AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of Caenorhabditis elegans.
AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of Caenorhabditis elegans.
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DOI：
10.1371/journal.pone.0008482
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发表时间：
2009-12-29
期刊：
影响因子：
3.7
通讯作者：
James C
中科院分区：
文献类型：
--
作者：
Dillon J;Andrianakis I;Bull K;Glautier S;O'Connor V;Holden-Dye L;James C
线虫线虫的咽部微电路作为一个模型，用于分析神经网络的活动，并服从电生理记录技术。其中一项技术是咽电图（EPG），它提供了对摄食行为、神经传递和肌肉兴奋性的遗传基础的深入了解。然而，对数字记录进行详细的手动分析是必要的，以识别反映底层网络内调节变化的活动的细微差异是耗时且低吞吐量的。为了解决这个问题，我们开发了一个自动化系统，用于EPG记录的高通量和离散分析（AutoEPG）。AutoEPG采用定制的信号处理算法，自动检测EPG信号的不同特征，包括报告肌肉松弛和收缩以及神经元活动的特征。检测算法的手动验证已证明AutoEPG具有非常高的准确度。我们通过分析EPG可检测到的已知咽部表型的现有突变菌株，进一步验证了该软件。在这样做的过程中，我们更精确地定义了钙依赖性钾通道SLO-1在调节神经网络节律活动中的进化保守作用。AutoEPG能够对EPG记录进行一致的分析，显著提高分析吞吐量，并能够稳健地识别咽神经系统电活动的细微变化。预计AutoEPG将进一步增加C. elegans咽as a model模型neural神经circuit电路.
The pharyngeal microcircuit of the nematode Caenorhabditis elegans serves as a model for analysing neural network activity and is amenable to electrophysiological recording techniques. One such technique is the electropharyngeogram (EPG) which has provided insight into the genetic basis of feeding behaviour, neurotransmission and muscle excitability. However, the detailed manual analysis of the digital recordings necessary to identify subtle differences in activity that reflect modulatory changes within the underlying network is time consuming and low throughput. To address this we have developed an automated system for the high-throughput and discrete analysis of EPG recordings (AutoEPG). AutoEPG employs a tailor made signal processing algorithm that automatically detects different features of the EPG signal including those that report on the relaxation and contraction of the muscle and neuronal activity. Manual verification of the detection algorithm has demonstrated AutoEPG is capable of very high levels of accuracy. We have further validated the software by analysing existing mutant strains with known pharyngeal phenotypes detectable by the EPG. In doing so, we have more precisely defined an evolutionarily conserved role for the calcium-dependent potassium channel, SLO-1, in modulating the rhythmic activity of neural networks. AutoEPG enables the consistent analysis of EPG recordings, significantly increases analysis throughput and allows the robust identification of subtle changes in the electrical activity of the pharyngeal nervous system. It is anticipated that AutoEPG will further add to the experimental tractability of the C. elegans pharynx as a model neural circuit.
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