Three-dimensional niche stiffness synergizes with Wnt7a to modulate the extent of satellite cell symmetric self-renewal divisions
Bibliographic record
Abstract
Satellite cells (SCs), the resident adult stem cells of skeletal muscle, are required for tissue repair throughout life. While many signaling pathways are known to control SC self-renewal, less is known about the mechanisms underlying the spatiotemporal control of self-renewal during skeletal muscle repair. Here, we measured biomechanical changes that accompany skeletal muscle regeneration and determined the implications on SC fate. Using atomic force microscopy, we quantified a 2.9-fold stiffening of the SC niche at time-points associated with planar-oriented symmetric self-renewal divisions. Immunohistochemical analysis confirms increased extracellular matrix deposition within the basal lamina. To test whether three-dimensional (3D) niche stiffness can alter SC behavior or fate, we embedded isolated SC-associated muscle fibers within biochemically inert agarose gels tuned to mimic native tissue stiffness. Time-lapse microscopy revealed that a stiff 3D niche significantly increased the proportion of planar-oriented divisions, without effecting SC viability, fibronectin deposition, or fate change. We then found that 3D niche stiffness synergizes with WNT7a, a biomolecule shown to control SC symmetric self-renewal divisions via the noncanonical WNT/planar cell polarity pathway, to modify stem cell pool expansion. Our results provide new insights into the role of 3D niche biomechanics in regulating SC fate choice.
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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".