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Saseendran Kumar, S.

Publications and source records attributed to Saseendran Kumar, S..

2 recordsLinked to original sources

Inferring monosynaptic connections from paired dendritic spine Ca2+ imaging and large-scale recording of extracellular spiking

Techniques to identify monosynaptic connections between neurons have been vital for neuroscience research, facilitating important advancements concerning network topology, synaptic plasticity, and synaptic integration, among others. Here, we introduce a novel approach to identify and monitor monosynaptic connections using high-resolution dendritic spine Ca2+ imaging combined with simultaneous large-scale recording of extracellular electrical activity by means of high-density microelectrode arrays (HD-MEAs). We introduce an easily adoptable analysis pipeline that associates the imaged spine with its presynaptic unit and test it on in vitro recordings. The method is further validated and optimized by simulating synaptically-evoked spine Ca2+ transients based on measured spike trains in order to obtain simulated ground-truth connections. The proposed approach offers unique advantages as i) it can be used to identify monosynaptic connections with an accurate localization of the synapse within the dendritic tree, ii) it provides precise information of presynaptic spiking, and iii) postsynaptic spine Ca2+ signals and, finally, iv) the non-invasive nature of the proposed method allows for long-term measurements. The analysis toolkit together with the rich data sets that were acquired are made publicly available for further exploration by the research community.

neuroscience↗

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↗