Browsing by Subject "developmental"

Development of a single cell into an adult organism is accomplished through an elaborate and complex cascade of spatiotemporal gene expression. While methods exist for capturing spatiotemporal expression patterns---in situ hybridization, reporter constructs, fluorescent tags---these methods have been highly laborious, and results are frequently assessed by subjective qualitative comparisons. To address these issues, methods must be developed for automating the capture of images, as well as for the normalization and quantification of the resulting data. In this thesis, I design computational approaches for high throughput image analysis which can be grouped into three main areas. First, I develop methods for the capture of high resolution images from high throughput platforms. In addition to the informatics aspect of this problem, I also devise a novel multiscale probabilistic model that allows us to identify and segment objects in an automated fashion. Second, high resolution images must be registered and normalized to a common frame of reference for cross image comparisons. To address these issues, I implement approaches for image registration using statistical shape models and non-rigid registration. Lastly, I validate the spatial expression data obtained from microscopy images to other known spatial expression methods, and develop methods for comparing and calculating the significance between spatial expression patterns. I demonstrate these methods on two model developmental organisms: Arabidopsis and Drosophila.

Medulloblastoma (MB) remains incurable in one third of patients despite aggressive multi-modality standard therapies. The heterogeneity of MB molecular subtypes as well as the failure of standard therapies to treat metastatic or recurrent disease necessitates more potent targeted approaches that minimize collateral toxicity. Immunotherapy presents a promising strategy by specifically targeting cancer cells and to date, there have been few successful immunologic applications targeting MB. Emerging evidence from integrated genomic studies has suggested MB variants arise from deregulation of pathways affecting the proliferation and differentiation of progenitor cell populations within the developing cerebellum. To test the developing cerebellum as a source of tumor rejection antigens, we adapted two animal models of MB recapitulating human Sonic Hedgehog (SHH) and Group 3 tumors for immunotherapeutic evaluation. Immunologic characterization of these murine models revealed subtype-specific differences in the tumor microenvironment and a differential response to immune checkpoint blockade. We used total embryonic RNA from the developing mouse cerebellum (P5) to generate antigen-specific T cells and confirmed the immunogenicity of targeting developmentally regulated antigens in vitro. Developmental antigen-specific T cells produced high levels of Th1-type cytokines in response to two immunologically distinct subtypes of MB. Interestingly, developmental antigen specific T cells did not show any cross reactivity with the normal brain or subsequent stages of the developing brain after P5. Targeting developmental antigens conferred a significant survival benefit and long term cures in intracranial treatment models of SHH and Group 3 tumor bearing animals. We additionally tested whether the enrichment of select developmental antigens through the exclusion of normal brain transcripts would potentiate antitumor responses in both animal models. Finally, we evaluated the relevance of targeting fetal antigens across human MB subtypes. Our studies demonstrate that developmental antigens can safely target multiple MB subtypes and can be further refined to preferentially target individual subgroups. Further studies targeting immunogenic developmental antigens and leveraging this strategy with specific immune modulatory interventions represent a novel approach at utilizing patient molecular classification information to mediate safe and effective immunotherapy.