Longitudinal in vivo PET imaging of P2X7R and TSPO neuroinflammation markers and myelin load in the TgF344-AD rat model of Alzheimer’s disease

Longitudinal in vivo PET imaging of P2X7R and TSPO neuroinflammation markers and myelin load in the TgF344-AD rat model of Alzheimer’s disease | 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 Longitudinal in vivo PET imaging of P2X7R and TSPO neuroinflammation markers and myelin load in the TgF344-AD rat model of Alzheimer’s disease Oscar Moreno, Izaro Fernández, Sandra Plaza-García, Daniel Padro, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7072571/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Neuroinflammation and myelin loss are hallmark features of Alzheimer’s disease (AD) associated with cognitive decline. Emerging evidence highlights the significant role of glial cell activation, particularly microglia, in driving neuroinflammation and disease progression. While translocator protein (TSPO) PET imaging is commonly used to detect neuroinflammation, limitations have prompted investigation into alternative targets such as the P2X7 receptor (P2X7R), which is upregulated during pathological conditions. Additionally, myelin loss has gained recognition as an important pathological feature in AD, potentially linked to chronic neuroinflammation. However, the temporal dynamics and interplay between neuroinflammation and myelin loss remain poorly understood in the context of AD. Methods: We conducted a longitudinal PET study from 4 to 22 months of age in TgF344-AD rats and wild-type controls to assess neuroinflammation with [ 18 F]JNJ-64413739 (P2X7R) and [ 18 F]DPA-714 (TSPO), alongside myelin content using [ 18 F]Florbetaben. Diffusion tensor imaging (DTI) was used to study variations on myelin structure in old AD and WT rats. In vitro studies, including autoradiography, immunoflusorescence and staining were used to support the in vivo results. Results: [ 18 F]JNJ-64413739 PET showed increased P2X7 receptor expression in AD and control animals over time, while [ 18 F]DPA-714 PET showed significant differences between groups at 22 months. [ 18 F]Florbetaben PET showed different uptake in white matter rich areas between groups with observed demyelination in AD rats at 20 months in the brain stem, supported by diffusional MRI findings. Conclusions: In our study, P2X7R overexpression was attributed to aging rather than genotype effects, and no link was found to the observed demyelination in AD rats. Conversely, increased TSPO neuroinflammation in TgF344-AD rats correlated with myelin loss and the reported cognitive decline in this model. Our results support the use of the TgF344-AD model to study early AD pathology, focusing on neuroinflammation and white matter integrity. Neuroinflammation Myelin PET P2X7R TgF344-AD rat TSPO DTI MRI. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. BACKGROUND Alzheimer’s disease (AD) is a chronic and a neurodegenerative disorder that affects both cognitive and non-cognitive functions, memory and behaviour in humans. Traditionally, AD is defined by Aβ plaque formation and accumulation of tau-based neurofibrillary tangles in the brain (1). However, while neuronal damage is a hallmark of AD, growing evidence highlights the critical role of glial cell activation on disease progression (2,3) . In AD, misfolded Aβ and tau proteins bind to pattern recognition receptors, triggering an innate immune response and releasing inflammatory mediators. Activated microglia migrate toward Aβ plaques and phagocytose them, revealing a functional connection to AD pathology (4). However, prolonged microglial activation impairs Aβ clearance, resulting in sustained inflammation, neuronal injury, and a self-perpetuating cycle of activation (5–7). Imaging of activated microglia is achieved by targeting molecular markers that are overexpressed during neuroinflammation. The 18-kDa translocator protein (TSPO) is one of the most commonly used targets for detecting activated microglia, with TSPO PET widely regarded as the gold standard for imaging of neuroinflammation (8–10). However, extensive research has also recognized certain drawbacks that limit TSPO as the definitive target for inflammation (11–17). Alternatively, various purinergic ion channel receptor subtypes (P2X) have been explored as potential targets for imaging microglia activation (18,19). Among these, the P2X7 receptor (P2X7R) is notably expressed in microglia, astrocytes, and Schwann cells, both peripherally and in the CNS (20). Typically silent under normal conditions, P2X7R is upregulated during pathological ATP imbalances and is linked to pro-inflammatory cytokine release (21). To this, several PET tracers targeting P2X7R have been developed as potential alternatives to TSPO for imaging brain inflammation, including the [ 18 F]JNJ-64413739 radiotracer (11,22–26). Although AD has long been associated with grey matter neuronal loss, white matter (WM) degeneration and demyelination are increasingly recognized as relevant pathological features (27–30). Myelin is produced and maintained by oligodendrocytes, which wrap around axons to form myelin sheaths that provide electrical insulation (31). Myelin degradation during AD severely disrupts neural communication, leading to axonal death (32). This, in turn, results in slower neural processing and impaired cognitive abilities—key hallmarks of Alzheimer’s progression. Some studies suggest that myelin damage may precede the appearance of other hallmark features of Alzheimer’s disease, although the mechanisms—such as oligodendrocyte death, impaired repair, or exposure to neurotoxins—remain to be fully elucidated (30). Chronic neuroinflammation, marked by sustained activation of microglia and astrocytes, has also been suggested to affect WM tracts in AD, leading to demyelination (33–35). Particularly, pro-inflammatory cytokines released by microglia, astrocytes, and infiltrating immune cells have been shown to induce myelin loss (36). Yet, the exact timeline of events through which neuroinflammation appears to induce demyelination during AD remains unclear. Myelin imaging via PET has become feasible using tracers that bind to beta-sheet structures of the myelin basic protein (MBP), allowing in vivo quantification (37). However, few studies have combined myelin PET with other radiotracers to explore the interplay between demyelination and other pathological processes over time (38). In the present study, we aimed to describe the dynamics of neuroinflammation and myelin loss simultaneously in the context of AD. For this, we employed the TgF344-AD rat model, currently one of the most accurate models in replicating human AD pathology, exhibiting increased Aβ and Tau pathology over time, along with sustained neuroinflammation and neuronal loss (39). We performed in vivo longitudinal PET imaging in TgF344-AD rats and age-matched wild-type animals using [ 18 F]JNJ-64413739 (P2X7R), [ 18 F]DPA-714 (TSPO) and [ 18 F]Florbetaben (myelin). Additionally, we employed magnetic resonance imaging (MRI), as well as in vitro techniques, providing new insights into the temporal resolution of these key pathophysiological hallmarks during disease progression. 2. METHODS 2.1. Animals Female TgF344-AD rats (n = 18) were purchased from the Mutant Mouse Resource and Research Centre (MMRRC), the Rat Resource and Research Centre (RRRC), and the MU Metagenomics Centre (Columbia, MO, USA). Female wild-type F344 rats (n = 12) were purchased from Janvier Labs (France). Animals arrived at our facilities at the age of 11 weeks and were housed in groups of 2-3 per cage with individual ventilation, environmental enrichment, constant access to food and water and a 12:12 hour cycle of light and dark. Imaging studies were performed at different ages (as defined below) during light phase of the light–dark cycle. Animal weights were recorded before each PET imaging session, during the longitudinal study. 2.2. Study design Rats were divided in two groups: AD group (TgF344-AD rats, n = 18) and control group (wild-type F344 rats, n = 12) (see Figure 1 for experimental design). Four to ten animals per group and per radiotracer were scanned using PET at each time point, in line with previous studies (see Table S1 in the supporting information for details on animal distribution) (40). In general, the same animals were scanned longitudinally throughout the study, except those that were retrieved for tissue collection, met humane endpoint criteria or died spontaneously (see Figures S1 and S2 and Table S2 for detailed information). At each time point, animals were scanned using tracers from the same production (same batch). In cases where some animals could not be scanned on the same day, a second tracer production was performed the following day to accommodate the remaining animals. Notably, no animal was scanned on two consecutive days. All animals surviving at 22 months in both groups underwent structural and diffusional MRI scanning. Brain tissue was collected from selected animals at different ages and used for ex vivo analysis at specific time points (see below). Female rats were chosen for their higher prevalence to the disease, their moderate weight at older ages (facilitating in vivo imaging) and lower aggressively compared to males. 2.3. Longitudinal PET-CT studies PET-CT studies were performed using β- and X-cube microsystems (Molecubes, Belgium) with [ 18 F]JNJ-64413739 at 8, 15 and 22 months of age, with [ 18 F]DPA-714 at 22 months of age and with [ 18 F]Florbetaben at 4, 8, 12, 16 and 20 months of age for both groups (AD and control) (see radiosynthesis details in the supporting information). In all cases, anaesthesia was induced with isoflurane (3.0 – 5.0%) in pure oxygen, and maintained during imaging studies at 1.5 – 2.0% of isoflurane in pure oxygen. Prior to image acquisition, anesthetized animals were injected intravenously in the tail vein with a saline solution (max. 10% EtOH) of either [ 18 F]JNJ-64413739 (3.5 ± 0.3 MBq), [ 18 F]DPA714 (3.6 ± 0.2 MBq) or [ 18 F]Florbetaben (4.0 ± 1.0 MBq) (see Tables S3-S5 for a detailed injection list). For [ 18 F]JNJ-64413739 and [ 18 F]DPA714 studies, dynamic PET imaging was started immediately before injection of the radiotracer with a total scan time of 60 minutes (frames: 1 x 5s, 10 x 60s, 4 x 300s, 2 x 600s, 1 x 595s). For [ 18 F]Florbetaben studies, 15-min static PET images were acquired 40 minutes post-injection. In this case, animals were allowed to recover from anaesthesia between tracer administration and image acquisition. All scans were acquired in list mode, one bed position and a field of view (FOV) of 100 mm ranging from the nose to the kidneys of the animal. A 5-min CT scan (X-Ray energy: 40 kV; intensity: 140 μA) was acquired immediately after each PET scan. PET images were reconstructed with OSEM-3D iterative algorithm and applying random, scatter and attenuation corrections. Once reconstructed, images were analysed using PMOD image analysis software (PMOD Technologies Ltd, Zurich, Switzerland). Volumes of interest (VOIs) were delineated in different brain regions: cortex (CTX), hippocampus (HIPP), striatum (STR), cerebellum (CB) and brain stem (BS), using the Schiffer-T2 MRI template (Figure S4). For dynamic PET scans, time activity curves were obtained for each region and expressed as standardized uptake values (SUV). For [ 18 F]JNJ-64413739, averaged SUVs relative to the cerebellum (SUVR CB ) were calculated 40 to 60 min post-injection in the selected regions and for every animal. Whole-brain single PET images were obtained by averaging individual SUVR CB images at every time point in each group. For [ 18 F]DPA-714, SUVs were calculated 45 to 60 min post-injection in the selected regions and for every animal. Whole-brain single PET images were obtained by averaging individual SUV images in each group. In the case of [ 18 F]Florbetaben, SUVR CB values were obtained in the selected regions for every animal. Whole-brain single PET images were obtained by averaging individual SUVR CB images at every time point in each group. 2.4. Brain tissue processing Animals used for ex vivo analysis were culled from the same cohort as those involved in the imaging studies for both AD and WT groups. Animals were euthanized and transcardially perfused with saline (50 mL, 0.9 % NaCl solution) to collect their brains, separating the two hemispheres through the longitudinal fissure using a blade. The left hemisphere was directly frozen in isopentane (2-methylbutane; Sigma-Aldrich) on dry ice and stored at -80ºC. The right hemisphere was fixed in a 4% formaldehyde solution at 4ºC for 24 hours, subsequently transferred to a 30% sucrose solution for 48 hours at 4°C and stored at –80ºC afterwards. Coronal sections of either 20 μm (left hemisphere) or 10 μm (right hemisphere) were cut using a Cryomicrotome (Leica CM3050S, Germany), collected on a glass slide (Superfrost Ultra Plus; Thermo Fisher) and stored at –80ºC before further analysis. 2.5. In vitro autoradiography Brain tissue from two animals of the AD group (23 months old) and two of the control group (22 months old) was subjected to in vitro autoradiography studies with [ 18 F]JNJ-64413739. One animal per group was used at the same ages for [ 18 F]DPA-714 studies. For [ 18 F]Florbetaben, brain tissue from two young AD animals (10 months), two old AD (23 months) and two old WT (22 months) was used. For each animal, one representative slide containing 4-5 coronal brain slices (20 μm) from the left hemisphere including cortex and striatum ( ca. +0.20 from Bregma), one slide including cortex and hippocampus ( ca. -2.30 and ca. -4.0 from Bregma) and one slide including cerebellum and brain stem ( ca. -10.04 from Bregma) were thawed, dried and pre-incubated for 15 min with Tris-HCl buffer (50 mM, pH 7.4, supplemented with 1 mM MgCl 2 , 1 mM CaCl 2 , 2 mM KCl and 1% of bovine serum albumin) at room temperature. Subsequently, the slices were incubated in a Tris-HCl buffer (50 mM, pH 7.4) solution containing either [ 18 F]JNJ-64413739, [ 18 F]DPA-714 or [ 18 F]Florbetaben (0.5 MBq/mL) for 30 min at room temperature. For the determination of non-specific binding, successive slices were additionally incubated in a 10 μM solution of the corresponding non-labelled reference compound. After incubation, the slices were removed from the bath, washed for 10 minutes in ice-cold buffer (50 mM Tris-HCl, pH 7.4, 4ºC) and dipped once in ice-cold ultra-pure water. After drying over a heating plate (1 min, 40°C), the slices were exposed to a phosphor sensitive plate for 5 minutes and the plate was scanned in a phosphor imager (Amersham Typhoon 5, GE, USA) at the highest resolution (10 μm). For image quantification, regions of interest (cortex, hippocampus, striatum, cerebellum and brain stem) were manually drawn for each slice with specific software (ImageQuantTL, Cytiva, USA) using a rat brain atlas as reference (Paxinos G., 2013). Averaged pixel intensity values were obtained per each region as the average of 4-5 replicates, and ratio values calculated per each animal and region using the cerebellum as reference. Percentage of self-blocking (homologous blocking) was calculated as [(No block- Block)/No block)] x 100 using the corresponding blocked tissue. 2.6. Immunofluorescence Previously obtained brain sections from the right hemisphere ( ca. -5.20 from Bregma) were fixed in 4% paraformaldehyde during 15 min, washed with phosphate-buffered saline (PBS) and incubated 5 min in NH 4 Cl, following two PBS rinses and methanol-acetone (1:1) permeabilization during 5 min at -20 ºC. After PBS washing, samples were saturated with a solution of bovine serum albumin (BSA) 5%/Tween 0.5% in PBS during 15 min at room temperature and incubated with combinations of anti-Iba1 antibody (guinea pig, 1:1,000, Synaptic Systens, Goettingen, Germany), anti-GFAP (chicken, 1:1000, AbCam, Cambridge, UK), anti-P2X7 (rabbit, 1:50, Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), and anti-TSPO (rabbit, 1:1000, Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) at room temperature during 1 hour in BSA (5%)/Tween (0.5%). Then, sections were washed with PBS again for 5 min followed by incubation with the appropriate fluorescent secondary antibody (1:1000): Alexa Fluor 594-conjugated anti-rabbit IgG (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), Alexa Fluor 647-conjugated anti-guinea pig (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), Alexa Fluor 647-conjugated anti-chicken (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) or Alexa Fluor 488-conjugated anti-rabbit secondary antibody (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) and DAPI (D9564, Sigma-Aldrich) at room temperature for 30 min. Immunoreaction controls were carried out by omission of the primary antibodies. For X34 staining (amyloid plaques), sections were additionally incubated with a X34 10 µM solution (Sigma-Aldrich) in 70% EtOH for 10 minutes at room temperature and washed with 0.1 M PBS. Finally, sections were mounted with Fluoromont-G® (SouthernBiothech, AL, USA) and dried for 24 hours at room temperature. Structured illumination fluorescent images were obtained using a Zeiss AxioImager Z1 with attached ApoTome (Carl Zeiss Microimaging) using a x20 or a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA). 2.7. Thioflavin staining Thioflavin staining was used to visualize fibrillar amyloid plaques. Previously obtained brain slices from a 23-month-old TgF344-AD rat (n = 1) corresponding to the right hemisphere were fixed with 4% paraformaldehyde for 15 min and rinsed with 0.1 M PBS 5 min 3 times. Sections were then incubated with 0.01% Thioflavin S in 70% ethanol, diluted 1:10 in 0.1 M PBS for 10 min, washed with 0.1 M PBS and mounted with Fluoromont-G® (SouthernBiothech, AL, USA). Immunoreaction controls were carried out by omission of the staining solution. After drying for 24 hours, images of the whole slice containing the cortex and hippocampus were acquired with a Zeiss AxioImager Z1 using a x20 objective in tile mode. Detailed images of plaques were obtained with a Zeiss AxioImager Z1 attached ApoTome using a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA). 2.8. In vitro fluorescence assay with Florbetaben Staining with non-labelled Florbetaben was performed in coronal brain slices from a 10-month-old TgF344-AD rat (n = 1) and a 22-month-old WT animal (n = 1) corresponding to the right hemisphere. First, tissue autofluorescence was minimized by treatment of sections with 0.25% KMnO 4 /PBS for 20 min prior to washing (0.1M PBS) and incubation with 1% K 2 S 2 O 5 /1% oxalic acid/PBS for 5 min. Following autofluorescence quenching, sections were blocked in 2% BSA/PBS pH 7.0 for 10 min and stained with 10 μM Florbetaben for 30 min. Finally, sections were washed in 0.1M PBS and mounted with Fluoromont-G® (SouthernBiothech, AL, USA). After drying for 24 hours, images of the whole slice containing the cortex and hippocampus were acquired with a Zeiss AxioImager Z1 using a x20 objective in tile mode. Detailed images of plaques were obtained with a Zeiss AxioImager Z1 attached ApoTome using a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA). 2.9. Luxol Fast Blue staining To assess myelin and neuronal cell bodies, a combined Luxol Fast Blue and Cresyl Fast Violet staining protocol was used with coronal brain sections from the right hemisphere ( ca. -2.30 from Bregma) of a 19-month old AD rat and a 20-month old WT rat. First, tissue sections were dehydrated down to 95% ethanol, followed by overnight incubation in a 1:1 mixture of absolute EtOH and chloroform at room temperature. Sections were then stained with 0.1% Luxol Fast Blue at 50-56°C for 6-7 hours. After staining, sections were washed in 95% EtOH and rinsed in distilled water. Differentiation was carried out using 0.05% lithium carbonate for 90 seconds, followed by a brief incubation in 70% EtOH for 30 seconds and another rinse in distilled water. Next, sections were stained with 0.1% Cresyl Fast Violet for 2 minutes, with a brief heating step of 30 seconds to enhance staining. Finally, dehydration was performed through 95% EtOH for 5 minutes, followed by two successive incubations in absolute EtOH (5 minutes each). Sections were cleared in xylene (two changes, 3 minutes each) and mounted using DPX mounting medium (Sigma-Aldrich). Images were obtained using a Zeiss AxioImager Z1 with attached ApoTome (Carl Zeiss Microimaging) using a x20 or a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA). 2.10. Magnetic Resonance Imaging (MRI) acquisition and processing Seven animals from each group (AD and WT) were scanned for diffusion tensor imaging (DTI) MRI at 22 months. In vivo MRI studies were performed on a 7 T horizontal bore Bruker Biospec USR 70/30 MRI system (Bruker Biospin GmbH, Ettlingen, Germany), interfaced to an AVANCE III console, and with a BGA12-S imaging gradient insert (maximal gradient strength 400 mT/m, switchable within 80 µs). These measurements were performed with a 72-mm volumetric quadrature coil for excitation and a 20-mm rat brain surface coil for reception. Animals were anesthetized with isoflurane (4% induction, 1.5–2% maintenance) in a 50:50 oxygen/nitrogen mixture and positioned in a stereotaxic holder. Body temperature was maintained at 37 ± 0.5 °C using a temperature-controlled air stream (SAII Instruments, model M1030), which also monitored respiration and temperature throughout the session. MRI data acquisition and reconstruction were performed using ParaVision 6.0.1 (Bruker Biospin GmbH, Ettlingen, Germany). Imaging Protocol included the acquisition of: 1) a T2-weighted image acquired using a 3D RARE sequence (TR = 1800 ms, TE = 32 ms, RARE factor = 8, 1 average). The field of view (FOV) was 25.6 × 25.6 × 14 mm³ with a matrix of 128 × 128 × 70, yielding an isotropic resolution of 0.2 mm³. Fat suppression was applied using a Gaussian pulse (bandwidth: 1050 Hz), and acquisition bandwidth was 100 kHz. Total scan time: ~29 minutes. 2) Diffusion Tensor Imaging was acquired using a spin-echo EPI sequence (TR = 7500 ms, TE = 26 ms, 2 segments, 1 average). A total of 75 volumes were acquired: 60 diffusion directions (b = 800 s/mm²) and 15 b0 images. Gradient duration: 4 ms; separation: 12 ms. FOV: 30 × 30 mm²; matrix: 128 × 128; 20 slices (0.7 mm thickness, 0.1 mm gap); in-plane resolution: 234 × 234 µm². Bandwidth: 220 kHz. Fat suppression: Gaussian pulse (1050 Hz). Total scan time: ~18 min 45 s. Diffusion metrics (FA, MD, AD, RD) were computed using the DIPY library (41). The processing pipeline included: 1) Denoising: MPPCA denoising (42) using code from https://github.com/NYU-DiffusionMRI/mppca_denoise. 2) Tensor Estimation: Motion correction and tensor fitting using DIPY. 3) Bias Field Correction: N4ITK algorithm applied to anatomical images (43). 4) Brain Extraction: rBET, a rodent-optimized version of BET (44). 5) Image Registration: Two-step registration using ANTs: affine + nonlinear (SyN) to align T2-weighted images to a modified Waxholm Space atlas (45). DTI b0 images were rigidly registered to T2-weighted images. 6) Atlas Reference: A modified version of the Waxholm Space atlas was used, grouping small ROIs to improve alignment and analysis. A structured pipeline was implemented to extract and analyze regional diffusion metrics, consisting in preprocessing of T2-weighted images (bias correction, brain extraction). Followed by incorporation of FA, MD, AD, and RD maps into the pipeline, nonlinear registration of T2 images to atlas space and propagation of anatomical labels, rigid registration of b0 images to T2-weighted images and transfer of labels to diffusion maps, and extraction of regional mean values for each diffusion metric. 2.11. Statistical analysis For PET imaging, statistical analysis was performed in GraphPad Prism 9 (GraphPad Software, CA, USA). PET values, pixel intensity values from autoradiography and FA values were analysed using a two-tailed unpaired t-test with Welch’s correction. Differences between groups (AD vs WT) at each time point and differences between time points within the same group were studied, calculated as percentage differences. No outliners were removed. Differences were concluded significant for p values < 0. 05: p < 0.05, * ; p < 0.01, ** , p < 0.001, *** ; and p < 0.0001, **** . For MRI imaging, robustness of the data was ensured by an outlier detection step applied to each DTI metric (FA, MD, AD, RD) across all brain regions and experimental groups. Outliers were defined using the interquartile range (IQR) method, as values falling below Q1 − 1.5×IQR or above Q3 + 1.5×IQR within each group and region, and were excluded from further analysis (46).To compare DTI metrics between WT and AD groups across multiple brain regions, we conducted a region-wise statistical analysis. For each DTI parameter, we first assessed the normality of the data distributions within each group using the Shapiro-Wilk test and evaluated homogeneity of variances using Levene’s test. Based on these assumptions, we applied either an independent samples t-test assuming equal variances, Welch’s t-test when variances were unequal, or the non-parametric Mann-Whitney U test when normality was not met. All comparisons were two-tailed, as no directional hypotheses were assumed a priori. 3. RESULTS 3.1. Animals In our hands, female TgF344-AD rats appeared very resistant even after multiple imaging sessions under anaesthesia (between 6 to 9 sessions per animal) over a 24-month lifespan, demonstrating the robustness of the model in extensive longitudinal studies. TgF344-AD female rats exhibited no mortality up to 18 months of age (Figure S1, Table S2). Subsequently, survival probability decreased as spontaneous conditions necessitated the application of humane endpoints. Additionally, two rats died after radiotracer injection at 17 months, with an incompatible dose formulation suspected but not confirmed as the cause; these animals were excluded from survival analysis. Wild-type F344 rats displayed spontaneous mortality only after 19 months, with a marked decrease in survival probability observed after 23 months. Survival rates between groups did not differ significantly (p = 0.8940, Log-rank Mantel-Cox test). Transgenic females presented similar rates to those previously reported in males at 19 months, whereas wild-type age-matched females exhibited higher survival compared to males (40,47). Rats in both groups were easy to handle at all ages, although a moderate anxiety-like behaviour was noticeable when handled for extended periods, a trait previously reported for this strain and model (48,49). Body weights were significantly higher in WT rats compared to TgF344-AD rats only at 4 months of age (p = 0.0026, two-tailed unpaired t-test with Welch’s correction); no significant differences were observed at later time points (Figure S3). Notably, old TgF344-AD rats exhibited an average weight loss of 8% after 20 months of age, which was not observed in control littermates. 3.2. Longitudinal [ 18 F]JNJ-64413739 PET imaging To investigate the neuroinflammatory response over time, we performed longitudinal [ 18 F]JNJ-64413739 PET imaging to monitor in vivo P2X7R expression, in both AD and WT rats at 8, 15 and 22 months of age (see Figure 2-A for representative images). SUV time activity curves (TACs) were obtained in different brain regions (cortex, hippocampus, striatum, brain stem and cerebellum) for each time point and group (Figure S5). In all cases, we observed a rapid tracer uptake with curves peaking at 10-15 seconds before reaching steady-state values at ca. 5 min post-injection. For analysis, we statistically compared normalized uptake (SUVR, cerebellum as a pseudo-reference region) of [ 18 F]JNJ-64413739 between time points (age effect) and between groups (genotype effect), following previous reports (Figure 2-B) (25). Although P2X7R expression appeared lower in the cerebellum compared to other regions, this region is not entirely devoid of receptors, therefore we suggested using this area as a pseudo-reference region in our analysis as previously reported (50). In AD animals, increased tracer uptake was detected from 8 to 15 months in the cortex (+11 ± 8%, p = 0.0004), hippocampus (+9 ± 17%, p = 0.0125), striatum (+14 ± 13%, p < 0.0001) and brain stem (+8 ± 13%, p = 0.0008). At 22 months, similar results were observed in the cortex (+9 ± 11%, p = 0.0311), hippocampus (+9 ± 12%, p = 0.0029) and striatum (+15 ± 2%, p = 0.0025) but not in the brain stem (0 ± 17%, p = 0.4338), compared to 8 months. A similar increasing trend was observed in the control group in the cortex (15mo. +10 ± 1%, p = 0.0250; 22mo. +7 ± 1%, p = 0.1745) and hippocampus (15mo. +15 ± 3%, p = 0.0433; 22mo. +19 ± 4%, p = 0.0802), while differences in the striatum were significant only at 22 months of age (15mo. +6 ± 1%, p = 0.3345; 22mo. +20 ± 4%, p = 0.0395). No significant differences were detected in the brain steam for control animals (15mo. +3 ± 1%, p = 0.5757; 22mo. +4 ± 1%, p = 0.6419) compared to the initial time point. We did not identify significant genotype differences between AD and WT animals in any of the regions that we studied, suggesting that longitudinal changes in P2X7R expression were mainly driven by aging effects in our study. 3.3. [ 18 F]JNJ-64413739 in vitro autoradiography High-resolution autoradiography with [ 18 F]JNJ-64413739 revealed widespread tracer binding in both AD and WT tissue, consistent with in vivo observations. Notably, higher uptake was observed in the corpus callosum ex vivo , although this region was not specifically quantified in the in vivo analysis (Figure 2C-D). This result is consistent with previous reports of the widespread distribution of the P2X7 receptor throughout the brain, although it also shows a degree of off-target binding of the radiotracer to white matter (20). Higher binding, measured as ratio to the cerebellum, was detected in AD tissue compared to controls in the cortex (+6 ± 1%, p = 0.7787), hippocampus (+24 ± 3%, p = 0.3324) and brain stem (+11 ± 1%), but not in the striatum (+17 ± 3%, p = 0.6998) (Figure 2-E). [ 18 F]JNJ-64413739 binding was found to be highly specific ( ca. 80% blocking) under incubation with non-labelled reference (homologous blocking) in the entire tissue for both AD and WT, including in the corpus callosum (Figure S7). 3.4. [ 18 F]DPA-714 PET imaging To enable comparison, neuroinflammation was assessed in both AD and WT rats at a later stage (22 months) using the gold-standard PET tracer [ 18 F]DPA-714 (see Figure 3-A for representative images). SUV time activity curves (TACs) were obtained in different brain regions (cortex, hippocampus, striatum, brain stem and cerebellum) for each time point and group (Figure S6). In all cases, we observed a slow tracer uptake with curves peaking at 10-15 minutes with steady wash-out thereafter. For analysis, SUV obtained 45 to 60 minutes post-injection were used to compare uptake between groups (genotype effect) (Figure 3-B). While previous studies have proposed using the cerebellum as a reference region for [ 18 F]DPA-714 PET, in our study we observed significantly higher uptake in this region for AD animals compared to WT, suggesting that the cerebellum may not be completely free of TSPO expression (see below) (40). At 22 months, significant genotype differences were found between AD and WT rats in all of the brain regions including cortex (+72 ± 0.2%, p = 0.0001), hippocampus (+61 ± 0.3%, p = 0.0020), striatum (+56 ± 0.2%, p = 0.0020), brain stem (+44 ± 0.3%, p = 0.0076) and cerebellum (+61 ± 0.2%, p = 0.0003), revealing a distinct neuroinflammatory response in the TgF344-AD rat model. 3.5. [ 18 F]DPA-714 in vitro autoradiography Autoradiography images with [ 18 F]DPA-714 showed widespread tracer binding, with markedly increased uptake observed in white matter-rich areas such as the corpus callosum in both AD and WT tissue (Figure 3 C-D). We found significant higher binding intensity of [ 18 F]DPA-714 in AD tissue compared to WT in all brain areas, including the cerebellum, supporting the obtained in vivo data (Figure 3-E). This finding limited the use of the cerebellum as a true reference region for [ 18 F]DPA-714 PET analysis in our study. [ 18 F]DPA-714 binding was found to be specific ( ca. 30% blocking) under incubation with non-labelled reference (homologous blocking) in all brain tissue for both AD and WT (Figure S8). 3.6. Immunofluorescence To support the in vivo results, immunohistochemistry studies were conducted with brain tissue collected at the endpoint of the study for both groups (AD, 23 months; WT, 22 months) using Iba1 (microglia), anti-P2X7R and anti-TSPO antibodies. Qualitative image analysis showed an abundance of round-shaped (amoeboid) Iba1-positive microglia in the hippocampus of AD animals (Figure 4-A), in contrast to the larger, more ramified microglial processes observed in control tissue cells (Figure 4-B), suggesting increased microglial reactivity in the AD group. P2X7R-positive cells were detected in both AD and WT tissue. In contrast, TSPO-positive cells were more abundant in AD animals compared to control (Figure 4-C-D). A neuroinflammatory response characterized by Iba1-positive microglia and GFAP-positive astrocytes was observed surrounding amyloid plaques in AD tissue, with P2X7R and TSPO expression co-localizing with microglia but not with astrocytes (Figure 4 E-H). Microglia were present in both grey and white matter in the cerebellum, with apparent lower P2X7R expression in the white matter (Figure S10). This finding suggests that [ 18 F]JNJ-64413739 uptake in WM-rich areas ( e.g. , corpus callosum) is likely due to off-target binding to lipid structures rather than to P2X7R. 3.7. Longitudinal [ 18 F]Florbetaben PET imaging [ 18 F]Florbetaben PET was used to assess in vivo myelin levels at 4, 8, 12, 16 and 20 months in both groups (see Figure 5-A for representative images). The use of stilbene-derivative β-amyloid tracers (i.e., [¹⁸F]Florbetaben) as myelin markers has been previously proposed, presenting an opportunity to repurpose these radiotracers for studying demyelinating disorders, including Alzheimer’s disease (51). In this sense, it has been shown that planar-like stilbenes bind to proteins or aggregates displaying a particular molecular conformation with adjacent beta-sheet structures (52,53). Given that these are found in both amyloid plaques and the myelin basic protein (MBP), [ 18 F]Florbetaben can potentially serve as a marker of myelin in vivo . Longitudinal [¹⁸F]Florbetaben PET imaging revealed no significant differences between groups in traditionally plaque-rich regions, such as the cortex and hippocampus, over time (Figure 5 B-C). This aligns with previous studies with this radiotracer, which reported only slight differences between TgF344-AD rats and wild-type littermates at 18 months of age in these regions (40). We then focused on the image quantification of two white matter-containing areas: brain stem, and to a lesser extent, the striatum, for the study of myelin content in vivo (Figure 5 D-E). SUVR analysis (cerebellum as reference region) in the striatum revealed an increase in tracer uptake for the AD group from 4 to 16 months (+7%, p = 0.0875), followed by a decrease from 16 to 20 months (-10%, p = 0.0067). Significant differences to control animals were found only at 12 months (p = 0.0378) and 20 months (p = 0.0006) in this region. In the brain steam, a significant age effect was observed in AD animals, with [¹⁸F]Florbetaben uptake increasing by 11% from 4 to 12 months (p = 0.0009). Interestingly, a decrease of 10% (p = 0.0010) was detected from 12 to 20 months in the same animals. Significant differences were also observed from 4 to 16 months (p = 0.0042), from 8 to 12 months (p = 0.0057) and from 16 to 20 months (p = 0.0044). Conversely, WT rats plateau at 8 and 12 months with an increase of 12% from 4 months (p = 0.0051 and p = 0.0136, respectively), while no significant decrease was observed in 20-month-old animals compared to those at 12 months (-4%, p = 0.4070). A pronounced genotype effect between groups was found in the brain stem at 8 months (p = 0.0047) compared to animals at 20 months (p = 0.0395), suggesting that differences in myelin load are bigger at younger ages compared to later time points. 3.8. [ 18 F]Florbetaben in vitro autoradiography Visual analysis of high-resolution autoradiography images obtained with [¹⁸F]Florbetaben revealed widespread binding of the tracer, with higher retention in white matter-rich areas including the corpus callosum, cerebellum and brain stem (Figure 5 F-H). Binding ratio to the cerebellum was used for comparative purposes between AD and WT tissue and between young (10 month) and old (19 month) AD animals (Figure 5-I). Overall, no significant differences were observed between groups within any of the brain regions analysed, although overall binding in the brain steam was higher compared to the cortex and hippocampus. Interestingly, [¹⁸F]Florbetaben binding in the brain steam was 14% lower in 19-month-old AD animals compared to 10-month-old AD tissue and 17% lower compared to age-matched WT animals. This finding supports the idea of myelin loss at advanced stages in AD animals, as previously suggested in vivo . Homologous blocking using non-labelled Florbetaben revealed specific binding in plaque-rich regions, such as the cortex (blocking = 25%) and hippocampus (blocking = 23%) in 10-month-old AD tissue, which likely illustrates the specificity of [¹⁸F]Florbetaben to amyloid-plaques (Figure S9). Conversely, binding displacement was not detected in white matter-rich areas such as the cerebellum (blocking = -13%), the brain stem (blocking = -6%) and the corpus callosum (not quantified). 3.9. Thioflavin staining Given the lack of differences in plaque-rich areas observed with [¹⁸F]Florbetaben PET between AD and control animals, we performed in vitro Thioflavin staining to confirm the presence of amyloid plaque pathology in our transgenic animals (Figure S11). Staining for amyloid plaques was positive in 23-month-old AD tissue, predominantly in the cortex and hippocampus, and to a lesser extent in the cerebellum. We found that plaque loading in TgF344-AD rats was qualitatively lower compared to other more aggressive AD mouse models (54). This may explain the low signal-to-noise ratio observed in vivo with [¹⁸F]Florbetaben. Additionally, we did not observe plaque deposition in the brain stem of AD animals, suggesting that [¹⁸F]Florbetaben uptake in this region likely corresponds to myelin binding. 3.10. Florbetaben staining Like other stilbene derivatives, Florbetaben exhibits spontaneous fluorescence when excited by light at approximately 480 nm, enabling analysis of its tissue uptake via fluorescence imaging (55,56). Binding of Florbetaben to amyloid plaques was confirmed on whole brain slices of AD animals, mainly in the cortex and the hippocampus, as previously seen in vivo and in vitro (Figure S12). Images also revealed the anticipated binding of Florbetaben to white matter rich areas such as the corpus callosum, striatum, thalamus, cerebellum and brain steam in both AD and WT tissue. These results qualitatively support Florbetaben’s dual binding affinity for both amyloid plaques and white matter. 3.11. Luxol Fast Blue staining Luxol Fast Blue was used to visualize myelin content in coronal brain sections of both AD and WT rats (Figure S13). Qualitative analysis of images revealed a similar distribution of myelin in TgF344-AD and control animals in the corpus callosum, striatum and cortex, with no apparent differences in myelin content. 3.12. Diffusion tensor imaging MRI Fractional anisotropy values were calculated with diffusion tensor imaging in AD and WT rats at 22 months in 25 brain regions segmented as described in the methods section. (Figure 6). Fractional anisotropy, showed significant differences between WT and AD animals in the following regions: Cortex, FA WT = 0.215, FA AD = 0.258, ΔFA = +20.2%, p = 0.0091; Hippocampus, FA WT = 0.198, FA AD = 0.233, ΔFA = +18.1%, p = 0.0003; Striatum, FA WT = 0.225, FA AD = 0.244, ΔFA = +8.7%, p = 0.0424; Diencephalon, FA WT = 0.263, FA AD = 0.283, ΔFA = +7.8%, p = 0.0021; in the Corpus callosum, FA WT = 0.454, FA AD = 0.492, ΔFA = +8.3%, p = 0.0260; Amygdala, FA WT = 0.298, FA AD = 0.361, ΔFA = +21.4%, p = 0.0226; Stria terminalis, FA WT = 0.278, FA AD = 0.317, ΔFA = +14.0%, p = 0.0395. Mean diffusivity (MD x10 -3 mm 2 s -1 ) was significantly different in: Cingulum, MD WT = 0.712, MA AD = 0.642, ΔMD = -9.8%, p = 0.0004; Hindbrain, MD WT = 0.801, MA AD = 0.760, ΔMD = -5.2%, p = 0.0370; Diencephalon, MD WT = 0.741, MA AD = 0.710, ΔMD = -4.2%, p = 0.0147; Internal capsule, MD WT = 0.731, MA AD = 0.705, ΔMD = -3.6%, p = 0.0260; Hippocampus, MD WT = 0.758, MA AD = 0.729, ΔMD = -3.9%, p = 0.0007; Pallidum, MD WT = 0.688, MA AD = 0.661, ΔMD = -3.9%, p = 0.0016; Cerebellum white matter, MD WT = 0.758, MA AD = 0.726, ΔMD = -4.1%, p = 0.0410; Nucleus accumbens, MD WT = 0.680, MA AD = 0.646, ΔMD = -5.1%, p = 0.0016. In relation to radial diffusivity (RD x10 -3 mm 2 s -1 ), significant differences were observed in the following regions: Cingulum, RD WT = 0.503, RD AD = 0.400, ΔRD = -20.5%, p = 0.0000; Midbrain, RD WT = 0.733, RD AD = 0.705, ΔRD = -3.8%, p = 0.0188; Hindbrain, RD WT = 0.653, RD AD = 0.614, ΔRD = -6.0%, p = 0.0051; Septum, RD WT = 0.747, RD AD = 0.686, ΔRD = -8.1%, p = 0.0212; Diagonal domain, RD WT = 0.742, RD AD = 0.681, ΔRD = -8.1%, p = 0.0492; Striatum, RD WT = 0.642, RD AD = 0.614, ΔRD = -4.3%, p = 0.0098; Diencephalon, RD WT = 0.630, RD AD = 0.600, ΔRD = -4.9%, p = 0.0372; Internal capsule, RD WT = 0.519, RD AD = 0.493, ΔRD = -4.9%, p = 0.0111; Hippocampus, RD WT = 0.679, RD AD = 0.634, ΔRD = -6.7%, p = 0.0000; Pallidum, RD WT = 0.597, RD AD = 0.570, ΔRD = -4.6%, p = 0.0094; Corpus callosum, RD WT = 0.539, RD AD = 0.463, ΔRD = -14.0%, p = 0.0095; Amygdala, RD WT = 0.663, RDAD = 0.599, ΔRD = -9.6%, p = 0.0297; Cerebellum white matter, RD WT = 0.649, RD AD = 0.613, ΔRD = -5.6%, p = 0.0102. Axial diffusivity (AD x10 -3 mm 2 s -1 ) resulted significantly different in the Cingulum, AD WT = 1.131, AD AD = 1.066, ΔAD = -5.8%, p = 0.0231, and the Corpus callosum, AD WT = 1.139, AD AD = 1.083, ΔAD = -4.9%, p = 0.0175. 4. DISCUSSION PET imaging using animal models of AD enables quantitative analysis of pathophysiological aspects of AD through non-invasive longitudinal studies, offering high sensitivity and high throughput (57). Here, we demonstrated that a single cohort of TgF344-AD rats can undergo a multi-tracer longitudinal PET study, enabling true comparisons while minimizing variability. Despite requiring careful planning and extended care, this approach maximized data collection, reduced the number of animals needed, and mitigated cohort variability from environmental factors like diet, handling, and housing, ultimately enhancing statistical power. Persistent neuroinflammation is now widely recognized as a core pathological feature of AD, evidenced by both clinical and pre-clinical reports (40,58). Neuroinflammation in TgF344-AD rats has been previously reported in vivo with [ 18 F]DPA-714 PET by measuring increased levels of TSPO over time (40). However, other neuroinflammatory targets remain unexplored in this model to date. For the first time, we examined neuroinflammation in TgF344-AD rats via P2X7 receptor expression, which drives pro-inflammatory cytokine release in AD (21,59,60). In our study, we initially hypothesized that neuroinflammation in TgF344-AD rats, resulting from microglia activation around amyloid plaques, can be detected by means of increased P2X7R expression with [ 18 F]JNJ-64413739 PET. Here, we revealed a significant increase in [ 18 F]JNJ-64413739 uptake in 8- to 22-month-old TgF344-AD rats, although a similar trend was also observed in age-matched wild-type animals, with no significant differences between groups. These findings seem to indicate that P2X7R expression in the TgF344-AD model is mostly driven by aging effects in the Fischer-344 strain, suggesting that age-dependant inflammation is also a natural event in these animals. This is in line with previous reports indicating an increased microglia activation in old female F344 rats (61). However, previous in vitro analyses have demonstrated significant microgliosis (CD11b staining) around plaques in TgF344-AD rats compared to wild-type animals at 18 months, consistent with our observations using Iba1 immunofluorescence at 22 months (39,40). Another study also reported elevated levels of pro-inflammatory cytokines in TgF344-AD rats with age; which correlates with increased [ 18 F]JNJ-64413739 uptake in our study (62). To gain a deeper understanding of the differences in neuroinflammation between AD and WT rats, we also conducted TSPO PET imaging using [ 18 F]DPA-714 for comparison. Here, we observed significant differences in tracer uptake between TgF344-AD and control rats at 22 months compared to what was previously reported at 18 months, demonstrating an increased age-related neuroinflammatory response (40). Based on this data, it seems that TgF344-AD rats exhibit a weaker neuroinflammatory phenotype, leading to a poor correlation between TSPO-driven neuroinflammation and P2X7 receptor expression. Interestingly, our results contrast with previous studies using mouse models of AD, where P2X7R upregulation was closely associated with AD progression (60,63). However, Martinez-Frailes et al. found that P2X7R upregulation in microglial cells occurs only in the advanced and late stages of AD, but not in the early stages, when microglial priming has not yet occurred and senile plaques are still limited in number (64). Therefore, we believe that the TgF344-AD model may primarily mimic an early stage of the neuroinflammatory response seen in more aggressive mouse models of AD, as it shows glial reactivity but maintains basal P2X7R expression. Supporting this idea, previous studies showed cognitive deficits in TgF344-AD rats similar to those observed in individuals with preclinical AD, suggesting that the model primarily reflects the prodromal phase of the disease (65–67). In our study, the small number of ex vivo samples (n = 1 - 2) limited the in vitro studies to a qualitative analysis as no statistically relevant comparison was possible. Since the rats used for ex vivo studies came from the same group of animals used for imaging, tissue availability was restricted to maintain sufficient animals for the longitudinal study. In this sense, future research should focus on more extensive ex vivo analysis to quantitatively assess genotype-specific P2X7R expression over time in this model. Here, we also presented the first longitudinal study using [¹⁸F]Florbetaben PET imaging to assess myelin content in vivo in the TgF344-AD model. Although [¹⁸F]Florbetaben PET is primarily used as an amyloid tracer in humans, it did not reveal significant differences between AD and wild-type rats in our study, which is in line with previous findings that reported only slight differences between groups (40). The combination of limited resolution in preclinical PET systems, high tracer lipophilicity, and low plaque density in this model resulted in low signal-to-noise ratio PET images and is likely the reason why [¹⁸F]Florbetaben fails at detecting amyloid plaques in our study. Given that white matter is predominantly composed of lipid-rich myelin, lipophilic amyloid tracers often show high off-target binding in these regions. This aligns with our autoradiography findings, where non-displaceable [¹⁸F]Florbetaben binding was observed in white matter-rich areas. Additionally, we also showed how self-fluorescent Florbetaben binds to amyloid-free white matter-rich regions, such as the brain stem and corpus callosum, consistent with our observations in PET and autoradiography. Despite the exact binding of [¹⁸F]Florbetaben to myelin is not fully described, evidence suggests that stilbene derivatives bind selectively to the beta sheet structures of the myelin basic protein (MBP), which correlates with myelin content in demyelinating models (55). When myelin is damaged, MBP loses its beta sheet structures reducing the binding sites for the tracer which can therefore be used to capture the demyelination process. Overall, [¹⁸F]Florbetaben uptake in the striatum was lower than in the brain steam in AD rats, presumably due to the lower content of myelinated axons in this region, with only a moderate increase from 4 to 16 months but with a significant decline in myelin content at 20 months. Uptake values in the myelin-rich brain stem indicated that healthy F344 rats might display a higher production of myelin during their early ages (4 to 8 months), followed by a slight decline of this protein at later time points (12 to 20 months), likely due to the effects of aging. In contrast, TgF344-AD rats initially displayed a slower production of myelin (4 to 12 months), but eventually reaching similar levels to WT rats over a longer period of time. However, our data in the brain stem suggests that AD rats experience a significant loss of myelin at 20 months compared to healthy animals. These results support the idea that not only TgF344-AD rats suffer from myelin loss at advanced ages, but they also encounter disruptions in myelin generation over time, which can potentially be attributed to AD progression. Quantitative in vitro analysis of myelin content could not be performed due to limited sample availability, preventing direct support of the in vivo results. This limitation should be addressed in future studies employing this model. MRI data further supported our findings on myelin content by revealing significant differences between AD and wild-type animals in multiple brain regions across all four DTI metrics (FA, MD, RD, and AD). Notably, fractional anisotropy was increased in several regions in AD rats, including the cortex, hippocampus, and corpus callosum. This finding is somewhat unexpected, as demyelination is typically associated with reduced FA due to increased radial diffusivity and loss of directional coherence in white matter tracts. However, increased FA in the context of neurodegeneration has been previously reported in this model and may reflect compensatory mechanisms such as axonal reorganization, gliosis, or selective loss of crossing fibers, which can artificially elevate FA values (68,69). This interpretation is supported by the concurrent decrease in radial diffusivity (RD) in many of the same regions, a change more directly associated with reduced myelin content. These results suggest that while FA increases may not directly reflect improved white matter integrity, they may still indicate underlying microstructural remodeling in response to pathology. These complex microstructural changes in TgF344-AD rats revealed by DTI-MRI are partially consistent with demyelination. Specifically, the combination of increased FA and decreased RD suggests a complex pathology involving both myelin loss and structural reorganization. Overall, the results from our study align with multiple lines of evidence showing that myelin is significantly altered in AD-related pathology (70,71). Notably, myelin content, indicated by [ 18 F]Florbetaben uptake, inversely correlated with previously reported amyloid load in this model, with the brain steam showing the highest uptake and cortex the lowest (39). This supports the hypothesis that AD pathology reflects a reverse of the myelination pattern, where less-myelinated regions are more vulnerable (28,72). Some studies also suggest AD is a developmental disorder that manifests only after myelination is complete (29). In our study, both AD and WT rats reached peak myelination around 12 months in the brain stem. While amyloid oligomers and microglial activation occur as early as 6 months in this model, cognitive deficits only appear after 15 months, coinciding with observed demyelination (39). This might indicate that myelin loss contributes to cognitive decline in TgF344-AD rats, consistent with other studies linking demyelination to cognitive impairment in AD (73–76). Additionally, previous reports showed white matter deficits at the earliest or preclinical stages of AD, supporting the idea that the TgF344-AD model may primarily represent the prodromal phase of the disease (77–80). In our study, we hypothesized that a neuroinflammatory response in the TgF344-AD model could be measured by an increased expression of the pro-inflammatory P2X7 receptor, driving myelin loss in transgenic animals. However, our longitudinal data showed an age-related but genotype-independent increase of P2X7R expression, therefore we could not find a direct link between P2X7R-mediated neuroinflammation and demyelination in TgF344-AD rats. Despite increased P2X7R expression was similar in both AD and WT groups with age, only transgenic rats showed myelin loss in white matter rich areas, which suggests that demyelination in AD animals is pathologically driven. Considering previous reports, it seems that amyloid pathology precedes demyelination in TgF344-AD rats, where amyloid deposition has been detected as early as 6 months of age (39). Additionally, signs of neuroinflammation, indicated by TSPO expression, are detected around 12 months in this model and continue to increase up to 22 months, coinciding with the myelin loss observed in our study. This reinforces the idea that TSPO is a more sensitive marker of AD-related inflammation in this model and it correlates better with myelin changes. Interestingly, the brain regions where AD is typically detected, such as the cortex and hippocampus, did not overlap with the areas affected by demyelination in our study—possibly due to the inherently low myelin content of grey matter. 5. CONCLUSIONS To our knowledge, this is the first study to examine both neuroinflammation and demyelination simultaneously in an animal model of AD using in vivo PET imaging. TSPO PET ([¹⁸F]DPA-714) revealed a significant neuroinflammatory response in TgF344-AD rats, while P2X7R PET ([¹⁸F]JNJ-64413739) showed increased age-related expression in both AD and control animals, reflecting aging effects but not AD-specific inflammation. Thus, [¹⁸F]DPA-714 PET proved more suitable for tracking neuroinflammation in this model. Additionally, [¹⁸F]Florbetaben PET was not appropriate for amyloid imaging in TgF344-AD rats due to low plaque burden and high off-target binding. However, its uptake reflected myelin content, allowing us to monitor progressive demyelination—most notably at 20 months—supporting a link between white matter loss and reported AD-related cognitive decline. Additionally, DTI-MRI suggested that TgF344-AD rats undergo microstructural remodeling of white matter tracts compared to controls. Overall, we did not find a direct link between P2X7R expression and demyelination over time, although neuroinflammation detected with TSPO PET was increased together with myelin loss. Our findings support the use of the TgF344-AD model to study early and prodromal stages of AD, especially in relation to neuroinflammation and white matter integrity. Despite some limitations, such as small ex vivo sample size and tracer specificity, this work illustrates the temporal dynamics of AD pathology and reinforces the use of the TgF344-AD rat model as a lead animal model for AD research in the future. Abbreviations Aβ Amyloid beta AD Alzheimer’s disease Bq Becquerel BS Brain stem CB Cerebellum CT Computer tomography CTX Cortex DTI Diffusion Tensor Imaging HIPP Hippocampus MQW Milli-Q water MRI Magnetic resonance imaging PET Positron Emission Tomography ROI Region of interest STR Striatum SUV Standardized Uptake Value SUVR Standardized Uptake Value ratio TAC Time activity curve Tg Transgenic VOI Volume of interest WM White Matter WT Wild type Declarations Ethics approval and consent to participate Animal experimentation was conducted in accordance with the European Council Directive 2010/63/EU at CIC biomaGUNE (San Sebastián, Spain) facilities and approved by the Institutional Animal Care and Use Committee (IACUC) at CIC biomaGUNE and Diputación Foral de Guipúzcoa (Project: PRO-AE-SS-169). Data availability All datasets used or analysed in this study are available from the corresponding authors on reasonable request. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding Oscar Moreno and Jordi Llop acknowledge funding from the Spanish Ministry of Science and Education (grant PRE2019-089068). Author’s contribution O.M, A.M and J.L designed the study and the main conceptual ideas; O.M performed the radiochemistry, PET studies and in vitro experiments. O.M and Z.B. quantified and analysed the PET data. I.F. performed the Luxol Fast Blue experiments. S.P. performed the MRI studies. S.P and D.P. curated the MRI data. D.P. and P.R. analysed the MRI data. O.M. wrote the original draft with edits from A.M. and J.L.. All authors contributed to the interpretation of findings, critical review of the manuscript, approval of the final manuscript, and agreement to be accountable for all aspects of the work. 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Evidence of demyelination in mild cognitive impairment and dementia using a direct and specific magnetic resonance imaging measure of myelin content. Alzheimer’s and Dementia. 2018 Aug 1;14(8):998–1004. Dong YX, Zhang HY, Li HY, Liu PH, Sui Y, Sun XH. Association between Alzheimer’s disease pathogenesis and early demyelination and oligodendrocyte dysfunction. Neural Regen Res. 2018 May 1;13(5):908–14. Ota M, Sato N, Kimura Y, Shigemoto Y, Kunugi H, Matsuda H. Changes of Myelin Organization in Patients with Alzheimer’s Disease Shown by q-Space Myelin Map Imaging. Dement Geriatr Cogn Dis Extra. 2019 Jan 1;9(1):24–33. Naggara O, Oppenheim C, Rieu D, Raoux N, Rodrigo S, Dalla Barba G, et al. Diffusion tensor imaging in early Alzheimer’s disease. Psychiatry Res Neuroimaging. 2006 Apr 30;146(3):243–9. Desai MK, Mastrangelo MA, Ryan DA, Sudol KL, Narrow WC, Bowers WJ. Early oligodendrocyte/myelin pathology in Alzheimer’s disease mice constitutes a novel therapeutic target. American Journal of Pathology. 2010;177(3):1422–35. Dean DC, Hurley SA, Kecskemeti SR, O’Grady JP, Canda C, Davenport-Sis NJ, et al. Association of amyloid pathology with myelin alteration in preclinical Alzheimer disease. JAMA Neurol. 2017 Jan 1;74(1):41–9. Tse KH, Cheng A, Ma F, Herrup K. DNA damage-associated oligodendrocyte degeneration precedes amyloid pathology and contributes to Alzheimer’s disease and dementia. Alzheimer’s and Dementia. 2018 May 1;14(5):664–79. Additional Declarations The authors declare no competing interests. Supplementary Files 250605InflammationandmyelinmanuscriptADSupporting.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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both AD and WT groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/e0724df354e4c6e2ab821f4b.jpg"},{"id":87384998,"identity":"1a98ab65-6ec0-4bd7-8ad3-c4a5ea28a23e","added_by":"auto","created_at":"2025-07-23 08:51:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal assessment of P2X7R expression. \u003c/strong\u003eA) SUVR [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 PET images from the longitudinal study at 8, 15 and 22 months (mo.) for both AD (top) and WT (bottom) groups. Images are obtained 40 to 60 minutes post-injection as the average from all animals per time point and group and co-registered with the MRI brain template. From left to right, first column corresponds to sagittal view (Lateral = 0.9 mm), and second to fourth columns to axial views (Bregma = 0.2 mm; -5.2 mm and -10.0 mm, respectively). B) [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 uptake quantification, expressed as SUVR (cerebellum as reference region), in different brain regions in AD (green) and WT (red) rats at 8, 15 and 22 months of age. Data is expressed as mean ± SD. \u003csup\u003e*\u003c/sup\u003e indicate significant differences between time points within the same group (\u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05; \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001). C, D) Representative \u003cem\u003ein vitro\u003c/em\u003e autoradiography images with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 in AD (C) and WT tissue (D). Manual segmentation is showed on the left for clarity: cortex (blue), hippocampus (red), cerebellum (purple) and brain stem (green). Right to each slice is the corresponding self-blocked tissue. All images are obtained from the same incubation experiment. E) Bar plot shows [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 binding in the cortex (CTX), hippocampus (HIPP), striatum (STR) and brain stem (BS) in each group (AD: green, WT: red). Values are expressed as mean ± SD, calculated as the ratio to the cerebellum. Absolute percentage indicate difference between mean values. Dots indicate individual animals.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/4d97fe4917cef3923b570393.jpg"},{"id":87384996,"identity":"0956ea8e-3e21-42f3-a51a-a4e1a9d80fb6","added_by":"auto","created_at":"2025-07-23 08:51:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal assessment of TSPO expression. \u003c/strong\u003eA) SUV [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET images at 22 months (mo.) for both AD (left) and WT (right) groups. Images are obtained 45 to 60 minutes post-injection as the average from all animals per time point and group and co-registered with the MRI brain template. From left to right, first image corresponds to sagittal view (Lateral = 0.9 mm), and second to fourth images correspond to axial views (Bregma = 0.2 mm; -5.2 mm and -10.0 mm, respectively). B) [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 uptake quantification, expressed as SUV, in different brain regions in AD (green) and WT (red) rats at 22 months of age. Data is expressed as mean ± SD. C, D) Representative \u003cem\u003ein vitro\u003c/em\u003e autoradiography images with [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 in AD (C) and WT tissue (D). Manual segmentation is showed on the left for clarity: cortex (blue), hippocampus (red), cerebellum (purple) and brain stem (green). Right to each slice is the corresponding self-blocked tissue. All images are obtained from the same incubation experiment. E) Bar plot shows [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 binding as pixel intensity in the cortex (CTX), striatum (STR), hippocampus (HIPP), brain stem (BS) and cerebellum (CB) in each group (AD: green, WT: red). Dots indicate replicates within the same animal.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/6aa43f2fd4ae967e141f297c.jpg"},{"id":87386702,"identity":"3285d808-dfc3-4d38-84e0-74a19d01e073","added_by":"auto","created_at":"2025-07-23 08:59:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e analysis of neuroinflammation.\u003c/strong\u003e Representative images from the immunofluorescence study including microglia (Iba 1, red), P2X7R (anti-P2X7R, green) and nuclei (DAPI, blue) in the hippocampus of a 23-month-old AD rat (A) and a 22-month-old WT rat (B). Representative images from the immunohistochemistry study including microglia (Iba 1, red), TSPO (anti-TSPO, green) and nuclei (DAPI, blue) in the hippocampus of a 23-month-old AD rat (C) and a 22-month-old WT rat (D). Merged images from the immunohistochemistry study including amyloid plaques (X34, green), microglia (Iba 1, red), astrocytes (GFAP, red) and P2X7R (anti- P2X7R, light blue) in the hippocampus of a 23-month-old AD rat (E-H). Scale bars: 100 µm, unless stated otherwise.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/9531a7f7b7d4ecfa0e12d238.jpg"},{"id":87384999,"identity":"a200f4f3-774b-4f40-af6f-3dbd0c9651bb","added_by":"auto","created_at":"2025-07-23 08:51:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":215961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal assessment of myelin content with PET. \u003c/strong\u003eA) SUVR [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben PET images from the longitudinal study at 4, 8, 12, 16 and 20 months (mo.) for both AD and WT groups. Images are obtained 40 to 55 minutes post-injection as the average from all animals per time point and group and co-registered with the MRI brain template. From left to right, first column corresponds to sagittal view (Lateral = 0.9 mm), and second to fourth columns correspond to axial views (Bregma = 0.2 mm, -5.2 mm and -10.0 mm, respectively). B-E) [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben uptake quantification, expressed as SUVR (cerebellum as reference region), in the cortex (B), hippocampus (C), striatum (D) and brain stem (E) in AD (green) and WT (red) rats at the different time points. Data are expressed as mean ± SD. Dots indicate individual animals. Schematic plot at the bottom of each chart indicates the mean percentage increase or decrease with respect to the previous time point. \u003csup\u003e*\u003c/sup\u003e indicates significant differences between time points within the same group and \u003csup\u003e#\u003c/sup\u003e indicates significance between groups (\u003csup\u003e*/#\u003c/sup\u003ep \u0026lt; 0.05; \u003csup\u003e**/##\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e***/###\u003c/sup\u003ep \u0026lt; 0.001). F-H) Representative \u003cem\u003ein vitro\u003c/em\u003e autoradiography images with [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben in AD at 10 (F) and 19 months (G) and WT tissue (H). Manual segmentation is showed on the left for clarity: cortex (blue), hippocampus (red), cerebellum (purple) and brain stem (green). Right to each slice is the corresponding self-blocked tissue. All images are obtained from the same incubation experiment. I) Bar plot shows [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben binding in the cortex (CTX), hippocampus (HIPP) and brain stem (BS) in each group (10 months AD: light blue; 23 months AD: green; 22 months WT: red). Values are expressed as mean ± SD, calculated as the ratio to the cerebellum. Absolute percentage indicates difference between mean values. Dots indicate individual animals.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/bb5f68bb5ea35f45388d67d8.jpg"},{"id":87386701,"identity":"362fe7ca-1683-48df-a8c0-0fcdb618663a","added_by":"auto","created_at":"2025-07-23 08:59:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy of myelin integrity with diffusion tensor imaging MRI. \u003c/strong\u003eTop) orthogonal 2D and 3D views of the regions of interest of the brain atlas that showed significant changes of DTI parameters using a color code for percentage of change of the AD group versus the WT group. (A) FA; (B) MD; (C) RD; (D) AD. Bottom) bar plots showing the mean ± SD values of FA (E), MD (F), RD (G) and AD (H) on the regions of the brain presenting significant (p \u0026lt; 0.05) differences between AD and WT groups.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/b1aba93ebb653087a974279e.jpg"},{"id":87387498,"identity":"18a7987b-25c7-40cb-b36b-d90963aa70f8","added_by":"auto","created_at":"2025-07-23 09:07:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1963646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/b266a1f2-21b4-4e4d-82db-3a3b0a30e445.pdf"},{"id":87385001,"identity":"d961c7c7-08a6-4945-8a09-18892a5cf1c9","added_by":"auto","created_at":"2025-07-23 08:51:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3618059,"visible":true,"origin":"","legend":"","description":"","filename":"250605InflammationandmyelinmanuscriptADSupporting.docx","url":"https://assets-eu.researchsquare.com/files/rs-7072571/v1/2d878c353f8931f0ccb69c77.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLongitudinal \u003cem\u003ein vivo\u003c/em\u003e PET imaging of P2X7R and TSPO neuroinflammation markers and myelin load in the TgF344-AD rat model of Alzheimer’s disease\u003c/p\u003e","fulltext":[{"header":"1.\tBACKGROUND","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a chronic and a neurodegenerative disorder that affects both cognitive and non-cognitive functions, memory and behaviour in humans. Traditionally, AD is defined by A\u0026beta; plaque formation and accumulation of tau-based neurofibrillary tangles in the brain (1). However, while neuronal damage is a hallmark of AD, growing evidence highlights the critical role of glial cell activation on disease progression \u003cspan lang=\"EN-US\"\u003e(2,3)\u003c/span\u003e. In AD, misfolded A\u0026beta; and tau proteins bind to pattern recognition receptors, triggering an innate immune response and releasing inflammatory mediators. Activated microglia migrate toward A\u0026beta; plaques and phagocytose them, revealing a functional connection to AD pathology (4). However, prolonged microglial activation impairs A\u0026beta; clearance, resulting in sustained inflammation, neuronal injury, and a self-perpetuating cycle of activation (5\u0026ndash;7). Imaging of activated microglia is achieved by targeting molecular markers that are overexpressed during neuroinflammation. The 18-kDa translocator protein (TSPO) is one of the most commonly used targets for detecting activated microglia, with TSPO PET widely regarded as the gold standard for imaging of neuroinflammation (8\u0026ndash;10). However, extensive research has also recognized certain drawbacks that limit TSPO as the definitive target for inflammation (11\u0026ndash;17). Alternatively, various purinergic ion channel receptor subtypes (P2X) have been explored as potential targets for imaging microglia activation (18,19). Among these, the P2X7 receptor (P2X7R) is notably expressed in microglia, astrocytes, and Schwann cells, both peripherally and in the CNS (20). Typically silent under normal conditions, P2X7R is upregulated during pathological ATP imbalances and is linked to pro-inflammatory cytokine release (21). To this, several PET tracers targeting P2X7R have been developed as potential alternatives to TSPO for imaging brain inflammation, including the [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 radiotracer (11,22\u0026ndash;26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough AD has long been associated with grey matter neuronal loss, white matter (WM) degeneration and demyelination are increasingly recognized as relevant pathological features (27\u0026ndash;30). Myelin is produced and maintained by oligodendrocytes, which wrap around axons to form myelin sheaths that provide electrical insulation (31). Myelin degradation during AD severely disrupts neural communication, leading to axonal death (32). This, in turn, results in slower neural processing and impaired cognitive abilities\u0026mdash;key hallmarks of Alzheimer\u0026rsquo;s progression. Some studies suggest that myelin damage may precede the appearance of other hallmark features of Alzheimer\u0026rsquo;s disease, although the mechanisms\u0026mdash;such as oligodendrocyte death, impaired repair, or exposure to neurotoxins\u0026mdash;remain to be fully elucidated (30). Chronic neuroinflammation, marked by sustained activation of microglia and astrocytes, has also been suggested to affect WM tracts in AD, leading to demyelination (33\u0026ndash;35). Particularly, pro-inflammatory cytokines released by microglia, astrocytes, and infiltrating immune cells have been shown to induce myelin loss (36). Yet, the exact timeline of events through which neuroinflammation appears to induce demyelination during AD remains unclear. Myelin imaging via PET has become feasible using tracers that bind to beta-sheet structures of the myelin basic protein (MBP), allowing \u003cem\u003ein vivo\u003c/em\u003e quantification (37). However, few studies have combined myelin PET with other radiotracers to explore the interplay between demyelination and other pathological processes over time (38).\u003c/p\u003e\n\u003cp\u003eIn the present study, we aimed to describe the dynamics of neuroinflammation and myelin loss simultaneously in the context of AD. For this, we employed the TgF344-AD rat model, currently one of the most accurate models in replicating human AD pathology, exhibiting increased A\u0026beta; and Tau pathology over time, along with sustained neuroinflammation and neuronal loss (39). We performed \u003cem\u003ein vivo\u003c/em\u003e longitudinal PET imaging in TgF344-AD rats and age-matched wild-type animals using [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 (P2X7R), [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 (TSPO) and [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben (myelin). Additionally, we employed magnetic resonance imaging (MRI), as well as\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e techniques, providing new insights into the temporal resolution of these key pathophysiological hallmarks during disease progression.\u003c/p\u003e"},{"header":"2.\tMETHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1. Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale TgF344-AD rats (n = 18) were purchased from the Mutant Mouse Resource and Research Centre (MMRRC), the Rat Resource and Research Centre (RRRC), and the MU Metagenomics Centre (Columbia, MO, USA). Female wild-type F344 rats (n = 12) were purchased from Janvier Labs (France). Animals arrived at our facilities at the age of 11 weeks and were housed in groups of 2-3 per cage with individual ventilation, environmental enrichment, constant access to food and water and a 12:12 hour cycle of light and dark. Imaging studies were performed at different ages (as defined below) during light phase of the light\u0026ndash;dark cycle. Animal weights were recorded before each PET imaging session, during the longitudinal study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRats were divided in two groups: AD group (TgF344-AD rats, n = 18) and control group (wild-type F344 rats, n = 12) (see Figure 1 for experimental design). Four to ten animals per group and per radiotracer were scanned using PET at each time point, in line with previous studies (see Table S1 in the supporting information for details on animal distribution) (40). In general, the same animals were scanned longitudinally throughout the study, except those that were retrieved for tissue collection, met humane endpoint criteria or died spontaneously (see Figures S1 and S2 and Table S2 for detailed information). At each time point, animals were scanned using tracers from the same production (same batch). In cases where some animals could not be scanned on the same day, a second tracer production was performed the following day to accommodate the remaining animals. Notably, no animal was scanned on two consecutive days. All animals surviving at 22 months in both groups underwent structural and diffusional MRI scanning. Brain tissue was collected from selected animals at different ages and used for \u003cem\u003eex vivo\u003c/em\u003e analysis at specific time points (see below). Female rats were chosen for their higher prevalence to the disease, their moderate weight at older ages (facilitating \u003cem\u003ein vivo\u003c/em\u003e imaging) and lower aggressively compared to males. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Longitudinal PET-CT studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePET-CT studies were performed using \u0026beta;- and X-cube microsystems (Molecubes, Belgium) with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 at 8, 15 and 22 months of age, with [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 at 22 months of age and with [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben at 4, 8, 12, 16 and 20 months of age for both groups (AD and control) (see radiosynthesis details in the supporting information). In all cases, anaesthesia was induced with isoflurane (3.0 \u0026ndash; 5.0%) in pure oxygen, and maintained during imaging studies at 1.5 \u0026ndash; 2.0% of isoflurane in pure oxygen. Prior to image acquisition, anesthetized animals were injected intravenously in the tail vein with a saline solution (max. 10% EtOH) of either [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 (3.5 \u0026plusmn; 0.3 MBq), [\u003csup\u003e18\u003c/sup\u003eF]DPA714 (3.6 \u0026plusmn; 0.2 MBq) or [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben (4.0 \u0026plusmn; 1.0 MBq) (see Tables S3-S5 for a detailed injection list). For [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 and [\u003csup\u003e18\u003c/sup\u003eF]DPA714 studies, dynamic PET imaging was started immediately before injection of the radiotracer with a total scan time of 60 minutes (frames: 1 x 5s, 10 x 60s, 4 x 300s, 2 x 600s, 1 x 595s). For [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben studies, 15-min static PET images were acquired 40 minutes post-injection. In this case, animals were allowed to recover from anaesthesia between tracer administration and image acquisition. All scans were acquired in list mode, one bed position and a field of view (FOV) of 100 mm ranging from the nose to the kidneys of the animal. A 5-min CT scan (X-Ray energy: 40 kV; intensity: 140 \u0026mu;A) was acquired immediately after each PET scan. PET images were reconstructed with OSEM-3D iterative algorithm and applying random, scatter and attenuation corrections. Once reconstructed, images were analysed using PMOD image analysis software (PMOD Technologies Ltd, Zurich, Switzerland). Volumes of interest (VOIs) were delineated in different brain regions: cortex (CTX), hippocampus (HIPP), striatum (STR), cerebellum (CB) and brain stem (BS), using the Schiffer-T2 MRI template (Figure S4). For dynamic PET scans, time activity curves were obtained for each region and expressed as standardized uptake values (SUV). For [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739, averaged SUVs relative to the cerebellum (SUVR\u003csub\u003eCB\u003c/sub\u003e) were calculated 40 to 60 min post-injection in the selected regions and for every animal. Whole-brain single PET images were obtained by averaging individual SUVR\u003csub\u003eCB \u003c/sub\u003eimages at every time point in each group. For [\u003csup\u003e18\u003c/sup\u003eF]DPA-714, SUVs were calculated 45 to 60 min post-injection in the selected regions and for every animal. Whole-brain single PET images were obtained by averaging individual SUV\u003csub\u003e \u003c/sub\u003eimages in each group. In the case of [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben, SUVR\u003csub\u003eCB\u003c/sub\u003e values were obtained in the selected regions for every animal. Whole-brain single PET images were obtained by averaging individual SUVR\u003csub\u003eCB \u003c/sub\u003eimages at every time point in each group. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Brain tissue processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimals used for \u003cem\u003eex vivo\u003c/em\u003e analysis were culled from the same cohort as those involved in the imaging studies for both AD and WT groups. Animals were euthanized and transcardially perfused with saline (50 mL, 0.9 % NaCl solution) to collect their brains, separating the two hemispheres through the longitudinal fissure using a blade. The left hemisphere was directly frozen in isopentane (2-methylbutane; Sigma-Aldrich) on dry ice and stored at -80\u0026ordm;C. The right hemisphere was fixed in a 4% formaldehyde solution at 4\u0026ordm;C for 24 hours, subsequently transferred to a 30% sucrose solution for 48 hours at 4\u0026deg;C and stored at \u0026ndash;80\u0026ordm;C afterwards. Coronal sections of either 20 \u0026mu;m (left hemisphere) or 10 \u0026mu;m (right hemisphere) were cut using a Cryomicrotome (Leica CM3050S, Germany), collected on a glass slide (Superfrost Ultra Plus; Thermo Fisher) and stored at \u0026ndash;80\u0026ordm;C before further analysis. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. \u003cem\u003eIn vitro\u003c/em\u003e autoradiography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain tissue from two animals of the AD group (23 months old) and two of the control group (22 months old) was subjected to \u003cem\u003ein vitro\u003c/em\u003e autoradiography studies with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739. One animal per group was used at the same ages for [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 studies. For [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben, brain tissue from two young AD animals (10 months), two old AD (23 months) and two old WT (22 months) was used. For each animal, one representative slide containing 4-5 coronal brain slices (20 \u0026mu;m) from the left hemisphere including cortex and striatum (\u003cem\u003eca.\u003c/em\u003e +0.20 from Bregma), one slide including cortex and hippocampus (\u003cem\u003eca.\u003c/em\u003e -2.30 and \u003cem\u003eca.\u003c/em\u003e -4.0 from Bregma) and one slide including cerebellum and brain stem (\u003cem\u003eca.\u003c/em\u003e -10.04 from Bregma) were thawed, dried and pre-incubated for 15 min with Tris-HCl buffer (50 mM, pH 7.4, supplemented with 1 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 1 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 2 mM KCl and 1% of bovine serum albumin) at room temperature. Subsequently, the slices were incubated in a Tris-HCl buffer (50 mM, pH 7.4) solution containing either [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739, [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 or [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben (0.5 MBq/mL) for 30 min at room temperature. For the determination of non-specific binding, successive slices were additionally incubated in a 10 \u0026mu;M solution of the corresponding non-labelled reference compound. After incubation, the slices were removed from the bath, washed for 10 minutes in ice-cold buffer (50 mM Tris-HCl, pH 7.4, 4\u0026ordm;C) and dipped once in ice-cold ultra-pure water. After drying over a heating plate (1 min, 40\u0026deg;C), the slices were exposed to a phosphor sensitive plate for 5 minutes and the plate was scanned in a phosphor imager (Amersham Typhoon 5, GE, USA) at the highest resolution (10 \u0026mu;m). For image quantification, regions of interest (cortex, hippocampus, striatum, cerebellum and brain stem) were manually drawn for each slice with specific software (ImageQuantTL, Cytiva, USA) using a rat brain atlas as reference (Paxinos G., 2013). Averaged pixel intensity values were obtained per each region as the average of 4-5 replicates, and ratio values calculated per each animal and region using the cerebellum as reference. Percentage of self-blocking (homologous blocking) was calculated as [(No block- Block)/No block)] x 100 using the corresponding blocked tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Immunofluorescence \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreviously obtained brain sections from the right hemisphere (\u003cem\u003eca.\u003c/em\u003e -5.20 from Bregma) were fixed in 4% paraformaldehyde during 15 min, washed with phosphate-buffered saline (PBS) and incubated 5 min in NH\u003csub\u003e4\u003c/sub\u003eCl, following two PBS rinses and methanol-acetone (1:1) permeabilization during 5 min at -20 \u0026ordm;C. After PBS washing, samples were saturated with a solution of bovine serum albumin (BSA) 5%/Tween 0.5% in PBS during 15 min at room temperature and incubated with combinations of anti-Iba1 antibody (guinea pig, 1:1,000, Synaptic Systens, Goettingen, Germany), anti-GFAP (chicken, 1:1000, AbCam, Cambridge, UK), anti-P2X7 (rabbit, 1:50, Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), and anti-TSPO (rabbit, 1:1000, Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) at room temperature during 1 hour in BSA (5%)/Tween (0.5%). Then, sections were washed with PBS again for 5 min followed by incubation with the appropriate fluorescent secondary antibody (1:1000): Alexa Fluor 594-conjugated anti-rabbit IgG (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), Alexa Fluor 647-conjugated anti-guinea pig (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain), Alexa Fluor 647-conjugated anti-chicken (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) or Alexa Fluor 488-conjugated anti-rabbit secondary antibody (Invitrogen Molecular Probes, Life Technologies, Madrid, Spain) and DAPI (D9564, Sigma-Aldrich) at room temperature for 30 min. Immunoreaction controls were carried out by omission of the primary antibodies. For X34 staining (amyloid plaques), sections were additionally incubated with a X34 10 \u0026micro;M solution (Sigma-Aldrich) in 70% EtOH for 10 minutes at room temperature and washed with 0.1 M PBS. Finally, sections were mounted with Fluoromont-G\u0026reg; (SouthernBiothech, AL, USA) and dried for 24 hours at room temperature. Structured illumination fluorescent images were obtained using a Zeiss AxioImager Z1 with attached ApoTome (Carl Zeiss Microimaging) using a x20 or a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. \u003cem\u003e \u003c/em\u003eThioflavin staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThioflavin staining was used to visualize fibrillar amyloid plaques. Previously obtained brain slices from a 23-month-old TgF344-AD rat (n = 1) corresponding to the right hemisphere were fixed with 4% paraformaldehyde for 15 min and rinsed with 0.1 M PBS 5 min 3 times. Sections were then incubated with 0.01% Thioflavin S in 70% ethanol, diluted 1:10 in 0.1 M PBS for 10 min, washed with 0.1 M PBS and mounted with Fluoromont-G\u0026reg; (SouthernBiothech, AL, USA). Immunoreaction controls were carried out by omission of the staining solution. After drying for 24 hours, images of the whole slice containing the cortex and hippocampus were acquired with a Zeiss AxioImager Z1 using a x20 objective in tile mode. Detailed images of plaques were obtained with a Zeiss AxioImager Z1 attached ApoTome using a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. \u003cem\u003e In vitro\u003c/em\u003e fluorescence assay with Florbetaben\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStaining with non-labelled Florbetaben was performed in coronal brain slices from a 10-month-old TgF344-AD rat (n = 1) and a 22-month-old WT animal (n = 1) corresponding to the right hemisphere. First, tissue autofluorescence was minimized by treatment of sections with 0.25% KMnO\u003csub\u003e4\u003c/sub\u003e/PBS for 20 min prior to washing (0.1M PBS) and incubation with 1% K\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e/1% oxalic acid/PBS for 5 min. Following autofluorescence quenching, sections were blocked in 2% BSA/PBS pH 7.0 for 10 min and stained with 10 \u0026mu;M Florbetaben for 30 min. Finally, sections were washed in 0.1M PBS and mounted with Fluoromont-G\u0026reg; (SouthernBiothech, AL, USA). After drying for 24 hours, images of the whole slice containing the cortex and hippocampus were acquired with a Zeiss AxioImager Z1 using a x20 objective in tile mode. Detailed images of plaques were obtained with a Zeiss AxioImager Z1 attached ApoTome using a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9. \u003cem\u003e \u003c/em\u003eLuxol Fast Blue staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess myelin and neuronal cell bodies, a combined Luxol Fast Blue and Cresyl Fast Violet staining protocol was used with coronal brain sections from the right hemisphere (\u003cem\u003eca.\u003c/em\u003e -2.30 from Bregma) of a 19-month old AD rat and a 20-month old WT rat. First, tissue sections were dehydrated down to 95% ethanol, followed by overnight incubation in a 1:1 mixture of absolute EtOH and chloroform at room temperature. Sections were then stained with 0.1% Luxol Fast Blue at 50-56\u0026deg;C for 6-7 hours. After staining, sections were washed in 95% EtOH and rinsed in distilled water. Differentiation was carried out using 0.05% lithium carbonate for 90 seconds, followed by a brief incubation in 70% EtOH for 30 seconds and another rinse in distilled water. Next, sections were stained with 0.1% Cresyl Fast Violet for 2 minutes, with a brief heating step of 30 seconds to enhance staining. Finally, dehydration was performed through 95% EtOH for 5 minutes, followed by two successive incubations in absolute EtOH (5 minutes each). Sections were cleared in xylene (two changes, 3 minutes each) and mounted using DPX mounting medium (Sigma-Aldrich). Images were obtained using a Zeiss AxioImager Z1 with attached ApoTome (Carl Zeiss Microimaging) using a x20 or a x40 objective. Image composition was carried out using ImageJ software (Version 2, NIH, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10. Magnetic Resonance Imaging (MRI) acquisition and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven animals from each group (AD and WT) were scanned for diffusion tensor imaging (DTI) MRI at 22 months. \u003cem\u003eIn vivo\u003c/em\u003e MRI studies were performed on a 7\u0026thinsp;T horizontal bore Bruker Biospec USR 70/30 MRI system (Bruker Biospin GmbH, Ettlingen, Germany), interfaced to an AVANCE III console, and with a BGA12-S imaging gradient insert (maximal gradient strength 400\u0026thinsp;mT/m, switchable within 80\u0026thinsp;\u0026micro;s). These measurements were performed with a 72-mm volumetric quadrature coil for excitation and a 20-mm rat brain surface coil for reception. Animals were anesthetized with isoflurane (4% induction, 1.5\u0026ndash;2% maintenance) in a 50:50 oxygen/nitrogen mixture and positioned in a stereotaxic holder. Body temperature was maintained at 37 \u0026plusmn; 0.5 \u0026deg;C using a temperature-controlled air stream (SAII Instruments, model M1030), which also monitored respiration and temperature throughout the session. MRI data acquisition and reconstruction were performed using ParaVision 6.0.1 (Bruker Biospin GmbH, Ettlingen, Germany). Imaging Protocol included the acquisition of: 1) a T2-weighted image acquired using a 3D RARE sequence (TR = 1800 ms, TE = 32 ms, RARE factor = 8, 1 average). The field of view (FOV) was 25.6 \u0026times; 25.6 \u0026times; 14 mm\u0026sup3; with a matrix of 128 \u0026times; 128 \u0026times; 70, yielding an isotropic resolution of 0.2 mm\u0026sup3;. Fat suppression was applied using a Gaussian pulse (bandwidth: 1050 Hz), and acquisition bandwidth was 100 kHz. Total scan time: ~29 minutes. 2) Diffusion Tensor Imaging was acquired using a spin-echo EPI sequence (TR = 7500 ms, TE = 26 ms, 2 segments, 1 average). A total of 75 volumes were acquired: 60 diffusion directions (b = 800 s/mm\u0026sup2;) and 15 b0 images. Gradient duration: 4 ms; separation: 12 ms. FOV: 30 \u0026times; 30 mm\u0026sup2;; matrix: 128 \u0026times; 128; 20 slices (0.7 mm thickness, 0.1 mm gap); in-plane resolution: 234 \u0026times; 234 \u0026micro;m\u0026sup2;. Bandwidth: 220 kHz. Fat suppression: Gaussian pulse (1050 Hz). Total scan time: ~18 min 45 s. Diffusion metrics (FA, MD, AD, RD) were computed using the DIPY library (41). The processing pipeline included: 1) Denoising: MPPCA denoising (42) using code from https://github.com/NYU-DiffusionMRI/mppca_denoise. 2) Tensor Estimation: Motion correction and tensor fitting using DIPY. 3) Bias Field Correction: N4ITK algorithm applied to anatomical images (43). 4) Brain Extraction: rBET, a rodent-optimized version of BET (44). 5) Image Registration: Two-step registration using ANTs: affine + nonlinear (SyN) to align T2-weighted images to a modified Waxholm Space atlas (45). DTI b0 images were rigidly registered to T2-weighted images. 6) Atlas Reference: A modified version of the Waxholm Space atlas was used, grouping small ROIs to improve alignment and analysis. A structured pipeline was implemented to extract and analyze regional diffusion metrics, consisting in preprocessing of T2-weighted images (bias correction, brain extraction). Followed by incorporation of FA, MD, AD, and RD maps into the pipeline, nonlinear registration of T2 images to atlas space and propagation of anatomical labels, rigid registration of b0 images to T2-weighted images and transfer of labels to diffusion maps, and extraction of regional mean values for each diffusion metric.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor PET imaging, statistical analysis was performed in GraphPad Prism 9 (GraphPad Software, CA, USA). PET values, pixel intensity values from autoradiography and FA values were analysed using a two-tailed unpaired t-test with Welch\u0026rsquo;s correction. Differences between groups (AD vs WT) at each time point and differences between time points within the same group were studied, calculated as percentage differences. No outliners were removed. Differences were concluded significant for p values \u0026lt; 0. 05: p \u0026lt; 0.05, \u003csup\u003e*\u003c/sup\u003e; p \u0026lt; 0.01, \u003csup\u003e**\u003c/sup\u003e, p \u0026lt; 0.001, \u003csup\u003e***\u003c/sup\u003e; and p \u0026lt; 0.0001, \u003csup\u003e****\u003c/sup\u003e. For MRI imaging, robustness of the data was ensured by an outlier detection step applied to each DTI metric (FA, MD, AD, RD) across all brain regions and experimental groups. Outliers were defined using the interquartile range (IQR) method, as values falling below Q1 \u0026minus; 1.5\u0026times;IQR or above Q3 + 1.5\u0026times;IQR within each group and region, and were excluded from further analysis (46).To compare DTI metrics between WT and AD groups across multiple brain regions, we conducted a region-wise statistical analysis. For each DTI parameter, we first assessed the normality of the data distributions within each group using the Shapiro-Wilk test and evaluated homogeneity of variances using Levene\u0026rsquo;s test. Based on these assumptions, we applied either an independent samples t-test assuming equal variances, Welch\u0026rsquo;s t-test when variances were unequal, or the non-parametric Mann-Whitney U test when normality was not met. All comparisons were two-tailed, as no directional hypotheses were assumed a priori.\u003c/p\u003e"},{"header":"3.\tRESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1. Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our hands, female TgF344-AD rats appeared very resistant even after multiple imaging sessions under anaesthesia (between 6 to 9 sessions per animal) over a 24-month lifespan, demonstrating the robustness of the model in extensive longitudinal studies. TgF344-AD female rats exhibited no mortality up to 18 months of age (Figure S1, Table S2). Subsequently, survival probability decreased as spontaneous conditions necessitated the application of humane endpoints. Additionally, two rats died after radiotracer injection at 17 months, with an incompatible dose formulation suspected but not confirmed as the cause; these animals were excluded from survival analysis. Wild-type F344 rats displayed spontaneous mortality only after 19 months, with a marked decrease in survival probability observed after 23 months. Survival rates between groups did not differ significantly (p = 0.8940, Log-rank Mantel-Cox test). Transgenic females presented similar rates to those previously reported in males at 19 months, whereas wild-type age-matched females exhibited higher survival compared to males (40,47). Rats in both groups were easy to handle at all ages, although a moderate anxiety-like behaviour was noticeable when handled for extended periods, a trait previously reported for this strain and model (48,49). Body weights were significantly higher in WT rats compared to TgF344-AD rats only at 4 months of age (p = 0.0026, two-tailed unpaired t-test with Welch\u0026rsquo;s correction); no significant differences were observed at later time points (Figure S3). Notably, old TgF344-AD rats exhibited an average weight loss of 8% after 20 months of age, which was not observed in control littermates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Longitudinal [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 PET imaging \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the neuroinflammatory response over time, we performed longitudinal [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 PET imaging to monitor \u003cem\u003ein vivo\u003c/em\u003e P2X7R expression, in both AD and WT rats at 8, 15 and 22 months of age (see Figure 2-A for representative images). SUV time activity curves (TACs) were obtained in different brain regions (cortex, hippocampus, striatum, brain stem and cerebellum) for each time point and group (Figure S5). In all cases, we observed a rapid tracer uptake with curves peaking at 10-15 seconds before reaching steady-state values at \u003cem\u003eca.\u003c/em\u003e 5 min post-injection. For analysis, we statistically compared normalized uptake (SUVR, cerebellum as a pseudo-reference region) of [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 between time points (age effect) and between groups (genotype effect), following previous reports (Figure 2-B) (25). Although P2X7R expression appeared lower in the cerebellum compared to other regions, this region is not entirely devoid of receptors, therefore we suggested using this area as a pseudo-reference region in our analysis as previously reported (50). In AD animals, increased tracer uptake was detected from 8 to 15 months in the cortex (+11 \u0026plusmn; 8%, p = 0.0004), hippocampus (+9 \u0026plusmn; 17%, p = 0.0125), striatum (+14 \u0026plusmn; 13%, p \u0026lt; 0.0001) and brain stem (+8 \u0026plusmn; 13%, p = 0.0008). At 22 months, similar results were observed in the cortex (+9 \u0026plusmn; 11%, p = 0.0311), hippocampus (+9 \u0026plusmn; 12%, p = 0.0029) and striatum (+15 \u0026plusmn; 2%, p = 0.0025) but not in the brain stem (0 \u0026plusmn; 17%, p = 0.4338), compared to 8 months. A similar increasing trend was observed in the control group in the cortex (15mo. +10 \u0026plusmn; 1%, p = 0.0250; 22mo. +7 \u0026plusmn; 1%, p = 0.1745) and hippocampus (15mo. +15 \u0026plusmn; 3%, p = 0.0433; 22mo. +19 \u0026plusmn; 4%, p = 0.0802), while differences in the striatum were significant only at 22 months of age (15mo. +6 \u0026plusmn; 1%, p = 0.3345; 22mo. +20 \u0026plusmn; 4%, p = 0.0395). No significant differences were detected in the brain steam for control animals (15mo. +3 \u0026plusmn; 1%, p = 0.5757; 22mo. +4 \u0026plusmn; 1%, p = 0.6419) compared to the initial time point. We did not identify significant genotype differences between AD and WT animals in any of the regions that we studied, suggesting that longitudinal changes in P2X7R expression were mainly driven by aging effects in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 \u003cem\u003ein vitro\u003c/em\u003e autoradiography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-resolution autoradiography with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 revealed widespread tracer binding in both AD and WT tissue, consistent with \u003cem\u003ein vivo\u003c/em\u003e observations. Notably, higher uptake was observed in the corpus callosum \u003cem\u003eex vivo\u003c/em\u003e, although this region was not specifically quantified in the \u003cem\u003ein vivo\u003c/em\u003e analysis (Figure 2C-D). This result is consistent with previous reports of the widespread distribution of the P2X7 receptor throughout the brain, although it also shows a degree of off-target binding of the radiotracer to white matter (20). Higher binding, measured as ratio to the cerebellum, was detected in AD tissue compared to controls in the cortex (+6 \u0026plusmn; 1%, p = 0.7787), hippocampus (+24 \u0026plusmn; 3%, p = 0.3324) and brain stem (+11 \u0026plusmn; 1%), but not in the striatum (+17 \u0026plusmn; 3%, p = 0.6998) (Figure 2-E). [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 binding was found to be highly specific (\u003cem\u003eca.\u003c/em\u003e 80% blocking) under incubation with non-labelled reference (homologous blocking) in the entire tissue for both AD and WT, including in the corpus callosum (Figure S7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enable comparison, neuroinflammation was assessed in both AD and WT rats at a later stage (22 months) using the gold-standard PET tracer [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 (see Figure 3-A for representative images). SUV time activity curves (TACs) were obtained in different brain regions (cortex, hippocampus, striatum, brain stem and cerebellum) for each time point and group (Figure S6). In all cases, we observed a slow tracer uptake with curves peaking at 10-15 minutes with steady wash-out thereafter. For analysis, SUV obtained 45 to 60 minutes post-injection were used to compare uptake between groups (genotype effect) (Figure 3-B). While previous studies have proposed using the cerebellum as a reference region for [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET, in our study we observed significantly higher uptake in this region for AD animals compared to WT, suggesting that the cerebellum may not be completely free of TSPO expression (see below) (40). At 22 months, significant genotype differences were found between AD and WT rats in all of the brain regions including cortex (+72 \u0026plusmn; 0.2%, p = 0.0001), hippocampus (+61 \u0026plusmn; 0.3%, p = 0.0020), striatum (+56 \u0026plusmn; 0.2%, p = 0.0020), brain stem (+44 \u0026plusmn; 0.3%, p = 0.0076) and cerebellum (+61 \u0026plusmn; 0.2%, p = 0.0003), revealing a distinct neuroinflammatory response in the TgF344-AD rat model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 \u003cem\u003ein vitro\u003c/em\u003e autoradiography \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAutoradiography images with [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 showed widespread tracer binding, with markedly increased uptake observed in white matter-rich areas such as the corpus callosum in both AD and WT tissue (Figure 3 C-D). We found significant higher binding intensity of [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 in AD tissue compared to WT in all brain areas, including the cerebellum, supporting the obtained \u003cem\u003ein vivo\u003c/em\u003e data (Figure 3-E). This finding limited the use of the cerebellum as a true reference region for [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET analysis in our study. [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 binding was found to be specific (\u003cem\u003eca.\u003c/em\u003e 30% blocking) under incubation with non-labelled reference (homologous blocking) in all brain tissue for both AD and WT (Figure S8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Immunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo support the \u003cem\u003ein vivo\u003c/em\u003e results, immunohistochemistry studies were conducted with brain tissue collected at the endpoint of the study for both groups (AD, 23 months; WT, 22 months) using Iba1 (microglia), anti-P2X7R and anti-TSPO antibodies. Qualitative image analysis showed an abundance of round-shaped (amoeboid) Iba1-positive microglia in the hippocampus of AD animals (Figure 4-A), in contrast to the larger, more ramified microglial processes observed in control tissue cells (Figure 4-B), suggesting increased microglial reactivity in the AD group. P2X7R-positive cells were detected in both AD and WT tissue. In contrast, TSPO-positive cells were more abundant in AD animals compared to control (Figure 4-C-D). A neuroinflammatory response characterized by Iba1-positive microglia and GFAP-positive astrocytes was observed surrounding amyloid plaques in AD tissue, with P2X7R and TSPO expression co-localizing with microglia but not with astrocytes (Figure 4 E-H). Microglia were present in both grey and white matter in the cerebellum, with apparent lower P2X7R expression in the white matter (Figure S10). This finding suggests that [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 uptake in WM-rich areas (\u003cem\u003ee.g.\u003c/em\u003e, corpus callosum) is likely due to off-target binding to lipid structures rather than to P2X7R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7. Longitudinal [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben PET imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]Florbetaben PET was used to assess \u003cem\u003ein vivo\u003c/em\u003e myelin levels at 4, 8, 12, 16 and 20 months in both groups (see Figure 5-A for representative images). The use of stilbene-derivative \u0026beta;-amyloid tracers (i.e., [\u0026sup1;⁸F]Florbetaben) as myelin markers has been previously proposed, presenting an opportunity to repurpose these radiotracers for studying demyelinating disorders, including Alzheimer\u0026rsquo;s disease (51). In this sense, it has been shown that planar-like stilbenes bind to proteins or aggregates displaying a particular molecular conformation with adjacent beta-sheet structures (52,53). Given that these are found in both amyloid plaques and the myelin basic protein (MBP), [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben can potentially serve as a marker of myelin \u003cem\u003ein vivo\u003c/em\u003e. Longitudinal [\u0026sup1;⁸F]Florbetaben PET imaging revealed no significant differences between groups in traditionally plaque-rich regions, such as the cortex and hippocampus, over time (Figure 5 B-C). This aligns with previous studies with this radiotracer, which reported only slight differences between TgF344-AD rats and wild-type littermates at 18 months of age in these regions (40). We then focused on the image quantification of two white matter-containing areas: brain stem, and to a lesser extent, the striatum, for the study of myelin content \u003cem\u003ein vivo \u003c/em\u003e(Figure 5 D-E). SUVR analysis (cerebellum as reference region) in the striatum revealed an increase in tracer uptake for the AD group from 4 to 16 months (+7%, p = 0.0875), followed by a decrease from 16 to 20 months (-10%, p = 0.0067). Significant differences to control animals were found only at 12 months (p = 0.0378) and 20 months (p = 0.0006) in this region. In the brain steam, a significant age effect was observed in AD animals, with [\u0026sup1;⁸F]Florbetaben uptake increasing by 11% from 4 to 12 months (p = 0.0009). Interestingly, a decrease of 10% (p = 0.0010) was detected from 12 to 20 months in the same animals. Significant differences were also observed from 4 to 16 months (p = 0.0042), from 8 to 12 months (p = 0.0057) and from 16 to 20 months (p = 0.0044). Conversely, WT rats plateau at 8 and 12 months with an increase of 12% from 4 months (p = 0.0051 and p = 0.0136, respectively), while no significant decrease was observed in 20-month-old animals compared to those at 12 months (-4%, p = 0.4070). A pronounced genotype effect between groups was found in the brain stem at 8 months (p = 0.0047) compared to animals at 20 months (p = 0.0395), suggesting that differences in myelin load are bigger at younger ages compared to later time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8. [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben \u003cem\u003ein vitro\u003c/em\u003e autoradiography \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual analysis of high-resolution autoradiography images obtained with [\u0026sup1;⁸F]Florbetaben revealed widespread binding of the tracer, with higher retention in white matter-rich areas including the corpus callosum, cerebellum and brain stem (Figure 5 F-H). Binding ratio to the cerebellum was used for comparative purposes between AD and WT tissue and between young (10 month) and old (19 month) AD animals (Figure 5-I). Overall, no significant differences were observed between groups within any of the brain regions analysed, although overall binding in the brain steam was higher compared to the cortex and hippocampus. Interestingly, [\u0026sup1;⁸F]Florbetaben binding in the brain steam was 14% lower in 19-month-old AD animals compared to 10-month-old AD tissue and 17% lower compared to age-matched WT animals. This finding supports the idea of myelin loss at advanced stages in AD animals, as previously suggested \u003cem\u003ein vivo\u003c/em\u003e. Homologous blocking using non-labelled Florbetaben revealed specific binding in plaque-rich regions, such as the cortex (blocking = 25%) and hippocampus (blocking = 23%) in 10-month-old AD tissue, which likely illustrates the specificity of [\u0026sup1;⁸F]Florbetaben to amyloid-plaques (Figure S9). Conversely, binding displacement was not detected in white matter-rich areas such as the cerebellum (blocking = -13%), the brain stem (blocking = -6%) and the corpus callosum (not quantified). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9. Thioflavin staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the lack of differences in plaque-rich areas observed with [\u0026sup1;⁸F]Florbetaben PET between AD and control animals, we performed \u003cem\u003ein vitro\u003c/em\u003e Thioflavin staining to confirm the presence of amyloid plaque pathology in our transgenic animals (Figure S11). Staining for amyloid plaques was positive in 23-month-old AD tissue, predominantly in the cortex and hippocampus, and to a lesser extent in the cerebellum. We found that plaque loading in TgF344-AD rats was qualitatively lower compared to other more aggressive AD mouse models (54). This may explain the low signal-to-noise ratio observed \u003cem\u003ein vivo \u003c/em\u003ewith [\u0026sup1;⁸F]Florbetaben. Additionally, we did not observe plaque deposition in the brain stem of AD animals, suggesting that [\u0026sup1;⁸F]Florbetaben uptake in this region likely corresponds to myelin binding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10. Florbetaben staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike other stilbene derivatives, Florbetaben exhibits spontaneous fluorescence when excited by light at approximately 480 nm, enabling analysis of its tissue uptake via fluorescence imaging (55,56). Binding of Florbetaben to amyloid plaques was confirmed on whole brain slices of AD animals, mainly in the cortex and the hippocampus, as previously seen \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro \u003c/em\u003e(Figure S12). Images also revealed the anticipated binding of Florbetaben to white matter rich areas such as the corpus callosum, striatum, thalamus, cerebellum and brain steam in both AD and WT tissue. These results qualitatively support Florbetaben\u0026rsquo;s dual binding affinity for both amyloid plaques and white matter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.11. Luxol Fast Blue staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuxol Fast Blue was used to visualize myelin content in coronal brain sections of both AD and WT rats (Figure S13). Qualitative analysis of images revealed a similar distribution of myelin in TgF344-AD and control animals in the corpus callosum, striatum and cortex, with no apparent differences in myelin content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.12. Diffusion tensor imaging MRI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFractional anisotropy values were calculated with diffusion tensor imaging in AD and WT rats at 22 months in 25 brain regions segmented as described in the methods section. (Figure 6). Fractional anisotropy, showed significant differences between WT and AD animals in the following regions: Cortex, FA\u003csub\u003eWT\u003c/sub\u003e = 0.215, FA\u003csub\u003eAD\u003c/sub\u003e = 0.258, \u0026Delta;FA = +20.2%, p = 0.0091; Hippocampus, FA\u003csub\u003eWT\u003c/sub\u003e = 0.198, FA\u003csub\u003eAD\u003c/sub\u003e = 0.233, \u0026Delta;FA = +18.1%, p = 0.0003; Striatum, FA\u003csub\u003eWT\u003c/sub\u003e = 0.225, FA\u003csub\u003eAD\u003c/sub\u003e = 0.244, \u0026Delta;FA = +8.7%, p = 0.0424; Diencephalon, FA\u003csub\u003eWT\u003c/sub\u003e = 0.263, FA\u003csub\u003eAD\u003c/sub\u003e = 0.283, \u0026Delta;FA = +7.8%, p = 0.0021; in the Corpus callosum, FA\u003csub\u003eWT\u003c/sub\u003e = 0.454, FA\u003csub\u003eAD\u003c/sub\u003e = 0.492, \u0026Delta;FA = +8.3%, p = 0.0260; Amygdala, FA\u003csub\u003eWT\u003c/sub\u003e = 0.298, FA\u003csub\u003eAD\u003c/sub\u003e = 0.361, \u0026Delta;FA = +21.4%, p = 0.0226; Stria terminalis, FA\u003csub\u003eWT\u003c/sub\u003e = 0.278, FA\u003csub\u003eAD\u003c/sub\u003e = 0.317, \u0026Delta;FA = +14.0%, p = 0.0395. Mean diffusivity (MD x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) was significantly different in: Cingulum, MD\u003csub\u003eWT\u003c/sub\u003e = 0.712, MA\u003csub\u003eAD\u003c/sub\u003e = 0.642, \u0026Delta;MD = -9.8%, p = 0.0004; Hindbrain, MD\u003csub\u003eWT\u003c/sub\u003e = 0.801, MA\u003csub\u003eAD\u003c/sub\u003e = 0.760, \u0026Delta;MD = -5.2%, p = 0.0370; Diencephalon, MD\u003csub\u003eWT\u003c/sub\u003e = 0.741, MA\u003csub\u003eAD\u003c/sub\u003e = 0.710, \u0026Delta;MD = -4.2%, p = 0.0147; Internal capsule, MD\u003csub\u003eWT\u003c/sub\u003e = 0.731, MA\u003csub\u003eAD\u003c/sub\u003e = 0.705, \u0026Delta;MD = -3.6%, p = 0.0260; Hippocampus, MD\u003csub\u003eWT\u003c/sub\u003e = 0.758, MA\u003csub\u003eAD\u003c/sub\u003e = 0.729, \u0026Delta;MD = -3.9%, p = 0.0007; Pallidum, MD\u003csub\u003eWT\u003c/sub\u003e = 0.688, MA\u003csub\u003eAD\u003c/sub\u003e = 0.661, \u0026Delta;MD = -3.9%, p = 0.0016; Cerebellum white matter, MD\u003csub\u003eWT\u003c/sub\u003e = 0.758, MA\u003csub\u003eAD\u003c/sub\u003e = 0.726, \u0026Delta;MD = -4.1%, p = 0.0410; Nucleus accumbens, MD\u003csub\u003eWT\u003c/sub\u003e = 0.680, MA\u003csub\u003eAD\u003c/sub\u003e = 0.646, \u0026Delta;MD = -5.1%, p = 0.0016. In relation to radial diffusivity (RD x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e), significant differences were observed in the following regions: Cingulum, RD\u003csub\u003eWT\u003c/sub\u003e = 0.503, RD\u003csub\u003eAD\u003c/sub\u003e = 0.400, \u0026Delta;RD = -20.5%, p = 0.0000; Midbrain, RD\u003csub\u003eWT\u003c/sub\u003e = 0.733, RD\u003csub\u003eAD\u003c/sub\u003e = 0.705, \u0026Delta;RD = -3.8%, p = 0.0188; Hindbrain, RD\u003csub\u003eWT\u003c/sub\u003e = 0.653, RD\u003csub\u003eAD\u003c/sub\u003e = 0.614, \u0026Delta;RD = -6.0%, p = 0.0051; Septum, RD\u003csub\u003eWT\u003c/sub\u003e = 0.747, RD\u003csub\u003eAD\u003c/sub\u003e = 0.686, \u0026Delta;RD = -8.1%, p = 0.0212; Diagonal domain, RD\u003csub\u003eWT\u003c/sub\u003e = 0.742, RD\u003csub\u003eAD\u003c/sub\u003e = 0.681, \u0026Delta;RD = -8.1%, p = 0.0492; Striatum, RD\u003csub\u003eWT\u003c/sub\u003e = 0.642, RD\u003csub\u003eAD\u003c/sub\u003e = 0.614, \u0026Delta;RD = -4.3%, p = 0.0098; Diencephalon, RD\u003csub\u003eWT\u003c/sub\u003e = 0.630, RD\u003csub\u003eAD\u003c/sub\u003e = 0.600, \u0026Delta;RD = -4.9%, p = 0.0372; Internal capsule, RD\u003csub\u003eWT\u003c/sub\u003e = 0.519, RD\u003csub\u003eAD\u003c/sub\u003e = 0.493, \u0026Delta;RD = -4.9%, p = 0.0111; Hippocampus, RD\u003csub\u003eWT\u003c/sub\u003e = 0.679, RD\u003csub\u003eAD\u003c/sub\u003e = 0.634, \u0026Delta;RD = -6.7%, p = 0.0000; Pallidum, RD\u003csub\u003eWT\u003c/sub\u003e = 0.597, RD\u003csub\u003eAD\u003c/sub\u003e = 0.570, \u0026Delta;RD = -4.6%, p = 0.0094; Corpus callosum, RD\u003csub\u003eWT\u003c/sub\u003e = 0.539, RD\u003csub\u003eAD\u003c/sub\u003e = 0.463, \u0026Delta;RD = -14.0%, p = 0.0095; Amygdala, RD\u003csub\u003eWT\u003c/sub\u003e = 0.663, RDAD = 0.599, \u0026Delta;RD = -9.6%, p = 0.0297; Cerebellum white matter, RD\u003csub\u003eWT\u003c/sub\u003e = 0.649, RD\u003csub\u003eAD\u003c/sub\u003e = 0.613, \u0026Delta;RD = -5.6%, p = 0.0102. Axial diffusivity (AD x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) resulted significantly different in the Cingulum, AD\u003csub\u003eWT\u003c/sub\u003e = 1.131, AD\u003csub\u003eAD\u003c/sub\u003e = 1.066, \u0026Delta;AD = -5.8%, p = 0.0231, and the Corpus callosum, AD\u003csub\u003eWT\u003c/sub\u003e = 1.139, AD\u003csub\u003eAD\u003c/sub\u003e = 1.083, \u0026Delta;AD = -4.9%, p = 0.0175.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003ePET imaging using animal models of AD enables quantitative analysis of pathophysiological aspects of AD through non-invasive longitudinal studies, offering high sensitivity and high throughput\u0026nbsp;(57). Here, we demonstrated that a single cohort of TgF344-AD rats can undergo a multi-tracer longitudinal PET study, enabling true comparisons while minimizing variability. Despite requiring careful planning and extended care, this approach maximized data collection, reduced the number of animals needed, and mitigated cohort variability from environmental factors like diet, handling, and housing, ultimately enhancing statistical power.\u003c/p\u003e\n\u003cp\u003ePersistent neuroinflammation is now widely recognized as a core pathological feature of AD, evidenced by both clinical and pre-clinical reports (40,58). Neuroinflammation in TgF344-AD rats has been previously reported \u003cem\u003ein vivo\u003c/em\u003e with [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET by measuring increased levels of TSPO over time (40). However, other neuroinflammatory targets remain unexplored in this model to date. For the first time, we examined neuroinflammation in TgF344-AD rats via P2X7 receptor expression, which drives pro-inflammatory cytokine release in AD (21,59,60). In our study, we initially hypothesized that neuroinflammation in TgF344-AD rats, resulting from microglia activation around amyloid plaques, can be detected by means of increased P2X7R expression with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 PET. Here, we revealed a significant increase in [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 uptake in 8- to 22-month-old TgF344-AD rats, although a similar trend was also observed in age-matched wild-type animals, with no significant differences between groups. These findings seem to indicate that P2X7R expression in the TgF344-AD model is mostly driven by aging effects in the Fischer-344 strain, suggesting that age-dependant inflammation is also a natural event in these animals. This is in line with previous reports indicating an increased microglia activation in old female F344 rats (61). However, previous \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eanalyses have demonstrated significant microgliosis (CD11b staining) around plaques in TgF344-AD rats compared to wild-type animals at 18 months, consistent with our observations using Iba1 immunofluorescence at 22 months (39,40). Another study also reported elevated levels of pro-inflammatory cytokines in TgF344-AD rats with age; which correlates with increased [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739\u0026nbsp;uptake in our study (62). To gain a deeper understanding of the differences in neuroinflammation between AD and WT rats, we also conducted TSPO PET imaging using [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 for comparison. Here, we observed significant differences in tracer uptake between TgF344-AD and control rats at 22 months compared to what was previously reported at 18 months, demonstrating an increased age-related neuroinflammatory response (40). Based on this data, it seems that TgF344-AD rats exhibit a weaker neuroinflammatory phenotype, leading to a poor correlation between TSPO-driven neuroinflammation and P2X7 receptor expression. Interestingly, our results contrast with previous studies using mouse models of AD, where P2X7R upregulation was closely associated with AD progression (60,63). However, Martinez-Frailes et al. found that P2X7R upregulation in microglial cells occurs only in the advanced and late stages of AD, but not in the early stages, when microglial priming has not yet occurred and senile plaques are still limited in number (64). Therefore, we believe that the TgF344-AD model may primarily mimic an early stage of the neuroinflammatory response seen in more aggressive mouse models of AD, as it shows glial reactivity but maintains basal P2X7R expression. Supporting this idea, previous studies showed cognitive deficits in TgF344-AD rats similar to those observed in individuals with preclinical AD, suggesting that the model primarily reflects the prodromal phase of the disease (65\u0026ndash;67). In our study, the small number of \u003cem\u003eex vivo\u003c/em\u003e samples (n = 1 - 2) limited the \u003cem\u003ein vitro\u003c/em\u003e studies to a qualitative analysis as no statistically relevant comparison was possible. Since the rats used for \u003cem\u003eex vivo\u003c/em\u003e studies came from the same group of animals used for imaging, tissue availability was restricted to maintain sufficient animals for the longitudinal study. In this sense, future research should focus on more extensive \u003cem\u003eex vivo\u003c/em\u003e analysis to quantitatively assess genotype-specific P2X7R expression over time in this model.\u003c/p\u003e\n\u003cp\u003eHere, we also presented the first longitudinal study using [\u0026sup1;⁸F]Florbetaben PET imaging to assess myelin content \u003cem\u003ein vivo\u003c/em\u003e in the TgF344-AD model. Although [\u0026sup1;⁸F]Florbetaben PET is primarily used as an amyloid tracer in humans, it did not reveal significant differences between AD and wild-type rats in our study, which is in line with previous findings that reported only slight differences between groups (40). The combination of limited resolution in preclinical PET systems, high tracer lipophilicity, and low plaque density in this model resulted in low signal-to-noise ratio PET images and is likely the reason why\u0026nbsp;[\u0026sup1;⁸F]Florbetaben fails at detecting amyloid plaques in our study. Given that white matter is predominantly composed of lipid-rich myelin, lipophilic amyloid tracers often show high off-target binding in these regions. This aligns with our autoradiography findings, where non-displaceable [\u0026sup1;⁸F]Florbetaben binding was observed in white matter-rich areas. Additionally,\u0026nbsp;we also showed how self-fluorescent Florbetaben binds to amyloid-free white matter-rich regions, such as the brain stem and corpus callosum, consistent with our observations in PET and autoradiography. Despite the exact binding of\u0026nbsp;[\u0026sup1;⁸F]Florbetaben to myelin is not fully described, evidence suggests that stilbene derivatives bind selectively to the beta sheet structures of the myelin basic protein (MBP), which correlates with myelin content in demyelinating models (55). When myelin is damaged, MBP loses its beta sheet structures reducing the binding sites for the tracer which can therefore be used to capture the demyelination process. Overall, [\u0026sup1;⁸F]Florbetaben uptake in the striatum was lower than in the brain steam in AD rats, presumably due to the lower content of myelinated axons in this region, with only a moderate increase from 4 to 16 months but with a significant decline in myelin content at 20 months. Uptake values in the myelin-rich brain stem\u0026nbsp;indicated that healthy F344 rats might display a higher production of myelin during their early ages (4 to 8 months), followed by a slight decline of this protein at later time points (12 to 20 months), likely due to the effects of aging. In contrast, TgF344-AD rats initially displayed a slower production of myelin (4 to 12 months), but eventually reaching similar levels to WT rats over a longer period of time. However, our data in the brain stem suggests that AD rats experience a significant loss of myelin at 20 months compared to healthy animals. These results support the idea that not only TgF344-AD rats suffer from myelin loss at advanced ages, but they also encounter disruptions in myelin generation over time, which can potentially be attributed to AD progression. Quantitative \u003cem\u003ein vitro\u003c/em\u003e analysis of myelin content could not be performed due to limited sample availability, preventing direct support of the \u003cem\u003ein vivo\u003c/em\u003e results. This limitation should be addressed in future studies employing this model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRI data further supported our findings on myelin content by revealing significant differences between AD and wild-type animals in multiple brain regions across all four DTI metrics (FA, MD, RD, and AD). Notably, fractional anisotropy was\u0026nbsp;increased\u0026nbsp;in several regions in AD rats, including the cortex, hippocampus, and corpus callosum. This finding is somewhat unexpected, as demyelination is typically associated with\u0026nbsp;reduced\u0026nbsp;FA due to increased radial diffusivity and loss of directional coherence in white matter tracts. However, increased FA in the context of neurodegeneration has been previously reported in this model and may reflect compensatory mechanisms such as axonal reorganization, gliosis, or selective loss of crossing fibers, which can artificially elevate FA values (68,69). This interpretation is supported by the concurrent\u0026nbsp;decrease in radial diffusivity (RD)\u0026nbsp;in many of the same regions, a change more directly associated with reduced myelin content. These results suggest that while FA increases may not directly reflect improved white matter integrity, they may still indicate underlying microstructural remodeling in response to pathology. These complex microstructural changes in TgF344-AD rats revealed by DTI-MRI are partially consistent with demyelination. Specifically, the combination of increased FA and decreased RD suggests a complex pathology involving both myelin loss and structural reorganization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the results from our study align with multiple lines of evidence showing that myelin is significantly altered in AD-related pathology (70,71). Notably, myelin content, indicated by [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben uptake, inversely correlated with previously reported amyloid load in this model, with the brain steam showing the highest uptake and cortex the lowest (39). This supports the hypothesis that AD pathology reflects a reverse of the myelination pattern, where less-myelinated regions are more vulnerable (28,72). Some studies also suggest AD is a developmental disorder that manifests only after myelination is complete (29). In our study, both AD and WT rats reached peak myelination around 12 months in the brain stem. While amyloid oligomers and microglial activation occur as early as 6 months in this model, cognitive deficits only appear after 15 months, coinciding with observed demyelination (39). This might indicate that myelin loss contributes to cognitive decline in TgF344-AD rats, consistent with other studies linking demyelination to cognitive impairment in AD \u0026nbsp;(73\u0026ndash;76). Additionally, previous reports showed white matter deficits at the earliest or preclinical stages of AD, supporting the idea that the TgF344-AD model may primarily represent the prodromal phase of the disease (77\u0026ndash;80). In our study, we hypothesized that a neuroinflammatory response in the TgF344-AD model could be measured by an increased expression of the pro-inflammatory P2X7 receptor, driving myelin loss in transgenic animals. However, our longitudinal data showed an age-related but genotype-independent increase of P2X7R expression, therefore we could not find a direct link between P2X7R-mediated neuroinflammation and demyelination in TgF344-AD rats. Despite increased P2X7R expression was similar in both AD and WT groups with age, only transgenic rats showed myelin loss in white matter rich areas, which suggests that demyelination in AD animals is pathologically driven. Considering previous reports, it seems that amyloid pathology precedes demyelination in TgF344-AD rats, where amyloid deposition has been detected as early as 6 months of age (39). Additionally, signs of neuroinflammation, indicated by TSPO expression, are detected around 12 months in this model and continue to increase up to 22 months, coinciding with the myelin loss observed in our study. This reinforces the idea that TSPO is a more sensitive marker of AD-related inflammation in this model and it correlates better with myelin changes. Interestingly, the brain regions where AD is typically detected, such as the cortex and hippocampus, did not overlap with the areas affected by demyelination in our study\u0026mdash;possibly due to the inherently low myelin content of grey matter.\u0026nbsp;\u003c/p\u003e"},{"header":"5.\tCONCLUSIONS","content":"\u003cp\u003eTo our knowledge, this is the first study to examine both neuroinflammation and demyelination simultaneously in an animal model of AD using \u003cem\u003ein vivo\u003c/em\u003e PET imaging. TSPO PET ([\u0026sup1;⁸F]DPA-714) revealed a significant neuroinflammatory response in TgF344-AD rats, while P2X7R PET ([\u0026sup1;⁸F]JNJ-64413739) showed increased age-related expression in both AD and control animals, reflecting aging effects but not AD-specific inflammation. Thus, [\u0026sup1;⁸F]DPA-714 PET proved more suitable for tracking neuroinflammation in this model. Additionally, [\u0026sup1;⁸F]Florbetaben PET was not appropriate for amyloid imaging in TgF344-AD rats due to low plaque burden and high off-target binding. However, its uptake reflected myelin content, allowing us to monitor progressive demyelination\u0026mdash;most notably at 20 months\u0026mdash;supporting a link between white matter loss and reported AD-related cognitive decline. Additionally, DTI-MRI suggested that TgF344-AD rats undergo microstructural remodeling of white matter tracts compared to controls. Overall, we did not find a direct link between P2X7R expression and demyelination over time, although neuroinflammation detected with TSPO PET was increased together with myelin loss. Our findings support the use of the TgF344-AD model to study early and prodromal stages of AD, especially in relation to neuroinflammation and white matter integrity. Despite some limitations, such as small \u003cem\u003eex vivo\u003c/em\u003e sample size and tracer specificity, this work illustrates the temporal dynamics of AD pathology and reinforces the use of the TgF344-AD rat model as a lead animal model for AD research in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eA\u0026beta;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Amyloid beta\u003c/p\u003e\n\u003cp\u003eAD \u0026nbsp; \u0026nbsp; \u0026nbsp;Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eBq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Becquerel\u003c/p\u003e\n\u003cp\u003eBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Brain stem\u003c/p\u003e\n\u003cp\u003eCB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cerebellum\u003c/p\u003e\n\u003cp\u003eCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Computer tomography\u003c/p\u003e\n\u003cp\u003eCTX\u0026nbsp; \u0026nbsp;\u0026nbsp;Cortex\u003c/p\u003e\n\u003cp\u003eDTI \u0026nbsp; \u0026nbsp;\u0026nbsp;Diffusion Tensor Imaging\u003c/p\u003e\n\u003cp\u003eHIPP\u0026nbsp; \u0026nbsp;\u0026nbsp;Hippocampus\u003c/p\u003e\n\u003cp\u003eMQW\u0026nbsp;\u0026nbsp;Milli-Q water\u003c/p\u003e\n\u003cp\u003eMRI\u0026nbsp; \u0026nbsp; \u0026nbsp;Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ePET \u0026nbsp; \u0026nbsp;\u0026nbsp;Positron Emission Tomography\u003c/p\u003e\n\u003cp\u003eROI\u0026nbsp; \u0026nbsp; \u0026nbsp;Region of interest\u003c/p\u003e\n\u003cp\u003eSTR\u0026nbsp; \u0026nbsp; \u0026nbsp;Striatum\u003c/p\u003e\n\u003cp\u003eSUV\u0026nbsp; \u0026nbsp;\u0026nbsp;Standardized Uptake Value\u003c/p\u003e\n\u003cp\u003eSUVR\u0026nbsp;\u0026nbsp;Standardized Uptake Value ratio\u003c/p\u003e\n\u003cp\u003eTAC\u0026nbsp; \u0026nbsp;\u0026nbsp;Time activity curve\u003c/p\u003e\n\u003cp\u003eTg\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Transgenic\u003c/p\u003e\n\u003cp\u003eVOI\u0026nbsp; \u0026nbsp; \u0026nbsp;Volume of interest\u003c/p\u003e\n\u003cp\u003eWM\u0026nbsp; \u0026nbsp; \u0026nbsp;White Matter\u003c/p\u003e\n\u003cp\u003eWT \u0026nbsp; \u0026nbsp; \u0026nbsp;Wild type\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal experimentation was conducted in accordance with the European Council Directive 2010/63/EU at CIC biomaGUNE (San Sebasti\u0026aacute;n, Spain) facilities and approved by the Institutional Animal Care and Use Committee (IACUC) at CIC biomaGUNE and Diputaci\u0026oacute;n Foral de Guip\u0026uacute;zcoa (Project: PRO-AE-SS-169).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used or analysed in this study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOscar Moreno and Jordi Llop acknowledge funding from the Spanish Ministry of Science and Education (grant PRE2019-089068).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.M, A.M and J.L designed the study and the main conceptual ideas; O.M performed the radiochemistry, PET studies and \u003cem\u003ein vitro\u003c/em\u003e experiments. O.M and Z.B. quantified and analysed the PET data. I.F. performed the Luxol Fast Blue experiments. S.P. performed the MRI studies. S.P and D.P. curated the MRI data. D.P. and P.R. analysed the MRI data. O.M. wrote the original draft with edits from A.M. and J.L.. All authors contributed to the interpretation of findings, critical review of the manuscript, approval of the final manuscript, and agreement to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the staff from the radiochemistry and molecular imaging platforms of CIC biomaGUNE and R. Andrade for technical support in radiolabelling, PET imaging, MRI and microscopy, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMasters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL. Alzheimer\u0026rsquo;s disease. 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JAMA Neurol. 2017 Jan 1;74(1):41\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eTse KH, Cheng A, Ma F, Herrup K. DNA damage-associated oligodendrocyte degeneration precedes amyloid pathology and contributes to Alzheimer\u0026rsquo;s disease and dementia. Alzheimer\u0026rsquo;s and Dementia. 2018 May 1;14(5):664\u0026ndash;79. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"6d9d050b-7c37-447e-8e42-295a80d90b50","identifier":"10.13039/501100011033","name":"Agencia Estatal de Investigación","awardNumber":"PRE2019-089068","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"CIC biomaGUNE","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neuroinflammation, Myelin, PET, P2X7R, TgF344-AD rat, TSPO, DTI, MRI.","lastPublishedDoi":"10.21203/rs.3.rs-7072571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7072571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNeuroinflammation and myelin loss are hallmark features of Alzheimer’s disease (AD) associated with cognitive decline. Emerging evidence highlights the significant role of glial cell activation, particularly microglia, in driving neuroinflammation and disease progression. While translocator protein (TSPO) PET imaging is commonly used to detect neuroinflammation, limitations have prompted investigation into alternative targets such as the P2X7 receptor (P2X7R), which is upregulated during pathological conditions. Additionally, myelin loss has gained recognition as an important pathological feature in AD, potentially linked to chronic neuroinflammation. However, the temporal dynamics and interplay between neuroinflammation and myelin loss remain poorly understood in the context of AD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a longitudinal PET study from 4 to 22 months of age in TgF344-AD rats and wild-type controls to assess neuroinflammation with [\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 (P2X7R) and [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 (TSPO), alongside myelin content using [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben. Diffusion tensor imaging (DTI) was used to study variations on myelin structure in old AD and WT rats. \u003cem\u003eIn vitro\u003c/em\u003e studies, including autoradiography, immunoflusorescence and staining were used to support the \u003cem\u003ein vivo\u003c/em\u003e results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e[\u003csup\u003e18\u003c/sup\u003eF]JNJ-64413739 PET showed increased P2X7 receptor expression in AD and control animals over time, while [\u003csup\u003e18\u003c/sup\u003eF]DPA-714 PET showed significant differences between groups at 22 months. [\u003csup\u003e18\u003c/sup\u003eF]Florbetaben PET showed different uptake in white matter rich areas between groups with observed demyelination in AD rats at 20 months in the brain stem, supported by diffusional MRI findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In our study, P2X7R overexpression was attributed to aging rather than genotype effects, and no link was found to the observed demyelination in AD rats. Conversely, increased TSPO neuroinflammation in TgF344-AD rats correlated with myelin loss and the reported cognitive decline in this model. Our results support the use of the \u0026nbsp;TgF344-AD model to study early AD pathology, focusing on neuroinflammation and white matter integrity.\u003c/p\u003e","manuscriptTitle":"Longitudinal in vivo PET imaging of P2X7R and TSPO neuroinflammation markers and myelin load in the TgF344-AD rat model of Alzheimer’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 08:51:40","doi":"10.21203/rs.3.rs-7072571/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8831560-eec1-4c29-9bfd-dcdd0f7f9276","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T08:51:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 08:51:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7072571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7072571","identity":"rs-7072571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}