Széll András, Martínez-Bellver Sergio, Hegedüs Panna, Hangya Balázs
Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary.
Laboratory of Neural Circuitry, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
Front Neuroinform. 2020 May 20;14:21. doi: 10.3389/fninf.2020.00021. eCollection 2020.
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 optogenetic cell type identification or the times of behaviorally relevant events during 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 electrophysiology with behavior or optogenetic tagging of neurons.
单细胞电生理学仍然是系统神经科学中使用最广泛的方法之一。实验者在电生理记录过程中做出的决策在很大程度上决定了记录质量、项目持续时间和所收集数据的价值。因此，有助于这些决策的在线反馈可以降低金钱和时间投入，并大幅加快项目进度，还能开展因通量极低而原本无法进行的新研究。实时反馈在涉及通过光遗传学进行细胞类型识别的研究中尤为重要，它能实现对感兴趣神经元的系统搜索。然而，此类工具稀缺，且仅限于高度专业化的昂贵商业系统，这迄今为止阻碍了该领域广泛受益。为解决这一问题，我们展示了一种开源工具，它能在电生理实验期间实现在线反馈，并为广泛使用的开源数据采集系统Open Ephys提供Python接口。具体而言，我们的软件允许灵活地在线可视化尖峰与外部事件的对齐情况，即所谓的在线事件周围时间直方图（OPETH）。这些由数字逻辑信号传达的外部事件，可能表示用于光遗传学细胞类型识别的光刺激时间戳，或行为神经生理学实验中与行为相关事件的时间。因此，OPETH允许实时识别基因定义的神经元类型或行为反应群体。通过允许“搜寻”感兴趣的神经元，OPETH显著减少了实验时间，从而提高了将电生理学与行为或神经元的光遗传学标记相结合的实验效率。
