Rapid and Reliable Identification of Phospholipids for Untargeted Metabolomics with LC–ESI–QTOF–MS/MS

Abstract

Lipids are important components of biological systems, and their role can be currently investigated by the application of untargeted, holistic approaches such as metabolomics and lipidomics. Acquired data are analyzed to find significant signals responsible for the differentiation between the investigated conditions. Subsequently, identification has to be performed to bring biological meaning to the obtained results. Lipid identification seems to be relatively easy due to the known characteristic fragments; however, the large number of structural isomers and the formation of different adducts makes it challenging and at risk of misidentification. The inspection of data, acquired for plasma samples by a standard metabolic fingerprinting method, revealed multisignal formations for phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins by the formation of ions such as [M + H](+), [M + Na](+), and [M + K](+) in positive ionization mode and [M - H](-), [M + HCOO](-), and [M + Cl](-) in negative mode. Moreover, sodium formate cluster formation was found for [M + H·HCOONa](+) and [H-H·HCOONa](-). The MS/MS spectrum obtained for each of the multi-ions revealed significant differences in the fragmentation, which were confirmed by the analysis of the samples in two independent research centers. After the inspection of an acquired spectra, a list of characteristic and diagnostic fragments was proposed that allowed for easy, quick, and robust lipid identification that provides information about the headgroup, formed adduct, and fatty acyl composition. This ensures successful identification, which is of great importance for the contextualization of data and results validation.

Comprehensive shotgun lipidomics of human meibomian gland secretions using MS/MSall with successive switching between acquisition polarity modes

“…With the increased sensitivity of pPIS using this untargeted MS/MS all method, species low in abundance, including LPC, PC, and SM, could be detected with high sensitivity. These species generate a common characteristic product ion with the theoretical m/z 184.0733 (52,53). SM and PC were estimated to account for only 18 ± 5 ppm (22), 0.006 ± 0.003% (11), 0.6% (54), or below the detection limit in meibum in previous studies (9,12).…”

Development of LC‐QTOF‐MS method for human lung tissue fingerprinting. A preliminary application to nonsmall cell lung cancer

“…The identity of compounds was based on the spectral data of reference compounds available at public databases (HMDB, METLIN, and LIPIDMAPS) and confirmed by MS/MS analysis. Identity of phospholipids and acylcarnitines was confirmed based on the already described fragmentation patterns [22,23].…”

A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source

“…Lipidomics can be performed in either positive ion or negative ion mode depending upon the lipid class. Lipid identifications with mass spectrometry can be difficult due to the abundance of structural isomers and the various adducts that can generate lipid ions and confuse the fragmentation pattern (Godzien et al 2015). Several LC-MS based lipidomic studies have been reported whose identification rely upon LC retention time and high mass accuracy MS1 data, some of which focus on specific lipid classes (van der Kloet et al 2013; Tyurina et al 2014; Hartler et al 2011; Hein et al 2010; Collins et al 2016).…”