Rapid and Reliable Identification of Phospholipids for Untargeted Metabolomics with LC鈥揈SI鈥換TOF鈥揗S/MS

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• 作者：Joanna Godzien ; Michal Ciborowski ; Mar铆a Paz Mart铆nez-Alc谩zar ; Paulina Samczuk ; Adam Kretowski ; Coral Barbas
• 刊名：Journal of Proteome Research
• 出版年：2015
• 出版时间：August 7, 2015
• 年：2015
• 卷：14
• 期：8
• 页码：3204-3216
• 全文大小：812K
• ISSN：1535-3907

文摘

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]<sup>+sup>, [M + Na]<sup>+sup>, and [M + K]<sup>+sup> in positive ionization mode and [M 鈥?H]<sup>鈭?/sup>, [M + HCOO]<sup>鈭?/sup>, and [M + Cl]<sup>鈭?/sup> in negative mode. Moreover, sodium formate cluster formation was found for [M + H路HCOONa]<sup>+sup> and [H鈥揌路HCOONa]<sup>鈭?/sup>. 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.