Generation of Alpha-Synuclein Preformed Fibrils from Monomers and Use In Vivo
Bibliographic record
Abstract
Use of the in vivo alpha-synuclein preformed fibril (α-syn PFF) model of synucleinopathy is gaining popularity among researchers aiming to model Parkinson's disease synucleinopathy and nigrostriatal degeneration. The standardization of α-syn PFF generation and in vivo application is critical in order to ensure consistent, robust α-syn pathology. Here, we present a detailed protocol for the generation of fibrils from monomeric α-syn, post-fibrilization quality control steps, and suggested parameters for successful neurosurgical injection of α-syn PFFs into rats or mice. Starting with monomeric α-syn, fibrilization occurs over a 7-day incubation period while shaking at optimal buffer conditions, concentration, and temperature. Post-fibrilization quality control is assessed by the presence of pelletable fibrils via sedimentation assay, the formation of amyloid conformation in the fibrils with a thioflavin T assay, and electron microscopic visualization of the fibrils. Whereas successful validation using these assays is necessary for success, they are not sufficient to guarantee PFFs will seed α-syn inclusions in neurons, as such aggregation activity of each PFF batch should be tested in cell culture or in pilot animal cohorts. Prior to use, PFFs must be sonicated under precisely standardized conditions, followed by examination using electron microscopy or dynamic light scattering to confirm fibril lengths are within optimal size range, with an average length of 50 nm. PFFs can then be added to cell culture media or used in animals. Pathology detectable by immunostaining for phosphorylated α-syn (psyn; serine 129) is apparent days or weeks later in cell culture and rodent models, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".