Structural Stabilization of Tissue for Embryo Phenotyping Using Micro-CT with Iodine Staining
Bibliographic record
Abstract
The International Mouse Phenotyping Consortium has been established to conduct large-scale phenotyping of the approximately 23,000 single-gene knockout mice generated by the International Knockout Mouse Consortium to investigate the role of each gene in the mouse genome. Of the generated mouse lines, 30% are predicted to be embryonic lethal, requiring the implementation of imaging techniques and analysis tools specific to late gestation mouse embryo phenotyping. A well-adopted technique combines the use of iodinated contrast solutions and micro-computed tomography imaging. This simple iodine immersion technique provides superior soft-tissue contrast enhancement, however, the hypertonic nature of iodine promotes dehydration causing moderate to severe tissue deformation. Here, we combine the stabilizing properties of a hydrogel mesh with the enhanced contrast properties of iodine. The protocol promotes cross linking of tissue through formaldehyde fixation and the linking of hydrogel monomers to biomolecules. As a result, the hydrogel supports tissue structure and preserves its conformation taking advantage of iodine-enhanced soft tissue contrast to produce high quality mouse embryo images with minimal tissue distortion. Hydrogel stabilization substantially reduces intersample anatomical variation of mature mouse embryos subjected to iodine preparation protocols. A 20% and 50% reduction in intersample variation of normalized brain and lung volume is achieved through hydrogel stabilization, as well as a 20% reduction in variation in overall embryo anatomy as measured through image registration methods. This increases the sensitivity of computer automated analysis to reveal significant anatomical differences between mutant and wild-type mice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".