Computational protocol: Laquinimod rescues striatal, cortical and white matter pathology and results in modest behavioural improvements in the YAC128 model of Huntington disease

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Protocol publication

[…] Animals were scanned at 8 months of age on a 7T MRI scanner (ClinScan, Bruker BioSpin, Germany) using 4-channel array coils. The mice were anesthetized and maintained stably under anaesthesia by 1–2% isoflurane mixed with air and O2 (1:1) at a flow rate of 1L/min via a nose cone. An MRI-compatible stereotaxic holder was used to secure the head. The rectal temperature was monitored and maintained at 36 ± 0.5 C using heated air (SAII, Stony Brook, NY, USA), and the respiration rate was controlled at 100 ± 20 breaths per minute by varying the concentration of isoflurane. The structural image was acquired by a fast-spin-echo T2-weighted sequence with TR = 3080 ms, TE = 43 ms and 0.1 × 0.1 × 0.3 mm3 voxel resolution with coil inhomogeneity normalisation. The brain was extracted using 3D-PCNN followed by manual editing. Images were linearly registered to an in-house mouse brain template based on 8 YAC128 and 8 WT mice (9 months of age) and then averaged to create a study-specific template. Image of each subject were then non-linearly registered to the template using FSL (FMRIB Software Library v5.0, University of Oxford, Oxford, UK, To compare voxel-wise tissue volume differences between WT and YAC128 mice, TBM was calculated based on the Jacobian determinant (a measure of volume changes from non-linear registration). Total brain volumes were quantified based on the extracted brain. A binary mask of the caudate-putamen (CPu) was manually delineated on the study-specific template, and the CPu volume for each animal was calculated based on the mean Jacobian determinant within the mask. [...] The DTI was acquired using a spin-echo Echo Planar Imaging (EPI) sequence with 9 averages of 30 diffusion sensitising directions, b = 1500 s/mm2, TR = 11000 ms, TE = 41 ms, voxel size = 0.25 × 0.25 × 0.3 mm3. Reversed phase-encoding EPI images were also collected for the purpose of distortion correction. The quality of images was checked using an in-house Matlab (Mathworks, MA, USA) code to detect and eliminate corrupted images and hyper-intensive outliers. After eddy current distortion and motion correction, susceptibility distortion in EPI was corrected by FSL TOPUP. Fractional anisotropy (FA), radial diffusivity (Dr), and parallel diffusivity (Dp) were obtained by weighted least squares tensor fitting. A combination of linear registration from the B0 image to the corresponding T2-weighted structural image, and the nonlinear transformation from the T2-weighted image to the above mentioned study-specific template was applied to the FA map using FSL. The registered FA maps were then averaged to create a study-specific FA template for a second round of nonlinear registration. The same transformation was applied to Dr, Dp, and MD maps. After 2D Gaussian smoothing with a kernel of 0.3 mm full width at the half maximum, voxel-wise 2-sample t-tests between groups were conducted using SPM8 with a cluster threshold of p < 0.05 determined by a Monte-Carlo simulation using 3DClustSim in AFNI (NIH; Regions of interest (ROIs), including the anterior and posterior part of the corpus callosum, and cingulum, were defined on the FA template. The mean values of FA, MD, Dr, and Dp over each ROI were compared.IL-6 ELISA. Whole blood was collected retro-orbitally after animals were anesthetized with Ketamine (150mg/Kg) and Xylazine (10 mg/Kg) via intraperitoneal injection, and before sacrifice. Blood was collected in EDTA and heparin free tubes and kept on ice until sample processing. Samples were then incubated at 37 °C for 30 min in a thermo block and centrifuged 15 min at 3000 rpm and 4 °C. Approximately between 50–100 μL of serum was collected per animal. Samples were stored at −80 °C until IL-6 levels were measured using Quantikine ELISA Mouse IL-6 immunoassay (R&D Systems, cat no. M6000B) following the manufacturer’s instructions. […]

Pipeline specifications

Software tools FSL, AFNI
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Diseases Huntington Disease