A cell-based immunobiosensor with engineered molecular recognition--Part III: Engineering molecular recognition.
We have been studying the feasibility of exploiting the recognition and amplification abilities of living immune cells for the development of hybrid immunosensors. Our group has previously reported that cell metabolic activation responses, induced by calcium ionophore A23187, can be directly transduced using calorimetric transducers, and that enzyme systems can be integrated to enhance sensing response time and output. In this study our goal was to determine the feasibility of transducing the thermal activation responses of mast cells molecularly engineered to a specific antigen. Rat peritoneal mast cells were sensitized to the model antigenic analyte dinitrophenylated-albumin (DNP-A), with monoclonal anti-DNP-A IgE, and challenged with antigen at final concentrations of 10 or 100 ng/ml. The addition of antigen resulted in the molecular triggering of cell activation, yielding thermal responses similar to those obtained previously with the ionophore model. A peak thermal response of 1.7 microW/5 x 10(5) cells was obtained within approximately 7 min of addition of antigen. The incorporation of selected amplification enzyme systems increased peak thermal outputs approximately three-fold, and reduced peak thermal response times to less than 3 min. A preliminary regression analysis of these data suggests a quantitative relationship exists between analyte concentration and peak thermal response (R = 0.988). These results support the feasibility and potential versatility of cell-based immunobiosensors for the selective detection and quantification of immunological analytes of interest.
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Department of Chemical, Bio & Materials Engineering, Arizona State University, Tempe 85287-6006, USA.
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D000818:Animals; D000937:Antigen-Antibody Reactions; D001698:Biomedical Engineering; D015374:Biosensing Techniques; D005240:Feasibility Studies; D005260:Female; D008407:Mast Cells / Q000276:immunology*; D051381:Rats; D017208:Rats, Wistar; D012044:Regression Analysis