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Plant and Cell Physiology Advance Access published online on December 2, 2008
Plant and Cell Physiology, doi:10.1093/pcp/pcn183
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© The Author 2008. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved.
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Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants

Yuji Sawada1,2, Kenji Akiyama1, Akane Sakata1, Ayuko Kuwahara1,2, Hitomi Otsuki1, Tetsuya Sakurai1, Kazuki Saito1,3 and Masami Yokota Hirai1,2

1RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
2JST, CREST, 4-1-8 Hon-chou, Kawaguchi, Saitama 332-0012, Japan.
3Graduate School of Pharmaceutical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba 263-8522, Japan.

Corresponding author: Dr. Masami Yokota Hirai, RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. E-mail: myhirai{at}psc.riken.jp


  Abstract

Metabolomics is an ‘omics’ approach that aims to comprehensively analyze all metabolites in a biological sample. 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 - multiple reaction monitoring - metabolite accumulation pattern - targeted metabolomics

(Received October 8, 2008; Accepted November 23, 2008)
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