MS 2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence

As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Rel...

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Bibliographic Details
Published inJournal of chromatography. B, Analytical technologies in the biomedical and life sciences Vol. 1145; p. 122105
Main Authors Folberth, Julica, Begemann, Kimberly, Jöhren, Olaf, Schwaninger, Markus, Othman, Alaa
Format Journal Article
LanguageEnglish
Published Netherlands 15.05.2020
Subjects
Chromatography, Liquid - methods
Humans
Metabolome
Metabolomics - methods
Plasma - chemistry
Plasma - metabolism
Tandem Mass Spectrometry - methods
HRMS
Metabolomics
Hydrophilic interaction chromatography
Orbitrap
In-house library
LC-ESI-MS/MS
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Summary:As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Reliable compound annotation is essential for translating metabolomics data into meaningful biological information. Here, we developed a fast and transferable method for generating in-house MS libraries to improve metabolite identification. Using the new method we established an in-house MS library that includes over 4,000 fragmentation spectra of 506 standard compounds for 6 different normalized collision energies (NCEs). Additionally, we generated a comprehensive liquid chromatography (LC) library by testing 57 different LC-MS conditions for 294 compounds. We used the library information to develop an untargeted metabolomics screen with maximum coverage of the metabolome that was successfully tested in a study of 360 human serum samples. The current work demonstrates a workflow for LC-MS/MS-based metabolomics, with enhanced metabolite identification confidence and the possibility to select suitable analysis conditions according to the specific research interest.
ISSN:1873-376X