Biotinylation of the Fcγ receptor ectodomains by mammalian cell co-transfection: application to the development of a surface plasmon resonance-based assay
Bibliographic record
Abstract
We here report the production of four biotinylated Fcγ receptor (FcγR) ectodomains and their subsequent stable capture on streptavidin-biosensor surfaces. For receptor biotinylation, we first describe an in-cell protocol based on the co-transfection of two plasmids corresponding to one of the FcγR ectodomains and the BirA enzyme in mammalian cells. This strategy is compared with a standard sequential in vitro enzymatic biotinylation with respect to biotinylation level and yield. Biotinylated FcγR ectodomains that have been prepared with both strategies are then compared by analytical ultracentrifugation and surface plasmon resonance (SPR) analyses. Overall, we demonstrate that in-cell biotinylation is an interesting alternative to standard biotinylation protocol, as it requires less purification steps while yielding higher titers. Finally, biotin-tagged FcγRs produced with the in-cell approach are successfully applied to the development of SPR-based assays to evaluate the impact of the glycosylation pattern of monoclonal antibodies on their interaction with CD16a and CD64. In that endeavor, we unambiguously observe that highly galactosylated trastuzumab (TZM-gal), non-glycosylated trastuzumab (TZM-NG), and reference trastuzumab are characterized by different kinetic profiles upon binding to CD16a and CD64 that had been captured at the biosensor surface via their biotin tag. More precisely, while TZM-NG binding to CD16a was not detected, TZM-gal formed a more stable complex with CD16a than our reference TZM. In contrast, both glycosylated TZM bound to captured CD64 in a stable and similar fashion, whereas the interaction of their non-glycosylated form with CD64 was characterized by a higher dissociation rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".