Data independent acquisition-digital archiving mass spectrometry: Application to single kernel mycotoxin analysis of Fusarium graminearum infected maize

New and conjugated mycotoxins of concern to regulators are frequently being identified, necessitating the costly need for new method development and sample reanalysis. In response, we developed an LC-data independent acquisition (LC-DIA) method on a Q-Exactive Orbitrap mass spectrometer tailored for mycotoxins analysis. This method combines absolute quantification of targeted fungal metabolites with non-targeted digital archiving (DA) of data on all ionizable compounds for retrospective analysis. The quantitative power of this approach was assessed by spiking 23 mycotoxins at a range of concentrations into clean maize extracts. The linearity and limits of detection achieved were comparable to conventional LC-MS/MS and significantly better than 'all-ion-fragmentation' scanning mode. This method was applied to single kernel analysis of Fusarium infected maize, where we quantified nine Fusarium metabolites and three metabolites from unexpected contaminations by Alternaria and Penicillium species. Retrospective analysis of this data set allowed us to detect the recently reported 15-acetyldeoxynivalenol-3-O-β-D-glucoside without requiring re-analysis of the samples. To our knowledge, this is the first reported occurrence of this conjugated mycotoxin in naturally contaminated maize, and led us to further study maize artificially inoculated with the 3-acetyldeoxynivalenol and 15-acetyldeoxynivalenol chemotypes of Fusarium graminearum. Analysis of these samples showed that the maize genotype tested glycosylates 15-acetyldeoxynivalenol but not 3-acetyldeoxynivalenol likely because the glycosylation site was blocked. In addition to confirming that these two F. graminearum chemotypes behave differently when infecting the host plant, it demonstrates the utility of using a single screening method to quantify known mycotoxins and archive a completely non-targeted dataset for future analysis.