A novel method for single-grain-based metabolic profiling of Arabidopsis seed

作者:Sawada Yuji; Tsukaya Hirokazu; Li Yimeng; Sato Muneo; Kawade Kensuke; Hirai Masami Yokota*
来源:Metabolomics, 2017, 13(6): 75.
DOI:10.1007/s11306-017-1211-1

摘要

Introduction In plant metabolomics, metabolite contents are often normalized by sample weight. However, accurate weighing of very small samples, such as individual Arabidopsis thaliana seeds ( approximately 20 mu g), is difficult, which may lead to irreproducible results. Objectives We aimed to establish alternative normalization methods for seed-grain-based comparative metabolomics of A. thaliana. Methods Arabidopsis thaliana seeds were assumed to have a prolate spheroid shape. Using a microscope image of each seed, the lengths of major and minor axes were measured by fitting a projected 2-dimensional shape of each seed as an ellipse. Metabolic profiles of individual diploid or tetraploid A. thaliana seeds were measured by our highly sensitive protocol ("widely targeted metabolomics") that uses liquid chromatography coupled with tandem quadrupole mass spectrometry. Mass spectrometric analysis of 1 mu L of solution extract identified more than 100 metabolites. The data were normalized by various seed-size measures, including seed volume ( single-grain-based analysis). For comparison, metabolites were extracted from 4 mg of diploid and tetraploid A. thaliana seeds and their metabolic profiles were analyzed by normalization of weight (weight-based analysis). Results A small number of metabolites showed statistically significant differences in the single-grain-based analysis compared to weight-based analysis. A total of 17 metabolites showed statistically different accumulation between ploidy types with similar fold changes in both analyses. Conclusion Seed-size measures obtained by microscopic imaging were useful for data normalization. Single-grain-based analysis enables evaluation of metabolism of each seed and elucidates the metabolic profiles of precious bioresources by using small amounts of samples.

  • 出版日期2017-6
  • 单位RIKEN

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