Improving metabolite annotation and identification in untargeted UHPLC-MS metabolomics studies

Nash, William (2021). Improving metabolite annotation and identification in untargeted UHPLC-MS metabolomics studies. University of Birmingham. Ph.D.

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Abstract

Untargeted metabolomics applying UHPLC-MS is a powerful technique capable of reporting thousands of signals from within a complex sample in one analysis and is a technique with vast potential. However, metabolite annotation is a major challenge and bottleneck in untargeted metabolomics experiments partially preventing the field from advancing further and making more substantial impact in the wider scientific community. UHPLC-MS is most typically applied and involves the collection of MS1 and MS2 data but there is much scope for improvement in the acquisition and analysis of both. This thesis characterises and addresses improvements for both data types. Firstly, the relationships between electrospray-derived MS1 features will be investigated across 104 datasets providing new insight into the complexity of MS1 data and new recommendations for annotation of MS1 data. Secondly, MS2 data acquisition strategies were investigated on Orbitrap based instruments, with data dependent acquisition (DDA), data independent acquisition (DIA), all ion fragmentation (AIF) and intelligent data dependent acquisition (iDDA) MS2 types investigated. The volume of informative MS2 information that was acquired was assessed with iDDA techniques being shown to increase biological knowledge collected from any future biological studies applying an Orbitrap analyser. Finally, a library containing chromatographic retention times and MS2 data were constructed utilising the Orbitrap IDX Tribrid Mass Spectrometer (Thermo Fisher Scientific, USA), with human biofluids subsequently analysed utilising the systems new AcquireX software capabilities for automated on-the-fly iDDA acquisition. The results and new tools developed have provided enhancements in the metabolite annotation workflow in untargeted metabolomics applying UHPLC-MS.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):