Traction and attraction: haptotaxis substrates collagen and fibronectin interact with chemotaxis by HGF to regulate myoblast migration in a microfluidic device
Bibliographic record
Abstract
Cell migration is central to development, wound healing, tissue regeneration, and immunity. Despite extensive knowledge of muscle regeneration, myoblast migration during regeneration is not well understood. C2C12 mouse myoblast migration and morphology were investigated using a triple-docking polydimethylsiloxane-based microfluidic device in which cells moved under gravity-driven laminar flow on uniform (=) collagen (CN=), fibronectin (FN=), or opposing gradients (CN-FN or FN-CN). In haptotaxis experiments, migration was faster on FN= than on CN=. At 10 h, cells were more elongated on FN-CN and migration was faster than on the CN-FN substrate. Net migration distance on FN-CN at 10 h was greater than on CN-FN, as cells rapidly entered the channel as a larger population (bulk-cell movement, wave 1). Hepatocyte growth factor (HGF) stimulated rapid chemotaxis on FN= but not CN=, increasing migration speed at 10 h early in the channel at low HGF in a steep HGF gradient. HGF accelerated migration on FN= and bulk-cell movement on both uniform substrates. An HGF gradient also slowed cells in wave 2 moving on FN-CN, not CN-FN. Both opposing-gradient substrates affected the shape, speed, and net distance of migrating cells. Gradient and uniform configurations of HGF and substrate differentially influenced migration behavior. Therefore, haptotaxis substrate configuration potently modifies myoblast chemotaxis by HGF. Innovative microfluidic experiments advance our understanding of intricate complexities of myoblast migration. Findings can be leveraged to engineer muscle-tissue volumes for transplantation after serious injury. New analytical approaches may generate broader insights into cell migration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".