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Network dynamics of modular assemblies coupled to Micro-Electrode Arrays

Lara Faccani1, Martina Brofiga1, Pasqualina Farisello1, Sergio Martinoia1 and Paolo Massobrio1*
  • 1 Università di Genova, Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), Italy

The nervous system is a complex structure which generates and integrates information from multiple external and internal sources. As confirmed by recent studies reporting structural analyses of brain networks, brain areas were found to be neither completely connected with each other nor randomly linked [1-3]. Instead, their interconnections show a specific and intricate organization, typical of the complex networks. The coexistence of high degrees of local clustering and short path length is peculiar of small-world network topologies [4] nowadays considered as the best topological model to support the emergent electrophysiological patterns of activity. However, since the high complexity of the brain, the use of a simplified experimental in vitro model can facilitate the analysis of the emergent complex dynamics and their relationships with the underlying connectivity. We present the first results achieved by developing a poly-dimethyl-siloxane (PDMS) device to couple to planar Micro-Electrode Arrays (MEAs) for designing interconnected (i.e., modular) neuronal assemblies made up of three sub-populations. To characterize the complex on-going dynamics emerging from the interaction between neuronal populations, we used standard data analysis algorithms. In particular, since the richness of the recorded signal made up of spikes, bursts, and network bursts, we evaluated inter-spike interval (ISI) distributions, instantaneous firing rate (IFR), burst and network burst duration, inter burst interval, coincidence index (CI). The analyses have been performed using ad hoc Matlab (The Mathworks, Natik, US) functions. Chemical surface patterning, PDMS stencil, microstamping, and micro-contact printing of selective substrates are the state of the art techniques to engineer neuronal networks [5-8]. In this work, we used PDMS devices for recreating small neuronal assemblies interconnected by means of thin micro-channels. Such structure should satisfy the following requirements: (i) realization of arrays of thin micro-channels for preventing migration of cells between compartments while allowing only neurites to pass through allowing the communication. (ii) fluidic isolation among the populations to independently cultivate and pharmacological manipulate each assembly. (iii) perfect adhesion and compatibility with the layout of the MEAs. PDMS stamps are replicated using silicon master molds realized with conventional soft lithographic molding techniques. Practically, the realized device is done by three small compartments (the compartment #1 is bigger than the compartments #2 and #3 that are of the same size) able to contain about 10 and 9 µL of medium. The reservoirs are able to host about 50 µL of volume and are used to inject the cell suspension from one reservoir of each compartment. The micro-fluidic compartments of 3 mm width, 8 mm length and 135 µm in height are interconnected with micro-channels of 5 μm height, 10 μm width and 100 μm length that are spaced at regular intervals of 50 μm. The PDMS mask was oxygen-plasma treated to make the compartments and micro-channels hydrophilic while preserving hydrophobic contact surface. The oxygen-plasma treated device was reversibly bonded to MEA substrate (previously coated with adhesion molecules) made up of 120 planar microelectrodes (TiN/SiN, 30 µm diameter, 200 µm spaced) supplied by Multi Channel Systems (MCS, Reutlingen, Germany). The mask is manually aligned to include one compartment with 56 electrodes (compartment #1), and the two other ones with 26 (compartment #2 and #3). The remaining 12 electrodes are covered by the PDMS. The day before the plating, the surface of the MEA has been coated with adhesion molecules laminin and poly-lysine. Then, the plating procedure occurred by injecting the cell suspension from one reservoir of each compartment. We inserted a nominal concentration of about 2000 cells/mm2 per each compartment. Raw data were recorded using the MEA 2100 system (MCS), and sampled at 10 kHz. Experiments have been performed at DIV (days in vitro) 18. We record 30 minutes of spontaneous electrophysiological activity. Extracellularly recorded spikes were detected using the PTSD (Precise Timing Spike Detection) algorithm [9]. Spike trains were built using three parameters: (1) a differential threshold set to 8 times the standard deviation of the noise independently for each channel; (2) a peak lifetime period (set at 2 ms); (3) a refractory period (set at 1 ms). The mean firing rate (MFR) of each compartment was obtained by computing the firing rate of each channel averaged among the active electrodes of the same compartment (being an active electrode defined by an activity greater than 0.1 spikes/s). Data are expressed as mean (+/-) standard error of the mean. We record the spontaneous electrophysiological activity of interconnected neuronal cultures at DIV 18. The performed analysis focused on the capability of the compartmentalized system to record activity in a reliable way. The activity spans from 1.57 (+/-) 0.25 for the compartment 1 to 3.00 (+/-) 0.45 for the compartment 3. In this work, we presented a new device for the design of interconnected neuronal networks coupled to MEAs. The devised experimental protocol shows good results in the plating procedure and the recorded spontaneous activity shows the typical features of modular networks, where an irregular asynchronous spiking activity emerges. It is worth to notice that this device will allow to cultivate over the active area of a MEA both homogeneous (i.e., assembly with only one kind of neurons) and complex heterogeneous (i.e., assemblies with neurons coming from different brain areas) populations giving the possibility to recreate interconnected brain regions on a chip.

Acknowledgements

The authors thank Dr. Leandro Lorenzelli and dr. Cristian Collini (Fondazione Bruno Kessler (FBK), Microsystems Technology (MST) Research Unit, Center for Materials and Microsystems, Trento) for the realization of the silicon master used to realize the PDMS devices.

References

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Keywords: engineered networks, dynamics, Micro-Electrode Arrays, interconnected assemblies, PDMS

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

Presentation Type: Poster Presentation

Topic: Neural Networks

Citation: Faccani L, Brofiga M, Farisello P, Martinoia S and Massobrio P (2019). Network dynamics of modular assemblies coupled to Micro-Electrode Arrays. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00073

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

* Correspondence: Prof. Paolo Massobrio, Università di Genova, Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), Genoa, Italy, paolo.massobrio@unige.it