Show simple item record

dc.contributor.advisorSchafer, William
dc.contributor.authorYemini, Eviatar
dc.date.accessioned2013-09-12T09:46:17Z
dc.date.available2013-09-12T09:46:17Z
dc.date.issued2013-01-08
dc.identifier.otherPhD.36001
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/244966
dc.description.abstractCaenorhabditis elegans, a millimeter-sized, soil-dwelling nematode, is a model organism for biology research. Its whole genome has been sequenced. The lineage and fate, for each one of the cells in wild-type (N2) worms, is known. The connectivity, for all 302 neurons of wild-type hermaphrodites, has been mapped. Many of its genes have homologs within other organisms, including humans. C. elegans have a well-defined repertoire of observed behaviors. For these reasons, and due to a wealth of experimental data, C. elegans is a well-suited organism for mapping genetics to phenotype. This thesis details a system for relating genetics to phenotype. I present a methodology for semi-automated, high-throughput, high resolution investigation of gene effects on behavior and morphology using C. elegans. In the first section beyond the introduction, Chapter 2, I describe a new singleworm tracking system (hardware and software), titled Worm Tracker 2.0 (WT2), which was used to collect videos of worm behavior with high throughput. While multi-worm tracking systems exist, including ones that enable higher experimental throughput by recording multiple worms at once, their videos have insufficient resolution to resolve worm bodies well and these systems have been limited to only simple measurements. While other single-worm tracking systems also exist, they present, among other limitations, significant costs precluding high experimental throughput. I designed and built the hardware and software for a less expensive unit, which is approximately 1/4 the cost of previous single-worm trackers. This enabled us to purchase eight such units for high-throughput of experimentation. Other novelty for our system includes the ability to track worms at all larval stages and the ability to follow single-worms swimming. In Chapter 3, I describe a novel automated analysis for the worm videos collected using the aforementioned single-worm tracker. While analysis exists for other single-worm tracking systems, several limitations precluded adaptation. Our worm videos are on food and the worms are of variable size. Several previous algorithms attempted to deal with worms on food but, for our purposes, suffer from poor resolution at the head and tail, areas necessary to obtain significant phenotypic information. The analysis I built uses a novel algorithm driven by a need to obtain high-accuracy and precise worm contours (and their consequent skeletons) in our difficult conditions (e.g., on food and swimming environments) with invariance to worm size (bounded by a minimal limit of resolution). This accuracy was necessary due to the sheer size of the data set collected, roughly 1/3 of a billion frames, which precludes manual verification. In the final section, Chapter 4, I describe the results from my analysis of our collected data. Using our trackers we collected more than 12,000 videos, each 15 minutes in length, at 640x480 20-30Hz resolution, representing over 300 mutant strains matched to wild-type controls. This large set was filtered to obtain high-quality data and remove strains specific to private data sets (prepared for future publications). The filtered analysis covers 330 worm groups compiled from 300 mutant strains, 2 wild isolates, three descendants of N2, along with our N2 controls divided into hourly, daily, and monthly groups. A subset of 79 strains, representing 76 genes with no previously characterized phenotype, show significant measures in my analysis. Further sensitivity of the analysis is explored through measures of habituation, small morphological changes due to growth, and a phenotypic comparison of the three descendants from the ancestral, wild-type N2. With the sensitivity explored, I present an N2 phenotypic reference compiled from 1,218 worms, recorded over three years. Statistics of this set define a reference measure of the N2 phenotype (specific to the Schafer Lab wild type) with broad implications for performing and controlling C. elegans experiments. Three genes, implicated in mechanosensation as a result of genetic sequence but lacking any observed phenotypic support, reveal locomotory phenotypes in our analysis. This prompts a large clustering of all 330 groups, to assess the predictive capabilities of our system. The N2 groups cluster together in a large exclusive aggregate. Further support for the predictive capabilities of the clustering emerge among multiple published pathways that also form exclusive clusters. I end by discussing a set of genes, predicted to be acetylcholine receptors through genetic sequence and functional heterologous expression, which now receive further support through strong aggregation within their own exclusive phenotypic cluster.en
dc.description.sponsorshipThis work was supported by the Gates Trust and the Medical Research Council.en
dc.language.isoenen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.titleHigh-throughput, single-worm tracking and analysis in Caenorhabditis elegansen
dc.typeThesisen
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridgeen
dc.publisher.departmentMRC Laboratory of Molecular Biologyen
dc.identifier.doi10.17863/CAM.15948


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record