Parkinson’s disease (PD) is a basal ganglia movement disorder characterized by progressive degeneration of the nigrostriatal dopaminergic system. Immunohistochemical methods have been widely used to characterize dopaminergic neuronal injury in animal models of PD, including the MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. However, conventional immunohistochemical techniques applied to tissue sections have inherent limitations, such as loss of 3D resolution, providing insufficient information on the architecture of the dopaminergic system.
To address these limitations, we used iDISCO immunolabeling, light-sheet fluorescence microscopy (LSFM), and deep-learning computational methods for whole-brain three-dimensional visualization and automated quantification of tyrosine hydroxylase (TH)-positive neurons in the adult mouse brain. Mice were terminated 7 days after acute MPTP administration, revealing widespread alterations in TH expression.
Results: Compared to vehicle controls, MPTP-dosed mice showed:
- Significant loss of TH-positive neurons in the substantia nigra pars compacta and ventral tegmental area.
- Reduced overall TH signal intensity in basal ganglia nuclei, including the substantia nigra, caudate-putamen, globus pallidus, and subthalamic nucleus.
- Increased TH signal intensity predominantly in limbic regions, such as several subdivisions of the amygdala and hypothalamus.
Conclusion: Whole-brain 3D imaging using LSFM and deep learning enables unbiased, automated counting and densitometric analysis of TH-positive cells. The LSFM–deep learning pipeline effectively tracked brain-wide changes in catecholaminergic pathways in the MPTP mouse model of PD and may be applied for preclinical characterization of compounds targeting dopaminergic neurotransmission.
