The Myofibroblast Fate of Therapeutic Mesenchymal Stromal Cells: Regeneration, Repair, or Despair?
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) can be isolated from various tissues of healthy or patient donors to be retransplanted in cell therapies. Because the number of MSCs obtained from biopsies is typically too low for direct clinical application, MSC expansion in cell culture is required. However, ex vivo amplification often reduces the desired MSC regenerative potential and enhances undesired traits, such as activation into fibrogenic myofibroblasts. Transiently activated myofibroblasts restore tissue integrity after organ injury by producing and contracting extracellular matrix into scar tissue. In contrast, persistent myofibroblasts cause excessive scarring-called fibrosis-that destroys organ function. In this review, we focus on the relevance and molecular mechanisms of myofibroblast activation upon contact with stiff cell culture plastic or recipient scar tissue, such as hypertrophic scars of large skin burns. We discuss cell mechanoperception mechanisms such as integrins and stretch-activated channels, mechanotransduction through the contractile actin cytoskeleton, and conversion of mechanical signals into transcriptional programs via mechanosensitive co-transcription factors, such as YAP, TAZ, and MRTF. We further elaborate how prolonged mechanical stress can create persistent myofibroblast memory by direct mechanotransduction to the nucleus that can evoke lasting epigenetic modifications at the DNA level, such as histone methylation and acetylation. We conclude by projecting how cell culture mechanics can be modulated to generate MSCs, which epigenetically protected against myofibroblast activation and transport desired regeneration potential to the recipient tissue environment in clinical therapies.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".