U.S. patent application number 11/514031 was filed with the patent office on 2007-03-08 for nmr method for differentiating complex mixtures.
Invention is credited to Daniel Raftery, Peter Sandusky.
Application Number | 20070055456 11/514031 |
Document ID | / |
Family ID | 37831032 |
Filed Date | 2007-03-08 |
United States Patent
Application |
20070055456 |
Kind Code |
A1 |
Raftery; Daniel ; et
al. |
March 8, 2007 |
NMR method for differentiating complex mixtures
Abstract
A method for differentiating complex mixtures each having one or
more chemical species is provided. The method comprises producing a
sample NMR spectrum by subjecting a mixture to a selective
spectroscopy process, wherein the NMR spectrum has individual
spectral peaks representative of the one or more chemical species
within the mixture. The one or more chemical species within the
mixture are identified by analyzing the individual spectral peaks,
and the individual spectral peaks are then subjected to a
multivariate statistical analysis.
Inventors: |
Raftery; Daniel; (West
Lafayette, IN) ; Sandusky; Peter; (New Orleans,
LA) |
Correspondence
Address: |
BOSE MCKINNEY & EVANS LLP;JAMES COLES
135 N PENNSYLVANIA ST
SUITE 2700
INDIANAPOLIS
IN
46204
US
|
Family ID: |
37831032 |
Appl. No.: |
11/514031 |
Filed: |
August 31, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60712786 |
Aug 31, 2005 |
|
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Current U.S.
Class: |
702/19 ;
702/22 |
Current CPC
Class: |
G01R 33/4625 20130101;
G01N 24/08 20130101; G01R 33/465 20130101 |
Class at
Publication: |
702/019 ;
702/022 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Goverment Interests
[0002] This invention was made with government support under grant
reference number NIH/NIDDK 3R21 DK070290-01 awarded by the NIH
Roadmap Initiative on Metabolomics Technology. The Government has
or may have certain rights in the invention.
Claims
1. A method for differentiating complex mixtures each having one or
more chemical species, comprising: producing a sample NMR spectrum
by subjecting a mixture to a selective spectroscopy process, the
NMR spectrum having individual spectral peaks representative of the
one or more chemical species within the mixture; identifying the
one or more chemical species by analyzing the individual spectral
peaks within the mixture; and subjecting the individual spectral
peaks to a multivariate statistical analysis.
2. The method of claim 1, wherein the mixture comprises a biofluid
mixture.
3. The method of claim 1, further comprising using the multivariate
statistical analysis to determine purity of the individual spectral
peaks and to assign the individual spectral peaks into spin
systems.
4. The method of claim 1, wherein the selective spectroscopy
analysis is a total correlation spectroscopy analysis.
5. The method of claim 1, wherein the multivariate statistical
analysis comprises a Pearson product moment correlation test.
6. The method of claim 1, wherein the multivariate statistical
analysis comprises a principal component analysis process.
7. The method of claim 1, wherein the multivariate statistical
analysis comprises an orthogonal-partial least squares-discriminate
analysis.
8. The method of claim 1, wherein subjecting the mixture to a
selective spectroscopy process comprises optimizing the duration of
an excitation pulse during the spectroscopy analysis to maximize
sensitivity.
9. The method of claim 8, wherein the duration of the excitation
pulse is about 5 to about 40 ms.
10. The method of claim 1, wherein subjecting the mixture to a
selective spectroscopy process comprises optimizing a mixing pulse
during the spectroscopy analysis.
11. The method of claim 1, further comprising classifying the
individual spectral peaks by applying a frequency-domain multiplex
scheme.
12. The method of claim 11, wherein the frequency-domain multiplex
scheme comprises a Hadamard Transform NMR matrix.
13. A method for quantifying one or more chemical species within a
complex mixture, comprising: subjecting a first mixture to a total
correlation spectroscopy analysis to produce a first spectrum
composed of individual spectral peaks representative of the one or
more chemical species within the first mixture; acquiring a second
spectrum from an isolated standard sample from a second mixture,
the second spectrum being produced by subjecting the second mixture
to a second total correlation spectroscopy analysis; and comparing
the first spectrum to the second spectrum to quantify the one or
more chemical species within the first mixture.
14. The method of claim 13, further comprising subjecting the
individual spectral peaks of the first mixture to a multivariate
statistical analysis.
15. The method of claim 13, further comprising optimizing the
duration of an excitation pulse during the spectroscopy analysis of
the first mixture to maximize sensitivity.
16. The method of claim 13, wherein the first mixture comprises a
biofluid mixture.
17. The method of claim 13, wherein the multivariate statistical
analysis comprises a Pearson product moment correlation test.
18. The method of claim 13, wherein the multivariate statistical
analysis comprises a principal component analysis process.
19. The method of claim 13, wherein the multivariate statistical
analysis comprises an orthogonal-partial least squares-discriminate
analysis.
20. The method of claim 15, wherein the duration of the excitation
pulse is about 5 to about 40 ms.
21. The method of claim 13, further comprising classifying the
individual spectral peaks by applying a frequency-domain multiplex
scheme.
22. The method of claim 21, wherein the frequency-domain multiplex
scheme comprises a Hadamard Transform NMR matrix.
23. The method of claim 13, further comprising using the
multivariate statistical analysis to determine purity of the
individual spectral peaks and to assign the individual spectral
peaks into spin systems.
24. The method of claim 13, further comprising differentiating the
complex mixture by analyzing the concentrations of the chemical
species quantified during the total correlation spectroscopy
analysis.
25. The method of claim 13, further comprising optimizing a mixing
pulse during the spectroscopy analysis of the first mixture.
26. A method for quantifying one or more chemical species within a
complex mixture, comprising: subjecting the mixture to a first
total correlation spectroscopy analysis to produce a first spectrum
composed of individual spectral peaks representative of the one or
more chemical species within the mixture; acquiring a second
spectrum from an isolated standard sample within the mixture, the
second spectrum being produced by subjecting the mixture to a
second total correlation spectroscopy analysis; and comparing the
first spectrum to the second spectrum to quantify the one or more
chemical species within the mixture.
27. The method of claim 26, further comprising subjecting the
individual spectral peaks of the mixture to a multivariate
statistical analysis.
28. The method of claim 27, wherein the multivariate statistical
analysis comprises a Pearson product moment correlation test.
29. The method of claim 26, wherein the mixture comprises a
biofluid mixture.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 60/712,786 filed Aug. 31, 2005, the disclosure
of which is expressly incorporated herein by reference.
TECHNICAL FIELD
[0003] The present invention is directed toward high-resolution NMR
analysis of chemical structures, and more particularly to the use
of selective total correlation spectroscopy ("TOCSY") to quantify
and analyze a predetermined set of chemical species.
BACKGROUND OF THE INVENTION
[0004] It is well known that nuclear magnetic resonance (NMR)
spectroscopy provides extremely highly detailed information on
molecular structure. NMR is also quantitative because the detected
signal is linearly proportional to the absolute number of active
nuclei in the detected sample volume. Thus, relative numbers of
hydrogen, carbon or other atoms in a molecule can be directly
measured, the relative number of different molecular species in a
mixture can be computed, and by using an internal standard (or even
an external standard), the absolute concentration of species can be
calculated.
[0005] However, when measuring the components of complex mixtures,
overlapping resonances often result, which thus compromises the
ability to measure concentrations quantitatively. Even small
molecules often give rise to 20 or more spectral lines in the
.sup.1H NMR spectrum, leading to severe overlap for many complex
mixtures. For example in the .sup.1H NMR spectrum of human urine,
over 1000 spectral lines can be at least partially resolved,
corresponding to upwards of 100 compounds (See: J. C. Lindon, E.
Holmes, and J. K. Nicholson, Prog. NMR Spec. 39, 1 (2001)).
[0006] The metabolomics approach, combining high-resolution NMR
with multivariate statistical analysis, has been shown to be very
powerful for distinguishing biofluid sample subpopulations based on
subtle differences in the their spectra..sup.1,2 This approach can
be widely applied to many types of samples, including urine, body
fluids, and tissues. NMR based approaches are attractive because
they can look at essentially all of the components of a mixture
simultaneously, and thus avoid the sometimes difficult process of
sample fractionation. These methods can also be rapid and
quantitative.
[0007] The present invention is intended to address one or more of
the problems discussed above.
SUMMARY OF THE INVENTION
[0008] The present teachings are directed to a method for
differentiating complex mixtures each having one or more chemical
species. The method comprises producing a sample NMR spectrum by
subjecting a mixture to a selective spectroscopy process, wherein
the NMR spectrum has individual spectral peaks representative of
the one or more chemical species within the mixture. The one or
more chemical species within the mixture are identified by
analyzing the individual spectral peaks, and the individual
spectral peaks are then subjected to a multivariate statistical
analysis.
[0009] In another aspect of the present invention, a method for
quantifying one or more chemical species within a complex mixture
is provided. The method comprises subjecting a first mixture to a
total correlation spectroscopy analysis to produce a first spectrum
composed of individual spectral peaks representative of the one or
more chemical species within the first mixture. A second spectrum
is acquired from an isolated standard sample from a second mixture,
the second spectrum being produced by subjecting the second mixture
to a second total correlation spectroscopy analysis. The first
spectrum is then compared to the second spectrum to quantify the
one or more chemical species within the first mixture.
[0010] In yet another aspect of the present invention, a method for
quantifying one or more chemical species within a complex mixture
is provided. The method comprises subjecting the mixture to a first
total correlation spectroscopy analysis to produce a first spectrum
composed of individual spectral peaks representative of the one or
more chemical species within the first mixture. A second spectrum
is acquired from an isolated standard sample within the mixture,
the second spectrum being produced by subjecting the mixture to a
second total correlation spectroscopy analysis. The first spectrum
is then compared to the second spectrum to quantify the one or more
chemical species within the mixture.
[0011] The attached claims recite at least some of the novel
aspects of the present teachings. Other advantages may well be
apparent to one of skill in the art upon consideration of the
description of the invention and claims contained herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The above-mentioned aspects of the present teachings and the
manner of obtaining them will become more apparent and the
teachings will be better understood by reference to the following
description of the embodiments taken in conjunction with the
accompanying drawings, wherein:
[0013] FIG. 1 shows a representative selective TOCSY pulse
sequence, wherein the duration of the shaped pulse, designated
"SP," was varied to produce the data presented in FIGS. 2 and
3;
[0014] FIG. 2 shows the effect of shaped pulse duration, "SP," on
the selectivity of 1D TOCSY experiment. (A) 1D proton spectrum of a
mixture of 10 mM L-Proline and 10 mM L-Arginine in pH 7 phosphate
buffer and 10% D.sub.2O. Spectra were taken using 1D NOESY Presat
sequence for water suppression. (B) Selective TOCSY spectrum of
this sample with selective excitation frequency set on the proline
y peak at 2.0 ppm (*) and SP=40 ms. (C) Same experiment as shown in
B, but with SP=10 ms;
[0015] FIG. 3 shows the effect of shaped pulse duration, "SP," on
the signal to noise ratio of the TOCSY peaks produced in the
selective TOCSY experiment. All spectra were taken on the
Proline-Arginine mixture described in FIG. 2. (A) On resonance
irradiation of isolated target peaks, (.circle-solid.) S/N of
proline .beta. TOCSY peak (2.37 ppm) produced by selective
irradiation of the proline .alpha. peak (4.15 ppm), (.box-solid.)
S/N of arginine .alpha. TOCSY peak (3.77 ppm) produced by selective
irradiation of the arginine .delta. peak (3.25 ppm). (B) Effects
associated with off resonance irradiation of nearby peaks,
(.diamond-solid.) S/N of proline .alpha. TOCSY peak (4.15 ppm)
produced by selective irradiation centered on the proline .gamma.
peak (2.00 ppm), (.tangle-solidup.) S/N of arginine a TOCSY peak
(3.77 ppm) produced by selective irradiation centered on the
proline .gamma. peak (2.00 ppm);
[0016] FIG. 4 shows (A) Proton NMR spectrum of rat urine acquired
using the 1D presat NOESY sequence to achieve water suppression.
(B) Low field expansion of the rat urine proton NMR spectrum. (C)
Selective TOCSY of rat urine with the selective pulse frequency set
on the hippurate 7.88 ppm peak (*), and acquired with SP=10 ms;
[0017] FIG. 5 shows (A) High field expansion of the proton NMR
spectrum of human urine spiked with 1 mM each of leucine,
isoleucine and valine. Spectrum was acquired using the 1D presat
NOESY sequence to achieve water suppression. (B) High field
expansion of the selective TOCSY spectrum of the spiked human urine
sample from A with the selective pulse frequency set on the
leucine-isoleucine-valine methyl peaks around 1.00 ppm (*), and
acquired with SP=10 ms. (C) Selective TOCSY spectrum of a 10 mM
solution of leucine with the selective pulse frequency set on the
methyl peak around 1.00 ppm (*), and acquired with SP=10 ms. The
C-alpha peaks appear somewhat low in intensity due to the poor
efficiency of the TOCSY mixing cycle in this case;
[0018] FIG. 6 shows (A) High field expansion of proton NMR spectrum
of human urine. (B) High field expansion of the proton NMR spectrum
of human urine sample from A spiked with 250 .mu.M isoleucine. (C)
High field expansion of semiselective TOCSY spectrum of human urine
from A. (D) High field expansion of semiselective TOCSY spectrum of
human urine sample from A spiked with 250 .mu.M isoleucine. Both
TOCSY spectra were taken with SP=10 msec centered at a frequency of
1.00 ppm;
[0019] FIG. 7 shows PC1 vs. PC2 score plots from the isoleucine
spiking study of human urine. (A) Score plot from PCA calculated
using 298 bins of the semiselective TOCSY spectra as data inputs
(1.2 to 4.2 ppm data with selective excitation at 1.00 ppm). (B)
Score plot from PCA calculated using 298 bins of the 1D proton
spectra as data inputs (1.2 to 4.2 ppm). (C) Score plot from PCA
calculated using the 57 bins of the 1D proton spectra containing
isoleucine peaks (exclusive of the methyl peaks near 1.00 ppm)
.tangle-solidup.=isoleucine spiked, .diamond-solid.=control. PC1
and PC2 account for 56.3% of the total variance in (A), 90.0% in
(B) and 93.4% in (C);
[0020] FIG. 8 shows (A) High field expansion of proton NMR spectrum
of human urine. (B) High field expansion of the proton NMR spectrum
of human urine sample from A spiked with 250 .mu.M isoleucine. (C)
High field expansion of semiselective TOCSY spectrum of human urine
sample from A. (D) High field expansion of semiselective TOCSY
spectrum of human urine sample from A spiked with 250 .mu.M
isoleucine. Both TOCSY spectra were taken with SP=10 msec centered
at a frequency of 1.00 ppm (*); and
[0021] FIG. 9 shows PC1 vs PC2 score plots from an isoleucine
spiking study of human urine. (A) Score plot from PCA calculated
using 398 bins of the semiselective TOCSY spectra as data inputs
(0.2 to 4.2 ppm data with selective excitation at 1.00 ppm). (B)
Score plot from PCA calculated using 398 bins of the 1D proton
spectra as data inputs (0.2 to 4.2 ppm data). (C) Score plot from
PCA calculated using the 129 bins of the 1D proton spectra
containing isoleucine peaks (including methyl peaks at 0.90 to 1.1
ppm) .tangle-solidup.=isoleucine spiked,
.diamond-solid.=control.
[0022] Corresponding reference characters indicate corresponding
parts throughout the several views.
DETAILED DESCRIPTION
[0023] The embodiments of the present teachings described below are
not intended to be exhaustive or to limit the teachings to the
precise forms disclosed in the following detailed description.
Rather, the embodiments are chosen and described so that others
skilled in the art may appreciate and understand the principles and
practices of the present teachings.
[0024] An experimental selective total correlation spectroscopy
(TOCSY) method for quantifying several chemical species of honey is
described herein (see also a recently published study.sup.3 which
was authored by the present inventors and is hereby incorporated by
reference in its entirety). It has been discovered that this TOCSY
method is a useful alternative to the standard metabonomic analysis
of biofluids. According to this method, a selective excitation
pulse and TOCSY mixing period were used to focus the statistical
analysis of a few pre-selected components in honey, such as amino
acids for instance. Through this analysis, it was discovered that
the discrimination of subpopulations in a set of samples is
substantially improved, particularly as the signals used, come
almost exclusively from components that vary significantly between
samples. One aspect of this method is that it facilitates the
accurate quantification of a predetermined set of chemical species,
regardless of whether these species are major or minor components
of the mixture. As such, a set of chemical compounds to be studied
in a metabonomic analysis may then be chosen based on their
metabolic or pharmacological significance. For instance, a specific
subset of chemical compounds present in a biofluid may be chosen
for study because they are known to be metabolically related.
[0025] The present methods are capable of differentiating complex
and largely similar mixtures by enhancing the quantitative
measurement of minor components using NMR spectroscopy.
Determination of the concentration of these species can be used in
one of a number of multivariate statistical analyses to
differentiate similar but complex mixtures, such as those found in
biofluids and/or other liquids. To achieve this, the present
methods involve a combination of advanced NMR methods with
multivariate statistical correlations, such as the Pearson product
moment correlation test, unsupervised multivariate statistical
analyses, such as the principal component analysis ("PCA"), as well
as supervised multivariate statistical analyses, such as the
orthogonal-partial least squares-discriminate analysis
("O-PLS-DA"). Moreover, the methods are capable of detecting low
concentration species, as well as analyzing a wide range of
mixtures, including biofluids such as blood, urine, spinal fluid,
etc., liquid foods, chemical feedstocks, such as petroleum, and so
forth, where different classes of molecules are present producing
complicated, overlapping spectral features. The methods may also be
used to select one or more molecules in a mixture, simplify their
NMR spectra, increase their detection sensitivity, allow for
quantitative evaluation of those molecules, and to differentiate
mixtures that differ in the concentration of these molecular
species that may be minor components in the mixtures. Additionally,
the methods may also be used to differentiate sick and healthy
patient samples by focusing on lipids, sugars, amino acids or other
such metabolites.
[0026] The present methods enhance the ability of NMR to
differentiate complex mixtures, as well as cause selective
excitation of certain nuclear spins and nuclear spin polarization
transfer to other nuclear spins on the same molecule. The methods
are also capable of identifying and quantitating molecular
components by simplifying the NMR spectra of the mixture. This
approach can be used to select a certain molecular species, or
several species, simplify their NMR spectra, increase their
detection sensitivity in the presence of a complicating matrix, and
allow for quantitative evaluation of these selected molecules. The
concentrations, or more typically the NMR spectra of these species,
are then subjected to multivariate statistical analysis, such as
principle component analysis to allow differentiation of the
samples. Many types of multivariate statistical analysis can be
applied once the spectra are simplified by selective excitation.
While other NMR processes are available, such as LC-NMR for
instance, the present TOCSY processes have significant unique
advantages over these existing methods. For instance, the present
methods are more rapid, since the selective TOCSY experiments
require very little time or effort for sample preparation, and they
avoid any possibility of differential sample fractionation,
particularly since no physical separation of the mixture components
is involved.
[0027] The use of quantitative selective excitation (selective)
TOCSY, and multivariate statistical analysis can be very useful to
differentiate otherwise very similar samples. For instance, in the
above-referenced publication, the inventors differentiated 8
different honey samples based on the concentrations of their amino
acid content. The concentrations of these amino acids are typically
200 times less than the major components, .alpha.-glucose,
fructose, other sugars and water.
[0028] One challenge in using processes such as selective TOCSY to
detect single molecular species within complex mixtures is that
such processes can produce the simultaneous excitation of several
molecular spin systems at once. When this happens, problems with
the purity of the individual TOCSY peaks observed and/or with their
assignment into specific spin systems can occur. While it is in
principle possible to use very selective excitation approaches in
order to address this problem, unfortunately in most cases, greater
spin system selectivity can only be gained at the expense of
sensitivity. This is an unacceptable trade-off when dealing with
biofluid samples. To eliminate this challenge, the present
inventors have discovered an alternative two-stage modification
process to the basic selective TOCSY system. At an initial stage,
using a less selective excitation in the TOCSY pulse sequence
optimizes the sensitivity and data collection efficiency of the
experiment, at the expense of spin system selectivity. At a second
stage, application of the Pearson product moment correlation
coefficient method to the TOCSY peak integral intensities provides
a test for individual TOCSY peak purity, and allows for the
assignment of the peaks into spin systems.
[0029] Another known challenge of NMR and metabolomics analysis
approaches is that in many biofluids of interest (such as urine and
blood serum), only a fraction of the NMR spectral features of the
different chemical species are capable of being resolved. For
instance, the selective TOCSY process can behave "semiselectively"
when applied to these mixtures, wherein a single selective TOCSY
spectrum will very often contain peaks from several different
chemical species. Using human and rat urine as examples of typical
biofluid samples, the present inventors examined and compared two
different solutions to this problem. Longer shaped pulse durations
in the selective TOCSY pulse sequence were found to narrow the
selective excitation band, thus focusing the experiment more
selectively on individual chemical species. However, increasing the
shaped pulse duration resulted in a significant decrease in the
intensity of the TOCSY peaks, which also affects the sensitivity.
Alternatively, it was discovered that relatively short excitation
pulse durations can be used to produce a spectrum composed of more
intense TOCSY peaks. This results in a "semiselective" TOCSY
spectrum in which the TOCSY peaks will, in general, derive from
several different chemical species. While initially problems may be
encountered when identifying which chemical species are represented
by a particular TOCSY peak, or whether a given TOCSY peak
represents any single species, the judicious application of the
Pearson product moment correlation coefficient method can be used
to resolve these problems..sup.4,5 Statistical correlation methods
provide a good means to identify related peaks and even molecules
in metabonomics studies, as was recently shown by Nicholson and
coworkers..sup.6 A major issue in classical metabonomics studies is
the discriminating power of the method when minor components are
the significant varying species. Classical metabonomic studies
usually employ complete 1D proton NMR spectra as data inputs for
PCA calculations..sup.7-14 Because of significant spectral overlap
in samples such as urine or serum, the practical limit of detection
is relatively high and thus discrimination of similar samples is
challenging. It was found that the using semiselective TOCSY
spectra as PCA data inputs is more sensitive and reliable than the
classical metabonomic approach when dealing with small differences
in complex biofluid compositions. In essence the coupling inherent
in the NMR spectrum can be used as a filter to lower the threshold
of detection for discriminating different sample
subpopulations.
[0030] Additionally, it is possible to "multiplex" the selective
excitation process by using an approach called Hadamard Transform
NMR..sup.20 In Hadamard NMR spectroscopy, the peaks of interest are
irradiated selectively using a frequency-domain multiplex scheme.
With this approach, there is no loss in sensitivity per unit time.
Hadamard matrices are used to determine the multiplexed irradiation
frequencies and are then used to decode the NMR spectra as they are
being processed. Most types of NMR experiments can be implemented
in this fashion, as long as the frequencies of the signals of
interest are known in advance. Multidimensional NMR experiments can
be implemented using Hadamard principles, leading to large time
savings. This approach then, because of its inherently quantitative
nature, can be combined with multivariate statistical analysis such
that it can be used to differentiate complex samples.
[0031] Advantages and improvements of the processes of the present
invention are demonstrated in the following examples. The examples
are illustrative only and are not intended to limit or preclude
other embodiments of the invention.
EXAMPLES
[0032] NMR Samples: L-Amino Acids and TSP (sodium 3-trimethylsilyl
(2,2,3,3 .sup.2H.sub.4) i-propionate) were purchased from
Sigma-Aldrich (St. Louis, Mo.) and used without further
purification. For NMR analysis, amino acid solutions were prepared
in 50 mM phosphate buffer at pH 7. Human urine was collected from
the volunteers. A sample of urine collected from a male adult
Sprague-Dawley rat was the generous donation of Dr. Peter Kissenger
and Dr. Chester Duda of Bioanalytical Systems, Inc. (West
Lafayette. Ind.). For NMR analysis, urine samples were prepared by
the addition of 60 .mu.l of 1 M phosphate buffer, pH 7, to 540
.mu.l of neat urine. For the PCA study, urine samples were
collected at three time points during the day, and these samples
were split into thirds, forming 9 samples, 6 of which were spiked
with varying concentrations of isoleucine. All NMR samples were run
in 5 mm tubes with 10% added D.sub.2O (Cambridge Isotopes
Laboratory, Inc.) and 50 .mu.l TSP.
[0033] NMR Spectroscopy: NMR spectra were taken on a Bruker AVANCE
DRX 500 MHz spectrometer (Bruker-Biospin, Fremont, Calif.), using a
5 mm inverse HCN triple resonance probe equipped with XYZ axis
gradient coils. All spectra were acquired at 25.degree. C., and
were referenced to the TSP methyl peak at 0.00 ppm. Proton spectra
were acquired using a 1D NOESY pulse sequence incorporating
presaturation for water suppression during the relaxation delay and
mixing time..sup.15,16 The relaxation delay and mixing times were
set to 2s and 300 ms, respectively, and the presaturation power
used was the minimum needed to effect complete suppression of the
water peak. In order to achieve high signal-to-noise ratios for
minor components, 64 FID transients were averaged, resulting in a
total acquisition time of 7 min. Selective TOCSY experiments used
the standard pulse sequence found in the Bruker XWINNMR pulse
program library (see FIG. 1), and consists of a hard 90.degree.
pulse--z gradient--selective 180.degree. pulse--z gradient train to
achieve selective excitation of the target peak, followed by a
MLEV-17 TOCSY spin lock..sup.17-19 It should be understood that the
pulse sequence shown in FIG. 1 illustrates one example of carrying
out a selective TOCSY experiment. Many choices exist for the
selective pulse and the mixing cycle, and in some experiments, the
first pulse can be eliminated. Here, gaussian-shaped pulsed z-field
gradients were 1 ms in duration and 14 mT/m at maximum strength.
Secant-shaped selective 180.degree. pulses were found to be most
effective for selective excitations. The duration of the shaped
pulse was varied as described below. TOCSY mixing times were 70 ms.
Thirty-two 16 K point FID transients were averaged in each
selective TOCSY experiment, resulting in an acquisition time of 1
min. The "spdisp" utility incorporated in the Bruker XWINNMR
software package was used to determine shaped pulse excitation
frequency band widths. 1 Hz line broadening was used in processing
the spectra. It should be appreciated and understood that the
parameters included herein are for illustrative purposes only,
wherein other types of selective pulses, mixing cycles and mixing
times may be employed by those skilled in the art while still
encompassing the scope of the present teachings. As such, the
present teachings are not intended to be limiting herein.
[0034] Pearson Product Moment Experiments: A series of 64 random
numbers with a mean value of 1.00 and a standard deviation of 0.25
was generated using the Microsoft EXCEL random number utility
(Microsoft Corp., Redmond, Wash.). Negative values in the list of
random numbers were discarded, and the first 27 values of the
remaining numbers were used as random mM concentrations of leucine,
isoleucine and valine added to nine aliquots of a single human
urine sample. In this way, nine NMR samples of human urine with
random concentrations of leucine, isoleucine and valine were
produced. Selective TOCSY spectra taken on this set of samples were
processed and transformed using the same parameters, and the TOCSY
peaks over the resulting set of nine spectra were base-line
corrected and then integrated as a set using the XWINNMR multiple
integration macro written in-house. The same chemical shift limits
were used for all spectral integrations. The resulting text file
containing the relative integral intensities was read into a
Microsoft EXCEL spread sheet, and the matrix of numbers used as
input data for Pearson product moment correlation coefficient
calculations performed using the EXCEL utility.
[0035] PCA Calculations: For the isoleucine spiking PCA study, nine
human urine samples (three controls and six spiked with 250.+-.65
.mu.M isoleucine) were prepared as described above from 3 different
urine samples from the same individual. 1D proton and semiselective
TOCSY spectra for the nine urine samples were acquired, transformed
and phased using XWINNMR. The real parts of the transformed spectra
were converted to XY plot format JCAMP files. Each JCAMP file was
text edited to remove header text and X data, and then read in as a
column into an EXCEL spreadsheet. The 8 K points of each spectrum
were 4-fold binned to yield a 2 K data column. In this way two 2 K
by nine matrices were constructed, one matrix containing the set of
nine 1D proton spectra, and one matrix containing the set of nine
semiselective TOCSY spectra. 298 point segments of these two
matrices, corresponding to the 1.2 to 4.2 ppm chemical shift region
of the spectra, were used as input data for correlation PCA
calculations performed in MINITAB 13 (MINITAB Inc., State College,
Pa.). The relative variance contributions of the first two
principal components are indicated in the figure captions. In all
cases fewer than eight principal components were found to be
adequate to account for >99.9% of the variance.
[0036] The Effect of Shaped Pulse Duration: The pulse sequence used
in the selective TOCSY experiment is shown in FIG. 1..sup.17-19 The
duration of the shaped pulse, used in this experiment in a
180.degree. refocusing mode, and denoted as "SP" in FIG. 1, will
largely determine the frequency width excited by the selective
pulse sequence. In general, of course, lengthening the shaped pulse
duration will narrow the excitation frequency band width. This
relationship can be quantitatively evaluated using the Bruker
XWINNMR "spdisp" utility.
[0037] A system composed of 10 mM L-arginine and 10 mM L-proline
was used to examine experimentally the effects of varying the
selective TOCSY sequence shaped pulse duration (FIG. 2). The
results of particular interest in these experiments are the effects
on the TOCSY peak intensities, a consideration of central
importance if the selective TOCSY experiment is to be used for the
quantitative analysis of specific chemical components of a biofluid
mixture. In general the results observed when varying the shaped
pulse duration will depend on the NMR spectrum of the target
chemical compound. However, two general cases can be described. In
cases where the target excitation peak is well separated in the
spectrum from any other peak of its own spin system, the
intensities of the TOCSY peaks will increase with an increase in
the shaped pulse excitation frequency band width, until the
excitation frequency band width equals the spectral width of the
target peak. This case is illustrated by the two experiments
presented in FIG. 3A. Note that for these examples the target peak
width is approximately 20 Hz, corresponding to an optimal
shaped-pulse duration of 40 ms.
[0038] It should be noted that the simple relationship between
shaped pulse duration and TOCSY peak intensity described above is
observed only when the target excitation peak is well separated in
the spectrum from other peaks of its own spin system. In the
examples presented in FIG. 3A target peaks were 350 to 275 Hz away
from the nearest neighbor peaks of their own spin systems. The
second general case that can be described occurs when the target
excitation peak is close to another peak of its own spin system. In
this case off-resonance excitation of this second neighboring peak
will increasingly occur as the shaped pulse duration is shortened.
The effects of this in the selective TOCSY experiment are presented
in FIG. 3B, where the signal to noise ratio of the proline .alpha.
TOCSY peak is plotted as a function of the shaped pulse duration,
with the shaped pulse excitation frequency centered on the proline
V peak (.diamond-solid.). The proline y peak width is approximately
56 Hz, which should correspond to an optimal selective-pulse
duration of 14 ms. However, the proline .beta.2 peak occurs only 13
Hz down field from the proline y peak. Consequently as the shaped
pulse duration centered on the proline y peak is shortened below 10
ms dramatic increases in the proline .alpha. TOCSY peak intensity
are observed, due to the off resonance excitation of the proline
.beta.2 peak. Concurrently strong arginine peaks are also observed
in the TOCSY spectrum due to the off-resonance irradiation of
arginine y peak (FIG. 3B-.tangle-solidup.). The experiment has
become "semiselective."
[0039] When discussing the quantitative results to be expected from
the selective TOCSY experiment, it is obvious that each spin system
in a mixture constitutes its own special case. However, all spin
systems show an increase in TOCSY peak intensity when the
shaped-pulse duration is shortened. So in terms of sensitivity, it
beneficial to use a relatively short, or "semiselective," shaped
pulse duration. Since a shorter shaped-pulse duration will in
general excite more spin systems at the same time, shorter pulse
durations are also more efficient in terms of surveying the
chemical species present in a biofluid sample.
[0040] Use of the Pearson Product Moment Method to Test the Purity
of TOCSY Peaks: In the case of urine samples, spectral overlap
varies from mild to severe. FIG. 4A shows the proton spectrum of
urine collected from an adult male Sprague-Dawley rat. In some
cases, the selective TOCSY experiment can yield relatively pure
single component spectra. An example of this is rat urine
hippurate, the selective TOCSY spectrum of which is shown in FIG.
4C. More typically, excitation of any given peak in the urine
spectrum will give rise to a spectrum containing peaks from several
different spin systems. An example of this is shown in FIG. 5B,
where excitation of the human urine amino acid methyl peak at 1 ppm
yields a TOCSY spectrum containing peaks from leucine, isoleucine
and valine. In either case however, regardless of whether a single
spin system or multiple spin systems are excited, the separate
individual TOCSY peaks, if pure, may be used to measure the
concentrations of the chemical species present in the
mixture..sup.3
[0041] With the use of less selective excitation, or in the case of
severely overlapped spectra in complex mixtures, it becomes more
likely that any resolved TOCSY peak produced will contain
contributions from several different chemical species. For example,
it is not clear a priori that any of the urine sample TOCSY peaks
resolved in FIG. 4C or 5B are pure. This of course raises the
possibility that a particular TOCSY peak can no longer be accepted
as an accurate measure of the concentration of a particular
chemical species.
[0042] In addition, since the application of a less selective
excitation pulse to a biofluid mixture also generally produces a
more complex TOCSY spectrum, containing peaks from several
different spin systems, it may also become difficult to assign
specific peaks to specific chemical species. An example of this is
the .alpha.-proton region in the human urine TOCSY spectrum shown
in FIG. 5B. Here it is clear that the four TOCSY peaks resolved
between 3.7 and 3.8 ppm are amino acid .alpha.-proton peaks.
However which of the three target amino acids each of the four a
peaks belongs to is unclear.
[0043] Fortunately, however, the Pearson product moment correlation
coefficient method can be used as a statistical test to determine
the purity of any particular TOCSY peak, and is also useful to help
define which peaks belong to the same spin system. For two
independent variables x.sub.i and y.sub.i, measured in sample i
over a set of samples, with average values X and Y, the Pearson
product moment correlation coefficient, PM, is given by:.sup.4,5 PM
= i .times. .times. ( x i - X ) .times. ( y i - Y ) / i .times.
.times. ( x i - X ) 2 .times. i .times. .times. ( y i - Y ) 2
##EQU1##
[0044] If x and y are the integral intensities of two TOCSY peaks
belonging to the same spin system, and neither is significantly
contaminated by peaks of another spin system, then the peak
intensities of the two will be highly correlated when they are
measured over a set of samples, and the PM calculated for the two
peaks will be close to one. Otherwise, the correlation will be
significantly diminished.
[0045] Table 1 summarizes the results from a set of experiments in
which semiselective TOCSY measurements were made on a set of 9
human urine samples generated by spiking a single sample of urine
with random concentrations of leucine, isoleucine and valine. Most
urine samples will show widely variable amounts of these amino
acids, as well as certain other unidentified species with peaks
occurring near 1.00 ppm. The particular urine sample chosen for
these experiments had negligible amounts of these amino acids
before spiking, which allowed for good experimental control over
the amounts of these three amino acids present. A single
semiselective TOCSY experiment was performed on each of the 9
samples, using a 10 ms shaped pulse duration centered on the amino
acid methyl peak of 1.00 ppm. The PM values calculated from the
integrated TOCSY peaks in these experiments were 0.88 to 0.99 for
peaks belonging to the same spin systems, and less than 0.57 for
peaks belonging to different spin systems (Table 1). If any peak
contained significant contamination from a spin system of a second
molecule, which presumably would not be statistically related over
the sample set to the target spin system, then its intra spin
system PM values would be significantly reduced. TABLE-US-00001
TABLE 1 Pearson Product Moment Correlation Coefficients for
Aliphatic Amino Acid Semiselective TOCSY Peaks Measured in Human
Urine LEU .alpha. LEU .beta., Y VAL .alpha.1 VAL .alpha.2 VAL
.beta. ILE .alpha. ILE .beta. ILE v1 ILE v2 LEU .alpha. 1.000 0.916
0.234 0.326 0.389 0.397 .424 0.440 0.469 LEU .beta., y 0.916 1.000
0.484 0.554 0.576 0.382 0.292 0.323 0.320 VAL .alpha.1 0.234 0.484
1.000 0.934 0.886 0.367 0.126 0.152 0.070 VAL .alpha.2 0.326 0.554
0.934 1.000 0.919 0.204 -0.007 0.056 -0.007 VAL .beta. 0.389 0.576
0.886 0.919 1.000 0.479 0.320 0.393 0.326 ILE .alpha. 0.397 0.382
0.367 0.204 0.479 1.000 0.901 0.918 0.887 ILE .beta. 0.424 0.292
0.126 -0.007 0.320 0.901 1.000 0.967 0.961 ILE y.sup.1 0.440 0.323
0.152 0.056 0.393 0.918 0.967 1.000 0.994 ILE y.sup.2 0.469 0.320
0.070 -0.007 0.326 0.887 0.961 0.994 1.000
[0046] Using the product moment correlation coefficients listed in
Table 1, it is possible to make definitive assignments of the amino
acid spin systems, including those peaks in the confusing
.alpha.-proton region. The three amino acids, leucine, isoleucine
and valine, are ubiquitous in biofluids, and have been identified
in metabonomic studies on urine,.sup.7,8 blood plasma,.sup.9-11
aqueous liver extracts,.sup.10-11 brain fluid,.sup.12 wine.sup.13
and beer..sup.14 However the identification of these three amino
acids in those complex mixtures has often been made based simply on
the observation of proton NMR peaks near 1 ppm. While connectivity
or spiking experiments have been used, they are time consuming. In
contrast, the use of the fast 1D semiselective TOCSY experiment in
combination with Pearson product moment correlation coefficient
analysis clearly defines the unique spectral signature of a
complete spin system. Thus the product moment correlation
coefficient method can be used both as a test to confirm the
integrity of any given TOCSY peak, and also as a means to identify
the spin system and confirm the identity of the chemical species
detected from multiple, correlated peaks in the spectra.
[0047] A Test of the Sensitivity of Semiselective TOCSY Spectra as
Data Inputs for Metabonomic PCA: An important feature of the
selective TOCSY experiment is the ability to focus the analysis of
different samples on components that can be used to draw
distinction between subtlety different subpopulations in sets of
very similar very complex samples. An experiment was performed on
human urine samples to test the ability of semiselective TOCSY to
make such subtle distinctions. FIG. 6 presents 1D proton NMR and
semiselective TOCSY spectra of a control human urine sample, and
the same sample spiked with 250 .mu.M isoleucine. It should be
clear that addition of 250 .mu.M isoleucine to urine produces only
very subtle, almost undetectable, differences in the 1D proton NMR
spectrum, as can be seen by comparing FIGS. 6A and 6B. In contrast,
dramatic differences are observed in the semiselective TOCSY
spectra of the spiked and control samples (FIGS. 6D and 6C).
[0048] Intuitively it would seem that the use of semiselective
TOCSY spectra as data inputs for PCA calculations would make
metabonomic studies more sensitive to small differences in
metabolite concentrations. To test this idea, three samples of
human urine were collected from a single individual over the course
of a single day. Each of the three samples was divided into three
aliquots, and two of the aliquots from each sample were spiked with
amounts of isoleucine varying between 185 and 315 .mu.M. The exact
amount of isoleucine added to each spiked sample was determined
from the first six numbers between 185 and 315 occurring in a list
generated using the EXCEL random number utility. This sample
preparation procedure generated a set of nine human urine samples
of which six were spiked with 250.+-.65 .mu.M isoleucine. 1D proton
spectra and semiselective TOCSY spectra, taken with the selective
excitation pulse centered at 1 ppm, were acquired on each of these
samples. The resulting spectra were subjected to correlation PCA
calculations using the spectral region from 1.2 to 4.2 ppm. That
is, the region of the complete NMR spectrum containing isoleucine
peaks, exclusive of the methyl peak at 1 ppm that was used for the
selective excitation (see additional discussion below).
[0049] The PC1 vs. PC2 PCA score plots shown in FIG. 7 clearly
indicate the advantage of the semiselective TOCSY approach. The
score plot calculated using the semiselective TOCSY spectra as data
inputs shows a clear discrimination between the spiked and control
samples (FIG. 7 A). In contrast, the PCA score plot calculated
using 1D proton NMR spectra as data inputs does not discriminate
between the isoleucine spiked and control samples (FIG. 7B).
Rather, the clustering observed in the PCA score plot calculated
using 1D proton spectra is random, and does not derive either from
the origin of the urine nor from the spiking of the samples. The
correct clustering is dramatically improved using the selective
TOCSY approach. It is also interesting to note that the
discrimination is largely along PC2. We found that PC1 was in fact
dominated by (chemical) noise due to the variation of the many
components among the different urine samples.
[0050] As an alternative, it is possible to use the 1D proton
spectra as data inputs but use only on those frequency bins that
include the isoleucine signals. In this third PCA calculation, data
inputs were limited to the 57 bins of the 1D proton spectra
containing the isoleucine .alpha. peak, at 3.65 to 3.75 ppm, .beta.
peak, at 1.95 to 2.05 ppm, .gamma.1 peak, at 1.45 to 1.55 ppm, and
.gamma.2 peak, at 1.25 to 1.35 ppm. However, this approach also
fails to produce any clean score plot discrimination between the
spiked and control samples (FIG. 7C).
[0051] The most intense peaks of the isoleucine spectra are .gamma.
and .delta. methyl peaks found between 0.90 and 1.1 ppm. These
peaks were used as the selective TOCSY target peaks in the
isoleucine spiking study, and were excluded from the PCA
calculations presented in FIG. 7. In fact inclusion of these
intense methyl peaks in the PCA calculation has very little effect
on the quality of the spiked vs. control clustering observed in the
resulting PCA score plots (see FIGS. 8 and 9). The clustering
produced in the selective TOCSY spectra based calculations remains
very good (FIG. 9A), while no actual discriminatory clustering is
produced in the 1D proton based calculations (FIGS. 9B and 9C).
Thus the calculations, both exclusive and inclusive of the
isoleucine methyl peaks, demonstrate that the selective TOCSY
approach is more sensitive to small differences in metabolite
concentration, or less intense spectral features, than the standard
metabonomics approach.
[0052] It should be noted that the above procedure, introduction of
complete selective TOCSY spectra as PCA data inputs, was performed
in this particular case only as a test of the greater
discriminatory sensitivity of the semiselective TOCSY spectra
relative to 1D proton spectra. In practice, in an actual
metabonomics study, the selective TOCSY peaks for a number of
compounds of interest would be integrated, and the integral
intensity numbers used as PCA data inputs..sup.3 The use of the
peak integral intensities is preferable in that it facilitates more
rapid statistical analysis, allows for the testing of the TOCSY
peaks for purity using the Pearson correlation method, and allows
for the establishment of the statistical significance of the
different chemical components using MANOVA..sup.3
[0053] The use of semiselective TOCSY spectra as data inputs for
PCA calculations provides a relatively rapid means of
distinguishing between sets of biofluid samples with subtle
differences in metabolite concentrations. When multiple components
are excited by the semiselective pulse, the Pearson moment
correlation coefficient provides a method to distinguish pure TOCSY
peaks and to aid in their assignment. Finally, the use of
semiselective TOCSY spectra as PCA data inputs is more sensitive to
small differences in metabolite concentrations than PCA
calculations using 1D proton spectra as data inputs. Good
separation of spiked and control samples could be made easily at
the level of 250 .mu.M with the use of selective TOCSY using a 1
min acquisition time.
[0054] It should be understood and appreciated that additional
external standardization processes, such as electronic quantitating
methods (e.g., ERETIC), may also be used in addition to and/or in
conjunction with the selective TOCSY processes of the present
teachings. As such, the present teachings are not intended to be
limiting in nature herein.
[0055] While exemplary embodiments incorporating the principles of
the present teachings have been disclosed hereinabove, the present
teachings are not limited to the disclosed embodiments. Instead,
this application is intended to cover any variations, uses, or
adaptations of the invention using its general principles. Further,
this application is intended to cover such departures from the
present disclosure as come within known or customary practice in
the art to which this invention pertains and which fall within the
limits of the appended claims.
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* * * * *