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Development of Chemical Isotope Labeling Liquid Chromatography Orbitrap Mass Spectrometry for Comprehensive Analysis of Dipeptides.

Zhan ChengLiang Li
Published in: Analytical chemistry (2023)
Dipeptides have recently attracted considerable attention due to their newly found biological functions and potential biomarkers of diseases. Global analysis of dipeptides (400 common dipeptides in total number) in samples of complex matrices would enable functional studies of dipeptides and biomarker discovery. In this work, we report a method for high-coverage detection and accurate relative quantification of dipeptides. This method is based on differential chemical isotope labeling (CIL) of dipeptides with dansylation and liquid chromatography Orbitrap tandem mass spectrometry (LC-Orbitrap-MS). An optimized LC gradient ensured the separation of dansyl-dipeptides, including positional isomers (e.g., leucine- and isoleucine-containing dipeptides). MS/MS collision energy in Orbitrap MS was optimized to provide characteristic fragment ion information to sequence dansyl-dipeptides. Using the optimized conditions, a CIL standard library consisting of retention time, MS, and MS/MS information of a whole set of 400 dansyl-dipeptides was constructed to facilitate rapid dipeptide identification. For qualitative analysis of dipeptides in real samples, IsoMS data processing software's parameters were tuned to improve the coverage of dipeptide annotation. Data-dependent acquisition was also carried out to improve the reliability of dipeptide identification. As examples of applications, we successfully identified a total of 321 dipeptides in rice wines and 105 dipeptides in human serum samples. For quantitative analysis, we demonstrated that the intensity ratios of the peak pairs from 96% of the dansyl-dipeptides detectable in a 1:1 mixture of 12 C- and 13 C-labeled rice wine samples were within ±20% of an expected value of 1.0. More than 90% of dipeptides were detected with a relative standard deviation of less than 10%, showing good performance of relative quantification.
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