DOI: 10.5061/dryad.905qfttwb
title: Data from: Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics

description: Glioblastomas are aggressive brain tumors with dismal prognosis. One of
 the main bottlenecks for developing more effective therapies for
 glioblastoma stems from their histologic and molecular heterogeneity,
 leading to distinct tumor microenvironments and disease phenotypes.
 Effectively characterizing these features would improve the clinical
 management of glioblastoma. Glucose flux rates through glycolysis and
 mitochondrial oxidation have been recently shown to quantitatively depict
 glioblastoma proliferation in mouse models (GL261 and CT2A tumors) using
 dynamic glucose-enhanced (DGE) deuterium spectroscopy. However, the
 spatial features of tumor microenvironment phenotypes remain hitherto
 unresolved. Here, we develop a DGE Deuterium Metabolic Imaging (DMI)
 approach for profiling tumor microenvironments through glucose conversion
 kinetics. Using a multimodal combination of tumor mouse models, novel
 strategies for spectroscopic imaging and noise attenuation, and
 histopathological correlations, we show that tumor lactate turnover
 mirrors phenotype differences between GL261 and CT2A mouse glioblastoma,
 whereas recycling of the peritumoral glutamate-glutamine pool is a
 potential marker of invasion capacity in pooled cohorts, linked to
 secondary brain lesions. These findings were validated by
 histopathological characterization of each tumor, including cell density
 and proliferation, peritumoral invasion and distant migration, and immune
 cell infiltration. Our study bodes well for precision neuro-oncology,
 highlighting the importance of mapping glucose flux rates to better
 understand the metabolic heterogeneity of glioblastoma and its links to
 disease phenotypes.
description: <strong>Animals and cell lines</strong>
 This study was performed in strict accordance with European
 Directive 2010/63 and the Portuguese law (Decreto-Lei 113/2013), following
 the FELASA (Federation of European Laboratory Animal Science Associations)
 guidelines and recommendations concerning laboratory animal welfare, and
 aligned with the ARRIVE (Animal Research: Reporting of In Vivo
 Experiments) guidelines. All animal experiments were performed at the
 Champalimaud Foundation Vivarium under project #05318, pre-approved by the
 competent institutional and national authorities: ORBEA (Champalimaud
 Foundation Animal Welfare Body) and DGAV (Direcção Geral de Alimentação e
 Veterinária), respectively. All the surgeries were performed under
 isoflurane anesthesia, and every effort was made to minimize suffering. A
 total of n=10 C57BL/6j male mice were used in this study, bred at the
 Champalimaud Foundation Vivarium, and housed with <em>ad
 libitum</em> access to food and water and 12h light cycles. GL261
 mouse glioma cells were obtained from the Tumor Bank Repository at the
 National Cancer Institute (Frederick MD, USA): “GLIOMA 261”, sample number
 0507815. CT2A mouse glioma cells were kindly provided by Prof. Thomas
 Seyfried at Boston College (Boston MA, USA). Both cell lines were grown in
 RPMI-1640 culture medium supplemented with 2.0 g/l Sodium Bicarbonate,
 0.285 g/l L-glutamine, 10% Fetal Bovine Serum (Gibco) and 1%
 Penicillin-Streptomycin solution. The cell lines tested negative for
 mycoplasma contamination using the IMPACT Mouse FELASA 1 test
 (Idexx-BioResearch, Ludwigsburg, Germany).
 <strong>Glioma
 </strong><strong>models</strong>
 Tumors were induced in previously described [66]. Briefly,
 intracranial stereotactic injection of 1 x10<sup>5</sup> GL261
 or CT2A cells was performed in the caudate nucleus (n=5 and n=5 mice,
 respectively); analgesia (Meloxicam 1.0 mg/Kg s.c.) was administered 30
 min before the procedure. Mice were anesthetized with isoflurane (1.5-2.0%
 in air) and immobilized on a stereotactic holder (Kopf Instruments,
 Tujunga/CA, USA) where they were warmed on a heating pad at 37 ºC, while
 body temperature was monitored with a rectal probe (WPI ATC-2000, Hitchin,
 UK). The head was shaved with a small trimmer, cleaned with iodopovidone,
 and the skull exposed through an anterior-posterior incision in the
 midline with a scalpel. A 1 mm hole was drilled in the skull using a
 micro-driller, 0.1 mm posterior to the bregma and 2.32 mm lateral to the
 midline. The tumor cells (1x10<sup>5</sup> in 4 μL PBS) were
 inoculated 2.35 mm below the cortical surface using a 10 µL Hamilton
 syringe (Hamilton, Reno NV, USA) connected to an automatic push-pull
 microinjector (WPI
 <em>Smartouch<sup>TM</sup></em>, Sarasota FL,
 USA), by advancing the 26G needle 3.85 mm from the surface of the skull
 (~1mm skull-to-brain surface distance), pulling it back 0.5 mm, and
 injecting at 2 μL/min rate. The syringe was gently removed 2 min after the
 injection had finished, the skin sutured with surgical thread (5/0 braided
 silk, Ethicon, San Lorenzo Puerto Rico) and wiped with iodopovidone.
 During recovery from anesthesia, animals were kept warm on a heating pad
 and given an opioid analgesic (Buprenorphine 0.05 mg/Kg s.c.) before
 returning to their cage. Meloxicam analgesia was repeatedly administered
 at 24- and 48-hours post-surgery.
 <strong><em>In
 vivo</em></strong><strong>
 Studies</strong> <em>Longitudinal
 MRI</em> GBM-bearing mice were imaged every 5-7
 days on a 1 Tesla Icon MRI scanner (Bruker BioSpin, Ettlingen, Germany;
 running <em>ParaVision 6.0.1 </em>software), to measure tumor
 volumes. For this, each mouse was placed in the animal holder under
 anesthesia (1-2 % isoflurane in 31% O<sub>2</sub>), heated
 with a recirculating water blanket, and monitored for rectal temperature
 (36-37 ºC) and breathing (60-90 BPM). Tumor volume was measured with
 T2-weighted <sup>1</sup>H-MRI (<em>RARE</em>
 sequence, ×8 acceleration factor, repetition time TR = 2500 ms, echo time
 TE = 84 ms, 8 averages, 1 mm slice thickness, and 160×160
 µm<sup>2</sup> in-plane resolution), acquired in two
 orientations (coronal and axial). Each session lasted up to 30
 min/animal.<strong> </strong>
 <em>End-point MRI and DMI</em>
 GBM-bearing mice with tumors ≥35 mm<sup>3</sup>
 (longitudinal MRI assessment) were scanned on a 9.4T BioSpec MRI scanner
 (Bruker BioSpin, Ettlingen, Germany; running under <em>ParaVision
 6.0.1</em>), using a
 <sup>2</sup>H/<sup>1</sup>H transmit-receive
 surface coilset customized for the mouse brain (NeosBiotec, Pamplona,
 Spain), as described before [31]. Before each experiment, GBM-bearing mice
 fasted 4-6h, were weighed, and cannulated in the tail vein with a catheter
 connected to a home-built 3-way injection system filled with:
 6,6′-<sup>2</sup>H<sub>2</sub>-glucose (1.6M in
 saline); Gd-DOTA (25 mM in saline); and with heparinized saline (10 U/mL).
 Mice were placed on the animal holder under anesthesia (as in 2.3.1).
 Coilset quality factors (Q) for <sup>1</sup>H and
 <sup>2</sup>H channels were estimated in the scanner for each
 sample based on the ratio of the resonance frequency (400.34 and 61.45
 MHz, for protons and deuterium, respectively) to its bandwidth (full width
 at half-minimum of the wobbling curve during the initial tuning
 adjustments): 175±8 and 200±12, respectively. Mice were imaged first with
 T2-weighted <sup>1</sup>H-MRI (<em>RARE</em>
 sequence, x8 acceleration factor, 3000 ms TR, 40 ms TE; 2 averages, 1 mm
 slice thickness, 70 µm in-plane resolution) in two orientations (coronal
 and axial). Then, the magnetic field homogeneity was optimized over the
 tumor region based on the water peak with <sup>1</sup>H-MRS
 (<em>STEAM</em> localization: 6x6x3 mm volume of interest,
 i.e. 108 µL) using localized 1<sup>st</sup> and
 2<sup>nd</sup> order shimming with the <em>MapShim
 Bruker </em>macro, leading to full widths at half-maximum (FWHM) of
 28±5 Hz. DMI was performed using a
 <em>slice-FID</em> <em>chemical-shift imaging</em>
 pulse sequence, with 175 ms TR, 256 spectral points sampled over a 1749 Hz
 window, and Shinnar-Le Roux RF pulse [67, 68] (0.42ms, 10kHz) with 55º
 flip angle, to excite a brain slice including the tumor: 18×18 mm
 field-of-view, and 2.27 mm slice thickness. After RF pulse calibration
 (using the natural abundance semi-heavy water peak, DHO), DGE-DMI data
 were acquired for 2h23min (768 repetitions), with i.v. bolus of
 6,6′-<sup>2</sup>H<sub>2</sub>-glucose (2 mg/g, 4
 µL/g injected over 30 s; Euroisotop, St Aubin Cedex, France). Data were
 sampled with an 8×8 matrix and 4-fold Fourier interpolated [69], rendering
 a 560 µm in-plane resolution. A reference T2-weighted image was
 additionally acquired with matching field-of-view and slice thickness, and
 70 µm in-plane resolution. Finally, animals underwent
 DCE T1-weighted <sup>1</sup>H-MRI (<em>FLASH</em>
 sequence, 8º flip-angle, 16ms TR, 4 averages, 150 repetitions, 1 slice
 with 140 µm in-plane resolution and 2.27 mm thickness, FOV size and
 position matching the DGE-DMI experiment), with i.v. bolus injection of
 Gd-DOTA (0.1 mmol/Kg, injected over 30 s; Guerbet, Villepinte, France).
 Animals were then sacrificed, brains were removed, washed in PBS, and
 immersed in 4% PFA. <strong>MRI/DMI
 </strong><strong>Processing</strong>
 <em>T2-weighted <sup>1</sup>H-MRI
 </em> T2-weighted MRI data were processed in
 ImageJ 1.53a (Rasband, W.S., ImageJ, U. S. National Institutes of Health,
 Bethesda, Maryland, USA, https://imagej.nih.gov/ij/, 1997-2018). For each
 animal, the tumor region was manually delineated on each slice, and the
 sum of the areas multiplied by the slice thickness to estimate the volume,
 which was averaged across the two orientations acquired (coronal and
 axial). <em>DGE-DMI</em>
 DGE-DMI data were processed in MATLAB<sup>®</sup>
 R2018b (Natick, Massachusetts: The MathWorks Inc.) and jMRUI 6.0b [70].
 Each dataset was averaged to 12 min temporal resolution and noise regions
 outside the brain, as well as the olfactory bulb and cerebellum, were
 discarded, rendering a 4D spectral-spatial-temporal matrix of 256×32×32×12
 points. After automated phase-correction of each spectrum, the 4D matrix
 was denoised with  a tensor PCA denoising approach [32]. For this, a [8 8
 8] window and tensor structure [1 2:3 4] were used for patch processing
 the spectral, spatial, and temporal dimensions with, whereas the
 <em>a priori </em>average standard deviation of the noise in
 each spectrum (calculated σ<sup>2</sup>) was used to avoid
 deleterious effects of spatially-correlated noise [34]. Then, these
 denoised spectra were analyzed voxel-wise by individual peak fitting with
 AMARES (similarly to the single-spectrum analysis reported previously in
 [52]), using a basis set for DHO (4.76 ppm: short- and long-T2 fractions
 [18]) and deuterium-labelled: glucose (Glc, 3.81 ppm), glutamate-glutamine
 (Glx, 2.36 ppm), and lactate (Lac, 1.31 ppm); relative linewidths
 referenced to the estimated short-T2 fraction of DHO, according to the
 respective T2 relaxation times reported by de Feyter <em>et
 al</em> [18]. The natural abundance DHO peak (DHOi) was further used
 to select and quantify both original and denoised spectra:
 SNR<sub>DHOi</sub> &gt;3.5 and 13.88 mM reference
 (assuming 80 % water content in the brain and 0.03 % natural abundance of
 DHO), respectively. Metabolite concentrations (CRLB&lt;50%; otherwise
 discarded) were corrected for T1 and labeling-loss effects, according to
 the values reported by de Feyter <em>et al</em> (T1, ms: DHO,
 320; Glc, 64; Glx, 146; Lac, 297) [18] and de Graaf <em>et
 al</em> (number of magnetically equivalent deuterons: DHO, 1; Glc,
 2; Glx, 1.2; Lac, 1.7) [71], respectively. Thus, the concentration of each
 metabolite (m) at each time point was estimated as (Eq 1):
 (Eq 1) <em>Area </em>= peak area;
 <em>Area0</em> = average peak area before injection;
 <em>d </em>= number of magnetically equivalent deuterons
 corrected for labelling-loss effects; <em>C</em> = T1
 correction factor (1-exp(-TR/T1)); and
 <em>Conc<sub>ref</sub></em> = reference DHO
 concentration. The time-course changes of
 <sup>2</sup>H-labelled metabolite (Glc, Glx and Lac)
 concentrations were fitted using a modified version of the kinetic model
 reported by Kreis et al [19], to estimate the maximum rate of Glc
 consumption (total, <em>V<sub>max</sub></em>) for
 Glx synthesis (mitochondrial oxidation,
 <em>V<sub>glx</sub></em>) and Lac synthesis
 (glycolysis, <em>V<sub>lac</sub></em>), and the
 confidence intervals for all estimated parameters: (Eq
 2) The coupled differential equations describing the
 concentration kinetics of each metabolite were: (Eq
 3) (Eq 4)
                                                    (Eq
 5) where:
 <em>k<sub>g</sub></em>, apparent rate constant of
 glucose transfer between blood and tumor
 (min<sup>−1</sup>);<em>
 k<sub>glx</sub></em>, apparent rate constant of Glx
 elimination (min<sup>−1</sup>);<em>
 k<sub>lac</sub></em>, apparent rate constant of lactate
 elimination (min<sup>−1</sup>); <em> </em>Glc
 concentration in plasma (mM);
 <em>a<sub>1</sub></em>, the Glc concentration
 after the bolus injection (mM); and
 <em>k<sub>p</sub></em>, the effective rate
 constant of labeled glucose transfer to tissue
 (min<sup>−1</sup>). As reported previously [31], the following
 parameters were fixed: fraction of deuterium enrichment
 (<em>f</em>), at 0.6 [19]; constant for glucose uptake
 (<em>k<sub>m</sub></em>), at 10 mM [72, 73]; and
 the extravascular-extracellular volume fraction (<em>v</em>),
 at 0.22 – average estimation from DCE-T1-weighted MRI analysis
 (<strong>Table 1 - </strong><strong>Supplementary file
 1a</strong>). All the other parameters were fitted without any
 restrictions to their range. Metabolic rate maps were displayed and
 analyzed pixel-wise using cut-off points defined by 5 times their
 respective confidence intervals. <em>DCE
 T1-weighted MRI </em> DCE T1-weighted MRI data
 were processed with DCE@urLab [74], as before [31]. First, ROIs were
 manually delineated for each tumor and the time-course data was fitted
 with the Extended Tofts 2-compartment model [75], to derive the volume
 transfer constant between plasma and tumor extravascular-extracellular
 space (<em>k<sup>trans</sup></em>), the washout
 rate between extravascular-extracellular space and plasma
 (<em>k<sub>ep</sub></em>), and the
 extravascular-extracellular volume fraction
 (<em>v<sub>e</sub></em>). Then, each dataset was
 reprocessed by down-sampling the original in-plane resolution to match the
 DGE-DMI experiment (0.56×0.56×2.27 mm<sup>3</sup>), and
 fitting the time-course data pixel-wise with the Extended Tofts
 2-compartment model to derive
 <em>k<sup>trans</sup></em>,
 <em>k<sub>ep</sub></em>, and
 <em>v<sub>e</sub></em> maps (pixels with root-mean
 square error &gt;0.005 discarded).
 <strong>Histopathology and
 Immunohistochemistry</strong> Whole brains fixed
 in 4% PFA were embedded in paraffin and sectioned at 30 different levels
 on the horizontal plane, spanning the whole tumor area. 4 µm sections were
 stained with H&E (Sigma-Aldrich, St. Louis MO, USA), digitized
 (Nanozoomer, Hamamatsu, Japan), and analyzed by an experimental
 pathologist blinded to experimental groups, according to previously
 established criteria [31]. Then, QuPath v0.4.3 built-in tools [76] were
 used to highlight different tumor regions: Tumor ROIs, corresponding to
 the bulk tumor, were delineated first with “create threshold” and then
 manually corrected; P-Margin ROIs, including areas of peritumoral
 infiltration, were delineated with “expand annotations” by expanding 100
 µm the tumor margin toward the adjacent brain parenchyma; Infiltrative
 ROIs, corresponding to specific infiltrative regions, were manually
 annotated. Between 3 to 6 sections of each tumor were also immunostained
 for Ki67 (mouse anti-ki67, BD, San Jose CA, USA; blocking reagent, M.O.M
 ImmPRESS kit, Vector Laboratories, Burlingame CA, USA; liquid
 DAB<sup>+</sup>, Dako North America Inc, Carpinteria CA, USA),
 digitized (Nanozoomer, Hamamatsu, Japan), and analyzed with QuPath
 built-in tools [76] for Tumor and P-Margin ROIs, defined as detailed
 above. Thus, Ki67<sup>+/-</sup> cells were counted
 semi-automatically to determine the total number of cells, the cell
 density, and the proliferation index (% Ki67<sup>+</sup>
 cells) as the average across slices for each ROI, and respective
 Tumor/P-Margin ratios. This procedure was repeated for each animal. In
 addition, one histologic section corresponding to each DGE-DMI slice was
 immunostained for Iba-1 (rabbit anti-Iba-1, Fujifilm Wako PCC, Osaka,
 Japan; NovolinkTM Polymer, Leica Biosystems, UK; liquid DAB+, Dako North
 America Inc, Carpinteria CA, USA), digitalized (Philips UFS v1.8.6614
 slide scanner) and analyzed in QuPath. Tumor region and peritumoral margin
 regions were automatically annotated as outlined above, and Iba-1 positive
 staining was quantified across all annotations using the threshold tools,
 adjusted for each slide to account for variations in staining intensity,
 to calculate the percentage of Iba-1 positive area: (Iba-1+ area / total
 annotation area) * 100. <strong>Statistical
 analyses</strong> Data were analyzed in
 MATLAB<sup>®</sup> R2018b (Natick, Massachusetts: The
 MathWorks Inc.) using the two-tailed Student’s
 <em>t-</em>test, either unpaired (comparing different animal
 cohorts) or paired (comparing the same animal cohort in different
 conditions). Differences at the 95% confidence level (p=0.05) were
 considered statistically significant. Correlation analyses were carried
 out with the Pearson R coefficient. Error bars indicate standard deviation
 unless indicated otherwise.
description: # Simoes\_eLife2024\_Data
 \[[https://doi.org/10.5061/dryad.905qfttwb](https://doi.org/10.5061/dryad.905qfttwb)]([https://doi.org/10.5061/dryad.905qfttwb](https://doi.org/10.5061/dryad.905qfttwb)) This dataset includes all the original and processed data, and respective code, generated during the study reported in the manuscript by Simões et al. eLife 2025, entitled "Deuterium Metabolic Imaging Phenotypes Mouse Glioblastoma Heterogeneity Through Glucose Turnover Kinetics", described there as supporting files. Briefly, the study reports a new MRI technique - dynamic glucose-enhance deuterium metabolic imaging (DGE-DMI) - that provides quantitative maps of glucose metabolism through different pathways fluxes in mouse glioblastoma, demonstrating potential as a non-invasive tool for in vivo metabolic phenotyping of aggressive brain tumors. DGE-DMI is applied to two cohorts of syngeneic mouse models of glioblastoma (CT2A and GL261), together with anatomical imaging (T2w-MRI), dynamic contrast-enhanced imaging (DGE-T1 MRI), and post-mortem histopathologic and immunohistochemistry analysis for each tumor. Thus, the study generated multi-parametric maps for each imaging modality, used for ROI analysis in tumor, tumor margin, and peri-tumoral regions: * DGE-DMI metabolic concentrations maps, for semi-heavy water (DHO), and deuterated-glucose (Glc), -glutamate-glutamine (Glx) and -lactate (Lac); * DGE-DMI synsthesis/consumption rate maps after model fititng of the metabolic concentration kinetics for Glc (Vmax), Glx (Vglx/kglx) and Lac (Vlac/klac), includind the apparent rate constant of glucose transfer between blood and tumor (kg), the Glc concentration after the bolus injection (a1), and the effective rate constant of labeled glucose transfer to tissue (kp); * DCE-T1 MRI permeability maps, according to the extended Tofts model, including washout rate between extravascular-extracellular space and plasma (Kep), volume transfer constant between plasma and tumor extravascular-extracellular space (Ktrans), extravascular-extracellular volume fraction (Ve), and root-mean square error (RMSE); * T2w-MRI anatomical imaging, used for volume rendering of each tumor; * Histopathology and immunohistochemistry maps, including cell count estimation for total cells, proliferating cells (Ki67 staining), and immune cell infiltration (iba-1 staining). ## Description of the data and file structure The data included was pre-processed as described in the Methods section and includes the following matlab code, files and folders for different parametric maps and/or their respective averages. ### Files and variables #### File: Simoes\_eLife2024\_Data.zip **Description:**  * ***CSI_2Dfid_eLife.m***, Matlab script for DGE-DMI analyses, from the original raw data to generating metabolic concentration and kinetic maps; * ***DCE_Maps_RMSEtrsh.mat***, Matlab file, including DCE-T1 MRI maps: Kep (min^-1), Ktrans (min^-1), and Ve (0-1), under RMSE threshold 0.005; * ***Variables_AveragesROIs.mat***, Matlab file, including average values in 3 ROIs (1:3 = all brain, tumor, and margin) for different parameters (1:9 = tumor size T2 (mm^3), Post-injection (days), Weight (g), H&E Class (1-4), Cells Detected (#), Ki67+ (%), Area (um^2), Cells/um^2, % Ki67%/mm^2)); * ***T2ref_ROIs*** folder, including: the original T2 reference images (mat file 'T2_raw') for both cohorts, CT2A ('T2refImage_ct2a': 1:5 = C1:5) and GL261 ('T2refImage_gl261': 1:5 = G1:5); and the ROI masks, over the reference T2 images (mat file 'ROIs_tumor_margin', including ROIs for 'CT2A' and 'GL261' cohorts, each one with 'tumor' and 'margin') and resized for the DGE-DMI matrix size (mat file 'ROIs_tumor_margin_nontumor': 1:10 subjects, 1:10: 1:5, CT2A cohort = C1:5; 6:10, GL261 cohort = G1:5; 32x32 matrix; 1:3 regions, for tumor, margin, and peritumor, respectively); * ***SNR*** folder, including: TIF figures, with SNR plots; dgeDMI_paper_MTX.mat file, with maps (32x32 matrix) for signal-to-noise ratio (snrMTX) and time-course spectra (specMTX: 1024 spectral points and 12 time points) for the original and denoised datasets (1:2) and each subject (1:10: 1:5, CT2A cohort = C1:5; 6:10, GL261 cohort = G1:5); and dgeDMI_paper_SNR.mat file, with the mean signal-to-noise ratio (SNRav) for the original and denoised datasets (1:2) and each subject (1:10: 1:5, CT2A cohort = C1:5; 6:10, GL261 cohort = G1:5), and respective standard deviations (SNRsd) and denoised/original fold changes (SNRav_fold and SNRsd_fold); * ***ConcentrationMaps*** folder, including a file ('dgeDMI_paper_MapsConcAll_n10') with the time-course concentration maps (1:12, where 1:3 are baseline, i.e. before Glc injection) for each metabolite (1:4 = DHO (mM), Glc (mM), Glx (mM), Lac (mM)) generated with and without spectral denoising (den and orig, respectively); also included additional figures (TIF) - average concentration maps (for each metabolite, subject, and condition: original and denoised), average concentration plots, and 2D correlation plots between average metabolite concentrations (denoised) and respective permeability metrics (Ktrans and Kep) for each subject; * ***MetabKineticsMaps*** folder, including the metabolite kinetics maps file ('dgeDMI_paper_MapsKinCIall_km10_n10_ve022_new'), specifically (1:8): kg (min^-1), a1 (mM), kp (min^-1), Vglx (mM/min), kglx (mM/min), Vlac (mM/min), klac (mM/min), Vmax (mM/min)); and their respective confidence interval maps (CI, upper and lower), generated from the concentration maps with model fitting; different ROIs included (all brain, tumor, tumor margin, and non-tumor); and also including 4 additional folders with kinetic maps (TIF) generated from each ROI (All brain, 'All'; Peritumor region, 'non-tumor'; tumor border, 'margin'; and 'tumor'); * ***rawData_SpectralFitting*** folder, including all DGE-DMI raw data and respective files ('ser') for original ('origPh') and denoised ('denPh') data, including the starting values & prior knowlege files for AMARES quantification in jMRUI ('jMRUI_AMARES_fitting' folder), for each subject (C1:5 and G1:5): input data (exported from Matlab, '_Alt3.txt'; and preprocessed in jMRUI, '_Alt3.mrui'); and output data ('_Alt3_AMARES.results'; and '_Alt3_AMARES_tr2.txt') All matlab files ordered according to the sample size (1:10): 1:5 = C1:5, CT2A cohort; 6:10 = G1:5, GL261 cohort. ## Code/software Matlab, version R2023b  ## Access information Other publicly accessible locations of the data: * N.A. Data was derived from the following sources: * N.A.