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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 ense...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580960/ https://www.ncbi.nlm.nih.gov/pubmed/36304329 http://dx.doi.org/10.3389/fbinf.2022.842964 |
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author | Gaudry, Arnaud Huber, Florian Nothias, Louis-Félix Cretton, Sylvian Kaiser, Marcel Wolfender, Jean-Luc Allard, Pierre-Marie |
author_facet | Gaudry, Arnaud Huber, Florian Nothias, Louis-Félix Cretton, Sylvian Kaiser, Marcel Wolfender, Jean-Luc Allard, Pierre-Marie |
author_sort | Gaudry, Arnaud |
collection | PubMed |
description | 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. 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. |
format | Online Article Text |
id | pubmed-9580960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95809602022-10-26 MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets Gaudry, Arnaud Huber, Florian Nothias, Louis-Félix Cretton, Sylvian Kaiser, Marcel Wolfender, Jean-Luc Allard, Pierre-Marie Front Bioinform Bioinformatics 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. 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. Frontiers Media S.A. 2022-04-13 /pmc/articles/PMC9580960/ /pubmed/36304329 http://dx.doi.org/10.3389/fbinf.2022.842964 Text en Copyright © 2022 Gaudry, Huber, Nothias, Cretton, Kaiser, Wolfender and Allard. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Gaudry, Arnaud Huber, Florian Nothias, Louis-Félix Cretton, Sylvian Kaiser, Marcel Wolfender, Jean-Luc Allard, Pierre-Marie MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title | MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title_full | MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title_fullStr | MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title_full_unstemmed | MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title_short | MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets |
title_sort | memo: mass spectrometry-based sample vectorization to explore chemodiverse datasets |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580960/ https://www.ncbi.nlm.nih.gov/pubmed/36304329 http://dx.doi.org/10.3389/fbinf.2022.842964 |
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