Literature DB >> 28691762

Comparison of fully wettable RPLC stationary phases for LC-MS-based cellular metabolomics.

Le Si-Hung1, Tim J Causon1, Stephan Hann1.   

Abstract

Reversed-phase LC combined with high-resolution mass spectrometry (HRMS) is one of the most popular methods for cellular metabolomics studies. Due to the difficulties in analyzing a wide range of polarities encountered in the metabolome, 100%-wettable reversed-phase materials are frequently used to maximize metabolome coverage within a single analysis. Packed with silica-based sub-3 μm diameter particles, these columns allow high separation efficiency and offer a reasonable compromise for metabolome coverage within a single analysis. While direct performance comparison can be made using classical chromatographic characterization approaches, a comprehensive assessment of the column's performance for cellular metabolomics requires use of a full LC-HRMS workflow in order to reflect realistic study conditions used for cellular metabolomics. In this study, a comparison of several reversed-phase LC columns for metabolome analysis using such a dedicated workflow is presented. All columns were tested under the same analytical conditions on an LC-TOF-MS platform using a variety of authentic metabolite standards and biotechnologically relevant yeast cell extracts. Data on total workflow performance including retention behavior, peak capacity, coverage, and molecular feature extraction repeatability from these columns are presented with consideration for both nontargeted screening and differential metabolomics workflows using authentic standards and Pichia pastoris cell extract samples.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Biotechnology; Column comparison; Fully wettable column; HPLC; Metabolite; Pichia pastoris; Reversed-phase; TOFMS; Yeast

Mesh:

Year:  2017        PMID: 28691762     DOI: 10.1002/elps.201700157

Source DB:  PubMed          Journal:  Electrophoresis        ISSN: 0173-0835            Impact factor:   3.535


  4 in total

1.  Metabolic Changes in Brain Slices over Time: a Multiplatform Metabolomics Approach.

Authors:  Carolina Gonzalez-Riano; Silvia Tapia-González; Gertrudis Perea; Candela González-Arias; Javier DeFelipe; Coral Barbas
Journal:  Mol Neurobiol       Date:  2021-03-02       Impact factor: 5.590

2.  Metabolic Dynamics of In Vitro CD8+ T Cell Activation.

Authors:  Joy Edwards-Hicks; Michael Mitterer; Erika L Pearce; Joerg M Buescher
Journal:  Metabolites       Date:  2020-12-28

3.  automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.

Authors:  Daniel Eilertz; Michael Mitterer; Joerg M Buescher
Journal:  Anal Chem       Date:  2022-04-12       Impact factor: 8.008

4.  Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries.

Authors:  Evelyn Rampler; Gerrit Hermann; Gerlinde Grabmann; Yasin El Abiead; Harald Schoeny; Christoph Baumgartinger; Thomas Köcher; Gunda Koellensperger
Journal:  Metabolites       Date:  2021-03-10
  4 in total

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