Jarno M. A. Tanskanen

Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro

The production of functional human embryonic stem cell (hESC)-derived neuronal cells is critical for the application of hESCs in treating neurodegenerative disorders. To study the potential functionality of hESC-derived neurons, we cultured and monitored the development of hESC-derived neuronal networks on microelectrode arrays. Immunocytochemical studies revealed that these networks were positive for the neuronal marker proteins beta-tubulin(III) and microtubule-associated protein 2 (MAP-2). The hESC-derived neuronal networks were spontaneously active and exhibited a multitude of electrical impulse firing patterns. Synchronous bursts of electrical activity similar to those reported for hippocampal neurons and rodent embryonic stem cell-derived neuronal networks were recorded from the differentiated cultures until up to 4 months. The dependence of the observed neuronal network activity on sodium ion channels was examined using tetrodotoxin (TTX). Antagonists for the glutamate receptors NMDA [D(-)-2-amino-5-phosphonopentanoic acid] and AMPA/kainate [6-cyano-7-nitroquinoxaline-2,3-dione], and for GABAA receptors [(-)-bicuculline methiodide] modulated the spontaneous electrical activity, indicating that pharmacologically susceptible neuronal networks with functional synapses had been generated. The findings indicate that hESC-derived neuronal cells can generate spontaneously active networks with synchronous communication in vitro, and are therefore suitable for use in developmental and drug screening studies, as well as for regenerative medicine.

Substantial variation in the cardiac differentiation of human embryonic stem cell lines derived and propagated under the same conditions—a comparison of multiple cell lines

Aim. The differentiation efficiencies of human embryonic stem cell (hESC) lines differ from each other. To assess this in more detail we studied the cardiac differentiation of eight hESC lines derived in the same laboratory. Results. Substantial variation in growth and in the ability to form beating areas was seen between the different hESC lines; line HS346 gave the best efficiency (9.4%), while HS293 did not differentiate into beating colonies at all. Nine germ layer and differentiation markers were quantified during early differentiation in four hESC lines. The expression levels of Brachyury T, MESP1 and NKX2.5 were highest in the most efficient cardiac line (HS346). A systematic characterization of the beating cells revealed proper cardiac marker expression, electrophysiological activity, and pharmacological response. Conclusions. The hESC lines derived in the same laboratory varied considerably in their potential to differentiate into beating cardiomyocytes. None of the expression markers could clearly predict cardiac differentiation potential, although the expression of early cardiomyogenic genes was upregulated in the best cardiac line. The proper cardiomyocyte characteristics and pharmacological response indicate that these cells could be used as a model for human cardiomyocytes in pharmacological and toxicological analyses when investigating new heart medications or cardiac side-effects.

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.

Independent component analysis of neural populations from multielectrode field potential measurements

Independent component analysis (ICA) is proposed for analysis of neural population activity from multichannel electrophysiological field potential measurements. The proposed analysis method provides information on spatial extents of active neural populations, locations of the populations with respect to each other, population evolution, including merging and splitting of populations in time, and on time lag differences between the populations. In some cases, results of the proposed analysis may also be interpreted as independent information flows carried by neurons and neural populations. In this paper, a detailed description of the analysis method is given. The proposed analysis is demonstrated with an illustrative simulation, and with an exemplary analysis of an in vivo multichannel recording from rat hippocampus. The proposed method can be applied in analysis of any recordings of neural networks in which contributions from a number of neural populations or information flows are simultaneously recorded via a number of measurement points, as well in vivo as in vitro.

Epileptic EEG signal classification with marching pursuit based on harmony search method

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

Averaging in vitro cardiac field potential recordings obtained with microelectrode arrays

Extracellular field potential (FP) recordings with microelectrode arrays (MEAs) from cardiomyocyte cultures offer a non-invasive way of studying the electrophysiological properties of these cells at the population level. Several studies have examined the FP properties of cardiomyocytes of various origins, including stem cell-derived cardiomyocytes. This focus reflects growing importance and interest in the field of MEA. High-quality cardiac FP signals are often difficult to obtain, especially from stem cell-derived cardiomyocyte cultures, which represent an important new field in cardiac electrophysiology. One way to improve the quality of these recordings is to average the cardiac FP signals. To date, however, no studies have examined the effect of averaging on cardiac FP signals. We report here that cardiac FP averaging can yield higher-quality signals than original individual FPs, and therefore promise more accurate detection of different phases and analysis of the cardiac FP signal. Averaged signals improved the signal-to-noise ratio (SNR), and obtaining reliable averages required approximately 50 cardiac cycles. We therefore propose that routine cardiac FP averaging can serve as a tool to compare the effects of different experimental conditions or stimuli on the properties of cardiac FPs.

Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

Independent component analysis of parameterized ECG signals.

Independent component analysis (ICA) of measured signals yields the independent sources, given certain fulfilled requirements. Properly parameterized signals provide a better view to the considered system aspects, while reducing the amount of data. It is little acknowledged that appropriately parameterized signals may be subjected to ICA, yielding independent components (ICs) displaying more clearly the investigated properties of the sources. In this paper, we propose ICA of parameterized signals, and demonstrate the concept with ICA of ST and R parameterizations of electrocardiogram (ECG) signals from ECG exercise test measurements from two coronary artery disease (CAD) patients

Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements

Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the real in vitro MEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.

Atomic layer deposited iridium oxide thin film as microelectrode coating in stem cell applications

Microelectrodes of microelectrode arrays (MEAs) used in cellular electrophysiology studies were coated with iridium oxide (IrOx) thin film using atomic layer deposition (ALD). This work was motivated by the need to find a practical alternative to commercially used titanium nitride (TiN) microelectrode coating. The advantages of ALD IrOx coating include decreased impedance and noise levels and improved stimulation capability of the microelectrodes compared to uncoated microelectrodes. The authors’ process also takes advantage of ALD’s exact process control and relatively low source material start costs compared to traditionally used sputtering and electrochemical methods. Biocompatibility and suitability of ALD IrOx microelectrodes for stem cell research applications were verified by culturing human embryonic stem cell derived neuronal cells for 28 days on ALD IrOx MEAs and successfully measuring electrical activity of the cell network. Electrode impedance of 450 kΩ at 1 kHz was achieved with ALD IrOx in t...