Large-scale visualization of α-synuclein oligomers in Parkinson’s disease brain tissue
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative condition characterized by the presence of intraneuronal aggregates containing fibrillar ɑ-synuclein known as Lewy bodies. These large end-stage species are formed by smaller soluble protein nanoscale assemblies, often termed oligomers, which are proposed as early drivers of pathogenesis. Until now, this hypothesis has remained controversial, at least in part because it has not been possible to directly visualize nanoscale assemblies in human brain tissue. Here we present Advanced Sensing of Aggregates-Parkinson's Disease, an imaging method to generate large-scale α-synuclein aggregate maps in post-mortem human brain tissue. We combined autofluorescence suppression with single-molecule fluorescence microscopy, which together enable the detection of nanoscale α-synuclein aggregates. To demonstrate the use of this platform, we analysed ~1.2 million nanoscale aggregates from the anterior cingulate cortex in human post-mortem brain samples from patients with PD and healthy controls. Our data reveal a disease-specific shift in a subpopulation of nanoscale assemblies that represent an early feature of the proteinopathy that underlies PD. We anticipate that quantitative information about this distribution provided by Advanced Sensing of Aggregates-Parkinson's Disease will enable mechanistic studies to reveal the pathological processes caused by α-synuclein aggregation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".