Spatial protein analysis in developing tissues: a sampling-based image processing approach

Advances in fluorescence microscopy approaches have made it relatively easy to generate multi-dimensional image volumes and have highlighted the need for flexible image analysis tools for the extraction of quantitative information from such data. Here we demonstrate that by focusing on simplified feature-based nuclear segmentation and probabilistic cytoplasmic detection we can create a tool that is able to extract geometry based information from diverse mammalian tissue images. Our open-source image analysis platform, called SilentMark can cope with noisy images and with crowded fields of cells to quantify signal intensity in different cellular compartments. Additionally, it provides tissue geometry related information, which allows one to quantify protein distribution with respect to marked regions of interest. The lightweight SilentMark algorithms have the advantage of not requiring multiple processors and graphics cards and can be run even with just several hundred MB of memory. This makes it possible to use the method as a web application, effectively eliminating setup hurdles and compatibility issues with operating systems. We test this platform on mouse pre-implantation embryos, embryonic stem cell derived embryoid bodies and mouse embryonic heart and relate protein localisation to tissue geometry.