An Intact Tissue Analysis Pipeline for Quantifying Brain Pathology in Preclinical Research

Abstract

Histology is a foundational technique for assessing disease phenotypes and therapeutic interventions. In mouse models of Alzheimer’s disease (AD) and Parkinson’s disease (PD), it is used to visualise and quantify hallmark pathologies such as amyloid plaques in AD, and dopaminergic neuron degeneration in PD. Accurate quantification is essential for deriving meaningful biological insights and enabling clinical translation. While stereology is considered the gold standard for quantification, its reliance on thin tissue sections that incompletely represent 3D structures can lead to inaccurate estimates of pathology.

This project developed an optimised pipeline for the visualisation and absolute quantification of pathological features in intact mouse brain tissue, using amyloid plaques and dopaminergic neurons as exemplars. Two tissue clearing methods, CLARITY and iDISCO+, were compared, and iDISCO+ was selected for subsequent studies. Amyloid plaques and dopaminergic neurons were successfully visualised in cleared specimens. A deep learning-based 3D analysis pipeline integrated with brain atlas registration was optimised to segment amyloid plaques from volumetric light-sheet imaging data. Plaque load measurements from the 3D pipeline were validated against 2D stereology and showed strong correlation. Notably, 3D analysis revealed regional differences in plaque load not detected in 2D, and offered advantages in time, cost, and scalability. The utility of the 3D pipeline was demonstrated in a study evaluating the neuroprotective potential of saffron in a mouse model of AD. Saffron-treated mice exhibited significantly lower hippocampal plaque load (p < 0.01) compared to controls.

In summary, this intact tissue analysis pipeline enables the absolute quantification of amyloid plaques in the intact mouse brain, overcomes limitations of 2D histology, and provides a framework for 3D quantification of other pathologies in preclinical research.