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Sample Set Information

Title A novel method for single-grain-based metabolic profiling of Arabidopsis seed
Description 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 µg), is difficult, which may lead to irreproducible results.
Authors Yuji Sawada, Hirokazu Tsukaya, Yimeng Li, Muneo Sato, Kensuke Kawade, Masami Yokota Hirai
Reference Sawada et al. (2017) Metabolomics 13:75

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The raw data files are available at DROP Met web site in PRIMe database of RIKEN.

Sample Information

Title Arabidopsis thaliana seeds
Organism - Scientific Name Arabidopsis thaliana
Organism - ID NCBI taxonomy 3702
Compound - ID
Compound - Source
Preparation Tetraploidization of A. thaliana was performed in an accession Col-0, as described previously (Breuer et al. 2007), and the ploidy type was confirmed by flow cytometry analysis (Kozuka et al. 2005). Diploid and autotetraploid plants were maintained at 23 °C under continuous illumination (ca. 60 µmol/sec/m2) on rockwool supplied with 0.2 g/L HYPONeX solution, as described elsewhere (Fujikura et al. 2007). To harvest mature seeds for metabolomic analysis, 1-week-old plants grown on agar-solidified, half-strength Murashige and Skoog medium were transferred to soil [PRO-MIX (Premier Horticulture, Canada): vermiculite = 2:1] and grown under 16 h light/8 h dark with a nutrient solution reported as a control medium in Hirai et al. (1995). For biological replicates, seeds were independently collected from 12 individuals for each genotype (Supplementary Table S1).
Sample Preparation Details ID

Analytical Method Information

Title LC-QqQ-MS
Method Details ID MS1
Sample Amount 1 µL of solution extract (40 µg/mL)

Analytical Method Details Information

Title LC-MS/MS (triple quadrupole)
Instrument Waters XEVO-TQS
Instrument Type
Ionization ESI
Ion Mode Positive
Description Two sample preparation methods were employed for comparison. For the single-grain-based analysis, a visually normal seed was selected from each of the 12 individual plants under a microscope. After the estimation of seed volume as described above, each seed was placed in a separate well of a 96-well plate using a Vacuseed (Polaris Instruments Ltd, Cambridge, U.K.) with a 3-mm zirconia bead. The metabolites were extracted using 500 µL extraction solvent (80% methanol, 0.1% formic acid, 16.8 nmol/L lidocain, and 105 nmol/L 10-camphorsulfonic acid as internal standards) with a Multi-beads Shocker (Shake Master NEO, Bio Medical Science) at 1000 rpm for 10 min. After centrifugation, 250 µL of the extract was transferred to a new plate, dried, dissolved in 250 µL of ultra-pure water, and filtered using Whatman® UNIFILTER® plates 384 (GE Healthcare). The sample preparation process was automatically performed by a liquid handling system (Microlab Star Plus, Hamilton) [see widely targeted metabolomics section in PRIMe ( (Sakurai et al. 2013)]. 1 µL of the solution extract (final concentration = ca. 40 µg/mL) prepared from a seed (ca. 20 µg in the case of diploid) was subjected to widely targeted metabolomics using LC-QqQ-MS (UPLC-TQS, Waters).

For the weight-based analysis, approximately 200 seeds, weighing 4 ± 0.4 mg, from each of 12 plants, were collected using a Seed Spoon (BMS-SS200, Bio Medical Science). Seeds were accurately weighed, placed in a 2-mL tube with a 5-mm zirconia bead and proportional volume of the extraction solvent (4.0 mg/mL), and crushed using a Multi-beads Shocker at 1000 rpm for 10 min. After centrifugation, the extracts were diluted to 40 µg/mL with the extraction solvent. A 250 µL volume of the extract was transferred to a 96-well plate, dried, dissolved in 250 µL of ultra-pure water, and filtered using Whatman® UNIFILTER® plates 384. 1 µL of solution extract (40 µg/mL) was injected into an LC-QqQ-MS (UPLC-TQS, Waters) (Fig. 2).

Comment_of_details All raw data (Waters LC-MS/MS data) and metadata are downloadable from DROP Met ( in PRIMe(Sakurai et al. 2013).

Link icon database.png Link icon dropmet.png

The raw data files are available at DROP Met web site in PRIMe database of RIKEN.

Data Analysis Information

Title Data analysis
Data Analysis Details ID DS1
Recommended decimal places of m/z

Data Analysis Details Information

Title Data analysis
Description A total of 513 metabolites, including two internal standards contained in the extraction solvent, were detected based on optimized SRM conditions and retention time of LC-QqQ-MS. PubChem ID (Kim et al. 2016) and KEGG ID (Kanehisa et al. 2014) were assigned to each metabolite as well as our internal ID (serial number) (for detailed information, see Supplementary Tables S2–S4).

The procedure for data pre-processing is summarized in Supplementary Fig. S1. Metabolomic data matrix of 513 metabolite intensities, which was obtained by LC-QqQ-MS analysis (Data matrix 1 in Supplementary Table S5), was comprised of 48 samples: four experimental groups (2 ploidy types × 2 sample preparation methods) × 12 replicates derived from the 12 individual plants (Supplementary Table S1). The metabolomic data were analyzed using the statistical software R (version 3.1.2, After the missing values were set to 10, the signal intensities of each experimental group were averaged in individual metabolites. The metabolites with signal-to-noise ratio (defined as ratio of averaged signal intensity to those of extraction solvent control) <3 in all four experimental groups were removed. In addition, the metabolites with relative standard deviation greater than 50% in all experimental groups were removed, leaving 125 metabolites for further analysis to compare single-grain-based and weight-based analyses. Using these criteria for data pre-processing, more than 100 metabolites can be detected in A. thaliana seeds and leaves (Tsukaya et al. 2015). The intensities of the 125 metabolites were divided by those of the internal standards (Data matrix 2 in Supplementary Table S6). Following this, the data distribution of each sample was checked using boxplots (Supplementary Fig. S1). We found that the data trend of sample no. 14 differed from that of the others, and thus omitted the data of this sample as an outlier. The resulting data matrix (Data matrix 3 in Supplementary Table S7) was used for comparative analysis. In the case of single-grain-based analysis, metabolite intensities were further normalized by various measurements of seed size; namely, volume, lengths of major and minor axes, and 2-dimensional projected area. We performed multivariate analyses to evaluate the global trend in the metabolomic data. After transformation into log2, the data obtained from single-grain-based analysis (samples no. 1–13 and 15–24) and weight-based analysis (samples no. 25–48) were respectively transformed into z-score and used for boxplot, volcano plot, and Welch's t test using R.

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