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

被引:234
作者
Sawada, Yuji [1 ,2 ]
Akiyama, Kenji [1 ]
Sakata, Akane [1 ]
Kuwahara, Ayuko [1 ,2 ]
Otsuki, Hitomi [1 ]
Sakurai, Tetsuya [1 ]
Saito, Kazuki [1 ,3 ]
Hirai, Masami Yokota [1 ,2 ]
机构
[1] RIKEN Plant Sci Ctr, Tsurumi Ku, Kanagawa 2300045, Japan
[2] CREST, JST, Kawaguchi, Saitama 3320012, Japan
[3] Chiba Univ, Grad Sch Pharmaceut Sci, Inage Ku, Chiba 2638522, Japan
基金
日本科学技术振兴机构;
关键词
MODEL DATA-ANALYSIS; MASS-SPECTROMETRY; DISCRIMINATING SIGNALS; ARABIDOPSIS-THALIANA; EXPRESSION DATA; DATA SET; GENES; IDENTIFICATION; GENETICS; GENOMICS;
D O I
10.1093/pcp/pcn183
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
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.
引用
收藏
页码:37 / 47
页数:11
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