Abstract
BackgroundLipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.ResultsLMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.ConclusionsLMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.
Highlights
Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease
We developed LipidMatch Normalizer (LMN), which can be integrated in an open source workflow to select the most appropriate internal standards for relative quantitation within acquired LC-high-resolution mass spectrometry (HRMS) data
Comparison of targeted MS/MS versus all-ion fragmentation (AIF) A total of 129 unique lipid molecular species across 16 lipid types were identified in negative ion mode, of which 122 had appropriate internal standards for relative quantification
Summary
Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. Absolute/accurate quantification typically employs matrix-matched external calibration curves and/or isotopically labeled internal standards for each lipid quantified. This quantitative approach has limited application to untargeted lipidomics analyses due to the enormous diversity of the lipidome, limited availability of appropriate standards to cover this diversity, and the cost associated with purchasing hundreds of standards [10,11,12]. Semi-quantification is used when stoichiometric differences between lipid species is of interest, but exact quantitative levels within 10–20% are not obtained Often both an internal calibrant and external calibration are used for semi-quantification [10, 13]. Relative quantification, which does not employ a calibration curve, and involves the addition of a smaller set of internal standards representative of the classes of lipids analyzed, is the most commonly used approach for quantitation in untargeted lipidomics experiments
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