University of Applied Sciences Aschaffenburg, BioMEMS Lab, Würzburger Str. 45, 63743 Aschaffenburg, Germany.
GSI Helmholtz Centre for Heavy Ion Research, Biophysics division, Planckstr. 1, 64291 Darmstadt, Germany.
Biosens Bioelectron. 2018 Feb 15;100:462-468. doi: 10.1016/j.bios.2017.09.034. Epub 2017 Sep 19.
Microelectrode array (MEA) technology in combination with three-dimensional (3D) neuronal cell models derived from human embryonic stem cells (hESC) provide an excellent tool for neurotoxicity screening. Yet, there are significant challenges in terms of data processing and analysis, since neuronal signals have very small amplitudes and the 3D structure enhances the level of background noise. Thus, neuronal signal analysis requires the application of highly sophisticated algorithms. In this study, we present a new approach optimized for the detection of spikes recorded from 3D neurospheres (NS) with a very low signal-to-noise ratio. This was achieved by extending simple threshold-based spike detection utilizing a highly sensitive algorithm named SWTTEO. This analysis procedure was applied to data obtained from hESC-derived NS grown on MEA chips. Specifically, we examined changes in the activity pattern occurring within the first ten days of electrical activity. We further analyzed the response of NS to the GABA receptor antagonist bicuculline. With this new algorithm method we obtained more reliable results compared to the simple threshold-based spike detection.
微电极阵列 (MEA) 技术与源自人类胚胎干细胞 (hESC) 的三维 (3D) 神经元细胞模型相结合，为神经毒性筛选提供了极好的工具。然而，在数据处理和分析方面存在重大挑战，因为神经元信号的幅度非常小，而 3D 结构会增加背景噪声的水平。因此，神经元信号分析需要应用非常复杂的算法。在这项研究中，我们提出了一种新的方法，该方法针对从信噪比非常低的 3D 神经球 (NS) 记录的尖峰进行了优化。这是通过扩展简单的基于阈值的尖峰检测来实现的，该检测利用了一种名为 SWTTEO 的高灵敏度算法。该分析程序应用于在 MEA 芯片上生长的 hESC 衍生 NS 获得的数据。具体来说，我们检查了电活动的最初十天内发生的活动模式变化。我们进一步分析了 NS 对 GABA 受体拮抗剂荷包牡丹碱的反应。与简单的基于阈值的尖峰检测相比，我们使用这种新算法方法获得了更可靠的结果。
