Metabolomics techniques
Metabolomics is a multi-disciplinary science that includes
aspects of biology, chemistry, and mathematics. It
requires analytical techniques such as chromatography, molecular
spectroscopy and mass spectrometry, coupled with multivariate
data analysis methods.
For target compound analysis and metabolic
profiling, main techniques are gas
chromatography (GC),
high performance liquid chromatography (HPLC) and
nuclear magnetic resonance (NMR). These approaches rely
on chromatographic separations, often coupled with well-developed
calibrations for specific analytes.
In metabolic fingerprinting, samples
are analyzed as crude extracts without any separation step,
using NMR, direct
injection mass spectrometry (MS), or
Fourier transform infrared (FT-IR) spectroscopy.
These fingerprinting
approaches are often combined with multivariate
analysis, to get the most out of the data.
The aim of metabolomics is to obtain the
widest possible coverage, in terms of the type and number
of compounds analyzed. This is achieved by making use of
several, complementary analytical methods. In particular,
the 'hyphenated' techniques of LC/MS,
LC/MS/MS and LC/NMR are likely to make increased
impact in the future.
An overview of each of the approaches is given below.
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Gas Chromatography (GC)
Developments involving gas chromatography have been responsible
for the recent upsurge of interest in plant metabolomics. GC provides
high-resolution compound separations, and can be used in conjunction
with a flame ionisation detector (GC/FID) or a mass spectrometer
(GC/MS). Both detection methods are highly sensitive and universal,
able to detect almost any organic compound, regardless of its
class or structure. However, most of the metabolites found in
plant extracts are too involatile to be analysed directly by GC
methods. The compounds have to be converted to less polar, more
volatile derivatives before they are applied to the GC column.
Efficient derivatisation methods are available, but relatively
low sample throughput is a drawback of the GC method, particularly
when there are many samples to be examined.
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High
Performance Liquid Chromatography (HPLC)
HPLC, with UV detection, is probably the most common method used
for targeted analysis of plant materials, and for metabolic profiling
of individual classes. A derivatisation step is not essential
(unless needed for detection), since involatile and volatile substances
may be measured equally well. Selection of compounds arises initially
from the type of solvent used for extraction (as with all methods
that use an extraction step), and then from the type of column
and detector. For example HPLC/UV will only detect compounds with
a suitable chromophore; a column selected for its ability to separate
one class of compounds will not generally be useful for other
types. HPLC profiling methods all rely to a great extent on comparisons
with reference compounds. The full UV spectrum (measured for each
peak when UV-diode array detectors are used) gives some useful
information on the nature of compounds in complex profiles, but
often indicates the class of the compound rather than its exact
identity.
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'Fingerprinting' Methods
'Fingerprinting' techniques can be used for rapid profiling of
large numbers of samples, whilst still being able to provide,
to different extents, specific chemical information. Samples can
be examined after solvent extraction (no derivatisation required),
or as intact tissues (magic angle spinning NMR), liquids or semi-solids
(NMR and FT-IR), or dried materials (FT-IR).
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NMR.
In principle, proton (1H) NMR can detect any metabolites
containing hydrogen. Signals can be assigned by comparison with
libraries of reference compounds, or by two-dimensional NMR. 1H
NMR spectra of plant extracts are inevitably crowded not only
because there is a large number of contributing compounds, but
also because of the low overall chemical shift dispersion. 1H
spectra are also complicated by spin-spin couplings which add
to signal multiplicity, although they are an important source
of structural information. In 13C NMR, the chemical
shift dispersion is twenty times greater and spin-spin interactions
are removed by decoupling. Despite these advantages, the low sensitivity
of 13C NMR prevents its routine use with complex extracts.
Sensitivity can be enhanced when seedlings are grown in the presence
of 13C enriched carbon dioxide, but this is obviously
only an option for laboratory based studies.
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Direct Injection
MS.
It is also possible to obtain metabolite 'mass profiles' without
any chromatographic separation. Such profiles are obtained by
injecting crude extracts into the electrospray ionisation source
of a high-resolution mass spectrometer. ESI generates mainly protonated,
deprotonated or adduct molecules, such as [M+H]+, [M+cation]+
or [M-H]- for each species present in the mixture,
with little or no fragmentation. Thus a fingerprint spectrum is
obtained with a single peak for each metabolite, separated from
other metabolites according to (accurate) molecular mass. The
fingerprint can be used as a classification tool, for example
in taxonomy. Some mass analysers are capable of ultra-high resolution
and permit the mass to be determined to four or five decimal places.
This allows unique formulae to be assigned to peaks with masses
of a few hundred or so. The coupling of high sensitivity with
high resolution provides a method of determining a rough estimate
of the number of metabolites present and a valuable first indication,
from the formulae, of their identities. Its main weakness is the
inability to separate isomers of the same molecular mass.
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FTIR spectroscopy.
The attraction of FTIR spectroscopy as a fingerprinting method
is the ease of sample preparation, the speed with which data can
be acquired, and the high degree of reproducibility attainable.
Samples that can be poured or spread to make good contact with
a flat surface can be measured by the attenuated total reflectance
(ATR) method, whereas powdered or dried samples are measured by
diffuse reflectance. The spectra are less easily interpreted than
with the other methods, but extremely subtle differences may be
picked out using chemometrics, providing a powerful classification
tool.
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LC/MS, LC/MS/MS and LC/NMR
LC/MS, LC/MS/MS and LC/NMR are potentially powerful solutions
to the problems of detector generality and structure determination.
LC/MS can be used to detect compounds that are not well characterised
by other methods (those that are not easily derivatised, lie above
the available GC/MS mass range, or do not contain good chromophores
for conventional HPLC). The electrospray ionisation (ESI) technique
has made polar molecules accessible to direct analysis by MS,
as well as being compatible with HPLC separations. Quantification
of multiple compounds in crude extracts can, in principle, be
achieved in the same way as described for GC/MS, although automation
of the procedure presents greater practical difficulties. LC/MS/MS
provides additional structural information that can be a very
useful aid in the identification of new or unusual metabolites,
or in the characterisation of known metabolites in cases where
ambiguity exists.
The lower sensitivity of LC/NMR means that at present it is most
often used for structural characterisation of unknowns, rather
than for comparative analysis of numerous samples. However, NMR
is a very general detection method, and can provide unique structural
information, so with improvements in sensitivity, the use of LC/NMR
is likely to grow.
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Multivariate Analysis
Plant extracts are very complex in composition and, if many samples
are examined, it is difficult to make meaningful comparisons of
large numbers of spectra or chromatograms 'by eye'. Multivariate
statistical methods can be extremely useful, as they are able
to compress data into a more easily managed form. This can
assist in visualizing, for example, how a given sample relates
to other samples - a central issue in metabolomics. Multivariate
analysis is practically essential in the fingerprinting approaches,
but is also helpful in techniques where individual metabolites
are explicitly quantified (eg GC/MS). |
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Principal component analysis (PCA) is a well-known and effective
method of data compression. PCA transforms the original data (e.g.
intensity values in a spectrum) into a set of 'scores' for each
sample, measured with respect to the principal component axes
('loadings'). The PC scores replace the original variates, and
are: (i) ordered, with successive PCs accounting for decreasing
amounts of variance, and (ii) orthogonal, with no correlation
between the scores on different axes. Due to these properties,
a small number of PCs can replace the many original variates
without much loss of information.
Scatter plots of the scores on the first few PC loadings provide
an excellent means of visualizing and summarising the data and
often reveal patterns that cannot be discerned in the original
data. The scores plots may show clustering of similar samples,
separation of different sample types, or the presence of outliers.
Plots of the loadings themselves may be used to explore which
compounds are most responsible for, say, separating samples into
groups: the most important compounds (peaks) tend to correspond
to high absolute loading values. |
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