Photoreversible Surfaces to Regulate Cell Adhesion
Bibliographic record
Abstract
We report the development of a photoreversible cell culture substrate. We demonstrate the capacity to modify the adhesivity of the substrate using light, altering its capacity to support cell growth. Polyelectrolyte multilayers (PEMs) were used to produce tunable substrates of different thickness and matrix stiffness, which have different intrinsic capacities to support cell adhesion and survival. Surfaces were top-coated with a poly(acrylic acid)-poly(allylamine hydrochloride) polyelectrolyte bilayer functionalized with a small fraction (<1%) of an azobenzene-based photoswitchable sidegroup, which included the cell-adhesive three-amino-acid peptide RGD. Irradiation with light-induced geometric switching of the azo bond, resulting in changes to RGD exposure and consequently to cell adhesion and survival, was investigated on a variety of surfaces of different thickness and stiffness. Substrate stiffness, as modified by the thickness, had a significant influence on the adhesion of NIH 3T3 cells, consistent with previous studies. However, by disrupting the isomerization state of the azobenzene-linked RGD and exposing it to the surface, cell adhesion and survival could be enhanced up to 40% when the positioning of the RGD peptide was manipulated on the softest substrates. These findings identify permissive, yet less-than-optimal, cell culture substrate conditions that can be substantially enhanced using noninvasive modification of the substrate triggered by light. Indeed, where cell adhesion was tuned to be suboptimal under baseline conditions, the light-induced triggers displayed the most enhanced effect, and identification of this 'Goldilocks zone' was key to enabling light triggering.
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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) | 0.001 | 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 | 0.001 |
| 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".