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- Language: English
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Vaccines are one of the most effective ways of combating infectious diseases and developing vaccine platforms that can be used to produce vaccines can greatly assist in combating global public health threats. This dissertation focuses on the development and pre-clinical testing of vaccine platforms that are highly immunogenic, easily modifiable, economically viable to produce, and stable. These criteria are met by the recombinant immune complex (RIC) universal vaccine platform when produced in plants. The RIC platform is modeled after naturally occurring immune complexes that form when an antibody, a component of the immune system that recognizes protein structures or sequences, binds to its specific antigen, a molecule that causes an immune response. In the RIC platform, a well-characterized antibody is linked via its heavy chain, to an antigen tagged with the antibody-specific epitope. The RIC antibody binds to the epitope tags on other RIC molecules and forms highly immunogenic complexes. My research has primarily focused on the optimization of the RIC platform. First, I altered the RIC platform to enable an N-terminal antigenic fusion instead of the previous C-terminal fusion strategy. This allowed the platform to be used with antigens that require an accessible N-terminus. A mouse immunization study with a model antigen showed that the fusion location, either N-terminal or C-terminal, did not impact the immune response. Next, I studied a synergistic response that was seen upon co-delivery of RIC with virus-like particles (VLP) and showed that the synergistic response could be produced with either N-terminal or C-terminal RIC co-delivered with VLP. Since RICs are inherently insoluble due to their ability to form complexes, I also examined ways to increase RIC solubility by characterizing a panel of modified RICs and antibody-fusions. The outcome was the identification of a modified RIC that had increased solubility while retaining high immunogenicity. Finally, I modified the RIC platform to contain multiple antigenic insertion sites and explored the use of bioinformatic tools to guide the design of a broadly protective vaccine.