Reprogramming the glycocalyx: Advances in glycoengineering for immunomodulation and regenerative medicine
Bibliographic record
Abstract
This review explores the evolving landscape of glycoengineering on the cell surface, a pivotal field in translational medicine with profound implications for immunomodulation and regenerative therapies. The cell membrane and glycocalyx, composed of proteins, lipids, and glycans, govern cellular interactions, immune recognition, and tissue regeneration. Given the dynamic nature and important biological roles of these structures, precise glycoengineering strategies are essential for modulating cell behavior without compromising function and safety. We delve into genetic and non-genetic approaches to manipulation of cell surface patterns, including targeted glycan and glycosaminoglycan modifications, metabolic glycoengineering, the use of synthetic glycopolymers, and bio-orthogonal glycan functionalization. Enzymatic strategies for glycan cleavage and addition further expand the toolkit for controlled cell surface remodeling. These advancements hold promise for cancer immunotherapy, organ transplantation, islet transplantation, and cardiovascular disease treatments. Despite the considerable potential of glycoengineering, a number of challenges, such as membrane instability and unintended cellular alterations remain, necessitating precise control over modifications. This review critically evaluates emerging strategies, discussing their limitations and future directions in biomedical and bioengineering applications. By integrating glycocalyx biology with advanced engineering techniques, we provide a roadmap for harnessing cell surface engineering to enhance therapeutic efficacy, immune modulation, biomaterial innovation, and regenerative medicine. • Glycoengineering and genetic engineering enables precise control of cell surface glycans and GAGs. • Bio-orthogonal and mucin-mimetic tools allow dynamic cell surface modulation. • Enzymatic glycan editing supports targeted therapies in major disease areas. • Integrating glycocalyx biology with molecular and biomaterial engineering advances regenerative medicine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".