SE57:/S05/M01/D01

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 original paper is available at Plant and Cell Physiology.

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

Sample Information

ID	S05
Title	Oat
Organism - Scientific Name	Avena sativa
Organism - ID	NCBI taxonomy:4498
Compound - ID
Compound - Source
Preparation	Seed coats of Gramineae species were purchased from Cuoca Planning Co., Ltd. (Tokushima, Japan).
Sample Preparation Details ID
Comment

Analytical Method Information

ID	M1
Title	Acquisition of ESI-MS and ESI-MS/MS data
Method Details ID	MS01
Sample Amount	20 μ l aliquots containing 1 nmol of compound
Comment

Analytical Method Details Information

ID	MS01
Title	Acquisition of ESI-MS and ESI-MS/MS data, UPLC-TQMS analysis
Instrument	Waters Xevo TQD, UPLC (Waters)
Instrument Type	UPLC-QTOF-MS
Ionization	ESI
Ion Mode	Positive and Negative
Description	<Automated liquid handling for sample preparation> The plant tissue samples were frozen in liquid nitrogen, quenched using a Mixer Mill (MM300, Retsch, Hann, Germany) as previously reported ( Hirai et al. 2007 ), and the extraction buffer (80% MeOH) was added. The resulting extracts consisted of 400 μ l with 20 mg ml –1 (fresh weight) of tissue. The extracts were transferred and treated using an ALHS as follows (Supplementary Fig. S1): 350 μ l of each plant extract were transferred to a 96-well plate, and the solutions were dried under N 2 gas using a 96-well format spray instrument (40°C, 25 min and 30°C, 20 min). The dried samples were dissolved in 350 μ l of H 2 O using a vortex system (1,300 r.p.m., 6 min), and then fi ltered through a 96-well filter [Captiva 96-well Filter Plate (pore size 0.45 μ m, polyvinylidene fl uoride), Varian, CA, USA], using a vacuum manifold with the following program: 30 s, 50 Hpa; 20 s, 100 Hpa; 20 s, 200 Hpa; 30 s, 300 Hpa; and 60 s, 300 Hpa. Plate handling was carried out automatically using robot arms (iSWAP, Hamilton). <Acquisition of ESI-MS and ESI-MS/MS data> All the solutions of authentic compounds (250 μ M) were transferred from 10 ml vials to 1.2 ml glass inserts in 96-well plates (Webseal system, GL Sciences, Tokyo, Japan) and diluted to 50 pmol μ l –1 with H 2 O using an ALHS (Microlab STARplus, Hamilton, Reno, NV, USA). The solutions (20 μ l aliquots containing 1 nmol of compound) were analyzed by the flow injection method using a CTC-PAL injection system (AMR, Tokyo, Japan) and a Waters GI Pump solvent system (Waters, Milford, MA, USA) consisting of: 0.05 ml min –1 flow using a micro splitter (GL Sciences), 80% acetonitrile (0.1% formic acid) and 20% water (0.1% formic acid), with a 2.5 min cycle in the isocratic mode. The ionized authentic compounds were detected by TQMS (TQD, Waters) according to the following conditions: capillary voltage +3.0 keV or –2.8 keV, CV 10–60 eV (six levels), source temperature 120°C, desolvation temperature 350°C, cone gas fl ow 50 l h –1 , desolvation gas fl ow 600 l h –1 and CE 10–60 eV (six levels). A total of 61,920 spectra of 860 compounds were obtained.The optimal MRM conditions, including positive/negative polarity (e.g. [M] + , [M + H] + , [M] – , [M – H] – ), m / z of precursor ion and product ion, and optimal CV and CE were determined automatically for 497 of these compounds using the software QuanOptimize (Waters). <UPLC-TQMS analysis> The UPLC (Waters) conditions were manually optimized based on the separation patterns of 12 methionine-derived glucosinolates and were as follows: flow rate 0.24 ml min –1 ; solvents A, 0.1% formic acid in water and B, 0.1% formic acid in acetonitrile; gradient program of B (0 min, 0%; 0.25 min, 0%; 0.4 min, 9%; 0.8 min, 17%; 1.9 min, 100%; 2.1 min, 100%;2.11 min, 0%); 3 min cycles with a temperature of 38°C.The TQMS detection conditions were the same as those for FIA-TQMS, except that the source temperature was 130°C.
Comment_of_details

Hirai et al. 2007 (PubMed)

Data Analysis Information

ID	D1
Title	Analysis of metabolite accumulation patterns
Data Analysis Details ID	DS01
Recommended decimal places of m/z	Default
Comment

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