mRNA fluorescence in situ hybridization to determine overlapping gene expression in whole‐mount mouse embryos
Bibliographic record
Abstract
BACKGROUND: Whole-mount in situ hybridization (ISH) is a prevalent tool to examine the spatial distribution of gene transcripts in intact embryos. Chromogenic-based methods of signal development are commonly used in mouse embryos because of their high sensitivity. Fluorescence techniques, however, offer several advantages over chromogenic methods including the ability to visualize multiple signals in a specimen at once. RESULTS: We describe a procedure for fluorescence in situ hybridization (FISH) for whole mouse embryos up to embryonic day 13.5. We show that this approach successfully produces a bright expression signal for several genes, validating the procedure in multiple tissues. Further, we show that double FISH can be used to visualize the expression of two genes in a single embryo by determining that Hoxd13 and Shh are co-expressed in both the limb bud and the hindgut. Finally, we demonstrate that FISH can be paired with confocal microscopy to take optical sections of interior regions of the embryo. CONCLUSIONS: FISH is a valid alternative to chromogenic-based ISH for visualizing gene expression in whole mouse embryos. This work provides a framework to add additional fluorescence signals in the mouse such as visualizing both mRNA and protein by pairing the procedure with immunofluorescence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".