Enhanced Lipidome Coverage in Shotgun Analyses by using Gas-Phase Fractionation

Abstract

A high resolving power shotgun lipidomics strategy using gas-phase fractionation and data-dependent acquisition (DDA) was applied toward comprehensive characterization of lipids in a hen ovarian tissue in an untargeted fashion. Using this approach, a total of 822 unique lipids across a diverse range of lipid categories and classes were identified based on their MS/MS fragmentation patterns. Classes of glycerophospholipids and glycerolipids such as glycerophosphocholines (PC), glycerophosphoethanolamines (PE), and triglycerides (TG) are often the most abundant peaks observed in shotgun lipidomics analyses. These ions suppress the signal from low abundance ions, and hinder the chances of characterizing low abundant lipids when DDA is used. These issues were circumvented by utilizing gas-phase fractionation, where DDA was performed on narrow m/z ranges instead of a broad m/z range. Employing gas-phase fractionation resulted in an increase in sensitivity by more than an order of magnitude in both positive- and negative-ion modes. Furthermore, the enhanced sensitivity increased the number of lipids identified by a factor of ≈4, and facilitated identification of low abundant lipids from classes such as cardiolipins that are often difficult to observe in untargeted shotgun analyses and require sample-specific preparation steps prior to analysis. This method serves as a resource for comprehensive profiling of lipids from many different categories and classes in an untargeted manner, as well as for targeted and quantitative analyses of individual lipids. Furthermore, this comprehensive analysis of the lipidome can serve as a species- and tissue-specific database for confident identification of other MS-based datasets, such as mass spectrometry imaging.

Targeted High Resolution LC/MS3 Adductomics Method for the Characterization of Endogenous DNA Damage

“…Another improvement in data analysis of the approach presented here would be to add the MS 2 and MS 3 fragmentation spectra to the endogenous DNA adduct database reported here to allow for automated confirmation of adduct identity. In addition, the sensitivity of the overall methodology could be improved through the addition of gas phase fractionation to the methodology, whereby the single full scan event is broken up into multiple ranges, allowing for greater ion injection times and enhanced ability to detect trace level DNA adducts or to measure endogenous DNA adducts using less DNA (Nazari and Muddiman, 2016). Another possible approach to increase the method sensitivity would include multiple injections of the same sample to create exclusion lists based on previous injections to allow for the detection of lower level DNA adducts with each subsequent injection (Koelmel et al, 2017).…”

Challenges and emergent solutions for LC‐MS/MS based untargeted metabolomics in diseases

“…For example, Calderón‐Santiago et al conducted LC‐MS untargeted metabolomics with GFP over four m/z ranges ( m/z 100‐325, 325‐550, 550‐775, and 775‐1000), and detected 81% and 79% more features in serum in the positive‐ and negative‐ion modes, respectively, than conventional LC‐MS detection . GFP was also applied on to untargeted, shotgun lipidomics to increase sensitivity by more than an order of magnitude in the positive‐ and negative‐ion modes to increase lipidome coverage by a factor of 3.7‐fold, and the identification of low‐abundant lipids such as cardiolipins . With the addition of LC‐MS/MS, tremendous opportunity to expand the lipidome is expected.…”

“…98 GFP was also applied on to untargeted, shotgun lipidomics to increase sensitivity by more than an order of magnitude in the positive-and negative-ion modes to increase lipidome coverage by a factor of 3.7-fold, and the identification of lowabundant lipids such as cardiolipins. 111 With the addition of LC-MS/MS, tremendous opportunity to expand the lipidome is expected.…”

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation

“…For lipidomics, IE type analyses have been used in direct infusion approaches by increasing the duration of dynamic exclusion to the length of the analysis. For example, Nazari and Muddiman [26] used gas phase fractionation and dynamic exclusion to increase coverage of the lipidome, especially of low abundance species. Schwudke et al [27] emulated precursor and neutral loss scanning using data-dependent analysis with a dynamic exclusion and inclusion list based workflow for increasing the lipidome coverage.…”