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

• Autor(a) principal: Perestrelo, Tânia
• Data de Publicação: 2017
• Outros Autores: Chen, Weitong, Correia, Marcelo, Le, Christopher, Pereira, Sandro, Rodrigues, Ana S., Sousa, Maria I., Ramalho-Santos, João, Wirtz, Denis
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/108394; https://doi.org/10.1016/j.stemcr.2017.06.006
• topic: Algorithms; Animals; Biomarkers; Cell Line; Cells, Cultured; Embryonic Stem Cells; Gene Expression; Image Processing, Computer-Assisted; Immunohistochemistry; Machine Learning; Mice; Pluripotent Stem Cells; Protein Transport; Reproducibility of Results; Sensitivity and Specificity; User-Computer Interface; Molecular Imaging; Software

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.