One-Step Selective Labeling of Native Cell Surface Sialoglycans by Exogenous α2,8-Sialylation
Bibliographic record
Abstract
Exo-enzymatic glycan labeling strategies have emerged as versatile tools for efficient and selective installation of terminal glyco-motifs onto live cell surfaces. Through employing specific enzymes and nucleotide-sugar probes, cells can be equipped with defined glyco-epitopes for modulating cell function or selective visualization and enrichment of glycoconjugates. Here, we identify Campylobacter jejuni sialyltransferase Cst-II I53S as a tool for cell surface glycan modification, expanding the exo-enzymatic labeling toolkit to include installation of α2,8-disialyl epitopes. Labeling with Cst-II was achieved with biotin- and azide-tagged CMP-Neu5Ac derivatives on a model glycoprotein and native sialylated cell surface glycans across a panel of cell lines. The introduction of modified Neu5Ac derivatives onto cells by Cst-II was also retained on the surface for 6 h. By examining the specificity of Cst-II on cell surfaces, it was revealed that the α2,8-sialyltransferase primarily labeled N-glycans, with O-glycans labeled to a lesser extent, and there was an apparent preference for α2,3-linked sialosides on cells. This approach thus broadens the scope of tools for selective exo-enzymatic labeling of native sialylated glycans and is highly amenable for the construction of cell-based arrays.
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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 | 0.001 |
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".