Back

Recapitulating Parkinson's pathology in human iPSC dopaminergic neurons reveals new mechanistic insights into Lewy body formation and heterogeneity.

Mahul-Mellier, A.-L.; van den Heuvel, L.; Teixeira, M.; Boussouf, M. L. N.; Oudinot, G.; Thonet, A.; Speri, D.; Jasiqi, Y.; Ulrich, C.; Sheta, R.; Idi, W.; Croisier, M.-C.; Clerc-Rosset, S.; Blanc, J.; Knott, G.; Oueslati, A.; Lashuel, H. A.

2025-10-27 neuroscience
10.1101/2025.10.26.684610 bioRxiv
Show abstract

The accumulation of alpha-synuclein (aSyn) into intraneuronal inclusions of heterogeneous morphology, known as Lewy bodies (LB), is one of the defining diagnostic features of Parkinsons disease (PD); yet, our understanding of the mechanisms underpinning their formation and heterogeneity remains incomplete. Here, we present a human isogenic iPSC-derived dopaminergic neuron (iDA) model that faithfully recapitulates the diverse biochemical, morphological, and ultrastructural features of LB neuropathology in PD. Unlike other iDA seeding models, our model does not rely on aSyn overexpression, mutations, or genetic engineering, making it a more physiologically relevant system for studying PD. We demonstrate that the iDA model accurately reproduces the temporal relationships between neuritic and cell-body aSyn pathology, recapitulating the full biochemical spectrum, post-translational modifications (PTM), and morphological diversity of aSyn aggregates found in human PD tissue. Moreover, our work provides critical insight into how different pathways to aSyn fibrillization and the complex interaction between aSyn fibrils and membranous organelles influence the morphological diversity of LB-like inclusions. This model represents a versatile platform for investigating the mechanisms of pathology formation, maturation, and neuronal dysfunction, as well as supporting the development of diagnostics that capture the diversity of aSyn pathology in PD and related synucleinopathies.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.