SE186:/DS1
From Metabolonote
Sample Set Information
| ID | TSE1344 |
|---|---|
| Title | Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based metabolite profiling |
| Description | The full-length cDNA over-expressing (FOX) gene hunting system is useful for genome-wide gain-of-function analysis. The screening of FOX lines requires a high-throughput metabolomic method that can detect a wide range of metabolites. Fourier transform-near-infrared (FT-NIR) spectroscopy in combination with the chemometric approach has been used to analyze metabolite fingerprints. Since FT-NIR spectroscopy can be used to analyze a solid sample without destructive extraction, this technique enables untargeted analysis and high-throughput screening focusing on the alteration of metabolite composition. We performed non-destructive FT-NIR-based fingerprinting to screen seed samples of 3000 rice-Arabidopsis FOX lines; the samples were obtained from transgenic Arabidopsis thaliana lines that overexpressed rice full-length cDNA. Subsequently, the candidate lines exhibiting alteration in their metabolite fingerprints were analyzed by gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) in order to assess their metabolite profiles. Finally, multivariate regression using orthogonal projections to latent structures (O2PLS) was used to elucidate the predictive metabolites obtained in FT-NIR analysis by integration of the datasets obtained from FT-NIR and GC-TOF/MS analyses. FT-NIR-based fingerprinting is a technically efficient method in that it facilitates non-destructive analysis in a high-throughput manner. Furthermore, with the integrated analysis used here, we were able to discover unique metabotypes in rice-Arabidopsis FOX lines; thus, this approach is beneficial for investigating the function of rice genes related to metabolism. |
| Authors | Suzuki, M., Kusano, M., Takahashi, H., Nakamura, Y., Hayashi, N., Kobayashi, M., Ichikawa, T., Matsui, M., Hirochika, H. and Saito, K. |
| Reference | Metabolomics, March 2010, Volume 6, Issue 1, pp 137–145 |
| Comment |
Data Analysis Details Information
| ID | DS1 |
|---|---|
| Title | Statistical analysis |
| Description | Before multivariate analysis, the corrected FT-NIR spectral datasets were mean-centered and the GC-TOF/MS dataset were scaled to unit variance following log10-transformation. The multivariate models were calculated using PCA, OPLS-DA, and O2PLS implemented in SIMCA-P + version 12 (Umetrics AB, Umeå, Sweden). The ellipse in the PC score plot represents the confidence region of the model based on Hotteling’s T2 statistic (Hotelling 1931; Mason et al. 2001). The significance level of the confidence region was defined at 0.05, and the data that fell outside the ellipse were determined to belong to candidate lines. These models were validated using 7-fold cross-validation or analysis of variance of cross-validated predictive residuals (CV-ANOVA) (Eriksson et al. 2008). Cross-validation is an internal predictive validation method for determining the number of significant components by calculating the total amount of explained X-variance (R2X), Y-variance (R2Y), and cross-validated predictive ability (Q2Y). A component is significant when Q2Y is positive value. Additionally, the variance related to class separation (RP2X) was calculated by OPLS-DA. CV-ANOVA is based on an ANOVA assessment of the cross-validatory predictive residuals of the models. The statistical Welch’s t test was performed and false discovery rate (FDR), which have been proven to be reliable for determining the significance of multiple testing (Storey 2002), were calculated using Microsoft Office Excel 2003 software. Q-value for FDR less than 0.05 was regarded as significant. |
| Comment_of_details |
