Vonck, E. | bioRxiv Science

bioRxiv2026

Protein aggregation inhibitors induce divergent transcriptional responses in a cellular model of a-synuclein seeded aggregation

Parkinsons disease (PD), dementia with Lewy Bodies (DLB) and multiple system atrophy (MSA) are progressive neurodegenerative disorders marked by the pathological aggregation of alpha-synuclein ([a]Syn). Despite significant research efforts, effective therapeutic interventions remain elusive due to limited understanding of the cellular effects of [a]Syn aggregation and propagation. This study presents the development of a scalable cellular seeding assay for screening small molecules targeting cellular [a]Syn seeded aggregation. By leveraging a fluorescent reporter of [a]Syn and phenotypic screening, the assay enables high-throughput evaluation of potential inhibitors in a cellular environment mimicking disease pathology. We evaluated three different Syn aggregation inhibitors tested in clinical trials for PD: Minzasolmin, Emrusolmin and EGCG and profiled gene expression using multiplexed single cell RNA sequencing in order to examine their distinct effects on cellular pathways associated with [a]Syn overexpression or seeded aggregation. Two cellular activities were prominently affected: lipid metabolism and rRNA processing. Notably, while EGCG effects were confined to cells with aggregated Syn, Minzasolmin and Emrusolmin also produced transcriptional changes in cells without aggregated Syn. Each of the compounds tested induced a partial reversal of transcriptional effects resulting from Syn seeded aggregation. We identified 391 genes that were no longer significantly differentially expressed upon addition of compound, relative to cells with seeded aggregation. This platform bridges phenotypic screening and molecular pathway analysis, providing insights into druggable pathways for synucleinopathies. The molecular signatures identified here can assist in testing and benchmarking future drug discovery leads.

neuroscience↗

bioRxiv2025

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.

neuroscience↗