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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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"On-Chip Phenotypic Screening & Characterization of C. elegans Enabled by Microfluidics & Image Analysis Methods"
Advisor: Dr. Hang Lu (BioE / ChBE)
Committee:
Xiaoping Hu, PhD (BioE / BMED)
Oliver Brand, PhD (ECE)
Todd Streelman, PhD (Bio)
Robert Butera, PhD (BioE / ECE)
Since its introduction in 1960’s, the model organism Caenorhabditis elegans has played a crucial role towards scientific discoveries because of its relatively simple anatomy, conserved biological mechanisms, and mapped genome. The organism also has a rapid generation time and produces a large number of isogenic progeny, making C. elegans an excellent system for conducting forward genetic screens. Conventional screening methods, however, are labor intensive and introduce potential experimental bias; typically, large-scale screens can take months to years. Thus, automated screening and characterization platforms can provide an opportunity to overcome this bottleneck.
The objective of this thesis is to develop tools to perform rapid phenotypical characterization of C. elegans to enable automated genetic screening systems for neural development. To achieve this goal, I developed methods to increase throughput of worm handling using microfluidic devices and demonstrate software modules to phenotype unknown mutants using quantitative and morphological image analysis methods. Microfluidic devices are constructed from PDMS using established soft-lithography techniques. The emphasis on the simplification of existing designs greatly facilitates the adoption of our developed systems by other scientists. This thesis also includes image processing modules using various techniques to determine animal phenotypes. For example, we adapted standard thresholding methods to detect animal motor neurons, developed a modified granulometry algorithm to rapidly characterize large numbers of lipid droplets in 3D, and developed a probability model to determine neuronal process morphology.
This work is significant because it increases current capabilities of screening small animals with morphological phenotypes by enhancing throughput and reducing human bias. Genes or gene functions that can be discovered using these methods can further elucidate mechanisms relevant to neural development, degeneration, maintenance, and function; these discoveries in turn can facilitate discoveries of potential therapeutic strategies for human neurological diseases.
