Protocol for single-molecule FISH in the developing mouse retinal vasculature
Bibliographic record
Abstract
The developing vasculature of the post-natal mouse retina is a powerful model to discover mechanisms of vessel formation and to test modulators of neovascularization. We present a protocol for single-molecule fluorescent in situ hybridization (smFISH) in whole-mount mouse retinas enabling the detection of individual mRNAs in vascular endothelial cells. We describe procedures from initial retina preparation to smFISH and detection. Our approach offers simple steps to overcome challenges related to tissue permeabilization, mRNA and protein co-detection, and post-acquisition image processing. • Guidance on dissecting neonatal mouse retinas and preparation for smFISH • Step-by-step protocol for detecting mRNAs in the retinal endothelium • Steps for mRNA and protein co-detection in the developing retinal vasculature Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The developing vasculature of the post-natal mouse retina is a powerful model to discover mechanisms of vessel formation and to test modulators of neovascularization. We present a protocol for single-molecule fluorescent in situ hybridization (smFISH) in whole-mount mouse retinas enabling the detection of individual mRNAs in vascular endothelial cells. We describe procedures from initial retina preparation to smFISH and detection. Our approach offers simple steps to overcome challenges related to tissue permeabilization, mRNA and protein co-detection, and post-acquisition image processing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.022 |
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".