Human tripartite cortical network model for temporal assessment of alpha-synuclein aggregation and propagation in Parkinson's Disease

人类三部分皮质网络模型用于帕金森病中 α-突触核蛋白聚集和传播的时间评估

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作者:Fikret Emre Kapucu, Iisa Tujula, Oskari Kulta, Lassi Sukki, Tomi Ryynänen, Hjalte Gram, Valtteri Vuolanto, Andrey Vinogradov, Joose Kreutzer, Poul Henning Jensen, Pasi Kallio, Susanna Narkilahti

Abstract

Previous studies have shown that aggregated alpha-synuclein (α-s) protein, a key pathological marker of Parkinson's disease (PD), can propagate between cells, thus participating in disease progression. This prion-like propagation has been widely studied using in vivo and in vitro models, including rodent and human cell cultures. In this study, our focus was on temporal assessment of functional changes during α-s aggregation and propagation in human induced pluripotent stem cell (hiPSC)-derived neuronal cultures and in engineered networks. Here, we report an engineered circular tripartite human neuronal network model in a microfluidic chip integrated with microelectrode arrays (MEAs) as a platform to study functional markers during α-s aggregation and propagation. We observed progressive aggregation of α-s in conventional neuronal cultures and in the exposed (proximal) compartments of circular tripartite networks following exposure to preformed α-s fibrils (PFF). Furthermore, aggregated forms propagated to distal compartments of the circular tripartite networks through axonal transport. We observed impacts of α-s aggregation on both the structure and function of neuronal cells, such as in presynaptic proteins, mitochondrial motility, calcium oscillations and neuronal activity. The model enabled an assessment of the early, middle, and late phases of α-s aggregation and its propagation during a 13-day follow-up period. While our temporal analysis suggested a complex interplay of structural and functional changes during the in vitro propagation of α-s aggregates, further investigation is required to elucidate the underlying mechanisms. Taken together, this study demonstrates the technical potential of our introduced model for conducting in-depth analyses for revealing such mechanisms.

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