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

Perestrelo, Tânia et al.

Stem cell reports. Volume 9:Number 2 (2017, August 8th); pp 697-709 -- Cell Press

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  • Title:
    Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software
  • Author: Perestrelo, Tânia;
    Chen, Weitong;
    Correia, Marcelo;
    Le, Christopher;
    Pereira, Sandro;
    Rodrigues, Ana S.;
    Sousa, Maria I.;
    Ramalho-Santos, João;
    Wirtz, Denis
  • Found In: Stem cell reports. Volume 9:Number 2 (2017, August 8th); pp 697-709
  • Journal Title: Stem cell reports
  • Subjects: Stem cells--Periodicals; ESC--pluripotency--automated image analysis--alkaline phosphatase--pluripotency quantification--Pluri-IQ
  • Rights: Licensed
  • Publication Details: Cell Press
  • Abstract: 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. Graphical Abstract Highlights Open-source software to evaluate pluripotency in low-magnification images Automatic colony detection and segmentation Supervised machine-learning platform with high characterization accuracy Software tools for easy data validation, visualization, and data analysis comparison 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.
  • Identifier: System Number: ETOCvdc_100066088688.0x000001; Journal ISSN: 2213-6711; 10.1016/j.stemcr.2017.06.006
  • Publication Date: 2017
  • Physical Description: Electronic
  • UIN: ETOCvdc_100066088688.0x000001

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