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analysis after subjecting the data to preprocessing
steps such as baseline correction, peak alignment,
and solvent peak removal. Subsequently, metabo-
lite features that distinguish sample classes are
identifi-
analysis. Because of the reliable peak identi
ca-
tion and measurement of metabolite integrals,
quantitative metabolomics can provide greater
insights into the dynamics and
fluxes of metabo-
lites and promises robust statistical models for
distinguishing classes with better classi
ed and then the structures of distinguish-
ing metabolic features are established.
19
Amajor
drawback is that this approach often differentiates
sample classes based on a long list of minormetab-
olite features that make small contributions.
Possible solutions to this issue often involve
scaling the data or
cation
accuracy.
MASS SPECTROMETRY
filtering (feature selection, or
targeted analysis as discussed below) based on
a set of criteria such as univariate analysis.
Another challenge is that errors due to imperfect
spectral baselines and peak alignments and strong
uneven solvent backgrounds can cause signi
Due to its high sensitivity (typically pg level)
and fast data acquisition speed, mass spectrom-
etry (MS) is one of the most commonly employed
analytical tools in metabolomics. Since early 2000,
there has been tremendous growth in MS-based
methods, including chromatography separation,
ionization, and detection strategies.
2,25,26
Advanced software combinedwith rich databases
have enabled automatic peak alignment, identifi-
cant
problems for the analysis. Metabolite peaks from
both MS and NMR spectra are sensitive to sample
conditions. Positions of NMR signals, for example,
can be sensitive to subtle differences between
samples such as pH, ionic strength, temperature,
and concentration of macromolecules. Sensitivity
to these parameters is more pronounced for bio-
-
cation, and quantitation of metabolites. Because
of the complexity of biological matrices, it is often
necessary to separate metabolites of interest prior
to MS acquisition, especially in case of metabolite
quantitation. Thus, hyphenated analytical plat-
forms that combine chromatography with MS
have proved effective for metabolomics applica-
tions. Common separation techniques used
include liquid chromatography (LC), gas chroma-
tography (GC), and capillary electrophoresis
(CE).
27
Common MS techniques include quadru-
pole, triple-quadrupole,
fluids such as urine.
20,21
Spectral binning, inwhich
spectra are divided into several regions and the
data points within each region are integrated,
has been suggested to alleviate the deleterious
effects of small peak shifts.
13,22,23
Nevertheless,
peak shifts combined with baseline distortions
can still translate into spectral bins that do not
represent truepeak intensity andpose a signi
cant
challenge to the accuracy of the outcome. The
problem becomes more severe when the metabo-
lite peaks involved are of low intensity.
Quantitative metabolomics, on the other
hand, follows a targeted approach wherein the
metabolites are
ion-trap, time-of-
ight
(TOF), and Orbitrap
mass analyzers, which
have been described in detail.
28
LC- and
GC-based MS methods are particularly wide-
spread, and the latest advances enable improved
quantitation by canceling errors arising from
sample complexity (i.e., matrix) effects.
ed and quantitated.
24
The identities of metabolites are established
generally based on the available databases of
standard compounds; the identi
first identi
ed metabolite
Liquid Chromatography Resolved MS
(LC-MS) Methods
Among the MS methods, LC-MS is by far the
most widely used for metabolomics applica-
tions.
29
peaks are then quanti
ed based on internal or
external reference compounds. The resulting
data can then be used as input variables for
statistical analysis using a variety of methods
as described previously for global chemometric
It allows direct detection of metabolites
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