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Published on: September 5, 2011
An extendable all-in-one injection twin derivatization LC-MS/MS strategy for the absolute quantification of multiple
Yezhen Huang1, Yu Jiao2, Yiqiao Gao1
1Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing, 210009, PR China.
Abstract:
The ability of LC-MS/MS for high coverage metabolite analysis lags behind the requirements of global metabolomics. The introduction of chemical derivatizations could significantly extend the ability of LC-MS/MS with enhanced MS response and improved LC separation, which has been serving as a promising quantitative tool for metabolomic analysis. However, as one specific derivatization reagent usually targets to a certain moiety, only a single chemical-group-based submetabolome could be analyzed in one injection. Therefore, the coverage of detected metabolites by derivatization-based LC-MS/MS is largely limited. To overcome this technical obstacle of derivatization-based LC-MS and increase submetabolome coverage, we proposed an extendable all-in-one injection LC-MS/MS strategy. 5-dimethylamino-naphthalene-1-sulfonyl chloride (Dns-Cl)/5-diethylamino-naphthalene-1-sulfonyl chloride (Dens-Cl) and 5-dimethylamino-naphthalene-1-sulfonyl piperazine (Dns-PP)/5-diethylamino-naphthalene-1-sulfonyl piperazine (Dens-PP) were used as twins labeling reagents for amino/phenol and carboxyl submetabolomes, respectively. "Series Mode" and "Parallel Mode" were proposed and investigated using eight representative standards with the consideration of interaction between different derivatization systems, time-consumption, and extendability. As a result, we found that "Series Mode" led to yield reduction, while "Parallel Mode" gave identical results with those of individual derivatization. Finally, a "Parallel Mode" was chosen to develop an extendable all-in-one injection twin derivatization LC-MS/MS strategy to quantify eighty metabolites assigned to five classes of microbial metabolites, including polyamines, amino acids, indole derivatives, bile acids, and free fatty acids. This well-validated method quantified 67 metabolites absolutely and discovered additional 40 differential metabolites compared with the untargeted method in rat serum from irinotecan (CPT-11)-induced gastrointestinal toxicity model.
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