Astrocytes have the capacity to act as antigen-presenting cells in the Parkinson’s disease brain
Bibliographic record
Abstract
BACKGROUND: Many lines of evidence suggest that accumulation of aggregated alpha-synuclein (αSYN) in the Parkinson's disease (PD) brain causes infiltration of T cells. However, in which ways the stationary brain cells interact with the T cells remain elusive. Here, we identify astrocytes as potential antigen-presenting cells capable of activating T cells in the PD brain. Astrocytes are a major component of the nervous system, and accumulating data indicate that astrocytes can play a central role during PD progression. METHODS: To investigate the role of astrocytes in antigen presentation and T-cell activation in the PD brain, we analyzed post mortem brain tissue from PD patients and controls. Moreover, we studied the capacity of cultured human astrocytes and adult human microglia to act as professional antigen-presenting cells following exposure to preformed αSYN fibrils. RESULTS: T cells to be surrounded by MHC-II expressing astrocytes, confirming an astrocyte T cell cross-talk in the PD brain. Moreover, we showed that αSYN accumulation in cultured human astrocytes triggered surface expression of co-stimulatory molecules critical for T-cell activation, while cultured human microglia displayed very poor antigen presentation capacity. Notably, intercellular transfer of αSYN/MHC-II deposits occurred between astrocytes via tunneling nanotubes, indicating spreading of inflammation in addition to toxic protein aggregates. CONCLUSIONS: In conclusion, our data from histology and cell culture studies suggest an important role for astrocytes in antigen presentation and T-cell activation in the PD brain, highlighting astrocytes as a promising therapeutic target in the context of chronic inflammation.
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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".