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Throughput Screening in C. Elegans Using AUTOMATED QUANTITATIVE PHENOTYPING AND HIGH- THROUGHPUT SCREENING IN C. ELEGANS USING MICROFLUIDICS AND COMPUTER VISION A Dissertation Presented to The Academic Faculty by Matthew Muria Crane In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Bioengineering Georgia Institute of Technology August 2011 AUTOMATED QUANTITATIVE PHENOTYPING AND HIGH- THROUGHPUT SCREENING IN C. ELEGANS USING MICROFLUIDICS AND COMPUTER VISION Approved by: Dr. Hang Lu, Advisor Dr. Michelle LaPlaca School of Chemical and Biomolecular Engineering School of Biomedical Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Robert Butera Dr. James Rehg School of Electrical and Computer Engineering School of Interactive Computing Georgia Institute of Technology Georgia Institute of Technology Dr. Oliver Brand School of Electrical and Computer Engineering Georgia Institute of Technology Date Approved: March 10, 2011 To my AsianCajun ACKNOWLEDGEMENTS I wish to thank my labmates for making this process interesting, educational and above all, entertaining. I’d like especially to thank Jeffrey Stirman and Kwanghun Chung for teaming up with me (or allowing me to team up with you), and for the years of coffee breaks and discussions. Ed Park for hours of political distraction and fierce debate. My advisor, Hang Lu, for providing me with all of the feedback and support a graduate student could ask for, while letting me explore this research topic with abandon. Thank you for helping me learn how to research, not just do research. My committee for providing me with valuable feedback and advance to improve this work. My family for supporting and encouraging me and helping to keep me from losing myself in the process. My fiancée, Lauren, for all of her support, forbearance and for making every day brighter than it could have ever been without her. TABLE OF CONTENTS ACKNOWLEDGEMENTS .................................................................................................................................... i List of Tables ................................................................................................................................................... ix List of Figures................................................................................................................................................. xii Summary .................................................................................................................................................... xxiii Chapter 1: Introduction ................................................................................................................................... 1 C. elegans as a model system ..................................................................................................................... 1 Standard methods: advantages and limitations ..................................................................................... 2 Using C. elegans as a model for neuroscience and synaptogenesis ....................................................... 4 Fluorescent Phenotpying Methods ........................................................................................................ 9 Challenges Facing C. elegans neurobiology .......................................................................................... 12 Microfluidics ............................................................................................................................................. 13 Microfluidics as a tool for biology ........................................................................................................ 14 General Microfluidic Methods .............................................................................................................. 16 State of the art microfluidics for multicellular model organisms ......................................................... 18 Previous microfluidics work within the Lu Lab ..................................................................................... 25 Problems with the original device ........................................................................................................ 29 iv Computer Vision ....................................................................................................................................... 32 Machine Learning Methods .................................................................................................................. 33 Feature Extraction Methods ................................................................................................................. 36 Machine Learning and Vision Applied to C. elegans ............................................................................. 39 External control components for microfluidic systems and lack thereof ................................................. 40 Thesis Objective and Contributions .......................................................................................................... 43 Chapter 2: Microfluidic device development for high-throughput screening ............................................... 45 Reducing failure rate by paring features and microfluidic redesign ......................................................... 45 Turning drawbacks into advantages: using partial closure valves ........................................................ 48 The first genetic screen of a multicellular model organism in a microfluidic device ........................... 54 Evaluating device performance during the large-scale screening ........................................................ 56 Incorporating features for an automated screening device ..................................................................... 60 Automated device performance ............................................................................................................... 66 Chapter 3: Creating a system for automated sorting .................................................................................... 67 Computer controlled valve actuation ....................................................................................................... 67 Pressure regulation, actuation and control .......................................................................................... 68 Packaged system .................................................................................................................................. 68 Components for Injecting animal suspension ........................................................................................... 70 v Immobilization .......................................................................................................................................... 71 Cooling system ...................................................................................................................................... 72 Cooling efficacy and biological Consequences ..................................................................................... 77 Computerized control sufficient for sorting known, relatively simple phenotypes ................................. 78 Expression pattern analysis enabled by large scale rapid processing .................................................. 80 Phenotyping, sorting, and screen based on 3-D cellular features ........................................................ 82 Phenotyping, sorting, and screen based on gross synaptic features ................................................... 84 Performance of systems level components .............................................................................................. 89 Chapter 4: an autonomous system for extended imaging and screening ..................................................... 90 Systemic optimization considerations ...................................................................................................... 91 Identifying bottlenecks ......................................................................................................................... 91 Handling errors associated with automated screening ............................................................................ 96 Loading and exiting errors .................................................................................................................... 98 Head/Tail/Non-tail identification ....................................................................................................... 101 BOW for head and tail classification ................................................................................................... 107 Performance of the automated imaging and screening ......................................................................... 108 Chapter 5: Computer vision for synapse identification and animal phenotyping ....................................... 109 Ground-Truth Dictionary ......................................................................................................................... 111 vi Synapse identification ............................................................................................................................. 113 Upfront Image processing .................................................................................................................. 116 Local features ..................................................................................................................................... 119 Regional
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