Genome-wide analyses of single cell phenotypes using cell microarrays


Genome-wide analyses of single cell phenotypes using cell microarrays

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dc.contributor.advisor Ellington, Andrew D.
dc.contributor.advisor Marcotte, Edward M.
dc.creator Narayanaswamy, Rammohan, 1978- 2008-08-29T00:22:48Z 2008-08-29T00:22:48Z 2008-05 2008-08-29T00:22:48Z
dc.description.abstract The past few decades have witnessed a revolution in recombinant DNA and nucleic acid sequencing technologies. Recently however, technologies capable of massively high-throughout, genome-wide data collection, combined with computational and statistical tools for data mining, integration and modeling have enabled the construction of predictive networks that capture cellular regulatory states, paving the way for ‘Systems biology’. Consequently, protein interactions can be captured in the context of a cellular interaction network and emergent ‘system’ properties arrived at, that may not have been possible by conventional biology. The ability to generate data from multiple, non-redundant experimental sources is one of the important facets to systems biology. Towards this end, we have established a novel platform called ‘spotted cell microarrays’ for conducting image-based genetic screens. We have subsequently used spotted cell microarrays for studying multidimensional phenotypes in yeast under different regulatory states. In particular, we studied the response to mating pheromone using a cell microarray comprised of the yeast non-essential deletion library and analyzed morphology changes to identify novel genes that were involved in mating. An important aspect of the mating response pathway is large-scale spatiotemporal changes to the proteome, an aspect of proteomics, still largely obscure. In our next study, we used an imaging screen and a computational approach to predict and validate the complement of proteins that polarize and change localization towards the mating projection tip. By adopting such hybrid approaches, we have been able to, not only study proteins involved in specific pathways, but also their behavior in a systemic context, leading to a broader comprehension of cell function. Lastly, we have performed a novel metabolic starvation-based screen using the GFP-tagged collection to study proteome dynamics in response to nutrient limitation and are currently in the process of rationalizing our observations through follow-up experiments. We believe this study to have implications in evolutionarily conserved cellular mechanisms such as protein turnover, quiescence and aging. Our technique has therefore been applied towards addressing several interesting aspects of yeast cellular physiology and behavior and is now being extended to mammalian cells.
dc.format.medium electronic
dc.language.iso eng
dc.rights Copyright © is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.
dc.subject.lcsh Genomics--Databases
dc.subject.lcsh Genomics--Data processing
dc.subject.lcsh DNA microarrays--Databases
dc.subject.lcsh Phenotype--Data processing
dc.subject.lcsh Saccharomyces cerevisiae--Genetics--Data processing
dc.subject.lcsh Pheromones--Data processing
dc.subject.lcsh Proteomics--Data processing
dc.subject.lcsh Proteins--Data processing
dc.title Genome-wide analyses of single cell phenotypes using cell microarrays
dc.description.department Cellular and Molecular Biology, Institute for
dc.identifier.oclc 244204512
dc.identifier.recnum b70687377
dc.type.genre Thesis
dc.type.material text Cellular and Molecular Biology, Institute for Cell and Molecular Biology The University of Texas at Austin Doctoral Doctor of Philosophy

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