title: Modeling microelectrode biosensors: free-flow calibration can substantially underestimate tissue concentrations.
abstract: Microelectrode amperometric biosensors are widely used to measure concentrations of analytes in solution and tissue including acetylcholine, adenosine, glucose, and glutamate. A great deal of experimental and modeling effort has been directed at quantifying the response of the biosensors themselves; however, the influence that the macroscopic tissue environment has on biosensor response has not been subjected to the same level of scrutiny. Here we identify an important issue in the way microelectrode biosensors are calibrated that is likely to have led to underestimations of analyte tissue concentrations. Concentration in tissue is typically determined by comparing the biosensor signal to that measured in free-flow calibration conditions. In a free-flow environment the concentration of the analyte at the outer surface of the biosensor can be considered constant. However, in tissue the analyte reaches the biosensor surface by diffusion through the extracellular space. Because the enzymes in the biosensor break down the analyte, a density gradient is set up resulting in a significantly lower concentration of analyte near the biosensor surface. This effect is compounded by the diminished volume fraction (porosity) and reduction in the diffusion coefficient due to obstructions (tortuosity) in tissue. We demonstrate this effect through modeling and experimentally verify our predictions in diffusive environments.NEW & NOTEWORTHYMicroelectrode biosensors are typically calibrated in a free-flow environment where the concentrations at the biosensor surface are constant. However, when in tissue, the analyte reaches the biosensor via diffusion and so analyte breakdown by the biosensor results in a concentration gradient and consequently a lower concentration around the biosensor. This effect means that naive free-flow calibration will underestimate tissue concentration. We develop mathematical models to better quantify the discrepancy between the calibration and tissue environment and experimentally verify our key predictions.
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom; and.
authors.forename: Adam J H
authors.identifier: 0000-0002-4726-2644
authors.initials: AJ
authors.lastname: Newton
authors.affiliation: School of Life Sciences, University of Warwick, Coventry, United Kingdom.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Wall
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom; and magnus.richardson@warwick.ac.uk.
authors.forename: Magnus J E
authors.initials: MJ
authors.lastname: Richardson
mesh_terms: D000818:Animals; D015374:Biosensing Techniques / Q000295:instrumentation* / Q000379:methods*; D001921:Brain / Q000378:metabolism*; D001923:Brain Chemistry; D002138:Calibration; D004058:Diffusion; D004798:Enzymes / Q000378:metabolism; D006801:Humans; D008839:Microelectrodes; D008959:Models, Neurological
chemical_list: D004798:Enzymes
keywords: biosensors; calibration; modeling