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

• Main Authors: Tânia Perestrelo, Weitong Chen, Marcelo Correia, Christopher Le, Sandro Pereira, Ana S. Rodrigues, Maria I. Sousa, João Ramalho-Santos, Denis Wirtz
• Format: Article
• Language: English
• Published: Elsevier 2017-08-01
• Series: Stem Cell Reports
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213671117302692

Summary: 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. : 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. Keywords: ESC, pluripotency, automated image analysis, alkaline phosphatase, pluripotency quantification, Pluri-IQ