Patent Application: US-68065508-A

Abstract:
a method for the parallel identification of one or more metabolite species within a biological sample is provided . the method comprises producing a first spectrum by subjecting the sample to a nuclear magnetic resonance analysis , the first spectrum containing individual spectral peaks representative of the one or more metabolite species contained within the sample ; producing a second spectrum by subjecting the sample to a mass spectrometry analysis , the spectrum containing individual spectral peaks representative of the one or more metabolite species contained within the sample ; subjecting each of the individual spectral peaks to a statistical pattern recognition analysis to identify the one or more metabolite species contained within the sample ; and identifying the one or more metabolite species contained within the sample by analyzing the individual spectral peaks of the mass and nuclear magnetic resonance spectra .

Description:
the embodiments of the present invention described below are not intended to be exhaustive or to limit the invention 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 invention . the present teachings are generally related to the combination of gc × gc - ms and nmr for the metabolomics - based analysis of human serum samples from breast cancer patients and matched healthy controls . after gc × gc - ms and nmr measurements , a multivariate statistical method , such as pca , is applied to reduce the data set size and complexity . while both gc × gc - ms and nmr independently have the ability to differentiate healthy and cancer patients using serum samples , the present inventors have found that combining the score plots of these two techniques results in an improved classification system . more particularly , the present method allows gc × gc - ms spectra to be statistically correlated with nmr using the statistical heterospectroscopy (“ shy ”) method 37 , thereby allowing important metabolites that correlated with one another across the two spectroscopic methods to be identified . advantages and improvements of the processes and methods of the present invention are demonstrated in the following example . this example is illustrative only and is not intended to limit or preclude other embodiments of the invention . commercial human blood serum samples from breast cancer patients ( n = 20 ) and gender and age - matched healthy controls ( n = 20 ) were purchased from asterand ( detroit , mich .). all the serum samples were obtained from female volunteers of ages ranging from 40 to 75 years old . samples were stored at − 80 ° c . until the measurements were made . nmr measurements were performed on a bruker drx 500 mhz spectrometer equipped with a room temperature hcn probe . samples were prepared by mixing 400 μl serum with a 300 μl solution of 1 . 5 mm 3 -( trimethylsilyl ) propionic - ( 2 , 2 , 3 , 3 - d4 ) acid sodium salt ( tsp ) in d 2 o . tsp was used as the frequency standard ( δ = 0 . 00 ). after vortexing and centrifugation , 580 μl aliquots were transferred to standard 5 mm nmr tubes for analysis . samples were measured using a standard 1d cpmg ( carr - purcell - meiboom - gill ) pulse sequence coupled with water presaturation . for each spectrum , 32 transients were collected resulting in 32 k data points using a spectral width of 6000 hz . an exponential weighting function corresponding to 0 . 3 hz line broadening was applied to the free induction decay ( fid ) before fourier transformation . after phasing and baseline correction using bruker &# 39 ; s xwinnmr software , the processed data were saved in ascii format for further multivariate analysis . the spectral region from 4 . 5 to 6 ppm , which contains water and urea signals , was removed from each spectrum prior to data analysis . for gc × gc - ms measurements , protein precipitation was first performed for each sample by mixing 200 μl serum with 200 μl methanol in a 1 . 5 ml eppendorf tube . the mixture was incubated on a thermomixer at 33 ° c . for 15 min and then centrifuged at 15000 rpm for 5 min . the upper portion was transferred into another eppendorf tube for future use . 200μl chloroform was mixed with the precipitate for 1 hour at room temperature . after centrifugation , the aliquot was transferred and combined with the methanol solution from the previous step . the mixture was then lyophilized to remove the organic solvents . each dried sample was dissolved in 100 μl pyridine , and 20 μl of this solution was mixed with 10 μl ethylhydroxylamine on a thermomixer at 60 ° c . for 30 min . 20 μl of the derivatization reagent mtbstfa ( n - methyl - n -( tert - butyldimethylsilyl ) trifluoroacetamide ) was then added to the mixture on the same thermomixer for 60 min , resulting in the addition of a tert - butyldimethylsilyl group to active functional groups such as the hydroxyl , amino or carboxylic groups . derivatized samples were then transferred to the leco pegasus 4d gc × gc - ms instrument ( st . joseph , mich .) for measurements . the automated leco system facilitates a two dimensional gc capacity using a cryo - trapping unit between the two chromatographic columns . the first dimension chromatographic column was a 10 m db - 5 capillary column with an internal diameter of 180 μm and a film thickness of 0 . 18 μm , and the second dimension chromatographic column was a 1 m db - 17 capillary column with an internal diameter of 100 μm and a film thickness of 0 . 1 μm . high purity helium was used as carrier gas at a flow rate of 1 . 0 ml / min . the first dimension column oven ramp began at 50 ° c . with a hold time of 0 . 2 min , then increased to 300 ° c . at a rate of 10 ° c / min and held at this temperature for 5 min . the second dimension column oven began 20 ° c . higher than the corresponding first dimension column oven ramp with the same program rate and hold time . the second dimension separation cycle time was set for 4 seconds . 2 μl of the derivatized solutions was injected in a split mode with a ratio of 20 : 1 . the temperatures for the inlet and transfer line were set at 280 ° c ., and the ion source was set at 200 ° c . the detection and filament bias voltages were set at 1600 v and − 70 v , respectively . mass spectra ranging from 50 to 900 m / z were collected at a rate of 50 hz , and each sample was measured twice to assure the reproducibility of the instrument . the nist ms database ( nist ms search 2 . 0 , nist / epa / nih mass spectral library ; nist 2002 ) was used for data processing and peak matching . all pre - processing and multivariate analyses of the experimental data were carried out using matlab 7 . 1 . 0 . 246 ( mathworks inc ., natick , mass .) with the pls toolbox ( eigenvector research inc , wenatchee , wash .). both gc × gc - ms and nmr data were transferred from the instruments in plain text format and then imported into matlab . three - way gc × gc - ms data was composed of the first dimension gc retention time ( in sec ), the second dimension gc retention time ( in sec ) and corresponding tic ( total ion current ). gc × gc - ms data were unfolded by inserting the second dimension data into the first dimension to form a two - column dataset . prior to data analysis , the spectral region below 252 sec , which contains large peaks from pyridine , tert - butyldimethylsilanol and 2 , 2 , 2 - trifluoro - n - methyl - acetamide , was removed . in addition , the data containing the tert - butyldimethylsilanol background peak eluting in the second gc column at about 0 . 6 sec was also excluded from the data analysis . nmr spectra were binned to 1000 frequency buckets of equal width in order to remove the errors resulting from the small fluctuations of chemical shifts due to ph or ion concentration variations while the gc × gc - ms spectra were kept at full resolution . both nmr and gc × gc - ms data were normalized against the total spectral intensity and then mean - centering was carried out prior to multivariate analysis . pca was all performed using the pls toolbox . for gc × gc - ms results , the corresponding loading or weight plot was folded back into the 3 - way presentation format for visualization . typical gc × gc - ms spectra derived from the serum of a cancer patient and a healthy volunteer are shown in fig1 a and 1 b , respectively . the three - dimensional gc × gc - ms spectra are shown in the bottom of each panel , while the corresponding contour plots are shown in the upper panels . compared with the chromatogram resulting from the first column ( shown in white in the three - dimensional spectra ), the unique feature of gc × gc - ms is clearly the much improved resolution due to the separation on the second gc column . more than 800 compounds could be identified in these spectra by searching against the nist database . this complexity made it impossible to differentiate the spectra visually , and thus multivariate analysis was applied . pca was first applied to the 40 gc × gc - ms spectra . original spectra were projected onto the two - dimensional space of the first two principal components , pc1 and pc2 . the classification between breast cancer and normal samples is clearly shown in the score plot ( fig2 ), where circles indicate 95 % confidence limits . separation between the two sample groups is along pc 1 while the variation within one sample group is primarily along pc2 . in the pca results , breast cancer and normal samples are separated mostly along the first principal component ( pc 1 ). the weight of pc1 is shown in fig3 a , in which orange and blue colors indicate up - and down - regulation , respectively , of metabolite concentrations observed in the breast cancer samples , compared to the normal samples . identification of the seven most significant peaks was carried out by the nist database search . six of the compounds , which are observed to be the most altered in concentration in the cancer samples , are listed in table 1 , along with calculated p - values from the student &# 39 ; s t - test . these p - values are shown before any bonferroni correction . 40 a very conservative estimate of this correction would be 1000 , since approximately 800 compounds were identified in the serum samples . five of these 6 compounds would remain significant or very close to significant ( p & lt ; 0 . 05 ) even after a bonferroni correction of this magnitude , and the corresponding box plot indicating the altered concentrations of these five compounds is shown in fig3 b . the identities of the six compounds were confirmed by running gc × gc - ms experiments of authentic ( commercial ) compounds under the same preparation , derivatization and acquisition conditions . among these compounds , a number of alpha - hydroxy acids ( aha ) have been reported to be related to the development of skin cancer by affecting the expression of epidermal growth factor receptor ( egfr ). 41 fatty acids , glycerol and cholesterol are also reported to be general markers for cancer growth . among them , hexadecanoic acid ( palmitic acid ), which is up - regulated in breast cancer samples , has been reported to induce cancer cell death . 42 , 43 more generally , it has also been reported that unsaturated acids stimulate the proliferation of human cancer cells and saturated acids inhibit it by causing apoptosis . certainly , there are more species , which may also explain the difference between breast cancer and normal samples by comparing the library search and pca loading ; however , even using just these six compounds as a starting point for discrimination yields high classification accuracy . in addition to the exemplary compounds listed above , it should be understood and appreciated herein that other metabolite species useful as biomarkers may also be identified in accordance with the present invention . some of these additional metabolite species include , but are not limited to , lipid - ch2 signals ( which are detectable by nmr ), as well as lactic acid and glucose . nmr has been one of the major techniques in metabolomics research because of its reproducible and quantitative features . fig4 a and 4 b compare typical 1d cpmg spectra of breast cancer and normal samples , where only the aliphatic region is shown and compared ( the intensities of the peaks in the aromatic region were very small ). from visual inspection , a number of peaks appear to be clearly different between the cancer and normal samples . for example , the glucose level is decreased in the cancer sample , while the intensity of the doublet belonging to lactate at 1 . 34 ppm is significantly larger . several of the broad lipid signals also have higher intensities in the nmr spectra of cancer samples . pca was applied to the 40 nmr spectra and the score plot is shown in fig5 . all the samples are classified by projecting corresponding nmr spectra onto the 2d plane of pc1 and pc2 where ellipses indicate 95 % confidence limits . using a linear discrimination , a discriminant accuracy of approximately 90 % was achieved . in contrast to the results of the gc × gc - ms , the separation occurs along the first two pc &# 39 ; s , primarily along pc2 , which showed 32 % of the variance in the nmr data . several putative biomarkers responsible for the separation were identified from pca . the levels of lactate ( 1 . 33 ppm ) and glucose ( 3 . 53 , 3 . 73 , 3 . 86 ppm ) are observed to increase in breast cancer patients . in addition , lipid signals at 0 . 92 and 1 . 31 ppm are also more concentrated in the serum samples from cancer patients . the pc 1 loading is mainly contributed by the variation within the breast cancer patients , for instance , the concentration of sugars and lipids . previously conducted research 13 has suggested an approach to combine the pca scores from two independent analytical techniques , desi - ms and nmr spectroscopy , when the 2d score plot is insufficient to differentiate the samples . as shown in fig5 , pc1 score values from the gc × gc - ms pca as the third dimension of the 3d score plot were used . only a single gc × gc - ms measurement for each sample was included in the plot . the classification accuracy of nmr pca can be improved by combining with gc × gc - ms resulting in all the samples being correctly classified . this combined approach indicates that inclusion of a second orthogonal analytical technique can be useful for improving the discrimination of similar samples . the combination of gc × gc - ms and nmr spectroscopy , using a metabolomics approach , provides a novel and powerful approach for analyzing serum samples from breast cancer patients and healthy controls . advanced gc × gc - ms possesses the ability to resolve and identify many more metabolites than conventional gc - ms . a good discrimination can be achieved from gc × gc - ms measurements after multivariate analysis . nmr spectroscopy also shows the capability of differentiating breast cancer from healthy control samples . more importantly , the composite pca score plot of gc × gc - ms and nmr data improves the accuracy of classification . while an exemplary embodiment incorporating the principles of the present invention has been disclosed hereinabove , the present invention is 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 . references : the following references are incorporated herein by reference in their entirety : ( 1 ) lindon , j . c . ; 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