Synapses do not facilitate prion-like transfer of alpha-synuclein: a quantitative study in reconstructed unidirectional neural networks
Bibliographic record
Abstract
Alpha-synuclein (aSyn) aggregation spreads between cells and underlies the progression of neuronal lesions in the brain of patients with synucleinopathies such as Parkinson's diseases. The mechanisms of cell-to-cell propagation of aggregates, which dictate how aggregation progresses at the network level, remain poorly understood. Notably, while prion and prion-like spreading is often simplistically envisioned as a "domino-like" spreading scenario where connected neurons sequentially propagate protein aggregation to each other, the reality is likely to be more nuanced. Here, we demonstrate that the spreading of preformed aSyn aggregates is a limited process that occurs through molecular sieving of large aSyn seeds. We further show that this process is not facilitated by synaptic connections. This was achieved through the development and characterization of a new microfluidic platform that allows reconstruction of binary fully oriented neuronal networks in vitro with no unwanted backward connections, and through the careful quantification of fluorescent aSyn aggregates spreading between neurons. While this allowed us for the first time to extract quantitative data of protein seeds dissemination along neural pathways, our data suggest that prion-like dissemination of proteinopathic seeding aggregates occurs very progressively and leads to highly compartmentalized pattern of protein seeding in neural networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".