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

Tânia Perestrelo, Weitong Chen, Marcelo Correia, Christopher Le, Sandro Pereira, Ana S. Rodrigues, Maria I. Sousa, João Ramalho-Santos, Denis Wirtz

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)697-709
Number of pages13
JournalStem Cell Reports
Volume9
Issue number2
DOIs
StatePublished - Aug 8 2017

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Keywords

  • alkaline phosphatase
  • automated image analysis
  • ESC
  • Pluri-IQ
  • pluripotency
  • pluripotency quantification

ASJC Scopus subject areas

  • Biochemistry
  • Genetics
  • Developmental Biology
  • Cell Biology

Cite this

Perestrelo, T., Chen, W., Correia, M., Le, C., Pereira, S., Rodrigues, A. S., Sousa, M. I., Ramalho-Santos, J., & Wirtz, D. (2017). Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software. Stem Cell Reports, 9(2), 697-709. https://doi.org/10.1016/j.stemcr.2017.06.006