Spatial Characterization of Protein Localization Patterns
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Today's imaging technologies generate a wealth of data that requires visualization and multi-dimensional quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provides the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology.
There has been a lot of work on solving some of the fundamental problems in the image based analysis like segmentation, image restoration, shape normalization etc. One of the limiting factors of such analysis is the statistical and morphological variation present across different biological samples. Thus, these standalone solutions can not be used off the shelf. The challenge is to develop a workflow for each problem considering the goals of the study, underlying biology and technical limitations.
During pregnancy, the antibodies are transferred from mother to the fetus. This transfer takes place through placenta. It is well established that a MHC-1 receptor FcRn in essential for transport of IgG across placenta. But the exact mechanism of this process is not known. This thesis focuses on designing a workflow based on 3D analysis of confocal microscopy images and statistical modeling to understand the IgG transport in the yolk sac endoderm of the mouse. The workflow consists of image correction, active contour based segmentation, distance transform based protein distribution and clustering.
Document number: osu1282088474
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=osu1282088474
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