Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson’s disease
Year: 2025
Type: article
Abstract: Purpose: Metabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson's disease (PD). Here, we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of the transgenic M83 murine model of PD overexpressing the mutated A53T alpha-synuclein form in comparison with non-transgenic littermates. Methods: In vivo spiral volumetric optoacoustic tomography (SVOT) was performed to assess oxygen saturation (sO2) in the spinal cords of M83 mice and non-transgenic littermates. Ex vivo high-field T1-weighted (T1w) magnetic resonance imaging (MRI) at 9.4T was used to assess volumetric alterations in the spinal cord. 3D SVOT analysis and deep learning-based automatic segmentation of T1w MRI data for the mouse spinal cord were developed for quantification. Immunostaining for phosphorylated alpha-synuclein (pS129 α-syn), as well as vascular organization (CD31 and GLUT1), was performed after MRI scan. Results: In vivo SVOT imaging revealed a lower sO2SVOT in the spinal cord of M83 mice compared to non-transgenic littermates at sub-100 μm spatial resolution. Ex vivo MRI-assisted by in-house developed deep learning-based automatic segmentation (validated by manual analysis) revealed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates at 50 μm spatial resolution. The vascular network was not impaired in the spinal cord of M83 mice in the presence of pS129 α-syn accumulation. Conclusion: We developed tools for deep-learning-based analysis for the segmentation of mouse spinal cord structural MRI data, and volumetric analysis of sO2SVOT data. We demonstrated non-invasive high-resolution imaging of reduced sO2SVOT in the absence of volumetric structural changes in the spinal cord of PD M83 mouse model.
DOI: 10.48620/20049
Authors: Benjamin F. Combes, Sandeep Kumar Kalva, Pierre-Louis Benveniste, Agathe Tournant, Man Hoi Law, Joshua Newton, Maik Krüger, Rebecca Z. Weber, Inês Dias, Daniela Noain, Xose Luis Dean-Ben, Uwe Konietzko, Christian R. Baumann, Per-Göran Gillberg, Christoph Hock, Roger M. Nitsch, Julien Cohen-Adad, Daniel Razansky, Ni, Ruiqing
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910914925
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