, 12:168 | Cite as

Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with 34S labeling

  • Ryo Nakabayashi
  • Hiroshi Tsugawa
  • Tetsuya Mori
  • Kazuki Saito
Original Article



Sulfur-containing metabolites (S-metabolites) in organisms including plants have unique benefits to humans. So far, few analytical methods have explored such metabolites.


We aimed to develop an automatic chemically assigning platform by metabolomics and chemoinformatics with 34S labeling to identify the molecular formula of S-metabolites.


Direct infusion analysis using Fourier transform ion cyclotron resonance-mass spectrometry provided ultra-high-resolution data including clearly separated isotopic ions—15N, 34S, 18O, and 13C2—in the flower, silique, leaf, stem, and root of non-labeled and 34S-labeled Arabidopsis thaliana. Chemoinformatic analysis assigned several elemental compositions of S-metabolites to the acquired S-containing monoisotopic ions using mass accuracy and peak resolution in the non-labeled metabolome data. Possible elemental compositions were characterized on the basis of diagnostic scores of the exact mass and isotopic ion pattern, and a database search. By comparing elemental compositions assigned to the 34S-labeled data with those assigned to the non-labeled data, the elemental composition of S-metabolites were determined. The determined elemental compositions were surveyed using the in-house database, which stores molecular formulae downloaded from metabolome databases.


We identified 35 molecular formulae for known S-metabolites and characterized 72 for unknown. Chemoinformatics required around 1.5 min to analyze a pair of the non-labeled and 34S-labeled data of the organ.


In this study, we developed an automation platform for automatically identifying the presence of S-metabolites. We identified the molecular formula of known S-metabolites, which are accessible in free databases, together with that of unknown. This analytical method did not focus on identifying the structure of S-metabolites, but on the automatic identification of their molecular formula.


Metabolomics FTICR-MS S-containing metabolite Chemical assignment Molecular formula 



We would like to thank Yutaka Yamada and Tetsuya Sakurai (RIKEN CSRS) for managing the metabolome data. This work was partially supported by Strategic International Collaborative Research Program (SICORP), JST, and Japan Advanced Plant Science Network. H.T. was supported by a grant-in-aid for scientific research (C) 15K01812.


The authors declare no competing financial interest.

Compliance with ethical standards

Conflict of interest

Authors declare that there is no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ryo Nakabayashi
    • 1
  • Hiroshi Tsugawa
    • 1
  • Tetsuya Mori
    • 1
  • Kazuki Saito
    • 1
    • 2
  1. 1.RIKEN Center for Sustainable Resource ScienceYokohamaJapan
  2. 2.Graduate School of Pharmaceutical SciencesChiba UniversityChibaJapan

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