Биосенсоры и биоэлектроника

Surface Acoustic Wave Actuated Lab-on-Chip System for Single Cell Analysis

Abstract

Stefan Thalhammer and Achim Wixforth

The advent of multi-sensor microelectrodes for extracellular action potential recordings has significantly improved the quality of recorded signals, allowing more reliable detection and classification of action potentials recorded in vivo. These microelectrodes can also be used to localize neuronal signal sources, which may allow experimentalists to estimate other parameters including the neurons’ migration trends, intensities and sizes. This information can also be used to resolve neurons based on their location and type. However, as the exact characteristics of neurons are unknown during in vivo experiments, current attempts to localize neuronal signal sources have not been validated. This article presents experimental validation of a method capable of estimating both the location and intensity of an electrical source. To this end, a stimulating electrode was immersed in a saline solution and its stimulus patterns were recorded by a commercially available four-sensor microelectrode (tetrode). The location of the tetrode was varied with respect to the stimulator, and for each tetrode position, the stimulus was generated at multiple intensity levels. The location and intensity of the source were estimated using the Multiple Signal Classification (MUSIC) algorithm, and the results were quantified by comparison to the parameters’ true values. Localization results, with an accuracy
and precision of ~10 μm, and ~11 μm respectively, imply the method’s ability to resolve individual neuronal sources. Similarly, source intensity estimation results indicate that this approach can accurately track changes in neuronal signal amplitude. Together, these results demonstrate the potential of the presented approach in characterizing neuronal signal sources in vivo, which may significantly improve the extracellular recording process and enable a more accurate interpretation of experimental data.