CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

To couple the fidelity of patch-clamp recording with a more high-throughput screening capability, we pioneered a, to our knowledge, novel approach to single-channel recording that we named "optical patch clamp." By using highly sensitive fluorescent Ca ²⁺ indicator dyes in conjunction with total internal fluorescence microscopy techniques, we monitor Ca ²⁺ flux through individual Ca ²⁺ -permeable channels. This approach provides information about channel gating analogous to patch-clamp recording at a time resolution of ∼2 ms with the additional advantage of being massively parallel, providing simultaneous and independent recording from thousands of channels in the native environment. However, manual analysis of the data generated by this technique presents severe challenges because a video recording can include many thousands of frames. To overcome this bottleneck, we developed an image processing and analysis framework called CellSpecks capable of detecting and fully analyzing the kinetics of ion channels within a video sequence. By using randomly generated synthetic data, we tested the ability of CellSpecks to rapidly and efficiently detect and analyze the activity of thousands of ion channels, including openings for a few milliseconds. Here, we report the use of CellSpecks for the analysis of experimental data acquired by imaging muscle nicotinic acetylcholine receptors and the Alzheimer's disease-associated amyloid β pores with multiconductance levels in the plasma membrane of Xenopus laevis oocytes. We show that CellSpecks can accurately and efficiently generate location maps and create raw and processed fluorescence time traces; histograms of mean open times, mean close times, open probabilities, durations, and maximal amplitudes; and a "channel chip" showing the activity of all channels as a function of time. Although we specifically illustrate the application of CellSpecks for analyzing data from Ca ²⁺ channels, it can be easily customized to analyze other spatially and temporally localized signals.
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