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 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".