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.

Human-Relevant Sensitivity of iPSC-Derived Human Motor Neurons to BoNT/A1 and B1

“…In the data recorded here, neuronal activity was low and did not increase after finalization of the differentiation on the day set by the authors of the original differentiation protocols. In addition, the low spike amplitudes resemble rather immature neurons [ 49 ]. Due to this, the activity of MNs needs to be improved prior to further experiments such as BoNT potency evaluation utilizing MEAs.…”

An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays

“…Subsequently, we made several improvements that were facilitated by the model-based validation that we present here. First, we extended the list of available filters for channel selection; in [34] only detection and kurtosis filters were used; second, we changed the interrogation of the graph to find axonal branches from using only the distance criterion, i.e., shortest distance (which could result in shortcuts and undetected axonal segments) to using a combination of distance and amplitude (h edge ) criteria with the A * method; third, we changed the strategy to avoid duplicates in the path: instead of looking for and removing duplicate paths a-posteriori, we here utilized the set of neighboring channels to existing paths to avoid finding duplicates a-priori, which also resulted in a more efficient implementation. Finally, we added pruning, merging, and splitting steps that were not implemented in [34], which arguably provide a better estimation of the axon branches.…”

“…In this section, we describe the proposed algorithm, the application of which includes four main steps: i) channel selection, ii) graph construction, iii) axonal-branch reconstruction, and iv) axonal-arbor pruning and velocity estimation. The method originated from ideas and concepts in our group [20,34], which have been organized, modified, validated and assembled to obtain a coherent and fully functional method for axonal-arbor reconstruction. In Section 4 we compare the presented new approach to the previously used approaches.…”

“…This local search can result in zig-zag paths, as the algorithm has no information on the global structure of the signal landscape. To overcome this limitation, in Ronchi et al 2020 [34] we introduced a very first version of a graph-based algorithm. Subsequently, we made several improvements that were facilitated by the model-based validation that we present here.…”

“…Such cultures are available from patients suffering from neurological disorders and from healthy donors, so that electrophysiological biomarkers associated to neurological diseases can be established. In Ronchi et al [34], for example, we made a first attempt to characterize axonal velocities of hiPSC-derived neuronal cultures and found significant differences between healthy motor and dopaminergic neurons and disease phenotypes featuring mutations related to Amyotrophic Lateral Sclerosis (ALS) and Parkinson's disease (PD).…”