Quantitative 3D Imaging of the Cranial Microvascular Environment at Single-Cell Resolution
Bibliographic record
Abstract
Abstract Vascularization is critical for skull development, maintenance, and healing. Yet, there remains a significant knowledge gap in the relationship of blood vessels to cranial skeletal progenitors during these processes. Here, we introduce a quantitative 3D imaging platform to enable visualization and analysis of high-resolution datasets (>100 GB) throughout the entire murine calvarium. Using this technique, we provide single-cell resolution 3D maps of vessel phenotypes and skeletal progenitors in the frontoparietal cranial bones. Through these high-resolution datasets, we demonstrate that CD31 hi Emcn hi vessels are spatially correlated with both Osterix+ and Gli1+ skeletal progenitors during postnatal growth, healing, and stimulated remodeling, and are concentrated at transcortical canals and osteogenic fronts. Interestingly, this relationship was weakened in mice with a conditional knockout of PDGF-BB in TRAP+ osteoclasts, suggesting a potential role for osteoclasts in maintaining the native cranial microvascular environment. Our findings provide a foundational framework for understanding how blood vessels and skeletal progenitors spatially interact in cranial bone, and will enable more targeted studies into the mechanisms of skull disease pathologies and treatments. Additionally, our technique can be readily adapted to study numerous cell types and investigate other elusive phenomena in cranial bone biology.
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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 | 0.000 | 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 | 0.000 |
| 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".