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The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards

The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
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A major goal of the Center for Biosignatures Discovery Automation (CBDA) is to design a diagnostic tool that detects novel cancer biosignatures at the single-cell level. We designed the Single-cell QUantitative In situ Reverse Transcription-Polymerase Chain Reaction (SQUIRT-PCR) system by combining a two-photon laser lysis (2PLL) system with a

A major goal of the Center for Biosignatures Discovery Automation (CBDA) is to design a diagnostic tool that detects novel cancer biosignatures at the single-cell level. We designed the Single-cell QUantitative In situ Reverse Transcription-Polymerase Chain Reaction (SQUIRT-PCR) system by combining a two-photon laser lysis (2PLL) system with a microfluidic reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) platform. It is important to identify early molecular changes from intact tissues as prognosis for premalignant conditions and develop new molecular targets for prevention of cancer progression and improved therapies. This project analyzes RNA expression at the single-cell level and presents itself with two major challenges: (1) detecting low levels of RNA and (2) minimizing RNA absorption in the polydimethylsiloxane (PDMS) microfluidic channel. The first challenge was overcome by successfully detecting picogram (pg) levels of RNA using the Fluidigm (FD) BioMark™ HD System (Fluidigm Corporation, South San Francisco, CA) for RT-qPCR analysis. This technology incorporates a highly precise integrated fluidic circuit (IFC) that allows for high-throughput genetic screening using microarrays. The second challenge entailed collecting data from RNA flow-through samples that were passed through microfluidic channels. One channel was treated with a coating of polyethylene glycol (PEG) and the other remained untreated. Various flow-through samples were subjected to RT-qPCR and analyzed using the FD FLEXsix™ Gene Expression IFC. As predicted, the results showed that the treated PDMS channel absorbed less RNA than the untreated PDMS channel. Once the optimization of the PDMS microfluidic platform is complete, it will be implemented into the 2PLL system. This novel technology will be able to identify cell populations in situ and could have a large impact on cancer diagnostics.
ContributorsBlatt, Amy Elissa (Author) / Meldrum, Deirdre R. (Thesis director) / Tran, Thai (Committee member) / Chao, Joseph (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05