Analysis of lipid storage in C. elegans enabled by image processing and microfluidics

Abstract

While the genetic origin of obesity has yet to be fully understood, the risk of this serious health issue has been linked to fat and lipid storage. C. elegans is a convenient model to understand key lipid droplet storage mechanisms conserved through evolution; however, current imaging and analysis methods are labor intensive and time consuming. Microfluidics can provide a solution to this problem by enabling high-throughput imaging and screening. Engineering methods and tools to improve object quantification would also dramatically enhance the ability to study lipids. The purpose of my thesis is to use microfluidics, image processing, and manipulation of diet to further understand fat storage in C. elegans. This is accomplished by: 1) developing an image processing method to automatically distinguish lipid droplet phenotypes in C. elegans; 2) conducting an automated forward genetic screen for C. elegans mutants that suppress the lipid droplet phenotype of atlastin; 3) conducting a diet screen with genetically varied E. coli fed to C. elegans and developing methods to characterize differences in phenotype populations, and 4) developing a method to reversibly bond microfluidic devices in order to selectively recover worms (and other organisms) and conducting individual animal analysis after high magnification imaging. The development of a platform using microfluidics and image processing will facilitate genetic and diet screens allowing for the discovery and understanding of proteins and genes vital to lipid metabolism and storage in C. elegans, and this approach will enable studies of fat storage unfeasible with traditional methods.