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A genetically engineered line of human induced pluripotent stem cells was used to study the effects of gene expression on cell fate. These cells were designed to activate expression

A genetically engineered line of human induced pluripotent stem cells was used to study the effects of gene expression on cell fate. These cells were designed to activate expression of the gene GATA6 when exposed to the small molecule doxycycline. This gene was chosen because it plays an important role in the developmental biology stages of liver formation. Because of the way the cells were engineered, a given population would have a heterogeneous expression of GATA6 because each cell could have a different copy number of the exogenous gene. This variation allows for the differentiation of multiple cell types, and is used to grow liver organoids. The early liver organoid samples were studied via immunofluorescent staining, imaging, and quantitative image analysis. It was originally hypothesized that absolute gene expression was not the most important factor in determining cell fate, but relative gene expression was. This meant that the spatial location of the cells and their local environment were critical in determining cell fate. In other words, the level of GATA6 of a cell is important, but so is the level of GATA6 in the surrounding cells, or neighborhood, of that cell. This hypothesis was analyzed with the creation of various Neighborhood Impact Factor (NIF) methods. Multiple time points of growth were analyzed to study the temporal effect, in addition to the gene expression and NIF influence on a cell’s fate. Direct gene expression level showed correlation with certain cell fate markers. In addition to GATA6 expression levels, NIF results from early and late time point experiments show statistical significance with relatively small neighborhood radii. The NIF analysis was useful for examining the effect of neighboring cells and determining the size of the neighborhood – how far cells influence one another. While these systems are complex, the NIF analysis provides a way to look at gene expression and its influence in spatial context.

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    • 2017
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