Genetically Encoded Toolbox for Glycocalyx Engineering: Tunable Control of Cell Adhesion, Survival, and Cancer Cell Behaviors
Bibliographic record
Abstract
The glycocalyx is a coating of protein and sugar on the surface of all living cells. Dramatic perturbations to the composition and structure of the glycocalyx are frequently observed in aggressive cancers. However, tools to experimentally mimic and model the cancer-specific glycocalyx remain limited. Here, we develop a genetically encoded toolkit to engineer the chemical and physical structure of the cellular glycocalyx. By manipulating the glycocalyx structure, we are able to switch the adhesive state of cells from strongly adherent to fully detached. Surprisingly, we find that a thick and dense glycocalyx with high O -glycan content promotes cell survival even in a suspended state, characteristic of circulating tumor cells during metastatic dissemination. Our data suggest that glycocalyx-mediated survival is largely independent of receptor tyrosine kinase and mitogen activated kinase signaling. While anchorage is still required for proliferation, we find that cells with a thick glycocalyx can dynamically attach to a matrix scaffold, undergo cellular division, and quickly disassociate again into a suspended state. Together, our technology provides a needed toolkit for engineering the glycocalyx in glycobiology and cancer research.
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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".