Development and validation of an expanded antibody toolset that captures alpha-synuclein pathological diversity in Lewy body diseases
Bibliographic record
Abstract
The abnormal aggregation and accumulation of alpha-synuclein (aSyn) in the brain is a defining hallmark of synucleinopathies. Various aSyn conformations and post-translationally modified forms accumulate in pathological inclusions and vary in abundance among these disorders. Relying on antibodies that have not been assessed for their ability to detect the diverse forms of aSyn may lead to inaccurate estimations of aSyn pathology in human brains or disease models. To address this challenge, we developed and characterized an expanded antibody panel that targets different sequences and post-translational modifications along the length of aSyn, and that recognizes all monomeric, oligomeric, and fibrillar aSyn conformations. Next, we profiled aSyn pathology across sporadic and familial Lewy body diseases (LBDs) and reveal heterogeneous forms of aSyn pathology, rich in Serine 129 phosphorylation, Tyrosine 39 nitration and N- and C-terminal tyrosine phosphorylations, scattered both to neurons and glia. In addition, we show that aSyn can become hyperphosphorylated during processes of aggregation and inclusion maturation in neuronal and animal models of aSyn seeding and spreading. The validation pipeline we describe for these antibodies paves the way for systematic investigations into aSyn pathological diversity in the human brain, peripheral tissues, as well as in cellular and animal models of 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".