Integrating MS1 and MS2 Scans in High-Resolution Parallel Reaction Monitoring Assays for Targeted Metabolite Quantification and Dynamic ^sup 13^C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. Initially developed for proteomic analysis, the parallel reaction monitoring (PRM) technique exploiting high-resolution MS2 fragment...

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Published inAnalytical chemistry (Washington) Vol. 89; no. 1; p. 877
Main Authors Li, Zhucui, Li, Yujing, Chen, Wujiu, Cao, Qichen, Guo, Yufeng, Wan, Ni, Jiang, Xiaolong, Tang, Yinjie J, Wang, Qinhong, Shui, Wenqing
Format Journal Article
LanguageEnglish
Published Washington American Chemical Society 03.01.2017
Subjects
Amino acids
E coli
Metabolism
Metabolites
Proteomics
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Summary:Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. Initially developed for proteomic analysis, the parallel reaction monitoring (PRM) technique exploiting high-resolution MS2 fragment ion data has shown high promise for targeted metabolite quantification. Notably, MS1 ion intensity data acquired independently as part of each PRM scan cycle are often underutilized in the PRM assay. In this study, we developed an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids and shikimate pathway-related metabolites on an orthogonal QqTOF system. Concentration curve assessment revealed that exploiting both MS1 and MS2 scans in PRM analysis afforded higher sensitivity, wider dynamic range and better reproducibility than relying on either scan mode for quantification. Furthermore, Skyline was incorporated into our workflow to process the MS1/MS2 ion intensity data, and eliminate noisy signals and transitions with interferences. This integrated MS1/MS2 PRM approach was applied to targeted metabolite quantification in engineered E. coli strains for understanding of metabolic pathway modulation. In addition, this new approach, when first implemented in a dynamic 13C-labeling experiment, showed its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary 13C-labeling metabolism analysis.
ISSN:0003-2700
1520-6882