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Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks

Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along...

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Autores principales: Mamaeva, Anastasiya, Krasnova, Olga, Khvorova, Irina, Kozlov, Konstantin, Gursky, Vitaly, Samsonova, Maria, Tikhonova, Olga, Neganova, Irina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9820636/
https://www.ncbi.nlm.nih.gov/pubmed/36613583
http://dx.doi.org/10.3390/ijms24010140
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author Mamaeva, Anastasiya
Krasnova, Olga
Khvorova, Irina
Kozlov, Konstantin
Gursky, Vitaly
Samsonova, Maria
Tikhonova, Olga
Neganova, Irina
author_facet Mamaeva, Anastasiya
Krasnova, Olga
Khvorova, Irina
Kozlov, Konstantin
Gursky, Vitaly
Samsonova, Maria
Tikhonova, Olga
Neganova, Irina
author_sort Mamaeva, Anastasiya
collection PubMed
description Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with “good” and “bad” morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.
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spelling pubmed-98206362023-01-07 Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks Mamaeva, Anastasiya Krasnova, Olga Khvorova, Irina Kozlov, Konstantin Gursky, Vitaly Samsonova, Maria Tikhonova, Olga Neganova, Irina Int J Mol Sci Article Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with “good” and “bad” morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells. MDPI 2022-12-21 /pmc/articles/PMC9820636/ /pubmed/36613583 http://dx.doi.org/10.3390/ijms24010140 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mamaeva, Anastasiya
Krasnova, Olga
Khvorova, Irina
Kozlov, Konstantin
Gursky, Vitaly
Samsonova, Maria
Tikhonova, Olga
Neganova, Irina
Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title_full Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title_fullStr Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title_full_unstemmed Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title_short Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks
title_sort quality control of human pluripotent stem cell colonies by computational image analysis using convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9820636/
https://www.ncbi.nlm.nih.gov/pubmed/36613583
http://dx.doi.org/10.3390/ijms24010140
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