3 years ago

Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software

Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software
Image-based assays, such as alkaline phosphatase staining or immunocytochemistry for pluripotent markers, are common methods used in the stem cell field to assess pluripotency. Although an increased number of image-analysis approaches have been described, there is still a lack of software availability to automatically quantify pluripotency in large images after pluripotency staining. To address this need, we developed a robust and rapid image processing software, Pluri-IQ, which allows the automatic evaluation of pluripotency in large low-magnification images. Using mouse embryonic stem cells (mESC) as a model, we combined an automated segmentation algorithm with a supervised machine-learning platform to classify colonies as pluripotent, mixed, or differentiated. In addition, Pluri-IQ allows the automatic comparison between different culture conditions. This efficient user-friendly open-source software can be easily implemented in images derived from pluripotent cells or cells that express pluripotent markers (e.g., OCT4-GFP) and can be routinely used, decreasing image assessment bias.

Graphical abstract

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Teaser

Perestrelo et al. show an image-analysis methodology coupled with a machine-learning platform to analyze and quantify pluripotency with high accuracy in different contexts. Requiring low user input, this software allows the pluripotency evaluation of large low-magnification images, with colony visualization and automated data analysis comparison among experiments.

Publisher URL: www.sciencedirect.com/science

DOI: S2213671117302692

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