Light-inducible alpha-synuclein aggregation in the midbrain impairs nigrostriatal dopaminergic transmission
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by intracellular inclusions of misfolded α-synuclein, known as Lewy bodies, and by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) that leads to dopamine (DA) depletion at the striatum. However, it remains elusive how the aggregates of α-synuclein can affect the normal function of dopaminergic projections, especially due to the absence of proper models that can reproduce the features of PD. In this context, we have recently developed an in vitro and in vivo model of PD based on the optogenetics technology named LIPA (light-inducible protein aggregation) that controls the aggregation of α-synuclein under the control of blue light. We showed that LIPA mimics the histopathological characteristics of PD, and allow thus to study how the aggregation of α-synuclein in dopaminergic cells of SNc can cause a progressive disruption in the nigrostriatal pathway. To investigate the physiological impact of LIPA-induced α-synuclein aggregation on the dopaminergic projections, we assessed the activity of striatal cells. Briefly, we implanted mini-endoscopes coupled with an optic fiber to induce α-synuclein aggregation in the SNc, and analyzed the neuronal activity using the calcium indicator GCaMP6s in the striatum of freely moving mice. Our results show a progressive decrease in the synchronized activity of striatal cells caused by the aggregation of α-synuclein. Altogether, our data showed that the use of this new LIPA-α-synuclein system offers a unique tool to elucidate the morphological, and physiological changes occurring in the dopaminergic projections in the context of PD.
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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) | 0.001 | 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 | 0.000 | 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".