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Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays.

ABSTRACT: Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, HD-MEAs are used in vitro to characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinson's disease and amyotrophic lateral sclerosis. Reproducible electrophysiological network, single-cell and subcellular metrics are used for phenotype characterization and drug testing. Metrics, such as burst shape and axonal velocity, enable the distinction of healthy and diseased neurons. The HD-MEA metrics can also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, it is shown that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.

SUBMITTER: Ronchi S

PROVIDER: S-EPMC7610355 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays.

Ronchi Silvia S Buccino Alessio Paolo AP Prack Gustavo G Kumar Sreedhar Saseendran SS Schröter Manuel M Fiscella Michele M Hierlemann Andreas A

Advanced biology 20210114 3

Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, HD-MEAs are used in vitro to characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic and motor neurons, including ...[more]

PMID: 33729694

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Project description:Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.

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.

Dataset Information

Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays.

Publications

Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays.

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