Advances in genetics and fluorescent protein chemistry have enabled us to fuse fluorescent probes directly to biomolecules in stably growing organisms; making it easier to image the precise position and movement of cells in three dimensions. Fluorescent stains and dyes can be employed in a similar fashion to visualize nano-scale fluctuations in active cellular structures without fixation. While informative and exciting on a qualitatively level, microscopy truly becomes powerful when we can extract meaningful quantitative information. To accomplish this, custom MATLAB (Mathworks, Natick, MA) image analysis algorithms were developed to specifically measure the biophysical parameters related to pathogenesis and function in microbes and mammalian cells. These parameters can then be exploited in the development of biophysical models to validate current measurements, and make critical predictions about the system's behavior, often addressing quantities inaccessible by experimental methods. The following research chapters of this dissertation thoroughly describe how these techniques were developed and applied to study the biophysical mechanisms of bacterial and cellular invasion.

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2017

Cell wall mannans of Candida albicans mask β-(1,3)-glucan from recognition by Dectin-1, contributing to innate immune evasion. Glucan exposures are predominantly single receptor-ligand interaction sites of nanoscale dimensions. Candida species vary in basal glucan exposure and molecular complexity of mannans. We used super-resolution fluorescence imaging and a series of protein mannosylation mutants in C. albicans and C. glabrata to investigate the role of specific N-mannan features in regulating the nanoscale geometry of glucan exposure. Decreasing acid labile mannan abundance and α-(1,6)-mannan backbone length correlated most strongly with increased density and nanoscopic size of glucan exposures in C. albicans and C. glabrata, respectively. Additionally, a C. albicans clinical isolate with high glucan exposure produced similarly perturbed N-mannan structures and elevated glucan exposure geometry. Thus, acid labile mannan structure influences the nanoscale features of glucan exposure, impacting the nature of the pathogenic surface that triggers immunoreceptor engagement, aggregation, and signaling. Graus et al. find that N-mannan structural features regulated by Candida mannosyltransfersases control glucan exposure. Loss of mannan increased the frequency and size of glucan exposures and changed multivalent receptor engagement. Changes to mannan structure in a bloodstream isolate are associated with elevated glucan exposure at the nanoscale.

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