Identification of triacylglycerol using automated annotation of high resolution multistage mass spectral trees

Abstract

High complexity of identification for non-target triacylglycerols (TAGs) is a major challenge in lipidomics analysis. To identify non-target TAGs, a powerful tool named accurate MS(n) spectrometry generating so-called ion trees is used. In this paper, we presented a technique for efficient structural elucidation of TAGs on MS(n) spectral trees produced by LTQ Orbitrap MS(n), which was implemented as an open source software package, or TIT. The TIT software was used to support automatic annotation of non-target TAGs on MS(n) ion trees from a self-built fragment ion database. This database includes 19108 simulate TAG molecules from a random combination of fatty acids and corresponding 500582 self-built multistage fragment ions (MS ≤ 3). Our software can identify TAGs using a "stage-by-stage elimination" strategy. By utilizing the MS(1) accurate mass and referenced RKMD, the TIT software can discriminate unique elemental composition candidates. The regiospecific isomers of fatty acyl chains will be distinguished using MS(2) and MS(3) fragment spectra. We applied the algorithm to the selection of 45 TAG standards and demonstrated that the molecular ions could be 100% correctly assigned. Therefore, the TIT software could be applied to TAG identification in complex biological samples such as mouse plasma extracts.

Keywords: Multistage mass (MS(n)); Referenced Kendrick mass defect (RKMD); TAG ion tree (TIT); Triacylglycerols; “Stage-by-stage elimination” strategy.