Development of convolutional neural networks for automated brain-wide histopathological analysis in mouse models of synucleinopathies

Preclinical animal models are indispensable for uncovering disease mechanisms and developing novel therapeutic interventions in synucleinopathies. Key readouts including neuronal cell death, neuroinflammation and alpha-synuclein protein aggregation, are routinely assessed by histological methods. However, traditional characterization of histological samples is labor-intensive and time-consuming. There is a growing need for reproducible and high-throughput tools to capture region- and cell type-specific changes, ultimately improving the predictive value of preclinical studies.

To address this, our study introduces a pipeline using convolutional neural networks (CNNs) for high-throughput, unbiased analysis of immunohistological data in mouse brains. We have trained five CNN-based models to autonomously identify brain regions and detect markers of neurodegeneration, neuroinflammation, and alpha-synuclein aggregation. These models provide accurate, region-specific insights at cellular resolution without manual annotation, significantly speeding up analysis time from weeks to minutes. Our approach enhances the precision and efficiency of histological assessments, providing robust, brain-wide results in various animal models of synucleinopathies.