1. Spatial protein analysis in developing tissues: a sampling-based image processing approach
- Author
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Tyser Rcv., Miranda Ama., Christophe Royer, Karolis Leonavicius, Annemarie Kip, and Shankar Srinivas
- Subjects
protein quantification ,Computer science ,Image processing ,Embryoid body ,Biology ,General Biochemistry, Genetics and Molecular Biology ,Mice ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Mammalian tissue ,image analysis ,Fluorescence microscope ,Image Processing, Computer-Assisted ,Animals ,Web application ,Segmentation ,Computer vision ,immunofluorescence ,Graphics ,030304 developmental biology ,Cell Nucleus ,0303 health sciences ,business.industry ,segmentation ,Probabilistic logic ,Sampling (statistics) ,Proteins ,Pattern recognition ,Articles ,cell and developmental biology ,Embryo, Mammalian ,Protein distribution ,Microscopy, Fluorescence ,gene expression ,Artificial intelligence ,Analysis tools ,General Agricultural and Biological Sciences ,business ,030217 neurology & neurosurgery ,Research Article - 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’.
- Published
- 2017
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