HIGH RESOLUTION MAPPING OF PLACENTAL GENE EXPRESSION

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DESCRIPTION (provided by applicant): There is ample showing adult cardiovascular disease takes root from maternal/placental physiologic conditions during fetal development. Barker (1990) demonstrated higher risk of hypertension in adults who had been small babies with large placentas. Lesage et al (2002) showed that fetal growth restriction from uteroplacental dysfunction causes increased risk for cardiovascular disease and diabetes in adults. The etiology of intrauterine growth restriction (IUGR) and low birth weight (LBW) relates to maternal undernutrition (Bajoria, 2002, Greenwood, 2003, Fall, 2003). Placental insufficiency is central to the etiology of IUGR and LBW which predispose individuals to coronary heart disease, diabetes, hypertension and stroke {Anthony, 2003 2271 /id}. The placental terminal villi are altered in placental disease states (Biagini, 1989 1656, Mayhew, 2003). To advance preventative and interventional practices, we must understand all gene expression programs governing adaptive and maladaptive placental physiology. New methods for analyzing placenta-related genomic, proteomic, and morphological datasets are needed. Tools for visualizing multiple gene markers within a comprehensive anatomical context to correlate gene expression with phenotype are needed. We propose to produce methods for visualizing placental gene expression idata in high-resolution 3D graphical models of terminal villi. Based on histological, immunohistochemical, and in situ hybridization information we will produce a 3D computer prototype of a terminal villus and its constituent tissue types upon which gene expression data can be mapped. Specific Aims: 1. Revise methodologies for labeling placental terminal villus tissue types; 2. Generate 3D canonical models describing villous architecture with cell-level resolution to include all cell-types. This aim includes segmentation of tissues and 3D standardization of villous morphology. 3. Develop a computational approach for combining gene expression data with 3D canonical placenta models.