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- Creators: Grandhi, Taraka Sai Pavan
Description
Despite significant advances in digital pathology and automation sciences, current diagnostic practice for cancer detection primarily relies on a qualitative manual inspection of tissue architecture and cell and nuclear morphology in stained biopsies using low-magnification, two-dimensional (2D) brightfield microscopy. The efficacy of this process is limited by inter-operator variations in sample preparation and imaging, and by inter-observer variability in assessment. Over the past few decades, the predictive value quantitative morphology measurements derived from computerized analysis of micrographs has been compromised by the inability of 2D microscopy to capture information in the third dimension, and by the anisotropic spatial resolution inherent to conventional microscopy techniques that generate volumetric images by stacking 2D optical sections to approximate 3D. To gain insight into the analytical 3D nature of cells, this dissertation explores the application of a new technology for single-cell optical computed tomography (optical cell CT) that is a promising 3D tomographic imaging technique which uses visible light absorption to image stained cells individually with sub-micron, isotropic spatial resolution. This dissertation provides a scalable analytical framework to perform fully-automated 3D morphological analysis from transmission-mode optical cell CT images of hematoxylin-stained cells. The developed framework performs rapid and accurate quantification of 3D cell and nuclear morphology, facilitates assessment of morphological heterogeneity, and generates shape- and texture-based biosignatures predictive of the cell state. Custom 3D image segmentation methods were developed to precisely delineate volumes of interest (VOIs) from reconstructed cell images. Comparison with user-defined ground truth assessments yielded an average agreement (DICE coefficient) of 94% for the cell and its nucleus. Seventy nine biologically relevant morphological descriptors (features) were computed from the segmented VOIs, and statistical classification methods were implemented to determine the subset of features that best predicted cell health. The efficacy of our proposed framework was demonstrated on an in vitro model of multistep carcinogenesis in human Barrett's esophagus (BE) and classifier performance using our 3D morphometric analysis was compared against computerized analysis of 2D image slices that reflected conventional cytological observation. Our results enable sensitive and specific nuclear grade classification for early cancer diagnosis and underline the value of the approach as an objective adjunctive tool to better understand morphological changes associated with malignant transformation.
ContributorsNandakumar, Vivek (Author) / Meldrum, Deirdre R (Thesis advisor) / Nelson, Alan C. (Committee member) / Karam, Lina J (Committee member) / Ye, Jieping (Committee member) / Johnson, Roger H (Committee member) / Bussey, Kimberly J (Committee member) / Arizona State University (Publisher)
Created2013
Description
Relapse after tumor dormancy is one of the leading causes of cancer recurrence that ultimately leads to patient mortality. Upon relapse, cancer manifests as metastases that are linked to almost 90% cancer related deaths. Capture of the dormant and relapsed tumor phenotypes in high-throughput will allow for rapid targeted drug discovery, development and validation. Ablation of dormant cancer will not only completely remove the cancer disease, but also will prevent any future recurrence. A novel hydrogel, Amikagel, was developed by crosslinking of aminoglycoside amikacin with a polyethylene glycol crosslinker. Aminoglycosides contain abundant amount of easily conjugable groups such as amino and hydroxyl moieties that were crosslinked to generate the hydrogel. Cancer cells formed 3D spheroidal structures that underwent near complete dormancy on Amikagel high-throughput drug discovery platform. Due to their dormant status, conventional anticancer drugs such as mitoxantrone and docetaxel that target the actively dividing tumor phenotype were found to be ineffective. Hypothesis driven rational drug discovery approaches were used to identify novel pathways that could sensitize dormant cancer cells to death. Strategies were used to further accelerate the dormant cancer cell death to save time required for the therapeutic outcome.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
ContributorsGrandhi, Taraka Sai Pavan (Author) / Rege, Kaushal (Thesis advisor) / Meldrum, Deirdre R (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Caplan, Michael (Committee member) / Tian, Yanqing (Committee member) / Arizona State University (Publisher)
Created2016