SE57:/DS01
From Metabolonote
Sample Set Information
| ID | SE57 |
|---|---|
| Title | Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants |
| Description | 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 quantifi ed 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-specifi c 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 profi ling and comparative metabolomics. |
| Authors | Yuji Sawada, Kenji Akiyama, Akane Sakata, Ayuko Kuwahara, Hitomi Otsuki, Tetsuya Sakurai, Kazuki Saito, Masami Yokota Hirai |
| Reference | Sawada Y et al. (2009) Plant and Cell Physiology 50: 37-47 |
| Comment |
The raw data files are available at DROP Met web site in PRIMe database of RIKEN.
Data Analysis Details Information
| ID | DS01 |
|---|---|
| Title | Analysis of metabolite accumulation patterns |
| Description | Quantitative data for metabolite accumulation in the seeds or seed coats of the 14 accessions or species were obtained by UPLC-TQMS. Peaks that showed S/N ratios >30 were selected as the detected metabolites to be used in further analyses. Areas under the selected peaks were converted into logarithms (base 2) after missing values, which appeared when a metabolite was not detected in a sample, were replaced with 0.1. Data were normalized by z -score transformation using the software TM4 MEV (Chu et al. 2008). The resulting data matrix was analyzed using hierarchical clustering based on the Euclidean distance and visualized by MEGA4 (Tamura et al. 2007) as a dendrogram. The family-specifi c metabolites were identifi ed by BL-SOM analysis of the matrix in combination with a model data set consisting of one hypothetical metabolite specifi c to each of the families Brassicaceae, Gramineae and Fabaceae. |
| Comment_of_details |
