title: Automated Algorithm for Detection of Transient Adenosine Release.
abstract: 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.
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: Ryan P
authors.initials: RP
authors.lastname: Borman
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Nguyen
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: Mallikarjunarao
authors.initials: M
authors.lastname: Ganesana
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: Scott T
authors.initials: ST
authors.lastname: Lee
authors.affiliation: Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.
authors.forename: B Jill
authors.identifier: 0000-0002-5096-9309
authors.initials: BJ
authors.lastname: Venton
mesh_terms: D000241:Adenosine / Q000378:metabolism*; D000465:Algorithms; D000818:Animals; D055664:Electrochemical Techniques; D001330:Electronic Data Processing / Q000379:methods*; D006632:Histamine / Q000494:pharmacology; D006861:Hydrogen Peroxide / Q000494:pharmacology; D006863:Hydrogen-Ion Concentration; D066298:In Vitro Techniques; D051379:Mice; D008839:Microelectrodes; D017397:Prefrontal Cortex / Q000187:drug effects / Q000378:metabolism*; D013997:Time Factors
chemical_list: D006632:Histamine; D006861:Hydrogen Peroxide; D000241:Adenosine
keywords: Adenosine; automated analysis; brain slice; caudate; hippocampus; in vivo voltammetry