Genetic targeting and anatomical registration of neuronal populations in the zebrafish brain with a new set of BAC transgenic tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genetic access to small, reproducible sets of neurons is key to an understanding of the functional wiring of the brain. Here we report the generation of a new Gal4- and Cre-driver resource for zebrafish neurobiology. Candidate genes, including cell type-specific transcription factors, neurotransmitter-synthesizing enzymes and neuropeptides, were selected according to their expression patterns in small and unique subsets of neurons from diverse brain regions. BAC recombineering, followed by Tol2 transgenesis, was used to generate driver lines that label neuronal populations in patterns that, to a large but variable extent, recapitulate the endogenous gene expression. We used image registration to characterize, compare, and digitally superimpose the labeling patterns from our newly generated transgenic lines. This analysis revealed highly restricted and mutually exclusive tissue distributions, with striking resolution of layered brain regions such as the tectum or the rhombencephalon. We further show that a combination of Gal4 and Cre transgenes allows intersectional expression of a fluorescent reporter in regions where the expression of the two drivers overlaps. Taken together, our study offers new tools for functional studies of specific neural circuits in zebrafish.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it