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bioRxiv · 10.1101/2024.10.18.619167

Development of an on-chip fluorescence anisotropy immunoassay for human C-peptide secretion reveals a general roadmap for tracer optimization

Wang, Y.·Gulati, N.·Regeenes, R.·Migliorini, A.·Oakie, A.·Nostro, M. C.·Rocheleau, J. V.
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Abstract

Fluorescence anisotropy immunoassays (FAIAs) are widely used to quantify the concentration of target proteins based on competition with a tracer in binding a monoclonal antibody. We recently designed an FAIA to measure mouse C-peptide secretion from living islets in a continuous-flow microfluidic device (InsC-chip). To develop an assay for human C-peptide, our initial selection of antibody-tracer pairings revealed the need to optimize both the dynamic range and the binding kinetics to measure the assay on-chip effectively. Here, we present strategies for developing an on-chip FAIA using two different monoclonal antibodies to achieve both a large dynamic range and high temporal resolution. The two monoclonal antibodies (Ab1 & Ab2) to human C-peptide initially showed low dynamic range and slow kinetics, preventing them from being used in an on-chip assay. 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 insulin secretion from individual hESC-islets in our InsC-chip. These strategies provide a general roadmap for developing FAIAs with high sensitivity and sufficiently fast kinetics to be measured in continuous-flow microfluidic devices.

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Wang, Y., Gulati, N., Regeenes, R., Migliorini, A., Oakie, A., Nostro, M. C., Rocheleau, J. V.. 2024-10-22. Development of an on-chip fluorescence anisotropy immunoassay for human C-peptide secretion reveals a general roadmap for tracer optimization. https://doi.org/10.1101/2024.10.18.619167

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