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Triangular neuronal networks on microelectrode arrays: an approach to improve the properties of low-density networks for extracellular recording.

ABSTRACT: Multi-unit recording from neuronal networks cultured on microelectrode arrays (MEAs) is a widely used approach to achieve basic understanding of network properties, as well as the realization of cell-based biosensors. However, network formation is random under primary culture conditions, and the cellular arrangement often performs an insufficient fit to the electrode positions. This results in the successful recording of only a small fraction of cells. One possible approach to overcome this limitation is to raise the number of cells on the MEA, thereby accepting an increased complexity of the network. In this study, we followed an alternative strategy to increase the portion of neurons located at the electrodes by designing a network in confined geometries. Guided settlement and outgrowth of neurons is accomplished by taking control over the adhesive properties of the MEA surface. Using microcontact printing a triangular two-dimensional pattern of the adhesion promoter poly-D-lysine was applied to the MEA offering a meshwork that at the same time provides adhesion points for cell bodies matching the electrode positions and gives frequent branching points for dendrites and axons. Low density neocortical networks cultivated under this condition displayed similar properties to random networks with respect to the cellular morphology but had a threefold higher electrode coverage. Electrical activity was dominated by periodic burst firing that could pharmacologically be modulated. Geometry of the network and electrical properties of the patterned cultures were reproducible and displayed long-term stability making the combination of surface structuring and multi-site recording a promising tool for biosensor applications.

SUBMITTER: Jungblut M

PROVIDER: S-EPMC2776171 | biostudies-other | 2009 Dec

REPOSITORIES: biostudies-other

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Project description:High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the "curse of dimensionality" and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

Project description:AbstractStudies have provided evidence that human cerebral organoids (hCOs) recapitulate fundamental milestones of early brain development, but many important questions regarding their functionality and electrophysiological properties persist. High-density microelectrode arrays (HD-MEAs) represent an attractive analysis platform to perform functional studies of neuronal networks at the cellular and network scale. Here, we use HD-MEAs to derive large-scale electrophysiological recordings from sliced hCOs. We record the activity of hCO slices over several weeks and probe observed neuronal dynamics pharmacologically. Moreover, we present results on how the obtained recordings can be spike-sorted and subsequently studied across scales. For example, we show how to track single neurons across several days on the HD-MEA and how to infer axonal action potential velocities. We also infer putative functional connectivity from hCO recordings. The introduced methodology will contribute to a better understanding of developing neuronal networks in brain organoids and provide new means for their functional characterization.Impact statementHuman cerebral organoids (hCOs) represent an attractive in vitro model system to study key physiological mechanisms underlying early neuronal network formation in tissue with healthy or disease-related genetic backgrounds. Despite remarkable advances in the generation of brain organoids, knowledge on the functionality of their neuronal circuits is still scarce. Here, we used complementary metal-oxide-semiconductor (CMOS)-based high-density microelectrode arrays (HD-MEAs) to perform large-scale recordings from sliced hCOs over several weeks and quantified their activity across scales. Using single-cell and network metrics, we were able to probe aspects of hCO neurophysiology that are more difficult to obtain with other techniques, such as patch clamping (lower yield) and calcium imaging (lower temporal resolution). These metrics included, for example, extracellular action potential (AP) waveform features and axonal AP velocity at the cellular level, as well as functional connectivity at the network level. Analysis was enabled by the large sensing area and the high spatiotemporal resolution provided by HD-MEAs, which allowed recordings from hundreds of neurons and spike sorting of their activity. Our results demonstrate that HD-MEAs provide a multi-purpose platform for the functional characterization of hCOs, which will be key in improving our understanding of this model system and assessing its relevance for translational research.Graphical abstractSupplementary informationThe online version contains supplementary material available at 10.1557/s43577-022-00282-w.

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Triangular neuronal networks on microelectrode arrays: an approach to improve the properties of low-density networks for extracellular recording.

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