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Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis 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 availabili...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549834/ https://www.ncbi.nlm.nih.gov/pubmed/28712847 http://dx.doi.org/10.1016/j.stemcr.2017.06.006 |
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author | Perestrelo, Tânia Chen, Weitong Correia, Marcelo Le, Christopher Pereira, Sandro Rodrigues, Ana S. Sousa, Maria I. Ramalho-Santos, João Wirtz, Denis |
author_facet | Perestrelo, Tânia Chen, Weitong Correia, Marcelo Le, Christopher Pereira, Sandro Rodrigues, Ana S. Sousa, Maria I. Ramalho-Santos, João Wirtz, Denis |
author_sort | Perestrelo, Tânia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5549834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-55498342017-08-17 Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software Perestrelo, Tânia Chen, Weitong Correia, Marcelo Le, Christopher Pereira, Sandro Rodrigues, Ana S. Sousa, Maria I. Ramalho-Santos, João Wirtz, Denis Stem Cell Reports Resource 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. Elsevier 2017-07-14 /pmc/articles/PMC5549834/ /pubmed/28712847 http://dx.doi.org/10.1016/j.stemcr.2017.06.006 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Resource Perestrelo, Tânia Chen, Weitong Correia, Marcelo Le, Christopher Pereira, Sandro Rodrigues, Ana S. Sousa, Maria I. Ramalho-Santos, João Wirtz, Denis Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title | Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title_full | Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title_fullStr | Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title_full_unstemmed | Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title_short | Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software |
title_sort | pluri-iq: quantification of embryonic stem cell pluripotency through an image-based analysis software |
topic | Resource |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549834/ https://www.ncbi.nlm.nih.gov/pubmed/28712847 http://dx.doi.org/10.1016/j.stemcr.2017.06.006 |
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