Contribution of <i>α</i>‐synuclein cytopathologies to distinct seeding of misfolded <i>α</i>‐synuclein
Bibliographic record
Abstract
Synucleinopathies are a group of neurodegenerative diseases characterized by the deposition of misfolded α-synuclein (αSyn), predominantly in oligodendrocytes in multiple system atrophy (MSA) and in neurons in Lewy body diseases (LBD). The contribution of αSyn cytopathologies to the pathogenesis of these diseases is underappreciated. Seed amplification assays of MSA and LBD brains have revealed striking differences in αSyn seeding between regions and cases. Therefore, our aim was to evaluate whether different brain regions containing distinct αSyn cytopathologies contribute to different seeding characteristics. We collected 2-mm micro-punches of regions in MSA (n = 10) and LBD (n = 15) cases from formalin-fixed paraffin-embedded tissues. We performed double immuno-labeling for disease-associated αSyn and cellular markers on tissue microarrays, evaluated co-deposition of other neurodegenerative disease-related proteins and, from the same micro-punched samples, we analyzed αSyn seeding. Based on these variables, machine learning algorithms were used to reduce dimensionality of the dataset and cluster the regions in MSA and LBD cases, revealing that different compositions of αSyn cytopathologies influence αSyn seeding patterns. Our results support the notion of different cellular processing of αSyn and its contribution to the variability in seeding. This has implications for understanding disease progression, interpretation of seed amplification assays, and opens avenues for the development of cell type-specific antibodies against αSyn.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".