Cell surface profiling of cultured cells by direct hydrazide capture of oxidized glycoproteins
Bibliographic record
Abstract
Glycoproteins are a particularly interesting subset of the cellular proteome as a high proportion of proteins present on the extracellular cell surface are glycosylated. These cell surface proteins are ideal targets for biologic drug therapies or for diagnostics tests. Here, we describe a modification of the well-described Cell Surface Capture (CSC) method for the selective isolation and identification of cell surface glycoproteins that contain N-linked carbohydrates. This modification, which we refer to as Direct Cell Surface Capture (D-CSC), is based on oxidation of cell surface glycans on intact cells, followed by direct conjugation of the oxidized oligosaccharides to a solid support using hydrazide chemistry, with no biotinylation step. As a proof-of-principle, we applied D-CSC to the analysis of cell surface membrane proteins of three adherent cancer cell lines (A549, OVCAR3, and U87MG) and compared our results to those published using the well-established Cell Surface Capture (CSC) method, demonstrating comparable selectivity for cell surface proteins. •A method enabling the identification of cell surface proteins from cells in culture is described.•Application of this method to profile the cell surface on three different cancer cell lines is included.
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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) | 0.000 | 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) | 0.001 | 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".