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

Arnaud Gaudry; Florian Huber; Louis-Félix Nothias; Sylvian Cretton; Marcel Kaiser; Jean-Luc Wolfender; Pierre-Marie Allard

doi:10.3389/fbinf.2022.842964

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

Front Bioinform. 2022 Apr 13:2:842964. doi: 10.3389/fbinf.2022.842964. eCollection 2022.

Authors

Arnaud Gaudry^{1

2}, Florian Huber³, Louis-Félix Nothias^{1

2}, Sylvian Cretton^{1

2}, Marcel Kaiser^{4

5}, Jean-Luc Wolfender^{1

2}, Pierre-Marie Allard^{1

2

6}

Affiliations

¹ Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
² School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
³ Center for Digitalization and Digitality, HSD-Düsseldorf University of Applied Sciences, Düsseldorf, Germany.
⁴ Department of Medical and Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.
⁵ Faculty of Science, University of Basel, Basel, Switzerland.
⁶ Department of Biology, University of Fribourg, Fribourg, Switzerland.

Abstract

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.

Keywords: computational metabolomics; drug discovery; mass spectrometry; natural products; vectorization.