Three-dimensional Kendrick mass plots as a tool for graphical lipid identification.

Abstract

The rising field of lipidomics strongly relies on the identification of lipids in complex matrices. Recent technical advances regarding liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) enable the mapping of the lipidome of an organism with short data acquisition times. However, interpretation and evaluation of resulting multidimensional datasets are challenging and this is still the bottleneck regarding overall analysis times. A novel adaption of Kendrick mass plot analysis is presented for a rapid and accurate analysis of lipids in complex matrices. Separation of lipids by their respective head group was achieved via hydrophilic interaction liquid chromatography (HILIC) coupled to HRMS. The resulting LC/HRMS datasets are processed to a list of chromatographically separated features by applying an optimized MZmine 2 workflow. All features are plotted in a three-dimensional Kendrick mass plot, which allows a fast identification of present lipid classes, based on equidistant features with fitting retention times and the same Kendrick mass defect. Suspected lipid classes are used for exact mass database matching to annotate features. A second three-dimensional Kendrick mass plot of annotated features of a single lipid class helps to reveal potential database mismatches, resulting in a curated list of identified lipid species. The use of the novel adaption of the Kendrick mass plot has accelerated the identification of the relevant lipid species in the green alga Chlamydomonas reinhardtii. A total of 106 species were identified within the lipid classes: phosphatidylserine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol, and sulfoquinovosyldiacylglycerol. This work shows how the addition of chromatographic information, i.e. the retention time, to a classical two-dimensional Kendrick mass plot enables rapid and accurate analysis of LC/HRMS datasets, exemplified on a green alga (C. reinhardtii) sample. Three-dimensional Kendrick mass plots have improved lipid class identification and fast spotting of falsely annotated lipid species.