Joint Research Center for Computational Biomedicine, RWTH Aachen University, Paulwelstrasse 19, 52074, Aachen, NRW, Germany.
Department of Chemistry and Molecular Biology, University of Gothenburg, Universitetsplatsen 1, 40530, Gothenburg, Sweden.
Sci Rep. 2024 Oct 24;14(1):25142. doi: 10.1038/s41598-024-76665-7.
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.
电流测定法是一种常用于实时研究胞吐过程的电化学方法。由于电流测定程序提供了高精度的记录，因此生成的数据量可能跨越数百兆字节到几个千兆字节，因此需要系统且可重复的分析方法。虽然电流测定迹线在时域中的尖峰特征包含有关胞吐动力学的信息，但这些生化信号通常具有时变的信号特性。具有时变特性的此类信号可能出现在不同的频率下，因此在频域中分析它们可能为已经在时域中建立的观察结果提供统计验证。这需要使用时变、频率选择的信号处理方法，这些方法可以灵活地量化信号中的主导或平均频率。快速傅里叶变换（FFT）是一种成熟的计算工具，常用于在噪声中找到信号的频率分量。在这项工作中，我们概述了一种使用 FFT 对电流测定迹线进行基于尖峰的频率分析的方法，该方法还提供了对时域中尖峰特征观察结果的统计验证。我们通过利用模拟信号并随后在不同化学刺激的不同实验生成的各种电流测定数据集上对其进行测试来证明该方法。据我们所知，这是第一个专门用于分析从电流测定信号中提取的尖峰的全自动化开源工具。
