Automated algorithm for measurement of spontaneous adenosine transients in large electrochemical data sets

Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry data has been performed manually and is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10-18 hours to about 40 minutes per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their peak ratios. In order to validate the program, 4 data sets from 3 independent researchers were analyzed by the algorithm and then verified by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate, histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.