Transgenic Zebrafish Expressing mCherry in the Mitochondria of Dopaminergic Neurons
Bibliographic record
Abstract
Genetic mutations and environmental toxins are known to affect mitochondrial health and have been implicated in the progressive degeneration of dopaminergic neurons in Parkinson's disease. To visualize mitochondria in dopaminergic neurons of live zebrafish, we used the regulatory elements of the dopamine transporter (dat) gene to target a reporter, mCherry, after fusion with the mitochondrial localizing signal (MLS) of Tom20. Immunoblot analysis of mitochondrial and cytosolic fractions from Tg(dat:tom20 MLS-mCherry) larvae shows that mCherry is efficiently targeted to the mitochondria. Confocal imaging of live fish was carried out from 1 day postfertilization (dpf) to 9 dpf. We also colocalized dat mRNA expression with the mCherry protein in the olfactory bulb (OB), subpallium (SP), pretectum (Pr), diencephalic clusters 2 and 3 (DC2/3), caudal hypothalamus (Hc), locus coeruleus (LC), anterior preoptic area (POa), retinal amacrine cells (RAC), caudal hypothalamus (Hc), and preoptic area (PO). Treating Tg(dat:tom20 MLS-mCherry) larvae with the dopaminergic neurotoxin MPTP (1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine) at 2 or 3 dpf resulted in a decrease in mCherry fluorescence in the pretectum, olfactory bulb, subpallium, diencephalic clusters 2 and 3, and the caudal hypothalamus. Labeling of mitochondria in nigrostriatal dopaminergic neurons of zebrafish could allow their visualization in vivo following genetic or pharmacological manipulations.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".