Advances in cell surface glycoengineering reveal biological function
Bibliographic record
Abstract
Cell surface glycans are critical mediators of cell-cell, cell-ligand, and cell-pathogen interactions. By controlling the set of glycans displayed on the surface of a cell, it is possible to gain insight into the biological functions of glycans. Moreover, control of glycan expression can be used to direct cellular behavior. While genetic approaches to manipulate glycosyltransferase gene expression are available, their utility in glycan engineering has limitations due to the combinatorial nature of glycan biosynthesis and the functional redundancy of glycosyltransferase genes. Biochemical and chemical strategies offer valuable complements to these genetic approaches, notably by enabling introduction of unnatural functionalities, such as fluorophores, into cell surface glycans. Here, we describe some of the most recent developments in glycoengineering of cell surfaces, with an emphasis on strategies that employ novel chemical reagents. We highlight key examples of how these advances in cell surface glycan engineering enable study of cell surface glycans and their function. Exciting new technologies include synthetic lipid-glycans, new chemical reporters for metabolic oligosaccharide engineering to allow tandem and in vivo labeling of glycans, improved chemical and enzymatic methods for glycoproteomics, and metabolic glycosyltransferase inhibitors. Many chemical and biochemical reagents for glycan engineering are commercially available, facilitating their adoption by the biological community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".