Modulating the Kinetics of a Fluorescence Anisotropy Immunoassay Using Tracer Point Mutations to Measure Human C-Peptide Secretion On-Chip
Bibliographic record
Abstract
Fluorescence anisotropy immunoassays (FAIAs) are widely used to quantify the concentration of target proteins based on competitive binding to a monoclonal antibody with a tracer. We recently designed an FAIA to measure mouse C-peptide secretion from living islets in a continuous-flow microfluidic device (InsC-chip). To develop a similar assay for human C-peptide, we selected two monoclonal antibodies (Ab1 and Ab2) that initially showed a low dynamic range and slow kinetics. One option to measure this assay on-chip was to extend the length of the mixing channels. However, this strategy would increase dispersion and ultimately lower the temporal resolution of secreted C-peptide. To shorten the time-to-reach equilibrium for Ab1, we reengineered the tracer based on a comparison between the human and mouse C-peptide sequences, resulting in >30-fold shorter time-to-reach equilibrium. To increase the relatively small dynamic range for Ab2, we used partial epitope mapping and targeted point mutations to increase the dynamic range by 45%. Finally, we validated both FAIAs by measuring depolarization-induced secretion from individual human stem cell-derived islets in our InsC-chip. These data demonstrate a strategy to optimize FAIA kinetics to be measured in continuous-flow microfluidic devices.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".