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Carbon fiber microelectrodes are commonly used for real-time monitoring of individual exocytosis events at single cells. Since the nature of an electrochemical signal is fundamentally governed by mass transport to the electrode surface, microelectrode geometry can be exploited to achieve precise and accurate measurements. Researchers traditionally pair amperometric measurements of exocytosis with a ∼10-μm diameter, disk microelectrode in an "artificial synapse" configuration to directly monitor individual release events from single cells. Exocytosis is triggered, and released molecules diffuse to the "post-synaptic" electrode for oxidation. This results in a series of distinct current spikes corresponding to individual exocytosis events. However, it remains unclear how much of the material escapes detection. In this work, the performance of 10- and 34-μm diameter carbon fiber disk microelectrodes was directly compared in monitoring exocytosis at single chromaffin cells. The 34-μm diameter electrode was more sensitive to catecholamines and enkephalins than its traditional, 10-μm diameter counterpart, and it more effectively covered the entire cell. As such, the larger sensor detected more exocytosis events overall, as well as a larger quantal size, suggesting that the traditional tools underestimate the above measurements. Both sensors reliably measured l-DOPA-evoked changes in quantal size, and both exhibited diffusional loss upon adjustment of cell-electrode spacing. Finite element simulations using COMSOL support the improved collection efficiency observed using the larger sensor. Overall, this work demonstrates how electrode geometry can be exploited for improved detection of exocytosis events by addressing diffusional loss─an often-overlooked source of inaccuracy in single-cell measurements.

Citation

A Chathuri De Alwis, J Dylan Denison, Ruby Shah, Gregory S McCarty, Leslie A Sombers. Exploiting Microelectrode Geometry for Comprehensive Detection of Individual Exocytosis Events at Single Cells. ACS sensors. 2023 Aug 25;8(8):3187-3194

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PMID: 37552870

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