Imaging vascular characteristics and glycolytic metabolism of glioblastoma in a chick embryo model using 1H MRI and [18F]FDG-PET

Imaging vascular characteristics and glycolytic metabolism of glioblastoma in a chick embryo model using 1H MRI and [18F]FDG-PET | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Imaging vascular characteristics and glycolytic metabolism of glioblastoma in a chick embryo model using 1 H MRI and [ 18 F]FDG-PET Elisabeth Non Gash, Jan Schulze, Sarah E Barnett, Mahon Maguire, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7761553/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Molecular Imaging and Biology → Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: To assess hypoxia-associated host-tumour vascular adaptations and glycolytic metabolism in the chick chorioallantoic membrane (CAM) glioblastoma model. Procedures: U251 GBM cells were conditioned under normoxia (21% O₂) or hypoxia (1% O₂) for 72 hours before implantation onto the CAM on embryonic day 7 (E7). Imaging was performed on E13 using MRI (control-CAM n=8, normoxic-tumour n=7, hypoxic-tumour n=6) and brightfield microscopy (control-CAM n=7, normoxic-tumour n=8, hypoxic-tumour n=7). Tumours were harvested on E14 for histology and gene expression analyses. In a separate cohort, under normoxic conditioning, glucose metabolism was assessed using [ 18 F]FDG-PET on E12 followed by lactate MRS on E13 (n=25). Results Normoxia- and hypoxia-conditioned tumour-bearing CAMs exhibited vascular remodelling and significant upregulation of VEGFA and ADM compared to cultured cells . αSMA staining confirmed vessel infiltration in normoxia-conditioned tumours. CAIX staining revealed a hypoxic core in these tumours while hypoxia-conditioned tumours displayed heterogeneous staining. In both conditions, GLUT1 staining colocalised with CAIX staining, indicating hypoxia-associated glycolysis. GLUT1, PDK1 and LDHA expression was elevated in CAM tumours relative to tumour cells in vitro. In the metabolic imaging cohort, most tumours exhibited [¹⁸F]FDG uptake and lactate signal. However, no statistically significant relationship was observed between the two methods. Conclusions The CAM model provides a versatile platform for investigating GBM vascularisation and metabolism. Hypoxic conditioning amplifies transcriptional and vascular changes to the CAM. Although both [¹⁸F]FDG uptake and lactate were measurable, no significant correlation between the two was observed, potentially reflecting variability in tumour engraftment, vascular delivery of [¹⁸F]FDG, and microenvironmental influences on lactate accumulation. Chick chorioallantoic membrane glioblastoma MRI PET metabolism vasculature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Glioblastoma (GBM), also referred as WHO grade 4 glioma [ 1 ], is the most common and aggressive malignant brain tumour in adults. It is characterized by rapid proliferation, diffuse infiltration, and resistance to conventional therapies [ 2 ]. In comparison to low-grade glioma, the hypoxic tumour microenvironment plays a critical role in driving GBM malignant progression [ 3 ]. Hypoxia promotes vascularisation and induces metabolic rewiring, shifting tumour cells towards glycolytic metabolism to sustain growth and survival under oxygen-deprived conditions [ 4 ]. Hypoxia leads to the stabilisation of hypoxia-inducible factor α (HIF-α), which orchestrates a cellular transcriptional programme to promote tumour adaptation and progression [ 5 ]. A major consequence of HIF-α activation in GBM is the induction of pro-angiogenic signalling, which compensates for reduced oxygen supply by stimulating neovascularization in and around the tumour. HIF-α upregulates expression of proangiogenic genes such as vascular endothelial growth factors ( VEGFA ), angiopoietins ( ANGPT ), platelet-derived growth factors ( PDGF ), and adrenomedullin ( ADM ), which promote endothelial cell proliferation, migration, and survival [ 6 ]. HIF-α-mediated upregulation of matrix metalloproteinases ( MMPs ) facilitates extracellular matrix remodelling, enabling endothelial sprouting and expansion of the vascular network [ 7 ]. The resulting vasculature is abnormal; newly formed blood vessels are disorganized, tortuous, and highly permeable due to defective pericyte coverage and endothelial junction instability. This further exacerbates intratumoural hypoxia, sustaining HIF activation [ 8 ]. In addition to promoting angiogenesis, GBM cells can also alter the host vasculature through vessel co-option, allowing for tumour cell invasion alongside host blood vessels without requiring new capillary formation [ 9 ]. The hypoxia-driven angiogenic response in GBM is closely linked with metabolic adaptations that support tumour survival. HIFs shift cellular metabolism towards glycolysis, in which glucose is converted to lactate (Warburg effect), facilitating energy production in the absence of oxygen (Fig. 1 ) [ 10 ]. This meets the increased energy demand of rapidly dividing tumour cells and enables survival in hypoxic microenvironments through bypassing the oxygen-dependent tricarboxylic acid (TCA) cycle. Consequently, the switch to glycolytic metabolism is characterised by high glucose uptake, mediated by increased expression of glucose transporter 1 ( GLUT1 (gene name Solute Carrier Family 2 Member 1 ( SLC2A1 )) and elevated lactate production, via lactate dehydrogenase A ( LDHA ). In addition to its well-established role as a metabolic end-product, lactate has recently been implicated in the epigenetic regulation of gene expression via histone lactylation, a post-translational modification [ 11 ]. This emerging mechanism may serve as a metabolic-epigenetic link, particularly under hypoxic conditions where glycolytic flux is heightened [ 12 ]. Metabolic imaging facilitates non-invasive detection of glycolytic markers and, since changes in tumour metabolism can occur early during treatment, these markers may provide a powerful tool to detect early evidence of treatment response. PET/CT measurements of [ 18 F]fluoro-D-glucose ([ 18 F]FDG) uptake are widely used clinically for tumour staging, prognosis and treatment monitoring [ 14 – 16 ]. Upon injection, [ 18 F]FDG enters the cells through glucose transporters, where it is phosphorylated and trapped, with higher signal intensity on the images reflecting elevated glucose uptake. Similarly, proton magnetic resonance spectroscopy ( 1 H MRS) is a well-established non-invasive technique for monitoring tumour metabolism, based on the distinct signal profiles of different individual metabolites. Increased signal from lactate, as well as choline and lipid are typically observed in GBM [ 17 ]. Rodent models of cancer have played a critical role in imaging studies of tumour biology and for development of novel drugs prior to clinical assessment. However, these models impose substantial financial and practical husbandry requirements and have an extended lag-time to establish xenografts (2–8 months). Additionally, variability in xenograft uptake across animal strains adds complexity, often limiting reproducibility and accessibility [ 18 , 19 ]. The complexity of rodent GBM models makes it challenging to study tumour-host vascular interactions in a controlled manner, creating a need for alternative in vivo platforms that allow investigation of GBM pathophysiology and rapid evaluation of treatment responses alongside clinical therapies. The chick chorioallantoic membrane (CAM) model has recently gained attention as a bridging model between in vitro and in vivo studies [ 20 , 21 ]. The CAM is a highly vascularized, extraembryonic membrane surrounding the developing chick embryo, providing a naturally immunodeficient environment that readily supports tumour engraftment [ 22 ]. Importantly, its well-defined and accessible vascular network enables controlled investigation of tumour-host vascular interactions, while also supporting in vivo imaging for real-time analysis of vascular and metabolic dynamics [ 23 ]. Furthermore, CAM vessels are suitable for radiotracer injection, and several CAM studies have explored various radiotracers, cancer models, imaging techniques, and comparisons to mouse models [ 24 – 31 ]. Despite the known links between hypoxia, aberrant vasculature and glycolysis, they have rarely been studied together in the same model system. In this study, we investigated how hypoxia influences GBM growth in a CAM model through two critical facets of the tumour microenvironment: host-tumour vascular dynamics and metabolic reprogramming. While distinct, these processes jointly sustain tumour growth and therapeutic resistance. We utilised brightfield microscopy and MRI to assess hypoxia-induced host-tumour vascular remodelling. We also used clinically relevant imaging, [ 18 F]FDG-PET and a lactate-selective MRS sequence - selective multiple quantum coherence (SelMQC) - to measure glycolytic metabolism in GBM xenografts in the CAM model. 2. Materials and Methods The experimental workflow used in the study is briefly summarized in Fig. 2 . Cell lines and cell culture The human GBM cell line U251 (RRID: CVCL_0021) was maintained in Glutamax (Gibco, Waltham, MA, USA) with 10% foetal bovine serum (Gibco, Waltham, MA, USA) and penicillin and streptomycin (100 mg/ml, Thermo Fisher Scientific, Cheshire, UK), in a 5% CO 2 humidified incubator at 37°C. The cells were split at 80% confluency every 3–4 days and regularly tested for mycoplasma (Lonza MycoAlert, Manchester, UK). For hypoxia studies, cells were pre-incubated in a humidified 1% O₂/5% CO₂/94% N₂ environment for 72 hours in an InVivO 2 300 workstation (Baker Ruskinn, Bridgend, UK), maintained at 37°C. CAM xenograft generation Tumour xenografts were generated as previously described [ 32 ]. Briefly, fertilised chicken eggs (Medeggs Ltd, Fakenham, UK) were incubated at 37°C and 45% humidity to initiate embryonic development (embryonic day 0; E0) in a BrinseaOvaEasy poultry incubator (Brinsea, Cheshire, UK). 2×10 6 U251 cells were pipetted onto the CAM on E7 (Fig. 2 A). On E14, tumours were dissected and experiments terminated in accordance with the UK Animals (Scientific Procedures) Act 1986 (amended 2012). [ 18 F]FDG-PET/CT PET and CT imaging was performed using a β-CUBE and X-CUBE (Molecubes, Ghent, Belgium) as previously described [ 31 ]. Briefly, on E12, [ 18 F]FDG (5 ± 1 MBq in a final volume of 100–150 µL, Alliance Medical Radiopharmacy, UK) was injected distal to xenografted tumours into a large blood vessel of the CAM using a 33 G 12 mm hypodermic needle (Meso-relle; UKMedi, UK). The eggs (n = 25) were incubated for ~ 30 minutes after injection, ensuring complete uptake and distribution of [ 18 F]FDG (as denoted in Fig. 2 B). Magnetic Resonance Imaging and spectroscopy MRI was performed on E13 using a horizontal bore 9.4 T Bruker Biospec 94/20 USR (Bruker, Ettlingen, Germany) system equipped with 440 mT/m imaging gradients (Fig. 2 C). To minimise image artifacts arising from embryonic motion, eggs were pre-cooled on ice for 90 minutes prior to scanning, as previously described [ 32 , 33 ], after which they were placed in a custom-built cradle. An 86 mm inner diameter quadrature volume coil was used for signal transmission/reception (Bruker, Ettlingen, Germany). High-resolution 3D images of CAM tumour and vasculature (control (CAM only) n = 8, tumour n = 7, hypoxia-preconditioned tumour n = 6) were acquired using a 3D TurboRARE (spin-echo, T 2 -weighted) pulse sequence with the following parameters: field of view 40 × 40 × 2 mm 3 , matrix size 256 × 256 × 12, voxel size = 156 × 156 × 167 µm 3 , TR/TE = 2000/45.81 ms, ESP = 9.162 ms, RARE factor = 14, slab thickness = 2 mm, 4 averages, flip angle = 90°, acquisition time = 33 m 36 s. For the metabolic imaging study, Image-Selected In vivo Spectroscopy with Selective Multiple Quantum coherence (ISIS-SelMQC) [ 34 ] was performed on a separate cohort of eggs on E13 following [ 18 F]FDG-PET imaging. MR images were acquired for voxel placement within the tumour, with a 3D TurboRARE (spin-echo, T 2 -weighted) pulse sequence with the following parameters: field of view 40 × 40 × 2 mm 3 , matrix size 267 × 267 × 14, voxel size = 156 × 156 × 167 µm 3 , TR/TE = 1000/69.52 ms, ESP = 9.162 ms, RARE factor = 14, slab thickness = 2 mm, 1 average, flip angle = 90°, acquisition time = 8 m 48 s. Voxel dimensions were adjusted according to tumour size, ranging from 1.8–12.5 mm 3 . MRS sequence parameters included: TR = 2000 ms, 8012.82 Hz bandwidth, flip angle excitation/inversion = 90°/180°, 2048 complex points, averages = 4, acquisition time 8 m 32 s. Microscopy On E14, eggs were imaged with brightfield microscopy (Fig. 2 D) using a Leica M165FC fluorescence stereomicroscope with 16.5:1 zoom optics, fitted with a Leica DFC425 C camera (Leica Biosystems, Wetzlar, Germany). Data analysis The PET data were reconstructed, co-registered with the CT image and quantified using Invicro Vivoquant (Invicro LLCm, MA, USA, RRID:SCR_025778). A 3D ROI was manually drawn around each xenografted tumour to quantify the concentration of [ 18 F]FDG. The SUVsum representing the total radiotracer uptake within the ROI, provides a measure of overall FDG accumulation in the tumour [ 24 ]. For MR images, Amira image analysis software (v9, Thermo Fisher Scientific, UK, RRID:SCR_007353) was used to manually segment and determine the volume of tumour xenografts and vessels within a region of interest of 10 × 10 × 2 mm 3 to ensure a consistent and objective measurement between different eggs. Lactate and water ISIS-selMQC spectra were quantified using TopSpin (Bruker, RRID:SCR_014227) by integrating their respective peak areas, and relative lactate (RelLac) was calculated as the lactate-to-water integral ratio. For brightfield microscopy, the IKOSA CAM Assay (v3.1.0, Kolaido, Thal, Switzerland) was used to determine relative blood vessel area, thickness, length and number of branching points for the control tumour and hypoxic tumour-bearing CAM. The output data were normalised to the analysed area, excluding the tumour region in tumour-bearing CAMs. Histology and immunostaining Dissected CAM tumour samples were collected at E14 and placed in 1 mL 10% neutral buffered formalin (Sigma, St. Louis, MO, USA) for 16 hours and then transferred to 70% ethanol. The samples underwent automated tissue processing and embedding into paraffin blocks and sectioned at 4 µm onto SuperFrost Plus slides (Thermo Fisher Scientific, Warrington, UK). H&E staining was carried out for assessment of tissue architecture. To assess tumour hypoxia, vascularisation and glucose transporter expression, immunohistochemical staining was performed on the automated Leica BOND RXM using the following primary antibodies: Carbonic Anhydrase IX (CAIX) (D47G3; Cell Signalling Technology; 1:200 pH9), Alpha Smooth Muscle Actin (αSMA) (AB5694; Abcam; 1:200 pH9), GLUT1 (D3J3A; Cell Signalling Technology; 1:200 pH9). Diaminobenzidine (DAB) was used to visualise antibody binding, and haematoxylin counterstain to distinguish human tumour and chick cell nuclei. Sections were mounted with DPX mountant (Sigma, St. Louis, MO, USA). Slides were imaged using a digital slide scanner (Leica Aperio GT450 digital slide scanner). Aperio ImageScope software (Leica Microsystems Ltd, UK) was used for viewing images. RNA extraction and gene expression analysis Immediately after dissection on E14, tumours (n = 9) were rinsed in ice cold Dulbeccos phosphate buffered saline (DPBS) (Thermo Fisher Scientific #14190094) before transferring to RNAlater (Ambion, Life Technologies, Carlsbad, CA, USA). Total RNA was extracted from tumours, as well as from cell lines cultured in vitro , using a peqGOLD total RNA kit (VWR Life Sciences). RNA was converted to complementary DNA using iScript reverse transcriptase kit (BioRad, Watford, UK). qRT-PCR was performed using iQ sybr green supermix (Bio-Rad, UK) or power-track SYBRGreen master mix (Applied Biosystems, UK) on the QuantStudio™ 1 System (Thermo Fisher Scientific, UK) with 96-well plates. Β-Actin was used as a normalising gene, and results were analysed using the ∆∆Ct method [ 35 ]. Target primers used for gene expression analysis are listed in Supplementary Table S1 . All primers were designed to be human-specific for evaluating the human GBM U251 xenograft without transcript amplification from chick CAM tissue. Analysis was performed using QuantStudio™ Design and Analysis Software Version 1.5.2 (Thermo Fisher Scientific, UK). Statistical Analysis Statistical analyses were performed in GraphPad Prism version 10.4 for Windows (GraphPad Software, San Diego, CA, USA, RRID:SCR_002798). Spearman’s Rank correlation coefficients were calculated to assess the correlation between SUVsum and relative lactate. For brightfield microscopy and MR image data, the Shapiro-Wilk test was used to determine whether data were normally distributed, and analysis performed using parametric or non-parametric tests as appropriate. For image data with three conditions or more, statistical significance was determined using one-way ANOVA with post-hoc Tukey multiple comparison. p -values less than 0.05 were considered significant. 3. Results 3.1 GBM xenografts remodel the CAM vasculature Brightfield microscopy images revealed that GBM tumours remodelled the CAM vasculature from a typical linear branching pattern to a radial ‘spokes of a wheel’ pattern around the tumour nodule (Fig. 3 A- 3 D). Relative vessel thickness was significantly higher in both normoxia- and hypoxia-preconditioned tumour-bearing CAMs compared with CAM controls (Control CAM vs. Nmx-GBM: p < 0.0001; Control CAM vs. Hpx-GBM: p < 0.0001), with no difference between Nmx-GBM and Hpx-GBM (p = 0.99) (Fig. 3 H). There were no significant differences between the three conditions for the other vessel parameters: relative vessel area (Control CAM vs. Nmx-GBM: p = 0.32; Control CAM vs. Hpx-GBM: p = 0.16; Nmx-GBM vs. Hpx-GBM: p = 0.87), number of branching points (Control CAM vs. Nmx-GBM: p = 0.54; Control CAM vs. Hpx-GBM: p = 0.09; Nmx-GBM vs. Hpx-GBM: p = 0.5), and mean vessel length (Control CAM vs. Nmx-GBM: p = 0.6; Control CAM vs. Hpx-GBM: p = 0.17; Nmx-GBM vs. Hpx-GBM: p = 0.63) (Fig. 3 E-G). Since brightfield microscopy only provides a two-dimensional (2D) cross sectional view of the CAM, we performed MRI to obtain 3D volumetric measurements of the tumour and CAM vessels using high-resolution images of the tumour and surrounding host vasculature (Fig. 4 A). As with brightfield microscopy images, a radial pattern of CAM vasculature could be observed with MRI, with small vessels appearing to sprout from larger CAM vessels (Fig. 4 B). Tumour volumes were similar between normoxia and hypoxia-preconditioned tumours (Fig. 4 E). There were no statistically significant differences between CAM and normoxia-conditioned tumour-bearing CAMs, and again no difference between normoxia and hypoxia-conditioned tumour-bearing CAMs (Control vs. Nmx GBM: p = 0.48; Control vs. Hpx GBM: p = 0.08; Nmx GBM vs. Hpx GBM: p = 0.49) (Fig. 4 F). 3.2 GBM-CAM xenografts exhibit hallmark metabolic switch to glycolysis [ 18 F]FDG-PET imaging was performed on E12 followed by MRS on E13 (n = 25). [ 18 F]FDG uptake was observed in the tumour with notable uptake in the chick embryos (Fig. 5 A), reflecting successful radiotracer injection and the high metabolic demand required for embryonic development. Lactate was detected in the CAM xenografts (Fig. 5 B). MR spectra acquired from the tumour and adjacent CAM regions demonstrated differential metabolite signals, with tumour regions showing a prominent lactate peak at ~ 1.3 ppm (Fig. 5 B, Figure S3A,C), whereas spectra from the CAM exhibited no lactate signal (Figure S3B,D). Some tumours had undetectable FDG uptake or lactate signal, and zero values were assigned to these cases for analysis. Examples of tumours that exhibited or lacked [ 18 F]FDG uptake are shown in Figure S1 . H&E and immunohistochemical staining for vasculature (αSMA), hypoxia (CAIX) and glucose transporter expression (GLUT1) was also performed to evaluate metabolic marker expression and assess tumour viability and engraftment quality. Histological analysis revealed that some tumours were detached from the CAM (Figure S2 ), indicating poor engraftment, which likely limited [ 18 F]FDG delivery to these regions. [ 18 F]FDG uptake and relative lactate values are summarised in supplementary Table S2 . No correlation was observed between FDG uptake and lactate ((Fig. 5Ci) when zero values were included. In order to avoid the impact of zero-clustered data, we performed the correlation excluding the zero values, and in this case again, there was no significant correlation between FDG and lactate, however the shape of the correlation trended negative (Fig. 5Cii). 3.3 GBM-CAM xenografts form a hypoxic core Vascularised tumour nodules formed on the CAM in most cases (Fig. 6 ). However, in some instances, vascularised tumours developed within the CAM, beneath the initial site of implantation (Figure S4). H&E staining revealed viable, differentiating tumour cells (Fig. 6 A). IHC staining for the endothelial marker αSMA revealed large vessels in the CAM and at the tumour periphery in both normoxia- and hypoxia-preconditioned tumours (Figs. 6 B and 6 F). Normoxia-preconditioned xenografts showed strong CAIX staining concentrated in the centre of the tumour, surrounding a region in the core lacking nuclei, indicative of the necrosis (Fig. 6 C). In contrast, hypoxia-preconditioned tumours exhibited a more heterogeneous CAIX distribution throughout the tumour mass (Fig. 6 G), suggesting a sustained hypoxic phenotype driven by ‘hypoxic memory’, as previously described [ 36 ]. GLUT1 staining strongly colocalised with CAIX staining in both normoxia- and hypoxia-preconditioned tumours (Figs. 6 D and 6 H). 3.4 GBM-CAM tumour exhibits upregulation of proangiogenic and proglycolytic genes mRNA expression levels of proglycolytic and proangiogenic targets of HIF-α demonstrated elevated transcription in tumours compared with U251 cells grown in culture in normoxia for proangiogenic genes VEGFA and ADM (p = 0.02 and p = 0.01, respectively), and for proglycolytic genes GLUT1 , PDK1 and LDHA , although not statistically significant (p = 0.60, p = 0.37, p = 0.27 respectively) (Fig. 7 ). Housekeeping gene expression was stable across conditions, as Ct values for actin remained consistent between in vitro cell studies and CAM xenografts under both normoxic and hypoxic conditions (Figure S5). 4. Discussion In this study, we demonstrate that the U251 chick CAM model recapitulates several clinically relevant hallmark features of GBM. Hypoxic tumour cells within the xenograft core may be activating HIF-α signalling to promote angiogenesis and metabolic reprogramming. These phenotypes can be assessed using a range of complementary techniques, highlighting the versatility of the model for investigating both cellular and microenvironmental processes. Collectively, these attributes establish the CAM model as a physiologically relevant platform for studying tumour metabolism and vascular remodelling in GBM. U251 xenografts remodelled the CAM vasculature (Fig. 3 & 4 ), in concordance with recent reports describing radial remodelling of blood vessels around mesothelioma cell line-derived CAM xenografts [ 32 ]. Relative vessel thickness was significantly increased in tumour-bearing CAMs compared to control CAMs; however, no difference was observed between normoxia-conditioned and hypoxia-conditioned tumour-bearing CAMs. This may suggest that hypoxic signalling differences between normoxia- and hypoxia-conditioned cells are less substantial once tumours establish on the CAM, possibly due to the development of a hypoxic core in normoxia-conditioned grafts, as shown histologically in Fig. 6 . [ 18 F]FDG uptake and lactate signal demonstrated that GBM-CAM xenografts exhibited a glycolytic phenotype (Fig. 5 ). However, no significant correlation between these two measures was observed, challenging the assumption that higher [ 18 F]FDG uptake would correspond with increased lactate production. Previous studies have investigated the relationship between [ 18 F]FDG uptake and lactate levels, yielding varied results. In one study, Herholz et al reported a significant positive correlation between lactate concentration and [ 18 F]FDG uptake in patients with gliomas [ 37 ]. However, their study included a heterogeneous mix of glioma subtypes and grades and, in many instances, regions of elevated [¹⁸F]FDG uptake did not spatially coincide with areas of increased lactate, suggesting a lack of direct correspondence between [ 18 F]FDG uptake and lactate production. Moreover, as the patients were undergoing variable treatments during imaging including corticosteroids or radiotherapy, the treatment itself could have confounded the observed correlation between FDG uptake and lactate concentration. On the other hand, Guo et al reported no significant correlation between [ 18 F]FDG uptake and relative lactate in patients with lung adenocarcinoma [ 38 ]. Here again, ongoing treatment at the time of imaging may have confounded the assessment of tumour-intrinsic metabolic activity. More recently, Van Heijster et al, using hyperpolarised [1- 13 C]pyruvate MRS in murine xenografted human prostate cancer cell lines, reported a significant negative correlation between [ 18 F]FDG uptake and lactate production (measured as the pyruvate-to-lactate conversion rate) [ 39 ]. Collectively, these varied findings align with our results, indicating that increased [ 18 F]FDG uptake may not necessarily be a predictor of elevated lactate production, but that the two modalities reflect distinct, partially overlapping facets of tumour glycolytic metabolism. Several technical and biological factors could explain the lack of correlation between FDG uptake and lactate in our GBM-CAM xenografts. Firstly, [¹⁸F]FDG-PET and lactate MRS have very different sensitivities and detection limits. In the case of lactate, non-detectable signals may not necessarily reflect an absence of production, but rather the limited sensitivity of MRS combined with rapid vascular clearance or metabolic reutilisation of lactate. The ISIS pulse sequence is also particularly sensitive to motion, further contributing to apparent zero values. Secondly, [¹⁸F]FDG uptake is strongly dependent on tumour vascularisation, which determines both tracer delivery and clearance. Heterogenous vascular recruitment across xenografts may therefore underlie some of the variability in [¹⁸F]FDG signal, with poorly vascularised tumours showing minimal or no [¹⁸F]FDG uptake despite active glycolysis. Indeed, histological assessment of tumours in which no FDG uptake was observed, but in which lactate was detected, demonstrated poor overall engraftment, both in terms of attachment to the CAM and of vascular supply (Figure S2 ). Thirdly, [¹⁸F]FDG uptake itself is limited by the expression and activity of GLUT, as well as by hexokinase activity, meaning that high [¹⁸F]FDG accumulation may not necessarily equate to high glycolytic metabolism. Furthermore, lactate is not merely a terminal byproduct influenced by glycolytic flux; its concentration is also influenced by downstream metabolic and microenvironmental processes. Lactate can be rapidly exported and cleared from the tumour via the vasculature or be metabolised through conversion back to pyruvate in order to fuel the TCA cycle [ 40 , 41 ]. In addition, lactate may be diverted into alternative pathways such as histone lactylation [ 11 , 42 ]. Thus, while both [¹⁸F]FDG uptake and lactate signal are indicative of glycolytic activity, they capture different aspects of tumour metabolism - glucose transport and phosphorylation versus steady-state lactate metabolism and clearance. The absence of a correlation between FDG uptake and lactate signal in our model may be reflective of such complexities. Notably, in our dataset, the inclusion of samples with non-detectable lactate signal as well as non-detectable [¹⁸F]FDG uptake resulted in no correlation between [¹⁸F]FDG uptake and lactate. In contrast, exclusion of these apparent zero values produced a negative trend. This change in correlation highlights that the apparent relationship is strongly influenced by zero-inflated data from both modalities: in FDG, zeros can arise from limited vascular delivery or transporter activity, while in lactate they may reflect MRS sensitivity, clearance, or reutilisation. Thus, the correlation outcome appears highly sensitive to methodological and detection thresholds across both readouts, rather than purely reflecting biological differences. Immunoreactivity of hypoxia-inducible molecular marker CAIX was localised to the centre of the tumour, demonstrating that GBM-CAM tumours develop a hypoxic core driven by an oxygen gradient similar to that observed in spheroid, rodent, and patient-derived GBM models (Fig. 6 ) [ 43 – 45 ]. The colocalization of GLUT1 and CAIX staining suggests a hypoxia-driven shift towards glycolytic metabolism. Furthermore, the absence of CAIX with correspondingly low levels of GLUT1 staining in tumour cells on the periphery of the tumour, or close to intratumoural vessels, suggests an oxygen supply adequate to support aerobic respiration thereby preventing activation of the hypoxic response. This reasoning is corroborated by hypoxia-preconditioned tumours displaying reduced expression of GLUT1 and CAIX staining at the site in which the tumour appears integrated with the vascularised CAM (Fig. 6 , G&H). Tumours also exhibited elevated mRNA levels of canonical HIF-α targets - VEGFA , ADM, GLUT1, LDHA , and PDK1 - further supporting the hypothesised hypoxia-induced transcriptional reprogramming (Fig. 7 ). Although not all differences between cultured cells and CAM xenografts reached statistical significance, the overall pattern indicates a shift toward pro-angiogenic and glycolytic gene expression in xenografts. While the chick CAM offers a partial solution to the cost, husbandry, timescale and ethical issues posed by rodent models, it imposes some unique constraints. In the UK for example, the CAM tumour model can persist until E14 before legal restrictions from the Animal (Scientific Procedure) Act apply. This imposes a restricted timeframe, presenting challenges for studying tumour progression, vascular remodelling, and treatment responses, which can be followed over extended periods in mammalian models. Additionally, seasonal changes and temperature extremes, particularly in the winter and summer, can negatively affect embryo viability leading to inconsistent survival rates. Such limitations can partially be mitigated by starting experiments with larger numbers of eggs. Beyond egg viability, intra- and inter-batch variability presents another challenge. Even within the same shipment, embryos may develop at slightly different rates, affecting tumour engraftment, vascularization, and response to experimental conditions. To account for this, experimental design should be kept simple to avoid additional variables and be powered accordingly to account for survival and engraftment rates. Standardized protocols for egg handling, incubation, and tumour implantation improve reproducibility, but some degree of biological variability remains inevitable. By accounting for these challenges through careful experimental design, the CAM model remains a powerful tool for preclinical research, particularly for rapid screening of tumour-host dynamics and therapeutic interventions. In order to reduce egg mortality and minimise motion artefacts associated with warming during prolonged scan times, the MR-derived vascular volume measurements were obtained in a cohort distinct from the eggs imaged by MRS and PET. Future work should ideally implement faster, integrated vascular imaging protocols to acquire all modalities in a single cohort, enabling direct pairing of vascular volume with metabolic readouts. In summary, this study demonstrates the utility of the GBM-CAM model for investigating hypoxia-driven metabolic reprogramming and tumour-induced vascular remodelling using molecular imaging techniques. The integration of [¹⁸F]FDG-PET, lactate MRS, brightfield vascular imaging, histology, and measurement of gene expression provides a comprehensive assessment of GBM cell xenografts in a physiologically relevant, cost-effective, and imaging-compatible model. These findings highlight the potential of the CAM for preclinical evaluation of metabolic imaging biomarkers and therapeutic strategies targeting the hypoxic tumour microenvironment. Declarations Conflict of Interest: None of the authors have any conflict of interest to disclose with regards to this manuscript and its contents Author contributions All authors contributed to experimental design, writing and editing the manuscript. Elisabeth N Gash performed most of the experiments, data acquisition, analysis as well as statistics. Jan Schulze, Sarah Barnett, and Mohesh Moothanchery assisted with the CAM model as well as PET experiments. Mahon L Maguire and Stephen Pickup helped with MRI and MRS data acquisition and analysis while Ian Scott assisted in histological analysis. Michael Batie, Rasheed Zakaria, Judy M Coulson and Sonia Rocha helped with conception and design of in vitro and in ovo studies. Harish Poptani conceived the study, supervised data analysis and aided funding as well as editing of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to acknowledge the use of the Egg Facility (RRID:SCR_026195; all egg experiments), Centre for Pre-clinical Imaging (RRID:SCR_026605; all preclinical imaging), Histology SRF (RRID:SCR_026606; Tissue processing, embedding, sectioning and H&E staining), all provided by the Liverpool Shared Research Facilities (LIV-SRF), Faculty of Health and Life Sciences, University of Liverpool. We would also like to acknowledge the Liverpool University Biobank (slide scanning), Dr Laura Bowker (assistance in CAM experiments as well as PET imaging) and Dr Helen Kalirai (training on the Leica Bond RxM). ENG was funded by The Isle of Man Anti-Cancer Association. References Louis, D.N., et al., The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol, 2021. 23 (8): p. 1231-1251. Yalamarty, S.S.K., et al., Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM). Cancers (Basel), 2023. 15 (7). Feldman, L., Hypoxia within the glioblastoma tumor microenvironment: a master saboteur of novel treatments. Front Immunol, 2024. 15 : p. 1384249. Eales, K.L., K.E. Hollinshead, and D.A. Tennant, Hypoxia and metabolic adaptation of cancer cells. Oncogenesis, 2016. 5 (1): p. e190. Torrisi, F., et al., The Hallmarks of Glioblastoma: Heterogeneity, Intercellular Crosstalk and Molecular Signature of Invasiveness and Progression. 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1","display":"","copyAsset":false,"role":"figure","size":271754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePro-glycolytic and pro-angiogenic transcriptional roles of HIF-α.\u003c/strong\u003e HIF-α upregulates pyruvate dehydrogenase kinase (PDK), which phosphorylates and inactivates pyruvate dehydrogenase (PDH), thereby reducing its active form and limiting acetyl-CoA production. Additionally, HIF-α enhances the expression of glucose transporters (GLUT) on the cell membrane, increasing glucose uptake to sustain glycolytic metabolism. This metabolic shift leads to increased conversion of pyruvate to lactate via lactate dehydrogenase (LDH), whose expression is also upregulated by HIF-α. Elevated intracellular lactate serves as a substrate for protein lysine lactylation. Lactylation has been implicated in the regulation of gene expression under hypoxia, potentially influencing chromatin accessibility and HIF targeted gene transcription. In parallel, HIF-α promotes the transcription of VEGF and ADM, promoting angiogenesis for greater oxygen and nutrient delivery to support biomass synthesis and cellular proliferation. Figure adapted from [13] and used with permission.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/ad4f639d4e24e7042a6963dc.png"},{"id":95320560,"identity":"9cf00fa7-c76f-495d-985b-8bec2a7e4a1a","added_by":"auto","created_at":"2025-11-06 16:39:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental workflow of GBM-CAM imaging.\u003c/strong\u003e \u0026nbsp;A: On embryonic day 7 (E7), the tumour is implanted on the CAM. B: For the metabolic imaging study, eggs are imaged with [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET on E12, and C: with MRI and MRS on E13. D: On E14 the eggs are imaged using microscopy and terminated. E: Tumours are dissected from the CAM and fixed/frozen for RNA or histological analysis. Figure created with BioRender.com.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/d08018c87f9ae99af6bef5c9.png"},{"id":95320565,"identity":"f0d6fb09-3378-4ff5-bb42-a4441036751d","added_by":"auto","created_at":"2025-11-06 16:39:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1406195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBM tumours remodel the CAM vasculature. \u003c/strong\u003eRepresentative brightfield microscopy images of control CAM (A) and tumour-bearing CAM (B) \u003cem\u003ein ovo\u003c/em\u003e. The IKOSA CAM Assay mask (IKOSA® CAM assay KML Vision) of the control (C) and tumour-bearing CAM (D). Scale bar: 2 mm. ROI: 10 × 10 mm\u003csup\u003e2\u003c/sup\u003e. Violin plots of (E) relative vessel thickness, (F) branching points, (G) vessel length and (H) vessel area (relative to ROI analysed, excluding tumour region in tumour-bearing CAM images) for untreated non-tumour bearing (Control; n=8), normoxia-preconditioned tumour-bearing (Nmx GBM; n=7) and hypoxia-preconditioned tumour-bearing CAM (Hpx GBM; n=6) output from the IKOSA CAM Assay. One-way ANOVA with Tukey’s post-hoc test., **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/0f3c508cd7c875728a199445.png"},{"id":95320572,"identity":"b652ae25-ea18-422f-a9f4-7719ee78d618","added_by":"auto","created_at":"2025-11-06 16:39:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1873404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMRI enables 3D \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein-ovo \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003evisualisation of the tumour-CAM vasculature relationship. \u003c/strong\u003eRepresentative MR image slices in the coronal plane, moving up towards the surface of the CAM (A, B). These images were chosen to focus on the tumour and its feeding vessels. The tumour (white arrow) and the associated vessels (red arrows) appear dark, the CAM appears as grey. The chick embryo organs are also observed as variable intensities, with the spine and flank of the chick observable near the centre of the image (yellow arrow). (C, D) Representative segmented masks, top and bottom view, used to determine tumour and vessel volume. (E) Violin plots showing relative tumour volume, calculated as tumour volume/ total ROI volume, for normoxia (Nmx GBM; n=8) and hypoxia-preconditioned (Hpx GBM; n=7) CAM tumours, and (F) relative blood vessel volume for untreated non-tumour bearing CAM (Control; n=7), normoxia (Nmx GBM; n=8) and hypoxia-preconditioned (Hpx GBM; n=7) tumour-bearing CAMs.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/6806a0505480d430fa8422e7.png"},{"id":95523630,"identity":"649375c0-f793-4f4b-b6ee-e566ed2605e0","added_by":"auto","created_at":"2025-11-10 09:59:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":386266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FDG uptake and lactate levels in GBM.\u003c/strong\u003e (A) Representative image of [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake into the chick embryo and tumour after injection of 8 ± 2 MBq [\u003csup\u003e18\u003c/sup\u003eF]FDG into a large vessel on E12. FDG signal is visible from the chick embryo and the GBM. The white arrow points to an inset, where an enlarged image of the same tumour is shown to reflect the heterogeneity in the FDG uptake within the tumour. (B) SelMQC sequence demonstrated a clearly visible lactate resonance from the tumour (red), but not from the CAM (blue). (C) Spearman’s Rank correlation between relative lactate and FDG uptake (SUVsum\u003cbr\u003e\n) shown for (i) all tumours, including 0 values (R = 0.161, p = 0.441, n = 25), and (ii) both detectable PET and lactate signals (R = -0.71, p = 0.058, n = 8).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/54d613de2d01dee5631c3846.png"},{"id":95320573,"identity":"6809393b-80c0-417d-9118-dd282701fec0","added_by":"auto","created_at":"2025-11-06 16:39:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4412366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistological characterisation of GBM-CAM tumour morphology, vascularisation, hypoxia and glucose transport.\u003c/strong\u003e H\u0026amp;E staining of normoxia-preconditioned (Nmx) (A) and hypoxia-preconditioned (Hpx) (E) U251 tumours (scale bars: 600 μm, 200 μm), focussing on the tumour-CAM interface. αSMA staining revealed blood vessels in the CAM and at the periphery of both Nmx (B) and Hpx (F) tumours. Nucleated chick blood cells appear as blue dots inside the blood vessels and spaces between tumour cells (scale bars: 600 μm, 60 μm). CAIX staining showed hypoxic tumour cells localised to the centre of Nmx tumour (C), surrounding a necrotic core, whereas the Hpx tumour (G) exhibited heterogenous staining throughout the whole tumour (scale bars: 800 μm, 200 μm). GLUT1 staining was localised to the central region of the Nmx tumour (D), while the Hpx tumour (H) displayed a heterogeneous pattern that mirrored CAIX distribution (scale bar: 800 μm, 100 μm). Note the CAM has folded over the tumour in the case of the Nmx tumour.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/472a42d3816a02b62eb18ba5.png"},{"id":95523625,"identity":"0c0a2c96-f56f-4c15-9b82-554447e965b5","added_by":"auto","created_at":"2025-11-10 09:59:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":100129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGBM-CAM xenografts demonstrate higher expression of HIF-α target genes involved in angiogenesis and glycolytic metabolism relative to cells in culture. \u003c/strong\u003eqPCR analysis of U251 cells cultured \u003cem\u003ein vitro \u003c/em\u003eand normoxia/control-conditioned CAM-tumour xenografts \u003cem\u003ein ovo\u003c/em\u003e. A) \u003cem\u003eVEGFA\u003c/em\u003e and B) \u003cem\u003eADM\u003c/em\u003e promote angiogenesis to restore oxygen and nutrient supply. C) \u003cem\u003eGLUT1\u003c/em\u003e is involved in glucose transport. D) \u003cem\u003ePDK1\u003c/em\u003e phosphorylates pyruvate dehydrogenase for ubiquitination, preventing conversion of pyruvate to acetyl-coA. E) \u003cem\u003eLDHA\u003c/em\u003e catalyzes conversion of pyruvate to lactate. For the \u003cem\u003ein vitro \u003c/em\u003econdition, a single biological replicate yielded no detectable Ct value for target gene \u003cem\u003eLDHA\u003c/em\u003e and was therefore excluded from analysis. For the above genes, expression is elevated \u003cem\u003ein ovo\u003c/em\u003e compared to \u003cem\u003ein vitro\u003c/em\u003e. Mean expression is shown relative to the mean of\u0026nbsp;β-actin\u0026nbsp;(2\u003csup\u003e∆Cq\u003c/sup\u003e). Bars represent the mean ± standard deviation (SD) of biological replicates, with individual data points shown. Unpaired t-test, * p\u0026lt;0.05, ** p\u0026lt;0.01. Supporting data for housekeeping genes is shown in Supplementary Figure S5.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/4883199595eb2767dec299dc.png"},{"id":103766143,"identity":"33528f28-da91-40aa-9a8d-bdd2319946c7","added_by":"auto","created_at":"2026-03-02 16:12:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10594459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/83d5fb2c-326a-48ed-a9e0-160654333f13.pdf"},{"id":95523887,"identity":"59ced58f-76d4-4fe6-b0d8-a193f9e2344a","added_by":"auto","created_at":"2025-11-10 10:01:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9667711,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresFDGPETMRSpaper01.10.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7761553/v1/9e79ba6b7992b780aac278f5.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eImaging vascular characteristics and glycolytic metabolism of glioblastoma in a chick embryo model using \u003csup\u003e1\u003c/sup\u003eH MRI and [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlioblastoma (GBM), also referred as WHO grade 4 glioma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], is the most common and aggressive malignant brain tumour in adults. It is characterized by rapid proliferation, diffuse infiltration, and resistance to conventional therapies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In comparison to low-grade glioma, the hypoxic tumour microenvironment plays a critical role in driving GBM malignant progression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hypoxia promotes vascularisation and induces metabolic rewiring, shifting tumour cells towards glycolytic metabolism to sustain growth and survival under oxygen-deprived conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHypoxia leads to the stabilisation of hypoxia-inducible factor α (HIF-α), which orchestrates a cellular transcriptional programme to promote tumour adaptation and progression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A major consequence of HIF-α activation in GBM is the induction of pro-angiogenic signalling, which compensates for reduced oxygen supply by stimulating neovascularization in and around the tumour. HIF-α upregulates expression of proangiogenic genes such as vascular endothelial growth factors (\u003cem\u003eVEGFA\u003c/em\u003e), angiopoietins (\u003cem\u003eANGPT\u003c/em\u003e), platelet-derived growth factors (\u003cem\u003ePDGF\u003c/em\u003e), and adrenomedullin (\u003cem\u003eADM\u003c/em\u003e), which promote endothelial cell proliferation, migration, and survival [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. HIF-α-mediated upregulation of matrix metalloproteinases (\u003cem\u003eMMPs\u003c/em\u003e) facilitates extracellular matrix remodelling, enabling endothelial sprouting and expansion of the vascular network [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The resulting vasculature is abnormal; newly formed blood vessels are disorganized, tortuous, and highly permeable due to defective pericyte coverage and endothelial junction instability. This further exacerbates intratumoural hypoxia, sustaining HIF activation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition to promoting angiogenesis, GBM cells can also alter the host vasculature through vessel co-option, allowing for tumour cell invasion alongside host blood vessels without requiring new capillary formation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe hypoxia-driven angiogenic response in GBM is closely linked with metabolic adaptations that support tumour survival. HIFs shift cellular metabolism towards glycolysis, in which glucose is converted to lactate (Warburg effect), facilitating energy production in the absence of oxygen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This meets the increased energy demand of rapidly dividing tumour cells and enables survival in hypoxic microenvironments through bypassing the oxygen-dependent tricarboxylic acid (TCA) cycle. Consequently, the switch to glycolytic metabolism is characterised by high glucose uptake, mediated by increased expression of glucose transporter 1 (\u003cem\u003eGLUT1\u003c/em\u003e (gene name Solute Carrier Family 2 Member 1 (\u003cem\u003eSLC2A1\u003c/em\u003e)) and elevated lactate production, via lactate dehydrogenase A (\u003cem\u003eLDHA\u003c/em\u003e). In addition to its well-established role as a metabolic end-product, lactate has recently been implicated in the epigenetic regulation of gene expression via histone lactylation, a post-translational modification [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This emerging mechanism may serve as a metabolic-epigenetic link, particularly under hypoxic conditions where glycolytic flux is heightened [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMetabolic imaging facilitates non-invasive detection of glycolytic markers and, since changes in tumour metabolism can occur early during treatment, these markers may provide a powerful tool to detect early evidence of treatment response. PET/CT measurements of [\u003csup\u003e18\u003c/sup\u003eF]fluoro-D-glucose ([\u003csup\u003e18\u003c/sup\u003eF]FDG) uptake are widely used clinically for tumour staging, prognosis and treatment monitoring [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Upon injection, [\u003csup\u003e18\u003c/sup\u003eF]FDG enters the cells through glucose transporters, where it is phosphorylated and trapped, with higher signal intensity on the images reflecting elevated glucose uptake. Similarly, proton magnetic resonance spectroscopy (\u003csup\u003e1\u003c/sup\u003eH MRS) is a well-established non-invasive technique for monitoring tumour metabolism, based on the distinct signal profiles of different individual metabolites. Increased signal from lactate, as well as choline and lipid are typically observed in GBM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRodent models of cancer have played a critical role in imaging studies of tumour biology and for development of novel drugs prior to clinical assessment. However, these models impose substantial financial and practical husbandry requirements and have an extended lag-time to establish xenografts (2\u0026ndash;8 months). Additionally, variability in xenograft uptake across animal strains adds complexity, often limiting reproducibility and accessibility [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The complexity of rodent GBM models makes it challenging to study tumour-host vascular interactions in a controlled manner, creating a need for alternative \u003cem\u003ein vivo\u003c/em\u003e platforms that allow investigation of GBM pathophysiology and rapid evaluation of treatment responses alongside clinical therapies.\u003c/p\u003e\u003cp\u003eThe chick chorioallantoic membrane (CAM) model has recently gained attention as a bridging model between \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The CAM is a highly vascularized, extraembryonic membrane surrounding the developing chick embryo, providing a naturally immunodeficient environment that readily supports tumour engraftment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, its well-defined and accessible vascular network enables controlled investigation of tumour-host vascular interactions, while also supporting \u003cem\u003ein vivo\u003c/em\u003e imaging for real-time analysis of vascular and metabolic dynamics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, CAM vessels are suitable for radiotracer injection, and several CAM studies have explored various radiotracers, cancer models, imaging techniques, and comparisons to mouse models [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the known links between hypoxia, aberrant vasculature and glycolysis, they have rarely been studied together in the same model system. In this study, we investigated how hypoxia influences GBM growth in a CAM model through two critical facets of the tumour microenvironment: host-tumour vascular dynamics and metabolic reprogramming. While distinct, these processes jointly sustain tumour growth and therapeutic resistance. We utilised brightfield microscopy and MRI to assess hypoxia-induced host-tumour vascular remodelling. We also used clinically relevant imaging, [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET and a lactate-selective MRS sequence - selective multiple quantum coherence (SelMQC) - to measure glycolytic metabolism in GBM xenografts in the CAM model.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe experimental workflow used in the study is briefly summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCell lines and cell culture\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe human GBM cell line U251 (RRID: CVCL_0021) was maintained in Glutamax (Gibco, Waltham, MA, USA) with 10% foetal bovine serum (Gibco, Waltham, MA, USA) and penicillin and streptomycin (100 mg/ml, Thermo Fisher Scientific, Cheshire, UK), in a 5% CO\u003csub\u003e2\u003c/sub\u003e humidified incubator at 37\u0026deg;C. The cells were split at 80% confluency every 3\u0026ndash;4 days and regularly tested for mycoplasma (Lonza MycoAlert, Manchester, UK). For hypoxia studies, cells were pre-incubated in a humidified 1% O₂/5% CO₂/94% N₂ environment for 72 hours in an InVivO\u003csub\u003e2\u003c/sub\u003e 300 workstation (Baker Ruskinn, Bridgend, UK), maintained at 37\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCAM xenograft generation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTumour xenografts were generated as previously described [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Briefly, fertilised chicken eggs (Medeggs Ltd, Fakenham, UK) were incubated at 37\u0026deg;C and 45% humidity to initiate embryonic development (embryonic day 0; E0) in a BrinseaOvaEasy poultry incubator (Brinsea, Cheshire, UK). 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e U251 cells were pipetted onto the CAM on E7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). On E14, tumours were dissected and experiments terminated in accordance with the UK Animals (Scientific Procedures) Act 1986 (amended 2012).\u003c/p\u003e\u003cp\u003e[\u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF]FDG-PET/CT\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePET and CT imaging was performed using a β-CUBE and X-CUBE (Molecubes, Ghent, Belgium) as previously described [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Briefly, on E12, [\u003csup\u003e18\u003c/sup\u003eF]FDG (5\u0026thinsp;\u0026plusmn;\u0026thinsp;1 MBq in a final volume of 100\u0026ndash;150 \u0026micro;L, Alliance Medical Radiopharmacy, UK) was injected distal to xenografted tumours into a large blood vessel of the CAM using a 33 G 12 mm hypodermic needle (Meso-relle; UKMedi, UK). The eggs (n\u0026thinsp;=\u0026thinsp;25) were incubated for ~\u0026thinsp;30 minutes after injection, ensuring complete uptake and distribution of [\u003csup\u003e18\u003c/sup\u003eF]FDG (as denoted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMagnetic Resonance Imaging and spectroscopy\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMRI was performed on E13 using a horizontal bore 9.4 T Bruker Biospec 94/20 USR (Bruker, Ettlingen, Germany) system equipped with 440 mT/m imaging gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). To minimise image artifacts arising from embryonic motion, eggs were pre-cooled on ice for 90 minutes prior to scanning, as previously described [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], after which they were placed in a custom-built cradle. An 86 mm inner diameter quadrature volume coil was used for signal transmission/reception (Bruker, Ettlingen, Germany). High-resolution 3D images of CAM tumour and vasculature (control (CAM only) n\u0026thinsp;=\u0026thinsp;8, tumour n\u0026thinsp;=\u0026thinsp;7, hypoxia-preconditioned tumour n\u0026thinsp;=\u0026thinsp;6) were acquired using a 3D TurboRARE (spin-echo, T\u003csub\u003e2\u003c/sub\u003e-weighted) pulse sequence with the following parameters: field of view 40 \u0026times; 40 \u0026times; 2 mm\u003csup\u003e3\u003c/sup\u003e, matrix size 256 \u0026times; 256 \u0026times; 12, voxel size\u0026thinsp;=\u0026thinsp;156 \u0026times; 156 \u0026times; 167 \u0026micro;m\u003csup\u003e3\u003c/sup\u003e, TR/TE\u0026thinsp;=\u0026thinsp;2000/45.81 ms, ESP\u0026thinsp;=\u0026thinsp;9.162 ms, RARE factor\u0026thinsp;=\u0026thinsp;14, slab thickness\u0026thinsp;=\u0026thinsp;2 mm, 4 averages, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, acquisition time\u0026thinsp;=\u0026thinsp;33 m 36 s.\u003c/p\u003e\u003cp\u003eFor the metabolic imaging study, Image-Selected \u003cem\u003eIn vivo\u003c/em\u003e Spectroscopy with Selective Multiple Quantum coherence (ISIS-SelMQC) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] was performed on a separate cohort of eggs on E13 following [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET imaging. MR images were acquired for voxel placement within the tumour, with a 3D TurboRARE (spin-echo, T\u003csub\u003e2\u003c/sub\u003e-weighted) pulse sequence with the following parameters: field of view 40 \u0026times; 40 \u0026times; 2 mm\u003csup\u003e3\u003c/sup\u003e, matrix size 267 \u0026times; 267 \u0026times; 14, voxel size\u0026thinsp;=\u0026thinsp;156 \u0026times; 156 \u0026times; 167 \u0026micro;m\u003csup\u003e3\u003c/sup\u003e, TR/TE\u0026thinsp;=\u0026thinsp;1000/69.52 ms, ESP\u0026thinsp;=\u0026thinsp;9.162 ms, RARE factor\u0026thinsp;=\u0026thinsp;14, slab thickness\u0026thinsp;=\u0026thinsp;2 mm, 1 average, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, acquisition time\u0026thinsp;=\u0026thinsp;8 m 48 s. Voxel dimensions were adjusted according to tumour size, ranging from 1.8\u0026ndash;12.5 mm\u003csup\u003e3\u003c/sup\u003e. MRS sequence parameters included: TR\u0026thinsp;=\u0026thinsp;2000 ms, 8012.82 Hz bandwidth, flip angle excitation/inversion\u0026thinsp;=\u0026thinsp;90\u0026deg;/180\u0026deg;, 2048 complex points, averages\u0026thinsp;=\u0026thinsp;4, acquisition time 8 m 32 s.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMicroscopy\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOn E14, eggs were imaged with brightfield microscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) using a Leica M165FC fluorescence stereomicroscope with 16.5:1 zoom optics, fitted with a Leica DFC425 C camera (Leica Biosystems, Wetzlar, Germany).\u003c/p\u003e\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe PET data were reconstructed, co-registered with the CT image and quantified using Invicro Vivoquant (Invicro LLCm, MA, USA, RRID:SCR_025778). A 3D ROI was manually drawn around each xenografted tumour to quantify the concentration of [\u003csup\u003e18\u003c/sup\u003eF]FDG. The SUVsum representing the total radiotracer uptake within the ROI, provides a measure of overall FDG accumulation in the tumour [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For MR images, Amira image analysis software (v9, Thermo Fisher Scientific, UK, RRID:SCR_007353) was used to manually segment and determine the volume of tumour xenografts and vessels within a region of interest of 10 \u0026times; 10 \u0026times; 2 mm\u003csup\u003e3\u003c/sup\u003e to ensure a consistent and objective measurement between different eggs. Lactate and water ISIS-selMQC spectra were quantified using TopSpin (Bruker, RRID:SCR_014227) by integrating their respective peak areas, and relative lactate (RelLac) was calculated as the lactate-to-water integral ratio. For brightfield microscopy, the IKOSA CAM Assay (v3.1.0, Kolaido, Thal, Switzerland) was used to determine relative blood vessel area, thickness, length and number of branching points for the control tumour and hypoxic tumour-bearing CAM. The output data were normalised to the analysed area, excluding the tumour region in tumour-bearing CAMs.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHistology and immunostaining\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDissected CAM tumour samples were collected at E14 and placed in 1 mL 10% neutral buffered formalin (Sigma, St. Louis, MO, USA) for 16 hours and then transferred to 70% ethanol. The samples underwent automated tissue processing and embedding into paraffin blocks and sectioned at 4 \u0026micro;m onto SuperFrost Plus slides (Thermo Fisher Scientific, Warrington, UK). H\u0026amp;E staining was carried out for assessment of tissue architecture. To assess tumour hypoxia, vascularisation and glucose transporter expression, immunohistochemical staining was performed on the automated Leica BOND RXM using the following primary antibodies: Carbonic Anhydrase IX (CAIX) (D47G3; Cell Signalling Technology; 1:200 pH9), Alpha Smooth Muscle Actin (αSMA) (AB5694; Abcam; 1:200 pH9), GLUT1 (D3J3A; Cell Signalling Technology; 1:200 pH9). Diaminobenzidine (DAB) was used to visualise antibody binding, and haematoxylin counterstain to distinguish human tumour and chick cell nuclei. Sections were mounted with DPX mountant (Sigma, St. Louis, MO, USA). Slides were imaged using a digital slide scanner (Leica Aperio GT450 digital slide scanner). Aperio ImageScope software (Leica Microsystems Ltd, UK) was used for viewing images.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRNA extraction and gene expression analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eImmediately after dissection on E14, tumours (n\u0026thinsp;=\u0026thinsp;9) were rinsed in ice cold Dulbeccos phosphate buffered saline (DPBS) (Thermo Fisher Scientific #14190094) before transferring to RNAlater (Ambion, Life Technologies, Carlsbad, CA, USA). Total RNA was extracted from tumours, as well as from cell lines cultured \u003cem\u003ein vitro\u003c/em\u003e, using a peqGOLD total RNA kit (VWR Life Sciences). RNA was converted to complementary DNA using iScript reverse transcriptase kit (BioRad, Watford, UK). qRT-PCR was performed using iQ sybr green supermix (Bio-Rad, UK) or power-track SYBRGreen master mix (Applied Biosystems, UK) on the QuantStudio\u0026trade; 1 System (Thermo Fisher Scientific, UK) with 96-well plates. Β-Actin was used as a normalising gene, and results were analysed using the ∆∆Ct method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Target primers used for gene expression analysis are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. All primers were designed to be human-specific for evaluating the human GBM U251 xenograft without transcript amplification from chick CAM tissue. Analysis was performed using QuantStudio\u0026trade; Design and Analysis Software Version 1.5.2 (Thermo Fisher Scientific, UK).\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were performed in GraphPad Prism version 10.4 for Windows (GraphPad Software, San Diego, CA, USA, RRID:SCR_002798). Spearman\u0026rsquo;s Rank correlation coefficients were calculated to assess the correlation between SUVsum and relative lactate. For brightfield microscopy and MR image data, the Shapiro-Wilk test was used to determine whether data were normally distributed, and analysis performed using parametric or non-parametric tests as appropriate. For image data with three conditions or more, statistical significance was determined using one-way ANOVA with post-hoc Tukey multiple comparison. \u003cem\u003ep\u003c/em\u003e-values less than 0.05 were considered significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 GBM xenografts remodel the CAM vasculature\u003c/h2\u003e\u003cp\u003eBrightfield microscopy images revealed that GBM tumours remodelled the CAM vasculature from a typical linear branching pattern to a radial \u0026lsquo;spokes of a wheel\u0026rsquo; pattern around the tumour nodule (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Relative vessel thickness was significantly higher in both normoxia- and hypoxia-preconditioned tumour-bearing CAMs compared with CAM controls (Control CAM vs. Nmx-GBM: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Control CAM vs. Hpx-GBM: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with no difference between Nmx-GBM and Hpx-GBM (p\u0026thinsp;=\u0026thinsp;0.99) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). There were no significant differences between the three conditions for the other vessel parameters: relative vessel area (Control CAM vs. Nmx-GBM: p\u0026thinsp;=\u0026thinsp;0.32; Control CAM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.16; Nmx-GBM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.87), number of branching points (Control CAM vs. Nmx-GBM: p\u0026thinsp;=\u0026thinsp;0.54; Control CAM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.09; Nmx-GBM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.5), and mean vessel length (Control CAM vs. Nmx-GBM: p\u0026thinsp;=\u0026thinsp;0.6; Control CAM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.17; Nmx-GBM vs. Hpx-GBM: p\u0026thinsp;=\u0026thinsp;0.63) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSince brightfield microscopy only provides a two-dimensional (2D) cross sectional view of the CAM, we performed MRI to obtain 3D volumetric measurements of the tumour and CAM vessels using high-resolution images of the tumour and surrounding host vasculature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As with brightfield microscopy images, a radial pattern of CAM vasculature could be observed with MRI, with small vessels appearing to sprout from larger CAM vessels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Tumour volumes were similar between normoxia and hypoxia-preconditioned tumours (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). There were no statistically significant differences between CAM and normoxia-conditioned tumour-bearing CAMs, and again no difference between normoxia and hypoxia-conditioned tumour-bearing CAMs (Control vs. Nmx GBM: p\u0026thinsp;=\u0026thinsp;0.48; Control vs. Hpx GBM: p\u0026thinsp;=\u0026thinsp;0.08; Nmx GBM vs. Hpx GBM: p\u0026thinsp;=\u0026thinsp;0.49) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 GBM-CAM xenografts exhibit hallmark metabolic switch to glycolysis\u003c/h2\u003e\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG-PET imaging was performed on E12 followed by MRS on E13 (n\u0026thinsp;=\u0026thinsp;25). [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake was observed in the tumour with notable uptake in the chick embryos (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), reflecting successful radiotracer injection and the high metabolic demand required for embryonic development. Lactate was detected in the CAM xenografts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). MR spectra acquired from the tumour and adjacent CAM regions demonstrated differential metabolite signals, with tumour regions showing a prominent lactate peak at ~\u0026thinsp;1.3 ppm (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Figure S3A,C), whereas spectra from the CAM exhibited no lactate signal (Figure S3B,D). Some tumours had undetectable FDG uptake or lactate signal, and zero values were assigned to these cases for analysis. Examples of tumours that exhibited or lacked [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake are shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. H\u0026amp;E and immunohistochemical staining for vasculature (αSMA), hypoxia (CAIX) and glucose transporter expression (GLUT1) was also performed to evaluate metabolic marker expression and assess tumour viability and engraftment quality. Histological analysis revealed that some tumours were detached from the CAM (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), indicating poor engraftment, which likely limited [\u003csup\u003e18\u003c/sup\u003eF]FDG delivery to these regions.\u003c/p\u003e\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and relative lactate values are summarised in supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. No correlation was observed between FDG uptake and lactate ((Fig.\u0026nbsp;5Ci) when zero values were included. In order to avoid the impact of zero-clustered data, we performed the correlation excluding the zero values, and in this case again, there was no significant correlation between FDG and lactate, however the shape of the correlation trended negative (Fig.\u0026nbsp;5Cii).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 GBM-CAM xenografts form a hypoxic core\u003c/h2\u003e\u003cp\u003eVascularised tumour nodules formed on the CAM in most cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, in some instances, vascularised tumours developed within the CAM, beneath the initial site of implantation (Figure S4). H\u0026amp;E staining revealed viable, differentiating tumour cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). IHC staining for the endothelial marker αSMA revealed large vessels in the CAM and at the tumour periphery in both normoxia- and hypoxia-preconditioned tumours (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Normoxia-preconditioned xenografts showed strong CAIX staining concentrated in the centre of the tumour, surrounding a region in the core lacking nuclei, indicative of the necrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In contrast, hypoxia-preconditioned tumours exhibited a more heterogeneous CAIX distribution throughout the tumour mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), suggesting a sustained hypoxic phenotype driven by \u0026lsquo;hypoxic memory\u0026rsquo;, as previously described [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. GLUT1 staining strongly colocalised with CAIX staining in both normoxia- and hypoxia-preconditioned tumours (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 GBM-CAM tumour exhibits upregulation of proangiogenic and proglycolytic genes\u003c/h2\u003e\u003cp\u003emRNA expression levels of proglycolytic and proangiogenic targets of HIF-α demonstrated elevated transcription in tumours compared with U251 cells grown in culture in normoxia for proangiogenic genes \u003cem\u003eVEGFA\u003c/em\u003e and \u003cem\u003eADM\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02 and p\u0026thinsp;=\u0026thinsp;0.01, respectively), and for proglycolytic genes \u003cem\u003eGLUT1\u003c/em\u003e, \u003cem\u003ePDK1\u003c/em\u003e and \u003cem\u003eLDHA\u003c/em\u003e, although not statistically significant (p\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;=\u0026thinsp;0.27 respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Housekeeping gene expression was stable across conditions, as Ct values for actin remained consistent between \u003cem\u003ein vitro\u003c/em\u003e cell studies and CAM xenografts under both normoxic and hypoxic conditions (Figure S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we demonstrate that the U251 chick CAM model recapitulates several clinically relevant hallmark features of GBM. Hypoxic tumour cells within the xenograft core may be activating HIF-α signalling to promote angiogenesis and metabolic reprogramming. These phenotypes can be assessed using a range of complementary techniques, highlighting the versatility of the model for investigating both cellular and microenvironmental processes. Collectively, these attributes establish the CAM model as a physiologically relevant platform for studying tumour metabolism and vascular remodelling in GBM.\u003c/p\u003e\u003cp\u003eU251 xenografts remodelled the CAM vasculature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026amp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), in concordance with recent reports describing radial remodelling of blood vessels around mesothelioma cell line-derived CAM xenografts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Relative vessel thickness was significantly increased in tumour-bearing CAMs compared to control CAMs; however, no difference was observed between normoxia-conditioned and hypoxia-conditioned tumour-bearing CAMs. This may suggest that hypoxic signalling differences between normoxia- and hypoxia-conditioned cells are less substantial once tumours establish on the CAM, possibly due to the development of a hypoxic core in normoxia-conditioned grafts, as shown histologically in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and lactate signal demonstrated that GBM-CAM xenografts exhibited a glycolytic phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, no significant correlation between these two measures was observed, challenging the assumption that higher [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake would correspond with increased lactate production. Previous studies have investigated the relationship between [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and lactate levels, yielding varied results. In one study, Herholz et al reported a significant positive correlation between lactate concentration and [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake in patients with gliomas [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, their study included a heterogeneous mix of glioma subtypes and grades and, in many instances, regions of elevated [\u0026sup1;⁸F]FDG uptake did not spatially coincide with areas of increased lactate, suggesting a lack of direct correspondence between [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and lactate production. Moreover, as the patients were undergoing variable treatments during imaging including corticosteroids or radiotherapy, the treatment itself could have confounded the observed correlation between FDG uptake and lactate concentration. On the other hand, Guo et al reported no significant correlation between [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and relative lactate in patients with lung adenocarcinoma [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Here again, ongoing treatment at the time of imaging may have confounded the assessment of tumour-intrinsic metabolic activity. More recently, Van Heijster et al, using hyperpolarised [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRS in murine xenografted human prostate cancer cell lines, reported a significant negative correlation between [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake and lactate production (measured as the pyruvate-to-lactate conversion rate) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Collectively, these varied findings align with our results, indicating that increased [\u003csup\u003e18\u003c/sup\u003eF]FDG uptake may not necessarily be a predictor of elevated lactate production, but that the two modalities reflect distinct, partially overlapping facets of tumour glycolytic metabolism.\u003c/p\u003e\u003cp\u003eSeveral technical and biological factors could explain the lack of correlation between FDG uptake and lactate in our GBM-CAM xenografts. Firstly, [\u0026sup1;⁸F]FDG-PET and lactate MRS have very different sensitivities and detection limits. In the case of lactate, non-detectable signals may not necessarily reflect an absence of production, but rather the limited sensitivity of MRS combined with rapid vascular clearance or metabolic reutilisation of lactate. The ISIS pulse sequence is also particularly sensitive to motion, further contributing to apparent zero values. Secondly, [\u0026sup1;⁸F]FDG uptake is strongly dependent on tumour vascularisation, which determines both tracer delivery and clearance. Heterogenous vascular recruitment across xenografts may therefore underlie some of the variability in [\u0026sup1;⁸F]FDG signal, with poorly vascularised tumours showing minimal or no [\u0026sup1;⁸F]FDG uptake despite active glycolysis. Indeed, histological assessment of tumours in which no FDG uptake was observed, but in which lactate was detected, demonstrated poor overall engraftment, both in terms of attachment to the CAM and of vascular supply (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Thirdly, [\u0026sup1;⁸F]FDG uptake itself is limited by the expression and activity of GLUT, as well as by hexokinase activity, meaning that high [\u0026sup1;⁸F]FDG accumulation may not necessarily equate to high glycolytic metabolism.\u003c/p\u003e\u003cp\u003eFurthermore, lactate is not merely a terminal byproduct influenced by glycolytic flux; its concentration is also influenced by downstream metabolic and microenvironmental processes. Lactate can be rapidly exported and cleared from the tumour via the vasculature or be metabolised through conversion back to pyruvate in order to fuel the TCA cycle [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, lactate may be diverted into alternative pathways such as histone lactylation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Thus, while both [\u0026sup1;⁸F]FDG uptake and lactate signal are indicative of glycolytic activity, they capture different aspects of tumour metabolism - glucose transport and phosphorylation versus steady-state lactate metabolism and clearance. The absence of a correlation between FDG uptake and lactate signal in our model may be reflective of such complexities.\u003c/p\u003e\u003cp\u003eNotably, in our dataset, the inclusion of samples with non-detectable lactate signal as well as non-detectable [\u0026sup1;⁸F]FDG uptake resulted in no correlation between [\u0026sup1;⁸F]FDG uptake and lactate. In contrast, exclusion of these apparent zero values produced a negative trend. This change in correlation highlights that the apparent relationship is strongly influenced by zero-inflated data from both modalities: in FDG, zeros can arise from limited vascular delivery or transporter activity, while in lactate they may reflect MRS sensitivity, clearance, or reutilisation. Thus, the correlation outcome appears highly sensitive to methodological and detection thresholds across both readouts, rather than purely reflecting biological differences.\u003c/p\u003e\u003cp\u003eImmunoreactivity of hypoxia-inducible molecular marker CAIX was localised to the centre of the tumour, demonstrating that GBM-CAM tumours develop a hypoxic core driven by an oxygen gradient similar to that observed in spheroid, rodent, and patient-derived GBM models (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The colocalization of GLUT1 and CAIX staining suggests a hypoxia-driven shift towards glycolytic metabolism. Furthermore, the absence of CAIX with correspondingly low levels of GLUT1 staining in tumour cells on the periphery of the tumour, or close to intratumoural vessels, suggests an oxygen supply adequate to support aerobic respiration thereby preventing activation of the hypoxic response. This reasoning is corroborated by hypoxia-preconditioned tumours displaying reduced expression of GLUT1 and CAIX staining at the site in which the tumour appears integrated with the vascularised CAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, G\u0026amp;H). Tumours also exhibited elevated mRNA levels of canonical HIF-α targets - \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eADM, GLUT1, LDHA\u003c/em\u003e, and \u003cem\u003ePDK1\u003c/em\u003e - further supporting the hypothesised hypoxia-induced transcriptional reprogramming (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Although not all differences between cultured cells and CAM xenografts reached statistical significance, the overall pattern indicates a shift toward pro-angiogenic and glycolytic gene expression in xenografts.\u003c/p\u003e\u003cp\u003e While the chick CAM offers a partial solution to the cost, husbandry, timescale and ethical issues posed by rodent models, it imposes some unique constraints. In the UK for example, the CAM tumour model can persist until E14 before legal restrictions from the Animal (Scientific Procedure) Act apply. This imposes a restricted timeframe, presenting challenges for studying tumour progression, vascular remodelling, and treatment responses, which can be followed over extended periods in mammalian models. Additionally, seasonal changes and temperature extremes, particularly in the winter and summer, can negatively affect embryo viability leading to inconsistent survival rates. Such limitations can partially be mitigated by starting experiments with larger numbers of eggs.\u003c/p\u003e\u003cp\u003eBeyond egg viability, intra- and inter-batch variability presents another challenge. Even within the same shipment, embryos may develop at slightly different rates, affecting tumour engraftment, vascularization, and response to experimental conditions. To account for this, experimental design should be kept simple to avoid additional variables and be powered accordingly to account for survival and engraftment rates. Standardized protocols for egg handling, incubation, and tumour implantation improve reproducibility, but some degree of biological variability remains inevitable. By accounting for these challenges through careful experimental design, the CAM model remains a powerful tool for preclinical research, particularly for rapid screening of tumour-host dynamics and therapeutic interventions.\u003c/p\u003e\u003cp\u003eIn order to reduce egg mortality and minimise motion artefacts associated with warming during prolonged scan times, the MR-derived vascular volume measurements were obtained in a cohort distinct from the eggs imaged by MRS and PET. Future work should ideally implement faster, integrated vascular imaging protocols to acquire all modalities in a single cohort, enabling direct pairing of vascular volume with metabolic readouts.\u003c/p\u003e\u003cp\u003eIn summary, this study demonstrates the utility of the GBM-CAM model for investigating hypoxia-driven metabolic reprogramming and tumour-induced vascular remodelling using molecular imaging techniques. The integration of [\u0026sup1;⁸F]FDG-PET, lactate MRS, brightfield vascular imaging, histology, and measurement of gene expression provides a comprehensive assessment of GBM cell xenografts in a physiologically relevant, cost-effective, and imaging-compatible model. These findings highlight the potential of the CAM for preclinical evaluation of metabolic imaging biomarkers and therapeutic strategies targeting the hypoxic tumour microenvironment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\u003cp\u003eNone of the authors have any conflict of interest to disclose with regards to this manuscript and its contents\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eAll authors contributed to experimental design, writing and editing the manuscript. Elisabeth N Gash performed most of the experiments, data acquisition, analysis as well as statistics. Jan Schulze, Sarah Barnett, and Mohesh Moothanchery assisted with the CAM model as well as PET experiments. Mahon L Maguire and Stephen Pickup helped with MRI and MRS data acquisition and analysis while Ian Scott assisted in histological analysis. Michael Batie, Rasheed Zakaria, Judy M Coulson and Sonia Rocha helped with conception and design of \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein ovo\u003c/em\u003e studies. Harish Poptani conceived the study, supervised data analysis and aided funding as well as editing of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the use of the Egg Facility (RRID:SCR_026195; all egg experiments), Centre for Pre-clinical Imaging (RRID:SCR_026605; all preclinical imaging), Histology SRF (RRID:SCR_026606; Tissue processing, embedding, sectioning and H\u0026amp;E staining), all provided by the Liverpool Shared Research Facilities (LIV-SRF), Faculty of Health and Life Sciences, University of Liverpool. We would also like to acknowledge the Liverpool University Biobank (slide scanning), Dr Laura Bowker (assistance in CAM experiments as well as PET imaging) and Dr Helen Kalirai (training on the Leica Bond RxM). ENG was funded by The Isle of Man Anti-Cancer Association.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLouis, D.N., et al., \u003cem\u003eThe 2021 WHO Classification of Tumors of the Central Nervous System: a summary.\u003c/em\u003e Neuro Oncol, 2021. \u003cstrong\u003e23\u003c/strong\u003e(8): p. 1231-1251.\u003c/li\u003e\n\u003cli\u003eYalamarty, S.S.K., et al., \u003cem\u003eMechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM).\u003c/em\u003e Cancers (Basel), 2023. \u003cstrong\u003e15\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eFeldman, L., \u003cem\u003eHypoxia within the glioblastoma tumor microenvironment: a master saboteur of novel treatments.\u003c/em\u003e Front Immunol, 2024. \u003cstrong\u003e15\u003c/strong\u003e: p. 1384249.\u003c/li\u003e\n\u003cli\u003eEales, K.L., K.E. Hollinshead, and D.A. 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Watson, \u003cem\u003eA novel 3D in vitro model of glioblastoma reveals resistance to temozolomide which was potentiated by hypoxia.\u003c/em\u003e J Neurooncol, 2019. \u003cstrong\u003e142\u003c/strong\u003e(2): p. 231-240.\u003c/li\u003e\n\u003cli\u003ePerez-Aliacar, M., et al., \u003cem\u003eModelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro.\u003c/em\u003e Comput Biol Med, 2024. \u003cstrong\u003e180\u003c/strong\u003e: p. 108866.\u003c/li\u003e\n\u003cli\u003eSattiraju, A., et al., \u003cem\u003eHypoxic niches attract and sequester tumor-associated macrophages and cytotoxic T cells and reprogram them for immunosuppression.\u003c/em\u003e Immunity, 2023. \u003cstrong\u003e56\u003c/strong\u003e(8): p. 1825-1843 e6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-imaging-and-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mibi","sideBox":"Learn more about [Molecular Imaging and Biology](http://link.springer.com/journal/11307)","snPcode":"11307","submissionUrl":"https://www.editorialmanager.com/mibi/default2.aspx","title":"Molecular Imaging and Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chick chorioallantoic membrane, glioblastoma, MRI, PET, metabolism, vasculature","lastPublishedDoi":"10.21203/rs.3.rs-7761553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7761553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To assess hypoxia-associated host-tumour vascular adaptations and glycolytic metabolism in the chick chorioallantoic membrane (CAM) glioblastoma model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures: \u003c/strong\u003eU251 GBM cells were conditioned under normoxia (21% O₂) or hypoxia (1% O₂) for 72 hours before implantation onto the CAM on embryonic day 7 (E7). Imaging was performed on E13 using MRI (control-CAM n=8, normoxic-tumour n=7, hypoxic-tumour n=6) and brightfield microscopy (control-CAM n=7, normoxic-tumour n=8, hypoxic-tumour n=7). Tumours were harvested on E14 for histology and gene expression analyses. In a separate cohort, under normoxic conditioning, glucose metabolism was assessed using [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET on E12 followed by lactate MRS on E13 (n=25). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eNormoxia- and hypoxia-conditioned tumour-bearing CAMs exhibited vascular remodelling and significant upregulation of \u003cem\u003eVEGFA\u003c/em\u003e and \u003cem\u003eADM compared to cultured cells\u003c/em\u003e. αSMA staining confirmed vessel infiltration in normoxia-conditioned tumours. CAIX staining revealed a hypoxic core in these tumours while hypoxia-conditioned tumours displayed heterogeneous staining. In both conditions, GLUT1 staining colocalised with CAIX staining, indicating hypoxia-associated glycolysis. \u003cem\u003eGLUT1,\u003c/em\u003e \u003cem\u003ePDK1\u003c/em\u003e and \u003cem\u003eLDHA\u003c/em\u003e expression was elevated in CAM tumours relative to tumour cells \u003cem\u003ein vitro.\u003c/em\u003e In the metabolic imaging cohort, most tumours exhibited [¹⁸F]FDG uptake and lactate signal. However, no statistically significant relationship was observed between the two methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThe CAM model provides a versatile platform for investigating GBM vascularisation and metabolism. Hypoxic conditioning amplifies transcriptional and vascular changes to the CAM. Although both [¹⁸F]FDG uptake and lactate were measurable, no significant correlation between the two was observed, potentially reflecting variability in tumour engraftment, vascular delivery of [¹⁸F]FDG, and microenvironmental influences on lactate accumulation.\u003c/p\u003e","manuscriptTitle":"Imaging vascular characteristics and glycolytic metabolism of glioblastoma in a chick embryo model using 1H MRI and [18F]FDG-PET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 16:39:20","doi":"10.21203/rs.3.rs-7761553/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2026-01-05T04:51:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-11-07T15:12:50+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T11:03:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T07:21:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Imaging and Biology","date":"2025-10-02T09:35:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-imaging-and-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mibi","sideBox":"Learn more about [Molecular Imaging and Biology](http://link.springer.com/journal/11307)","snPcode":"11307","submissionUrl":"https://www.editorialmanager.com/mibi/default2.aspx","title":"Molecular Imaging and Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"083fdabb-29b0-4369-84a9-b62d468e7d9f","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:08:42+00:00","versionOfRecord":{"articleIdentity":"rs-7761553","link":"https://doi.org/10.1007/s11307-026-02084-x","journal":{"identity":"molecular-imaging-and-biology","isVorOnly":false,"title":"Molecular Imaging and Biology"},"publishedOn":"2026-02-23 15:59:04","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-11-06 16:39:20","video":"","vorDoi":"10.1007/s11307-026-02084-x","vorDoiUrl":"https://doi.org/10.1007/s11307-026-02084-x","workflowStages":[]},"version":"v1","identity":"rs-7761553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7761553","identity":"rs-7761553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}