OPETH: Open Source Solution for Real-Time Peri-Event Time Histogram Based on Open Ephys

Abstract

Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as allow novel studies otherwise not possible due to prohibitively low throughput. Real-time feedback is especially important in studies that involve optogenetic cell type identification by enabling a systematic search for neurons of interest. However, such tools are scarce and limited to costly commercial systems with high degree of specialization, which hitherto prevented wide-ranging benefits for the community. To address this, we present an open-source tool that enables online feedback during electrophysiology experiments and provides a Python interface for the widely used Open Ephys open source data acquisition system. Specifically, our software allows flexible online visualization of spike alignment to external events, called the online peri-event time histogram (OPETH). These external events, conveyed by digital logic signals, may indicate photostimulation time stamps for in vivo optogenetic cell type identification or the times of behaviorally relevant events during in vivo behavioral neurophysiology experiments. Therefore, OPETH allows real-time identification of genetically defined neuron types or behaviorally responsive populations. By allowing "hunting" for neurons of interest, OPETH significantly reduces experiment time and thus increases the efficiency of experiments that combine in vivo electrophysiology with behavior or optogenetic tagging of neurons.

Cholinergic activity reflects reward expectations and predicts behavioral responses

“…For optogenetic tagging, 1 ms laser pulses were delivered (473 nm, Sanctity) at 20 Hz for 2 seconds, followed by 3 seconds pause, repeated 20–30 times. Light-evoked spikes and potential artifacts were monitored online using the OPETH plugin (SCR_018022) 84 and laser power was adjusted as necessary to avoid light-induced photoelectric artifacts and population spikes that could mask individual action potentials. Significance of photoactivation was assessed during offline analyses by the SALT test based spike latency distributions after light pulses, compared to a surrogate distribution using Jensen-Shannon divergence (information radius).…”

Parvalbumin-expressing basal forebrain neurons mediate learning from negative experience

“…Before the training session has begun, to optogenetically tag BFPVNs, 1 ms laser pulses were applied (473 nm, Sanctity) at 20 Hz for 2 seconds, followed by 3 seconds pause, repeated 20-30 times. Light-evoked spikes and incidental artifacts were monitored online using the OPETH plugin (SCR_018022) 82 for Open Ephys and when light-induced photoelectric artifacts or population spikes were observed, laser power was adjusted as necessary to avoid masking of individual action potentials. Significance of photoactivation was assessed with offline data analysis by examining the SALT test based on spike latency distributions after light pulses, compared to a surrogate distribution using Jensen-Shannon divergence (information radius) 75,83 .…”

Internet of Things Architecture for Cellular Biology

“…Human cell cultures, especially recently developed 3-D organoid models, enable the study of disease, drug interactions, genetic alterations, and response to neural stimulation in ways infeasible in vivo (Kim et al, 2020;Schutgens & Clevers, 2020;Gao et al, 2014). Dozens of cultures are commonly grown (Lancaster et al, 2013(Lancaster et al, , 2017Paşca et al, 2015) and measured (Széll et al, 2020;Putzeys et al, 2019;Rolston et al, 2009) at a time, but massively parallel longitudinal experiments on organoid models are required to provide insights into organ development and function, and to use them as platforms for drug discovery (Schuster et al, 2020;Khan et al, 2021). Recent complex culturing experiments have focused on the large scale production of samples (Sarrafha et al, 2021), but managing experiments at this scale leads to a host of technical challenges in data management and analysis.…”