The rotation of mouse myoblast nuclei is dependent on substrate elasticity
Bibliographic record
Abstract
The complex interplay of biochemical signaling and mechanical traction forces regulate the position of cellular nuclei. Although the phenomenon of nuclear rotation has been observed for many years, the influence of substrate elasticity was unknown. We discovered another layer of complexity to this phenomenon: nuclear rotation is dependent on substrate elasticity. Nuclear rotation is drastically reduced on physiologically relevant stiffnesses. Here, we studied nuclear rotation in mouse C2C12 myoblasts cultured on soft substrates designed to mimic resting tissue (∼26 kPa) and on hard glass substrates. We examined the roles of the actin and microtubule cytoskeleton on the presence and dynamics of nuclear rotation in these two different microenvironments. We demonstrated the clear dependence of nuclear rotation dynamics on matrix stiffness. These results will have important implications for the design of future studies of nuclear rotation and our understanding of the phenomenon as a whole. Unnaturally, hard substrates do not only fail to mimic the in vivo microenvironment, but can also induce cellular processes that would not normally occur in the natural cellular environment.
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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".