Development of ion mobility mass spectrometry deconvolution strategies for use in data-independent acquisition proteomics.


Modern mass spectrometers are among the most sensitive and suitable analytical tools for high-throughput protein characterization. However, limitations in ion mobility (IM) and mass spectrometry (MS) resolving powers can limit the success of MS-based proteomics approaches. This work highlights the development of IM-MS deconvolution techniques and their utilization with commercial data processing software to improve results from IM-enhanced data-independent acquisition (HDMSᴱ) proteomics strategies. Chemometric deconvolution and post-IM/collision-induced dissociation (postIM/CID) MS were used to extract “pure component” IM profiles and construct CID mass spectra of IM-MS unresolved species. Collision-energy resolved and broadband IM-MS deconvolution strategies were developed and used for successful characterization of simulated complex mixtures and biological samples, respectively. As described in Chapter Two, utilizing m/z-isolation in a novel collision-energy resolved approach, a multicomponent IM unresolved peak was deconvoluted to construct fully resolved individual IM peaks.A broadband data acquisition approach (i.e., without the use of m/z-isolation) for improved IM-MS deconvolution is presented in Chapter Three where it is demonstrated that broadband IM-MS deconvolution provides time-saving and sensitivity advantages over the scan mode approach. Elimination of the ion isolation step also expands the applicability of IM-MS deconvolution to HDMSᴱ acquisition. Ultra-performance liquid chromatography (UPLC) was used with HDMSᴱ for high-throughput bottom-up proteomics analysis of rat brain tissue. The combined use of broadband IM-MS deconvolution and commercial HDMSᴱ data processing software allowed more efficient precursor and product ion detections and improved proteins’ sequence coverages. The use of data-dependent acquisition (DDA) and HDMSᴱ for high-throughput MS-based investigations of rat brain tissue is presented in Chapter Four. Infrared laser ablation microsampling was used to extract spatially localized rat brain samples for subsequent bottom-up proteomics analysis using both DDA and HDMSᴱ strategies. The use of HDMSᴱ acquisition for bottom-up proteomics of laser ablated rat brain samples provided greater numbers of peptides and proteins detected than DDA. Finally, in Chapter Five, potential future research directions are presented and discussed.



Ion Mobility (IM), Mass Spectrometry (MS), Deconvolution, Bottom-up Proteomics, Data-independent Acquisition (DIA)