Needle BioFET sensors rapidly detect algal toxins in fish on-site
Toxins from harmful algal blooms are known to accumulate in fish, presenting risks to human health through seafood consumption. Further, the potent toxin microcystin is the predominant cyanotoxin found in fish species in the Great Lakes.
Current methods for detecting algal toxins in fish are both time and labor intensive and require specialized laboratory equipment and trained personnel. The processes also sacrifice the fish examined. Researchers at The Ohio State University are working to change this.
Led by Electrical and Computer Engineering Professor Wu Lu, they have developed sensor devices that enable untrained users to measure toxin levels in fish tissue on-site without sacrificing the animal. The device, a needle housing a special biosensor, can detect microcystin in fish tissue in just a few minutes. The team designed the sensor based on semiconductor technology with a field effect transistor (FET) approach to sense toxins. This immunoFET sensor uses electrical conductance to determine concentrations of microcystin.
Through the project, researchers validated and calibrated the needle sensor devices in water samples, which showed the devices can sense toxins at concentrations much lower than those that have known human health impacts. The team saw major success when testing the devices on actual fish tissue; all control samples read as false, giving no false alarms, and all contaminated samples correctly showed positive responses. Compared to a conventional ELISA sensor, the newly developed sensors are six orders more sensitive while taking measurements in far less time.
A patent for the needle sensor device has been issued by the U.S. Patent Office. Meanwhile, the team is collaborating with the Ohio Environmental Protection Agency to share and test fish samples. Results from the project will be shared with relevant Ohio agencies, and their feedback and suggestions will in turn guide future research.
original article appeared in the Harmful Algal Bloom Research Initiative 2023 Report