Yuji Sawada1,2, Kenji Akiyama1, Akane Sakata1, Ayuko Kuwahara1,2, Hitomi Otsuki1, Tetsuya Sakurai1,
Kazuki Saito1,3 and Masami Yokota Hirai1,2,*
1RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan
2JST, CREST, 4-1-8 Hon-chou, Kawaguchi, Saitama, 332-0012 Japan
3Graduate School of Pharmaceutical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba, 263-8522 Japan

Metabolomics is an ‘omics’ approach that aims to analyze
all metabolites in a biological sample comprehensively.
The detailed metabolite proﬁling of thousands of plant
samples has great potential for directly elucidating plant
metabolic processes. However, both a comprehensive
analysis and a high throughput are difﬁcult to achieve at
the same time due to the wide diversity of metabolites
in plants. Here, we have established a novel and practicalmetabolomics methodology for quantifying hundredsof targeted metabolites in a high-throughput manner.
Multiple reaction monitoring (MRM) using tandem
quadrupole mass spectrometry (TQMS), which monitors
both the speciﬁc precursor ions and product ions ofeach metabolite, is a standard technique in targeted
metabolomics, as it enables high sensitivity, reproducibility
and a broad dynamic range. In this study, we optimized
the MRM conditions for speciﬁc compounds by performing
automated ﬂow injection analyses with TQMS. Based ona 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 quantiﬁed in each of 14 plant accessions from
Brassicaceae, Gramineae and Fabaceae. A hierarchicalcluster analysis based on the metabolite accumulation
patterns clearly showed differences among the plantfamilies, and family-speciﬁc metabolites could be
predicted using a batch-learning self-organizing map
analysis. Thus, the automated widely targeted
metabolomics approach established here should pave theway for large-scale metabolite proﬁling and comparative
metabolomics.

Special Issue – Regular Paper

Widely Targeted Metabolomics Based on Large-Scale
MS/MS Data for Elucidating Metabolite Accumulation
Patterns in Plants

Keywords: Automated • Comparative metabolomics• Liquid chromatography–mass spectrometry • Metabolite
accumulation pattern • Multiple reaction monitoring• Targeted metabolomics.
Abbreviations: ALHS, automated liquid handling system;
APCI, atmospheric pressure chemical ionization; CE, collisionenergy; CV, cone voltage; EI, electron ionization; ESI,
electrospray ionization; FIA, ﬂow injection analysis; GC, gas
chromatography; LC, liquid chromatography; MRM, multiple
reaction monitoring; MS, mass spectrometry; MS/MS,
tandem mass spectrometry; MS2T, MS/MS spectral tag;
NMR, nuclear magnetic resonance; Q, quadrupole; S/N ratio,
signal-to-noise ratio; TOF, time-of-ﬂight; TQMS, tandem
quadrupole mass spectrometry; UPLC, ultra performanceliquid chromatography.

*Corresponding author: E-mail, myhirai@psc.riken.jp; Fax, +81-45-503-9491.
Plant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183, available online at www.pcp.oxfordjournals.org© The Author 2008. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open
access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and theJapanese Society of Plant Physiologists are attributed as the original place of publication with the correct citation details given; if an article is subsequently
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Plant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183 © The Author 2008.

37

Y. Sawada et al.

Introduction
Metabolomics is a novel experimental methodology categorized as an ‘omics’ approach along with genomics, transcriptomics and proteomics (Fiehn et al. 2001, Fiehn 2002,
Nicholson et al. 2002, Sumner et al. 2003, Saito et al. 2008).
Metabolomics is often used in combination with the otheromics approaches for deeper understanding of biologicalprocesses, especially metabolism. The number of metabolites
in the plant kingdom is considered to be far greater thanthat in the animal kingdom, and is estimated to exceed
200,000 (Fiehn et al. 2001). This is due to the great diversityof metabolic pathways that each plant species has evolved
to survive under varying environmental conditions.
Metabolomics deals with diverse metabolites that differ
greatly in their physical and chemical properties. The otheromics approaches are used to analyze molecules with similar
chemical properties (namely DNA, RNA and proteins) using
standardized analytical platforms (e.g. the DNA sequencer
in genomics). In contrast, metabolomics requires multiple
instruments based on different analytical principles. In thepast decade, chromatography-coupled mass spectrometry(MS)-based metabolomics and nuclear magnetic resonance(NMR)-based metabolomics have been developed in orderto establish non-targeted analysis procedures (Werner et al.
2008a, Werner et al. 2008b). The ‘ultimate’ metabolomics,
i.e. quantiﬁcation of all metabolites in a biological sample,
can only be achieved through the integration of dataobtained using these various techniques. Non-targeted
metabolomics enables us to identify the broad metabolite
proﬁles of samples and to ﬁnd novel metabolites that can beused as biomarkers (Glinski and Weckwerth 2006). However,many difﬁculties exist in achieving this goal, e.g. it is technically difﬁcult and extremely time-consuming to merge all thedata obtained in different formats by different instruments,
and to identify the unknown metabolites (Werner et al.
2008b). Hence, the application of non-targeted metabolomics
to hundreds of biological samples is not yet practical. Therefore, we need an alternative methodology for targeted but
high-throughput metabolomics.
Generally, MS provides us with information on the molecular masses of compounds and the fragment patterns ofcompounds, depending on the ionization techniques used.
Electron ionization (EI), used in gas chromatography(GC)-MS, gives rise to fragment ions derived by cleavage ofa compound. GC-MS spectra are highly reproducible, andstandard mass spectrum databases of GC-MS data havealready been constructed (Kopka et al. 2005, Schauer et al.
2005). Electrospray ionization (ESI), used in liquid chromatography (LC)-MS, is a soft ionization method (Fenn et al.
1989) and causes much less fragmentation of compounds
than GC-EI-MS. LC-ESI-MS is a highly sensitive techniquethat provides information on the molecular masses of

38

compounds. To obtain structural information based on the
fragmentation patterns of compounds, ESI-tandem mass
spectrometry (MS/MS) is applied to LC-MS-based methods.
In the case of targeted metabolomics, multiple reaction
monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS) enables high sensitivity, reproducibility
and a broad dynamic range of analysis. During MRM, theﬁrst quadrupole (Q) transmits only an ion of speciﬁc m/z,
which is then fragmented in the collision cell. The second
quadrupole is set to transmit a speciﬁc product ion fromamong the fragments. Detection of this product is therefore
diagnostic information for compound identiﬁcation.
MRM requires two ions (a precursor and a product ion) togenerate a positive result, making it very speciﬁc and withvery low background, enhancing the sensitivity of detection(Unwin et al. 2005). Because the amount of publicly available
information about MRM conditions is currently very limited,
we needed to determine the optimal ionization conditions
for each compound used in our experiments.
Here, we describe a highly selective and sensitive procedure that utilizes ultra performance liquid chromatography(UPLC)-TQMS for widely targeted metabolomics studies.
First we established a method for the automated ﬂow
injection analysis (FIA) of a library of authentic compounds.
This method enabled us to obtain a huge data set of ESI-MSand ESI-MS/MS spectra, leading to optimization of MRM
conditions for approximately 500 authentic compounds.
We then applied MRM conditions to the analysis of biologicalsamples using high-throughput UPLC-TQMS to obtain
metabolite accumulation patterns. The strategy followed inthis study is summarized in Fig. 1. In terms of comprehensiveness and throughput, the analytical system establishedhere is, to our knowledge, the ﬁrst that can be used to quantify
hundreds of metabolites in thousands of biological samples.
As a case study, we determined the accumulation patternsof about 100 metabolites in representative plant speciesof Brassicaceae, Gramineae and Fabaceae. The data sets ofthe ESI-MS and ESI-MS/MS spectra, MRM conditions andmetabolite accumulation patterns are accessible at our website PRIMe (Platform for RIKEN Metabolomics; http://prime.
psc.riken.jp/) (Akiyama et al. 2008).

Results
Establishment of a 96-well-formatted authentic
compound library
In metabolomics, the metabolites detected in biologicalsamples are generally identiﬁed by reference to data fromauthentic compounds. To obtain the reference data for awide range of compounds, we constructed an authentic
compound library as part of our PRIMe project (Akiyama et al.
2008). The library consists of >1,000 commercially available
compounds which we selected from the metabolite

Plant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183 © The Author 2008.

Automated widely targeted metabolomics

automated liquid handling system (ALHS, see Supplementary
Fig. S1), because this plate format is standard for automatedrobotics in high-throughput analyses. The ALHS can detect
the liquid level in the plate using electrical potential andpressure, and pressure monitoring also enables anti-droplet
control for organic solvents (Palandra et al. 2007). All solutions except those in chloroform, which is difﬁcult to use in
LC-MS analysis, were successfully transferred.

Optimization of MRM conditions by FIA

Fig. 1 Flowchart of the strategy used to conduct a widely targeted
metabolomics analysis. Automated FIA-TQMS was applied to a
total of 967 authentic compounds to obtain an ESI-MS/MS data
set consisting of 61,920 spectra for 860 authentic compounds. The
remaining 107 compounds did not show reproducible spectra with
FIA-TQMS. The MRM data set consists of optimized analyticalconditions for 497 compounds, determined automatically, based on
the ESI-MS/MS data set. UPLC-TQMS analysis was applied to these497 compounds using the optimized UPLC and MRM conditions.
Approximately 80% (378) of the compounds were successfully
detected, to give a retention time index data set. The conditions thatwere established, as described above, were used in combination withan ALHS to conduct a widely targeted metabolomics study of 14
accessions representing nine plant species from three families, and themetabolite accumulation patterns were determined. All large-scaledata sets mentioned here are freely available at the PRIMe website.

databases KEGG (Kanehisa and Goto 2000), AraCyc (Mueller
et al. 2003) and KNApSAcK (Shinbo et al. 2006). In this study,
967 compounds were weighed and dissolved in water, methanol, ethanol, acetone or chloroform, depending on thesolubility of each compound. The concentrations of thestock solutions were adjusted to 250 µM and the solutions
were stored in 10 ml glass vials. Then the stock solutions
were transferred from the vials to 96-well plates using an

In GC-MS analysis, the GC retention index and standardized
EI-MS spectra are essential for the identiﬁcation of compounds. More than 100,000 spectra obtained by EI-MS havebeen stored in commercial databases, such as the NIST
library (http://www.nist.gov). However, in the case of ESI
and atmospheric pressure chemical ionization (APCI), which
are applied to LC- and capillary electrophoresis-MS, the ionization conditions have not been standardized, and only a
few hundred to a thousand MS and MS/MS spectra obtainedby ESI and APCI are accessible in databases, including MassBank (http://www.massbank.jp/) (Horai et al. 2008), The
Human Metabolome Database (HMDB, http://www.hmdb.
ca/), METLIN (http://masspec.scripps.edu/index.php) and
NIST2008 (http://www.nist.gov).
In this study, we optimized the MRM conditions for polar
metabolites, which can be easily applied to LC-MS analysis.
To establish a large-scale ESI-MS and ESI-MS/MS data library,authentic compounds were analyzed by automated FIA
using TQMS (Fig. 2A). During the automated FIA, the ESI-MS(Fig. 2B) and ESI-MS/MS (Fig. 2C) ionization patterns were
measured after two injections as follows. In the ﬁrst injection, the MS conditions were optimized in quadrupole
1 (Q1), which transmits only an ion of speciﬁc m/z, using fastpolarity switching with six levels of cone voltage (CV). In thesecond injection, the second MS conditions were optimized
in quadrupole 2 (Q2), which transmits a speciﬁc product ion
generated by fragmentation of the transmitted ion in thecollision cell, using fast polarity switching with six levelsof collision voltage. The experiments were performed intriplicate for each of 967 compounds (Fig. 2D). As a resultof these analyses the MRM conditions were successfully
optimized for 497 compounds, based on reproducibility inconditions for both the selected precursor ion and the production in the triplicate experiments. During this procedure,>60,000 ESI-MS and ESI-MS/MS spectra were acquired anddeposited in the publicly available database PRIMe. The
number of spectra deposited is much greater than the numbers in NIST2008 and MassBank. NIST2008 is a commerciallyavailable database which consists of 14,802 ESI-MS spectra(as of September 2008) of 5,308 precursor ions (3,898 positive ions and 1,410 negative ions) obtained by ion trap MS.
MassBank is a web-searchable database (http://www.mass
bank.jp) of ESI-MS and ESI-MS/MS spectra obtained by using

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39

Y. Sawada et al.

column have markedly improved ﬂow speed, resolution andsensitivity compared with conventional LC particles (5 µm)(Citova et al. 2007). We determined the best gradientconditions for UPLC using acetonitrile and water with 0.1%
formic acid, which are representative of solvents used in
LC-MS analysis. Since we were already familiar with the analysis of glucosinolates by UPLC-QTOFMS (Hirai et al. 2007),
we optimized the UPLC gradient program with these solventsusing 12 selected compounds of methionine-derived glucosinolates with different side chains. We chose the gradient
program by which these glucosinolates were distributed
over a wide range of retention times (0.3–1.5 min) among the
MRM data recording times (0–2.1 min) (see Materials andMethods). The 497 compounds for which MRM conditions
were determined were analyzed by UPLC-TQMS under optimized gradient conditions. A total of 390 compounds weresuccessfully detected from the 497 independent injections
before column conditioning steps (2.2–3.0 min) in the gradient cycle. Quantiﬁcation limits were determined based on
signal-to-noise ratios (S/N ratios), and 378 compounds
showed quantitative S/N ratios >10.

High-throughput analysis of metabolite
accumulation patterns

Fig. 2 Strategy for the optimization of MRM conditions for authentic
compounds. (A) Schematic diagram of automated FIA using TQMS
for high-throughput acquisition of ESI-MS and ESI-MS/MS spectra.
FIA was applied to the authentic compound library using samples(shown in red) in a 96-well format. (B) In the first injection, theprecursor ion was detected within a range of ﬁve atomic mass units ofthe speciﬁed molecular masses of each compound. The optimal CV,
which determines ionization in ESI-MS, was identified from amongsix levels in the range 10–60 eV, in the positive and negative polarity.
(C) In the second injection, the optimal product ion and CE were
examined using the conditions determined for the first injection.
(D) Triplicate experiments were used to determine optimal MRM
conditions for 967 compounds. Q1, quadrupole 1; Q2, quadrupole 2.

multiple instruments [TQMS, time-of-ﬂight (TOF) MS,
QTOF MS and ion trap MS], consisting of 13,125 spectra(as of September 2008).

Optimization of UPLC for widely targeted
metabolomics
For high-throughput analyses of biological samples, we chose
UPLC-TQMS. In a novel analytical UPLC platform, smaller
particles (<2 µm; called sub-2-µm particles) packed in the40

As a case study of high-throughput, widely targeted metabolomics, we chose to analyze dry matter such as mature
seeds due to their ease of handling compared with fresh
samples such as leaves. Almost all of the steps of sample
preparation after sample quenching and metabolite
extraction were successfully automated by using the ALHS
(Supplementary Fig. S1). In this case, the MS conditions [CV,collision energy (CE) and polarity, see Fig. 2] were set todetect ﬁve compounds with similar MRM conditions in eachrun of 3 min per cycle, i.e. 390 compounds could be detected
in 78 runs. The combination of automated sample preparation
and widely targeted UPLC-TQMS analysis was applied to seeds
and seed coats of 14 accessions derived from nine species ofBrassicaceae, Fabaceae and Gramineae (see Materials andMethods for a complete list of the accessions and species
analyzed). A data matrix of metabolite accumulation patterns
in the 14 samples was acquired by reference to the databaseof authentic compounds described above.
The number of detected metabolites was determined foreach of the 14 plant samples (Fig. 3). A total of 343 metabolites were detected in at least one of the samples, and 18% of
the 378 compounds detectable in UPLC-TQMS analysis (seeFig. 1) were identiﬁed in all of the plants analyzed (Fig. 3).
For an overview, the detected metabolites and detectableauthentic compounds were superimposed on a map of metabolic pathways using the KEGG Atlas (Okuda et al. 2008)(Supplementary Fig. S2). They were broadly distributed
across the metabolic map. To evaluate the ability of thisPlant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183 © The Author 2008.

Automated widely targeted metabolomics

Fig. 3 Histogram showing the frequencies of metabolites detected
in 14 plant accessions by widely targeted metabolomics. The verticalaxis indicates the number of metabolites that were detected in a given
number of plant accessions, as shown on the horizontal axis. The red bar
indicates the number of metabolites detected in all accessions, while theblue bar indicates the number of compounds that were detected in noneof the 14 accessions.

analytical method to detect and quantify metabolites in biological samples, we then classiﬁed the 14 plant accessions
based on accumulation patterns of detected metabolites
whose S/N ratios were >30. Classiﬁcation was conducted byhierarchical clustering analysis and visualized as a dendrogram (Fig. 4). Fourteen plant accessions were classiﬁed as
Brassicaceae, Gramineae or Fabaceae, indicating that thisanalysis could be used to determine metabolite accumulation
patterns speciﬁc to plant families.
The data generated from actual detected metabolites
were combined with ﬁctitious, model metabolite data
representing three families, and metabolites were classiﬁed
in a batch-learning self-organizing map (BL-SOM) (Fig. 5, seeMaterials and Methods). This analysis was used to search
for family-speciﬁc metabolites, and classiﬁed the actual andﬁctitious metabolites into 48 classes according to theiraccumulation patterns in the 14 plant accessions (data notshown). The ﬁctitious metabolites were classiﬁed into one ofthree classes (Fig. 5). Glucosinolates, which are well-known
Brassicaceae-speciﬁc metabolites, were correctly classiﬁed
with the model data for Brassicaceae-speciﬁc metabolites(Fig. 5, top panel), indicating that this analysis can be used in
at least some cases to predict family-speciﬁc metabolites.

Fig. 4 Hierarchical clustering of plant accessions and species basedon their metabolite accumulation patterns. The dendrogram shows
the cluster relationships among the 14 accessions representing ninespecies from the families Brassicaceae, Gramineae and Fabaceae (seeMaterials and Methods for further details). The families are colored
red, green and yellow.

Data storage at the PRIMe website
In order to create a powerful research infrastructure forpractical metabolomics, a large-scale database, consisting of
ESI-MS and ESI-MS/MS spectra, MRM conditions, UPLCTQMS retention time indices and metabolite accumulation
patterns in representative plant species, was created. The
database can be used for widely targeted metabolomics andis available to the public at the PRIMe website. The download page is linked to the top page of PRIMe (http://prime.
psc.riken.jp/).

Discussion
Comparison between metabolomics andtranscriptomics
Here we introduce an automated high-throughput methodology for widely targeted metabolomics as an alternative to
non-targeted metabolomics, which includes time-consuming
steps, and therefore cannot be applied to large numbers of
biological samples. The difference between non-targeted
metabolomics and widely targeted metabolomics is analogous to the difference between a whole genome array and an
expressed sequence tag-based custom array in transcriptomics. Needless to say, the custom array often works
effectively for many applications. Likewise, widely targeted

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Y. Sawada et al.

Fig. 5 Prediction of family-speciﬁc metabolites by BL-SOM analysis. Three ﬁctitious model data sets, Brassicaceae (guide), Gramineae (guide) and Fabaceae(guide), were combined with actual metabolite accumulation data, and metabolites were classiﬁed by BL-SOM based on the accumulation patterns in 14 plant
accessions. The actual metabolites that were grouped with the respective model data are shown as putative family-speciﬁc metabolites of Brassicaceae (upper),Gramineae (middle) and Fabaceae (lower). Vertical values represent normalized peak area values in each accession (see Materials and Methods). IG1,
indol-3-ylmethyl glucosinolate; MSc4, 4-methylsulﬁnylbutyl glucosinolate; MSc7, 7-methylsulﬁnylheptyl glucosinolate; MSc8, 8-methylsulﬁnyloctyl glucosinolate; MTc3, 3-methylthiopropyl glucosinolate; MTc4, 4-methylthiobutyl glucosinolate; MTc5, 5-methylthiopentyl glucosinolate; MTc6, 6-methylthiohexyl
glucosinolate; MTc7, 7-methylthioheptyl glucosinolate; MTc8, 8-methylthiooctyl glucosinolate; OHc3, 3-hydroxypropyl glucosinolate.

42

Plant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183 © The Author 2008.

Automated widely targeted metabolomics

analyses based on available authentic compounds canprovide the information needed to elucidate whole biological processes (DellaPenna and Last 2008, Lu et al. 2008a,
Lu et al. 2008b).

Signiﬁcance of the large-scale ESI-MS and
ESI-MS/MS spectrum data sets
We have developed an automated procedure to determine
optimal MRM conditions using FIA-TQMS. This means that
if thousands of authentic compounds become available in
the future, we can easily determine the optimal MRM conditions of each compound, and thus can quantify their levels
in biological samples. Once the MRM conditions have been
determined in our analytical system, they can be applied
to other LC-MS-based analytical systems with different
instruments to obtain the MS/MS spectra in those systems.
This is necessary because ESI-MS and ESI-MS/MS spectra arehighly instrument dependent and difﬁcult to standardize(Lu et al. 2008a). The concept of MRM can be applied tometabolites only when the corresponding authentic compounds are available and reference data can be obtained. In
the case of non-targeted metabolomics, the identiﬁcation of
unknown peaks is the most time-consuming step, because
no pre-existing reference data are available. Recently,
Matsuda et al. performed non-targeted metabolomics ofArabidopsis using UPLC-QTOFMS, and gathered ESI-MS/MS
spectra of about 500 unidentiﬁed metabolites which theyreferred to as MS/MS spectral tags (MS2Ts) (Matsuda et al.
2008). These can be used as a reference data set for comparison
with unidentiﬁed metabolites, in the same way that
expressed sequence tags are used in transcriptomics. We
intend to establish a methodology for ultra-widely targeted
metabolomics using UPLC-TQMS by combining the nontargeted MS2Ts with the large-scale reference data set ofauthentic compounds. In this case, metabolites detected inbiological samples will be given either compound names or
metabolite IDs (not identiﬁed as speciﬁc compounds), by
reference to the combined data sets.
The advantage of MRM also resides in its potential todistinguish between metabolites of the same molecularmass, which give the same retention times in chromatography.
For example, in LC-MS analysis the peaks corresponding toleucine and isoleucine cannot be separated either in LC
chromatograms or in MS chromatograms. However, if MRM
is performed, leucine and isoleucine can be separated bymonitoring their respective product ions (Gu et al. 2007).

Large-scale acquisition of metabolite accumulation
patterns in plants
In the case of transcriptomics, thousands of microarray data
were systematically acquired using the same analytical platforms, i.e. Affymetrix GeneChip, in AtGenExpress (Schmid et al.

2005, Kilian et al. 2007, Goda et al. 2008) and NASCArrays(Craigon et al. 2004). These data sets are available to the
public and are used to develop web-based tools for gene coexpression analyses (Steinhauser et al. 2004, Zimmermann
et al. 2004, Touﬁghi et al. 2005, Zimmermann et al. 2005,
Obayashi et al. 2007, Horan et al. 2008, Srinivasasainagendra
et al. 2008). The availability of these tools has accelerated thefunctional identiﬁcation of unknown genes (Saito et al. 2008).
High-throughput analysis of widely targeted metabolites will
enable the systematic acquisition of metabolite accumulation
pattern data for large numbers of biological samples from asingle species (e.g. in screening mutants) as well as from awide range of species. In this study, an interspecies comparison of metabolite accumulation patterns was successfully
conducted (Figs. 4, 5), indicating that our methodology has
paved the way for comparative metabolomics. Certain
taxon-speciﬁc metabolites and/or metabolite accumulation
patterns are highly relevant to the usefulness of plants tohumans (e.g. as resources of phytochemicals) and therefore
inﬂuence the commercial values of crops (Surh 2003).
Comparative metabolomics will be a key approach to understanding diverse plant metabolisms and to developing
value-added crops.
By systematically acquiring the metabolite accumulation
patterns in large numbers of samples, such as mutant collections and natural variants/ecotypes within a single species,
we can carry out large-scale reverse genetics and quantitative
trait locus analyses aimed at the elucidation of regulatory
mechanisms in plant metabolism (Keurentjes et al. 2006,
Kuromori et al. 2006, Loudet et al. 2007). The metabolite
accumulation pattern is the ﬁnal phenotype, resulting fromgene expression and protein function in metabolic pathways.
Thus, the acquisition of a large-scale data set of metabolite
accumulation patterns is essential to our work in understanding plant metabolism, which cannot be fully elucidated
using the other omics approaches.

High-throughput UPLC-TQMS analysis for widely
targeted metabolomics
In this study, ﬁve metabolites were simultaneously monitoredin each 3 min run, and thus a total of about 400 metabolites ina single sample were quantiﬁed in 240 min. This level ofthroughput may seem relatively low. However, unlike nontargeted metabolomics, the metabolites to be measured are
already identiﬁed and directly monitored in this process,without the necessity for signal separation from otherunnecessary signals and noises. Moreover, the number of
metabolites monitored in a single run could be increased by
up to about 100 compounds by adjusting the MRM monitoring times based on the speciﬁc retention times. Thus, we
expect that the actual throughput will be 1,000 biologicalsamples per week for the quantiﬁcation of a few hundred

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Y. Sawada et al.

metabolites, depending on the number of detectablecompounds.
By employing our prototype method, based on UPLCsingle quadrupole MS, we have already analyzed the contents of soluble amino acids and glucosinolates in the seedsof about 3,000 transposon-tagged lines (Kuromori et al.
2006) and about 400 natural variants/ecotypes of Arabidopsis(to be published elsewhere). In the novel method introduced
here, the sensitivity of detection and selectivity of compounds, as well as the rate of throughput, have been greatly
improved. We intend to re-analyze the Arabidopsis lines
mentioned above for the accumulation patterns of a few
hundred metabolites, by using UPLC-TQMS. This will provide us with a large data set that will help us to understandplant metabolism, and it will be used as a model data set forother bioinformatics studies.

metabolites. Such analyses could be conducted independently from genome information, and thus it may be possible
to extrapolate the results to many species. Furthermore,unlike genes, a given metabolite (compound) is identical in
all organisms (i.e. there is no ‘orthologous’ metabolite); thus,
metabolite accumulation patterns can be directly comparedamong different species.
Our ﬁnal goal is to understand complex plant metabolic
systems which are ﬁne-tuned to sense and react to both developmental and environmental changes. The high-throughput
and widely targeted metabolomics methodologies established here will enable us to acquire a huge metabolome
data set and to generate data-driven hypotheses about plant
metabolism. They will also provide the basis for comparative
metabolomics that can be used to elucidate plant-speciﬁc
but widely diverse metabolic phenomena.

Future prospects—systems analysis andcomparative metabolomics
Metabolomics is a young ﬁeld, and thus the necessary
analytical technologies and informatics are still developing
in response to the specialized demands of this new area. In
order to develop metabolomics effectively as a robust tool forbasic biology, some technical difﬁculties, such as metabolite
identiﬁcation, will need to be overcome.
Over the last decade, many studies have investigated the‘metabolomes’ of biological samples from plants, mammals
and bacteria. In some cases, metabolic ﬁngerprints without
metabolite identiﬁcation were used for clinical diagnosis orfor a description of metabolic status. In other cases, targeted
metabolomics for hundreds of identiﬁed metabolites
possessing similar chemical properties were conducted
to understand a particular biological event fully. As
above, developing ‘metabolic descriptions’ and ‘targeted
approaches’ is still a major trend in metabolomics, partly
due to technical limitations. In contrast, the widely targeted
metabolomics approach established in this study covers abroad range of metabolites encompassing plant primary andsecondary metabolism. Such wide coverage is necessary tounderstand fully the plant metabolic system as a whole. For
example, any perturbation in primary metabolism almost
certainly affects many secondary metabolic pathways, andvice versa (Kaimoyo et al. 2008).
In addition, the high-throughput measurement advances
realized in this study are essential to metabolomics-based
biology. Scant metabolome data require other types of
biological data and/or a priori knowledge, such as gene
expression patterns or kinetic parameters of enzymes, tomake sense of the biological information embedded inmetabolomic data. On the other hand, metabolome data inthe order of thousands or tens of thousands of data points
would allow the statistical treatment of data sets and, forexample, create algorithms describing relationships among

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Materials and Methods
Acquisition of ESI-MS and ESI-MS/MS data
All the solutions of authentic compounds (250 µM) weretransferred from 10 ml vials to 1.2 ml glass inserts in 96-wellplates (Webseal system, GL Sciences, Tokyo, Japan) anddiluted to 50 pmol µl–1 with H2O using an ALHS (Microlab
STARplus, Hamilton, Reno, NV, USA). The solutions (20 µl
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 mincycle 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 ﬂow 50 l h–1,
desolvation gas ﬂow 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 acidin 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.

Plant Cell Physiol. 50(1): 37–47 (2009) doi:10.1093/pcp/pcn183 © The Author 2008.

Automated widely targeted metabolomics

The TQMS detection conditions were the same as those for
FIA-TQMS, except that the source temperature was 130°C.

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 theextraction buffer (80% MeOH) was added. The resulting
extracts consisted of 400 µl with 20 mg ml–1 (fresh weight) of
tissue. The extracts were transferred and treated using an
ALHS as follows (Supplementary Fig. S1): 350 µl of each plant
extract were transferred to a 96-well plate, and the solutions
were dried under N2 gas using a 96-well format spray instrument (40°C, 25 min and 30°C, 20 min). The dried samples
were dissolved in 350 µl of H2O using a vortex system(1,300 r.p.m., 6 min), and then ﬁltered through a 96-wellﬁlter [Captiva 96-well Filter Plate (pore size 0.45 µm,
polyvinylidene ﬂuoride), 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).

Plant materials
In this study, we selected 14 accessions representing nine
plant species from three families, and analyzed whole seeds
or seed coats. The samples tested were as follows: seeds ofthe three Brassicaceae species Arabidopsis thaliana (accessions Columbia-0, C24, Landsberg erecta, Wassilewskija andNossen-0), white mustard (Sinapis alba) and black mustard(Brassica nigra); seed coats of the four Gramineae species
wheat (Triticum aestivium), oat (Avena sativa), rye (Secale
cereale) and durum wheat (Triticum durum); and seeds ofthe two Fabaceae species Lotus japonicus (accessions B129
gifu and MG20 miyakojima) and soybean (Glycine max).
Arabidopsis seeds were obtained from the Arabidopsis Biological Resource Center (Ohio State University, USA). Seedsof white and black mustards were purchased from MikasaEngei (Tokyo, Japan). Seed coats of Gramineae species andsoybean seeds were purchased from Cuoca Planning Co.,
Ltd. (Tokushima, Japan). Lotus japonicus accessions were a giftfrom Dr. Toshio Aoki (Nihon University, Japan) (Kawaguchi
et al. 2001).

Analysis of metabolite accumulation patterns
Quantitative data for metabolite accumulation in the seeds
or seed coats of the 14 accessions or species were obtainedby UPLC-TQMS. Peaks that showed S/N ratios >30 wereselected 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-speciﬁc
metabolites were identiﬁed by BL-SOM analysis of the matrix
in combination with a model data set consisting of one
hypothetical metabolite speciﬁc to each of the families Brassicaceae, Gramineae and Fabaceae.

Supplementary data
Supplementary data are available at PCP online.

Funding
The Japan Science and Technology Agency (CREST grant).

Acknowledgments
We thank Ms. Etsuko Suzuki (Nihon Waters KK), Mr. Takehiro
Nozawa (Nihon Waters KK) and Mr. Hiroshi Hike (AMR Inc.)for technical support with the FIA-TQMS analysis. We also
thank Mr. Kazuto Hashimoto (GL Sciences Inc.) andMr. Masahito Shimoya (GL Sciences Inc.) for technical support with the ALHS. We wish to thank Dr. Aoki Toshio(Department of Applied Biological Sciences, Nihon University)
for the kind gift of Lotus japonicus (accessions B129 gifu andMG20 miyakojima). We are grateful to Dr. Fumio Matsuda
for useful comments on our widely targeted metabolomics
approach.

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(Received October 08, 2008; Accepted November 23, 2008)

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