Access Type

Open Access Thesis

Date of Award

January 2022

Degree Type

Thesis

Degree Name

M.S.

Department

Physics and Astronomy

First Advisor

Christopher V. Kelly

Abstract

Lipolysis is a metabolic pathway in which free fatty acids are mobilized from stored triglycerides. The rate-limiting enzyme in this process is adipose triglyceride lipase, which is regulated by α/β-hydrolase domain-containing protein 5 (ABHD5) via both natural and synthetic pathways. With advanced artificial neural networks, image processing methods can extract quantitative results from fluorescence images. The segmentation of complex biological images, in which regions of the image are labeled as distinct masks, is the first step in image analysis. Ilastik, a machine-learning software, performs image segmentation with a user-trained neural network and custom key feature labels. The software’s results are evaluated using a custom Python script, resulting in a new workflow that incorporates Ilastik for the construction of single-cell data from confocal fluorescence images. We analyzed multi-color fluorescence images of tissues to determine the growth and metabolism of lipid droplets. Moreover, the use of neural-network-based fluorescence image analysis to measure single-cell triglyceride storage and ABHD5 expression upon stimulation with isoproterenol, SR3420, dimethyl sulfoxide, or forskolin is reported. We demonstrate enhanced quantitative information for hypothesis testing in the assessment of single-cell behaviors and metabolic pathways.

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