Literature DB >> 24678888

Quantitative metabolite profiling utilizing parallel column analysis for simultaneous reversed-phase and hydrophilic interaction liquid chromatography separations combined with tandem mass spectrometry.

Kristaps Klavins1, Hedda Drexler, Stephan Hann, Gunda Koellensperger.   

Abstract

In this work, a fully automated parallel LC column method was established in order to perform orthogonal hydrophilic interaction chromatography (HILIC) and reversed-phase (RPLC) chromatography within one analytical run for targeted quantitative mass spectrometric determination of metabolites from central carbon metabolism. In this way, the analytical throughput could be significantly improved compared to previously established dual separation work flows involving two separate analytical runs. Two sample aliquots were simultaneously injected onto a dual column setup columns using a ten-port valve, and parallel separations were carried out. Sub 2 μm particle size stationary phases were employed for both separation methods. HILIC and RPLC eluents were combined post column followed by ESI-MS/MS detection. The orthogonal separations were optimized, aiming at an overall separation with 2 retention time segments, while reversed-phase separation was accomplished within 5.5 min; metabolites on the HILIC phase were retained for a minimum time of 6 min. The overall run time was 15 min. The setup was applied to the quantification of 30 primary intercellular metabolites, including amino acids, organic acids, and nucleotides employing internal standardization by a fully (13)C-labeled yeast extract. The comparison with HILIC-MS/MS and RPLC-MS/MS in separate analytical runs revealed that an excellent analytical performance was achieved by the parallel LC column method. The experimental repeatability (N = 5) was on average <5% (only for 2 compounds >5%). Moreover, limits of detection for the new approach ranging from 0.002-15 μM were in a good agreement with ones obtained in separate HILIC-MS/MS and RPLC-MS/MS runs (ranging from 0.01-44 μM).

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Year:  2014        PMID: 24678888     DOI: 10.1021/ac5003454

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  16 in total

1.  Profiling of Polar Metabolites in Mouse Feces Using Four Analytical Platforms to Study the Effects Of Cathelicidin-Related Antimicrobial Peptide in Alcoholic Liver Disease.

Authors:  Liqing He; Fengyuan Li; Xinmin Yin; Patrick Bohman; Seongho Kim; Craig J McClain; Wenke Feng; Xiang Zhang
Journal:  J Proteome Res       Date:  2019-06-12       Impact factor: 4.466

2.  Comparison of GC-MS and GC×GC-MS in the analysis of human serum samples for biomarker discovery.

Authors:  Jason H Winnike; Xiaoli Wei; Kevin J Knagge; Steven D Colman; Simon G Gregory; Xiang Zhang
Journal:  J Proteome Res       Date:  2015-03-16       Impact factor: 4.466

3.  Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics.

Authors:  Md Aminul Islam Prodhan; Biyun Shi; Ming Song; Liqing He; Fang Yuan; Xinmin Yin; Patrick Bohman; Craig J McClain; Xiang Zhang
Journal:  Analyst       Date:  2019-06-13       Impact factor: 4.616

4.  Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography-mass spectrometry-based metabolomics.

Authors:  Yi-Fan Xu; Wenyun Lu; Joshua D Rabinowitz
Journal:  Anal Chem       Date:  2015-01-27       Impact factor: 6.986

5.  Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival.

Authors:  Hunter A Miller; Shesh N Rai; Xinmin Yin; Xiang Zhang; Jason A Chesney; Victor H van Berkel; Hermann B Frieboes
Journal:  Metabolomics       Date:  2022-05-14       Impact factor: 4.290

6.  Using Multiple Analytical Platforms to Investigate the Androgen Depletion Effects on Fecal Metabolites in a Mouse Model of Systemic Lupus Erythematosus.

Authors:  Fang Yuan; James Harder; Jing Ma; Xinmin Yin; Xiang Zhang; Michele M Kosiewicz
Journal:  J Proteome Res       Date:  2019-12-27       Impact factor: 4.466

7.  Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods.

Authors:  Hunter A Miller; Ramy Emam; Chip M Lynch; Samuel Bockhorst; Hermann B Frieboes
Journal:  Metabolomics       Date:  2021-03-27       Impact factor: 4.290

Review 8.  Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism.

Authors:  Joseph Chen; Hyunchul Lee; Philipp Schmitt; Caleb J Choy; Donald M Miller; Brian J Williams; Elaine L Bearer; Hermann B Frieboes
Journal:  J Neuropathol Exp Neurol       Date:  2021-09-15       Impact factor: 3.148

9.  Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data.

Authors:  Hunter A Miller; Xinmin Yin; Susan A Smith; Xiaoling Hu; Xiang Zhang; Jun Yan; Donald M Miller; Victor H van Berkel; Hermann B Frieboes
Journal:  Lung Cancer       Date:  2021-04-15       Impact factor: 6.081

10.  Complementing reversed-phase selectivity with porous graphitized carbon to increase the metabolome coverage in an on-line two-dimensional LC-MS setup for metabolomics.

Authors:  Karin Ortmayr; Stephan Hann; Gunda Koellensperger
Journal:  Analyst       Date:  2015-03-31       Impact factor: 4.616

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