Capturing variations in nuclear phenotypes.

• We propose a way for 3D quantification of nuclear phenotypes in a tissue microenvironment using shape, texture and contextual features. • We implement on a tumor microenvironment for PTEN and TP53 deleted stromal fibroblasts in mouse mammary glands. • TP53 deletion in fibroblasts results in smaller, more spindly nuclei and reorganization of chromatin especially in nuclear periphery. • We also propose a scheme for visualizing all the three types of features together. Relating genotypes with phenotypes is important to understand diseases like cancer, but extremely challenging, given the underlying biological variability and levels of phenotypes. 3D quantitative tools are increasingly used to provide robust inferences pertaining to variations across collections of cells. We especially focus on the changes wrought to the nucleus of specific genotypes. Fibroblasts in the tumor microenvironment of mammary epithelial tissue serve as our model system and provide the context, although our methods are applicable to a broader range of biological systems. Using an image based approach, we analyze in 3D and compare phenotypes at nuclear level using estimates of texture, morphology and spatial context based on confocal images. Our data demonstrates that deletion of TP53 in stromal fibroblasts results in reorganization of chromatin content across the nucleus, especially the nuclear periphery, while simultaneously reducing nuclear size and making it more spindly. No such shape change was observed for PTEN-deleted genotype, although there were some differences in distribution of chromatin and an increase in the local nuclear density. The relative changes in phenotypes are in line with the larger role that the TP53 plays in tumor initiation and progression.These findings play an important role in uncovering the relationships of those genes with the subcellular phenotypes, as well as formulating new hypotheses, especially pertaining to the relative impact of genes in specific pathways. More importantly, they demonstrate the efficacy of methodology of analyzing a large number of cellular phenotypes. [ABSTRACT FROM AUTHOR]