Fabrication and Optimization of a Molecularly Imprinted Carbon Fiber Microelectrode for Selective Detection of Met-enkephalin Using Fast-Scan Cyclic Voltammetry.

Methionine-enkephalin (Met-Enk) is an endogenous opioid peptide that is involved in various physiological processes including memory. A technological gap in the understanding of Met-Enk's role in memory is the lack of rapid measurement tools to selectively quantify Met-Enk concentrations in situ. Here, we integrate molecularly imprinted polymers (MIPs) with carbon fiber microelectrodes (CFMs) to selectively detect Met-Enk by using fast-scan cyclic voltammetry (FSCV). We report two MIP conditions that yield 2-fold and 5-fold higher selectivity toward Met-Enk than the tyrosine-containing hexapeptide fragment angiotensin II (3-8). We demonstrate that MIP technology can be combined with FSCV at CFMs to create rapid and selective sensors for Met-Enk. This technology is a promising platform for creating selective sensors for other peptides and biomarkers.

Novel, User-Friendly Experimental and Analysis Strategies for Fast Voltammetry: Next Generation FSCAV with Artificial Neural Networks.

Fast-scan adsorption-controlled voltammetry (FSCAV) was recently derived from fast-scan cyclic voltammetry to estimate the absolute concentrations of neurotransmitters by using the innate adsorption properties of carbon fiber microelectrodes. This technique has improved our knowledge of serotonin dynamics in vivo. However, the analysis of FSCAV data is laborious and technically challenging. First, each electrode requires post-experimental in vitro calibration. Second, current analysis methods are semi-manual and time-consuming and require a steep learning curve. Finally, the calibration methods used do not adapt to nonlinear electrode responses. In this work, we provide freely accessible computational solutions to these issues. First, we design an artificial neural network (ANN) and train it with a large data set (calibrations from 140 electrodes by six different researchers) to achieve calibration-free estimations and improve predictive error. We discuss the power of the ANN to obtain a low predictive error without electrode-specific calibrations as a function of being able to predict the sensitivity of the electrode. We use the ANN to successfully predict the absolute serotonin concentrations of real in vivo data. Finally, we create a fast and user-friendly, fully automated analysis web platform to simplify and reduce the expertise required for the postanalysis of FSCAV signals.