Temporal dynamics of macrophages transcriptional profiles during zebrafish wound healing
Bibliographic record
Abstract
Abstract Macrophages participate to wound healing by contributing to host defense and orchestrating inflammation and repair. They adapt dynamically to the wound microenvironment by adopting diverse polarization states, which are determinant of wound outcome, influencing whether healing is successful or becomes chronic. The zebrafish embryo, widely used for live imaging of immune responses, is a powerful model to study macrophage behavior after injury. However, the transcriptional landscape of polarized macrophages in this model during wound healing remains insufficiently characterized. Here, we employ bulk RNA sequencing to characterize macrophage transcriptional programs following tail fin wounding a robust model for studying sterile inflammation. Our findings reveal that zebrafish macrophages undergo large transcriptomic changes along different wound healing phases, particularly between 2 and 5 hours post-amputation, suggesting a fast reprogramming leading to different functional states. We further show that, at 2h, macrophages acquire a pro-inflammatory profile with a gene signature closed to M1 signature. At 5h, macrophages express genes involved in immunoregulation and healing associated with shutoff of pro-inflammatory pathways and the activation of glucose and glycogen metabolism. Finally, we show that macrophage reprogramming becomes deeply attenuated by 29h. Our findings provide a foundation for understanding macrophage polarization in zebrafish, revealing underpinning molecular mechanisms, including both specific and evolutionarily conserved pathways with a potential impact on translational medicine.
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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".