Comprehensive Lipidome Analysis by Shotgun Lipidomics on a Hybrid Quadrupole-Orbitrap-Linear Ion Trap Mass Spectrometer

Abstract

Abstract. Here we report on the application of a novel shotgun lipidomics platform featuring an Orbitrap Fusion mass spectrometer equipped with an automated nanoelectrospray ion source. To assess the performance of the platform for indepth lipidome analysis, we evaluated various instrument parameters, including its high resolution power unsurpassed by any other contemporary Orbitrap instrumentation, its dynamic quantification range and its efficacy for in-depth structural characterization of molecular lipid species by quadrupole-based higher-energy collisional dissociation (HCD), and ion trap-based resonant-excitation collision-induced dissociation (CID). This evaluation demonstrated that FTMS analysis with a resolution setting of 450,000 allows distinguishing isotopes from different lipid species and features a linear dynamic quantification range of at least four orders of magnitude. Evaluation of fragmentation analysis demonstrated that combined use of HCD and CID yields complementary fragment ions of molecular lipid species. To support global lipidome analysis, we designed a method, termed MS ALL , featuring high resolution FTMS analysis for lipid quantification, and FTMS 2 analysis using both HCD and CID and ITMS 3 analysis utilizing dual CID for in-depth structural characterization of molecular glycerophospholipid species. The performance of the MS ALL method was benchmarked in a comparative analysis of mouse cerebellum and hippocampus. This analysis demonstrated extensive lipidome quantification covering 311 lipid species encompassing 20 lipid classes, and identification of 202 distinct molecular glycerophospholipid species when applying a novel high confidence filtering strategy. The work presented here validates the performance of the Orbitrap Fusion mass spectrometer for in-depth lipidome analysis.

Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences

“…Lipidomics has led us to identify new signaling molecules, reveal the underlying mechanisms responsible for patho(physio)logical conditions, discover potential biomarkers for early diagnosis and prognosis of diseases, screen drug targets and/or test drug efficacy, guide nutritional intervention, and achieve personalized medicine. These accomplishments are due to not only technique development, but also to the nature of lipidomics in being able to comprehensively analyze hundreds to thousands of lipid species at its current development [10, 32, 67, 68] and to study lipid metabolism [69]. …”

“…The broader the coverage is, the better the approach allows us to map the entire metabolic pathways of lipid classes/subclasses and individual species of a system and to better understand the inter-relationship between these classes and species within a metabolic pathway or between the metabolic networks. Luckily, different approaches have already demonstrated their power for “visualizing” and understanding the changes of hundreds to thousands of individual species [3, 40, 67, 68]. However, further increases in the coverage of lipid classes and individual species (particularly for those very low abundance species) using an automated, high throughput manner in any platform remain demanding.…”

Modulation of the Lactobacillus acidophilus La-5 lipidome by different growth conditions

“…All ITMS 2 data were recorded using collision-induced dissociation, normalized collision energy at 35 %, isolation width at 2 amu, activation Q parameter at 0.2, one microscan and data-dependent acquisition of the ten most abundant precursor ions per cycle with dynamic exclusion, such that each precursor ion was fragmented only once. Lipid species were identified and quantified by ALEX software, SAS and Tableau, as previously described (Almeida et al, 2015;Husen et al, 2013). Lipid species detected by FTMS analysis were identified using an m/z tolerance setting of +0.004 amu and annotated using sum composition (e.g.…”

Computational Lipidomics and Lipid Bioinformatics: Filling In the Blanks

“…by a multi-pole mass filter, and in the case of MSn (n > 1) subsequently fragmented n − 1 times. A fragment's m/z ratio may then reveal unique molecular details [49]. A typical fragment that derives from a PC molecule in positive ion mode has an m/z of 184.073321 which represents the phosphocholine that dissociated from the sn-3 position of the glycerol.…”