Nanoscale surface topography reduces focal adhesions and cell stiffness by enhancing integrin endocytosis
Bibliographic record
Abstract
Abstract Both substrate stiffness and surface topography regulate cell behavior through mechanotransduction signaling pathways. Such intertwined effects suggest that engineered surface topographies might substitute or cancel the effects of substrate stiffness in biomedical applications. However, the mechanisms by which cells recognize topographical features are not fully understood. Here we demonstrate that the presence of nanotopography drastically alters cell behavior such that neurons and stem cells cultured on rigid glass substrates behave as if they were on soft hydrogels. We further show that rigid nanotopography resembles the effect of soft hydrogels in reducing cell stiffness and membrane tension as measured by atomic force microscopy. Finally, we demonstrate that nanotopography reduces focal adhesions and cell stiffness by enhancing the endocytosis and the subsequent removal of integrin receptors. This mechanistic understanding will support the rational design of nanotopography that directs cells on rigid materials to behave as if they were on soft ones. TOC graphic
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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) | 0.003 | 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".