Selection, Characterization, and Application of High Affinity Microcystin-Targeting Aptamers in a Graphene-Based Biosensing Platform

The development of successful biosensing platforms is highly dependent upon the biorecognition properties of the recognition receptor and the sensitivity of the transducer of the binding signal. The integration of the high affinity and specificity of DNA aptamers, and the unique properties of the carbon nanomaterial graphene, offers an excellent avenue for sensitive and selective biosensing architectures. In this work, novel microcystin-targeting DNA aptamers were successfully selected using systematic evolution of ligand by exponential enrichement (SELEX). Then, highly sensitive and selective aptasensor which utilizes an unlabeled aptamer noncovalently assembled on a graphene electrode for microcystin-LR detection was developed. Assembly of the DNA aptamer on the graphene-modified electrodes caused a marked drop in the square wave voltammetric reduction signal of the [Fe(CN)₆]^4−/3− redox couple. The presence of microcystin-LR, on the other hand, caused a dose-responsive increase in peak current, allowing the quantification of microcystin-LR through the measurement of peak current change. Under optimal conditions, the detection limit of the developed aptasensor was 1.9 pM in buffer, a concentration much lower than those offered by previously reported biosensors for microcystin-LR. The developed aptasensor also exhibited excellent selectivity for microcystin-LR with no detectable cross-reactivity to okadaic acid, microcystin-LA and -YR. Moreover, the proposed aptasensor has been applied for the analysis of spiked tap water and fish samples showing good recovery percentages. We also demonstrated in this work that the mechanism of the detection was based on the conformation change in part of the aptamer without complete release from the graphene surface. This novel, simple, high performance and low-cost detection platform would facilitate the routine monitoring of microcystin –LR in real samples.