Borman Ryan P, Wang Ying, Nguyen Michael D, Ganesana Mallikarjunarao, Lee Scott T, Venton B Jill
Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
ACS Chem Neurosci. 2017 Feb 15;8(2):386-393. doi: 10.1021/acschemneuro.6b00262. Epub 2016 Dec 8.
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 (FSCV) data 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 to 18 h to about 40 min per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs time peak ratios. In order to validate the program, four data sets from three independent researchers were analyzed by the algorithm and then compared to manual identification 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 (ATP), 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.
大脑中已发现持续仅几秒的自发性腺苷释放事件。由于腺苷释放的随机性，从快速扫描循环伏安法（FSCV）数据中识别这些腺苷事件具有难度。在本研究中，我们开发了一种算法，可自动识别并表征腺苷瞬态特征，包括事件时间、浓度和持续时间。自动化数据分析将每个实验的分析时间从10至18小时减少至约40分钟。该算法基于腺苷的两个氧化峰、它们之间的时间延迟以及它们的电流与时间峰值比来识别腺苷。为了验证该程序，算法分析了来自三位独立研究人员的四个数据集，然后与分析师的人工识别结果进行比较。该算法产生了10±4%的假阴性和9±3%的假阳性。通过比较三磷酸腺苷（ATP）、组胺、过氧化氢和pH变化的校准数据验证了该算法的特异性，这些分析物未被识别为腺苷。体内刺激的组胺释放也未被识别为腺苷。该代码在设计上具有模块化，可轻松调整以检测FSCV数据中自发性多巴胺或其他神经化学瞬态的特征。
