No abstract

Liquid chromatography tandem mass spectrometry (LC/MS) and other mass spectrometric technologies have been widely applied for triacylglycerol profiling. One challenge for targeted identification of fatty acyl moieties that constitute triacylglycerol species in biological samples is the numerous combinations of 3 fatty acyl groups that can form a triacylglycerol molecule. Manual determination of triacylglycerol structures based on peak intensities and retention time can be highly inefficient and error-prone. To resolve this, we have developed TAILOR-MS, a Python (programming language) package that aims at assisting: (1) the generation of targeted LC/MS methods for triacylglycerol detection and (2) automating triacylglycerol structural determination and prediction. To assess the performance of TAILOR-MS, we conducted LC/MS triacylglycerol profiling of bovine milk and two infant formulas. Our results confirmed dissimilarities between bovine milk and infant formula triacylglycerol composition. Furthermore, we identified 247 triacylglycerol species and predicted the possible existence of another 317 in the bovine milk sample, representing one of the most comprehensive reports on the triacylglycerol composition of bovine milk thus far. Likewise, we presented here a complete infant formula triacylglycerol profile and reported >200 triacylglycerol species. TAILOR-MS dramatically shortened the time required for triacylglycerol structural identification from hours to seconds and performed decent structural predictions in the absence of some triacylglycerol constituent peaks. Taken together, TAILOR-MS is a valuable tool that can greatly save time and improve accuracy for targeted LC/MS triacylglycerol profiling.