Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESCs), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI) histograms. Moreover, the algorithm calculates ISI thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average (CMA) and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA) data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays

Human Cell-Based Micro Electrode Array Platform for Studying Neurotoxicity

At present, most of the neurotoxicological analyses are based on in vitro and in vivo models utilizing animal cells or animal models. In addition, the used in vitro models are mostly based on molecular biological end-point analyses. Thus, for neurotoxicological screening, human cell-based analysis platforms in which the functional neuronal networks responses for various neurotoxicants can be also detected real-time are highly needed. Microelectrode array (MEA) is a method which enables the measurement of functional activity of neuronal cell networks in vitro for long periods of time. Here, we utilize MEA to study the neurotoxicity of methyl mercury chloride (MeHgCl, concentrations 0.5–500 nM) to human embryonic stem cell (hESC)-derived neuronal cell networks exhibiting spontaneous electrical activity. The neuronal cell cultures were matured on MEAs into networks expressing spontaneous spike train-like activity before exposing the cells to MeHgCl for 72 h. MEA measurements were performed acutely and 24, 48, and 72 h after the onset of the exposure. Finally, exposed cells were analyzed with traditional molecular biological methods for cell proliferation, cell survival, and gene and protein expression. Our results show that 500 nM MeHgCl decreases the electrical signaling and alters the pharmacologic response of hESC-derived neuronal networks in delayed manner whereas effects can not be detected with qRT-PCR, immunostainings, or proliferation measurements. Thus, we conclude that human cell-based MEA platform is a sensitive online method for neurotoxicological screening