  Amperometry is a commonly used electrochemical method for studying the
process of exocytosis in real-time. Given the high precision of recording that
amperometry procedures offer, the volume of data generated can span over
several hundreds of megabytes to a few gigabytes and therefore necessitates
systematic and reproducible methods for analysis. Though the spike
characteristics of amperometry traces in the time domain hold information about
the dynamics of exocytosis, these biochemical signals are, more often than not,
characterized by time-varying signal properties. Such signals with time-variant
properties may occur at different frequencies and therefore analyzing them in
the frequency domain may provide statistical validation for observations
already established in the time domain. This necessitates the use of
time-variant, frequency-selective signal processing methods as well, which can
adeptly quantify the dominant or mean frequencies in the signal. The Fast
Fourier Transform (FFT) is a well-established computational tool that is
commonly used to find the frequency components of a signal buried in noise. In
this work, we outline a method for spike-based frequency analysis of
amperometry traces using FFT that also provides statistical validation of
observations on spike characteristics in the time domain. We demonstrate the
method by utilizing simulated signals and by subsequently testing it on diverse
amperometry datasets generated from different experiments with various chemical
stimulations. To our knowledge, this is the first fully automated open-source
tool available dedicated to the analysis of spikes extracted from amperometry
signals in the frequency domain.
