Monocyte and macrophage derived myofibroblasts: Is it fate? A review of the current evidence
Bibliographic record
Abstract
Since the discovery of the myofibroblast over 50 years ago, much has been learned about its role in wound healing and fibrosis. Its origin, however, remains controversial, with a number of progenitor cells being proposed. Macrophage-myofibroblast transition (MMT) is a recent term coined in 2014 that describes the mechanism through which macrophages, derived from circulating monocytes originating in the bone marrow, transformed into myofibroblasts and contributed to kidney fibrosis. Over the past years, several studies have confirmed the existence of MMT in various systems, suggesting that MMT could potentially occur in all fibrotic conditions and constitute a reasonable therapeutic target to prevent progressive fibrotic disease. In this perspective, we examined recent evidence supporting the notion of MMT in both human disease and experimental models across organ systems. Mechanistic insight from these studies and information from in vitro studies is provided. The findings substantiating plausible MMT showcased the co-expression of macrophage and myofibroblast markers, including CD68 or F4/80 (macrophage) and α-SMA (myofibroblast), in fibroblast-like cells. Furthermore, fate-mapping experiments in murine models exhibiting myeloid-derived myofibroblasts in the tissue further provide direct evidence for MMT. Additionally, we provide some evidence from single cell RNA sequencing experiments confirmed by fluorescent in situ hybridisation studies, showing monocyte/macrophage and myofibroblast markers co-expressed in lung tissue from patients with fibrotic lung disease. In conclusion, MMT is likely a significant contributor to myofibroblast formation in wound healing and fibrotic disease across organ systems. Circulating precursors including monocytes and the molecular mechanisms governing MMT could constitute valid targets and provide insight for the development of novel antifibrotic therapies; however, further understanding of these processes is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".