Measuring binding kinetics of surface-bound molecules using the surface plasmon resonance technique

Surface plasmon resonance has long been used to study fluid-phase biomolecular interactions. While the Biacore biosensor is well established for such measurements, its application to surface-bound molecules has remained limited. Prior research has shown that SPR can detect binding events in solution, but cell surface interactions have been less quantified. No prior work had resolved how to measure binding kinetics when both molecules are immobilized. That uncertainty drove the need for a new approach to capture cell-level interactions. Existing methods lack the ability to predict per-cell kinetic rates. This gap motivated the development of a cellular kinetic model. The study introduces a novel way to measure binding and dissociation rates at the cell surface. The approach combines experimental SPR with a new modeling framework.

The aim of this work is to develop a method for measuring binding kinetics of surface-bound molecules using SPR. The specific problem is the lack of reliable tools to quantify cell-level interactions. The motivation comes from the limitations of current SPR applications, which focus on fluid-phase interactions. This study proposes a new approach to measure cell binding rates. The method uses human red blood cells and a biosensor chip. The goal is to predict on and off-rates at the cellular level. The model is designed to fit sensorgram data and extract kinetic parameters. The study seeks to validate the feasibility of this new approach.

The study uses SPR technology with a Biacore 3000 biosensor. Human red blood cells are coated with bovine serum albumin. Anti-BSA monoclonal antibodies are immobilized on the sensor chip. The cells are allowed to bind and then dissociate from the chip surface. Sensorgrams are collected during binding and debinding processes. A cellular kinetic model is developed to analyze the sensorgram data. The model fits the data and predicts binding and dissociation rates. The effect of flow duration, flow rate, and site density is tested systematically.

The cellular kinetic model successfully fits the sensorgram data. The model predicts both on and off-rates as well as binding affinities. Binding and dissociation rates are extracted from the curve fitting process. The study shows that flow duration affects binding kinetics. Flow rate and site density also influence the measured rates. The model accurately captures the dependence of these parameters. Experimental validation confirms the model's reliability. The approach is feasible for measuring cell-level interactions.

The authors propose that the cellular kinetic model is a reliable tool for measuring binding kinetics. The model fits the sensorgram data well and predicts kinetic rates. The study demonstrates the feasibility of using SPR for surface-bound interactions. The model accounts for flow duration, flow rate, and site density effects. The results suggest that the new approach can be used for cell-level binding studies. The method is validated through systematic testing of multiple parameters. The findings support the use of SPR for measuring cell binding kinetics. The approach provides a new way to quantify interactions at the cellular level.