Optogenetic stimulation of nigral astrocytes is neuroprotective in a 6-OHDA model of neurodegeneration
Bibliographic record
Abstract
Summary Highlights Optogenetic stimulation of nigral astrocytes attenuates motor deficits & Th+ cell loss in a 6-OHDA model of neurodegeneration Bulk RNA-seq analysis reveals optogenetic stimulation of nigral astrocytes induces early changes in microglia snRNA-seq shows 6-OHDA alone induces extensive gene expression changes across all cell populations within the SN Oligodendrocytes within the SNc express Th, which is upregulated with DA neuron loss Parkinson’s disease is characterized by the loss of dopaminergic neurons in the substantia nigra. Glial-glial crosstalk is essential for maintaining the regional milieu, and appears to be particularly important in modulating neuroinflammation and many aspects of neurodegeneration. In particular, astrocytes are critical for maintaining dopamine neuronal integrity and survival, and astroglial dysfunction is prominent in Parkinson’s disease. As such, astrocytes represent a potentially critical therapeutic target in neurodegeneration. In this study, in vivo optogenetics were used to selectively stimulate astrocytes in the substantia nigra following a striatal 6-OHDA lesion. Remarkably, a single bout of optogenetic stimulation was sufficient to attenuate motor deficits and dopamine neuron loss induced by the neurotoxin. Furthermore, bulk RNA-seq and snRNA-seq analysis of the substantia nigra revealed extensive changes in both microglia and oligodendrocytes, suggesting that the neuroprotective effects of stimulating astrocytes may be mediated through alterations in glia-glia crosstalk. Altogether, this work demonstrates the importance of understanding glia-glia interactions in neurodegeneration.
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 | 0.001 |
| 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".