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Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation
Jeremy P Koelmel1, Nicholas M Kroeger2, Emily L Gill1
1Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.
Journal of the American Society for Mass Spectrometry
|March 8, 2017
Summary
New software, iterative exclusion omics (IE-Omics), enhances lipidome coverage by automating precursor exclusion in mass spectrometry. This method significantly increases molecular identifications in biological samples, aiding disease biomarker discovery.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Computational Biology
Background:
- Untargeted omics aims for comprehensive biomolecule characterization, but lipidomics identification is limited.
- High-resolution tandem mass spectrometry (HR-MS/MS) improved lipidome coverage but is constrained by precursor selection.
- Current methods struggle to identify the full spectrum of lipids due to limitations in precursor fragmentation during analysis.
Purpose of the Study:
- To develop and validate a software tool, IE-Omics, for automating iterative exclusion (IE) in lipidomic analyses.
- To significantly increase the coverage of the lipidome by overcoming limitations in precursor selection for HR-MS/MS.
- To enhance the identification of trace lipid species and improve biomarker discovery for disease etiology.
Main Methods:
- Developed IE-Omics software to automate iterative exclusion (IE) of selected precursors in sequential mass spectrometry injections.
- Implemented IE to exclude already fragmented precursors, allowing subsequent injections to target unique ions.
- Applied IE-Omics to lipidomic analysis of Red Cross plasma and substantia nigra tissue using HR-MS/MS.
Main Results:
- IE-Omics drastically improved lipidome coverage, yielding 69% more identifications in plasma and 40% in substantia nigra.
- The method enhanced the detection of low-abundance lipid species, including odd-chained, short-chained diacylglycerides, and oxidized lipids.
- Iterative exclusion significantly increased the number of molecular identifications achievable within a single lipidomic workflow.
Conclusions:
- IE-Omics software effectively automates iterative exclusion, substantially enhancing lipidome coverage in complex biological samples.
- Increased lipid identification through IE-Omics improves the potential for discovering novel disease biomarkers.
- This approach provides deeper insights into biological perturbations and aids in understanding disease etiology.
