Available online at www.sciencedirect.com
Separation and mass spectrometry in microbial metabolomics
David E Garcia1,2
, Edward E Baidoo3,4
, Peter I Benke1,3,4
,
Francesco Pingitore3,4
, Yinjie J Tang1,3,4,5
, Sandra Villa1,2,4
and
Jay D Keasling1,3,4,6,7
Measurements of low molecular weight metabolites have been
increasingly incorporated in the characterization of cellular
physiology, qualitative studies in functional genomics, and
stress response determination. The application of cutting edge
analytical technologies to the measurement of metabolites and
the changes in metabolite concentrations under defined
conditions have helped illuminate the effects of perturbations in
pathways of interest, such as the tricarboxylic acid cycle, as
well as unbiased characterizations of microbial stress
responses as a whole. Owing to the complexity of microbial
metabolite extracts and the large number of metabolites
therein, advanced and high-throughput separation techniques
in gas chromatography, liquid chromatography, and capillary
electrophoresis have been coupled to mass spectrometry usually
high-resolution mass spectrometry, but not exclusively
­ to make these measurements.
Addresses
1
Joint BioEnergy Institute, Emeryville, CA 94608, USA
2
Department of Chemistry, University of California, Berkeley, CA 94720,
USA
3
Physical Biosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA
4
Virtual Institute for Microbial Stress and Survival, Lawrence Berkeley
National Laboratory, Berkeley, CA 94720, USA
5
Energy Environmental & Chemical Engineering Department,
Washington University, St. Louis, MO 63130, USA
6
Department of Chemical Engineering, University of California, Berkeley,
CA 94720, USA
7
Department of Bioengineering, University of California, Berkeley, CA
94720, USA
Corresponding authors: Keasling,
Jay D (keasling@berkeley.edu)
Current Opinion in Microbiology 2008, 11:233­239
This review comes from a themed issue on
Ecology and industrial microbiology
Edited by Juan Ramos and Martin Keller
Available online 4th June 2008
1369-5274/$ ­ see front matter
# 2008 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.mib.2008.04.002
Introduction
Genomics, proteomics, and transcriptomics have all made
considerable contributions to the field of functional genomics.
However, understanding of the genome, transcriptome,
and proteome is not enough to fully characterize
cellular function. For example, the proteome cannot be
completely predicted from the transcriptome owing to
differences in regulatory mechanisms at the protein level
(e.g. post-translational modifications). Furthermore, an
approach based solely on transcriptomics will also be
inadequate, since there are many genes that are not under
transcriptional control.
The metabolome, however, is further from gene expression
and, thus, more closely reflects the activities of a cell at
a functional level. Furthermore, many metabolites are not
exclusively involved in a single metabolic pathway, so it is
only when the metabolome is characterized as a whole ­ or
all associated metabolic pathways ­ that the pathway(s) of
interest can be identified with a high degree of certainty.
The time it takes for the metabolome to reflect a change
may vary depending on the perturbation in question, thus
the timing of sample acquisition and the methods used to
identify and quantitate the metabolome are crucial.
A universal quenching and extraction protocol for
microbes does not yet exist; however, a detailed review
of such procedures has been written recently [1]. Previously
the extraction of phosphorylated compounds has
proven difficult, which is problematic because of the
importance of phosphorylated compounds in metabolism
(e.g. ATP). This difficulty is many-faceted owing to the
potential for cleavage of phosphate groups in a highly
aqueous environment [2], as a result of the interaction
with phospholipids in the cell [3
], and because triphosphates
are readily hydrolyzed (enzymatically or nonenzymatically)
even after exposure of the cells to organic
solvent [4]. Despite this, extraction procedures have been
developed to improve the yield of phosphorylated compounds
and continue to be improved [2­4].
Owing to the wide range of physiochemical properties
and concentration ranges of metabolites there is no one
method that can separate, detect, and identify all known
metabolites. Mass spectrometry (MS) is a popular tool
that, given the complexity of microbial metabolic
extracts, requires a chromatographic separation to reduce
isobaric interferences (i.e. compounds of the same mass
being indistinguishable in the mass spectrometer) and ion
suppression (i.e. more easily ionizable species masking
the presence of less ionizable species). In this review we
discuss several chromatographic techniques that compliment
each other because they are able to resolve compounds
of differing physiochemical properties.
www.sciencedirect.com Current Opinion in Microbiology 2008, 11:233­239
Metabolomics by GC­MS
Gas chromatography coupled to mass spectrometry (GCMS)
is a very popular tool within the field of metabolomics
[5­7,8
]. The majority of the GC­MS metabolite
profiling applications are focused on plant metabolomics
rather than microbiological or biomedical research. Since
all plant metabolites originate from the plant machinery
itself [9], interpretation of metabolite profiles in plants are
more straightforward than for microorganisms and mammalian
cells. However, rapid development in the field of
metabolic engineering and recent advances in analytical
techniques have resulted a growing focus in microbial
metabolomics [1,10
].
While MS provides individual mass spectra that can
differentiate between co-eluting metabolites that are
chemically diverse, the main advantages of GC over other
separation techniques are a high separation efficiency (as
highlighted by its ability to distinguish isomeric compounds),
ease of use, robustness, and low cost [6,7,8
].
GC­MS is a technique that usually yields extensive and
highly reproducible fragmentation because of the standardized
use of electron ionization (EI), which has
enabled metabolites to be identified by matching their
relative retention times, retention or Kovats indices, and
mass spectral fragmentation patterns, to known and predicted
information available from extensive databases
(e.g. the NIST and Wiley database) [5]. It is noteworthy
that the large amount of chromatographic and spectral
data generated in metabolomics by GC­MS requires
special de-convolution and identification software platforms
(e.g. AMDIS, AnalyzerPro and LECO ChromaTOF)
[11,12].
A major drawback of GC­MS is that analytes are required
to be volatile. Since a large number of metabolites are
non-volatile, time-consuming derivatization steps are
required [11,13]. Furthermore, thermally labile compounds,
such as phosphorylated metabolites, can easily
degrade when exposed to the high temperatures in the
GC oven. Additionally, metabolites can have varying
affinities for derivatizing agents, which could lead to
inaccurate quantification unless derivatized chemical
standards and data correction strategies are used to normalize
for such a bias [14]. One must also be aware of the
formation of byproducts from the derivatization procedure
[15], as well as the possible conversion of analytes
(e.g. arginine to ornithine, cyclization of open-chain
sugars, decarboxylation of alpha-ketoacids, etc.) and/or
degradation of the final product(s) (e.g. the hydrolysis of
trimethylsilyl derivatives), which could lead to misinterpretation
of the data generated. A two-step derivatization
method (e.g. methoximation followed by silylation), can
provide a wider coverage of analyzable metabolites [16].
Although GC­MS is a well-established analytical technique
and most of its metabolomics publications focus on
applications rather than method development and/or
optimization [17,18,19
], the extremely complex nature
of biological samples required enhanced separation performance
and improved instrumentation, which led to the
development of multidimensional separation techniques,
such as GC­GC time of flight (TOF)­MS [18,20]. This
novel approach presents a dramatically improved peak
separation capacity and an increase in sensitivity, while
TOF­MS provides a very fast scanning rate and
additional sensitivity for improved detection. Furthermore,
the application of two different GC columns (i.e.
polar/non-polar) can provide greater metabolite detection
coverage for metabolomics analysis, via improved selectivity
[21­23]. Despite this recent advance, GC­GCTOF­MS
is not yet routinely used in metabolomics
partly because of practical problems (e.g. optimization
of fraction modulation) and partly because of high instrument
cost.
Metabolomics by LC­MS
The combination of GC­MS with liquid chromatography
coupled to mass spectrometry (LC­MS) can provide
greater coverage of the metabolome [24]. Smilde et al.
were able to detect a very high number (93%) of the
commercially available metabolites of the in silico metabolome
of Bacillus subtilis and Escherichia coli. Similar
coverage (95­97%) was achieved for the same microorganisms
and Saccharomyces cervisiae with the application of
six different analytical methods [10
]. It must be noted,
however, that only around half of the estimated metabolites
were commercially available as reference compounds,
which confers a higher degree of confidence in
the identification of metabolites.
As a standalone technique, LC­MS is still prominent
among the technologies currently available to perform
metabolic profiling not only because there is no need to
derivatize analytes, but also because LC has demonstrated
its ability to resolve large numbers of metabolites
[25,26]. LC separations that are compatible with electrospray
ionization (ESI) are desirable because of the polar
and ionizable nature of most metabolites [10
]. Even
though ESI is the most commonly used ionization technique
with LC, techniques such as APCI [27] and APPI
[28] are becoming increasingly popular due to their applicability
to less polar metabolites. Additionally, LC is an
attractive alternative to GC because it is a particularly
versatile separation technique: varying the stationary and
mobile phases through approaches like ion pairing (IP)
LC­MS [29], hydrophilic interaction liquid chromatography
(HILIC) MS [24,30], and reverse phase LC­MS
[31­33] allows for the simultaneous quantitative analysis
of different classes of important metabolites.
The HILIC­MS obtained metabolic fingerprint of
starvation stress response by E. coli and Saccharomyces
cervisiae [34
] is an example of the benefit of LC­MS
234 Ecology and industrial microbiology
Current Opinion in Microbiology 2008, 11:233­239 www.sciencedirect.com
because the metabolites did not require derivatization.
Upon carbon or nitrogen starvation, the changes in concentration
of 68 cellular metabolites were measured.
Quantitative changes in metabolic profiling have also
been measured with an isotopic dilution strategy [35],
where fully isotopically labeled metabolites are used as
internal standards. LC­ESI­MS­MS analysis of these
pooled samples was utilized to measure glycolytic and
tricarboxylic acid cycle intermediates in Saccharomyces
cervisiae following a glucose pulse. In addition to quantification,
isotopic labeling has also been used to assign
molecular formulae to single compounds [36
].
Metabolomics by CE­MS
Capillary electrophoresis (CE) offers several potential
advantages over GC and LC for the analysis of complex
mixtures of metabolites, including even higher separation
efficiencies, extremely small sample injection volumes
(nL range), rapid method development, and low reagent
costs. CE­MS is commonly utilized with electrospray
ionization (ESI) and, like LC­MS, can also be used for
structural elucidation via tandem mass spectrometry [37].
CE may be used to perform highly efficient separations of
a wide range of sample types, and can even be used to
separate intact microorganisms as well [38]. The simplest
and most commonly applied mode of CE is capillary zone
electrophoresis (CZE), which has been used to separate
charged analytes from the lysate of microorganisms
[3
,39,40]. The incorporation of additives, such as surfactants,
into the separation buffer can be used to separate
neutral and charged compounds via Micellar Electrokinetic
Chromatography (MEKC) [41]. Both APCI [27] and
APPI [28] have been coupled with CE to measure less
polar compounds, and the latter has also been coupled to
MEKC separations, which are not normally compatible
with ESI [28]. Chiral compounds can also be separated by
adding cyclodextrins to the separation buffer [42]. Both
MEKC and the chiral method have been used as quality
control assays to determine the level of impurities that
have arisen from the production of amino acids by fermentation
[43].
Recently, a comprehensive and quantitative survey of
anionic and cationic metabolites from E. coli was conducted
via CE­MS [3
]. From the results obtained, 375
charged hydrophilic intermediates in primary metabolism
were identified, of which 198 were quantified, further
cementing the notion that CE­MS can make a major
contribution to the field of microbial metabolomics. CEMS
has also been used in functional genomics studies on
various microorganisms, including response to antibiotics
[44], salt [45], and cadmium stress [40].
The main limitation of CE is the lack of sensitivity due to
small sample injection volumes, especially when coupled
to MS. The sample can be further diluted by a sheath
liquid. However, the combination of a reduced sheath
flow rate and the employment of online sample preconcentration
procedures, such as pH-mediated stacking and
transient isotachophoresis [46], can achieve sensitivities
similar to that of current LC­MS protocols. Furthermore,
the dilution effect brought about by the sheath flow can
be avoided entirely by using a sheathless interface [47].
Current CE­MS protocols that utilize an acidic electrolyte,
such as formic acid, appear to be among the best
approaches for measuring cationic species, as they can
yield highly efficient separations, with extremely good
sensitivity [3
,39,40]. The measurement of anions
appears to be more difficult, primarily because a reversal
of the electroosmotic flow (EOF) is often required to
achieve highly efficient separations [3
]. Furthermore,
this separation strategy requires the use of rather expensive
coated fused silica capillaries. In 2006, however,
Harada et al. published a method that utilized conventional
CE separation with an EOF and a supplementary
pressure gradient to achieve highly efficient separations
of coenzyme-As, organic acids, nucleotides and sugar
phosphates [48
]. A similar approach was also used to
measure sugar phosphates, including regioisomers such as
hexose-6-phosphate and hexose-1-phosphate, from the
lysate of E. coli [37].
The characterization of unknown metabolites is one of
the biggest challenges that face metabolomics today. The
current strategy used by most laboratories is to utilize the
relative migration times of chemical standards, together
with mass spectral information, for the identification of
unknown metabolites. Such a strategy requires a large
chemical standards library, which can be extremely
costly. To circumvent this problem, a computer-simulated
algorithm was developed to predict the CE
migration profile of charged analytes [49
]. Physicochemical
properties such as mobility and acid dissociation
constants were used for the simulation. There was excellent
agreement between experimental and simulated
relative migration times, with an average error of <2%.
Thus, prediction simulations would ensure the characterization
of metabolites that are not readily synthesized
or commercially available [49
].
Data mining
Metabolomics data sets are complex owing to the large
amounts of data that are generated in three dimensions
(i.e. the mass spectra of individual components, their
retention times and their intensities). The main obstacle
is the extraction of all the useful information that is
present in the raw data. While peak de-convolution
algorithms have been developed, automated peak picking
and integration still remains an important challenge. This
is complicated by the wide range of peak intensities, peak
asymmetry, and spectral interferences (which is connected
to the resolving power of a mass analyzer and
Separation and mass spectrometry in microbial metabolomics Garcia et al. 235
www.sciencedirect.com Current Opinion in Microbiology 2008, 11:233­239
the abundance of an analyte), all of which can lead to
incorrect assignments of differences [50]. Current software
packages that can perform data extraction from
normalized mass spectra and total ion chromatograms
include Agilent MassHunter MFE, Bruker Metabolite
Detect, ACD/Labs IntelliXtract/CODA and NIST
AMDIS.
Extensive libraries are required to identify all the mined
data. There are well-established spectral libraries for GCMS
that contain up to 606,000 spectra (Palisade 600k
Ed.). However, in the case of LC­MS and CE­MS, it is
more difficult to obtain a standardized spectral library
owing to a high instrument dependent variability and
variable source conditions. Thus, laboratories are normally
responsible for building up their own spectral
libraries.
The final step to complete a metabolomics study is the
application of relevant statistical tests in order to determine
metabolic differences or target components that are
characteristic of a sample and/or conditions [50]. The use
of post-data collection statistical methods can greatly
minimize variations in analytical observations such as
retention time, mass accuracy, and signal intensity [51].
The most rigorous statistical method is multivariate data
analysis [52].
Application of metabolomics tools for
isotopomer-based flux analysis
Metabolic flux analysis via 13
C labeling is a high-throughput
technology to quantitatively track metabolic pathways
and determine overall enzyme function in cells.
Metabolic fluxes are the functional output of the combined
transcriptome, proteome, and metabolome. These
data bridge contemporary functional analyses of the cellular
phenotype [53,54]. The essence of 13
C-based metabolic
flux analysis is the precise measurement of the
labeling patterns of targeted metabolites from tracer
experiments to determine the complex metabolic network
[53,55]. Traditionally, isotopomer analysis of metabolites
is mainly carried out by the measurement of
proteinogenic amino acids via nuclear magnetic resonance
(NMR) spectroscopy [56­58] or GC­MS
[13,59,60,61
,62,63].
Recently, high-resolution and highly sensitive mass spectrometers
have been used to precisely measure the labeling
pattern of both amino acids and metabolites in central
metabolic pathways at nanomolar concentrations. LCMS­MS
has been used for the determination of intracellular
amino acids to profile metabolic flux changes
during fed-batch cultivation [64
]. CE coupled to timeof-flight
MS (CE­TOF­MS) has been applied to measure
the isotopomer distribution of 13 unstable metabolites in
central metabolism, including some unstable phosphorylated
molecules such as 3-P-glycerate, phosphoenolpyruvate,
and ribose-5-P [65
]. Direct infusion via ESI
coupled to Fourier transform ion cyclotron resonance
MS (FT-ICR MS) was able to measure the metabolite
isotopomer distribution in a biomass hydrolysate of Desulfovibrio
vulgaris Hildenborough, unveiling an unusual
citrate synthase activity [66,67]. Labeling measurements
of metabolites other than amino acids should enable the
flux analysis of more complicated metabolic networks
(such as mammalian cells) and therefore improve the
accuracy of flux determination.
Conclusions
As metabolic quenching and extraction protocols are
improved, the vast array of separation techniques that
can be coupled to MS in the pursuit of metabolomic
analysis will have to be further developed. Presently there
is no one technique that seems capable of easily resolving
the hundreds of metabolites present in microbial extracts.
Although there are academic endeavors to accomplish
exactly that, the most comprehensive metabolite coverage
has come from the combination of using multiple and
overlapping separations that compliment each other in
their ability to resolve compounds of differing physiochemical
properties. Furthermore, metabolome quantitation
is complicated by the lack of comprehensive and
automated data mining software, the need to perform
statistical analyses to minimize the effects of variations in
analytical observations, and may miss the mark entirely
when drawing conclusions about cellular activity and
function without the application of flux analysis to the
results. In a rather short period, however, the field of
metabolomics has blossomed from a seemingly impossible
undertaking to a fruitful laboratory practice that
allows us to address scientific questions that were previously
inaccessible.
Acknowledgements
The authors are funded by the Joint BioEnergy Institute and the Virtual
Institute of Microbial Stress and Survival (both of which are funded by the
US Department of Energy).
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