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ORIGINAL RESEARCH article

Front. Chem.
Sec. Analytical Chemistry
Volume 12 - 2024 | doi: 10.3389/fchem.2024.1477492

A complementary approach for detecting biological signals through a semi-automated feature selection tool

Provisionally accepted
Gabriel S. Arini Gabriel S. Arini 1Luiz G. Mencucini Luiz G. Mencucini 1Rafael De Felício Rafael De Felício 2Luís G. Feitosa Luís G. Feitosa 1Paula REZENDE-TEIXEIRA Paula REZENDE-TEIXEIRA 1Henrique M. Tsuji Henrique M. Tsuji 1Alan C. Pilon Alan C. Pilon 3Danielle R. Pinho Danielle R. Pinho 1Leticia V. Lotufo Leticia V. Lotufo 1Norberto P. Lopes Norberto P. Lopes 1Daniela Trivella Daniela Trivella 2Ricardo da Silva Ricardo da Silva 1*
  • 1 University of São Paulo, São Paulo, Brazil
  • 2 National Center for Research in Energy and Materials (Brazil), Campinas, São Paulo, Brazil
  • 3 Institute of Chemistry, São Paulo State University, Araraquara, Sao Paulo, Brazil

The final, formatted version of the article will be published soon.

    Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled to mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package, RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biological PAGE \* Arabic \* relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potencial biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

    Keywords: Mass Spectrometry, Data dependent acquisition, chemometrics, untargeted metabolomics, Natural Products

    Received: 16 Aug 2024; Accepted: 11 Oct 2024.

    Copyright: © 2024 Arini, Mencucini, De Felício, Feitosa, REZENDE-TEIXEIRA, Tsuji, Pilon, Pinho, Lotufo, Lopes, Trivella and da Silva. 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) or licensor 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.

    * Correspondence: Ricardo da Silva, University of São Paulo, São Paulo, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.