Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions.

Abstract

Lipidomics requires the accurate annotation of lipids in complex samples to enable determination of their biological relevance. We demonstrate that unintentional in-source fragmentation (ISF, common in lipidomics) generates ions that have identical masses to other lipids. Lysophosphatidylcholines (LPC), for example, generate in-source fragments with the same mass as free fatty acids and lysophosphatidylethanolamines (LPE). The misannotation of in-source fragments as true lipids is particularly insidious in complex matrixes since most masses are initially unannotated and comprehensive lipid standards are unavailable. Indeed, we show such LPE/LPC misannotations are incorporated in the data submitted to the National Institute of Standards and Technology (NIST) interlaboratory comparison exercise. Computer simulations exhaustively identified potential misannotations. The selection of in-source fragments of highly abundant lipids as features, instead of the correct recognition of trace lipids, can potentially lead to (i) missing the biologically relevant lipids (i.e., a false negative) and/or (ii) incorrect assignation of a phenotype to an incorrect lipid (i.e., false positive). When ISF is not eliminated in the negative ion mode, ∼40% of the 100 most abundant masses corresponding to unique phospholipids measured in plasma were artifacts from ISF. We show that chromatographic separation and ion intensity considerations assist in distinguishing precursor ions from in-source fragments, suggesting ISF may be especially problematic when complex samples are analyzed via shotgun lipidomics. We also conduct a systematic evaluation of electrospray ionization (ESI) source parameters on an Exactive equipped with a heated electrospray ionization (HESI-II) source with the objective of obtaining uniformly appropriate source conditions for a wide range of lipids, while, at the same time, reducing in-source fragmentation.