Mapping the relationship between proliferation and morphology in the developing mouse face
Bibliographic record
Abstract
There is a long‐standing prediction that small changes in proliferation and apoptosis during the time frame of facial morphogenesis act to shape the face. Further, many studies show genetic alterations that cause structural birth defects affect local proliferation or apoptosis. Yet, it is unclear how much of local change in regional proliferation would be necessary to cause a defect. Here, we set out to understand the relationship between growth, morphology and proliferation and test that prediction that targeted proliferation shapes the developing face by quantifying proliferation and apoptosis in 3D and relating it to the growth of the face. We use whole mount staining for proliferation and apoptosis markers, whole tissue clearing methods, lightsheet microscopy and atlas and machine learning based quantification methods to identify individual proliferating or apoptotic nuclei within a 3D tissue structure at a set time point. We also employee geometric morphometric analysis of the same tissue structure to quantify overall morphology. By collecting data at various time points across facial development (E9.5–E11.5) and quantifying the age of each embryo, we are able to relate cell biological level growth to tissue level growth and morphological change and relate these two parameters in a way not performed previously. Support or Funding Information NIH NIDCR R01‐DE019638 to RM and BH, NSERC Discovery to BH, and CIHR Foundation grant to BH and RM, CIHR postdoctoral fellowship to RMG. Atlas based quantification of proliferation: A) Maximum Projection of the embryo highlighting the external morphology ‐ lateral view. B) Proliferation staining (phospho‐Histone H3) ‐ lateral view. C–D) Surface morphology of the atlas (n=5) C ‐ anterior view, D ‐ Lateral view. E–F) Heat map of proliferating cells (no density correction) E ‐ anterior view, F ‐ Lateral view. Figure 1
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".