Due to the large extent to which important biological mechanisms are conserved evolutionarily, the study of a simple soil nematode, C. elegans, has provided the template for significant advances in biology. Use of this model organism has accelerated in recent years as developments of advanced reagents such as synapse localized fluorescent markers have provided powerful tools to study the complex process of synapse formation and remodeling. Even as much routine biology work, such as sequencing, has become faster and easier, imaging protocols have remained essentially unchanged over the past forty years of research. This, coupled with the ability to visualize small, complex features as a result of new fluorescent reagents, has resulted in genetic screens in C. elegans becoming increasingly labor intensive and slow because microscopy mainly relies on manual mounting of animals and phenotyping is usually visually done by experts. Genetic screens have become the rate limiting factor for much of modern C. elegans research. Furthermore, phenotyping of fluorescent expression has remained a primarily qualitative process which has prevented statistical analysis of subtle features.
To address these issues, a comprehensive system to allow autonomous screening for novel mutants was created. This was done by developing novel microfluidic devices to enable high-throughput screening, systems-level components to allow automated operation, and a computer vision framework for identification and quantitative phenotyping of synaptic patterns. The microfluidic platform allows for imaging and sorting of thousands of animals at high-magnification within hours. The computer vision framework employs a two-stage feature extraction to incorporate local and regional features and allows for synapse identification in near real-time with an extremely low error rate. Using this system thousands of mutagenized animals were screened to indentify numerous novel mutants expressing altered synaptic placement and development. Fully automated screening and analysis of subtle fluorescent phenotypes will allow large scale RNAi and drug screens. Combining microfluidics and computer vision approaches will have a significant impact on the biological community by removing a significant bottleneck and allowing large-scale screens that would have previously been too labor intensive to attempt.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/44784 |
| Date | 20 May 2011 |
| Creators | Crane, Matthew Muria |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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