From Exogenous to Endogenous: The Inevitable Imprint of Mass
Spectrometry in Metabolomics
Elizabeth J. Want, Anders Nordstro 1m, Hirotoshi Morita, and Gary Siuzdak*
Department of Molecular Biology, The Scripps Center for Mass Spectrometry, 10550 North Torrey Pines Road,
La Jolla, California 92037
Received September 27, 2006
Mass spectrometry (MS) is an established technology in drug metabolite analysis and is now expanding
into endogenous metabolite research. Its utility derives from its wide dynamic range, reproducible
quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims
of developing mass spectrometry for metabolomics range from understanding basic biochemistry to
biomarker discovery and the structural characterization of physiologically important metabolites. In
this review, we will discuss the techniques involved in this exciting area and the current and future
applications of this field.
Keywords: mass spectrometry • liquid chromatography • metabolomics • biomarker characterization • metabolite
The application of modern mass spectrometry technology
to endogenous metabolite research derives from its success in
drug metabolite studies, both quantitative and structural.1-11
Interest also originates from the ability to perform more
comprehensive metabolite analyses with new liquid chroma-
tography/mass spectrometry (LC/MS) technologies, such as
nanoESI-LC/MS, and the desire to unravel basic biochemical
events of cells and tissues, or to identify disease or pharma-
One of the first metabolite profiling experiments was by
Pauling and colleagues in 1971, who analyzed the metabolite
content of human urine vapor and breath of subjects on a
defined diet using gas chromatography (GC).12Approximately
250 substances were detected in a breath sample and 280 in a
urine vapor sample. This group then went on to profile amino
acids in urine, employing nonparametric statistical analysis for
detecting profile differences related to gender and other
variables.13This was the beginning of what we now call
metabolomics, the aim of which is to provide a comprehensive
profile of all the metabolites present in a biological sample.
From the 1970s, gas chromatography mass spectrometry
(GC/MS) became popular for metabolite profiling and is still
used for the detection of many metabolic disorders.14Advan-
tages of GC/MS include high resolution and reproducibility,
as well as the availability of EI spectral libraries for structural
identification.15In addition, since the 1990s,16nuclear magnetic
resonance (NMR) has also been applied to areas such as plant
metabolism, Duchenne Muscular Dystrophy, neurological dis-
orders, and hepatotoxicity and nephrotoxicity in rodents,17-24
with advantages in both speed and accuracy. However, because
of the limitations of NMR in terms of sensitivity, LC/MS has
emerged as a powerful alternative technology for metabolom-
ics. In this review, the role of mass spectrometry in metabo-
lomics will be discussed, encompassing data acquisition, data
analysis, metabolite characterization, and many exciting ap-
1. Data Acquisition
Due to the complex nature of biological samples, separation
is often performed before mass spectrometric analysis to
achieve the detection of as many metabolites as possible.
Traditionally, GC was employed, as it is well-known for high
resolution and reproducibility. However, disadvantages of GC
include convoluted sample preparation (such as derivatization),
lengthy analysis time, and the limitation on the size and type
of molecule that can be analyzed (nonvolatile, polar macro-
molecules are unsuitable). However, GC-MS is still widely used
in plant metabolomics due in part to the nature of the
metabolites being investigated.15,25-28
Liquid chromatography electrospray ionization mass spec-
trometry (LC/ESI-MS) (Figure 1) is now a common metabolo-
mics tool. Separation of the thousands of molecules present
in biofluids using LC can reduce ion suppression29-31by
decreasing the number of competing analytes entering the
mass spectrometer ion source at any one time. This results in
a selective approach that allows for both quantitation and
structural information, where sensitivities in the pg/mL range
can be achieved readily.32LC/MS techniques have replaced
some of the traditional specialized clinical laboratory meth-
ods33,34that used immunological, fluorometric, and biological
An important factor in LC metabolite separation is the choice
of column. Many biofluids, particularly urine, contain a vast
array of highly polar molecules that are not retained well on
the more traditional reverse phase (RP) LC columns. Normal
phase techniques, which result in the elution of less polar
molecules first and thus the retention of more polar molecules,
10.1021/pr060505+ CCC: $37.00
2007 American Chemical Society
Journal of Proteome Research 2007, 6, 459-468
Published on Web 11/18/2006
require a different solvent system to that used by RP chroma-
tography, typically containing no aqueous. A newer approach
is hydrophilic interaction chromatography (HILIC), which can
offer complementary information to that obtained using RP
chromatography.36Here, water and acetonitrile can still be
used, although starting from a high organic content and ending
at high aqueous. HILIC approaches combined with ESI-MS
techniques have already been applied to the analysis of
dichloroacetic acid in rat blood and tissues,37plant metabolites
such as oligosaccharides, glycosides, and sugar nucleotides,38
and with APCI mass spectrometry for the determination of
5-fluorouracil in plasma and tissues.39
The ability of LC to separate complex mixtures prior to mass
analysis comes at a cost of speed. An alternative to traditional
reverse phase (RP) approaches is ultrahigh performance liquid
chromatography (UPLC),40which utilizes columns with much
smaller particle size packing material (1.4-1.7 µm) than
traditional columns, thus allowing for improved separation and
higher resolution (Figure 2). This technology permits pumping
and injection of liquids at pressures exceeding 10 000 psi.41
Using this approach, sample analysis times can be reduced to
as little as 1 min,42resulting in much higher throughput. With
UPLC, narrower chromatographic peaks can be achieved (peak
widths at half-height <1 s), resulting in increased peak capacity,
lower ion suppression and improved signal-to-noise ratio, and
thus increased sensitivity (Figure 2). Recent studies comparing
UPLC and HPLC for their application to metabolomics studies
showed that UPLC can detect more components than HPLC,32
with a 20% increase reported over the same chromatographic
length.43This study also showed UPLC to display superior
retention time reproducibility and signal-to-noise ratios over
When coupled to separation techniques, MS analysis of
biofluids can offer high sensitivity and specificity. However,
despite LC/MS being the foremost technique for the analysis
of known compounds,44as well as the determination of
unknowns using MS/MS, one limitation is the inability of LC/
MS alone to unequivocally distinguish between some coeluting
stereoisomers.45However, the application of ion-mobility mass
spectrometry to metabolomics might be a powerful strategy
for addressing the problem of resolving isomers. Indeed, an
LC approach has been combined with ion mobility/time-of-
flight (TOF) mass spectrometry for the characterization of a
combinatorial peptide library, enabling many peptide isomers
with identical masses and retention times to be resolved.46
Furthermore, as LC-MS techniques for metabolomics can be
affected by high noise levels, retention time shifts, and high
variability in signal intensities, researchers are constantly
investigating ways to reduce analysis time and sample prepara-
tion in metabolomics studies. There have been some recent
explorations of chip-based mass spectrometry approaches for
the delivery of the biological sample to the mass spectrometer
with the aim of improving metabolite detection. One recent
study using protein precipitation of plasma combined with
chip-based nanospray infusion reported high reproducibility,
sample throughput, and the observation of over 1800 different
mass peaks up to 900Da.47Some of the samples were highly
diluted to minimize ion suppression, and so although MS runs
of 10 min were used, for MS/MS studies, runtimes of 60-90
min were needed to obtain meaningful data, offering no
advantages over LC-MS/MS. In fact, using UPLC-MS/MS
impressive fragmentation data can be collected in a run of 10
Ionization Techniques. Once the components of a biological
sample have been separated, ions must be produced. In GC,
samples are vaporized and then ionized by electron-impact (EI)
or chemical ionization (CI). Extensive libraries of EI spectra,
such as the NIST database, which contains over 100 000
compounds, are available to aid in the identification of
molecules (http://www.nist.gov/srd/nist1a.htm). EI has the
advantages of good sensitivity and unique fragmentation.
However, the molecular ion is often not detected due to the
extensive fragmentation, which may prove hinder the identi-
fication of unknown compounds. A disadvantage with EI is the
limited mass range due to the thermal desorption requirement.
As CI is much less energetic than electron ionization, it induces
less fragmentation and in general, more stable ions, and so can
be useful for identifying the molecular ion and thus determin-
ing the molecular weight of a compound. However, CI still
requires thermal desorption. Negative CI is particularly sensitive
for perfluorinated derivatives and proves a limited but powerful
approach for certain derivatized molecules such as steroids.
However, for metabolomics studies, electrospray ionization
(ESI) is most commonly used in conjunction with LC/MS. ESI
offers soft ionization, excellent quantitative analysis and high
sensitivity. With ESI, ions are generated directly from the liquid
phase into the gas phase, establishing this technique as a
convenient mass analysis platform for both liquid chromatog-
raphy and automated sample analysis. In its simplest form, ESI
can be quite effective even without separation, especially when
combined with tandem mass spectrometry (MS/MS) where its
direct application to metabolite screening is currently used for
over 35 diseases.49,50
Three alternative solution-based ionization strategies to ESI
are also being used for LC/MS-based metabolomics, namely
nanoESI, atmospheric pressure chemical ionization (APCI) and
atmospheric pressure photoionization (APPI). NanoESI liquid
chromatography, performed at low flow rates (∼200 nL/min),
has already proved useful in proteomics studies51,52where it
significantly enhances sensitivity and dynamic range.53-55In
nanoLC/nano-ESI-MS, ions are produced from small sub-
micron sized droplets requiring less evaporation and a greater
ability to focus the resulting ions into the analyzer, therefore
increasing sensitivity and ultimately offering a greater dynamic
range. APCI and APPI are widely used in the pharmaceutical
industry56-58yet have had limited exposure to metabolomics
Figure 1. Metabolomics aims to measure all the metabolites in
a biofluid or tissue. Common approaches include LC-MS and LC-
MS/MS using electrospray ionization. Alterations in metabolite
levels may reflect the activity of their corresponding enzymes.
Additional factors affecting metabolite profiles include drug
intake or the onset or progression of a disease. Metabolomics is
complementary to proteomics and transcriptomics and the
combination of data from all three approaches can provide
important information regarding the status of a cell or organism.
Want et al.
460Journal of Proteome Research • Vol. 6, No. 2, 2007
studies. Analogous to the ESI interface, APCI and APPI typically
induce little or no fragmentation and are considered robust
and relatively tolerant of high buffer concentrations. It is now
recognized that these approaches can be valuable for the
analysis of nonpolar and thermally stable compounds such as
lipids59,60with the apparent trend toward a “single” ionization
source containing combinations of ESI and APCI or ESI and
There is a small but growing body of work using other
ionization strategies for metabolomics. MALDI applications
have been limited, due in part to matrix suppression issues
for low molecular weight molecules. However, some research-
ers believe that there could be advantages to using this
approach. Using a novel sample deposition approach, semi-
quantitation of amino acids from mammalian cells has been
achieved using positive mode MALDI-TOF-MS.61Negative
mode MALDI, rarely used due to the lack of suitable matrices,
has been applied recently for the analysis of metabolites from
Islets of Langerhans and E. Coli62. In all, over 100 metabolites
were detected, although many could not be identified due to
the lack of complete databases and the inability to distinguish
isomers such as citrate and isocitrate. Recently, a matrix-
suppressed laser desorption/ionization (MSLDI) strategy was
evaluated. By decreasing the matrix/analyte ratio, less sup-
pressed spectra were obtained, enabling the detection of lower
abundance compounds.63However, it can be difficult to find
a suitable matrix/analyte ratio without prior knowledge of
Desorption ionization on porous silicon (DIOS) allows for
the detection of small molecules in both positive and negative
mode with little background interference64and has recently
been applied to metabolomics studies.65Here, 26/30 known
metabolites in a mixture were detected rapidly in positive mode
and in negative mode, showing the potential of DIOS as a
Recently, the combination of desorption electrospray ioniza-
tion mass spectrometry (DESI-MS) and NMR was investigated
for its application to metabolomics.66This group studied urine
without any sample preparation to differentiate between
diseased and healthy mice. DESI is an ambient ionization direct
analysis technique, providing high sensitivity and specificity
with minimal sample preparation.66There is no sample separa-
tion and because the sample is placed on the surface rather
than direct infusion, this affords a higher tolerance to salts.
However, some compounds do not ionize well using any of
the common ionization techniques and so will not be detected
using MS alone. The coupling of NMR and MS has been used
in combination with liquid chromatography (LC-NMR-MS)
and applied to metabolite studies, such as in the pharmaceuti-
cal drug discovery area.44,67This technique allows for the both
MS and NMR data to be collected from a single LC run and
the complementary information that can be provided makes
this approach a powerful tool for the detection and identifica-
tion of both known and unknown compounds.44Further,
software is being developed to cope with the analysis of the
complex data produced by these instruments, in particular,
statistical heterospectroscopy (SHY), an approach to the inte-
grated analysis of NMR and UPLC-MS data sets.68
Mass Analyzers. Along with advances in ionization sources,
mass analyzers have improved with respect to speed, accuracy,
and resolution. The most common mass analyzers are the
quadrupole and time-of-flight (TOF) based analyzers. Other
analyzers that can be used for metabolomics studies include
ion traps, Fourier transform mass spectrometers (FTMS), and
orbitraps, some of which will be discussed in this section.
Quadrupole mass analyzers can be coupled to many different
ionization sources, with advantages including comparatively
high pressure tolerance, good dynamic range, and excellent
stability, all at a relatively low cost. To perform tandem mass
analysis with a quadrupole instrument, three quadrupoles are
placed in series. Each quadrupole has a separate function: the
first quadrupole (Q1) scans across a preset m/z range to select
an ion of interest, which is then fragmented in the second
quadrupole (Q2), the collision cell, using argon or helium as
the collision gas. The third quadrupole (Q3) analyzes the
fragment ions generated in the collision cell (Q2).
The linear time-of-flight (TOF) mass analyzer is the simplest
mass analyzer, with virtually unlimited mass range, whereas
the TOF reflectron has mass range up to m/z ∼10 000. The TOF
reflectron is now widely used with ESI and MALDI, and more
recently for electron ionization in GC/MS applications. TOF
instruments offer high resolution, fast scanning capabilities
(ms), and accuracy on the order of 5 part per million (ppm).
Quadrupole-TOF (Q-TOF) mass analyzers combine the stability
of a quadrupole analyzer with the high efficiency, sensitivity,
and accuracy of a time-of-flight reflectron mass analyzer, and
are typically coupled to ESI sources. Q-TOF mass analyzers are
an obvious choice for obtaining metabolite fragmentation data.
Figure 2. Ultrahigh performance liquid chromatography (UPLC) utilizes columns with smaller particle size packing material (1.4-1.7
µm) than traditional columns and can enhance several aspects of chromatography in a metabolomics context. (1) Separation of
metabolites is improved, decreasing ion suppression and in turn improving data interpretability (2) Signal to Noise (S/N) is improved
due to narrower peak widths allowing for increased peak capacity and improved accuracy and sensitivity. (3) Sample run time is
decreased dramatically allowing for faster sample throughput.
Inevitable Imprint of MS in Metabolomics
Journal of Proteome Research • Vol. 6, No. 2, 2007
The quadrupole can act as any simple quadrupole analyzer to
scan across a specified m/z range, but can also be used to
selectively isolate a precursor ion and direct that ion into the
collision cell. The resultant fragment ions are analyzed by the
TOF reflectron mass analyzer. Q-TOF analyzers offer signifi-
cantly higher sensitivity and accuracy over tandem quadrupole
instruments when acquiring full fragment mass spectra.
The ion trap mass analyzer can be used for both MS scanning
and MS/MS studies. It allows the isolation of one ion species
by ejecting all others from the trap, whereby the isolated ions
can subsequently be fragmented. However, a major limitation
of the ion trap is its inability to perform high sensitivity triple
quadrupole-type precursor ion scanning and neutral loss
scanning experiments. Furthermore, the upper limit on the
ratio between precursor m/z and the lowest trapped fragment
ion is ∼0.3 (the “one-third rule”). The dynamic range is also
limited due to space charge effects when too many ions are in
the trap, which diminish the performance of the ion trap. Here,
the linear ion trap has an advantage over the 3D trap, with a
larger analyzer volume which lends itself to a greater dynamic
range and an improved range of quantitative analysis. Quad-
rupole ion traps have MSncapabilities, allowing for multiple
MS/MS experiments to be performed quickly without having
multiple analyzers, such that real time LC-MS/MS is now
routine. Other important advantages of quadrupole ion traps
include their compact size, and their ability to trap and
accumulate ions to provide a better ion signal.
Fourier transform mass spectrometry (FTMS) offers high
resolution and the ability to perform multiple collision experi-
ments (MSn). FTMS is capable of ejecting all but the ion of
interest, fragmenting the selected ion and yielding high-
accuracy fragment masses. Ultrahigh resolution FTMS can be
coupled to MALDI, ESI, APCI, and EI, although the new
quadrupole-FTMS and quadrupole linear ion trap-FTMS mass
analyzers are typically coupled to electrospray ionization
sources. Newer hybrid instrument designs are preferable over
coupling FTMS/MS to separation techniques such as LC, as
MS/MS experiments can be performed outside the magnet.69
This presents some advantages because high resolution in
FTMS is dependent on the presence of high vacuum. Perform-
ing MS/MS experiments outside the cell is thus faster because
the ICR cell is not exposed to a pulse of gas to initiate
dissociation and thus can be maintained at ultrahigh vacuum.
Instruments, such as the LTQ-FT, combine the excellent
performance and capabilities of the FT mass spectrometers
with the well-established, tested, and validated features of
quadrupoles and ion traps. Due to the robust, externally
calibrated accurate mass determination for both parent and
product ions, the LTQ-FT could be a very powerful analytical
tool for metabolomics studies, allowing for the confirmation
of known metabolites or to elucidate the structures of unknown
2. Data Analysis
LC/MS based metabolomics studies generate large, complex
datasets which require sophisticated software to enable inter-
pretation. A current challenge is achieving the high-throughput
conversion of these datasets into organized data matrices
necessary for further statistical processing, as well as visualiza-
tion. As two or more sample sets are often compared for
changes in metabolite levels, metabolites must first be detected
in all samples, matched between the samples and then their
levels compared (Figure 3). Multiple adducts (such as sodium
and potassium) can be formed using LC-ESI/MS, thus com-
plicating the data produced by increasing the number of peaks
detected. It is imperative that the same metabolites are
identified correctly in all samples to enable this comparison.
To this end, software has been produced in order to allow peak
picking and evaluation. Many instrument manufacturers have
produced their own software, which often works solely with
data generated from a particular instrument. These include
MarkerLynx (Waters), MassHunter (Agilent) and MarkerView
(Applied Biosystems/MDS SCIEX). However, some researchers
desire the freedom to modify many parameters and also to
compare data from different instruments and so have devel-
oped their own software. Examples of these include MZmine,70,71
XCMS,72and MET-IDEA73and are generally freely available for
download and in some cases, user modification.
A huge challenge with metabolomic data analysis can often
be the classification of data, either with or without prior
knowledge that such classes exist. The development of new data
analysis approaches74,75including multivariate statistical analy-
sis for biomarker discovery5,28,76,77has facilitated the discovery
of hidden structure in data. Therefore, with many of these
software programs, data (as peak lists or similar) can be output
in a suitable format to then be analyzed using multivariate
statistics (Figure 4). These multivariate techniques can help to
discern peaks with high discriminating power between the
sample groups being analyzed, i.e., potential biomarkers.78Most
companies and research groups involved in metabolite research
supplement these available data mining techniques with in-
house software to further enable compound identification and
A variety of multivariate statistics and pattern recognition
methods are currently in use for metabolomics studies, which
can be divided into two categories, unsupervised and super-
vised methods. In unsupervised methods, such as principal
component analysis and hierarchical cluster analysis, the
algorithm is not given a training set and so input data is
Figure 3. LC/MS metabolomics studies generate large, complex datasets, and there is the possibility of retention time drift between
samples over the course of the study. Alignment of chromatographic data is fundamental for the production of comparable data sets.
A data extraction tool like XCMS allows for nonlinear correction of retention time drift in the time domain. Other freely available data
processing software includes MZmine and MET-IDEA.
Want et al.
462Journal of Proteome Research • Vol. 6, No. 2, 2007
classified in an “unsupervised” manner. Conversely, with
supervised methods, a classification system is given some input
data together with the answers, known as the “training set”,
which can be used to build a model and estimate necessary
parameters. These include discriminant analysis, such as
projection to latent structures (also called partial least-squares)
(PLS) and orthogonal projection to latent structures (O-PLS),
artificial neural networks (ANNs), and evolutionary-based
Principal component analysis (PCA)79is often used for
metabolomics.80,81PCA can be used in the reduction of data
dimensionality, to investigate clustering tendency, such as with
gene expression data,82to detect outliers, and to visualize data
structure.83,84However, PCA gives a simplified representation
of the information contained in the spectra and cannot
generally use additional information about the data, such as
class information. Therefore, PCA is often followed by a
supervised analysis technique such as PLS-DA or O-PLS-DA.
In fact, Lutz and colleagues showed by comparison of PCA with
PLS-DA that there was a clear advantage in using a supervised
model where class details are known.78
Hierarchical cluster analysis organizes information about
variables in a data set, forming “clusters”, where the degree of
association is strong between samples within the same cluster
and weak between those in different clusters. This approach
may reveal associations and structure in data that were not
previously evident. Hierarchical clustering can be represented
as a tree, or dendrogram, where each step in the clustering
process is illustrated by a join of the tree. The combination of
proteomics and cluster analysis has been applied successfully
to the classification of normal breast, benign breast and breast
cancer tissues using just the protein expression profiles.85
Projection to latent structures, also called partial least-
squares discriminant analysis (PLS-DA) is performed in order
to enhance the separation between groups of observations,
often by rotating PCA components to achieve maximum
separation between classes, and to understand which variables
are responsible for separating the classes. Orthogonal projec-
tion on latent structure discriminant analysis (O-PLS-DA),
developed by Trygg and Wold, can be a powerful tool for the
analysis of metabolomics data.86,87Like PLS-DA, O-PLS-DA is
a supervised pattern recognition technique, but has improved
predictive quality because the structured noise is modeled
separately. O-PLS-DA has been used in conjunction with
STOCSY (statistical total correlation spectroscopy) in the
analysis of NMR metabolomics data.88
ANNs are powerful data modeling tools, capable of learning
patterns and relations from input data, making good pattern
recognition engines and robust classifiers. ANNs are being used
effectively for problems including building nonlinear classifica-
tion and regression models. Currently, ANNs are being devel-
oped which can predict patient responses to drugs, which
would enable ideal dosing regimes to be established.89
A newer approach to the mining of highly complex metabo-
lomics data is to apply evolutionary computational-based
methods.90These are explanatory supervised learning tech-
niques, including genetic algorithms, genetic programming,
evolutionary programming and genomic computing, which
could be ideal strategies for mining such high-dimensional data
as that obtained from metabolomic studies.90
However, there appears to be no consensus on which
multivariate statistics approach is truly superior, and so at
present it seems that individual companies and research groups
are employing their own combination of data analysis software
and multivariate statistics to address their individual metabo-
Databases. The collection of LC/MS data and subsequent
comparative analysis is becoming more straightforward, yet a
major challenge lies in characterizing the metabolites that have
interesting biological properties and whose mass is initially
identified. In contrast to the well-annotated gene and protein
databases that can be searched easily, at present, no such
comprehensive tools exist for metabolite researchers. However,
current metabolite databases, although incomplete, offer a
starting point for characterization. Among the databases cur-
rently available, the most widely used are the NIST database,
which includes mass spectral data for some known metabolites
(http://www.nist.gov/srd/nist1.htm), as well as the KEGG,
HumanCyc, ARM, and METLIN databases. The KEGG database
is a valuable resource for metabolomics researchers (http://
biocyc.org) includes known metabolites as well as those
predicted by algorithms which project metabolic pathways
from a genomic sequence. A database constructed as part of
the Atomic Reconstruction of Metabolism (ARM) project,
compiles metabolite structures together with molecular weight
and MS fragmentation data (http://www.metabolome.jp). In
addition, the University of Alberta hosts a mini-library of full
mass spectra of newer drugs, metabolites and some breakdown
products, (http://www.ualberta.ca/∼gjones/mslib.htm). Other
databases include the human metabolite database (http://
www.hmdb.ca/), which acts as an electronic repository for
identification of small molecule metabolites. The Spectral
Database for Organic Compounds SDBS provides access to a
wealth of spectra of organic compounds (NMR, MS, IR).
Another metabolite database is the “tumor metabolome”
database, established at the Justus-Liebig University Giessen
in Germany (http://www.metabolic-database.com).
LIPID MAPS (http://www.lipidmaps.org/tools/index.html)
and Lipid Search (http://lipidsearch.jp/LipidNavigator.htm) are
useful databases to search lipid metabolites. Although phos-
pholipids and some other lipids are important metabolites,
numbers of the registered secondary metabolites are still
The KNApSAcK database (http://kanaya.aist-nara.ac.jp/
KNApSAcK/Manual/KNApSAcKManual.html) can also be used
to pick up metabolites not registered in the above databases.
This database is specific for secondary metabolites and MS-
based data searches can also be performed. Some databases
focus purely on electron impact mass spectrometry data,
such as the Wiley Registry of Mass Spectral Data (http://
www.wileyregistry.com), the largest commercially available
reference library of mass spectra. The GOLM open access
database at the Max-Planck Institute of Molecular Plant
manually inspected before data are further processed using
multivariate statistics. These multivariate techniques can help to
discern potential biomarkers, which then undergo targeted
analysis to further validate their significance as biomarkers.
Once data has been aligned, the output can be
Inevitable Imprint of MS in Metabolomics
Journal of Proteome Research • Vol. 6, No. 2, 2007
Physiology also focuses on electron ionization mass spectrom-
etry data and is intended as a repository for experiments
performed at this institute, as well as for data from collabora-
To support the identification of metabolites we have devel-
oped METLIN, a web-based data repository (http://metlin-
.scripps.edu/) on endogenous and exogenous metabolites.
METLIN provides mass, elemental composition, CAS#, KEGG#,
some MS/MS data, and a diverse collection of LC/MS and high-
resolution Fourier transform mass spectrometry (FTMS) spec-
tra, primarily from human biofluids and also some model
organisms. The purpose of this data is to aid in metabolite
identification through accurate mass measurement and isotopic
pattern evaluation. METLIN also includes an annotated list of
known metabolite structural information, both endogenous and
drug metabolites can be easily cross-correlated with the LC/
MS and FTMS data. Further, METLIN provides a number of
data visualization tools including color 3D LC-MS plots and
A long-term aim in metabolomics is the establishment of
data standards, to standardize experiment descriptions, par-
ticularly within publications. ArMet92(http://www.armet.org),
is a data model to describe plant metabolomics experiments
and their results.93Other groups have produced reporting
requirements for metabolomics experiments,15to form a
checklist of the information necessary for the publication of
metabolomics data. A standard metabolic reporting structure
policy document (SMRS Group, 2004) has been developed by
a group from industry and academia.
Metabolite Identification. Once potential biomarkers have
been selected, identification is required. Some metabolites
observed in metabolomics studies may be well-known and
characterized. Databases such as KEGG, human metabolite
database, and METLIN can be used to search candidate
molecules. If samples are analyzed using high-resolution mass
spectrometry, then many candidates can be excluded. Once
candidate molecules are obtained, co-chromatography and
comparison of MS/MS data are necessary to confirm the
identification of the molecule.
If the molecule is not known then the next task is identifica-
tion, a significant challenge given the often limited sample
amount and trace quantities of some metabolites. The overall
procedure can be summarized in Figure 5 with the initial LC
isolation of molecule of interest followed by tandem mass
measurements on a Q-TOF for structural characterization and
FTMS analysis for accurate mass measurements. Typical
methods for obtaining elemental composition involve high-
resolution ESI-FTMS and FTMS/MS technology for accurate
mass determination, as well as the newer LTQ-FT technology.
Orthogonal acceleration Q-TOF mass spectrometry94is also
being used to obtain high accurate mass measurements.
Furthermore, UPLC/MSE, performed on a Q-TOF, has been
presented recently as an approach for obtaining fragmentation
data from LC/MS metabolomics studies.95This technique was
applied to small molecules in complex mixtures and was
achieved using simultaneous acquisition of exact mass at high
and low collision energy, without reported loss of quality in
the chromatographic data, offering an alternative approach to
structural elucidation in complex mixture analysis problems.
However, despite the usefulness of this mass spectrometry
data, the lack of comprehensive mass spectral libraries often
precludes identification of molecules based on this data alone.
Ultimately, the combination of many technologies will be
required to identify unknown metabolites in biofluids including
high sensitivity capillary NMR, which can provide metabolite
structure characterization down to low microgram level,96,97
chemical modification for functional group identification, and
finally independent synthesis for verification.
An example of the isolation and characterization of com-
pletely novel metabolites was recently shown with the discovery
of a family of taurine-conjugated fatty acids.11The challenge
in identifying these metabolites was addressed in a three-step
approach, (1) ultrahigh accuracy FTMS mass measurements,
(2) high accuracy tandem mass analysis using a Q-TOF, and
(3) chemical synthesis of potential candidates using the results
and structural information gained from experiments (1) and
3. Applications of Mass Spectrometry in Metabolomics
As metabolomics techniques become more robust and
sophisticated, their applications become more widespread
(Table 1). Combined with proteomics and genomics, metabo-
lomics can help gain insight into systems biology, by studying
the metabolite alterations and their relationships to changes
in gene expression, protein expression and enzyme activity.98-100
Despite the obvious challenges facing mass spectrometry in
metabolomics, including the confirmation of known metabo-
lites and the identification of unknown metabolites, many
studies are underway employing these techniques. One par-
ticularly successful application of metabolomics has been in
understanding gene function in model organisms such as yeast,
plants and mice.25,101-103Notably, metabolomics has been
applied to mouse models of Huntingtons Disease,104cardiac
disease,103and Duchenne muscular dystrophy.20
However, despite a great need and potential, there are
currently very few metabolomic studies in cancer therapeutics.
Metabolomics can be applied to the study of cancer by
monitoring tumor growth and regression, and has already been
used to study the function of hypoxia-inducible factor 1? in
tumors.105By combining functional genomics with metabolo-
mics, features of neuroendocrine cancers associated with a
poor outcome have been identified.99
Figure 5. Identification of an unknown metabolite or confirmation of a known metabolite can be performed using mass spectrometry.
The compound can be isolated using chromatography and characterization facilitated through accurate mass measurements to provide
an elemental composition and tandem mass spectral data for structural information. Ultimately, synthetic standards are generated to
validate (with perhaps other spectroscopic data), a structural hypothesis.
Want et al.
464 Journal of Proteome Research • Vol. 6, No. 2, 2007
Another important application of metabolomics is in the
pharmaceutical arena, where it can be used to investigate drug
efficacy and toxicity, to diagnose or predict disease states, or
to classify patient groups based on their specific metabolism.
By measuring alterations in biofluid metabolite concentrations
after administration of a therapeutic agent, and applying
multivariate statistical analysis techniques to highlight any
differences between dosed and control samples, the effect of a
potential drug can be studied.106Alterations in specific me-
tabolites, such as succinate, glycine, and dimethylamine in the
blood indicate kidney damage.17In addition, the nephrotoxin
gentamicin, when administered to male Wistar-derived rats,
has been shown to increase N-acetyl-beta-D-glucosaminidase
(NAG) activity significantly, accompanied by kidney damage.
Using a combination of NMR and HPLC-TOF-MS/MS, raised
glucose and reduced trimethylamine N-oxide (TMAO), as well
as reduced xanthurenic acid and kynurenic acid were observed
in the urine of treated animals.23Furthermore, bromobenzene
treatment to rats induces the formation of the novel biomarker,
5-oxoproline, in liver tissue, blood plasma, and urine.107These
studies could be eventually expanded to humans, where
metabolomics techniques may be able to highlight the re-
sponses of different groups of patients to a given drug. In this
way, metabolomics may dramatically reduce the costs of drug
development, by eliminating the progression of compounds
destined to fail due to toxicity. Additionally, in the drug
development phase, metabolomics could also aid in the
discovery of new preclinical and clinical safety and efficacy
biomarkers. The timing of the appearance of small molecule
markers in the particular biofluids may also be of importance.
The value of metabolomics in plant biotechnology has
increased significantly, and despite the convoluted nature of
plant metabolism, the interpretation of metabolomics data is
becoming easier, in part due to more sophisticated data
analysis approaches. Metabolomics can be used for the phe-
notyping of plants, and has been used in part to assess the
natural variance in metabolite profiles between plants, with the
potential to improve compositional quality.26Already, metabo-
lomic techniques have been applied to a vast array of plant
species, such as potato,27tomato,108,109wheat,110rice,111Arabi-
Over 1000 small molecules have been quantitated in a single
leaf extract, as well as more than 500 compounds from potato
Summary and Outlook. The area of metabolomics is ex-
panding rapidly and applications for this science range from
basic biochemistry to clinical biomarker discovery. The primary
challenge in metabolomics is in the generation of compre-
hensive, quantitative profiles of the thousands of components
present in biofluids, an issue that is largely being addressed
with LC/MS technology. Data analysis is becoming more
mature, due to the development of sophisticated bioinformatics
software packages that will ultimately drive the discovery
process. However, probably the greatest challenge in metabo-
lomics is in structurally characterizing physiologically important
molecules. The application of high-accuracy instruments and
advancements in the generation of fragmentation data, along
with the growing numbers of databases available, are gradually
making this task possible. As these challenges are being met,
it is encouraging that new potential biomarkers for diseases
such as myocardial ischemia,116atherosclerosis,117muscular
dystrophy,118influenza-associated encephalopathy,119and vari-
ous cancers66,99,120are being identified. As metabolomics data
is complementary to transcriptomics and proteomics, the data
from all three approaches can be meshed to provide a more
complete picture of cells and even whole organisms. Ultimately
it is the discovery of novel metabolites10,11as well as correlating
the changes of multiple metabolites with physiological events
that make this area alluring and challenging.
Acknowledgment. This work was supported by NIH
Grant MH062261 and DOE Grant DE-AC02-05CH11231. A.N.
is supported by a postdoctoral fellowship from the Swedish
Research Council (V.R.).
(1) Dear, G. J.; Ayrton, J.; Plumb, R.; Fraser, I. J. The rapid identifica-
tion of drug metabolites using capillary liquid chromatography
coupled to an ion trap mass spectrometer. Rapid Commun. Mass
Spectrom. 1999, 13 (5), 456-463.
(2) Zhang, N. Y.; Fountain, S. T.; Bi, H. G.; Rossi, D. T. Quantification
and rapid metabolite identification in drug discovery using API
time-of-flight LC/MS. Anal. Chem. 2000, 72 (4), 800-806.
(3) Shockcor, J. P.; Holmes, E. Metabonomic applications in toxicity
screening and disease diagnosis. Curr. Top. Med. Chem. 2002, 2
(4) Tiller, P. R.; Romanyshyn, L. A. Liquid chromatography/tandem
mass spectrometric quantification with metabolite screening as
a strategy to enhance the early drug discovery process. Rapid
Commun. Mass Spectrom. 2002, 16 (12), 1225-1231.
(5) Plumb, R. S.; Stumpf, C. L.; Granger, J. H.; Castro-Perez, J.;
Haselden, J. N.; Dear, G. J. Use of liquid chromatography/time-
of-flight mass spectrometry and multivariate statistical analysis
shows promise for the detection of drug metabolites in biological
fluids. Rapid Commun. Mass Spectrom. 2003, 17 (23), 2632-2638.
Table 1. Summary of Some of the Applications of Metabolomics, Together with Relevant References.
application of metabolomicsdescriptionreference
systems biologystudy of metabolite alterations and their
relationship to changes in gene expression,
protein expression, and enzyme activity
to study Huntington’s disease
to study cardiac disease
to study Duchenne muscular dystrophy
study of the function of hypoxia-inducible factor 1ss in tumors
in combination with functional genomics to
identify features of neuroendocrine cancers
associated with a poor outcome
investigate drug efficacy and toxicity,
diagnose or predict disease states, and
classify patient groups based on their specific metabolism
phenotyping of plants and the assessment of the
natural variance in metabolite profiles between
plants to improve compositional quality
88, 89, 15, 21
Inevitable Imprint of MS in Metabolomics
Journal of Proteome Research • Vol. 6, No. 2, 2007
(6) Deng, Y. Z.; Wu, J. T.; Zhang, H. W.; Olah, T. V. Quantitation of
drug metabolites in the absence of pure metabolite standards
by high-performance liquid chromatography coupled with a
chemiluminescence nitrogen detector and mass spectrometer.
Rapid Commun. Mass Spectrom. 2004, 18 (15), 1681-1685.
(7) Maurer, H. H. Advances in analytical toxicology: the current role
of liquid chromatography-mass spectrometry in drug quantifica-
tion in blood and oral fluid. Anal. Bioanal. Chem. 2005, 381 (1),
(8) Staack, R. F.; Varesio, E.; Hopfgartner, G. The combination of
liquid chromatography/tandem mass spectrometry and chip-
based infusion for improved screening and characterization of
drug metabolites. Rapid Commun. Mass Spectrom. 2005, 19 (5),
(9) Liu, D. Q.; Hop, C. E. C. A. Strategies for characterization of drug
metabolites using liquid chromatography-tandem mass spec-
trometry in conjunction with chemical derivatization and on-
line H/D exchange approaches. J. Pharm. Biomed. Anal. 2005,
37 (1), 1-18.
(10) Cravatt, B. F.; Prosperogarcia, O.; Siuzdak, G.; Gilula, N. B.;
Henriksen, S. J.; Boger, D. L.; Lerner, R. A. Chemical Characteriza-
tion of a Family of Brain Lipids That Induce Sleep. Science 1995,
268 (5216), 1506-1509.
(11) Saghatelian, A.; Trauger, S. A.; Want, E. J.; Hawkins, E. G.; Siuzdak,
G.; Cravatt, B. F. Assignment of endogenous substrates to
enzymes by global metabolite profiling. Biochemistry 2004, 43
(12) Pauling, L.; Robinson, A. B.; Teranishi, R.; Cary, P. Quantitative
analysis of urine vapor and breath by gas-liquid partition
chromatography. Proc. Natl. Acad. Sci. U.S.A. 1971, 68 (10), 2374-
(13) Dirren, H.; Robinson, A. B.; Pauling, L. Sex-related patterns in
the profiles of human urinary amino acids. Clin. Chem. 1975, 21
(14) Chace, D. H. Mass spectrometry in the clinical laboratory. Chem.
Rev. 2001, 101 (2), 445-477.
(15) Bino, R. J.; Hall, R. D.; Fiehn, O.; Kopka, J.; Saito, K.; Draper, J.;
Nikolau, B. J.; Mendes, P.; Roessner-Tunali, U.; Beale, M. H.;
Trethewey, R. N.; Lange, B. M.; Wurtele, E. S.; Sumner, L. W.
Potential of metabolomics as a functional genomics tool. Trends
Plant Sci. 2004, 9 (9), 418-425.
(16) Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Antti, H.; Bollard, M.
E.; Keun, H.; Beckonert, O.; Ebbels, T. M.; Reilly, M. D.; Robertson,
D.; Stevens, G. J.; Luke, P.; Breau, A. P.; Cantor, G. H.; Bible, R.
H.; Niederhauser, U.; Senn, H.; Schlotterbeck, G.; Sidelmann, U.
G.; Laursen, S. M.; Tymiak, A.; Car, B. D.; Lehman-McKeeman,
L.; Colet, J. M.; Loukaci, A.; Thomas, C. Contemporary issues in
toxicology - The role of metabonomics in toxicology and its
evaluation by the COMET project. Toxicol. Appl. Pharmacol. 2003,
187 (3), 137-146.
(17) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Metabo-
nomics: a platform for studying drug toxicity and gene function.
Nat. Rev. Drug Discovery 2002, 1 (2), 153-161.
(18) Bligny, R.; Douce, R. NMR and plant metabolism. Curr. Opin.
Plant Biol. 2001, 4 (3), 191-196.
(19) Ratcliffe, R. G.; Shachar-Hill, Y. Probing plant metabolism with
NMR. Annu. Rev. Plant Physiol. Plant Mol. Biol. 2001, 52, 499-
(20) Griffin, J. L.; Williams, H. J.; Sang, E.; Clarke, K.; Rae, C.; Nicholson,
J. K. Metabolic profiling of genetic disorders: A multitissue H-1
nuclear magnetic resonance spectroscopic and pattern recogni-
tion study into dystrophic tissue. Anal. Biochem. 2001, 293 (1),
(21) Holmes, E.; Tsang, T. M.; Tabrizi, S. J. The application of NMR-
based metabonomics in neurological disorders. NeuroRx 2006,
3 (3), 358-372.
(22) Bollard, M. E.; Keun, H. C.; Beckonert, O.; Ebbels, T. M. D.; Antti,
H.; Nicholls, A. W.; Shockcor, J. P.; Cantor, G. H.; Stevens, G.;
Lindon, J. C.; Holmes, E.; Nicholson, J. K. Comparative metabo-
nomics of differential hydrazine toxicity in the rat and mouse.
Toxicol. Appl. Pharmacol. 2005, 204 (2), 135-151.
(23) Lenz, E. M.; Bright, J.; Knight, R.; Westwood, F. R.; Davies, D.;
Major, H.; Wilson, I. D. Metabonomics with H-1-NMR spectros-
copy and liquid chromatography-mass spectrometry applied to
the investigation of metabolic changes caused by gentamicin-
induced nephrotoxicity in the rat. Biomarkers 2005, 10 (2-3),
(24) Craig, A.; Sidaway, J.; Holmes, E.; Orton, T.; Jackson, D.; Row-
linson, R.; Nickson, J.; Tonge, R.; Wilson, I.; Nicholson, J. Systems
toxicology: Integrated genomic, proteomic and metabonomic
analysis of methapyrilene induced hepatotoxicity in the rat. J.
Proteome Res. 2006, 5 (7), 1586-1601.
(25) Fiehn, O.; Kopka, J.; Dormann, P.; Altmann, T.; Trethewey, R. N.;
Willmitzer, L. Metabolite profiling for plant functional genomics.
Nat. Biotechnol. 2000, 18 (11), 1157-1161.
(26) Schauer, N.; Fernie, A. R. Plant metabolomics: towards biological
function and mechanism. Trends Plant Sci. 2006, in press.
(27) Roessner, U.; Wagner, C.; Kopka, J.; Trethewey, R. N.; Willmitzer,
L. Technical advance: simultaneous analysis of metabolites in
potato tuber by gas chromatography-mass spectrometry. Plant
J. 2000, 23 (1), 131-142.
(28) Jonsson, P.; Gullberg, J.; Nordstrom, A.; Kusano, M.; Kowalczyk,
M.; Sjostrom, M.; Moritz, T. A strategy for identifying differences
in large series of metabolomic samples analyzed by GC/MS. Anal.
Chem. 2004, 76 (6), 1738-1745.
(29) Matuszewski, B. K.; Constanzer, M. L.; Chavez-Eng, C. M. Matrix
effect in quantitative LC/MS/MS analyses of biological fluids: a
method for determination of finasteride in human plasma at
picogram per milliliter concentrations. Anal. Chem. 1998, 70 (5),
(30) Gangl, E. T.; Annan, M. M.; Spooner, N.; Vouros, P. Reduction of
signal suppression effects in ESI-MS using a nanosplitting device.
Anal. Chem. 2001, 73 (23), 5635-5644.
(31) Gustavsson, S. A.; Samskog, J.; Markides, K. E.; Langstrom, B.
Studies of signal suppression in liquid chromatography-electro-
spray ionization mass spectrometry using volatile ion-pairing
reagents. J. Chromatogr. A 2001, 937 (1-2), 41-47.
(32) Plumb, R.; Castro-Perez, J.; Granger, J.; Beattie, I.; Joncour, K.;
Wright, A. Ultra-performance liquid chromatography coupled to
quadrupole-orthogonal time-of-flight mass spectrometry. Rapid
Commun. Mass Spectrom. 2004, 18 (19), 2331-2337.
(33) Bremer, H. J. Disturbances of Amino Acid Metabolism; Urban &
Schwarzenberg: Baltimore, MD, 1981.
(34) Hommes, F. A. Techniques in Diagnostic Human Biochemical
Genetics: A Laboratory Manual; Wiley-Liss: New York, 1991.
(35) Niwa, T. Procedures for MS analysis of clinically relevant com-
pounds. Clin. Chim. Acta 1995, 241-242, 75-152.
(36) Idborg, H.; Zamani, L.; Edlund, P. O.; Schuppe-Koistinen, I.;
Jacobsson, S. P. Metabolic fingerprinting of rat urine by LC/MS
Part 1. Analysis by hydrophilic interaction liquid chromatography-
electrospray ionization mass spectrometry. J. Chromatogr., B:
Analyt. Technol. Biomed. Life Sci. 2005, 828 (1-2), 9-13.
(37) Delinsky, A. D.; Delinsky, D. C.; Muralidhara, S.; Fisher, J. W.;
Bruckner, J. V.; Bartlett, M. G. Analysis of dichloroacetic acid in
rat blood and tissues by hydrophilic interaction liquid chroma-
tography with tandem mass spectrometry. Rapid Commun. Mass
Spectrom. 2005, 19 (8), 1075-1083.
(38) Tolstikov, V. V.; Fiehn, O. Analysis of highly polar compounds of
plant origin: combination of hydrophilic interaction chroma-
tography and electrospray ion trap mass spectrometry. Anal.
Biochem. 2002, 301 (2), 298-307.
(39) Pisano, R.; Breda, M.; Grassi, S.; James, C. A. Hydrophilic
interaction liquid chromatography-APCI-mass spectrometry de-
termination of 5-fluorouracil in plasma and tissues. J. Pharm.
Biomed. Anal. 2005, 38 (4), 738-745.
(40) Wilson, I. D.; Nicholson, J. K.; Castro-Perez, J.; Granger, J. H.;
Johnson, K. A.; Smith, B. W.; Plumb, R. S. High resolution “ultra
performance” liquid chromatography coupled to oa-TOF mass
spectrometry as a tool for differential metabolic pathway profiling
in functional genomic studies. J. Proteome Res. 2005, 4 (2), 591-
(41) Swartz, M. E.; Murphy, B. J. Ultr performance liquid chromatog-
raphy: tomorrow’s HPLC technology today. Labplus Int. 2004,
18 (3), 6-9.
(42) Wilson, I. D.; Plumb, R.; Granger, J.; Major, H.; Williams, R.; Lenz,
E. M. HPLC-MS-based methods for the study of metabonomics.
J. Chromatogr., B: Analyt. Technol. Biomed. Life Sci. 2005, 817
(43) Nordstrom, A.; O’Maille, G.; Qin, C.; Siuzdak, G. Nonlinear data
alignment for UPLC-MS and HPLC-MS based metabolomics:
quantitative analysis of endogenous and exogenous metabolites
in human serum. Anal. Chem. 2006, 78 (10), 3289-3295.
(44) Yang, Z. Online hyphenated liquid chromatography-nuclear
magnetic resonance spectroscopy-mass spectrometry for drug
metabolite and nature product analysis. J. Pharm. Biomed. Anal.
2006, 40 (3), 516-527.
Want et al.
466Journal of Proteome Research • Vol. 6, No. 2, 2007
(45) Dachtler, M.; Glaser, T.; Kohler, K.; Albert, K. Combined HPLC-
MS and HPLC-NMR on-line coupling for the separation and
determination of lutein and zeaxanthin stereoisomers in spinach
and in retina. Anal. Chem. 2001, 73 (3), 667-674.
(46) Srebalus, B.; Hilderbrand, A. E.; Valentine, S. J.; Clemmer, D. E.
Resolving isomeric peptide mixtures: a combined HPLC/ion
mobility-TOFMS analysis of a 4000-component combinatorial
library. Anal. Chem. 2002, 74 (1), 26-36.
(47) Boernsen, K. O.; Gatzek, S.; Imbert, G. Controlled protein
precipitation in combination with chip-based nanospray infusion
mass spectrometry. An approach for metabolomics profiling of
plasma. Anal. Chem. 2005, 77 (22), 7255-7264.
(48) Wang, G.; Hsieh, Y.; Cui, X.; Cheng, K. C.; Korfmacher, W. A. Ultra-
performance liquid chromatography/tandem mass spectrometric
determination of testosterone and its metabolites in in vitro
samples. Rapid Commun. Mass Spectrom. 2006, 20 (14), 2215-
(49) Chace, D. H.; Millington, D. S.; Terada, N.; Kahler, S. G.; Roe, C.
R.; Hofman, L. F. Rapid diagnosis of phenylketonuria by quan-
titative analysis for phenylalanine and tyrosine in neonatal blood
spots by tandem mass spectrometry. Clin. Chem. 1993, 39 (1),
(50) Levy, H. L. Newborn screening by tandem mass spectrometry:
a new era. Clin. Chem. 1998, 44 (12), 2401-2402.
(51) Chelius, D.; Zhang, T.; Wang, G.; Shen, R. F. Global protein
identification and quantification technology using two-dimen-
sional liquid chromatography nanospray mass spectrometry.
Anal. Chem. 2003, 75 (23), 6658-6665.
(52) Nagele, E.; Vollmer, M.; Horth, P. Two-dimensional nano-liquid
chromatography-mass spectrometry system for applications in
proteomics. J. Chromatogr. A 2003, 1009 (1-2), 197-205.
(53) Chatman, K.; Hollenbeck, T.; Hagey, L.; Vallee, M.; Purdy, R.;
Weiss, F.; Siuzdak, G. Nanoelectrospray mass spectrometry and
precursor ion monitoring for quantitative steroid analysis and
attomole sensitivity. Anal. Chem. 1999, 71 (13), 2358-2363.
(54) Griffiths, W. J.; Liu, S.; Yang, Y.; Purdy, R. H.; Sjovall, J. Nano-
electrospray tandem mass spectrometry for the analysis of
neurosteroid sulphates. Rapid Commun. Mass Spectrom. 1999,
13 (15), 1595-1610.
(55) Abian, J.; Oosterkamp, A. J.; Gelpı ´, E. Comparison of conventional,
narrow-bore and capillary liquid chromatography/mass spec-
trometry for electrospray ionization mass spectrometry: practical
considerations. J. Mass Spectrom. 1999, 34 (4), 244-254.
(56) Keski-Hynnila, H.; Kurkela, M.; Elovaara, E.; Antonio, L.; Magdal-
ou, J.; Luukkanen, L.; Taskinen, J.; Kostiainen, R. Comparison of
electrospray, atmospheric pressure chemical ionization, and
atmospheric pressure photoionization in the identification of
apomorphine, dobutamine, and entacapone phase II metabolites
in biological samples. Anal. Chem. 2002, 74 (14), 3449-3457.
(57) Raffaelli, A.; Saba, A. Atmospheric pressure photoionization mass
spectrometry. Mass Spectrom. Rev. 2003, 22 (5), 318-331.
(58) Kratzsch, C.; Tenberken, O.; Peters, F. T.; Weber, A. A.; Kraemer,
T.; Maurer, H. H. Screening, library-assisted identification and
validated quantification of 23 benzodiazepines, flumazenil, zale-
plone, zolpidem and zopiclone in plasma by liquid chromatog-
raphy/mass spectrometry with atmospheric pressure chemical
ionization. J. Mass Spectrom. 2004, 39 (8), 856-872.
(59) Byrdwell, W. C. Atmospheric pressure chemical ionization mass
spectrometry for analysis of lipids. Lipids 2001, 36 (4), 327-346.
(60) De Marchi, N.; De Petrocellis, L.; Orlando, P.; Daniele, F.; Fezza,
F.; Di Marzo, V. Endocannabinoid signalling in the blood of
patients with schizophrenia. Lipids Health Dis. 2003, 2, 5.
(61) Dally, J. E.; Gorniak, J.; Bowie, R.; Bentzley, C. M. Quantitation
of underivatized free amino acids in mammalian cell culture
media using matrix assisted laser desorption ionization time-of-
flight mass spectrometry. Anal. Chem. 2003, 75 (19), 5046-5053.
(62) Edwards, J. L.; Kennedy, R. T. Metabolomic analysis of eukaryotic
tissue and prokaryotes using negative mode MALDI time-of-flight
mass spectrometry. Anal. Chem. 2005, 77 (7), 2201-2209.
(63) Vaidyanathan, S.; Gaskell, S.; Goodacre, R. Matrix-suppressed
laser desorption/ionisation mass spectrometry and its suitability
for metabolome analyses. Rapid Commun. Mass Spectrom. 2006,
20 (8), 1192-1198.
(64) Shen, Z.; Thomas, J. J.; Averbuj, C.; Broo, K. M.; Engelhard, M.;
Crowell, J. E.; Finn, M. G.; Siuzdak, G. Porous silicon as a versatile
platform for laser desorption/ionization mass spectrometry. Anal.
Chem. 2001, 73 (3), 612-619.
(65) Vaidyanathan, S.; Jones, D.; Broadhurst, D. I.; Ellis, J.; Jenkins,
T.; Dunn, W. B.; Hayes, A.; Burton, N.; Oliver, S. G.; Kell, D. B.;
Goodacre, R. A laser desorption ionisation mass spectrometry
approach for high throughput metabolomics. Metabolomics 2005,
1 (3), 243-250.
(66) Chen, H.; Pan, Z.; Talaty, N.; Raftery, D.; Cooks, R. G. Combining
desorption electrospray ionization mass spectrometry and nuclear
magnetic resonance for differential metabolomics without sample
preparation. Rapid Commun. Mass Spectrom. 2006, 20 (10),
(67) Corcoran, O.; Spraul, M. LC-NMR-MS in drug discovery. Drug
Discovery Today 2003, 8 (14), 624-631.
(68) Crockford, D. J.; Holmes, E.; Lindon, J. C.; Plumb, R. S.; Zirah, S.;
Bruce, S. J.; Rainville, P.; Stumpf, C. L.; Nicholson, J. K. Statistical
heterospectroscopy, an approach to the integrated analysis of
NMR and UPLC-MS data sets: application in metabonomic
toxicology studies. Anal. Chem. 2006, 78 (2), 363-371.
(69) Patrie, S. M.; Charlebois, J. P.; Whipple, D.; Kelleher, N. L.;
Hendrickson, C. L.; Quinn, J. P.; Marshall, A. G.; Mukhopadhyay,
B. Construction of a hybrid quadrupole/Fourier transform ion
cyclotron resonance mass spectrometer for versatile MS/MS
above 10 kDa. J. Am. Soc. Mass Spectrom. 2004, 15 (7), 1099-
(70) Katajamaa, M.; Oresic, M. Processing methods for differential
analysis of LC/MS profile data. BMC Bioinformatics 2005, 6, 179.
(71) Katajamaa, M.; Miettinen, J.; Oresic, M. MZmine: toolbox for
processing and visualization of mass spectrometry based mo-
lecular profile data. Bioinformatics 2006, 22 (5), 634-636.
(72) Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G.
XCMS: processing mass spectrometry data for metabolite profil-
ing using nonlinear peak alignment, matching, and identification.
Anal. Chem. 2006, 78 (3), 779-787.
(73) Broeckling, C. D.; Reddy, I. R.; Duran, A. L.; Zhao, X.; Sumner, L.
W. MET-IDEA: data extraction tool for mass spectrometry-based
metabolomics. Anal. Chem. 2006, 78 (13), 4334-4341.
(74) Hastings, C. A.; Norton, S. M.; Roy, S. New algorithms for
processing and peak detection in liquid chromatography/mass
spectrometry data. Rapid Commun. Mass Spectrom. 2002, 16 (5),
(75) Floter, A.; Nicolas, J.; Schaub, T.; Selbig, J. Threshold extraction
in metabolite concentration data. Bioinformatics 2004, 20 (10),
(76) Norton, S. M.; Huyn, P.; Hastings, C. A.; Heller, J. C. Data mining
of spectroscopic data for biomarker discovery. Curr. Opin. Drug
Discov. Devel. 2001, 4 (3), 325-331.
(77) Idborg, H.; Edlund, P. O.; Jacobsson, S. P. Multivariate approaches
for efficient detection of potential metabolites from liquid chro-
matography/mass spectrometry data. Rapid Commun. Mass
Spectrom. 2004, 18 (9), 944-954.
(78) Lutz, U.; Lutz, R. W.; Lutz, W. K. Metabolic profiling of glucu-
ronides in human urine by LC-MS/MS and partial least-squares
discriminant analysis for classification and prediction of gender.
Anal. Chem. 2006, 78 (13), 4564-4571.
(79) Jolliffe, I. T. Principal Component Analysis, 2nd ed.; Springer: New
(80) Taylor, J.; King, R. D.; Altmann, T.; Fiehn, O. Application of
metabolomics to plant genotype discrimination using statistics
and machine learning. Bioinformatics 2002, 18 Suppl 2, S241-
(81) Choi, H. K.; Choi, Y. H.; Verberne, M.; Lefeber, A. W.; Erkelens,
C.; Verpoorte, R. Metabolic fingerprinting of wild type and
transgenic tobacco plants by 1H NMR and multivariate analysis
technique. Phytochemistry 2004, 65 (7), 857-864.
(82) Yeung, K. Y.; Ruzzo, W. L. Principal component analysis for
clustering gene expression data. Bioinformatics 2001, 17 (9), 763-
(83) Martens, H.; Naes, T. Multivariate Calibration; John Wiley & Sons
Inc.: New York, 1989.
(84) Malinowski, E. R. Factor Analysis in Chemistry; Wiley-Inter-
science: New York, 1991.
(85) Dwek, M. V.; Alaiya, A. A. Proteome analysis enables separate
clustering of normal breast, benign breast and breast cancer
tissues. Br. J. Cancer 2003, 89 (2), 305-307.
(86) Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-
PLS). J. Chemom. 2002, 16 (3), 119-128.
(87) Trygg, J. O2-PLS for qualitative and quantitative analysis in
multivariate calibration. J. Chemom. 2002, 16 (6), 283-293.
Inevitable Imprint of MS in Metabolomics
Journal of Proteome Research • Vol. 6, No. 2, 2007
(88) Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Download full-text
Hudson, J.; Blancher, C.; Gauguier, D.; Lindon, J. C.; Holmes, E.;
Nicholson, J. Statistical total correlation spectroscopy: an ex-
ploratory approach for latent biomarker identification from
metabolic 1H NMR data sets. Anal. Chem. 2005, 77 (5), 1282-
(89) Gaweda, A. E.; Jacobs, A. A.; Brier, M. E.; Zurada, J. M. Pharma-
codynamic population analysis in chronic renal failure using
artificial neural networks-a comparative study. Neural Netw.
2003, 16 (5-6), 841-845.
(90) Goodacre, R. Making sense of the metabolome using evolutionary
computation: seeing the wood with the trees. J. Exp. Bot. 2005,
56 (410), 245-254.
(91) Kopka, J.; Schauer, N.; Krueger, S.; Birkemeyer, C.; Usadel, B.;
Bergmuller, E.; Dormann, P.; Weckwerth, W.; Gibon, Y.; Stitt, M.;
Willmitzer, L.; Fernie, A. R.; Steinhauser, D. GMD@CSB.DB: the
Golm Metabolome Database. Bioinformatics 2005, 21 (8), 1635-
(92) Jenkins, H.; Hardy, N.; Beckmann, M.; Draper, J.; Smith, A. R.;
Taylor, J.; Fiehn, O.; Goodacre, R.; Bino, R. J.; Hall, R.; Kopka, J.;
Lane, G. A.; Lange, B. M.; Liu, J. R.; Mendes, P.; Nikolau, B. J.;
Oliver, S. G.; Paton, N. W.; Rhee, S.; Roessner-Tunali, U.; Saito,
K.; Smedsgaard, J.; Sumner, L. W.; Wang, T.; Walsh, S.; Wurtele,
E. S.; Kell, D. B. A proposed framework for the description of plant
metabolomics experiments and their results. Nat. Biotechnol.
2004, 22 (12), 1601-1606.
(93) Jenkins, H.; Johnson, H.; Kular, B.; Wang, T.; Hardy, N. Toward
supportive data collection tools for plant metabolomics. Plant
Physiol. 2005, 138 (1), 67-77.
(94) Wolff, J. C.; Eckers, C.; Sage, A. B.; Giles, K.; Bateman, R. Accurate
mass liquid chromatography/mass spectrometry on quadrupole
orthogonal acceleration time-of-flight mass analyzers using
switching between separate sample and reference sprays. 2.
Applications using the dual-electrospray ion source. Anal. Chem.
2001, 73 (11), 2605-2612.
(95) Plumb, R. S.; Johnson, K. A.; Rainville, P.; Smith, B. W.; Wilson, I.
D.; Castro-Perez, J. M.; Nicholson, J. K. UPLC/MS(E); a new
approach for generating molecular fragment information for
biomarker structure elucidation. Rapid Commun. Mass Spectrom.
2006, 20 (13), 1989-1994.
(96) Schlotterbeck, G.; Ross, A.; Hochstrasser, R.; Senn, H.; Kuhn, T.;
Marek, D.; Schett, O. High-resolution capillary tube NMR. A
miniaturized 5-microL high-sensitivity TXI probe for mass-limited
samples, off-line LC NMR, and HT NMR. Anal. Chem. 2002, 74
(97) Olson, D. L.; Norcross, J. A.; O’Neil-Johnson, M.; Molitor, P. F.;
Detlefsen, D. J.; Wilson, A. G.; Peck, T. L. Microflow NMR:
concepts and capabilities. Anal. Chem. 2004, 76 (10), 2966-2974.
(98) Hirai, M. Y.; Yano, M.; Goodenowe, D. B.; Kanaya, S.; Kimura, T.;
Awazuhara, M.; Arita, M.; Fujiwara, T.; Saito, K. Integration of
transcriptomics and metabolomics for understanding of global
responses to nutritional stresses in Arabidopsis thaliana. Proc.
Natl. Acad. Sci. U.S.A. 2004, 101 (27), 10205-10210.
(99) Ippolito, J. E.; Xu, J.; Jain, S.; Moulder, K.; Mennerick, S.; Crowley,
J. R.; Townsend, R. R.; Gordon, J. I. An integrated functional
genomics and metabolomics approach for defining poor prog-
nosis in human neuroendocrine cancers. Proc. Natl. Acad. Sci.
U.S.A. 2005, 102 (28), 9901-9906.
(100) Clish, C. B.; Davidov, E.; Oresic, M.; Plasterer, T. N.; Lavine, G.;
Londo, T.; Meys, M.; Snell, P.; Stochaj, W.; Adourian, A.; Zhang,
X.; Morel, N.; Neumann, E.; Verheij, E.; Vogels, J. T.; Havekes, L.
M.; Afeyan, N.; Regnier, F.; van der Greef, J.; Naylor, S. Integrative
biological analysis of the APOE*3-leiden transgenic mouse. Omics
2004, 8 (1), 3-13.
(101) Raamsdonk, L. M.; Teusink, B.; Broadhurst, D.; Zhang, N.; Hayes,
A.; Walsh, M. C.; Berden, J. A.; Brindle, K. M.; Kell, D. B.; Rowland,
J. J.; Westerhoff, H. V.; van Dam, K.; Oliver, S. G. A functional
genomics strategy that uses metabolome data to reveal the
phenotype of silent mutations. Nat. Biotechnol. 2001, 19 (1), 45-
(102) Allen, J.; Davey, H. M.; Broadhurst, D.; Heald, J. K.; Rowland, J.
J.; Oliver, S. G.; Kell, D. B. High-throughput classification of yeast
mutants for functional genomics using metabolic footprinting.
Nat. Biotechnol. 2003, 21 (6), 692-696.
(103) Jones, G. L.; Sang, E.; Goddard, C.; Mortishire-Smith, R. J.;
Sweatman, B. C.; Haselden, J. N.; Davies, K.; Grace, A. A.; Clarke,
K.; Griffin, J. L. A functional analysis of mouse models of cardiac
disease through metabolic profiling. J. Biol. Chem. 2005, 280 (9),
(104) Griffin, J. L.; Cemal, C. K.; Pook, M. A. Defining a metabolic
phenotype in the brain of a transgenic mouse model of spino-
cerebellar ataxia 3. Physiol. Genomics 2004, 16 (3), 334-340.
(105) Griffin, J. L.; Shockcor, J. P. Metabolic profiles of cancer cells.
Nat. Rev. Cancer 2004, 4 (7), 551-561.
(106) Plumb, R. S.; Stumpf, C. L.; Gorenstein, M. V.; Castro-Perez, J.
M.; Dear, G. J.; Anthony, M.; Sweatman, B. C.; Connor, S. C.;
Haselden, J. N. Metabonomics: the use of electrospray mass
spectrometry coupled to reversed-phase liquid chromatography
shows potential for the screening of rat urine in drug develop-
ment. Rapid Commun. Mass Spectrom. 2002, 16 (20), 1991-1996.
(107) Waters, N. J.; Waterfield, C. J.; Farrant, R. D.; Holmes, E.;
Nicholson, J. K. Integrated metabonomic analysis of bromoben-
zene-induced hepatotoxicity: novel induction of 5-oxoprolinosis.
J. Proteome Res. 2006, 5 (6), 1448-1459.
(108) Schauer, N.; Zamir, D.; Fernie, A. R. Metabolic profiling of leaves
and fruit of wild species tomato: a survey of the Solanum
lycopersicum complex. J. Exp. Bot. 2005, 56 (410), 297-307.
(109) Moco, S.; Bino, R. J.; Vorst, O.; Verhoeven, H. A.; de Groot, J.;
van Beek, T. A.; Vervoort, J.; de Vos, C. H. A liquid chromatog-
raphy-mass spectrometry-based metabolome database for to-
mato. Plant Physiol. 2006, 141 (4), 1205-1218.
(110) Hamzehzarghani, H.; Kushalappa, A. C.; Dion, Y.; Rioux, S.;
Comeau, A.; Yaylayan, V.; Marshall, W. D.; Mather, D. E. Metabolic
profiling and factor analysis to discriminate quantitative resis-
tance in wheat cultivars against fusarium head blight. Physiol.
Mol. Plant Pathol. 2005, 66 (4), 119-133.
(111) Sato, S.; Soga, T.; Nishioka, T.; Tomita, M. Simultaneous deter-
mination of the main metabolites in rice leaves using capillary
electrophoresis mass spectrometry and capillary electrophoresis
diode array detection. Plant J. 2004, 40 (1), 151-163.
(112) Jonsson, P.; Johansson, E. S.; Wuolikainen, A.; Lindberg, J.;
Schuppe-Koistinen, I.; Kusano, M.; Sjostrom, M.; Trygg, J.; Moritz,
T.; Antti, H. Predictive metabolite profiling applying hierarchical
multivariate curve resolution to GC-MS data-a potential tool for
multi-parametric diagnosis. J. Proteome Res. 2006, 5 (6), 1407-
(113) Tagashira, N.; Plader, W.; Filipecki, M.; Yin, Z.; Wisniewska, A.;
Gaj, P.; Szwacka, M.; Fiehn, O.; Hoshi, Y.; Kondo, K.; Malepszy,
S. The metabolic profiles of transgenic cucumber lines vary with
different chromosomal locations of the transgene. Cell. Mol. Biol.
Lett. 2005, 10 (4), 697-710.
(114) Aharoni, A.; Ric de Vos, C. H.; Verhoeven, H. A.; Maliepaard, C.
A.; Kruppa, G.; Bino, R.; Goodenowe, D. B. Nontargeted metabo-
lome analysis by use of Fourier Transform Ion Cyclotron Mass
Spectrometry. Omics 2002, 6 (3), 217-234.
(115) Garratt, L. C.; Linforth, R.; Taylor, A. J.; Lowe, K. C.; Power, J. B.;
Davey, M. R. Metabolite fingerprinting in transgenic lettuce. Plant
Biotechnol. J. 2005, 3 (2), 165-174.
(116) Sabatine, M. S.; Liu, E.; Morrow, D. A.; Heller, E.; McCarroll, R.;
Wiegand, R.; Berriz, G. F.; Roth, F. P.; Gerszten, R. E. Metabolomic
identification of novel biomarkers of myocardial ischemia.
Circulation 2005, 112 (25), 3868-3875.
(117) Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.;
Bethell, H. W.; Clarke, S.; Schofield, P. M.; McKilligin, E.;
Mosedale, D. E.; Grainger, D. J. Rapid and noninvasive diagnosis
of the presence and severity of coronary heart disease using 1H-
NMR-based metabonomics. Nat. Med. 2002, 8 (12), 1439-1444.
(118) Griffin, J. L.; Sang, E.; Evens, T.; Davies, K.; Clarke, K. Metabolic
profiles of dystrophin and utrophin expression in mouse models
of Duchenne muscular dystrophy. FEBS Lett 2002, 530 (1-3),
(119) Kawashima, H.; Oguchi, M.; Ioi, H.; Amaha, M.; Yamanaka, G.;
Kashiwagi, Y.; Takekuma, K.; Yamazaki, Y.; Hoshika, A.; Watanabe,
Y. Primary biomarkers in cerebral spinal fluid obtained from
patients with influenza-associated encephalopathy analyzed by
metabolomics. Int J. Neurosci. 2006, 116 (8), 927-936.
(120) Yang, J.; Xu, G.; Zheng, Y.; Kong, H.; Pang, T.; Lv, S.; Yang, Q.
Diagnosis of liver cancer using HPLC-based metabonomics
avoiding false-positive result from hepatitis and hepatocirrhosis
diseases. J. Chromatogr., B: Analyt. Technol. Biomed. Life Sci.
2004, 813 (1-2), 59-65.
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