Abstract
Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimize approaches in busy MS facilities. In this work, we first provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost-efficient development of new DDA strategies using a previously developed virtual metabolomics mass spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an Instrument Application Programming Interface (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.