title: Automated online optical biosensing system for continuous real-time determination of microcystin-LR with high sensitivity and specificity: early warning for cyanotoxin risk in drinking water sources.
abstract: The accelerated eutrophication of surface water sources and climate change have led to an annual occurrence of cyanobacterial blooms in many drinking water resources. To minimize the health risks to the public, cyanotoxin detection methods that are rapid, sensitive, real time, and high frequency must be established. In this study, an innovative automated online optical biosensing system (AOBS) was developed for the rapid detection and early warning of microcystin-LR (MC-LR), one of the most toxic cyanotoxins and most frequently detected in environmental water. In this system, the capturing molecular MC-LR-ovalbumin (MC-LR-OVA) was covalently immobilized onto a biochip surface. By an indirect competitive detection mode, samples containing different concentrations of MC-LR were premixed with a certain concentration of fluorescence-labeled anti-MC-LR-mAb, which binds to MC-LR with high specificity. Then, the sample mixture was pumped onto the biochip surface, and a higher concentration of MC-LR led to less fluorescence-labeled antibody bound onto the biochip surface and thus to lower fluorescence signal. The quantification of MC-LR ranges from 0.2 to 4 μg/L, with a detection limit determined as 0.09 μg/L. The high specificity and selectivity of the sensor were evaluated in terms of its response to a number of potentially interfering cyanotoxins. Potential interference of the environmental sample matrix was assessed by spiked samples, and the recovery of MC-LR ranged from 90 to 120% with relative standard deviation values <8%. The immunoassay performance of the AOBS was validated with respect to that of conventional high-performance liquid chromatography, and the correlation between methods agreed well (R(2) = 0.9762). This system has successfully been applied to long-term, continuous determination and early warning for MC-LR in Lake Tai from June 2011 to May 2012. Thus, the AOBS paves the way for a vital routine online analysis that satisfies the high demand for ensuring the safety of drinking water sources. The AOBS can also serve as early warning system for accidental or intentional water pollution.
authors.affiliation: State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing, China. hanchang@tsinghua.edu.cn
authors.forename: Han-Chang
authors.initials: HC
authors.lastname: Shi
authors.forename: Bao-Dong
authors.initials: BD
authors.lastname: Song
authors.forename: Feng
authors.initials: F
authors.lastname: Long
authors.forename: Xiao-Hong
authors.initials: XH
authors.lastname: Zhou
authors.forename: Miao
authors.initials: M
authors.lastname: He
authors.forename: Qing
authors.initials: Q
authors.lastname: Lv
authors.forename: Hai-Yang
authors.initials: HY
authors.lastname: Yang
mesh_terms: D000911:Antibodies, Monoclonal / Q000276:immunology; D001331:Automation; D015374:Biosensing Techniques / Q000295:instrumentation*; D060766:Drinking Water / Q000032:analysis*; D057230:Limit of Detection; D008387:Marine Toxins; D052998:Microcystins / Q000032:analysis* / Q000276:immunology; D018570:Risk Assessment; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D000911:Antibodies, Monoclonal; D060766:Drinking Water; D008387:Marine Toxins; D052998:Microcystins; D014874:Water Pollutants, Chemical; C057862:cyanoginosin LR