Abstract
Understanding neuronal communication is fundamental in neuroscience, but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics (μEF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows the extracellular recording of spike propagation with a high signal-to-noise ratio over the course of several weeks. Addressing these μEF devices, we developed an advanced yet easy-to-use publically available computational tool, μSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of μEF devices and μSpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens the door to new approaches in studies of peripheral innervation, neural coding, and neuroregeneration, among many others. We demonstrate the use of μSpikeHunter in dorsal root ganglion neurons where we analyze the presence of both anterograde and retrograde signals in μEF devices. A fully functional version of µSpikeHunter is publically available for download from https://github.com/uSpikeHunter.
Highlights
Electrical signaling is recognized as the principal modality of communication in neurons, where it is used to encode and transmit information via action potentials (APs)
Analysis of axonal propagation velocities in μEF devices has been addressed in recent publications[11,13,28,29], but, typically, (i) the calculations are performed using simple custom-made MATLAB scripts for estimating the signal delay between pairs of electrodes; (ii) the analytical tools generally do not provide spike sorting tools nor automatic selection constraints for identifying travelling waves; and, importantly, (iii) the scripts are not made available in public repositories, nor are they easy to use (“out-of-the-shelf ”) by other research groups
Windows and Apple’s macOS operating systems, and is composed of two graphical user interfaces (GUIs): the main GUI, in which the data are imported and analyzed at the single-spike level, and the spike sorting GUI, in which the user can sort spikes into source clusters associated to different neurites. μSpikeHunter was developed to be compatible with recordings obtained using custom setups or commercial recording systems (MEA2100 from MultiChannel Systems MCS GmbH, Germany) for 60, 120, and 252-electrode Microelectrode arrays (MEAs)
Summary
Electrical signaling is recognized as the principal modality of communication in neurons, where it is used to encode and transmit information via action potentials (APs). The biochemical and morphological facets of the interaction between neurons and cells from other organ systems, including osteoblasts[20], dental pulp[21], and myocytes[22,23] have been investigated In both single- and co-culture configurations, more than the technical difficulty of combining microelectrodes and microfluidics, the challenge for most labs lies in the sheer volume and complexity of the recorded electrophysiological data. Analysis of axonal propagation velocities in μEF devices has been addressed in recent publications[11,13,28,29], but, typically, (i) the calculations are performed using simple custom-made MATLAB scripts for estimating the signal delay between pairs of electrodes; (ii) the analytical tools generally do not provide spike sorting tools nor automatic selection constraints for identifying travelling waves (depending on the noise, not all spikes in individual microelectrodes are associated to a propagating action potential); and, importantly, (iii) the scripts are not made available in public repositories, nor are they easy to use (“out-of-the-shelf ”) by other research groups
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