Purpose: Import pClamp files and perform common types of data analysis, mainly of biophysical properties of voltage-gated ion channels studied with voltage-step or voltage-ramp protocols, such as construction of current-voltage relationships, activation and inactivation curves, and fitting them by Boltzmann function to obtain the various parameters of various voltage-gated ion conductances. In addition, several tools for facilitation of data graphing and presentation (mask/unmask capacitive artifacts, leak correction, and data smoothing) are included in this application. Possible variations in the user voltage protocols are extensively addressed via user interface.
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)
Description of button functionality:
1. pClamp data import. Normally, one file is imported for subsequent data analysis by pressing this button. However, multiple files can also be selected, in which case pressing the button repeatedly opens the subsequent pClamp files (e.g. file viewer function). Data analysis can be commenced once the correct file was located.
2. Data smoothing by N number of points: each point is replaced by the mean of N/2 proceeding and N/2 subsequent points.
3. Masks capacitative artifacts associated with voltage steps and automatically rescales current traces (this procedure will have to be acknowledged in Methods).
4. Undo data points masking.
5. Current-voltage (I-V) relationship with options to measure max, min or mean values, or any combinations of min/max/mean values in different episodes (these options are more versatile than in Clampfit).
6. Input resistance calculation from the I-V curve and leak current correction (e.g. subtraction of a linear component of the I-V curve).
7. Activation (conductance) curve is created and automatically fitted by the Boltzmann function with the preset lower bound Gmin≥0 (since conductance should be ≥0) to determine the potential of half-maximal activation (V1/2) and slope factor (k).
8. Inactivation curve is created and automatically fitted by the Boltzmann function to determine the potential of half-maximal inactivation (V1/2) and slope factor (k).
9. Extracts I-V relationships from voltage ramp protocols. If file contains multiple episodes with the same voltage ramp protocol then all I-V curves are extracted in one go (this option is not available in Clampfit). If voltage protocol used contains several ramp epochs a pop-up dialog allows the user to choose any one for constructing the I-V curves.
10. Calculation of conductance-voltage (G-V) relationship based on ramp I-V curve: reversal potentials of the ramp I-V curves are measured by linear regression of data points around the estimated reversal potential to increase the accuracy, and based on these values the conductance curves are created with the option of simultaneously fitting them by the Boltzmann function. Slope factor can be shared for the cases when the activation curves shift on the voltage axis in a parallel manner. This tool is particularly suitable for analysis of cationic currents.
11. Quick Tutorial is provided in the form of PowerPoint slides with all steps of data analysis described in the Notes for each slide. Sample files used are included allowing the user to learn how to use this App for various types of pClamp data analysis. Sample files are saved in Apps folder: \\Simple pCLAMP Analyzer\Samples. Type "%@A=" in Script window to see App folder.