Laser-induced gene expression in specific cells of transgenic zebrafish
Bibliographic record
Abstract
Over the past few years, a number of studies have described the generation of transgenic lines of zebrafish in which expression of reporters was driven by a variety of promoters. These lines opened up the real possibility that transgenics could be used to complement the genetic analysis of zebrafish development. Transgenic lines in which the expression of genes can be regulated both in space and time would be especially useful. Therefore, we have cloned the zebrafish promoter for the inducible hsp70 gene and made stable transgenic lines of zebrafish that express the reporter green fluorescent protein gene under the control of a hsp70 promoter. At normal temperatures, green fluorescent protein is not detectable in transgenic embryos with the exception of the lens, but is robustly expressed throughout the embryo following an increase in ambient temperature. Furthermore, we have taken advantage of the accessibility and optical clarity of the embryos to express green fluorescent protein in individual cells by focussing a sublethal laser microbeam onto them. The targeted cells appear to develop normally: cells migrate normally, neurons project axons that follow normal pathways, and progenitor cells divide and give rise to normal progeny cells. By generating other transgenic lines in which the hsp70 promoter regulates genes of interest, it should be possible to examine the in vivo activity of the gene products by laser-inducing specific cells to express them in zebrafish embryos. As a first test, we laser-induced single muscle cells to make zebrafish Sema3A1, a semaphorin that is repulsive for specific growth cones, in a hsp70-sema3A1 transgenic line of zebrafish and found that extension by the motor axons was retarded by the induced muscle.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".