Whole Mount RNA Fluorescent <em>in situ</em> Hybridization of <em>Drosophila</em> Embryos
Bibliographic record
Abstract
Assessing the expression pattern of a gene, as well as the subcellular localization properties of its transcribed RNA, are key features for understanding its biological function during development. RNA in situ hybridization (RNA-ISH) is a powerful method used for visualizing RNA distribution properties, be it at the organismal, cellular or subcellular levels. RNA-ISH is based on the hybridization of a labeled nucleic acid probe (e.g. antisense RNA, oligonucleotides) complementary to the sequence of an mRNA or a non-coding RNA target of interest. As the procedure requires primary sequence information alone to generate sequence-specific probes, it can be universally applied to a broad range of organisms and tissue specimens. Indeed, a number of large-scale ISH studies have been implemented to document gene expression and RNA localization dynamics in various model organisms, which has led to the establishment of important community resources. While a variety of probe labeling and detection strategies have been developed over the years, the combined usage of fluorescently-labeled detection reagents and enzymatic signal amplification steps offer significant enhancements in the sensitivity and resolution of the procedure. Here, we describe an optimized fluorescent in situ hybridization method (FISH) employing tyramide signal amplification (TSA) to visualize RNA expression and localization dynamics in staged Drosophila embryos. The procedure is carried out in 96-well PCR plate format, which greatly facilitates the simultaneous processing of large numbers of samples.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".