Biology Reference
In-Depth Information
same sample load and probe tuning is main-
tained between samples.
promise for a number of quantitative metabolo-
mics applications.
Spectral Simpli
cation Methods
In a typical 1 H NMR spectrum, signals are
observed between 0 ppm and 9 ppm, and most
are crowded into two spectral regions that
roughly span 5 ppm (0.8
Metabolite Quantitation Using 1D NMR
Unambiguous peak identi
cation is a critical
step in quantitative metabolomics. To aid such
identi
cation, databases that containNMR chem-
ical shifts and spectra for several hundreds of
metabolites have been developed using standard
compounds that are publicly available. 94,95 For
applications involving large sample sets, automa-
tion of metabolite identi
8.0
ppm). Because of the signal overlap, the identifi-
4.4 ppm, 6.8
-
cation and quanti
cation of many metabolites of
interest often becomes impossible. This problem
is compounded because it is almost always the
case that a given bio
cation and quantitation
is often sought. Numerous method development
efforts have been focused on automated peak
identi
uid will contain a relatively
few species present at high concentrations that
will dominate the NMR spectra. Numerous
studies have been focused on alleviating this
challenge. One such approach uses selective total
correlation spectroscopy (TOCSY) methodology,
which can detect metabolites quantitatively even
if they are found at concentrations 10 to 100
times below those of the major components
and provides improved data inputs for principal
component analysis. 89,90 Recently, quantitative
aspects were examined by optimizing the 1D
TOCSY experiment and comparing integrations
of 1D TOCSY read peaks to the bucket integra-
tion of 1D proton NMR spectra. 91 An important
aspect of this approach is that selective TOCSY,
apart from metabolite quantitation, enables
unknown peak
cation and/or metabolite quantitation.
Automated integration of de
ned spectral
regions is the simplest approach for metabolite
quantitation. It reduces the number of variables
and, at the same time, provides integrals for the
reduced variables (metabolite signals). However,
this approach assumes that each variable
contains the same chemical information, which
is often not the case because many metabolites d
for example, citrate, histidine, and taurine d
exhibit signi
cant peak shifts due to altered pH
or ion concentrations. The severity of their peak
shifts is more prominent for bio
uids such as
urine. 96,97 Moreover, baseline distortions delete-
riously affect the quantitative measurement of
metabolites using this approach. 98
A number of curve
identi
cation
in
complex
fluids.92 92 More recently, a new method called
Add to Subtract for ef
fitting methods have been
proposed to focus on metabolite identi
cient suppression of back-
ground signals from highly concentrated metab-
olites
cation
and quantitative analysis. A frequency domain
data
uids such as strong glucose
background in serum and diabetic urine spectra
was shown. 93 This method is simple to perform,
as it requires only obtaining a second spectrum
after the addition of a small drop of concentrated
glucose solution. It can reduce the glucose
signals by 98% and allow retrieval of the hidden
metabolic information. This spectral simpli-
in bio
fitting approach was proposed to iden-
tify metabolite peaks from overlapped spectral
regions. 99 It uses a semiautomated approach
and can therefore be time consuming for the
analysis of large sample sets. A different
approach, which is insensitive to the variation
of peak shifts because it makes use of prior
knowledge of the spectra of pure compounds,
was suggested. 100 This method assumes that at
least one peak for the metabolite is isolated,
without overlap from other compounds, such
fication of
distinguishing low concentrated metabolites
by multivariate statistical analysis and shows
fication approach enables
identi
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