1 H-MRSI pattern perturbation in a mouse glioma: the effects of acute hyperglycemia and moderate...

Research Article

Received: 20 July 2008, Revised: 4 June 2009, Accepted: 5 June 2009, Published online in Wiley InterScience: 7 August 2009

(www.interscience.wiley.com) DOI:10.1002/nbm.1421

1H-MRSI pattern perturbation in a mouseglioma: the effects of acute hyperglycemiaand moderate hypothermiaR.V. Simoesa,b,c, T. Delgado-Gonia,c, S. Lope-Piedrafitac,d and C. Arusa,c*

MR spectroscopic Imaging (MRSI), with PRESS localizain the spectral pattern of 11 mice bearing GL261 glio

NMR Biom

tion, is used here to monitor the effects of acute hyperglycemiamas at normothermia (36.5–37.5-C) and at hypothermia (28.5–

29.5-C). These in vivo studies were complemented by ex vivo high resolution magic angle spinning (HR-MAS) analysisof GL261 tumor samples from 6 animals sacrificed by focused microwave irradiation, and blood glucose measure-ments in 12 control mice. Apparent glucose levels, monitored by in vivo MRSI in brain tumors during acutehyperglycemia, rose to an average of 1.6-fold during hypothermia (p< 0.05), while no significant changes weredetected at normothermia, or in control experiments performed at euglycemia, or in normal/peritumoral brainregions. Ex vivo analysis of glioma-bearing mouse brains at hypothermia revealed higher glucose increases in distinctregions during the acute hyperglycemic challenge (up to 6.6-fold at the tumor center), in agreement with maximalin vivo blood glucose changes (5-fold). Phantom studies on taurine plus glucose containing solutions explained thedifferences between in vivo and ex vivomeasurements. Our results also indicate brain tumor heterogeneity in the fouranimal tumors investigated in response to a defined metabolic challenge. Copyright � 2009 John Wiley & Sons, Ltd.

Supporting information may be found in the online versi
on of this article.
Keywords: acute hyperglycemia; brain tumor; GL261 cells; moderate hypothermia; MRS pattern

* Correspondence to: C. Arus, Bioquımica i Biologia Molecular, UniversitatAutonoma de Barcelona, Spain.E-mail: [email protected]

a R. V. Simoes, T. Delgado-Goni, C. Arus

Bioquımica i Biologia Molecular, Universitat Autonoma de Barcelona, Spain

b R. V. Simoes

Centro de Neurociencias e Biologia Celular, Universidade de Coimbra,

Portugal

c R. V. Simoes, T. Delgado-Goni, S. Lope-Piedrafita, C. Arus

Centro de Investigacion Biomedica en Red – Bioingenierıa, Biomateriales y

Nanomedicina (CIBER-BBN), Spain

d S. Lope-Piedrafita

Servei de Ressonancia Magnetica Nuclear, Universitat Autonoma de

Barcelona, Spain

Contract/grant sponsor: Ministerio de Educacion y Ciencia (Spain); contract/

grant number: SAF 2005-03650.

Contract/grant sponsor: Instituto de Salud Carlos III (Spain); contract/grant

number: PI051845.

Contract/grant sponsor: Fundacao para a Ciencia e a Tecnologia (Portugal);

contract/grant number: SFRH/BD/17643/2004.

Centro de Investigacion Biomedica en Red – Bioingenieria Biomateriales y

Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III (Spain).

Abbreviations used: GBM, glioblastoma multiforme; HR-MAS, high resol-

ution magic angle spinning; IST, inter-slice distance; MR-glc, MR-detectable

spectroscopic pattern change induced by glucose; MRSIdyn, dynamic MR

spectroscopic imaging; MTX, matrix size; NEX, number of excitations; NS,

number of slices; SRF, spatial response function; ST, slice thickness; SV, single

voxel; TAT, total acquisition time. 2

INTRODUCTION

Brain tumors contain abnormal cells with alteredmetabolism thatpresent distinct metabolic patterns according to their type andgrade. These metabolic patterns have been described bylocalized magnetic resonance spectroscopy (MRS) for the mostcommon brain tumors found in humans (1). More recently,classifiers, based on pattern recognition analysis of MRS data,have improved brain tumor characterization in vivo (2). However,these classifiers still fail to completely discriminate between sometumor types and do not recognize the large number of tumorsubtypes that classical anatomical pathology and molecularbiology are now defining (3–5). To overcome this limitation, werecently proposed a new approach to study brain tumors; in apreclinical study with a mouse model of glioma, tumormetabolism was challenged with acute hyperglycemia so thatits basal spectral pattern was perturbed (6). Our intention was toincrease the dynamic range for MRS pattern classification ofdifferent molecular phenotypes, as these may respond differentlyto a particular metabolic challenge. Nevertheless, our originalstudy still had the limitations of single-voxel MRS such that it didnot take into account that brain tumors are usually hetero-geneous masses composed of varied metabolic regions (7,8).Therefore, we have now studied GL261 tumors in mice using MRspectroscopic Imaging (MRSI) (9), a technique that provides aspatial mapping of the metabolite profiles throughout thedifferent regions of the mouse brain (10,11) and tumor (12).It has been described in both cats (13) and rats (14) that

anesthesia can reduce brain temperature independently of coretemperature, with external body warming unable to fullycompensate for this effect (anesthesia-induced brain hypother-

ed. 2010; 23: 23–33 Copyright � 2009 John Wiley & Sons, Ltd.

3

Table 1. Summary of the nine groups used for control (Cr),MRSI (Mr), and HR-MAS (Hr) studies, and the number ofanimals investigated in each group (n)

Group Study Description n

CrEug-NormT Control Euglycemia and normothermia 3CrEug-HypoT Control Euglycemia and hypothermia 3CrHyp-NormT Control Hyperglycemia and normothermia 3CrHyp-HypoT Control Hyperglycemia and hypothermia 3MrEug-HypoT MRSI Euglycemia and hypothermia 3MrHyp-HypoT MRSI Hyperglycemia and hypothermia 4MrHyp-NormT MRSI Hyperglycemia and normothermia 4HrEug-HypoT HR-MAS Euglycemia and hypothermia 3HrHyp-HypoT HR-MAS Hyperglycemia and hypothermia 3

R. V. SIMOES ET AL.

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mia). In this regard, although the body temperature of theanimals studied in Reference (6) was maintained as much aspossible with a water blanket, the restrictions imposed inpositioning the mice inside the magnet (7T Bruker PharmaScan,equipped with a volume coil) compromised thermoregulation ofthe animals. Unfortunately we only realized this after the workwas published, when we performed a retrospective analysis ofthe original spectra. We calculated the brain temperature of theanimals using the approach reported by others (15) (with amodification described in this work), and found that the micestudied in Reference (6) were at moderate hypothermicconditions: 31� 1.38C (n¼ 7).Accordingly, to investigate the possible role of brain

temperature in this brain tumor model with metabolicperturbation, we have now compared the effects of moderatebrain hypothermia (�308C) versus normothermia during theacute hyperglycemic challenges. To corroborate the MRSI results,we have performed blood glucose measurements in controlmice, at euglycemia and during acute hyperglycemic challenges,and carried out additional confirmatory MR experiments: HR-MAS(16), for ex vivo glucose quantification in distinct brain regions ofmice bearing GL261 tumors, at euglycemia and hyperglycemia;and 1H-MRS of phantoms prepared with Taurine-Glucosesolutions at different concentrations.

MATHERIALS AND METHODS

Animals and cells

A total of 29 C57BL/6 female mice, 20–23 g weight, were usedin this study. These were obtained from Charles RiverLaboratories (France) and housed at the animal facility of theAutonomous University of Barcelona (Servei d’Estabulari). All theanimal studies were approved by the local ethics committee,according to the regional and state legislation (protocol DARP-3255/CEEAH-530). GL261 mouse glioma cells were obtainedfrom the Tumor Bank Repository at the National CancerInstitute (Frederick/MD, USA) and were grown as described inReference (6).

Brain tumors

Tumors were induced in 17 mice by intracranial sterotacticinjection of 105 GL261 cells in the caudate nucleus, essentially asreported previously (6). Analgesia (Meloxicam subcutaneous, s.c.,1.0mg/kg) was given to each animal 15min before the inductionof anesthesia, and also after surgery, as they began to wake up(Buprenorphine s.c., 0.1mg/kg). Meloxicam analgesia wasrepeatedly administrated at 24 and 48 h post-surgery.

Experimental design

Mice were divided into 9 groups, as summarized in Table 1.Control bench experiments (12 normal mice) and MRSIexperiments (11 glioma-bearing mice) were carried out inparallel. This allowed us to separately monitor the effects of bodytemperature and acute hyperglycemia in blood glucoseconcentration, and brain MRSI-detectable changes in meta-bolic patterns induced by glucose (MR-glc). Additionally, ex vivoHR-MAS experiments were performed with 6 glioma-bearing mice in order to quantify glucose in distinct regionsof the brain. Animals studied at normothermia (CrEug-NormT,

www.interscience.wiley.com/journal/nbm Copyright � 200

CrHyp-NormT, and MrHyp-NormT) were kept at 36.5–37.58C bodytemperature while those inspected at moderate hypothermia(CrEug-HypoT, CrHyp-HypoT, MrEug-HypoT, MrHyp-HypoT, HrEug-HypoT,and HrHyp-HypoT) were maintained at 28.5–29.58C. Acute hyper-glycemia was induced in mice (CrHyp-NormT, CrHyp-HypoT,MrHyp-NormT, MrHyp-HypoT, and HrHyp-HypoT) by i.p. bolus injectionof 10mL/g D-glucose (Sigma-Aldrich, St. Louis/MO, USA),25% (w/v) in saline (1.4M), essentially as reported in Reference(6). Before starting the experiments, each animal was cannulatedi.p. with a 26G needle connected, through polyethylene tubing(1.5m length), to a 1mL syringe (Becton-Dickinson S.A., Madrid,Spain) pre-loaded with the glucose solution.

Control studies

Animals from groups CrEug-NormT, CrEug-HypoT, CrHyp-NormT, and CrEug-HypoTwere anesthetizedwith isoflurane (0.5–1.5% inO2) andplacedover a bed heated with a recirculating water system. The bodytemperature was constantly monitored using a K(CA) typethermocouple as a rectal probe, connected to a K/J thermometer(TES-1307, TES Electronic Electronical corp., Taipei, Taiwan). Bloodsamples (0.6mL) were collected in duplicate from the tail vein byneedle puncture at different time-points, and used for bloodglucose readings with two glucometers (Glucocard G Meter, ArkayInc., Kyoto, Japan), as previously described (6). Readings started atabout 90min after inducing anesthesia, to simulate theMRSI studyconditions described in the next section and were carried out at7 time-points: 0, 5, 15, 30, 60, 90, and 120min post-injection ofglucose. IngroupCrHyp-HypoT, 3 additional readingswereperformedat 150, 180, and 210min. Glucometer readings were displayed asmg/dL glucose and manually converted to mM units (D-Glucosemolar mass, 180.2 g/mol). After the studies were completed, micewere sacrificed with an intraperitoneal injection of pentobarbital(200mg/kg, 60mg/mL).

In vivo MR studies

MR studies were carried out at 7 Tesla in a horizontal magnet(BioSpec 70/30, Bruker BioSpin, Ettlingen, Germany) equippedwith actively shielded gradients (B-GA12 gradient coil insertedinto a B-GA20S gradient system) and a quadrature receive surfacecoil, actively decoupled from a volume resonator with 72mminner diameter. Animals from groups MrEug-HypoT, MrHyp-HypoT,and MrHyp-NormT were positioned in the scanner bed, whichallowed stereotactic immobilization of the head and localized

9 John Wiley & Sons, Ltd. NMR Biomed. 2010; 23: 23–33

MONITORING HYPERGLYCEMIA-HYPOTHERMIA IN A MOUSE GLIOMA MODEL BY MRSI

delivery of anesthesia (isoflurane, 0.5–1.5% in O2; respiratoryfrequency kept between 60–80 breaths/min). A recirculatingwater system, incorporated in the animal bed, was used to controlthe body temperature as measured with the rectal probe.Breathing pattern and rectal temperature of the animals wereconstantly monitored (SA Instruments, Inc., New York, USA).All animals were initially scanned to confirm the presence of a

brain tumor with a rapid T2-weighted sequence, RARE (RapidAcquisition by Relaxation Enhancement) (17): Turbo Factor, 8;field of view (FOV), 19.2� 19.2mm; matrix (MTX), 128� 128(150� 150mm/pixel); number of slices (NS), 14; slice thickness(ST), 1mm; interslice-thickness (IST), 1.1mm; TR/TE, 2000/36ms;number of excitations (NEX), 1; total acquisition time (TAT),24 sec.Dynamic MRSI (MRSIdyn) was performed for each animal at

approximately 45min after inducing anesthesia: i.e., the timerequired for reference image acquisitions and MRSI preparationadjustments for geometry and field homogeneity. At this point,animal body temperatures were stable within the range definedfor each experiment. Two reference MRSI scans were initiallyacquired, one with short echo time (12ms TE), followed byanother one at long echo time (136ms TE). At this point (90minafter induction of anesthesia), acute hyperglycemia was inducedin MrHyp-HypoT and MrHyp-NormT animals while MrEug-HypoT animalsremained in a euglycemic state. Consecutive short TE MRSI scanswere then acquired at different times, according to the temporalresolution of each MRSI scan (TAT, 21min 30 sec), and interleavedwith one additional long TE scan, as shown in Fig. 1.Specifically: MrHyp-HypoT – 5 short TE scans (experimental time-points: 22, 44, 66, 88, 110min), 1 long TE scan (132min), 3 short TEscans (154, 176, 194min); MrHyp-NormT – 3 short TE scans (22, 44,66min), 1 long TE scan (88min), 3 short TE scans (110, 132,154min); MrEug-HypoT – 3 short TE scans (22, 44, 66min), 1 long TEscan (88min), 2 short TE scans (110, 154min).MRSI was performed with PRESS localization (Point Resolved

Spectroscopy (18)): FOV, 17.6� 17.6mm; ST, 1mm; Volume ofinterest (VOI), 5.5� 5.5� 1.0mm; hermite-shaped pulses (exci-tation, 0.6ms/9000Hz; refocusing, 0.6ms/5400 Hz). The VOI wasmanually positioned approximately in the center of the brain,based on the reference image, in a way that it would includemostof the tumor mass and also part of the normal/peritumoral brainparenchyma. Also, 6 saturation slices (10mm thickness; sech-shaped pulses: 1.0ms/20250Hz) were positioned around the VOIto help reduce outer volume contamination in our signals. Spatialresolution was defined by an 8� 8 voxel matrix over the FOV(2.2� 2.2mm: 4.84mL/voxel), which was Fourier interpolatedbefore reconstruction to a 32� 32 matrix (0.55� 0.55mm:0.30mL/voxel). k-space was sampled symmetrically by applyinga Hanningwindow on a 13� 13 imaging grid, as described beforein Reference (19): 12 accumulations in the centre of k-space led to113 different phase encoding steps with a total of 512 NEX. Water

Figure 1. Experimental protocol for MRSIdyn studies. White and grey boxes rep

NMR Biomed. 2010; 23: 23–33 Copyright � 2009 John Wiley &

suppression was performed with VAPOR (Variable Pulse Powerand Optimized Relaxation Delays) (20), using a 300Hz bandwidth.Long TE MRSI spectra were only partially water-suppressed forpost-acquisition temperature measurement requirements; thiswas done by decreasing the VAPOR pulse amplitude until apartially-suppressed water signal was visible throughout the VOIregion. Linear and second order shims were automaticallyadjusted with FASTMAP (21) in a 5.8� 5.8� 5.8 voxel (whichincluded the MRSI-VOI region), leading to waterline widths at halfheight of 13–19Hz inside the MRSI-VOI. MRSI spectra wereacquired with 2500ms TR, 4006.41 Hz spectral width (13.34 ppm)and 2 k points in the time domain. The reference scan for eachMRSI study was acquired with a T2-W RARE sequence: FOV,17.6� 17.6mm; MTX, 256� 256 (69� 69mm/pixel); NS, 1; ST,1mm; TR/TE, 3000/36ms; NEX, 3; TAT, 3min 36 sec. After thestudies were completed, mice were sacrificed with an intraper-itoneal injection of pentobarbital (200mg/kg, 60mg/mL).

Ex vivo HR-MAS studies

Group HrEug-HypoT and HrHyp-HypoT mice were studied in a similarmanner as group CrEug-HypoT and CrHyp-HypoT mice (controlexperiments) respectively, except they were not monitored forblood glucose levels at any time. Mice were sacrificed with afocused Microwave Fixation System (TMW-6402-C 5 kW, Muroma-chi Kikai Co. Ltd., Tokyo, Japan), 1.1 sec irradiation at: 90min post-induction of anesthesia for group HrEug-HypoT mice; 170min post-induction of anesthesia (90min of anesthesia alone followed by80min of acute hyperglycemic period under anesthesia) forgroup HrHyp-HypoT mice. The whole brain was then removed andsamples were collected by dissection under a magnifying glassfrom distinct regions: normal/peritumoral parenchyma; tumorperiphery; center of the tumor (core). Samples were cut into smallpieces with a scalpel, inserted into a pre-weighted HR-MAS micro-rotor (HZ05538 BL4; 12mL Teflon spacer, Cortecnet, Paris, France)and weighed: average biopsy sample content was 12.2 � 6.4mg.Deuterium oxide (D2O 99.995%, Carlo Erba Reactifs-SDS, Val deReuil, France) was then added andmixed inside each rotor using a1mL syringe (Becton-Dickinson S.A., Madrid, Spain) and the rotorswere weighed again: average D2O content added was10.7� 4.6mg. Finally, the dissected samples were studied on a9.4Tmagnet (DMX 400MHz, Bruker BioSpin, Wissembourg, France)equipped with a 1H-13C/31P HR-MAS resonance probe. HR-MASstudies were carried out at room temperature and using thestandard 3000Hz spinning rate for the magnetic field strength.1H spectra were acquired for all samples with a pulse-acquiresequence, with and without water suppression (zg and zgpr,respectively), using in all cases a 908 Gauss pulse for excitation,12 ppm sweep-width and 16 k points. Unsuppressed-water spectrawere acquired with 28.5 sec TR and 8 scans while 4.04 sec TR and256 scans were used in the case of water-suppressed spectra.

resent short (12ms) and long (136ms) echo timeMRSI scans, respectively.

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R. V. SIMOES ET AL.

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In vitro MR studies

Two phantoms were prepared as follows: 10.8mM taurineþ2.1mM glucose; 10.8mM taurineþ 14.0mM glucose. Eachphantom was studied by single voxel 1H-MRS in the samespectrometer used for in vivo studies. The parameters of the MRSIstudies were also reproduced in this case: spatial localization withPRESS (hermite-shaped pulses with the same amplitude forexcitation/refocusing), water suppression with VAPOR (samebandwidth), linear-second order shims adjustments with FAS-TMAP (3.5� 3.5� 3.5mm, 1 Hz waterline widths at half heightwere obtained) and the same spectral width. In this case: voxelsize, 3� 3� 3mm (27mL); TR/TE, 2500/12ms; 4 k points in thetime domain; number of scans (NS), 128; TAT, 5min 30 sec.

Processing and post-processing of MR spectra

MRSI data were initially processed at the MR work station withParaVision 4.0 (Bruker BioSpin, Ettlingen, Germany) and laterpost-processed with different software: 3D Interactive ChemicalShift Imaging v1.9.10 - 3DiCSI (Dept. Radiology, ColumbiaUniversity/NY, USA), for line broadening adjustment (Lorentzianfilter, 4 Hz), zero order phase correction, and exporting the data inASCII format; MatLab (The MathWorks Inc., Natick/MA, USA)home written scripts, to align all spectra within each MRSI matrix(using the choline peak as reference, 3.21 ppm), quantify relativepeak intensity changes (3.43 ppm: MR-glc, based on the approachpreviously described for single-voxel (6)), and display thesechanges as color-coded maps (10� 10 voxels). The processingpath is summarized in Fig. 2.When color-coded maps were to be superimposed on the

respective reference T2-W image, inside the VOI region, they werefirst smoothed by linear interpolation and a 40% transparencymask was applied to allow for visualization of the underlying MRI.Time-course curves of selected regions of interest (ROI) withineach map were generated with home written scripts in R (22)

Figure 2. Flowdiagramof the post-processing strategy used to generate

color-coded maps from MRSIdyn scans. Supporting information to this

figure is available at: http://gabrmn.uab.es/SimoesEtAl_NMRB2009.pdf


while ImageJ and Gimp open-source software were also used forfinal image, and spectroscopic image, display adjustments.Signal-to-noise ratios (SNR) for the 3.43 ppm peak wereindividually measured for each spectrum as the ratio of thepeak intensity to the standard deviation of the region 9–10 ppm(noise). CSIapo software (Center for Magnetic Resonance inBiology andMedicine, Marseille, France) was used to calculate thespatial response function (SRF) to show the actual spatial origin ofthe MRSI signal in a pixel, (i.e., the nominal resolution, asdescribed in Reference (19)) and to display the 64% intensitycontour-line superimposed on the reference T2-W images.Long echo time MRSI data were used to measure local brain

temperature inside the VOI, essentially as described by others(15), but using the total choline peak, at 3.21 ppm, as a referenceinstead of NAA (2.02 ppm). Spectra from selected ROIs wereaveraged and the ppmdistance between the partially suppressedwater peak and the total choline peak was measured (Dist). Thisvalue was used to render the brain temperature in each ROI,using a modification of the calibration curve described for dogbrain (15) [eqn (1)], taking into account the constant distancebetween the total choline peak and the NAA peak at 2.02 ppm(1.21 ppm, measured in the striatum tissue from 3 healthy C57mice by single-voxel PRESS spectroscopy and also from normal/peritumoral brain regions of MRSI spectra acquired in this work).

Temperatureð�CÞ ¼ �82:33 � Distþ 1:21ð Þ þ 255:94 (1)

HR-MAS data were processed and analyzed with MestRecsoftware (23). Timedomain datawere line-broadenedwith a 1.0Hzexponential filter before Fourier transformation. After phaseadjustments, and before deconvoluting selected peak regions(manual line-fitting) for peak area calculations, baseline correctionswere performed for each spectrum: unsuppressed water peak(singlet, 4.75 ppm) – linear baseline correction; total creatine(creatine/phophocreatine singlets, 3.028/3.030ppm), taurine (tri-plet, 3.42 ppm), and glucose peaks (alpha/beta anomer doublets,5.22/4.63ppm) – multipoint manual baseline correction. Totalcreatine peaks were fitted in both unsupressed (ACr-unsup) andwater-suppressed (ACr-sup) spectra,while taurineandglucosepeakswere only fitted in water-suppressed spectra (AMetab). Taurine andglucose were then quantified in each case as in eqn (2), using theunsuppressed water peak area (AWater) as reference.

Metab½ � ¼ AMetab

hMetab

� ACr�unsup

ACr�sup� C1Cr�sup

C1Metab� hWater

AWater� 106

MWater(2)

where [Metab]¼ taurine or glucose concentrations in mmol/gwater; h¼ number of magnetically equivalent protons in theresonance used (water, 2; taurine, 2; glucose, 1); C1¼ [1�e(-TR/T1)],correction factor for T1 effects in water-suppressed spectra;M¼molar mass (water, 18 g/mol). This method is a slightmodification of the approach described in Reference (24) andreceiver gain differences between unsuppressed and water-suppressed spectra were compensated by comparing the areasof the creatine peak measured in each spectra. T2 effects weredisregarded since no echo-time was used. T1 effects were onlyconsidered in water-suppressed spectra due to the smaller TRvalue, because when assuming a 1.9 sec for water T1 and a typıcal1.4 sec for metabolites (25,26), the C1 variables cancel in eqn (2).In vitro spectra acquired from the two phantom solutions

prepared were also processed with MestRec software. In this case,timedomain datawere zero-filled to 8 k points and line-broadenedwithan8.0HzexponentialfilterbeforeFourier transformation.After



phase adjustments andbaseline corrections, thepeakheightsweremeasured at 3.42–3.43 ppm for each spectrum.One way ANOVA (analysis of variance, SPSS 15.0, SPSS Inc.,

Chicago, USA) was used for statistical analysis, with p< 0.05 assignificance level. In the case of MRSI studies, relative MR-glc levelsat each voxel, within the VOI, were considered as independentmeasurements/cases. Normality was first inspected in each groupby the Shapiro-Wilk test while ANOVA’s post-hoc multiplecomparisons were carried out using Bonferroni’s correction.

RESULTS

Control experiments with normal mice during acute hypergly-cemia showed different patterns of glucose accumulation inblood between mice kept at moderate hypothermia and thosestudied at normothermia. As shown in Fig. 3, blood glucoselevels, initially at 5.3� 1.4mM among the 12 mice, rose to26.9� 1.8mM in animals kept at moderate hypothermia at60min after inducing acute hyperglycemia (5-fold increase,p< 0.05), while only 9.0� 2.7mM (1.5-fold increase, p< 0.05) wasobserved in animals kept at normothermia, at the sametimepoint. The maximum accumulation of glucose in normother-mic animals occurred at 15min post-injection of glucose, withblood glucose rising up to 15.9� 1.3mM (3-fold increase). Micekept at euglycemia during these experiments showed nosignificant changes in blood glucose levels at either normother-mia or moderate hypothermia.All animals inoculated with GL261 glioma cells developed brain

tumors and were used for MR studies within 13-16 days post-injection of cells (Figs 4A and 5A). All the glioma-bearing animalsusedpresentednormalbehaviorat this stageof tumorgrowth,withonly slight signsof cachexia, andnodetriment tomotionor feedingabilities. Figures 4B and 4C show representative examples of theshort TE spectra collected fromMRSIdyn studies. Thesewereused togenerate MR-glc kinetic curves from different regions of the VOI(Fig. 4D) and also time-course color-coded variation maps(Fig. 4E: maps smoothed in this case by linear interpolation of

Figure 3. Control studies for blood glucose measurements. Time-course

curves show the average �SD values (mM) obtained in each group:squares, normothermia; circles, moderate hypothermia; open symbols,

euglycemia; and closed symbols, hyperglycemia. The shaded bar high-

lights the point of maximum accumulation of glucose in the CrHyp-HypoTgroup (60min post-injection of glucose), which showed a 5-fold increase.ANOVA: �, p< 0.05 versus all groups. y, p> 0.05 among all the points in the

time-course curve.


2

the 10� 10 voxelmatrix; themap collected at 88min is shown in itsoriginal format in Fig. 5B, second map from the left).Integrationof thewholeSRFenabledanestimationof the relative

spatial contributions to the signal in each nominal voxel of theMRSI matrices. Seventy-eight percent of the total signal ampli-tude in one pixel, before Fourier interpolation (nominal voxel size:2.2� 2.2� 1.0mm), was estimated to originate from that exactregion, displayed in 2D in Figs 4A and 5E. That is to say that only theremaining 22% of the total signal amplitude is the result ofcontamination from the pixels surrounding the nominal voxel, ontop of which the drawn SRFs are centered in Fig. 5E.The results obtained from the different MR groups are

summarized in Fig. 5. Here, MR-glc maps that displayedmaximumrelative increases in eachMRSIdyn study (Fig. 5B) can be comparedto the respective reference image (Fig. 5A). Also, time-course MR-glc curves (Fig. 5D, upper row) are plotted for the voxels andregions of interest shown in Fig. 5C, which are displayed overlaidon the reference image (tumor borders highlighted by dashedlines). The voxels, shown in black in Fig. 5C, indicate the cases ofreference spectra collected with SNR lower than 7.8 for the3.43 ppm peak. In the lower row of Fig. 5D, the same time-coursecurves are shown but overlaid with the time course profile ofthe relative blood glucose levels (% units) monitored in thecontrol groups for the same experimental conditions (as shown inFig. 3, in mM units). In Fig. 5E, the first MR-glc map inFig. 5B (starting from the left), is shown enlarged, highlighting thecomplete tumor borders and overlaying two contour-lines of theSRF (1 and 2) at its 64% intensity level. As previously explained,these contour-lines represent the spatial origin of 78% of the totalsignal amplitude in the nominal voxel that they define.In MrHyp-HypoT mice, the maximum accumulation of MR-glc was

observed at 88min after inducing hyperglycemia as indicated bythe red bar highlighting in Fig. 5D: showing 164� 9% averageincrease in the tumor region (208� 9% increase in the voxelswhere maximum variation was monitored and 172� 15%increase in the tumor center) and basal levels (103� 10%) innormal/peritumoral brain parenchyma regions. After this time-point, MR-glc levels began to decrease in the tumor region, fallingto 130� 29% at 198min, post-induction of acute hyperglycemia(149� 35% in the voxels where maximum variation wasmonitored and 133� 32% in the tumor center). In MrHyp-NormT

animals, MR-glc levels appeared to reach their maximum valuesin the tumor region sooner than in MrHyp-HypoT animals (66min ofhyperglycemia time: red bar highlighting), although thevariations monitored in this group were much smaller(113� 4%, average in the tumor region; 131� 15%, maximumintensity inside the tumor and 113� 6% in the tumor center) andnot significant compared to the changes observed in the normal/peritumoral regions (99� 11%). At 132min after inducing acutehyperglycemia, MR-glc levels had already decreased to theirreference levels at euglycemia in the different tumor regions:91� 5%, average; 100� 11%, maximum variation; 88� 10%,center. Finally, in control MrEug-HypoT mice, which were kept ateuglycemia throughout MRSI monitoring, MR-glc levels remainedessentially constant inside the tumor as compared to normal/peritumoral brain regions (the apparent differences were notstatistically significant) – at 66 and 154min: 91� 2 and 89� 2%,average of the tumor region; 90� 4 and 87� 7%, tumor center;98� 16 and 102� 14%, normal/peritumoral brain regions. Ingroups MrHyp-HypoT, MrHyp-NormT, and MrEug-HypoT, the average levelof MR-glc in normal/peritumoral brain parenchyma at 88, 66, and66min of the dynamic MRSI studies (respectively) was 100� 11%.


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Figure 4. Representative example from MrHyp-HypoT animals. A, T2-W reference MRSI image overlaid with the VOI geometric position (yellow square),

which includes most of the tumor region seen and also part of the normal/peritumoral brain parenchyma. In the lower-right corner, the 64% contour-line

of the SRF for MRSI is shown. B, VOI region enlarged and overlaid with the 1H-MRSI matrix and corresponding reference spectra at euglycemia. The tumorregion is identified by the red dashed line (manually drawn). Highlighted voxels correspond to the following positions/regions: 1, voxel inside the tumor

where the maximum relative increase of MR-glc was observed during MRSIdyn; 2, position that best describes the center of the tumor mass displayed on

the image; 3, normal/peritumoral brain parenchyma. C, Time-course spectral patterns collected from regions 1, 2, and 3 at: euglycemia (reference, black),

22min hyperglycemia (blue), 44min hyperglycemia (green) and 66min hyperglycemia (red). Black arrows indicate the 3.43 ppm peak used to quantifythe relative variations of apparent brain-MR detectable glucose. D, Time-course kinetic curves for the relative MR-glc variations observed in voxels 1

(yellow circles), 2 (green circles) and 3 (blue circles) and also the average kinetic pattern of the tumor region (red circles; region used for quantification

highlighted in Fig. 5C, second map). E, MR-glc variation maps (%) at different times post-induction of hyperglycemia (0–198min), color-coded accordingto the scale displayed. Each map is shown overlaid on the reference MRSI image inside the VOI region, as described in methods; tumor borders and voxel

positions used to monitor MR-glc changes are overlaid (black dashed lines and numbers in white, respectively).

www.interscience.wiley.com/journal/nbm Copyright � 2009 John Wiley & Sons, Ltd. NMR Biomed. 2010; 23: 23–33

R. V. SIMOES ET AL.

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Figure 5. Summary of the results collected from all groups: MrHyp-HypoT, left column (n¼ 4); MrHyp-NorrmT, column in the center (n¼ 4); MrEug-HypoT, right

column (n¼ 3). The images and maps displayed correspond all to the VOI region used for MRSIdyn studies. A, T2-W reference images for MRSI (tumors are

visible in all cases on the right side). B, MR-glc maps, showing the relative variations of the 3.43 ppm peak at each voxel (compared to the initial referencelevels) at times 88min (MrHyp-HypoT) and 66min (groups MrHyp-NorrmT andMrEug-HypoT). The color-coded scale is displayed on the upper-right corner (same

scale as in Fig. 4E). C, reference T2-W image overlaid with: 1H-MRSI grid inside the VOI (10� 10 voxels); highlighted tumor borders (dashed lines); voxels

with SNR lower than 7.8 highlighted in black; selected voxel positions (voxels in yellow, maximum relative increase of MR-glc; voxels in green, apparenttumor center) and regions of interest (voxels in red-yellow-green, tumor; voxels in blue, normal/peritumoral) used to generate local MR-glc kinetic curves.

D (upper-row), time-course curves for MR-glc relative variations, normalized to the initial reference levels in each case (100%). These correspond to the voxel

positions and regions of interest defined in C: red circles, average of tumor region (voxels in red-yellow-green, in C); blue circles, normal/peritumoral region

(voxels in blue, in C); yellow circles, maximum relative increase of MR-glc inside the tumor (voxels in yellow, in C); green circles, tumor center (voxels in green, inC). Vertical bars correspond to�SD values. D (lower-row), same as the upper row but with time-course patterns of blood glucose variations (black circles) for

the control groups performed in the same experimental conditions of temperature and glycemia (from left to right: CrHyp-HypoT, CrHyp-NormT, CrEug-HypoT). The red

transparent bar indicates the time-point formaximumchange ofMR-glc in bothMrHyp-HypoTandMrHyp-NormT groups. E, enlarged viewof the first MR-glcmap in

B (from the left), showing the complete tumor borders and overlaid with two contour-lines of the SRF (1 and 2) at its 64% intensity level.

NMR Biomed. 2010; 23: 23–33 Copyright � 2009 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm


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Figure 6. Box plot analysis of the MR-glc intensity with respect to time zero (100%), displayed in Fig. 5. For each box, the boundary closest to zero

indicates the 25th percentile, a line within the box marks the median and the boundary of the box farthest from zero indicates the 75th percentile;whiskers above and below the box indicate the 90th and 10th percentiles; the outlying points (beyond 5th and 95th percentiles) are also graphed for each

case. A, MR-glc levels monitored in the red and blue color-coded ROIs in Fig. 5C (tumor and normal/peritumoral brain parenchyma, respectively): ‘MrHyp-

HypoT’ group (88min post-injection of glucose), 126 voxels in ‘Tumor’ and 16 voxels in ‘Normal’ ROIs; ‘MrHyp-NormT’ group (66min post-injection of glucose),101 voxels in ‘Tumor’ and 16 voxels in ‘Normal’ ROIs; ‘MrEug-HypoT’ group (66min post-injection of glucose), 118 voxels in ‘Tumor’ and 12 voxels in ‘Normal’

ROIs. ANOVA: � p< 0.05 versus all groups and regions; y p< 0.05 versus MrEug-HypoT Tumor; z p< 0.05 versus MrHyp-HypoT Normal/Peritumoral. B, MR-glc

levels monitored in different tumor areas at 88min post-injection of glucose, specifically in two 4� 4 voxel regions defined by the SRF contour-lines at

64% intensity level (SRFs 1 and 2), as shown in Fig. 5E. ‘MrHyp-HypoT n¼ 1’ box plots refer to the case shown in Fig. 5E while ‘MrHyp-HypoT n¼ 4’ shows theaverage dispersions for the four cases in MrHyp-HypoT group. For each case, SRF 1 was always positioned over those voxels where the highest increase in

MR-glc was observed, while SRF 2 was tentatively positioned in a region of the tumor where lower values where detected. ANOVA: � p< 0.05 versus both

SRF 2 regions. Supporting information for this figure is available at: http://gabrmn.uab.es/SimoesEtAl_NMRB2009.pdf

R. V. SIMOES ET AL.

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The relative MR-glc changes observed in each MR study group atthe time of maximumMR-glc accumulation are shown in Fig. 6 asbox plots for different regions of the VOIs.Brain temperatures were measured in all study groups from the

tumorandnormal/peritumoralbrainparenchymaregionsshown inFig. 5C (red voxels and blue voxels, respectively) – Table II, availableas supporting information (http://gabrmn.uab.es/SimoesE-tAl_NMRB2009.pdf). Significant differences were found betweenthe normothermic group, MrHyp-NormT, and the two hypothermicgroups, MrEug-HypoT and MrHyp-HypoT (p< 0.05), but not betweenthese last two groups or within each of the 3 groups (p> 0.05).Analysis of ex vivo HR-MAS data (Fig. 7) obtained from the HrEug-

HypoT group showed the following glucose concentrations innormal/peritumoral regions, tumor periphery, and tumor center:2.3� 0.5, 2.6� 0.8, and 2.1� 0.5mmol/g, respectively; taurineconcentrations were 14.5� 0.5, 14.8� 1.4, and 10.8� 0.6mmol/gin the same regions. As for group HrHyp-HypoT mice, glucose levelswere higher compared to the HrEug-HpoT group, which was set asreference (100%): 4.9� 0.2 (219� 10%), 14.1� 3.1 (533� 118%),and 14.0� 1.4 (664� 64%) mmol/g. Glucose levels detectedin HrHyp-HypoT tumor regions (center and periphery) weresignificantly different from those found in the rest of the samplesanalyzed (p< 0.05). This is summarized in Fig. 8.In vitro 1H-MRS data acquired from the two Taurine-Glucose

phantoms revealed a 1.7-fold increase in the 3.42–3.43 peak


intensity when comparing the solution with higher glucosecontent (14.0mM) to the other one (2.1mM) – results not shown.

DISCUSSION

The mouse model of glioma used (GL261 cells), previouslycharacterized by others (27) and also studied in our group (6), hasproven again to be a very reproducible model for in vivo MRstudies, with 100% incidence and a relatively precise time-window for tumor growth.The translation of the dynamic MRSI data collected from

MrHyp-HypoT and MrHyp-NormT groups into maps showing thetime-course of MR-glc changes (Figs 4E and 5B) provides avisualization of non-phosphorylated apparent glucose accumu-lation caused by the contribution of tumor glucose uptake,consumption, andwashout in the different regions of each tumor.This accumulation of apparent glucose content was higher atmoderate hypothermia (MrHyp-HypoT cases) and showed a patternof selective accumulation inside the tumors that resembled a‘fried-egg’ morphology (Fig. 5B). Moreover, our results suggestspecific regional patterns of MR-glc changes in each tumor. In thisregard, although Hanning-weighted MRSI has been shown tosignificantly reduce signal contamination between adjacentvoxels compared to conventional MRSI (28), our nominal


Figure 7. Representative ex vivo HR-MAS water-suppressed spectra

obtained from HrEug-HypoT and HrHyp_HypoT groups (straight lines anddotted lines, respectively), shown overlaid. Left column: A, spectra

(LB¼ 1.0 Hz) corresponding to samples collected from the tumor center;

B, same spectra shown in A but with LB¼ 12.0Hz (to make themcomparable to the in vivo MRSI patterns); C, spectra (LB¼ 1.0 Hz) corre-

sponding to samples collected from normal/peritumoral brain parench-

yma. Right Column, enlarged views of the highlighted grey regions shown

in the left column spectra.


resolution (shown by the SRF in Fig. 4A and 5E) should certainlyaccount for the ‘smoothness’ observed in the MR-glc distributiongradients throughout the MrHyp-HypoT tumors, mostly betweenthe internal regions and the periphery. However, since most ofthe signal amplitude in eachMRSI nominal voxel (78%) originatedfrom the respective spatial region seen on the reference image,with little contamination from the nearby voxels (22%), it seemsunlikely that this effect alone could explain the MR-glc gradients

Figure 8. Glucose concentration in different brain regions of HrEug-HypoTrespectively): left side, absolute concentrations (mmol/g) with relevant statisti

normal/peritumoral); right side, relative increases from euglycemia (100%) to


themselves. If it did, then we would expect to find higher MR-glcconsistently in the tumor center, which is not the case: highestMR-glc regions do not coincide with the geometric tumor centerin the various GL261 tumors studied so far.Altogether, our results suggest heterogeneity in tumor

response to hyperglycemia during moderate hypothermia, notonly in each glioma microenvironment (gradient of accumu-lation) but also among the different tumor cases studied(different patterns of MR-glc) – Fig. 5B. This apparentheterogeneity is supported by statistical analyses (Fig. 6B), whichindicate significant differences in glucose accumulation indistinct regions of the tumor that correspond to the nominalvoxel sizes, as illustrated in Fig. 5E (SRF1 and SRF2).The hyperglycemic challenges studied by MRSIdyn (Figs 5D and

6) showed that MR-glc increased approximately by 1.6-fold (2.1-fold maximum) in GL261 tumors kept at moderate hypothermia(group MrHyp-HypoT). This accumulation of apparent glucose wasstatistically significant compared to that observed in the nearbynormal/peritumoral brain parenchyma or in tumor and normal/peritumoral regions from the other groups. Nevertheless,apparent glucose accumulation in tumors at normothermia(1.1-fold) was not significant compared to that observed innormal/peritumoral regions from any of the 3 groups, where nomajor changes in MR-glc were observed (100� 11%). Inthe MrEug-HypoT group, no relevant changes were found in MR-glc levels in any of the regions inspected throughout the MRSIdynstudies. Therefore, moderate brain hypothermia (�308C) wasfound to be a necessary requirement in the GL261 tumor modelin order to observe the hyperglycemia-induced pattern pertur-bations (Figs 4B and 4C) previously described by single-voxelstudies (6).The differences in brain MR-glc between moderate hypother-

mia and normothermia can now, perhaps, be explained bycomparing the distinct patterns of blood glucose changesobserved during the hyperglycemic challenges in the respectivecontrol groups: at the time of higher brain MR-glc changes ingroups MrHyp-HypoT and MrHyp-NormT (88 and 66min post-injectionof glucose, respectively), the blood glucose levels in theCrHyp-HypoT group (23mM) were about 2.6-fold higher than thoseobserved in the CrHyp-NormT group (9mM). These observationshighlight the importance of temperature on the blood glucoselevels reached upon a hyperglycemic challenge, which was muchhigher in moderate hypothermia than in normothermia, possiblybecause its consumption by the organism is much lower in the

and HrHyp_HypoT groups of mice (black-colored and grey-collored bars,cal analysis results (�, p< 0.05 versus all HrEug-HypoT groups and HrHyp_HypoThyperglycemia in each region. Error bars correspond to �SD values.


31

R. V. SIMOES ET AL.

32

first case (although differences in insulin secretion cannot beruled out without further investigation). Additionally, there is alsoa possible contribution of the metabolic rate of GL261 tumor cellsto the level of blood glucose achieved. In this respect, glucosetransport was shown to display a temperature coefficient (Q10) of2.9 in the rat insulinoma-derived RINm5F cell line (29) whileoverall glucose consumption rate dropped 5.2-fold upon adecrease of only 58C in a Chinese Hamster Ovary cell line (CHO)(30). Therefore, hypothermia should in principle reduce theactivity of the proteins/enzymes involved in processes likeglucose transport by GLUT, phosphorylation by hexokinase andfurther metabolism down the glycolytic pathway. Assuming thatglucose passage from blood to the glioma extracellular space isnot limited (broken BBB), hypothermia should probably facilitatethe overload of the GLUT transporters capacity and/or saturatethe glycolytic pathway glucose consumption capacity in GL261glioma, therefore explaining the transient accumulation of non-phosphorylated glucose in the tumors studied here and inReference (6). Thus, the lower accumulation of tumor MR-glc atnormothermia could be explained by the lower glucose levels inblood and, perhaps, by a faster consumption of glucose by GL261tumors driven by an initially non-saturated glycolytic pathway.Moreover, the time-course pattern of blood glucose changes(accumulation and clearance from the vascular bed) during theacute hyperglycemic challenge in moderate hypothermia wasvery similar to that of tumor MR-glc in the same conditions(accumulation and washout from the tumor interstitial space).This suggests that, under these experimental conditions, theglucose time-course distribution was close to its equilibriumbetween the bloodstream and the tumor extracellular space(Fig. 5D).With respect to the discrepancy between the MR-glc increase

in the tumor upon hyperglycemia (average 1.6-fold) and theglucose concentration increase measured by HR-MAS (average 6-fold), the explanation is trivial. The large taurine concentrationpresent in the mouse brain (average 12.8mmol/g for GL261tumors) significantly contributes to the basal MRS pattern at3.43 ppm, where MR-glc levels are measured to generate theMRSI maps. This diminishes the observable fold change whenglucose increases, assuming taurine to remain constant duringthe experimental time. To test this assumption, phantomexperiments were carried out. When the phantom contents ofglucose and taurine mimicked those measured ex vivo, from theHR-MAS spectra, the fold change observed at the MR-glc positionwas 1.7, which is about what had been previously measured fromliving mice in vivo upon hyperglycemic challenge (1.6-fold). Thisdoes not rule out other minor contributions to the MR-glcchange, which may be further studied after metabolite extractionand high resolution NMR analysis as in Reference (31).

CONCLUSIONS

The translation of the MRSI data obtained from GL261 glioma inmice into maps of temporal MR-glc changes provided avisualization of apparent extracellular glucose accumulationcaused by the simultaneous contribution of tumor glucoseuptake, consumption, and washout within the different regionsof each tumor. Moderate brain hypothermia (�308C) was foundto be a necessary requirement in the GL261 tumor model in orderto observe the hyperglycemia-induced metabolite patternperturbation previously described by single-voxel studies (6).


Our results suggest spatial heterogeneity in tumor response tohyperglycemia, not only in each glioma microenvironment(gradient of accumulation) but also among the different tumorcases studied (different pattern of glucose distribution). Attemptsto quantify in vivo MRSI data from peak heights results in anunderestimation of the real glucose change in the tumors, asdemonstrated by ex vivo HR-MAS analysis of tumor samples, dueto the basal contribution of taurine to the MR-glc pattern.Altogether, this shows the potential of both MRS pattern

perturbation protocols and MRSI proton spectroscopy to increasethe dynamic range for in vivo brain tumor characterization inpreclinical studies. It would be interesting to assess whether ornot different tumor types and grades respond in a similar way to acomparable metabolite perturbation. This methodology could beused to study other animal models of human brain tumors (i.e.,Genetically Engineered Mice) (32).

Acknowledgements

The authors thank Dr Markus von Kienlin for his most valuablehelp in understanding MRSI; Dr Qi Zhao and Dr Radka Stoyanovafor introducing the necessary modifications in 3DiCSI software toread our data; Prof. Patrick Cozzone and Dr Yann le Fur forproviding access to CSIapo software and for the helpful modi-fications introduced; and Mr. Oscar Tibaduisa for developing theMatLab scripts. Special thanks go to Dr Franklyn Howe for thefinal English language revision.This work was funded by Ministerio de Educacion y Ciencia(Spain), SAF 2005-03650; Instituto de Salud Carlos III (Spain),PI051845; Fundacao para a Ciencia e a Tecnologia (Portugal),SFRH/BD/17643/2004; and Centro de Investigacion Biomedica enRed – Bioingenieria Biomateriales y Nanomedicina (CIBER-BBN),an initiative of the Instituto de Salud Carlos III (Spain).

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