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Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics

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Abstract

Introduction

Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching.

Methods

This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation.

Results and Discussion

The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.

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Data availability

The NIST msp file can be accessed at Metabolomics Library Toxicology Centre (BE) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6979698.

References

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Acknowledgements

The authors would like to thank Michael Stravs for his help during adaptation of the data processing workflow and review of the manuscript, and Joao Pedro Cerqueira for his help during generation of in silico CCS values using DeepCCS. KMdS and RR were funded by the University of Antwerp (BOF DOCPRO 4-Antigoon ID 36893 and BOF-GOA─PS ID 41667, respectively). MvdL is funded by the University of Antwerp (BOF-Antigoon ID 46315) and by the Research Scientific Foundation-Flanders (FWO)-project number 1120623N. EI is funded by FWO-project number 1161620N. Graphical icons in Fig. 1 were provided by BioRender, license no 2641–5211.

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Author notes

  1. Katyeny Manuela da Silva, Maria van de Lavoir, Rani Robeyns, and Elias Iturrospe shared first authorship.

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    Contributions

    KMdS: conceptualized the study, designed the analytical methods, performed experiments, and wrote the manuscript. MvdL: conceptualized the study, performed experiments, and wrote the manuscript. EI: conceptualized the study, designed the analytical methods, performed experiments, and wrote the manuscript. RR: conceptualized the study, performed experiments, and wrote the manuscript. LV: performed the experiments and reviewed the manuscript. ALNvN and AC: conceptualized the study, provided funding, and reviewed the manuscript.

    Corresponding author

    Correspondence to Alexander L. N. van Nuijs.

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    da Silva, K.M., van de Lavoir, M., Robeyns, R. et al. Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics. Metabolomics 19, 4 (2023). https://doi.org/10.1007/s11306-022-01965-w

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    • DOI: https://doi.org/10.1007/s11306-022-01965-w

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