Introduction
Compound identification is a bottleneck in untargeted metabolomics, hindering biological interpretation of results. Here, we describe a data-informed workflow that maximizes the number of metabolites interrogated by MS/MS and MSn, while minimizing the acquisition of uninformative spectra. Human plasma was analyzed using this workflow resulting in high confidence identifications, deeper metabolome coverage, and enhanced biological knowledge generation.
Methods
Human plasma was extracted with methanol and injected on a Thermo Scientific™ Hypersil GOLD™ column. Instrumentation included a Thermo Scientific™ Vanquish™ UHPLC system and a Thermo Scientific™ Orbitrap ID-X™ Tribrid™ mass spectrometer. Data were analyzed using Thermo Scientific™ Mass Frontier software and Thermo Scientific™ Compound Discoverer™ software.
Results
During data-dependent MS/MS, ions are selected based on abundance, without any knowledge of biological relevance or ion type. In a typical DDA experiment, we determined, that >40% of MS/MS spectra could be attributed to background ions. By enabling the automatic generation and implementation of an exclusion list based on real-time feature detection in LC-MS data, background ion MS2 spectra were practically eliminated (<0.1%), allowing for the analysis of true sample components.
Small molecules form different adducts and cluster ions during electrospray ionization. Highly abundant compounds may prevent the fragmentation of metabolites of lower abundance. By populating the inclusion list with the preferred ion for each metabolite, more compounds can be sampled by MS/MS and MSn in a single run. Additionally, by automatically updating inter-run inclusion and exclusion lists, we can ensure that compounds not selected for MS/MS and MSn will be prioritized during a subsequent injection.
Conclusions
The combination of MSn and automatically generated inter-run inclusion and exclusion lists resulted in fragmentation of more unique metabolites and a greater number of metabolites confidently annotated. This innovative workflow addresses the identification bottleneck in untargeted metabolomics and enables confident biological interpretation of the results.