Bullmann Torsten, Radivojevic Milos, Huber Stefan T, Deligkaris Kosmas, Hierlemann Andreas, Frey Urs
RIKEN Quantitative Biology Center, RIKEN, Kobe, Japan.
Graduate School of Informatics, Kyoto University, Kyoto, Japan.
Front Cell Neurosci. 2019 Sep 6;13:404. doi: 10.3389/fncel.2019.00404. eCollection 2019.
Understanding the role of axons in neuronal information processing is a fundamental task in neuroscience. Over the last years, sophisticated patch-clamp investigations have provided unexpected and exciting data on axonal phenomena and functioning, but there is still a need for methods to investigate full axonal arbors at sufficient throughput. Here, we present a new method for the simultaneous mapping of the axonal arbors of a large number of individual neurons, which relies on their extracellular signals that have been recorded with high-density microelectrode arrays (HD-MEAs). The segmentation of axons was performed based on the local correlation of extracellular signals. Comparison of the results with both, ground truth and receiver operator characteristics, shows that the new segmentation method outperforms previously used methods. Using a standard HD-MEA, we mapped the axonal arbors of 68 neurons in <6 h. The fully automated method can be extended to new generations of HD-MEAs with larger data output and is estimated to provide data of axonal arbors of thousands of neurons within recording sessions of a few hours.
了解轴突在神经元信息处理中的作用是神经科学的一项基本任务。在过去几年中，精密的膜片钳研究提供了关于轴突现象和功能的意想不到且令人兴奋的数据，但仍需要能够以足够的通量研究完整轴突分支的方法。在此，我们提出了一种同时绘制大量单个神经元轴突分支图谱的新方法，该方法依赖于用高密度微电极阵列（HD-MEA）记录的细胞外信号。轴突的分割是基于细胞外信号的局部相关性进行的。将结果与真实情况和受试者工作特征进行比较表明，新的分割方法优于先前使用的方法。使用标准的HD-MEA，我们在不到6小时内绘制了68个神经元的轴突分支图谱。这种全自动方法可以扩展到具有更大数据输出的新一代HD-MEA，预计在几小时的记录过程中就能提供数千个神经元轴突分支的数据。
