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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 Ca2+ 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 Ca2+ channels and fluorescence microscopy of a cluster of Ca2+ channels and Ca2+ 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.
Pubmed ID: 29972815
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Software tool for automated microscope acquisition, device control, and image analysis. Used for integrating dissimilar fluorescent microscope hardware and peripherals into a single custom workstation, while providing all the tools needed to perform analysis of acquired images. Offers user friendly application modules for analysis such as cell signaling, cell counting, and protein expression.
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