Department of Physics, University of South Florida, Tampa, Florida.
Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California.
Biophys J. 2018 Jul 3;115(1):9-21. doi: 10.1016/j.bpj.2018.06.003.
Experimental records of single molecules or ion channels from fluorescence microscopy and patch-clamp electrophysiology often include high-frequency noise and baseline fluctuations that are not generated by the system under investigation and have to be removed. Moreover, multiple channels or conductance levels can be present at a time in the data that need to be quantified to accurately understand the behavior of the system. Manual procedures for removing these fluctuations and extracting conducting states or multiple channels are laborious, prone to subjective bias, and likely to hinder the processing of often very large data sets. We introduce a maximal likelihood formalism for separating signal from a noisy and drifting background such as fluorescence traces from imaging of elementary Ca release events called puffs arising from clusters of channels, and patch-clamp recordings of ion channels. Parameters such as the number of open channels or conducting states, noise level, and background signal can all be optimized using the expectation-maximization algorithm. We implement our algorithm following the Baum-Welch approach to expectation-maximization in the portable Java language with a user-friendly graphical interface and test the algorithm on both synthetic and experimental data from the patch-clamp electrophysiology of Ca channels and fluorescence microscopy of a cluster of Ca channels and Ca channels with multiple conductance levels. The resulting software is accurate, fast, and provides detailed information usually not available through manual analysis. Options for visual inspection of the raw and processed data with key parameters are provided, in addition to a range of statistics such as the mean open probabilities, mean open times, mean close times, dwell-time distributions for different number of channels open or conductance levels, amplitude distribution of all opening events, and number of transitions between different number of open channels or conducting levels in asci format with a single click.
来自荧光显微镜和膜片钳电生理学的单分子或离子通道的实验记录通常包含高频噪声和基线波动，这些噪声和波动不是由被研究的系统产生的，因此必须去除。此外，在数据中，多个通道或电导水平可能同时存在，需要对其进行量化，以便准确理解系统的行为。手动去除这些波动并提取传导状态或多个通道的过程繁琐、容易受到主观偏见的影响，并且可能会阻碍通常非常大的数据集的处理。我们引入了一种最大似然形式主义，用于分离信号与噪声和漂移背景，例如来自基本钙释放事件（称为 puff）的成像荧光轨迹，以及离子通道的膜片钳记录。可以使用期望最大化算法优化参数，例如开放通道或传导状态的数量、噪声水平和背景信号。我们使用便携式 Java 语言按照 Baum-Welch 方法实现我们的算法，具有用户友好的图形界面，并在钙通道的膜片钳电生理学和钙通道簇的荧光显微镜实验数据的合成数据和实验数据上测试了该算法。该算法的结果准确、快速，并提供了通常无法通过手动分析获得的详细信息。除了一系列统计信息（如平均开放概率、平均开放时间、平均关闭时间、不同开放通道数量或电导水平的停留时间分布、所有开放事件的幅度分布以及不同数量的通道开放或传导水平之间的转换次数）之外，还提供了对原始和处理后数据的关键参数的可视化检查选项，以 asci 格式输出，只需单击一下即可。
