Recapitulating Parkinson’s pathology in human iPSC dopaminergic neurons reveals new mechanistic insights into Lewy body formation and heterogeneity
Bibliographic record
Abstract
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 Parkinson’s 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".