CCR2 recruits an inflammatory macrophage subpopulation critical for angiogenesis in tissue repair
Bibliographic record
Abstract
Monocytes/macrophages are critical in orchestrating the tissue-repair response. However, the mechanisms that govern macrophage regenerative activities during the sequential phases of repair are largely unknown. In the present study, we examined the dynamics and functions of diverse monocyte/macrophage phenotypes during the sequential stages of skin repair. By combining the analysis of a new CCR2-eGFP reporter mouse model with conditional mouse mutants defective in myeloid cell-restricted CCR2 signaling or VEGF-A synthesis, we show herein that among the large number of inflammatory CCR2(+)Ly6C(+) macrophages that dominate the early stage of repair, only a small fraction strongly expresses VEGF-A that has nonredundant functions for the induction of vascular sprouts. The switch of macrophage-derived VEGF-A during the early stage of tissue growth toward epidermal-derived VEGF-A during the late stage of tissue maturation was critical to achieving physiologic tissue vascularization and healing progression. The results of the present study provide new mechanistic insights into CCR2-mediated recruitment of blood monocyte subsets into damaged tissue, the dynamics and functional consequences of macrophage plasticity during the sequential repair phases, and the complementary role of macrophage-derived VEGF-A in coordinating effective tissue growth and vascularization in the context of tissue-resident wound cells. Our findings may be relevant for novel monocyte-based therapies to promote tissue vascularization.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".