Abstract
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 three-dimensional 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, graphics cards or training datasets and can be run even with just several hundred megabytes 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 localization to tissue geometry.This article is part of a discussion meeting issue ‘Contemporary morphogenesis’.
Highlights
One of the main areas of focus in developmental biology is how tissue complexity is generated and how diverse cell types are consistently organized into a complete organism
The function of a protein can vary depending on the cellular compartment it is localized to and differential expression of proteins across tissues during development is important in pattering those tissues
Cutting edge optical microscopy and image analysis tools are routinely used for spatial analysis of developing tissues from various organisms [3,4,5,6,7,8,9]
Summary
One of the main areas of focus in developmental biology is how tissue complexity is generated and how diverse cell types are consistently organized into a complete organism. Even in cases where image quality prevents reliable segmentation, image data often contain a great deal of useful quantitative information To extract such information, we have developed a sampling-based approach to measure nuclear, cytoplasmic and plasma membrane fluorescence levels. The localization of these samples within the image volume is retained, which can be used for detailed statistical analysis of the spatial distribution of the detected proteins This approach works across the scale of individual cells to entire tissues. Since the spatial distribution of proteins is of particular interest to developmental biologists, we have incorporated the ability to define points or regions of interest in the image volume, so that the relative positions of these sampled regions with respect to these points of interest can be recovered We have implemented this approach as a software package called ‘SilentMark’, designed for general use. We demonstrate the use of our algorithm on mouse preand post-implantation embryos, as well as mouse embryonic stem cell-derived embryoid bodies
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