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Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells

Quantitative methods for assessing differentiative potency of adipose-derived stem/stromal cells may lead to improved clinical application of this multipotent stem cell, by advancing our understanding of specific processes such as adipogenic differentiation. Conventional cell staining methods are us...

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Autores principales: Brooks, Patrick Terrence, Munthe-Fog, Lea, Rieneck, Klaus, Banch Clausen, Frederik, Rivera, Olga Ballesteros, Kannik Haastrup, Eva, Fischer-Nielsen, Anne, Svalgaard, Jesper Dyrendom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632106/
https://www.ncbi.nlm.nih.gov/pubmed/34747303
http://dx.doi.org/10.1080/21623945.2021.2000696
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author Brooks, Patrick Terrence
Munthe-Fog, Lea
Rieneck, Klaus
Banch Clausen, Frederik
Rivera, Olga Ballesteros
Kannik Haastrup, Eva
Fischer-Nielsen, Anne
Svalgaard, Jesper Dyrendom
author_facet Brooks, Patrick Terrence
Munthe-Fog, Lea
Rieneck, Klaus
Banch Clausen, Frederik
Rivera, Olga Ballesteros
Kannik Haastrup, Eva
Fischer-Nielsen, Anne
Svalgaard, Jesper Dyrendom
author_sort Brooks, Patrick Terrence
collection PubMed
description Quantitative methods for assessing differentiative potency of adipose-derived stem/stromal cells may lead to improved clinical application of this multipotent stem cell, by advancing our understanding of specific processes such as adipogenic differentiation. Conventional cell staining methods are used to determine the formation of adipose areas during adipogenesis as a qualitative representation of adipogenic potency. Staining methods such as oil-red-O are quantifiable using absorbance measurements, but these assays are time and material consuming. Detection methods for cell characteristics using advanced image analysis by machine learning are emerging. Here, live-cell imaging was combined with a deep learning-based detection tool to quantify the presence of adipose areas and lipid droplet formation during adipogenic differentiation of adipose-derived stem/stromal cells. Different detection masks quantified adipose area and lipid droplet formation at different time points indicating kinetics of adipogenesis and showed differences between individual donors. Whereas CEBPA and PPARG expression seems to precede the increase in adipose area and lipid droplets, it might be able to predict expression of ADIPOQ. The applied method is a proof of concept, demonstrating that deep learning methods can be used to investigate adipogenic differentiation and kinetics in vitro using specific detection masks based on algorithm produced from annotation of image data.
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spelling pubmed-86321062021-12-01 Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells Brooks, Patrick Terrence Munthe-Fog, Lea Rieneck, Klaus Banch Clausen, Frederik Rivera, Olga Ballesteros Kannik Haastrup, Eva Fischer-Nielsen, Anne Svalgaard, Jesper Dyrendom Adipocyte Research Paper Quantitative methods for assessing differentiative potency of adipose-derived stem/stromal cells may lead to improved clinical application of this multipotent stem cell, by advancing our understanding of specific processes such as adipogenic differentiation. Conventional cell staining methods are used to determine the formation of adipose areas during adipogenesis as a qualitative representation of adipogenic potency. Staining methods such as oil-red-O are quantifiable using absorbance measurements, but these assays are time and material consuming. Detection methods for cell characteristics using advanced image analysis by machine learning are emerging. Here, live-cell imaging was combined with a deep learning-based detection tool to quantify the presence of adipose areas and lipid droplet formation during adipogenic differentiation of adipose-derived stem/stromal cells. Different detection masks quantified adipose area and lipid droplet formation at different time points indicating kinetics of adipogenesis and showed differences between individual donors. Whereas CEBPA and PPARG expression seems to precede the increase in adipose area and lipid droplets, it might be able to predict expression of ADIPOQ. The applied method is a proof of concept, demonstrating that deep learning methods can be used to investigate adipogenic differentiation and kinetics in vitro using specific detection masks based on algorithm produced from annotation of image data. Taylor & Francis 2021-11-19 /pmc/articles/PMC8632106/ /pubmed/34747303 http://dx.doi.org/10.1080/21623945.2021.2000696 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Brooks, Patrick Terrence
Munthe-Fog, Lea
Rieneck, Klaus
Banch Clausen, Frederik
Rivera, Olga Ballesteros
Kannik Haastrup, Eva
Fischer-Nielsen, Anne
Svalgaard, Jesper Dyrendom
Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title_full Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title_fullStr Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title_full_unstemmed Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title_short Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
title_sort application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632106/
https://www.ncbi.nlm.nih.gov/pubmed/34747303
http://dx.doi.org/10.1080/21623945.2021.2000696
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