Novel one-point calibration strategy for high-throughput quantitation of microcystins in freshwater using LC-MS/MS

Precise quantification of microcystins (MCs) in freshwater is crucial for environmental monitoring and human health. However, the preparation of traditional multi-sample external calibration curve (MSCC) is time consuming and prone to error. Here, a novel one-point calibration strategy including one sample multi-point calibration curve (OSCC) and in sample calibration curve (ISCC) is proposed for the quantitation of eight MCs in freshwater lakes using liquid chromatography tandem mass spectrometry (LC-MS/MS). The multiple isotopologue reaction monitoring (MIRM) of MCs and its 15N-labelled internal standards were used for OSCC and ISCC, respectively. The isotopic abundance of each MIRM channel could be calculated and measured accurately. Additionally, this strategy was comprehensively validated and showed good performance in selectivity, sensitivity, accuracy and precision as the traditional MSCC. Interestingly, OSCC could realize sample dilution by monitoring the less abundant MIRM transitions, while ISCC remove blank matrixes and generate calibration curve in each study samples. Furthermore, the proposed methodology was successfully applied to analyze several freshwater lake samples contaminated by MCs. Considering the advantages of excluding the MSCC preparation, simplified workflows and improved throughput, OSCC and ISCC will be favored for MCs monitoring and as an emerging approach in environmental pollutant control and prevention.

Automated summary

Accurate quantification of microcystins (MCs) in freshwater is essential for environmental monitoring and human health. A novel one-point calibration strategy, using liquid chromatography tandem mass spectrometry (LC-MS/MS), was proposed for the quantitation of eight MCs in freshwater lakes. This strategy showed good performance in selectivity, sensitivity, accuracy and precision, comparable to the traditional multi-sample external calibration curve (MSCC). The strategy allows for sample dilution and removal of blank matrixes, simplifying the workflow and improving throughput.