Neuronal Autophagy Failure Drives α-Synuclein Transfer to Microglia to Outsource Aggregate Clearance
Bibliographic record
Abstract
Abstract Tunneling nanotubes (TNTs) play a crucial role in intercellular communication, enabling a dynamic network for the transfer of molecular cargo over long distances between connected cells. Previous studies have demonstrated efficient, directional transfer of α -Synuclein ( α -Syn) aggregates from neurons to microglia, with endosomal trafficking and lysosomal processing identified as the primary events following α-Syn internalization. Using human neuronal and microglial cell lines, we found that microglia exhibit higher lysosomal turnover, particularly through lysophagy, whereas neuronal lysosomes display compromised degradative capacity and impaired autophagic flux. This deficiency results in less efficient degradation of aggregates in neurons. Moreover, perturbation of autophagy enhances TNT-mediated transfer of aggregate from neuronal cells to microglia. In contrast, microglia co-cultured with α -Syn-containing neurons upregulate autophagy flux, enabling efficient degradation of the transferred aggregates. These findings were further validated using human induced pluripotent stem cells (hiPSC)-derived neurons and microglia. Overall, our study highlights the distinct responses of neurons and microglia to α -Syn aggregates and identifies dysfunctional autophagy in neurons as a key driver of the preferential and directional transfer of aggregates to microglia.
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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 | 0.001 | 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".