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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".