Home   >   CSC-OpenAccess Library   >    Manuscript Information
DrCell – A Software Tool for the Analysis of Cell Signals Recorded with Extracellular Microelectrodes
Christoph Nick, Michael Goldhammer, Robert Bestel, Frederik Steger, Andreas Daus, Christiane Thielemann
Pages - 96 - 109     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 2    |    Publication Date - September 2013  Table of Contents
MATLAB® Toolbox, Bio Signal Processing, Spike Sorting, Network Analysis, Extracellular Recording.
Microelectrode arrays (MEAs) have been applied for in vivo and in vitro recording and stimulation of electrogenic cells, namely neurons and cardiac myocytes, for almost four decades. Extracellular recordings using the MEA technique inflict minimum adverse effects on cells and enable long term applications such as implants in brain or heart tissue.

Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.

In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.

For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
CITED BY (9)  
1 Frieß, J. L., Heselich, A., Ritter, S., Haber, A., Kaiser, N., Layer, P. G., & Thielemann, C. (2015). Electrophysiologic and cellular characteristics of cardiomyocytes after X-ray irradiation. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 777, 1-10.
2 Oster, S., Daus, A. W., Erbes, C., Goldhammer, M., Bochtler, U., & Thielemann, C. (2016). Long-term electromagnetic exposure of developing neuronal networks: A flexible experimental setup. Bioelectromagnetics, 37(4), 264-278.
3 Nick, C., & Thielemann, C. (2014). Are Carbon Nanotube Microelectrodes Manufactured from Dispersion Stable Enough for Neural Interfaces?. BioNanoScience, 4(3), 216-225.
4 Regalia, G., Coelli, S., Biffi, E., Ferrigno, G., & Pedrocchi, A. (2016). A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis. Computational Intelligence and Neuroscience, 2016.
5 Frieß, J., Heselich, A., Ritter, S., Layer, P. G., & Thielemann, C. (2014). Electrophysiologic and molecular characteristics of cardiomyocytes after heavy ion irradiation in the frame of the ESA IBER-10 program. Journal of radiation research, 55(suppl 1), i40-i41.
6 Frieß, J., Heselich, A., Ritter, S., Layer, P. G., & Thielemann, C. Effects of X-rays and titanium ions on cardiomyocyte cultures.
7 Daus, A. W. (2013). Zellbasierte Biosensoren--Hybride Systeme aus dreidimensionalen in vitro Netzwerken und Mikroelektroden Arrays.
8 Frieß, J. L., Heselich, A., Ritter, S., Layer, P. G., & Thielemann, C. Combined effects of ionizing radiation and cardio-active drugs on human iPSC-derived cardiomyocytes.
9 Frieß, J. L. (2016). Einfluss ionisierender Strahlung auf die elektrophysiologischen Eigenschaften kardialer Zellen.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Daus, M. Goldhammer, P. Layer, and C. Thielemann, “Electromagnetic exposure of scaffold-free three-dimensional cell culture systems.” Bioelectromagnetics, vol. 32, no. 5, pp.351–359, 2011.
A. Daus, P. Layer, and C. Thielemann, “A spheroid-based biosensor for the label-free detection of drug-induced field potential alterations,” Sensors and Actuators B: Chemical, vol 165, no. 1, pp. 53–58, 2012.
A. Grumet, J. Wyatt, and J. Rizzo, “Multi-electrode stimulation and recording in the isolated retina,” Journal of neuroscience methods, vol. 101, no. 1, pp. 31–42, 2000.
C. Legendy and M. Salcman, “Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons,” Journal of neurophysiology, vol. 53, no. 4, pp.926–939, 1985.
C. Nick, R. Joshi, J. Schneider, and C. Thielemann, “Three-dimensional carbon nanotube electrodes for extracellular recording of cardiac myocytes.” Biointerphases, vol. 7, no. 1-4, pp.58–64, 2012.
C. Nick, S. Quednau, R. Sarwar, H.F. Schlaak and C. Thielemann, “High Aspect Ratio Gold Nanopillars on Microelectrodes for Neural Interfaces”, submitted.
D. Eytan and S. Marom, “Dynamics and effective topology underlying synchronization in networks of cortical neurons,” The Journal of neuroscience, vol. 26, no. 33, pp. 8465–8476,2006.
D. Tam, “An alternate burst analysis for detecting intra-burst firings based on inter-burst periods,” Neurocomputing, vol. 44, pp. 1155–1159, 2002.
D. Wagenaar and S. Potter, “Real-time multi-channel stimulus artifact suppression by local curve fitting,” Journal of neuroscience methods, vol. 120, no. 2, pp. 113–120, 2002.
D. Wagenaar, R. Madhavan, J. Pine, and S. Potter, “Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation,” Journal of Neuroscience, vol. 25, no. 3, pp.680–688, 2005.
D. Wagenaar, T. DeMarse, and S. Potter, “Meabench: A toolset for multi-electrode data acquisition and on-line analysis,” in Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on. Ieee, 2005, pp. 518–521.
G. Wang, Y. Zhou, A. Chen, P. Zhang, and P. Liang, “A robust method for spike sorting with automatic overlap decomposition,” Biomedical Engineering, IEEE Transactions on, vol. 53,no. 6, pp. 1195–1198, 2006.
I. Cajigas, W. Malik, and E. Brown, “nstat: Open-source neural spike train analysis toolbox for matlab,” Journal of Neuroscience Methods, vol. 211, no. 2, pp. 245––264, 2012.
J. Choi, H. Jung, and T. Kim, “A new action potential detector using the mteo and its effects on spike sorting systems at low signal-to-noise ratios,” Biomedical Engineering, IEEE Transactions on, vol. 53, no. 4, pp. 738–746, 2006.
J. Cohen, “Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit,” Psychological bulletin, vol. 70, no. 4, pp. 213–220, 1968.
J. Rolston, D. Wagenaar, and S. Potter, “Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures,” Neuroscience, vol. 148, no. 1, pp. 294–303, 2007.
J. van Pelt, P. Wolters, W. Rutten, M. Corner, P. Van Hulten, and G. Ramakers, “Spatiotemporal firing in growing networks cultured on multi-electrode arrays,” in World Congress on Neuroinformatics 2001, 2001.
L. Turnbull, E. Dian, and G. Gross, “The string method of burst identification in neuronal spike trains,” Journal of neuroscience methods, vol. 145, no. 1-2, pp. 23–35, 2005.
M. Chiappalone, A. Novellino, I. Vajda, A. Vato, S. Martinoia, and J. Van Pelt, “Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons,” Neurocomputing, vol. 65, pp. 653–662, 2005.
M. Corner, J. Van Pelt, P. Wolters, R. Baker, and R. Nuytinck, “Physiological effects of sustained blockade of excitatory synaptic transmission on spontaneously active developing neuronal networks–an inquiry into the reciprocal linkage between intrinsic biorhythms and neuroplasticity in early ontogeny,” Neuroscience & Biobehavioral Reviews, vol. 26, no. 2, pp.127–185, 2002.
M. Jungblut, W. Knoll, C. Thielemann, and M. Pottek, “Triangular neuronal networks on microelectrode arrays: an approach to improve the properties of low-density networks for extracellular recording,” Biomedical Microdevices, vol. 11, no. 6, pp. 1269–1278, 2009.
M. Lidierth et al., “sigtool: A matlab-based environment for sharing laboratory-developed software to analyze biological signals,” Journal of neuroscience methods, vol. 178, no. 1, pp.188–196, 2009.
M. Nicolelis and J. Chapin, “Controlling robots with the mind,” Scientific American-American Edition, vol. 287, no. 4, pp. 46–55, 2002.
M. Ruaro, P. Bonifazi, and V. Torre, “Toward the neurocomputer: Image processing and pattern recognition with neuronal cultures,” Biomedical Engineering, IEEE Transactions on,vol. 52, no. 3, pp. 371–383, 2005.
M. Velliste, S. Perel, M. Spalding, A. Whitford, and A. Schwartz, “Cortical control of a prosthetic arm for self-feeding,” Nature, vol. 453, no. 7198, pp. 1098–1101, 2008.
P. Horton, A. Nicol, K. Kendrick, and J. Feng, “Spike sorting based upon machine learning algorithms (soma),” Journal of neuroscience methods, vol. 160, no. 1, pp. 52–68, 2007.
P. Thakur, H. Lu, S. Hsiao, and K. Johnson, “Automated optimal detection and classification of neural action potentials in extra-cellular recordings,” Journal of Neuroscience Methods, vol.162, no. 1-2, pp. 364–376, 2007.
R. Baker, M. Corner, and J. van Pelt, “Spontaneous neuronal discharge patterns in developing organotypic mega-co-cultures of neonatal rat cerebral cortex,” Brain research, vol.1101, no. 1, pp. 29–35, 2006.
R. Bestel, A. Daus, and C. Thielemann, “A novel automated spike sorting algorithm with adaptable feature extraction,” Journal of Neuroscience Methods, vol. 211, no. 1, pp. 168–178, 2012.
R. Meier, U. Egert, A. Aertsen, and M. Nawrot, “Find - a unified framework for neural data analysis,” Neural Networks, vol. 21, no. 8, pp. 1085–1093, 2008.
R. Quiroga, Z. Nadasdy, and Y. Ben-Shaul, “Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering,” Neural Computation, vol. 16, pp. 1661–1687,2004.
R. Segev, M. Benveniste, E. Hulata, N. Cohen, A. Palevski, E. Kapon, Y. Shapira, and E.Ben-Jacob, “Long term behavior of lithographically prepared in vitro neuronal networks,”Physical review letters, vol. 88, no. 11, p. 118102, 2002.
R. Vogelstein, K. Murari, P. Thakur, C. Diehl, S. Chakrabartty, and G. Cauwenberghs, “Spike sorting with support vector machines,” in Engineering in Medicine and Biology Society, 2004.IEMBS’04. 26th Annual International Conference of the IEEE, vol. 1. IEEE, 2004, pp. 546–549.
S. Martinoia, P. Massobrio, M. Bove, and G. Massobrio, “Cultured neurons coupled to microelectrode arrays: circuit models, simulations and experimental data,” Biomedical Engineering, IEEE Transactions on, vol. 51, no. 5, pp. 859–863, 2004.
T. Borghi, R. Gusmeroli, A. Spinelli, and G. Baranauskas, “A simple method for efficient spike detection in multiunit recordings,” Journal of neuroscience methods, vol. 163, no. 1, pp. 176–180, 2007.
U. Egert and T. Meyer, Heart on a Chip - Extracellular Multielectrode Recordings from Cardiac Myocytes in Vitro. Springer Berlin Heidelberg, 2005, ch. Heart on a Chip - Extracellular Multielectrode Recordings from Cardiac Myocytes in Vitro, pp. 432–453.
Y. Chagnac-Amitai, H. Luhmann, and D. Prince, “Burst generating and regular spiking layer 5 pyramidal neurons of rat neocortex have different morphological features,” The Journal of Comparative Neurology, vol. 296, no. 4, pp. 598–613, 1990.
Y. Jimbo and A. Kawana, “Electrical stimulation and recording from cultured neurons using a planar electrode array,” Bioelectrochemistry and Bioenergetics, vol. 29, no. 2, pp. 193–204,1992.
Y. Jimbo, H. Robinson, and A. Kawana, “Strengthening of synchronized activity by tetanic stimulation in cortical cultures: application of planar electrode arrays,” Biomedical Engineering, IEEE Transactions on, vol. 45, no. 11, pp. 1297–1304, 1998.
Mr. Christoph Nick
University of Applied Sciences Aschaffenburg - Germany
Mr. Michael Goldhammer
University of Applied Sciences Aschaffenburg - Germany
Mr. Robert Bestel
University of Applied Sciences Aschaffenburg - Germany
Mr. Frederik Steger
University of Applied Sciences Aschaffenburg - Germany
Dr. Andreas Daus
University of Applied Sciences Aschaffenburg - Germany
Professor Christiane Thielemann
University of Applied Sciences Aschaffenburg - Germany

View all special issues >>