Structural Characterization and Absolute Quantification of Microcystin Peptides Using Collision-Induced and Ultraviolet Photo-Dissociation Tandem Mass Spectrometry.

Microcystin (MC) peptides produced by cyanobacteria pose a hepatotoxic threat to human health upon ingestion from contaminated drinking water. While rapid MC identification and quantification in contaminated body fluids or tissue samples is important for patient treatment and outcomes, conventional immunoassay-based measurement strategies typically lack the specificity required for unambiguous determination of specific MC variants, whose toxicity can significantly vary depending on their structures. Furthermore, the unambiguous identification and accurate quantitation of MC variants using tandem mass spectrometry (MS/MS)-based methods can be limited due to a current lack of appropriate stable isotope-labeled internal standards. To address these limitations, we have systematically examined here the sequence and charge state dependence to the formation and absolute abundance of both "global" and "variant-specific" product ions from representative MC-LR, MC-YR, MC-RR, and MC-LA peptides, using higher-energy collisional dissociation (HCD)-MS/MS, ion-trap collision-induced dissociation (CID)-MS/MS and CID-MS, and 193 nm ultraviolet photodissociation (UPVD)-MS/MS. HCD-MS/MS was found to provide the greatest detection sensitivity for both global and variant-specific product ions in each of the MC variants, except for MC-YR where a variant-specific product uniquely formed via UPVD-MS/MS was observed with the greatest absolute abundance. A simple methodology for the preparation and characterization of O-stable isotope-labeled MC reference materials for use as internal standards was also developed. Finally, we have demonstrated the applicability of the methods developed herein for absolute quantification of MC-LR present in human urine samples, using capillary scale liquid chromatography coupled with ultra-high resolution / accurate mass spectrometry and HCD-MS/MS. Graphical abstract ᅟ.