Journal
STEM CELL REPORTS
Volume 9, Issue 2, Pages 697-709Publisher
CELL PRESS
DOI: 10.1016/j.stemcr.2017.06.006
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Funding
- Fundacao para a Ciencia e Tecnologia (FCT) Portugal [SFRH/BD/51684/2011, SFRH/BD/51681/2011, SFRH/BD/86260/2012]
- FCT [UID/NEU/04539/2013]
- FCT
- FEDER/COMPETE [POCI-01-0145-FEDER-007440, HealthyAging2020:CENTRO-01-0145-FEDER-000012]
- [SFRH/BPD/98995/2013]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/51681/2011, SFRH/BPD/98995/2013, SFRH/BD/51684/2011, SFRH/BD/86260/2012] Funding Source: FCT
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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.
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