ArticlePlacenta2025
A quantitative image analysis platform for assessing trophoblast differentiation.
Article in Placenta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
Immunofluorescence microscopy is extensively used in characterization of trophoblast differentiation in vitro. However, such data is primarily used to confirm the presence of protein markers or qualitatively compare levels of protein markers across experimental conditions. Imaging data, when processed and analyzed appropriately can provide quantitative and spatial information, and provide biological insight. Towards this end, here we present MATroph, an open-source MATLAB-based computational tool to process images generated by immunofluorescent microscopy. MATroph automatically executes a series of image processing operations, including the classification of red, blue, and green channels from images, background extraction, morphological operations, and image filtering. From the isolated blue channels corresponding to nuclear staining, this tool generates numerical values for cell number. Additionally, relative levels and spatial location of proteins are obtained by mapping red and green channel pixels to blue pixels by assigning minimum pixel distance between the blue and other color objects. Thus, this tool provides information about intracellular protein accumulation areas. Additionally, this tool can also classify cells as single cells or part of colonies, and extract information on protein levels for each; this is particularly useful for quantitative studies on extravillous trophoblast maturation. We provide a user-guide to analyze the relative levels of markers relevant to human trophoblast stem cell self-renewal and differentiation. Importantly, MATroph is composed of a simple MATLAB algorithm, and its implementation requires minimal expertise in programming.
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