Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/108394
Title: Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software
Authors: Perestrelo, Tânia 
Chen, Weitong
Correia, Marcelo 
Le, Christopher
Pereira, Sandro 
Rodrigues, Ana S. 
Sousa, Maria I. 
Ramalho-Santos, João 
Wirtz, Denis
Issue Date: 8-Aug-2017
Publisher: Elsevier
Project: PhD scholarships attributed to T.P. (SFRH/BD/ 51684/2011), M.C. (SFRH/BD/51681/2011), and M.I.S. (SFRH/ BD/86260/2012) and a postdoc fellowship attributed to S.P. (SFRH/BPD/98995/2013). Center for Neuroscience and Cell Biology (CNC) is also funded by FCT under the strategic project UID/NEU/04539/2013. The authors also give thanks for the grant support by FCT and FEDER/COMPETE 2020 (POCI-01-0145- FEDER-007440) and HealthyAging2020:CENTRO-01-0145- FEDER-000012. 
Serial title, monograph or event: Stem Cell Reports
Volume: 9
Issue: 2
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.
URI: https://hdl.handle.net/10316/108394
ISSN: 22136711
DOI: 10.1016/j.stemcr.2017.06.006
Rights: openAccess
Appears in Collections:FCTUC Ciências da Vida - Artigos em Revistas Internacionais
I&D CNC - Artigos em Revistas Internacionais
IIIUC - Artigos em Revistas Internacionais

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