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A Line of MEA Signal Analysis Methods for Human Stem Cell-Derived and Other Dynamic Neuronal Cultures

Jarno M. A. Tanskanen1*, Fikret Emre Kapucu2, 3 and Jari A. K. Hyttinen1
  • 1 Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Finland
  • 2 Aarhus University, Danish Research Institute of Translational Neuroscience - DANDRITE, Denmark
  • 3 Aarhus University, Department of Biomedicine, Denmark

Abstract Here, we review the line of MEA signal analysis methods we have developed. Our target has been the analysis needs of MEA signals measured from developing human pluripotent stem cell (hPSC)–derived neuronal cells and networks, which exhibit highly varying spiking statistics. Such MEA signals do not lend themselves well to analysis by traditional means. A hypothesis in our work has also been that MEA measurements carry more information than assessable by the commonly used spike and burst analysis methods. The MEA signal analysis areas advanced are: 1) Neuronal action potential spike detection [1,2]; 2) Adaptive burst detection [3]; 3) Burst quantification [4]; 4) Joint analysis of bursts and sorted spikes [5,6]; and 5) Network connectivity/synchronization analysis [7-11], including the developed correlated spectral entropy method (CorSE) [8,11]. A more comprehensive introduction and discussion of these methods has been presented by Kapucu [12]. Spike Detection Spike detection is the basis of all analysis based on spikes. Spike detection [13,14] is often performed by thresholding at a usually fixed or adaptive threshold (TH) based on a standard deviation of the signal. Here, an objective MEA signal analysis-based thresholding method was developed. In the smoothed derivative of a spike count histogram, shoulder-like features may be observable approximately above and below background noise amplitudes, and our method [1] sets the THs accordingly automatically. Matlab code for the methods is freely available [2]. Adaptive Burst Detection Bursts are generally considered to indicate network activity. Bursts are detected [15-17] typically by the fulfillment of at least two conditions: a fixed minimum number of spikes and a fixed maximum interspike interval (ISI) in a burst. ISI histograms have been employed to assess firing characteristics [18]. In some MEA signals, intra- and interburst ISIs are well separated (Fig. 1A). However, for hESC-derived neuronal cultures, many different cases have been observed (Fig. 1C). The spiking statistics of developing hPSC-derived neurons and networks vary greatly, and burst detection needs to be based on signal dynamics. Here, such a method was developed [3]. Our burst detection method [3] is based on the cumulative moving average (CMA) of ISI histogram (Fig. 1D), and does not depend on intraburst and other ISIs begin separable in the histogram. In our method, the maximum ISI allowed in a burst in a particular MEA signal, i.e., the ISI TH defining a burst, is found at the maximum of the CMA curve multiplied by alpha, whose value depends on the kurtosis of the ISI histogram (see [3] for a possible mapping between alpha and kurtosis) (Fig. 1D). A recommendable review of eight adaptive methods has been presented by Cotterill et al. [19]. Adaptive methods are crucial for detecting bursts recorded from dynamic cultures. Our CMA method has been successfully used in burst detection from MEA signals measured from hPSC-derived neuronal networks [20-23]. Burst Quantification Burst detection and quantification is possible also using e.g., entropy measures. In [4], we calculated sample entropy (SmE) and spectral entropy (SE) in running windows forming SmE and SE signals for MEA signals from rat cortical cell cultures. Comparing the entropy signals with raw signals and detected bursts indicated that these entropy measures have potential for burst detection and quantification. SmE appeared to be more sensitive to burst duration and number of spikes in bursts, whereas SmE was found potential also for quantifying/classifying presumably different kinds of bursts. In later unpublished experiments with hPSC-derived neurons, SE was able to indicate also possible bursts with spikes buried in background noise, i.e., undetectable by visual assessment. Joint Analysis of Bursts and Sorted Spikes To extract more information from the internal dynamics of network bursts, a method for the joint analysis of bursts and spike waveforms was developed [5]. In the joint analysis, spike sorting is used to generate spike types, and bursts are detected, using any methods suited for the signals at hand. Thereafter, each burst spike is associated with a spike type. Thus, spike type compositions of bursts are obtained. The method can be applied in the analysis of MEA recordings from any experimental paradigms in which changes in burst generating cells and/or networks can be expected. Changes may be observable comparing the spike type compositions of bursts before and after alterations, possibly even when no changes in burst durations, numbers of spikes in bursts, or in other traditional burst metrics could be observed. Matlab code for the methods is freely available [6]. CorSE for Functional Network Connectivity Assessment Entropy in its different forms can be used to assess neuronal networks based on MEA signals [7-11]. Particularly, we have demonstrated that spatial network relations can be revealed by time-variant SE signals, and devised CorSE [8]. In CorSE, an SE signal is formed for each MEA signal, and all pair-wise SE signal correlations (CorSE values) are calculated. A CorSE value quantifies how similarly the uniformities of the frequency spectra of the two signals change in time. Functional connectivity of the local networks in the vicinities of two MEA electrodes is deemed if the CorSE value is sufficiently high. A comparison of network connectivity detected by CorSE and a few other methods is shown in Fig. 2B-E, and an example of tracking network formation in Fig. 2F. CorSE could be particularly beneficial in tracking the formation of maturing neuronal networks since it does not depend on any predetermined spike/burst detection parameters or alike, or even of the spike timings: To observe functional connectivity, it is sufficient that the complexities of the frequency characteristics of the two MEA signals change similarly. Work using CorSE in the analysis of maturing hPSC-derived neuronal networks is ongoing. Matlab code for CorSE is freely available [11]. Conclusions and Discussion We have developed a line of MEA signal analysis methods motivated by the analysis needs of dynamic hPSC-derived neuronal networks. However, they can be applied in many other areas as well, from basic neuroscience to toxicology and drug studies. We hope that the methods will be found useful in gaining more information on the development and functioning of living neuronal networks.

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Acknowledgements

This work has been funded by the Human Spare Parts Project funded by Business Finland (formerly the Finnish Funding Agency for Technology and Innovation (TEKES)), 3DNeuroN project in the European Union’s Seventh Framework Programme, Future and Emerging Technologies (grant agreement number 296590). The work of JMAT has been funded by Jane and Aatos Erkko Foundation (grant ‘Biological Neuronal Communications and Computing with ICT’).

References

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Keywords: microelectrode array, MEA, Human pluripotent stem cell, HPSC, Electrophysiology, Spike Detection, thresholding, Burst detection, interspike interval, cumulative moving average, neuronal network, synchronization, functional connectivity, Spectral entropy, Corse

Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018.

Presentation Type: Poster Presentation

Topic: Microelectrode Array Technology

Citation: Tanskanen J, Kapucu F and Hyttinen J (2019). A Line of MEA Signal Analysis Methods for Human Stem Cell-Derived and Other Dynamic Neuronal Cultures. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00020

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Received: 18 Mar 2018; Published Online: 17 Jan 2019.

* Correspondence: Dr. Jarno M. A. Tanskanen, Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere, FI-33520, Finland, jarno.m.tanskanen@tut.fi