Strain-Distinct α-Synuclein and Tau Cross-Seeding Uncovered by Correlative Approach with Optical Photothermal Infrared Sub-Micron Imaging
Bibliographic record
Abstract
The co-occurrence of α-synuclein (αSyn) and Tau in synucleinopathies and tauopathies suggests a complex interplay between these proteins. Their cross-seeding enhances fibrillization, leading to the formation of diverse amyloid-specific structures enriched with β-sheets, which may influence their biological functions. However, existing tools cannot differentiate structural polymorphs directly in cells, as conventional microscopic approaches have limitations in providing structural insights into aggregates. As a result, a structurally relevant characterization of amyloids in their native cellular environment has not yet been achieved. In this study, we characterize the structural rearrangements of newly formed αSyn inclusions cross-seeded by different αSyn and Tau preformed fibrils (PFFs) directly in cells, using a correlative approach that combines submicron optical photothermal infrared (O-PTIR) microspectroscopy and confocal microscopy. We found that hybrid PFFs synthesized from αSyn, and two Tau isoforms (Tau3R and Tau4R) exhibit variations in αSyn and Tau composition. Specifically, structural polymorphs composed of αSyn and Tau3R exhibit the highest β-sheet content and most potent seeding potency, leading to enhanced phosphorylation within cellular inclusions. Importantly, we demonstrate that cellular inclusions inherit structural motifs from their donor seeds and exhibit distinct spatial and structural evolution. By providing subcellular-resolution structural imaging of amyloid proteins, our study uncovers divergent mechanisms of αSyn aggregation induced by αSyn/Tau PFFs in both mixed and hybrid formats.
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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".