Loss of Vimentin Enhances Cell Motility through Small Confining Spaces
Bibliographic record
Abstract
The migration of cells through constricting spaces or along fibrous tracks in tissues is important for many biological processes and depends on the mechanical properties of a cytoskeleton made up of three different filaments: F-actin, microtubules, and intermediate filaments. The signaling pathways and cytoskeletal structures that control cell motility on 2D are often very different from those that control motility in 3D. Previous studies have shown that intermediate filaments can promote actin-driven protrusions at the cell edge, but have little effect on overall motility of cells on flat surfaces. They are however important for cells to maintain resistance to repeated compressive stresses that are expected to occur in vivo. Using mouse embryonic fibroblasts derived from wild-type and vimentin-null mice, it is found that loss of vimentin increases motility in 3D microchannels even though on flat surfaces it has the opposite effect. Atomic force microscopy and traction force microscopy experiments reveal that vimentin enhances perinuclear cell stiffness while maintaining the same level of acto-myosin contractility in cells. A minimal model in which a perinuclear vimentin cage constricts along with the nucleus during motility through confining spaces, providing mechanical resistance against large strains that could damage the structural integrity of cells, is proposed.
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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".