Hydrogen peroxide (HO) is an endogenous molecule that plays several important roles in brain function: it is generated in cellular respiration, serves as a modulator of dopaminergic signaling, and its presence can indicate the upstream production of more aggressive reactive oxygen species (ROS). HO has been implicated in several neurodegenerative diseases, including Parkinson's disease (PD), creating a critical need to identify mechanisms by which HO modulates cellular processes in general and how it affects the dopaminergic nigrostriatal pathway, in particular. Furthermore, there is broad interest in selective electrochemical quantification of HO, because it is often enzymatically generated at biosensors as a reporter for the presence of nonelectroactive target molecules. HO fluctuations can be monitored in real time using fast-scan cyclic voltammetry (FSCV) coupled with carbon-fiber microelectrodes. However, selective identification is a critical issue when working in the presence of other molecules that generate similar voltammograms, such as adenosine and histamine. We have addressed this problem by fabricating a robust, HO-selective electrode. 1,3-Phenylenediamine (mPD) was electrodeposited on a carbon-fiber microelectrode to create a size-exclusion membrane, rendering the electrode sensitive to HO fluctuations and pH shifts but not to other commonly studied neurochemicals. The electrodes are described and characterized herein. The data demonstrate that this technology can be used to ensure the selective detection of HO, enabling confident characterization of the role this molecule plays in normal physiological function as well as in the progression of PD and other neuropathies involving oxidative stress.
Significance: Fiber photometry is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals, However, analyzing photometry data can be both laborious and time-consuming. Aim: We propose the FiPhA (Fiber Photometry Analysis) app, which is a general-purpose fiber photometry analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation. Approach: FiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface. Results: This application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events and quality control features. Conclusions: FiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based fiber photometry data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to fiber photometry analysis.
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