Development of a rapid detection method for Karenia mikimotoi by using CRISPR-Cas12a

Harmful algal blooms (HABs), mainly formed by dinoflagellates, have detrimental effects on marine ecosystems and public health. Therefore, detecting HABs is crucial for early warning and prevention of HABs as well as the mitigation of their adverse effects. Although various methods, such as light microscopy, electron microscopy, real-time PCR, and microarrays, have already been established for the detection of HABs, they are still cumbersome to be exploited in the field. Therefore, rapid nucleic detection methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP)-lateral flow dipstick (LFD) have been developed for monitoring bloom-forming algae. However, the CRISPR/Cas-based detection of HABs has yet to be applied to this field. In this study, we developed a method for detecting Karenia mikimotoi (K. mikimotoi), a typical ichthyotoxic dinoflagellate responsible for global blooms. Our method utilized Cas12a from Lachnospiraceae bacterium ND2006 (LbCas12a) to target and cleave the internal transcribed spacer (ITS) of K. mikimotoi, guided by RNA. We leveraged the target-activated non-specific single-stranded deoxyribonuclease cleavage activity of LbCas12a to generate signals that can be detected using fluorescence-read machines or LFDs. By combining RPA and LbCas12a with reporters, we significantly enhanced the sensitivity, enabling the detection of ITS-harboring plasmids at concentrations as low as 9.8 aM and genomic DNA of K. mikimotoi at levels as low as 3.6 x 10(-5) ng/& mu;l. Moreover, we simplified the genomic DNA extraction method using cellulose filter paper (CFP) by directly eluting the DNA into RPA reactions, reducing the extraction time to < 30 s. The entire process, from genomic DNA extraction to result reporting, takes less than an hour, enabling the identification of nearly a single cell. In conclusion, our method provided an easy, specific, and sensitive approach for detecting K. mikimotoi, offering the potential for efficient monitoring and management of K. mikimotoi blooms.