AtMetExpress LC-MS
10/8/2011 ver 1.1
Fumio Matsuda, Metabolome analysis research team, RIKEN PSC
AtMetExpress LC-MS ia a phytochemical atlas of Arabidopsis thaliana. Now, following two datasets are available.
AtMetExpress Development LC-MS
AtMetExpress 20 Ecotypes LC-MS
AtMetExpress Development LC-MS
We analyzed phytochemical accumulation during development of the model plant Arabidopsis thaliana using liquid chromatography-mass spectrometry (LC-MS) in samples covering many growth stages and organs. We also obtained MS/MS spectral tags of many metabolites as a resource for elucidation of metabolite structure. These are part of the AtMetExpress metabolite accumulation atlas. Based on the dataset, we detected 1,589 metabolite signals from which the structures of 167 metabolites were elucidated.
Publication
F. Matsuda et al. AtMetExpress development: A phytochemical atlas of Arabidopsis thaliana development. Plant Physiol.(2010). 152(2) 566-678. 10.1104/pp.109.148031 [Pubmed]
AtMetExpress Development LC-MS database
[AtMetExpress Development database]
AtMetExpress Development database produced a peak search function by using following information as queries such as polarity, accession number, retention time, m/z, annotation text, and MS/MS spectral similarity. From a list of search results, a metabolite signal of interest could be selected and its detailed information such as lists of tagged MS2Ts, results of database searching, final annotation, annotation levels, heat map representation of metabolite levels in each tissue, and raw chromatogram data of representative peak in all samples.
Integrated clustering of transcripotme and metabolome data using BL-SOM
BL-SOM viewer is able to map genes (by AGI code) and metabolites (by metabolite accession) of your interest on the results of BL-SOM analysis of AtGenExpress and AtMetExpress data.
In order to clarify any possible similarity of expression pattern of each gene to the accumulation pattern of its associated metabolites, we conducted a clustering analysis using the batch-learning self-organizing map (BL-SOM) method developed by Kanaya lab. in NAIST. BL-SOM is an improved and a reproducible method of the original SOM, and thus applied to integrated analysis of transcriptome and metabolome leading to successful prediction of genes' functions. All 1,589 metabolite signals with 10,147 metabolism-related genes (selected by GO terms) were classified into a 30 by 26 lattice according to their relative expression level across 36 tissues.
Download
All raw data files (36 tissues, 4 replicate, two acquisition modes (positive and negative ion mode) = 288 files) are downloadable from DROP Met. Meta-information of each data is also available.
[Raw and processed matrix data]
Raw and processed matirx data files (Intensity data of 1589 metabolite in 144 samples [36 tissues, 4 replicates]) produced in the project are downloadable.
MS/MS spectral tag data (MS2T) obtained in this project was available from MS2T viewer page.
Experimental Design
For determining the metabolite levels, we obtained quadruplicate metabolic profiles of 36 distinct tissues by using a liquid chromatography coupled with electrospray ionization-quadrupole-time-of-flight tandem mass spectrometer (LC-ESI-Q-Tof/MS) according to previously described methods (Matsuda et al (2009)). The obtained data matrix contains the relative peak intensity values of 1,589 metabolite signals from 144 samples as below (36 tissues by 4 replicates). The experimental design was compatible with that of the AtGenExpress developmental (Schmid et al., 2005) series for integrated analyses with transcriptome data.
| Sample name | Corresponding microarray data in AtGenExpress | Tissue | Age | Photoperiod | Substrate |
| ATME_1 | ATGE_1 | cotypedons | 10 days | continuous light | soil |
| ATME_7 | ATGE_7 | seedling, green parts | 10 days | continuous light | soil |
| ATME_9 | ATGE_9 | roots | 21 days | continuous light | soil |
| ATME_10 | ATGE_10 | rosette leaf #4, 1 cm long | 14 days | continuous light | soil |
| ATME_12 | ATGE_12 | rosette leaf #2 | 21 days | continuous light | soil |
| ATME_13 | ATGE_13 | rosette leaf #4 | 21 days | continuous light | soil |
| ATME_14 | ATGE_14 | rosette leaf #6 | 21 days | continuous light | soil |
| ATME_15 | ATGE_15 | rosette leaf #8 | 21 days | continuous light | soil |
| ATME_16 | ATGE_16 | rosette leaf #10 | 21 days | continuous light | soil |
| ATME_19 | ATGE_19 | leaf7. petiole | 21 days | continuous light | soil |
| ATME_20 | ATGE_20 | leaf7, proximal half | 21 days | continuous light | soil |
| ATME_21 | ATGE_21 | leaf7, distal half | 21 days | continuous light | soil |
| ATME_25 | ATGE_25 | senescing leaves | 35 days | continuous light | soil |
| ATME_26 | ATGE_26 | cauline leaves | 28 days | continuous light | soil |
| ATME_27 | ATGE_27 | stem, 2nd internode | 28 days | continuous light | soil |
| ATME_28 | ATGE_28 | 1st node | 28 days | continuous light | soil |
| ATME_29 | ATGE_29 | shoot apex, inflorescence (after bolting) | 28 days | continuous light | soil |
| ATME_32 | ATGE_32 | flowers stage 10/11 | 28 days | continuous light | soil |
| ATME_33 | ATGE_33 | flowers stage 12 | 28 days | continuous light | soil |
| ATME_39 | ATGE_39 | flowers stage 15 | 28 days | continuous light | soil |
| ATME_41 | ATGE_41 | flowers stage 15, sepals | 28 days | continuous light | soil |
| ATME_42 | ATGE_42 | flowers stage 15, petals | 28 days | continuous light | soil |
| ATME_45 | ATGE_45 | flowers stage 15, carpels | 28 days | continuous light | soil |
| ATME_76 | ATGE_76 | siliques, w/ seeds stage 3 | 4 wk | long day (16/8) | soil |
| ATME_77 | ATGE_77 | siliques, w/ seeds stage 4 | 4 wk | long day (16/8) | soil |
| ATME_78 | ATGE_78 | siliques, w/ seeds stage 5 | 4 wk | long day (16/8) | soil |
| ATME_91 | ATGE_91 | leaf | 15 days | long day (16/8) | 1x MS agar, 1% sucrose |
| ATME_92 | ATGE_92 | flower | 28 days | long day (16/8) | soil |
| ATME_93 | ATGE_93 | root | 15 days | long day (16/8) | 1x MS agar, 1% sucrose |
| ATME_95 | ATGE_95 | root | 8 days | continuous light | 1x MS agar, 1% sucrose |
| ATME_96 | ATGE_96 | seedling, green parts | 8 days | continuous light | 1x MS agar |
| ATME_97 | ATGE_97 | seedling, green parts | 8 days | continuous light | 1x MS agar, 1% sucrose |
| ATME_98 | ATGE_98 | root | 21days | continuous light | 1x MS agar |
| ATME_99 | ATGE_99 | root | 21days | continuous light | 1x MS agar, 1% sucrose |
| ATME_101 | ATGE_101 | seedling, green parts | 21days | continuous light | 1x MS agar, 1% sucrose |
| ATME_84 | RIKEN-NAKABAYASHI | mature seed | 16 wk | long day (16/8) | soil |
AtMetExpress 20 Ecotypes LC-MS
To investigate variations in the composition of secondary metabolites among Arabidopsis strains (accessions), metabolic profile data were obtained from the rosette leaves of 20 accessions of Arabidopsis by LC-ESI-Q-Tof/MS analysis. The 20 diverse accessions evaluated herein were previously selected by Clark et al. (2007) to investigate the genetic variations within the population of Arabidopsis. Metabolite signals were automatically indentified or annotated by the compound name, as well as characterized by the metabolite ontolgy terms.
Publication
F. Matsuda et al. Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity. Front. Plant Sci. 2:40. doi: 10.3389/fpls.2011.00040 [View]
Download
All raw data files (20 accessions, 5 replicate, 2 acquisition modes (positive and negative ion mode) = 200 files) are downloadable from DROP Met. Meta-information of each data is also available.
[Raw and processed matrix data]
Raw and processed matirx data files produced in the project are downloadable.
MS/MS spectral tag data (MS2T) obtained in this project was available from MS2T viewer page.
Experimental Design
For determining the metabolite levels, we obtained quadruplicate metabolic profiles of aerial part of 16-days-seedlings of 20 accessions by using LC-ESI-Q-Tof/MS according to previously described methods (Matsuda et al (2009)). The 20 diverse accessions evaluated herein were previously selected by Clark et al. (2007) to investigate the genetic variations within the population of Arabidopsis.
| Sample name | Accession |
| AtMetEcotype01 | CS22676 Bay-0 (BAYREUTH) |
| AtMetEcotype02 | CS22677 Bor-4 (BORKY) |
| AtMetEcotype03 | CS22678 Br-0 (BRUNN) |
| AtMetEcotype04 | CS22679 Bur-0 (BURREN) |
| AtMetEcotype05 | CS22680 C24 |
| AtMetEcotype06 | CS22681 Col-0 (COLUMBIA) |
| AtMetEcotype07 | CS22682 Cvi-0 (CAPE VERDI ISLANDS) |
| AtMetEcotype08 | CS22683 Est-1 (ESTLAND) |
| AtMetEcotype09 | CS22684 Fei-0 (ST. MARIA D. FEIRIA) |
| AtMetEcotype10 | CS22685 Goettingen-7 (GOETTINGEN) |
| AtMetEcotype11 | CS22686 Ler-1 (LANDSBERG ERECTA) |
| AtMetEcotype12 | CS22687 NFA-8 (NFA) |
| AtMetEcotype13 | CS22688 RRS-7 (RRS) |
| AtMetEcotype14 | CS22689 RRS-10 (RRS) |
| AtMetEcotype15 | CS22690 Sha (SHAKDARA) |
| AtMetEcotype16 | CS22691 Tamm-2 (TAMMISARI) |
| AtMetEcotype17 | CS22692 Ts-1 (TOSSA DEL MAR) |
| AtMetEcotype18 | CS22693 Tsu-1 (TSU) |
| AtMetEcotype19 | CS22694 Van-0 (VANCOUVER) |
| AtMetEcotype20 | CS22695 Lov-5 (LOVVIK) |
Modified: 2011-8-10
Metabolome analysis research team, LC-MS Branch