Author

Colby E. Witt

Date of Award

Fall 2021

Document Type

Open Access Dissertation

Department

Chemistry and Biochemistry

First Advisor

Parastoo Hashemi

Abstract

The brain is a complex matrix that is difficult to study. Signaling molecules, neurotransmitters, are constantly being released and sequestered back into neurons within milliseconds to facilitate communication and normal function, a process called neurotransmission. There are few analytical techniques available to selectively probe such a dynamic system, and even fewer can detect these discrete changes in real-time. In order to make robust measurements in the brain you need speed, sensitivity, selectivity and small probe size, which are encompassed by fast-voltammetry with microelectrodes. Traditional fast-voltammetry at carbon fiber microelectrodes (fast-scan cyclic voltammetry (FSCV)) requires background subtraction to overcome the large capacitive currents generated from scanning at such a high rate, therefore crucial basal information is lost. To capture this previously lost information, fast-scan controlled adsorption (FSCAV) was developed for serotonin detection in vivo. Serotonin is tightly regulated in the brain and thought to mark affective disorders, therefore it is crucial to develop and cultivate new tools to better understand this biomarker. Recently, our lab has employed FSCAV to understand niche mechanisms of action and regulatory processes of the central nervous system regarding serotonin. Here, we discuss improvements towards basal serotonin detection with FSCAV by first modifying CFMs to improve sensitivity and stability over prolonged periods of time and then demonstrate their use in elucidating the effects of antidepressants on tonic serotonin levels. These efforts have indeed improved our own measurements of serotonin and have clearly shown that exploiting the in vivo environment can improve biomarker detection and thus shows promise as a methodology to be adopted across the fast-voltammetry community.

Included in

Chemistry Commons

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