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Migration time correction for dual pressure capillary electrophoresis in semi-targeted metabolomics study

TitleMigration time correction for dual pressure capillary electrophoresis in semi-targeted metabolomics study
Publication TypeJournal Article
Year of Publication2022
AuthorsHuang, Z-A, Tan, J, Li, Y, Miao, S, Scotland, KB, Chew, BH, Lange, D, Chen, DDY
Start Page1626
Date Published06/2022
Type of ArticleResearch
Keywordscapillary electrophoresis mass spectrometry, dual pressure capillary electrophoresis, migration time correction, semi-targeted metabolomics

Abstract Migration time fluctuation strongly affects peak alignment and identification of unknown compounds, making migration time correction an essential step in capillary electrophoresis (CE)-based metabolomics. To obtain more reliable information, metabolites with different apparent mobilities are analyzed by tandem mass spectrometry. Applying a small pressure is a common practice for reducing the analysis time of anions in a positive mode CE, known as the pressure-assisted CE. However, applying pressure may reduce the separation efficiency and can be undesirable for cation analysis. A simple way to address this issue is to increase the pressure after a certain time, during the separation. We term this practice as dual pressure CE. However, changing the pressure during the CE separation complicates migration time correction. Previous migration time correction methods were established based on a consistent electroosmotic flow and a constant pressure-driven bulk-flow velocity. We proposed a new correction method to support the peak alignment when dual pressure CE is used. A Python-based script was developed to implement dual pressure CE migration time correction for semi-targeted metabolomics study performed by a multiple reaction monitoring–based method. This script can help select suitable endogenous metabolites as correction markers, perform migration time correction, and conduct peak alignment. A case study showed that migration time precision of 156 metabolites in 32 samples can be improved from 4.8 to 11.4%RSD (relative standard deviation) to less than 1.8%RSD.