MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets

  • In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMOIn natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.show moreshow less

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Author:Arnaud GaudryORCiD, Florian HuberORCiDGND, Louis-Félix NothiasORCiD, Sylvian CrettonORCiD, Marcel KaiserORCiD, Jean-Luc WolfenderORCiD, Pierre-Marie AllardORCiD
Qualitätssicherung:peer reviewed
open access:Gold - Erstveröffentlichung mit Lizenzhinweis
Institutes:Zentrum für Digitalisierung und Digitalität (ZDD)
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Medien
Document Type:Article
Year of Completion:2022
Language of Publication:English
Publisher:Frontiers
Parent Title (English):Frontiers in Bioinformatics
Volume:2
Article Number:842964
Page Number:13
URN:urn:nbn:de:hbz:due62-opus-54660
DOI:https://doi.org/10.3389/fbinf.2022.842964
ISSN:2673-7647
GND Keyword:Computational chemistryGND; MassenspektrometrieGND; MetabolomikGND; NaturstoffchemieGND
Information on the Research Data:The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2025/07/01
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