Altered dynamics of glymphatic flow in a mature-onset Tet-off APP mouse model of amyloidosis

Mouse strain, dox treatment and housing

Generation of the Tet-Off APP transgenic mice has been described in detail in our previous study [39]. Briefly, this inducible model of amyloidosis allows a time-controlled expression of a chimeric mouse/human APP695 transgene using the Tet-Off system. The bigenic tetO-APPswe/ind (line 107) animals (strain B6.Cg-Tg(tetO-APPSwInd)107Dbo/Mmjax, referred to as AD mice in the manuscript) were bred in-house by crossing APP mice, in which a tetracycline-responsive (tetO) promoter drives the expression of the chimeric APP transgene bearing the Swedish and Indiana mutations (mo/huAPP695swe/ind), with transgenic mice expressing the tetracycline transactivator (tTA) gene (strain B6;CBA-Tg(Camk2a-tTA)1Mmay/J). The single transgenic tTA and APP males (Prof. Dr JoAnne McLaurin, Sunnybrook Health Sciences Centre, Toronto, Canada) were initially crossed with non-transgenic females on a C57BL6/J background (Charles River, France) to establish the single transgenic colonies. Since the tTA transgene is under the control of the CaMKIIα promoter, the bigenic mice express APP in a neuron-specific manner at moderate levels, essentially in the forebrain [42]. The APP expression was ‘turned-off’ up to the age of 3 months (3 m, young adult mice) by feeding females with litters and weaned pups with a specific chow supplemented with doxycycline (DOX), a derivative of tetracycline (antibiotics, 100 mg/kg doxycycline diet, Envigo RMS B.V., The Netherlands) from P3 up to 3 months. To induce APP expression in the bigenic mice, all in-house bred transgene carrier and non-transgenic carrier (NTg) littermates were switched to a regular chow from 3 months onward until the day of the surgery (14 months old) resulting in a total APP expression duration of 11 months. Of note, we reused the animals that previously completed a longitudinal rsfMRI study, presented in our recent work [39]. The mouse genotypes are indicated in figure schemes or legends throughout the manuscript. Animals were housed in an environment with controlled temperature and humidity and on a 12-h light–dark cycle, and water was provided ad libitum.

Surgery

The injection of gadolinium (Gd)-based T1 contrast agent, gadoteric acid (Gd-DOTA), into the cisterna magna (CM) was performed in spontaneously breathing mice anaesthetised with 2% isoflurane (3% for short induction) delivered in oxygen by adapting a previously reported protocol [43, 44]. Briefly, the animal was positioned in a custom-made stereotaxic frame with its head pointing down to expose the CM. A midline incision was made under a microscope from the occipital crest down to the first vertebrae. Then, the underlying muscles were gently separated and maintained pulled aside using two curved forceps. The CM appeared as a small, inverted triangle overlaid with the translucent dural membrane, in between the cerebellum and the medulla. After exposing the CM, 2.5 μl of 50 mM solution of Gd-DOTA (DOTAREM®, Guerbert, France) was injected at 0.55 μl/min via a pulled haematological glass micropipette attached to a nanoinjector (Nanoject II Drummond). To avoid leakage, the micropipette was left in place for an additional 5 min and the incision was closed with biocompatible superglue. The body temperature was maintained at 37.0 °C with a heating pad.

MRI acquisition

Following surgery, animals were positioned in an MRI-compatible cradle/bed (animal in prone position) using an MRI-compatible mouse stereotactic device, including a nose cone to deliver anaesthetic gas 2% isoflurane (Isoflo®, Abbot Laboratories Ltd., IL, USA) administered in a gaseous mixture of 33% oxygen (200 cc/min) and 67% nitrogen (400 cc/min). During the MRI acquisition, the mice were allowed to breathe spontaneously. The physiological status of the animals was closely monitored during the entire acquisition. The respiration rate was maintained within the normal physiological range (80–120 breaths/min) using a small animal pressure-sensitive pad (MR-compatible Small Animal Monitoring and Gating System, SA instruments, Inc.). The body temperature was monitored by a rectal probe and maintained at 37.0 ± 0.5 °C using a feedback-controlled warm air system (MR-compatible Small Animal Heating System, SA Instruments, Inc.).

All imaging measurements were performed on a 9.4 T Biospec MRI system (Bruker BioSpin, Germany) with the Paravision 6.0 software (www.bruker.com) using a Bruker coil setup with a quadrature volume transmit coil and a 2 × 2 surface array mouse head receiver coil. Axial and sagittal 2D T2-weighted Turbo RARE images were acquired to ensure uniform slice positioning (RARE; TR/TE 2500/33 ms; 9 slices of 0.7 mm; FOV 20 × 20 mm2; pixel dimensions 0.078 × 0.078 mm2). Dynamic contrast-enhanced MRI (DCE-MRI) acquisition was performed using a 3D T1-weighted FLASH sequence (3D T1-FLASH; TR/TE 15/4.3 ms; flip angle 20°) in the sagittal plane. The field of view (FOV) was 18 × 15 × 12 mm3 and the matrix size 96 × 96 × 64, resulting in voxel dimensions of 0.188 × 0.156 × 0.188 mm3. The DCE-MRI scans were acquired every 5 min and started 30 min up to 150 min after the contrast agent injection. An overview of the experimental setup is summarised in Fig. 1.

Fig. 1figure 1

Overview of the experimental setup. The 14-month-old animals, AD and CTL, underwent the surgery after 11 months of amyloid-beta expression. The surgery started a few minutes after the anaesthesia induction (time point =  − 15 min) and the start of continuous Gd-DOTA infusion (2.5 µl, 0.55 µl/min) into the cisterna magna refers to the time point t = 0. The DCE-MRI acquisition (120 min) started 30 min post-Gd-DOTA injection, every 5 min, up to 150 min post-injection

MRI data pre-processing

Pre-processing of the DCE-MRI data was performed using Advanced Normalisation Tools (ANTs) including realignment, spatial normalisation and creation of a 3D study template. First, a mean image has been created across the time series for each subject and a mask larger than the brain has been delineated on it using AMIRA 5.4. Then, this broad mask has been applied to the DCE-MRI images to remove the surrounding muscle tissue. In parallel, a study-specific 3D template based on the last scan of the non-transgene carrier (NTg) group was created using a global 12-parameter affine transformation followed by a nonlinear deformation protocol. This template was used to estimate the spatial normalisation parameters of the mean images. Next, the realignment parameters of all masked DCE-MRI images within each session to the masked mean image were first estimated, using a symmetric image normalisation method (SyN transformation). Then, the transformation parameters of the realignment and the spatial normalisation were applied to the DCE-MRI images in one resampling step.

Signal intensity normalisation was performed in MATLAB (MATLAB R2020a, The MathWorks Inc. Natick, MA, USA). First, an ellipsoid-shaped region-of-interest (ROI) of 141 voxels was delineated in a cortical area where the variability of the intensity over time was negligible for the baseline and saline groups. More specifically, a time frame of six consecutive scans was selected based on the least changes in the time traces for this specific mask (i.e. intensity values remained approximately constant). Therefore, the mean intensity value of this mask for these six consecutive scans was used to convert each voxel of all images to percent signal change. Finally, a smoothing step was performed with a 3D Gaussian kernel of radius twice the voxel size. A second mask restricted to the brain was applied to all images.

MRI data analysis

Five groups of 14-month-old mice (14 months) were subjected to DCE-MRI experiments: a non-transgene carrier (NTg) non-injected group (NONE, N = 3), a NTg saline-injected group (SAL, N = 3), a NTg Gd-DOTA-injected group (CTL1; N = 5), a tTA Gd-DOTA-injected group (CTL2; N = 3) and a bigenic Tet-Off APP Gd-DOTA-injected group (AD; N = 7). In total, 21 mice (14 months, mixed in gender) were scanned that were reared on the Dox diet until 3 months of age. Five animals (2 NTg, 1 tTA and 2 AD) have been removed due to surgery failure. Given that tTA animals (CTL2) do not produce soluble Aβ or Aβ plaques and showed no difference to the NTg littermates (CTL1) in our previous resting-state experiments [39, 42], these two groups of animals were combined and are further referred to as the control group (CTL, NCTL = 5).

First, a principal component analysis (PCA) was performed per group on the average of the spatially smoothed and normalised time courses. As more than 99% of the data variability could be explained by the three largest components, we used them to reconstruct PCA-based time courses that effectively reduced high-frequency noise from the data. Subsequently, a hierarchical clustering (ward linkage, maximum 15 clusters) was performed on the reconstructed time courses of all animal groups. This analysis allowed for the identification of clusters of voxels with similar time courses and to observe the patterns across groups. Then, to have a fair comparison in the same voxels, the clusters of either the CTL group or the AD group were used to compare the voxel-averaged time courses of the CTL and AD groups and to assess the dynamics of glymphatic flow based on modelling. To this end, we sub-selected the clusters with at least 100 voxels that showed a difference between the maximum and minimum intensity of more than 10%. This criterion was selected based on the variability observed in the NONE and SAL groups for which the time courses were flat as expected (see the ‘Results’ section). Subsequently, PCA was also performed on a subject-by-subject basis to allow for statistical analysis between the groups (CTL vs. AD). Analyses were performed (a) on six predefined hypothesis-driven regions-of-interest (ROIs) and (b) on the clusters defined based on the group-level PCA of the CTL group as described above. For ROI-based analysis, six relevant ROIs (olfactory bulb, hippocampus, medulla, pons, aqueduct and cerebellum) were delineated with MRIcroGL software (https://www.nitrc.org/projects/mricrogl/) based on the 3rd edition of the Paxinos atlas [45]. Then, for each subject, the mean of the PCA-based time courses over the voxels included in each ROI was extracted and the area under the curve (AUC) was calculated and used for statistical analysis (t-test across the two groups). For cluster-based analysis, the mean over the voxels included in each cluster was extracted for each subject separately and then the time courses of each cluster were fitted using a model with two exponentials based on the following formula: \(f\left(t\right)=_\bullet \left(1-^_}}}\right)+_\bullet \left(^_}}}-1\right)\), with c1 and c2 representing gain constants, τin the influx time constant and τout the efflux time constant. The estimated τ’s for the influx and efflux for each cluster per subject were then used for statistical comparison (t-test) across the two groups.

Immunohistochemistry

Brain samples were collected directly after the MRI acquisition (NNTg = 3; NAD = 3) as described previously [39]. Briefly, the mice were deeply anaesthetised with an intraperitoneal injection of 60 mg/kg/BW pentobarbital (Nembutal; Ceva Sante Animale, Brussels, Belgium), followed by a transcardial perfusion with ice-cold PBS, and with 4% paraformaldehyde (Merck Millipore, Merck KGaA, Darmstadt, Germany). Brain samples were afterwards surgically removed and post-fixed in 4% paraformaldehyde for 4 h. Next, the fixed brains were freeze-protected using a sucrose gradient (sucrose, Sigma-Aldrich): 2 h at 5%, 2 h at 10%, and overnight at 20%. Then, the brain samples were snap frozen in liquid nitrogen and stored at − 80 °C. Finally, 14-μm-thickness sagittal brain sections were cut using a cryostat (CryoStar NX70; ThermoScientific).

For immunofluorescence analyses, the following primary antibodies were used: chicken anti-GFAP (Abcam ab4674, 1:1000), rabbit anti-IBA-1 (Wako 019–19,741, 1:1000), rabbit anti-AQP4 (Sigma-Aldrich HPA014784, 1:100) and the following secondary antibodies: donkey anti-chicken (Jackson 703–166-155, 1:1000), goat anti-rabbit (Jackson 111–096-114, 1:1000) and donkey anti-rabbit (Abcam AF555, 1:1000). Moreover, the Aβ plaques were stained with Thioflavin-S (Santa Cruz Biotechnology, sc-215969) and the vessels with lectin (Labconsult VEC.DL-1174 (green) or VEC.DL-1177 (red), 1:200). After the staining, the sections were mounted using Prolong Gold Antifade (P36930; Invitrogen).

Immunofluorescence images of GFAP/lectin, Iba1/lectin, AQP4/lectin and Thioflavin-S/lectin stainings were acquired using an Olympus BX51 fluorescence microscope equipped with an Olympus DP71 digital camera and the image acquisition was done with CellSens Imaging Software (Olympus, Tokyo, Japan, http://www.olympus-global.com). Obtained images were visually evaluated by at least three co-workers to ensure the selection of representative images. The 3rd edition of the mouse brain atlas from Paxinos and Franklin was used as a reference for the localisation of the regions of interest. Images were further processed with ImageJ Software 1.52 k (National Institutes of Health) and artificially pseudo-coloured in the representative images.

Histological quantifications were performed in FIJI version 2.9.0/1.53t. Estimation of the vascular density in the cortex was performed in two-channel immunofluorescence images of Lectin/ThioflavinS. First, the two channels were split and then the Lectin (vascular) channel was auto-thresholded using the Triangle method implemented in FIJI. To eliminate noise, only clusters with greater than 300 pixels were retained. In addition, areas where plaques were present in the ThioflavinS channel were excluded from the analysis. The vascular density was estimated as the fraction of the total area of clusters to the surrounding pixels. Similarly, (a) the total GFAP-positive signal reflecting potential changes in astrogliosis and (b) the fractional vascular coverage by astrocytes were performed using two-channel immunofluorescence images of GFAP/lectin that were also split into two image files for each area (cortex, amygdala, brainstem). For the GFAP analysis, each image was auto-thresholded using the Li method implemented in FIJI and only clusters greater than 50 pixels were retained. The intensity of each of the clusters was then measured and averaged over all clusters to calculate the total mean GFAP signal intensity. For vascular coverage analysis, the image from the corresponding lectin channel was auto-thresholded using the Triangle method in FIJI and only clusters over 300 pixels were retained. Subsequently, to calculate the overlap of astrocytes on vessels, the two thresholded images from the GFAP and lectin channels were merged, converted to RGB composite images and thresholded for colour hues between 10 and 80 (i.e. orange to greenish yellow). The number of pixels in this overlap was divided by the number of pixels in the thresholded lectin channel to calculate the fractional vessel coverage by astrocytes. Vascular density, total GFAP signal intensity and fractional vessel coverage were then used for statistical analysis.

Statistical analyses

For the MRI ROI-based analysis, a two-sample t-test was performed on the area under the curve (AUC) values per ROI for the CTL vs. AD groups. Similarly, for cluster-based analysis, a two-sample t-test was performed on the τin and τout values per cluster across the two groups. MRI statistical analyses were performed using MATLAB (version 9.8 (R2020a), Natick, Massachusetts, The Mathworks Inc.). Significance was defined with a criterion α = 0.05. All results are shown as mean ± standard errors.

For immunohistochemistry statistical analyses of vascular density, total GFAP signal intensity and fractional vessel coverage, we performed linear mixed-effects (LME) model analyses (fitlme function) in MATLAB (version 9.10 (R2021a)). For vessel density (that was only estimated in the cortex), genotype was used as a fixed effect and animal id as a random effect. For total GFAP signal intensity and fractional vessel coverage, genotype and brain area were used as fixed effects and animal id as a random effect. Significance was defined with a criterion α = 0.05. Result means are reported with their 95% confidence intervals (CIs). More details of the parameter fit for the LME analysis are reported in the Supplementary Data.

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