Power of isotopic fine structure for unambiguous determination of metabolite elemental compositions: in silico evaluation and metabolomic application.

Power of isotopic fine structure for unambiguous determination of metabolite elemental compositions: in silico evaluation and metabolomic application. Nagao, T., Yukihira, D., Fujimura, Y., Saito, K., Takahashi, K., Miura, D., & Wariishi, H. Analytica chimica acta, 813:70–76, 2014.
doi abstract bibtex

In mass spectrometry (MS)-based metabolomics studies, reference-free identification of metabolites is still a challenging issue. Previously, we demonstrated that the elemental composition (EC) of metabolites could be unambiguously determined using isotopic fine structure, observed by ultrahigh resolution MS, which provided the relative isotopic abundance (RIA) of (13)C, (15)N, (18)O, and (34)S. Herein, we evaluated the efficacy of the RIA for determining ECs based on the MS peaks of 20,258 known metabolites. The metabolites were simulated with a łe 25% error in the isotopic peak area to investigate how the error size effect affected the rate of unambiguous determination of the ECs. The simulation indicated that, in combination with reported constraint rules, the RIA led to unambiguous determination of the ECs for more than 90% of the tested metabolites. It was noteworthy that, in positive ion mode, the process could distinguish alkali metal-adduct ions ([M+Na](+) and [M+K](+)). However, a significant degradation of the EC determination performance was observed when the method was applied to real metabolomic data (mouse liver extracts analyzed by infusion ESI), because of the influence of noise and bias on the RIA. To achieve ideal performance, as indicated in the simulation, we developed an additional method to compensate for bias on the measured ion intensities. The method improved the performance of the calculation, permitting determination of ECs for 72% of the observed peaks. The proposed method is considered a useful starting point for high-throughput identification of metabolites in metabolomic research.

@Article{nagao14power,
  author    = {Nagao, Tatsuhiko and Yukihira, Daichi and Fujimura, Yoshinori and Saito, Kazunori and Takahashi, Katsutoshi and Miura, Daisuke and Wariishi, Hiroyuki},
  journal   = {Analytica chimica acta},
  title     = {Power of isotopic fine structure for unambiguous determination of metabolite elemental compositions: in silico evaluation and metabolomic application.},
  year      = {2014},
  pages     = {70--76},
  volume    = {813},
  abstract  = {In mass spectrometry (MS)-based metabolomics studies, reference-free identification of metabolites is still a challenging issue. Previously, we demonstrated that the elemental composition (EC) of metabolites could be unambiguously determined using isotopic fine structure, observed by ultrahigh resolution MS, which provided the relative isotopic abundance (RIA) of (13)C, (15)N, (18)O, and (34)S. Herein, we evaluated the efficacy of the RIA for determining ECs based on the MS peaks of 20,258 known metabolites. The metabolites were simulated with a \le 25% error in the isotopic peak area to investigate how the error size effect affected the rate of unambiguous determination of the ECs. The simulation indicated that, in combination with reported constraint rules, the RIA led to unambiguous determination of the ECs for more than 90% of the tested metabolites. It was noteworthy that, in positive ion mode, the process could distinguish alkali metal-adduct ions ([M+Na](+) and [M+K](+)). However, a significant degradation of the EC determination performance was observed when the method was applied to real metabolomic data (mouse liver extracts analyzed by infusion ESI), because of the influence of noise and bias on the RIA. To achieve ideal performance, as indicated in the simulation, we developed an additional method to compensate for bias on the measured ion intensities. The method improved the performance of the calculation, permitting determination of ECs for 72% of the observed peaks. The proposed method is considered a useful starting point for high-throughput identification of metabolites in metabolomic research.},
  doi       = {10.1016/j.aca.2014.01.032},
  keywords  = {isotope patterns; isotopologues; simulation; FT-ICR;},
  pmid      = {24528662},
  timestamp = {2018.04.24},
}

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Previously, we demonstrated that the elemental composition (EC) of metabolites could be unambiguously determined using isotopic fine structure, observed by ultrahigh resolution MS, which provided the relative isotopic abundance (RIA) of (13)C, (15)N, (18)O, and (34)S. Herein, we evaluated the efficacy of the RIA for determining ECs based on the MS peaks of 20,258 known metabolites. The metabolites were simulated with a łe 25% error in the isotopic peak area to investigate how the error size effect affected the rate of unambiguous determination of the ECs. The simulation indicated that, in combination with reported constraint rules, the RIA led to unambiguous determination of the ECs for more than 90% of the tested metabolites. It was noteworthy that, in positive ion mode, the process could distinguish alkali metal-adduct ions ([M+Na](+) and [M+K](+)). However, a significant degradation of the EC determination performance was observed when the method was applied to real metabolomic data (mouse liver extracts analyzed by infusion ESI), because of the influence of noise and bias on the RIA. To achieve ideal performance, as indicated in the simulation, we developed an additional method to compensate for bias on the measured ion intensities. The method improved the performance of the calculation, permitting determination of ECs for 72% of the observed peaks. 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However, a significant degradation of the EC determination performance was observed when the method was applied to real metabolomic data (mouse liver extracts analyzed by infusion ESI), because of the influence of noise and bias on the RIA. To achieve ideal performance, as indicated in the simulation, we developed an additional method to compensate for bias on the measured ion intensities. The method improved the performance of the calculation, permitting determination of ECs for 72% of the observed peaks. 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