Monitoring diacylglycerols in biofluids by non-isotopically paired charge derivatization combined with LC-MS/MS.

ABSTRACT

Diacylglycerols (DAGs) are important lipid mediators in cellular signaling transduction and metabolism. Imbalanced production or consumption of DAGs has a negative impact on the physiological functions of the body. However, comprehensive monitoring of structurally diverse DAGs remains a daunting task due to the rapid metabolism and ion suppression characteristics in biofluids. These bottlenecks call for developing a method that enables sensitive quantification of DAGs in biological sample. In this work, a straightforward charge derivatization strategy was developed to insert a series of structure analogs charge label, i.e., N, N-dimethylglycine (DMG) and N, N-dimethylalanine (DMA), on the free hydroxyl group of the DAGs. Owing to the existence of tertiary amino groups in charge label, the mass spectrometry ionization response of the derivatized DAGs was significantly increased in comparison with traditional metal ion adducts. After charge derivatization, the specific neutral loss diagnostic ions (DMG, 103 Da and DMA, 117 Da) were captured by mass spectrometry. Then, the DMG/DMA-oriented paired multiple reaction monitoring methods based on the characteristic diagnostic ions of the derivatized DAGs have been developed as sensitive methods for the detection (detection limit = 16 aM) and quantification (quantification limit = 62.5 aM) of DAGs in serum. Moreover, the tagged 1,2-DAGs and 1,3-DAGs sn-isomers have been well separated on the reversed-phase column in combination with ultra-performance liquid chromatography. Finally, metabolic characterizations of the tagged DAGs were further explored in L-Arginine-induced acute pancreatitis mice and resveratrol treated model mice. The results indicated that 1,2-DAGs were increased in the serum of model mice relative to normal controls and resveratrol significantly altered this metabolic abnormality. The currently established DMG/DMA-oriented paired charge derivatization strategy is promising for depicting DAGs changes more accurately in metabolic studies of lipid-related diseases and accurately evaluating drug treatment strategies.