFD approach demonstrated highly specificity during experimental testing, with no cross-reaction against different microalgae used as controls. In addition, the lowest detection limit was 10,000 times better than the lowest detection limit of the standalone RPA approach. The feasibility and robustness of this approach were further verified by using the different environmental samples. It also observed that P. parvum are widely distributed in Chinese Sea, but the cell density of P. parvum is relatively low (<0.1 cells/mL). SIGNIFICANCE: The developed approach has an excellent specificity and offers 10,000 times better sensitivity than the standalone RPA approach. These advantages make this approach suitable for early warning detection and prevention of HAB events in environmental water. Also, the outcomes of this study could promote a shift from traditional laboratory-based detection to on-site monitoring, facilitating early warning against HABs.
