Jin Dezhe Z, Zhao Ting, Hunt David L, Tillage Rachel P, Hsu Ching-Lung, Spruston Nelson
Department of Physics and Center for Neural Engineering, The Pennsylvania State University, University Park, PA, United States.
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States.
Front Neuroinform. 2019 Oct 31;13:68. doi: 10.3389/fninf.2019.00068. eCollection 2019.
Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.
神经元通过整合来自数千个突触（主要在树突树中）的输入来执行计算，以驱动轴突中的动作电位发放。研究这一过程的一种富有成效的方法是使用膜片钳电极从神经元进行记录，用一种能允许后续染色的物质填充被记录的神经元，重建树突的三维结构，并利用所得的功能和结构数据来开发树突整合的计算机模型。准确地生成树突的定量重建通常是一个繁琐的过程，需要花费许多小时进行人工检查和测量。在这里，我们展示了ShuTu，这是一个新的软件包，它有助于对使用明场显微镜成像的树突进行准确而高效的重建。该程序分两步运行：（1）自动识别树突过程，以及（2）对自动重建中的错误进行人工校正。这种方法能够快速而高效地重建具有复杂树突形态的神经元，从而便于使用计算机模型来研究树突结构 - 功能关系以及单个神经元执行的计算。
