Biology subjects

Fiscella, M.

Publications and source records attributed to Fiscella, M..

4 recordsLinked to original sources

bioRxiv2022

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

Parkinsons disease (PD) is a common debilitating neurodegenerative disorder, characterized by a progressive loss of dopaminergic (DA) neurons. Mutations, gene dosage increase, and single nucleotide polymorphisms in the -synuclein-encoding gene SNCA either cause or increase the risk for PD. However, neither the function of -synuclein in health and disease, nor its role throughout development is fully understood. Here, we introduce DeePhys, a new tool that allows for data-driven functional phenotyping of neuronal cell lines by combining electrophysiological features inferred from high-density microelectrode array (HD-MEA) recordings with a robust machine learning workflow. We apply DeePhys to human induced pluripotent stem cell (iPSC)-derived DA neuron-astrocyte co-cultures harboring the prominent SNCA mutation A53T and an isogenic control line. Moreover, we demonstrate how DeePhys can facilitate the assessment of cellular and network-level electrophysiological features to build functional phenotypes and to evaluate potential treatment interventions. We find that electrophysiological features across all scales proved to be highly specific for the A53T phenotype, enabled to predict the genotype and age of individual cultures with high accuracy, and revealed a mutant-like phenotype after downregulation of -synuclein.

neuroscience↗

bioRxiv2022

Homophilic wiring principles underpin neuronal network topology in vitro

Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.

neuroscience↗

bioRxiv2020

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

Recent advances in the field of cellular reprogramming have opened a route to study 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, we used HD-MEAs in vitro to characterize and compare human induced-pluripotent-stem-cell (iPSC)-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinsons disease and amyotrophic lateral sclerosis. We established reproducible electrophysiological network, single-cell and subcellular metrics, which were used for phenotype characterization and drug testing. Metrics such as burst shapes and axonal velocity enabled the distinction of healthy and diseased neurons. The HD-MEA metrics could also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, we showed that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.

neuroscience↗

bioRxiv2020

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

neuroscience↗