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

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

SUBMITTER: Leonavicius K 

PROVIDER: S-EPMC7482225 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature