1.1.1 Organ Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, Trethewey RN, Lange BM, Wurtele ES, Sumner LW (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci 9: 418-425
The harvested samples with fresh weight (FW) over 7 mg were used for GC-MS-based metabolite profiling. An equivalent of 55.6 µg of the derivatized samples were injected.
1.1.3 Growth condition
The sterilized seeds were stratified at 5ºC for 2 days, and were sown on Murashige and Skoog (MS) medium containing 1% sucrose. Seedlings of Arabidopsis Col-0 and the mutants were cultivated in controlled growth chambers at 22ºC in the 16-h light and 8-h dark condition for 18 days (Light strength, approx. 80 PPF).
1.1.4 Experimental condition
Same as section 1.1.3.
1.1.5 Sampling and sampling date
The leaves were harvested in September-December in 2008.
1.1.6 Metabolism quenching method
Samples were grown under the condition shown in section 1.1.4. Then, the aerial part of each sample was harvested. All the plant materials were frozen immediately in liquid nitrogen to quench the enzymatic activity.
1.2 Chemical analysis metadata
1.2.1 Sample processing and extraction
Each sample was extracted with a concentration of 5 mg flesh weight (FW) of tissues per ml extraction medium (methanol / chloroform/water [3:1:1 v/v/v]) containing 10 stable isotope reference compounds:
-[2H6]-2-hydoxybenzoic acid and
using a Retsch mixer mill MM310 at a frequency of 30 Hz for 3 min at 4ºC. Each
isotope compound was adjusted to a final concentration of 15 ng µl-1 for each 1-µl
injection. After centrifugation for 5 min at 15,100 ? g, a 100-µl aliquot of the
supernatant was drawn and transferred into a glass insert vial. The extracts were
evaporated to dryness in an SPD2010 SpeedVac® concentrator from ThermoSavant
(Thermo electron corporation, Waltham, MA, USA). For methoximation, 30 µl of
methoxyamine hydrochloride (20 mg/ml in pyridine) was added to the sample. After 24
h of derivatization at room temperature, the sample was trimethylsilylated for 1 h using
30 µl of MSTFA with 1% TMCS at 37ºC with shaking. For methoximation, 30 µl of
methoxyamine hydrochloride (20 mg ml-1 in pyridine) was added to the sample. After
24 h of derivatization at room temperature, the sample was trimethylsilylated for 1 h
using 30 µl of MSTFA with 1% TMCS at 37ºC with shaking. Thirty µl of n-heptane
was added following silylation. All the derivatization steps were performed in the
vacuum glove box VSC-100 (Sanplatec, Japan) filled with 99.9995% (G3 grade) of dry
1.2.2 GC-TOF/MS conditions
One microliter of each sample was injected in the splitless mode by an CTC CombiPAL
auto-sampler (CTC analytics, Zwin-gen, Switzerland) into an Agilent 6890N gas
chromatograph (Agilent Technologies, Wilmingston, USA) equipped with a 30 m ? 0.25
mm inner diameter fused-silica capillary column with a chemically bound 0.25-µl film
Rtx-5 Sil MS stationary phase (RESTEK, Bellefonte, USA) for metabolome analysis.
Helium was used as the carrier gas at a constant flow rate of 1 ml min-1. The
temperature program for metabolome analysis started with a 2-min isothermal step at
80 ºC and this was followed by temperature ramping at 30 ºC to a final temperature of
320 ºC, which was maintained for 3.5 min. The transfer line and the ion source
temperatures were 250 and 200 ºC, respectively. Ions were generated by a 70-eV
electron beam at an ionization current of 2.0 mA. Data acquisition was performed on a
Pegasus IV TOF mass spectrometer (LECO, St. Joseph, MI, USA) with an acquisition
rate of 30 spectra s-1 in the mass range of a mass-to-charge ratio of m/z = 60-800.
Alkane standard mixtures (C8-C20 and C21-C40) were purchased from Sigma-Aldrich
(Tokyo, Japan) and were used for calculating the retention index (RI). The normalized
response for the calculation of the signal intensity of each metabolite from the
mass-detector response was obtained by each selected ion current that was unique in
each metabolite MS spectrum to normalize the peak response. For quality control, we
injected methylstearate in every 6 samples. Quality control samples were prepared by
mixing 100 µl of extracts of each sample.
1.2.3 Data processing
Non-processed MS data from GC-TOF/MS analysis were exported in NetCDF format
generated by chromatography processing and mass spectral deconvolution software,
Leco ChromaTOF version 3.22 (LECO, St. Joseph, MI, USA) to MATLAB 6.5
(Mathworks, Natick, MA, USA), where all data-pretreatment procedures, such as
smoothing, alignment, time-window setting, and peak deconvolution, were carried out
by using hyphenated data analysis (HDA) (Jonsson et al., 2004; Jonsson et al., 2006).
The resolved MS spectra were matched against reference mass spectra using the NIST
mass spectral search program for the NIST/EPA/NIH mass spectral library (version 2.0)
and our custom software for peak annotation written in JAVA. Peaks were identified or
annotated based on RIs and the reference mass spectra comparison to the Golm
Metabolome Database (GMD) and our in-house spectral library. The metabolites were
identified by comparison with RIs from the library databases (GMD and our own
library) and with those of authentic standards, and the metabolites were defined as
annotated metabolites on comparison with mass spectra and RIs from these two libraries.
The five batches of metabolite profiles were combined using the HDA method (Jonsson
et al., 2004; Jonsson et al., 2006). To correct the "batch effect" we used COMBAT
normalization (Johnson et al., 2007) with our quality samples consist of Col-0 wild-type
plants for each batch. Data was normalized using the CCMN algorithm (Redestig et al.,
Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8: 118-127
Jonsson P, Gullberg J, Nordstrom A, Kusano M, Kowalczyk M, Sjostrom M, Moritz T (2004) A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Anal Chem 76: 1738-1745
Jonsson P, Johansson ES, Wuolikainen A, Lindberg J, Schuppe-Koistinen I, Kusano M, Sjostrom M, Trygg J, Moritz T, Antti H (2006) Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data--a potential tool for multi-parametric diagnosis. J Proteome Res 5:1407-1414
Redestig H, Fukushima A, Stenlund H, Moritz T, Arita M, Saito K, Kusano M(2009) Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Anal Chem 81: 7974-7980