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    Worm Paparazzi – A High Throughput Lifespan and Healthspan Analysis Platform for Individual Caenorhabditis elegans

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    Author
    Freitas, Samuel
    Issue Date
    2021
    Keywords
    C. elegans
    image processing
    individual
    neural networks
    Robotics
    YOLOv5
    Advisor
    Sutphin, George
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Aging is a near ubiquitous phenomenon prevalent across evolutionary lineages from single-celled organisms to humans. Natural aging is characterized by a progressive deterioration in cellular and tissue function. Age is the primary risk factor for the many of the most prevalent diseases and pathological conditions today. Lifespan and healthspan measurements are the gold standard phenotypes for aging research, integrating the influence of many complex molecular processes into a set of easily measurable physiological metrics. Due to the number of environmental, genetic, and phenotypic inputs, a high degree of variation is present between individuals within a given population. The roundworm Caenorhabditis elegans is among the most common model systems used to study aging, because of its short lifespan (~3 weeks), simplicity to culture in the lab, and availability of powerful genetic tools. Lifespan is traditionally measured through population-level survival assays counted by hand on petri plates containing solid nematode growth media (NGM) and seeded with a bacterial food sourec, with each plate containing a sample of a few dozen animals to represent a population. These studies can give an accurate measure of aging within restricted populations under experimental conditions but require many hours of human attention. The amount of resources required to create statistically well-powered data is inefficient in terms of cost, time, and labor. In order to alleviate this research bottleneck, different mechanical systems have been proposed and implemented, each with their own advantages and disadvantages. Robotic systems have been created that can recapitulate manual lifespan results. Most of these systems either rely on population-level assessment of lifespan, require manual input during data processing, or necessitate maintaining worms in a liquid culture environment. Liquid culture causes C. elegans to swim continuously, placing the animals into a stress state that can change the molecular processes that influence aging. Thus, by changing the environmental context, new discoveries in liquid culture cannot be directly compared to the majority of previous work on C. elegans aging conducted on solid culture. Here I describe Worm Paparazzi, a novel analysis system designed to automate lifespan and healthspan measurement for individual roundworms cultured on solid media. This system uses the WorMotel™ (Churgin et al. 2017) a 240-well plate constructed from polydimethylsiloxane (PDMS) that isolates individual C. elegans on solid media under conditions similar to traditional manual lifespan assays. By leveraging the characteristics of the WorMotel™, creating supporting robotics, and building a custom analysis architecture, the Worm Paparazzi analysis system is able to combine the accuracy of human lifespan screeners—while removing one source of human bias—with the high-throughput capabilities of a machine-based system for an individualized insight into C. elegans lifespan and healthspan assays, thereby providing researchers with a new, powerful tool to study the macroscopic result of lifespan and healthspan assays.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Biomedical Engineering
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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