High Throughput Whole Mount In Situ Hybridization of Zebrafish Embryos for Analysis of Tissue-Specific Gene Expression Changes After Environmental Perturbation
Bibliographic record
Abstract
Whole mount in situ hybridization is a process that allows the visualization of gene expression (mRNA) within the cells of an intact organism. By comparing gene expression domains between organisms that have been subjected to different environmental conditions, an understanding of the cellular and tissue-specific effects of these environmental exposures can be identified. This technique is complementary to gene expression profiling techniques such as DNA microarrays which can usually provide information only on the differential levels of gene expression within an organism or tissue. In the case of whole mount in situ hybridization there is the added ability to detect differences in the distribution of cells, within a whole organism, expressing a particular gene. Subtle changes in the distribution of cells expressing a gene may not be reflected in the overall level of gene expression when RNA samples are retrieved from a whole organism and assayed. Exploitation of automation technology has made whole mount in situ hybridization a procedure that is amiable to high-throughput genomic studies. Combining automation with computer-aided image analysis makes this an efficient strategy for quantifying subtle changes in tissues and genes expression that can result from sublethal exposures to environmental toxins, for example.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".