Journal Article

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Yuji Sawada, Kenji Akiyama, Akane Sakata, Ayuko Kuwahara, Hitomi Otsuki, Tetsuya Sakurai, Kazuki Saito and Masami Yokota Hirai

in Plant and Cell Physiology

Published on behalf of Japanese Society of Plant Physiologists

Volume 50, issue 1, pages 37-47
Published in print January 2009 | ISSN: 0032-0781
Published online December 2008 | e-ISSN: 1471-9053 | DOI: http://dx.doi.org/10.1093/pcp/pcn183
Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

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  • Biochemistry
  • Molecular and Cell Biology
  • Plant Sciences and Forestry

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Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics.

Keywords: Automated; Comparative metabolomics; Liquid chromatography–mass spectrometry; Metabolite accumulation pattern; Multiple reaction monitoring; Targeted metabolomics

Journal Article.  5517 words.  Illustrated.

Subjects: Biochemistry ; Molecular and Cell Biology ; Plant Sciences and Forestry

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