Magic Angle Spinning NMR Spectroscopic Metabolic Profiling of Gall Bladder Tissues for Differentiating Malignant From Benign Disease

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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
http://dx.doi.org/10.1007/s11306-012-0431-7http://dx.doi.org/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

102 S. K. Bharti et al.

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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

Metabolic profile of gall bladder tissues by HR-MAS NMR 103

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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.


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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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8/18

Table 1 continued


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


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


123


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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


123


11/18

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


123


12/18

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


123


13/18

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


123


14/18

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





MeanAreaSD

MeanAreaSD

MeanAreaSD

MeanAreaSD

MeanAreaSD

## #

#

# / # / #

# # /

* /# * /# * /# /

* /# /

* /# / */# /* /#

* /#


123


15/18

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


123


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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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Magic Angle Spinning NMR Spectroscopic Metabolic Profiling of Gall Bladder Tissues for Differentiating Malignant From Benign Disease