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O R I G I N A L A R T I C L E
Magic angle spinning NMR spectroscopic metabolic profilingof gall bladder tissues for differentiating malignant from benign
disease
Santosh Kumar Bharti Anu Behari
Vinay Kumar Kapoor Niraj Kumari
Narendra Krishnani Raja Roy
Received: 13 April 2012/ Accepted: 2 May 2012 / Published online: 26 May 2012
Springer Science+Business Media, LLC 2012
Abstract Gall bladder tissue specimens obtained from 112
patients were examined by high resolution magic anglespinning (HR-MAS) NMR spectroscopy. Fifty one metab-
olites were identified by combination of one and two-
dimensional NMR spectra. To our knowledge, this is the first
report on metabolic profiling of gall bladder tissues using
HR-MAS NMR spectroscopy. Metabolic profiles were
evaluated for differentiation between benign Chronic Cho-
lecystitis (CC, n = 66) and xantho-granulomatous chole-
cystitis (XGC, n = 21) and malignant gall bladder cancer
(GBC, n = 25). Increase in choline containing compounds,
amino acids, taurine, nucleotides and lactate as common
metabolites were observed in malignant tissues whereas lipid
content was found low as compared to benign tissues. Prin-
cipal component analysis obtained from the NMR data
showed clear distinction between CC and GBC tissue spec-
imens; however, 27 % of XGC tissues were classified with
GBC. The partial least square discriminant analysis (PLS-DA)
multivariate analysis between benign (CC, XGC) and malig-
nant (GBC) on the training data set (CC; n = 51, XGC;n = 15, GBC; n = 19 tissues specimens) provided 100 %
sensitivity and 94.12 % specificity. This PLS-DA model when
executed on the spectra of unknown tissue specimens (CC;
n = 15, XGC; n = 6, GBC; n = 6) classified them into the
three histological categories with more than 95 % of diagnostic
accuracy. Non-invasive in vivo MRS technique may be used
in future to differentiate between benign (CC and XGC) and
malignant (GBC) gall bladder diseases.
Keywords HR-MAS NMR spectroscopy Gall bladder
cancer (GBC) Xantho-granulomatous cholecystitis
(XGC) Chronic cholecystitis (CC) Metabolic profiling
Metabolomics
Abbreviations
BCA Branch chain amino acids
CC Chronic cholecystitis
CPMG Carr-purcell-meiboom-gill
DQF-COSY Double quantum filtered-correlation
spectroscopy
GBC Gall bladder cancer
HR-MAS High resolution-magic angle spinning
PLS-DA Partial least square regression discriminant
analysis
PCA Principal component analysis
XGC Xantho-granulomatous cholecystitis
1 Introduction
Gall bladder cancer (GBC) represents the fifth most com-
mon malignancy of the gastro-intestinal tract and the
commonest malignancy of the biliary tract worldwide
Electronic supplementary material The online version of thisarticle (doi:10.1007/s11306-012-0431-7 ) contains supplementarymaterial, which is available to authorized users.
S. K. Bharti R. Roy (&)
Centre of Biomedical Magnetic Resonance, Sanjay Gandhi
Postgraduate Institute of Medical Sciences Campus, RaibarelyRoad, Lucknow 226014, Uttar Pradesh, India
e-mail: [email protected]
A. Behari V. K. Kapoor (&)
Department of Surgical Gastroenterology, Sanjay Gandhi Post
Graduate Institute of Medical Sciences, Raibarely Road,
Lucknow 226014, Uttar Pradesh, India
e-mail: [email protected]
N. Kumari N. Krishnani
Department of Pathology, Sanjay Gandhi Post Graduate Institute
of Medical Sciences, Lucknow, Uttar Pradesh, India
123
Metabolomics (2013) 9:101118
DOI 10.1007/s11306-012-0431-7
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(Misra and Guleria 2006). It has an unusual geographic
distribution having more frequency in Chile, Bolivia, Israel
and northern India than in United States and Europe (Gupta
and Shukla 2005; Orth and Beger 2000). According to the
Indian Council for Medical Research (ICMR) reports, the
incidence rate of GBC is very high in northern India with
the highest in world and therefore GBC could be named an
Indian disease (Kapoor 2007). GBC is 23 times morecommon in women than men (Yalcin 2004); the incidence
of GBC in India is 10 times more frequent in women that is
10.1 per 100,000 for females and 1.01 per 100,000 for
males (ICMR 1996). Prognosis of GBC is very poor and
about 40 % of the patients have survival rate of five years
in case of early diagnosis and one year in case of advanced
stages (Lazcano-Ponce et al. 2001).
Chronic cholecystitis (CC) is inflammationof gall bladder
(GB) which is associated with gall stones in more than 90 %
of the cases (Schirmer et al. 2005). It occurs when gallstone
or solid sludge impact in the cystic duct and inflammation
develops behind the obstruction (Elwood 2008). Xantho-granulomatous cholecystitis (XGC), an uncommon variants
of chronic cholecystitis (Yang et al. 2007); (Chang et al.
2010; Roberts and Parsons 1987) is characterized by thick-
ening of gall bladder wall, focal or destructive inflammation
with accumulation of lipid laden macrophages, fibrous tis-
sue, with acute and chronic inflammatory cells (Jessurun and
Albores-Saavendra 1996). Clinically, it is very difficult to
distinguish XGC from any other inflammatory gall bladder
disease such as CC, acute cholecystitis and especially GBC.
The XGC mimics GBC which makes it difficult to differ-
entiate by imaging techniques like ultrasonography (US),
computed tomography (CT) and magnetic resonance imag-
ing (MRI) (Chang etal. 2010). Thefinal diagnosis is obtained
only through histopathological examination.
The exact etiology of GBC is unknown, but several risk
factors have been identified including age, gender, chole-
lithiasis, chronic inflammation of gall bladder, increased
stone size, family history, choledochal cyst, composition of
bile acids, infection, exposure to carcinogens etc. (Gupta
and Shukla 2005; Tazuma and Kajiyama 2001; Pandey
et al. 1995). The most accepted model for development of
GBC includes initial chronic inflammation leading to
metaplasia, dysplasia, carcinoma in situ and finally to
invasive cancer (Roa et al. 2009; Roa et al. 2006). Chronic
inflammation may arise due to gall stone obstruction,
bacterial infection, metabolic disturbances etc. (Roa et al.
1996). Discrimination between the benign (CC and XGC)
and malignant (GBC) has an important role in management
of patients care.
Metabolomics allows the qualitative and quantitative
measurements of all metabolites present in cells, biofluids,
pathological fluids, tissues, tissue extracts etc. (Hollywood
et al. 2006; Lindon et al. 2004). Common analytical
techniques used for metabolomics studies are HPLC, MS,
GC, GCMS and NMR spectroscopy. Among them high
resolution NMR spectroscopy is widely used for investi-
gating the composition of body fluids, tissues extracts,
pathological fluids etc. as a wide range of metabolites can
be detected simultaneously without separation of individ-
ual components (Lindon et al. 2000). NMR based meta-
bolic profiling followed by pattern recognition statisticaltechniques provides a comprehensive metabolic informa-
tion of various components in biofluids, reflecting levels of
endogenous metabolites/biomarkers involved in key cel-
lular pathways, which indicate physiological and patho-
physiological status, and also further used in diagnosis
(Lindon and Nicholson 2008). High-resolution magic angle
spinning (HR-MAS) NMR spectroscopy is a further
advancement of this technique and it provides fast, easy
and direct analysis of intact tissues cells, foods, paste, soils,
fruit, plant, tissues (Beckonert et al. 2010; Bharti et al.
2011) etc. It also offers qualitative and quantitative bio-
chemical information on small intact tissue samples bygenerating metabolic profile. The HR-MAS NMR analysis
of tissue specimens allows the simultaneous detection of
both lipids and small metabolites with a resolution com-
parable to that of liquid state NMR. The HR-MAS NMR
spectroscopy is nondestructive unlike extraction proce-
dures and tissue specimens can be further used for routine
molecular biology experiments (Stenman et al. 2010).
Extraction procedure requires large quantity of the samples
and increases the chance of loss of some low concentrated
metabolites or reduction in their intensity which leads to
the misinterpretation of data (Duportet et al. 2011; Cheng
et al. 1998). In recent years, HR-MAS NMR spectroscopy
has been successfully applied to characterize the metabolic
composition of control and pathological tissues from brain
(Wright et al. 2010), pancreas (Misra et al. 2008), lung
(Rocha et al. 2009), breast (Gribbestad et al. 1994), colo-
rectal cancer (Chan et al. 2009) etc. Therefore in the
present study, 1H HR-MAS NMR based metabolomic
approach has been applied with an aim to carry out meta-
bolic profiling of gall bladder tissues followed by chemo-
metric and quantitative analysis, for discrimination of
benign and malignant tissues types.
2 Materials and methods
2.1 Subjects and study protocol
Gall bladder tissue specimens (n = 112, CC = 66, XGC =
21, GBC = 25) were collected from patients undergoing
laparoscopic cholecystectomy/open cholecystectomy at the
Department of Surgical Gastroenterology, Sanjay Gandhi
Post Graduate Institute of Medical Sciences, Lucknow, a
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tertiary care super specialty hospital in northern India. Gall
bladder tissues specimens, confirmed to have CC, XGC and
GBC on histo-pathology were included in this study. Tissue
samples from male and female patients with age more than
18 and\70 years were taken after surgical removal. The
suspected region chosen by surgeons were visibly resected
and then subjected for histopathology and NMR analysis.
A Part of the same tissues specimen was sent to the depart-ment of pathology for routine histopathological testing and
other part for NMR analysis. All samples were snap frozen
in liquid nitrogen within 15 min after surgery and stored at
-80 C untilNMR spectra were recorded.The studyhas been
approved by SGPGIMS institute ethics committee and con-
sent from each patient was obtained prior to investigations.
2.2 Sample preparation and acquisition
Tissue specimen stored at -80 C were thawed at room
temperature and then washed with saline deuterated water
in order to remove blood from tissues specimens. Dissectedand weighed pieces of gall bladder were inserted in the
ZrO2 rotor of 50 ll. A volume of 20 ll of D2O was filled in
the rotor with tissue sample for locking the spectrometer
frequency. The sample-rotor-setup was then transferred in
the HR-MAS probe. A sample weight of 32 1.5 mg of
wet tissue was used for analysis. After NMR analysis all
the tissue specimens were fixed in formalin in order to
observe the high spinning effect on the tissue integrity by
histopathological examination.
2.3 NMR experimental conditions
1H NMR spectra were recorded on Bruker Biospin Avance-
III 800 MHz NMR (Bruker GmBH, Germany) spectrometer
operating at proton frequency of 800.21 using 4 mm HR-
MAS 1H/13C/31P triple resonance probehead equipped with
magic angle gradient accessories. A zirconium oxide rotor of
4 mm diameter was used for the spectral recording with a
spinning speed of 8000 2 Hz. Twenty microliter of deu-
terium oxide containing (Sigma-Aldrich, St. Louis, MO,
USA) was used for internal lock. Sample temperature was
regulated using Bruker BCU-05 unit at 280 0.5 K during
the acquisition of spectra to reduce the metabolic changes
during spectral acquisition (Beckonert et al. 2010). The
calibration of thetemperature wasperformed using methanol
during setup of the HR-MAS probe. HR-MAS NMR spectra
were recorded within few days and no further temperature
calibration was performed during the sample analysis.
2.3.1 One dimensional NMR analysis
The 1H HR-MAS spectra with water suppression were
acquired using one-dimensional single pulse and Carr-
Purcell-Meiboom-Gill (CPMG) pulse sequence with the
following experimental parameters: spectral width of
12,820.5 Hz,, time domain data points of 64 K, effective
90 flip angle, 9.0 ls, relaxation delay 4.0 s acquisition
time of 2.55 s, 64 number of scan with 4 dummy scan, a
constant receiver gain of 50.8 with a total recording time of
9 min. CPMG pulse sequence with water suppression
[PRESET-90-(d-180-d)n-Aq] was performed to removeshort T2 components arising due to the presence of proteins
as well as to obtain a good baseline for multivariate anal-
ysis. Echo time of 40 ms (2d 9 n, n = 200, d = 100 ls)
was used in CPMG pulse sequence. All spectra were pro-
cessed using line broadening for exponential window
function of 0.3 Hz prior to Fourier transformation. The 1H
HR-MAS spectra of gall bladder tissues were manually
phased and automatically baseline corrected using TOP-
SPIN 2.1 (Bruker Analytik, Rheinstetten, Germany). The1H NMR spectra were referenced to the methyl resonance
of alanine at 1.48 ppm. The total analysis time (including
sample preparation, optimization of NMR parameters anddata acquisition) of 1H HR-MAS NMR spectroscopy for
each sample was approximately 20 min.
2.3.2 Two dimensional NMR analysis
To confirm the assignments, two-dimensional homo
nuclear correlation spectroscopy (1H-1H COSY) and1H-13C hetero nuclear single quantum correlation spec-
troscopy (HSQC) experiments were performed using Bru-
kers standard pulse program library. The parameters used
for COSY were as follows: 2 K data points were collected
in the t2 domain over spectral width of 12820 Hz, 512 t1increments were collected with 64 transients, relaxation
delay of 1.5 s, acquisition time of 95 ms, and pre-satura-
tion of water resonance was carried out during the relax-
ation delay. The resulting data were zero-filled to 1 K and
were weighted with sine bell window functions in both the
dimensions prior to Fourier transformation. The parameters
used for 1H-13C HSQC were: 2 K data points were col-
lected in t2 dimension over spectral width of 12,820 Hz,
256 t1 increments were collected with 32 transients,
relaxation delay of 2.0 s, acquisition time of 80 ms and a
90 pulse of 9.0 ls. The phase sensitive data were obtained
by the antiecho-Time proportional phase increments
(Antiecho-TPPI) method. The resulting data were zero-
filled to 512 data points and were weighted with 90 shifted
squared sine bell window functions in both the dimensions
prior to Fourier transformation.
2.4 Statistical analysis
Multivariate principal component analysis (PCA) and
partial least square-discriminant analysis (PLS-DA) was
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performed on the HR-MAS NMR data. The external vali-
dation of the data was performed by dividing about 75 %
of the patients as training set and the remaining 25 % as
test set. In the present study, 85 samples obtained from 85
patients were randomly selected from Fisher and Yates
table (Fisher and Yates 1957) as training set for PLS-DA
model generation and the rest 27 samples from 27 patients
were predicted on the basis of that model. The training setcomprised of 85 tissue specimens with 66 tissues (CC;
n = 51, XGC; n = 15) having benign histology while rest
19 (GBC; n = 19) were malignant in nature. The 27 tis-
sues, comprising the test set, were not included during
construction of the model and treated as blinded samples
till the prediction of their respective pathological status and
then compared with their corresponding histopathology
reports.
Before subjecting the CPMG spectra for multivariate
analysis, these were reduced to discrete chemical shift
regions (between 0.5 and 9.0 ppm) by digitization to pro-
duce a series of sequentially integrated regions of 0.01 ppmwidth, using Bruker AMIX software (Version 3.8.7, Bruker
Biospin, Germany). Simple rectangular bucketing proce-
dure was chosen to integrate the peak area. The data
obtained was mean centered and normalized by dividing
each integral area of the segment by total area of the
spectrum in order to compensate for the differences in
overall metabolite concentration between individual sam-
ples. The resulting data matrices having normalized inte-
gral values were exported into Microsoft Office Excel 2007
(Microsoft Corporation, USA). This was further imported
to The Unscrambler X Software package (Version 10.0.1,
Camo USA) for multivariate Principal Component Analy-
sis (PCA) and Partial Least Square Discriminant Analysis
(PLS-DA) analysis. In PCA and PLS-DA, a full cross
validation using leave one out were applied in order to
avoid the overfitting of the mathematical model. To vali-
date the robustness of the PLS-DA model, unknown data
set obtained from 27 patients was subsequently analysed.
Univariate analysis of semi-quantitative data was per-
formed using MannWhitney U test (SPSS 15.0).
2.5 Semi-quantitative analysis
Absolute integral area of resolved metabolites were quan-
tified with respect to QUANTAS (QUANTification by
Artificial Signal) (Bharti and Roy 2012). QUANTAS is an
artificial signal generated by NMRSIM (or any NMR
simulation software) with a fixed line width and intensity
was added to real HR-MAS NMR spectrum. The main
condition for the QUANTAS is all spectra should be
recorded at same acquisition parameters using same
receiver gain setting. Integration of some of the metabo-
lites which are not overlapped, were performed for
quantification of absolute intensity and metabolites integral
area/intensity were normalized with QUANTAS which
provides information about variation in the quantitative
values of metabolites in different groups. Mean of integral
area with standard deviation was calculated for comparing
the CC, XGC and GBC individually.
3 Results
Hundred twelve patients were included in the study.
Chronic Cholecystitis was more frequent as compared to
the XGC and GBC. The routine histopathologies of the
surgically resected GBC tissues were found to be adeno-
carcinoma in nature. The number of females cases were
comparatively more in each groups whereas as there was
no significant difference in the mean age of male and
female. The detailed distribution of patients in each group,
mean age, range and gender are reported in Supplementary
Table S-1.
3.1 Metabolic profile of gall bladder tissues
A typical proton HR-MAS NMR spectrum of GBC tissues
along with assignments is shown in Fig. 1. Using HR-MAS
NMR spectroscopy, 51 endogenous metabolites were
assigned that includes lipids, amino acids, organic acids,
choline containing compound, creatine, sugars, etc. The
detailed list of metabolites and assignments is presented in
Table 1. Characterization of the metabolites was carried
out on the basis of chemical shift, coupling constant, and
splitting pattern of metabolites as reported in literature
(Gribbestad et al. 1994; Sitter et al. 2006; Martinez-
Granados et al. 2011; Rocha et al. 2009), two dimensional
NMR spectra (Supplementary Fig. 1 and 2), comparison
with standard NMR spectra of metabolites reported at
Biological Magnetic Resonance Bank (BMRB, www.bmrb.
wisc.edu) and Human Metabolome Data Base (HMDB,
www.hmdb.ca) (Markley et al. 2007; Wishart et al. 2009).
Metabolic profile of malignant (GBC) differed from benign
conditions (CC and XGC). The stack plot of one dimen-
sional proton NMR spectra of CC, XGC and GBC with
assignment of resonances is shown in Fig. 2. In order to
examine the effect of high spinning, few tissues samples
were subjected for further histopathological examination
after HR-MAS analysis. The tissue has not lost its integrity
but the lining epithelium is denuded. The denudation
(shedding off) of lining epithelium occurs when there is
poor fixation or due to high spinning speed. Comparison of
routine histopathological examination with histopathology
after HR-MAS analysis obtained from the same patient is
shown in Fig. 3, which clearly demonstrates similar his-
topathological findings.
104 S. K. Bharti et al.
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3.2 PCA of CC, XGC and GBC NMR spectra
Multivariate principal component analysis (PCA) was per-formed on the one-dimensional CPMG HR-MAS NMR
spectra of CC, XGC and GBC as it provides better baseline.
A four principal component model explained[95 % of the
variance, with the first two components explaining 89 % of
the total variance. A clear clustering separation between CC
and GBC groups in the PCA of spectra demonstrated sig-
nificant metabolic variations in CC and GBC groups
(Fig. 4A) whereas 27 % of the XGC samples were found to
be overlapped with GBC groups and rest of them were
classified with CC. The detailed examination of PC1 (prin-
cipal components), PC2 and PC3 loadings showed that the
cluster separation arising mainly due to TAG signals (FattyAcid; FA); terminal methyl, (CH3, 0.90 ppm), saturated
methylene ((CH2)n, 1.30 ppm), methylene attached with
carbonyl methylene (CH2CH2CO, 1.59 ppm), mono-
allylic methylene (CH2CH=CH, 2.05 ppm), carbonyl
methylene (CH2CO, 2.27 ppm), di-allylic methylene
(CH=CHCH2CH=CH, 2.78 ppm), TAG-glycerol back-
bone (TAG-Glycerol, 4.304.13 ppm), branch chain amino
acids (BCA; isoleucine, leucine and valine, 0.951.05 ppm),
beta-hydroxybutyrate (1.20 ppm), lactate (1.33, 4.12 ppm),
1.01.52.02.53.03.54.04.5 ppm
Methionine
Valine/Leucine/Is
oleucine
Methionine
Glutamine
Lysine
AsparticAcid
Asparagin
e
Cholester
ol
Glutamic
Acid
Lysine
Alanine
Lactate
Glucose
Lactate
Threonine
Taurine
Taurine
Lysine/Creatine
Myinositol
-Hydroxybutyrate
AscorbicAcid
Glycine
Tyrosine
CholineContaining
Compounds
Creatine
Urid
ine
Alanine
GlutamicAcid
Proline
5.56.06.57.07.58.08.5
B
A
ppm
Tyrosine
FumaricAcid
Tyrosine
Histidine Ur
acil
Uridine
Tryptophan
/Uracil
Tryptophan
Histidine
Phenylalanine/Tryptophan
Glucose
FattyAcidCH=CH
Formate I
nosine/A
denosine
Inosine/Ad
enosine
Adenine
Inosine/Adeno
sine
Fig. 1 A typical 800 MHz 1H HR-MAS NMR spectrum of gall bladder carcinoma (malignant) tissue recorded using CPMG pulse sequence
showing assignments of metabolites. Expansions of NMR spectrum from a 0.54.7 and b 5.09.0 ppm
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Table 1 Chemical shift assignments of metabolites observed in the
HR-MAS spectrum of gallbladder tissues using one dimensional (1D)
chemical shift reported in literature (LIT), two dimensional COSY,
HSQC and comparing standard NMR spectrum (STD) of individual
metabolites taken from Biological Magnetic Resonance Bank
(BMRB)
S. No. Name of metabolites Chem. shift Resonances Methods
1 Acetate 1.92 (s) CH3 1D, HSQC
2 Adenine 8.21 (s) 4H 1D, STD
8.23 (s) 8H
3 Alanine 1.48 (d) b-CH3 1D, COSY, HSQC
3.78 (q) a-CH
4 Beta-alanine 2.56 b-CH2 1D, COSY, HSQC
3.18 a-CH2
5 Arginine 1.68 (m) c-CH2 1D, COSY, HSQC
1.90 (m) b-CH2
3.25 d-CH2
3.77 a-CH
6 Ascorbic acid 4.07 C5H 1D, STD
3.73 CH2
4.55 (s) C4H-ring
7 Asparagine 2.87 (dd) b-CH 1D, COSY, HSQC2.95 (dd) b0-CH
4.01 (dd) a-CH
8 Aspartic acid 2.69 (dd) b-CH 1D, COSY, HSQC
2.82 (dd) b0-CH
3.90 (dd) a-CH
9 Cholesterol 0.72 (s) C18H 1D, STD
10 Choline 3.21 (s) N(CH3)3 1D, COSY, HSQC
3.53 N-CH2
4.07 O-CH2
11 Citric acid 2.53 (d) CH2 1D, STD
2.67 (d) CH2
12 Creatine 3.03 N-CH3 1D, HSQC
3.94 N-CH2
13 Ethanol 1.18 (t) CH3 1D, COSY, STD
3.62 CH2
14 Ethanolamine 3.15 N-CH2 1D, COSY, STD, HSQC
15 Fatty acids (TAG) 0.90/0.96 CH3 1D, COSY, LIT
1.3 (CH2)n
1.59 CH2CH2CO
2.04/2.07 CH=CHCH2
2.26 CH2CO
2.81 CH=CHCH2CH=CH
4.13 CH2CHOHCH2
4.31 CH2CHOHCH2
5.33 CH=CH/CH2CHOHCH2
16 Formic acid 8.45 (s) CH 1D, STD
17 Fumaric acid 6.52 (s) CH 1D, STD
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Table 1 continued
S. No. Name of metabolites Chem. shift Resonances Methods
31 Inosine/adenosine 3.86 C50500H ribose 1D, STD
4.28 C40H ribose
4.44 C30H ribose
4.78 (t) C20H ribose
6.10 (d) C10H ribose
8.23 (s) C2H ribose
8.36 (s) C8H ribose
32 Isoleucine 0.94 (t) d-CH3 1D, COSY, STD, HSQC
1.01 (d) c-CH3
1.26 (m) c-CH
1.47 (m) c0-CH
1.98 (m) b-CH
3.68 (d) a-CH
33 Isobutyrate 1.14 (d) CH3 1D, STD, COSY
3.88 CH
34 Lactate 1.33 (d) b-CH3 1D, STD, COSY, HSQC
4.12 (q) a-CH
35 Leucine 0.96 (d) d-CH3 1D, STD, COSY, HSQC
0.97 (d) d0-CH3
1.71 (m) c-CH/b-CH2
3.75 a-CH
36 Lysine 1.47 (m) c-CH2 1D, STD, COSY, HSQC
1.72 (m) b-CH2
1.9 (m) d-CH2
3.02 N-CH2
3.74 a-CH
37 Methionine 2.13 (s) S-CH3 1D, COSY, STD, HSQC
2.16 (s) b-CH22.64 (t) c-CH2
3.85 a-CH
38 Myo-inositol 3.28 (t) C2H-ring 1D, COSY, STD, HSQC
3.54 (d) C1, 3H-ring
3.62 (t) C5H-ring
4.06 (t) C4, 6H-ring
39 Phenylalanine 3.12 b-CH 1D, STD, COSY, HSQC
3.28 b0-CH
3.98 a-CH
7.32 (d) C2H, C6H-ring
7.37 (m) C4H-ring
7.41 (m) C3H, C5H-ring
40 Phosphocholine 3.22 N(CH3)3 1D, STD, HSQC
3.62 N-CH2
4.18 O-CH2
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alanine (1.48 ppm), creatine (3.03 ppm), choline containing
compounds (3.203.24 ppm), taurine (3.25, 3.41 ppm),
glycine (3.56 ppm) and glucose (4.65 ppm). TAG compo-
nents were significantly very high in CC whereas very low in
GBC tissues. Whereas negative loadings in PC1 are due to
isoleucine, leucine, valine, lactate, alanine, lysine, creatine,
choline containing compounds, taurine, glycine and glucose
which demonstrate that these metabolites were high in GBC
samples (Fig. 4B). Few of the XGC spectra were found to be
overlapped with GBC and and the rest with CC groups. The
detailed individual analysis of each HR-MAS spectrum of
XGC samples confirms that the samples overlapped with CC
groups have higher content of TAG resonances and resem-
bled to CC tissues HR-MAS spectra. However, XGC sam-
ples overlapped with GBC had lower TAG contents,
resembling to HR-MAS spectra of GBC.
Table 1 continued
S. No. Name of metabolites Chem. shift Resonances Methods
41 Proline 2.01 (m) c-CH2 1D, COSY, STD, HSQC
2.08 (m) b-CH
2.35 (m) b0-CH
3.35 d-CH
3.42 d0-CH
4.13 a-CH
42 Serine 3.85 a-CH 1D, COSY, HSQC
3.97 b-CH2
43 Scyllo-inositol 3.35 (s) CH 1D, STD, HSQC
44 Succinic acid 2.41 (s) a,b-CH2 1D, STD, HSQC
45 Taurine 3.25 (t) S-CH2 1D, COSY, STD, HSQC
3.41 (t) N-CH2
46 Threonine 1.34 (d) c-CH3 1D, COSY, STD, HSQC
3.6 (d) a-CH
4.25 (m) b-CH
47 Tryptophan 3.29 (dd) b-CH 1D, COSY, STD, HSQC
3.47 (dd) b0-CH
4.04 (dd) a-CH
7.19 (t) C5H-ring
7.26 (t) C6H-ring
7.30 (s) C2H-ring
7.53 (d) C4H-ring
7.72 (d) C7H-ring
48 Tyrosine 3.06 (dd) b-CH 1D, COSY, STD, HSQC
3.19 (dd) b0-CH
3.95 (dd) a-CH
6.89 (d) C3H, C5H-ring
7.18 (d) C2H, C6H-ring49 Uracil 5.8 (s) C5H-ring 1D, COSY, STD, HSQC
7.54 (d) C6H-ring
50 Uridine 4.13 C40H-ribose 1D, COSY, STD
4.23 (t) C30H-ribose
4.35 (t) C20H-ribose
5.89 (d)/5.92 (d) C10H-ribose/C5H-ring
7.89 (d) C6H-ring
51 Valine 0.99 (d) c-CH3 1D, COSY, STD, HSQC
1.04 (d) c0-CH3
3.62 (d) b-CH
2.28 a-CH
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3.3 PLS-DA analysis of CC, XGC and GBC NMR
spectra
PLS-DA analysis was also performed for better charac-
terization of the metabolites distinguishing CC, XGC and
GBC groups from each other. PLS-DA was performed on
the 1D CPMG HR-MAS spectra obtained from gall bladder
tissues associated with CC, XGC and GBC. The full cross
validated scores plot showed statistically significant dif-
ferences between CC and GBC while few of the XGC
samples overlapped with GBC (Fig. 5a) as observed in
PCA analysis. The PLS-DA model provided 100 % sen-
sitivity and 94.12 % specificity. Discrimination between
the benign and malignant tissues was due to TAG, BCA,
beta-hydroxybutyrate, lactate, alanine, lysine, creatine,
choline compounds, taurine, glycine and glucose. Analysis
of PLS-DA 1D loadings plot express high level of TAG in
CC whereas higher levels of amino acids, beta-hydroxy-
butyrate, lactate, alanine, lysine, choline compounds, cre-
atine, taurine, glucose, glycine etc. were detected in GBC
as similarly observed in PCA model. For validation of the
generated model, discriminating between benign (CC,XGC) and malignant (GBC) was done using the remaining
25 % of the sample (CC; n = 15, XGC; n = 6, GBC;
n = 6), which were predicted using this PLS-DA model.
Unsupervised prediction was performed and it demon-
strated more than 95 % prediction were correct (Fig. 5b)
when compared with histo-pathological findings.
3.4 Semi-quantitative analysis
The above PCA/PLS-DA analysis performed using the inte-
gral area of a particular bin divided by the integral area of
whole binned spectral region used for binning which dem-onstrateit as a relativemethod. Therefore it may or maynot be
able to reflect absolute concentration of metabolites in each
particular group i.e. CC, XGC and GBC. For example relative
intensity of glucose in GBC samples was found higher as
already represented in PCA and PLS-DA loading plots,
whereas absolute intensity is lower as compared to CC and
XGC (Fig. 6). For estimation of intensity of metabolites,
QUANTAS signal with a fixed intensity and line width at
desired chemicalshift wasadded in all the spectra individually
(Bharti and Roy 2012). Mean integral area with standard error
has been shown in Fig. 6 for lipids and small molecules.
Statistical significancefor metabolitesusing their integralarea
was determined by Man-Whitney U test and detailed report
has been provided in Supplementary Table S-2.
The entire lipid components including TAG and cho-
lesterol varied significantly in decreasing order from CC to
XGC and GBC. Small molecules such as taurine, glycine,
glucose, choline containing compounds, creatine, uracil,
tyrosine, amino acids etc. also vary from CC to XGC and
GBC. Few resonances (at 4.60 (broad singlet), 5.60 (mul-
tiplet), 5.90 (triplet), 6.30 (triplet) ppm) which remained
unassigned and their corresponding metabolites of these
resonances could not be identified. However their absolute
intensities in CC, XGC and GBC samples vary signifi-
cantly. Quantitative estimations of intensity of these reso-
nances were also performed and their respective bar plots
are also shown in Fig. 6.
4 Discussion
This study demonstrates that CC samples were mostly
dominated by TAG resonances whereas GBC samples were
6.06.57.07.58.0 ppm
Tyrosine
Unknown
Tyrosine
Histidine
Unknown
Unknown
FumaricAcid
Histidine
Phenylalanine
Uracil
Tryptophan
Farm
ate
Uracil
Nucleotides
Uridine
Inosine/Adenosine
1.01.52.0 ppm2.53.54.04.5
Lactate
Alanine
CreatineT
AG
Choline
compounds
Glycine
Myo-inositol
Taurine
Glucose
3.0
Myo-inositol
Val,Leu,Ile
Lysine
Lactate
Aspartic
Acid
F
E
D
C
B
A
5.5
TAG
TAG
TAG T
AG
TAG
TAG
TAG
Fig. 2 Stack plot of typical1
H HR-MAS NMR spectra ofa, d GBC, b,
e XGC, and c, fCC tissues showing difference in the metabolic profile
110 S. K. Bharti et al.
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dominated by small molecules like amino acids, choline,
creatine, lactate, etc. The lipid profile of gall bladder tis-
sues followed CC[XGC[GBC trends as observed by
HR-MAS NMR which supports earlier study on lipids
extract of gall bladder tissues (Jayalakshmi et al. 2011).
Intensity of cholesterol C-18 CH3 signal was quantified
with respect to QUANTAS signal which also represent
same trends as TAG (Fig. 6). The detailed investigation of
PCA and PLS-DA loadings/coefficients plots indicate high
content of choline containing compounds (choline, choline,etc.), creatine, amino acids, lactate, glycine, taurine and
b-hydroxybutyrate in GBC cases as compared to benign
(CC and XGC) ones. Relative content of glucose with
respect to total spectral area also varied significantly
among the groups.
Lipids are the main source of fuel in mammalian cells
for production of new cells. Carcinoma cells exhibit faster
growth of cells where rate of energy expenditure is higher
than its production results in utilization of stored lipids.
Consequently lipid depletion occurs in cancer cells
(McAndrew 1986). Hence lower content of lipid (TAG)
was observed in GBC as compared to CC samples. How-ever the similar results for lipid depletion in cancer cells of
oral, breast, liver, etc. have been reported by HR-MAS
NMR spectroscopy (Sitter et al. 2009; Srivastava et al.
2011). This study demonstrates significant variation in
TAG and cholesterol content among CC, XGC and GBC.
Depletion in the cholesterol level may be attributed to the
production of cholesterol ester in GBC. However, we were
not able to authenticate the identification of the cholesterol
ester signal at 4.60 ppm, but its presence as a broad
multiplet was observed in GBC spectra (Fig. 6) and
showed a significant increase in its level when compared
with other groups.
The absolute intensity of lactate could not be quantified
due to overlap of TAG resonance at 1.33 ppm and TAG-
Glycerol at 4.13 ppm. However, the loading profiles of
PCA and PLS-DA models have been recently used to
estimate the relative levels of lactate in malignant cells
(Cao et al. 2012). Similarly, in our study, the PCA loading
plot demonstrates increase in relative lactate concentrationin GBC samples. It may be attributed to high glycolytic
rate in malignant cells. Ischemia may develop as samples
were outside for 510 min during surgical removal of the
gallbladder. Glycolysis produces NADH which is oxidized
by the mitochondria, but during ischemia or hypoxia con-
ditions, this oxidative route becomes nonfunctional (Lane
and Gardner 2005). Thus, in the ischemic tissue, conver-
sion of pyruvate to lactate is the only way of oxidizing
cytosolic NADH (Sitter et al. 2002). Therefore, during this
period little contribution from ischemia and anaerobic
glycolysis to lactate concentration may affect its actual
tissues concentration (Tessem et al. 2008). Glucose tolactate conversion also protects cancer cells from oxidative
stress (due to reactive oxygen) resulting in reduction of
glucose level in cancer cells (McFate et al. 2008) as
compared to benign and non-involved tissues (Gribbestad
et al. 1994). The absolute intensity of glucose as shown in
Fig. 6 showed minor decrease from CC to XGC to GBC,
but no statistical significance could be deciphered from
analysis of the data. A proper explanation for this unusual
observation could not be ascertained. However, strong
1.02.03.04.0 ppm6.07.08.0 5.05.5
XGC
CC
GBC
9.0 Histopathological Examination
A
XGC
CC
GBC
B
Fig. 3 Representative 800 MHz proton CPMG NMR spectra of CC,
XGC and GBC tissues. a Routine histopathology photographs of thetissue specimens from same patients alongside of each NMR
spectrum. b The corresponding histopathology of the post HR-MAS
NMR tissues are also shown which signifies similar findings whencompared with a
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signal ofb-hydroxybutyrate in GBC samples was observed.
Ketone bodies like b-hydroxybutyrate, acetone, etc. are
produced in cancer cells under oxidative stress conditions
(Pavlides et al. 2010). Presence ofb-hydroxybutyrate may
be an indication of oxidative stress in GBC tissues.
Alanine, which is also an indication of hypoxic condi-
tion in cancer cells, was high in GBC tissues. Similarly,
relative and absolute increased content of glycine was alsoobserved in GBC samples. Increase in the lactate signal
accompanied with glycine and alanine is probably due to
increased rate of glycolysis in GBC tissues. Elevation in
the levels of amino acids like valine, lysine, etc. were
observed in GBC samples, again implying the involvement
of glycolysis (Yang et al. 2007). One of the most robust
indicator and widely used biomarker of malignant cells is
significant elevation in the choline containing compounds
involved in membrane phospholipid metabolism, cell sig-
naling, lipid transport etc. reported in several malignant
tissues of different organs like brain, oral, breast, prostate
etc. (Beloueche-Babari et al. 2009; Glunde et al. 2006;Srivastava et al. 2011). In sequence with previous pub-
lished reports, choline containing compounds were also
high in GBC samples as compared to CC and XGC. Both
absolute and relative intensities were high in GBC repre-
senting active cell proliferation in cancer tissues. It is one
of the biomarker which also used as in vivo MRS in
clinical practices (Bolan et al. 2003). Taurine, important in
osmoregulation and volume regulation process, also helps
in protecting cells from swelling and free radical under
hypoxic and oxidative stress conditions (Griffin and
Shockcor 2004; Shen et al. 2001). Taurine reported as a
potential diagnostic biomarker in differentiation of malig-
nant from benign and control tissues and its level was
found to be increased (Sitter et al. 2010; Wang et al. 2010;
Srivastava et al. 2011). In GBC samples, relative as well as
absolute intensities of taurine were significantly increased.
Three broad signals at 5.60, 5.90 and 6.30 ppm could
not be assigned and were predominantly observed in CC
and XGC samples, whereas it was not detected in any of
the GBC tissue specimens (Fig. 6). The characteristization
of these signals may provide biological correlation for
discriminating malignancy. Whereas, uracil, an integral
component of nucleotide/nucleoside was observed in XGC
and GBC samples, it was not detected in CC samples.
It is reported that nucleotide/nucleoside like ATP/ADP,
UTP/UDP-hexoses, NAD, etc. involved in the energy
metabolism, increases in malignant cells as compared to
benign and control (Gribbestad et al. 1994). In line with
this hypothesis, nucleotide/nucleoside signals resonating
between 8.1 and 8.3 ppm were integrated with respect to
QUANTAS signal. Elevation in their level was found to
increase sequentially from CC\XGC\GBC samples
(Fig. 6).
-0.20-0.15-0.10
-0.050.000.05
0.100.15
-0.10
-0.05
0.00
0.05
0.10
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
PC-2
(11%
)
PC-1(78%)
PC-3(4%)
CC
XGCGBC
123456
123456
123456
-0.2
-0.1
0.0
0.1
0.2
-0.2
-0.1
0.0
0.1
0.2
-0.2
-0.1
0.0
0.1
0.2
PC-1 (78%)
PC-2 (11%)
PC-3 (4%)
789
789-10
-5
0
5x10-3
789
-10
-5
0
5
A
Bx10-3
-10
-5
0
5 x10-3
LactateA
lanine B
CBCA
BHB
BCA
BHB
Alanine
Glucose
Lactate
Creatine
Choline-C
AminoAcid
Taurine
Glycine
Lysiine
Lysiine
TAG T
AG
TAG
TAG
TAG
TAG
TAG
TAG
TAG
Creatine
Choline-C
Taurine
GlycineG
lucose
Lactate
AminoAcids
Tyrosine
FumaricAcids
Nucleotide
Formate
Phenylalanine
TAG
TAG
TAG
TAG
TAG
TAG
TAG
Tyrosine
FumaricAcids
Formate
Phenylalanine
Nucleotide
Formate
Phenylalanine
Tyro
sine
Nucleotide G
lucose
TAG
A
minoAcid
Glycine
TAG
TAG
TAG
TAG
TAG
Creatine
Water
Water
Water
Fig. 4 A Three dimensional score plot derived using PC1m PC2 and
PC3 from PCA analysis of 1H HR-MAS CPMG NMR spectra of CC,
XGC and GBC tissues. B One dimensional principal component
(a) PC1, (b) PC2, and (c) PC3 loadings plot derived from PCA
analysis of CC, XGC and GBC tissues 1H HR-MAS CPMG NMR
spectra. The water region from 4.7 to 5.1 ppm have been omitted in
all the spectra during the binning procedure and hence shown as
dotted straight line in the loading plots
112 S. K. Bharti et al.
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CC and XGC are both inflammatory conditions usually
associated with gallstones but the factors that triggers CC
in one patients and XGC in another is still not clear. It is
also not known whether XGC represents an evolution of
and later stage of CC. The major problem in diagnosis of
XGC from GBC in clinical practice is mimicking GBC
characteristics by XGC samples in physical appearance,
wall thickening and similar image on diagnostic imaging
techniques and may coexist with GBC (Makino et al. 2009;
Ghosh et al. 2011; Karabulut et al. 2003). The individual
analysis of NMR spectra showed that metabolic profile ofsome XGC samples mimics GBC metabolic profile. Other
XGC samples had metabolic profile similar to CC samples
that is higher TAG content as compared to GBC samples. It
showed high content of TAG in XGC samples as compared
to GBC. This may be attributed to accumulation of lipid-
laden macrophages in the area of destructive inflammation
which is a characteristic feature of XGC (Karabulut et al.
2003). In case of XGC, more than 70 % of the samples
were predicted correctly by PLS-DA model and classified
as benign samples i.e., in CC groups The association of
XGC with GBC is controversial but several articles have
reported repetitively on the association of XGC with
malignancy (Benbow 1989; Benbow and Taylor 1988;
Ghosh et al. 2011; Goodman and Ishak 1981; Karabulut
et al. 2003; Krishnani et al. 2000; Lopez et al. 1991; Ros
and Goodman 1997) along with its mimicking and coex-
istence with GBC (Benbow 1990; Dixit et al. 1998;
Houston et al. 1994; Kim et al. 1999; Krishnani et al. 2000;
Kwon and Sakaida 2007; Parra et al. 2000; Ros and
Goodman 1997). In our study, XGC samples overlappedwith GBC in PCA/PLS-DA analysis and showed similar
metabolic profile with GBC indicating similar metabolic
disturbances. The premalignant potential of XGC therefore
remains controversial. However, quantitative estimation of
metabolites in XGC showed intermediate values between
CC and GBC as shown in Fig. 6.
All the above pattern recognition based PCA/PLS-DA
analyses were performed using the relative integral area
approach. It is a well established method, applied frequently
-40-20
020
4060
80
-20
-10
0
10
20
-15
-10
-5
0
5
10
A
B
15
Factor-1(72%,44%) Fac
tor-2
(8%
,19%
)
Factor-3(3%,19
%)
CC
XGC
GBC
-1
0
1
2
Samples
CC
XGC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
XGC
XGC
XGC
XGC
XGC
GBC
GBC
GBC
GBC
GBC
GBC
PredictedY
*
Fig. 5 a PLS-DA cross
validated score plot derived
using regression coefficient 1, 2
and 3 from PLS-DA analysis of
CC, XGC and GBC 1H HR-
MAS CPMG NMR spectra.
b Prediction of unknown gall
bladder tissues using PLS-DA
model which was prepared
using training data set (CC;
n = 51, XGC; n = 15 and
GBC; n = 19) samples. This
model was then used to predict
the unknown (CC, XGC and
GBC) samples. The predictions
are made on the basis of a priori
cut-off value of 0.5 and 1.5 for
class membership, using
y-predicted box-plot (class 0 for
CC, 1 for XGC and class 2 for
GBC). The predicted mean
values along with standard
deviation are depicted. All
samples except one XGC
sample denoted by * were
correctly predicted by the
training set model
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Unknown 4.60 ppm
Adenine/Adenosine
Taurine Glycine Creatine
MeanAreaSD
Choline Contn. Comps. Uracil Tyrosine
Amino Acids 3.78 ppm Glucose
CC XGC GBC
Unknown 5.90 ppm
CC
XGC
GBC
Unknown 5.60 ppmUnknown 6.30 ppm
Cholesterol TAG: 4.31 ppm
TAG 0.90 ppm TAG 2.80 ppm TAG 5.30 ppm
CC XGC GBC CC XGC GBC
CC XGC GBC CC XGC GBC CC XGC GBC
CC XGC GBC CC XGC GBC CC XGC GBC
CC XGC GBC CC XGC GBC CC XGC GBC
CC XGC GBC CC XGC GBC CC XGC GBC
CC XGC GBC CC XGC GBC CC XGC GBC
MeanAreaSD
MeanAreaSD
MeanAreaSD
MeanAreaSD
MeanAreaSD
## #
#
# / # / #
# # /
* /# * /# * /# /
* /# /
* /# / */# /* /#
* /#
114 S. K. Bharti et al.
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in many areas of research discipline to remove or minimizethe effects of variable dilutions, weight variations etc., has
its advantage over other methods in which integral area of a
bin is divided by sum of integral area of all bins (Craig
et al. 2006). Positive loadings of principal components
(PCA) or regression coefficients (PLS-DA) for metabolites
in one group indicate its higher content/concentration in all
samples of that group. It does not mean that absolute
concentration of those metabolites will be high in samples
of respective group. Suppose in NMR spectra of group A,
metabolite M has its absolute integral area X and total
spectral area is 1000X, then relative integral area used in
PLS-DA/PCA using such approach will be 0.00X. Insecond group B, suppose M has same absolute area X
but total absolute area of spectrum is 10X, therefore its
final relative area used for analysis using similar approach
will be 0.X. Hence PCA/PLS-DA analysis between A
and B will show positive loading for metabolites M in
group B but the absolute concentration of metabolites
M is same in both the groups. This is one of the advan-
tages of this approach that it separates group on the basis of
relative integration with respect to the total spectral area
but contrary does not reflect the absolute concentration. For
example, glucose showed positive loadings for GBC sam-
ples as observed in PCA and PLS-DA loadings plots
generated from PCA/PLS-DA analysis of CC, XGC and
GBC samples, but the absolute intensity of glucose in GBC
samples was found to be lower when compared with CC
and XGC tissue specimens (Fig. 7). Therefore, QUANTAS
signal can be used for scaling in such pattern recogni-
tion statistical analysis for evaluating the quantitative
(not relative) information of metabolites.
5 Conclusion
The HR-MAS NMR spectroscopy is an adequate option for
evaluating the metabolic profile of gall bladder tissues rather
than extraction procedures which are time consuming,
laborious and increase the risk of contamination. However,
this is the first study attempted by HR-MAS NMR based
metabolic profiling of small molecules as well as lipids and
its implementation for differentiation of different pathology
of gall bladder tissues i.e., CC, XGC and GBC. The CC
samples could easily be distinguished from GBC samplesusing either small molecule metabolites or lipid resonances.
Overlapping of 27 % of the XGC samples with GBC in the
PLS-DA model projected it to be a more aggressive benign
inflammatory condition with rapid cell proliferation and this
requires further investigation. XGC appears to be interme-
diate on a scale of metabolic changes from CC to GBC and
the clustering of some of the XGC specimens with GBC may
be a pointer to a later and perhaps premalignant stage of
XGC. Though the number of patients in the present study is
not too large and the results are preliminary. We believe
that monitoring the metabolites observed in the present
study could potentially be used in future as a diagnostic
0.0000
0.0005
0.0010
0.0015
0.0020
0.0025
GBCXGCCC
0.00
0.05
0.10
0.15
0.20
0.25
GBCXGCCC
BA
Fig. 7 a Mean value of relative intensity of glucose in CC, XGC and
GBC has been shown. Relative intensity of glucose is defined as area
of glucose signal is divided by area of the total spectrum (scaling used
for PCA and PLS-DA analysis). b Mean value of absolute intensity of
glucose has been calculated using QUANTAS and normalization was
carried out with respect to area of QUANTAS signal. The bar
diagram demonstrates that relative intensity of glucose has significant
variation between CC and GBC a, therefore in PCA loading plot,
glucose showed positive loading for GBC. However, absolute
intensity of glucose as calculated by QUANTAS shows insignificant
variations (Supplementary Table S-2). Therefore artificial signal can
be used for scaling the data and to estimate the quantitative
information of metabolites
Fig. 6 Quantitative variations of lipids and small molecules in CC,
XGC and GBC samples represented as bar plot of absolute integral
area normalized with respect to QUANTAS signal. Mean area is
represented as bar plotand standard deviation as error bars. Symbols
*, # and / denote significant changes among CC versus XGC, CC
versus GBC and XGC versus GBC respectively. The detailed
statistical analysis is shown in Supplementary Table S-2 using
median values
b
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bio-marker for gall bladder diseases using non-invasive
volume localized in vivo MRS technique.
Acknowledgments Financial assistance from the Department of
Science and Technology, Government of India is gratefully acknowl-
edged. S. K. Bharti thanks Dr. K. Jayalakshmi Mulge and Ms Kanchan
Sonkar for their help during the work.
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