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Description
The bleomycins are a family of glycopeptide-derived antibiotics isolated from various Streptomyces species and have been the subject of much attention from the scientific community as a consequence of their antitumor activity. Bleomycin clinically and is an integral part of a number of combination chemotherapy regimens. It has previously been shown that bleomycin has the ability to selectively target tumor cells over their non-malignant counterparts. Pyrimidoblamic acid, the N-terminal metal ion binding domain of bleomycin is known to be the moiety that is responsible for O2 activation and the subsequent chemistry leading to DNA strand scission and overall antitumor activity. Chapter 1 describes bleomycin and related DNA targeting antitumor agents as well as the specific structural domains of bleomycin. Various structural analogues of pyrimidoblamic acid were synthesized and subsequently incorporated into their corresponding full deglycoBLM A6 derivatives by utilizing a solid support. Their activity was measured using a pSP64 DNA plasmid relaxation assay and is summarized in Chapter 2. The specifics of bleomycin—DNA interaction and kinetics were studied via surface plasmon resonance and are presented in Chapter 3. By utilizing carefully selected 64-nucleotide DNA hairpins with variable 16-mer regions whose sequences showed strong binding in past selection studies, a kinetic profile was obtained for several BLMs for the first time since bleomycin was discovered in 1966. The disaccharide moiety of bleomycin has been previously shown to be a specific tumor cell targeting element comprised of L-gulose-D-mannose, especially between MCF-7 (breast cancer cells) and MCF-10A ("normal" breast cells). This phenomenon was further investigated via fluorescence microscopy using multiple cancerous cell lines with matched "normal" counterparts and is fully described in Chapter 4.
ContributorsBozeman, Trevor C (Author) / Hecht, Sidney M. (Thesis advisor) / Chaput, John (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.
ContributorsWallgren, Ross (Author) / Presse, Steve (Thesis advisor) / Armbruster, Hans (Thesis advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
Single cell heterogeneity plays an important role in the onset and progression of a variety of disease pathologies. One of the most notable examples of the impact of heterogeneity in the complexity of a disease is cancer. Traditionally, molecular analyses on cancer-related samples have been performed on bulk populations of cells, with the resultant data only representative of an average of the population, thereby concealing potentially relevant information about individual cells. Performing these studies at the single cell level is proposed to address this issue. However, current methods for the isolation and analysis of single cells often require specialized and expensive equipment that may be prohibitive to labs wishing to perform such analyses. Herein, a method for the isolation and gene expression analysis of single cells is described that (1) relies only on readily available, inexpensive materials, (2) is compatible with phase and fluorescent microscopy, and (3) allows for the ability to track specific cells throughout all measurements. This method utilizes random seeding of single cells on 72-well Terasaki plates (also called microtest plates) that have 20 µl, optically clear flat-bottomed wells in order to circumvent the need for specific hardware for cell isolation. Suspensions of the Barrett’s esophagus epithelial cell line CP-D stably expressing turboGFP and a related, GFP-negative BE cell line, CP-A, were prepared, seeded at a concentration of approximately 1-2 cells/well and incubated overnight. Wells containing single cells were visually identified using phase-contrast and fluorescent microscopy. Single cells were then lysed directly in the well, total RNA was isolated, and RT-qPCR was performed. RT-qPCR results reflected the ability to distinguish between turboGFP-expressing and non-expressing cells that matched previous identification by microscopy. These results indicate that this is a convenient and cost-effective method for studying gene expression in single cells.
ContributorsZiegler, Colleen Patricia (Author) / Chao, Joseph (Thesis director) / Tran, Thai (Committee member) / Yaron, Jordan (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2013-05
Description
The microbiome and virome are known to interact within the human body which in turn modulates the health and disease of an individual. While these interactions have been largely studied in bodily sites such as the gastrointestinal tract, the microbiome and virome of the female genital tract (FGT) remains largely understudied. Within the virome exists DNA and RNA viruses which are known to infect both eukaryotes and prokaryotes. While existing virome research within the FGT has focused largely on eukaryote infecting viruses, a large proportion of the virome consists of uncharacterized bacteriophages known as “dark matter”. Due to the lack of a specific gene marker for viruses, which is essential in qPCR quantification of other populations such as bacteria, determination of viral abundance and virome characterization has been limited. However, the staining of viral DNA has been found effective in visualizing and enumerating virus-like particles within various specimens. In this study, we seek to determine viral abundance within the FGT utilizing SYBR Gold nucleic acid stain to visualize VLP present within a cohort of cervicovaginal lavage (CVL) samples. Given these results we intend to draw conclusions regarding the interactions between the FGT virome and viral abundance as well as sexual-reproductive health. Understanding the complex relationship of the virome within the female reproductive tract is likely to have remarkable clinical implications and has the potential to progress both the diagnostic and treatment aspects of female sexual and reproductive health.
ContributorsFredenberg, Mara (Author) / Lim, Efrem (Thesis director) / Kaelin, Emily (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2023-05
Description
The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles, reconstructing potentials from trajectories corrupted by measurement noise, and inferring potential energy landscapes from fluorescence intensity experiments. This research demonstrates the power and potential of Bayesian methods for solving a variety of problems in fluorescence microscopy and biophysics more broadly.
ContributorsBryan IV, J Shepard (Author) / Presse, Steve (Thesis advisor) / Ozkan, Banu (Committee member) / Wadhwa, Navish (Committee member) / Shepherd, Doug (Committee member) / Arizona State University (Publisher)
Created2023