Iterative DDA LC-MS/MS method and molecular networking
Résumé
Metabolomics data analysis is a complex, multistep process, which is constantly evolving along with new analytical technologies, statistical methods, bioinformatics tools and databases. The annotation step in the metabolomics workflow is probably one of the most difficult part so much that only a few metabolites own a putative annotation when comparing their spectra with databases such as MoNA, MassBank or Lipidblast.
The online Workflow4Metabolomics infrastructure (https://workflow4metabolomics.usegalaxy.fr) includes a powerful annotation pipeline with easy spectral comparison to online databases [2]. In addition, the LC-MS/MS pipeline outputs are compatible with molecular networking tools and can easily be imported into MetGEM (https://metgem.github.io/) [3].
To expand the number of features annotated, an iterative Data Dependant Analysis (DDA) method [1] with an Orbitrap (ThermoFisher) was applied. The idea here first is to compare the number of all features obtained from iterative DDA on the Quality Control samples and from single DDA on all the samples. Then, a molecular network is built to find out which method provides the most annotated features and compare the ratio of annotation between those two methods. This new way of data acquisition by using iterative DDA has shown some promising results, allowing to increase and improve quantity and quality of the annotation step.
REFERENCES
[1] Koelmel JP, Kroeger NM, Gill EL, Ulmer CZ, Bowden JA, Patterson RE, Yost RA, Garrett TJ. Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation. J Am Soc Mass Spectrom, 2017 May 28, (5) : 908-917. [doi: 10.1007/s13361-017-1608-0].
[2] Giacomoni F., Le Corguillé et al (2014). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. In Bioinformatics. [doi: 10.1093/bioinformatics/btu813]
[3] F. Olivon et al (2018). MetGem software for the generation of molecular networks based on t-SNE algorithm. In In Analytical Chemistry, 90 (23), pp.13900--13908. [doi: 10.1021/acs.analchem.8b03099].
Domaines
Chimie analytique
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