Identification and analysis of the intranuclear protein pattern in fluorescence microscopy images

Advances in development of genetically encoded fluorescent proteins and in digital imaging has led to the rapid evolution of live-cell imaging methods. These methods are being applied to address biological questions, in particular the identification of the intranuclear protein pattern can help the analysis of specific processes, such as DNA repair, DNA integration, and chromatin folding. Here, we present an efficient tool that implements mathematical algorithms to detect the pattern of the Polycomb Group (PcG) of proteins in high resolution fluorescent image cell stacks. Our tool is composed of an automatic segmentation algorithm combining the globally convex Chan-Vese model and a classification method, to segment nuclei regions and detect intranuclear PcG areas. Then a 3d reconstruction step of nuclei and proteins is performed, followed by a set of algorithms designed to explore the 3d structure in order to produce a quantitative analysis of nuclei and proteins, and to evaluate the intranuclear positioning of the PcGs. The 3D reconstruction of several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, has showed that intranuclear positioning of PcG bodies is evolutionarily conserved, being horizontally coplanar and excluded from the nuclear periphery.