SE154:/DS1

Sample Set Information

ID	TSE1310
Title	Metabolomics of a single vacuole reveals metabolic dynamism in an alga Chara australis.
Description	Metabolomics is the most reliable analytical method for understanding metabolic diversity in single organelles derived from single cells. Although metabolites such as phosphate compounds are believed to be localized in different organelles in a highly specific manner, the process of metabolite compartmentalization in the cell is not thoroughly understood. The analysis of metabolites in single organelles has consequently presented a significant challenge. In this study, we used a metabolomic method to elucidate the localization and dynamics of 125 known metabolites isolated from the vacuole and cytoplasm of a single cell of the alga Chara australis. The amount of metabolites in the vacuole and the cytoplasm fluctuated asynchronously under various stress conditions, suggesting that metabolites are spatially regulated within the cell. Metabolite transport across the vacuolar membrane can be directly detected using the microinjection technique, which may reveal a previously unknown function of the vacuole.
Authors	Oikawa A, Matsuda F, Kikuyama M, Mimura T, Saito K.
Reference	Plant Physiol. 2011 Oct;157(2):544-51. doi: 10.1104/pp.111.183772. Epub 2011 Aug 16.
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PubMed

Data Analysis Details Information

ID	DS1
Title	Data Processing and Statistics
Description	Data processing Peak picking and alignment: Raw CE-MS data were analyzed with the proprietary software MasterHands (Sugimoto et al., 2010b; Sugimoto et al., 2010c). In brief, peaks were detected from sliced electropherograms (0.02 m/z width), and the accurate m/z value for each peak was calculated by Gaussian curve fitting. Migration times of the detected peaks were normalized by a dynamic time-warping method; numerical parameters were optimized using the simplex method and matching peaks across multiple data sets by dynamic programming (Sugimoto et al., 2010a). Peaks were picked and aligned using this software. Peak annotation: Metabolites in the standard compounds were assigned to the remaining features by matching their m/z values and normalized migration times using the software described in Peak picking and alignment. Quantification: For normalization, the area of the detected peak was divided by the area of the internal standard peak. Based on the of calibration curves for standard compounds, metabolite amounts were quantified. In C. australis internodal cells, the vacuolar solution accounts for approximately 95% of the whole cell volume (Sakano and Tazawa, 1984). Therefore, in the present study, the cytoplasmic fraction was estimated to occupy about 5% of the total cell volume; this value was used to calculate metabolite levels in the cytoplasmic fractions. Statistics Data transformation: Calculated amounts were standardized by subtracting the mean amount for each metabolite in a sequence experiment from the calculated amount of each sample and then dividing this value by the standard deviation of each metabolite (z-score). Levels of undetected metabolites were set at 0, and then standardized. Statistics: Standardized amounts were submitted for hierarchical clustering analysis (HCA) and processed with PermutMatrix software (http://www.atgc-montpellier.fr/permutmatrix) according to Euclidean distance and Ward’s method (Caraux and Pinloche, 2005; Ward, 1963).
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