Cross talk between matrix elasticity and mechanical force regulates myoblast traction dynamics
Bibliographic record
Abstract
Growing evidence suggests that critical cellular processes are profoundly influenced by the cross talk between extracellular nanomechanical forces and the material properties of the cellular microenvironment. Although many studies have examined either the effect of nanomechanical forces or the material properties of the microenvironment on biological processes, few have investigated the influence of both. Here, we performed simultaneous atomic force microscopy and traction force microscopy to demonstrate that muscle precursor cells (myoblasts) rapidly generate a significant increase in traction when stimulated with a local 10 nN force. Cells were cultured and nanomechanically stimulated on hydrogel substrates with controllable local elastic moduli varying from ~16-89 kPa, as confirmed with atomic force microscopy. Importantly, cellular traction dynamics in response to nanomechanical stimulation only occurred on substrates that were similar to the elasticity of working muscle tissue (~64-89 kPa) as opposed to substrates mimicking resting tissue (~16-51 kPa). The traction response was also transient, occurring within 30 s, and dissipating by 60 s, during constant nanomechanical stimulation. The observed biophysical dynamics are very much dependent on rho-kinase and myosin-II activity and likely contribute to the physiology of these cells. Our results demonstrate the fundamental ability of cells to integrate nanoscale information in the cellular microenvironment, such as nanomechanical forces and substrate mechanics, during the process of mechanotransduction.
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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".