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，因为神经元信号的振幅非常小，而3D结构则增强了背景噪声的水平。因此，神经元信号分析需要应用高度复杂的算法。在这项研究中，我们提出了一种优化的新方法，用于检测信噪比非常低的3D神经球（NS）记录的尖峰。这是通过使用名为SWTTEO的高灵敏度算法扩展基于阈值的简单峰值检测来实现的。将此分析程序应用于从在MEA芯片上生长的hESC衍生的NS获得的数据。具体来说，我们检查了在电活动的前十天内发生的活动模式的变化。我们进一步分析了NS对GABA受体拮抗剂bicuculline的反应。与简单的基于阈值的尖峰检测相比，使用这种新的算法方法，我们获得了更可靠的结果。