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:	Patrick Terrence Brooks, Lea Munthe-Fog, Klaus Rieneck, Frederik Banch Clausen, Olga Ballesteros Rivera, Eva Kannik Haastrup, Anne Fischer-Nielsen, Jesper Dyrendom Svalgaard
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	adipogenesis differentiation adipose-derived stem cells stem cells deep learning machine learning Diseases of the endocrine glands. Clinical endocrinology RC648-665 Cytology QH573-671 Physiology QP1-981
Acceso en línea:	https://doaj.org/article/bff1e437b2dc439eaaae392b6254a39b
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spelling	oai:doaj.org-article:bff1e437b2dc439eaaae392b6254a39b2021-11-26T11:19:49ZApplication of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells2162-39452162-397X10.1080/21623945.2021.2000696https://doaj.org/article/bff1e437b2dc439eaaae392b6254a39b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/21623945.2021.2000696https://doaj.org/toc/2162-3945https://doaj.org/toc/2162-397XQuantitative 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.Patrick Terrence BrooksLea Munthe-FogKlaus RieneckFrederik Banch ClausenOlga Ballesteros RiveraEva Kannik HaastrupAnne Fischer-NielsenJesper Dyrendom SvalgaardTaylor & Francis Grouparticleadipogenesisdifferentiationadipose-derived stem cellsstem cellsdeep learningmachine learningDiseases of the endocrine glands. Clinical endocrinologyRC648-665CytologyQH573-671PhysiologyQP1-981ENAdipocyte, Vol 10, Iss 1, Pp 621-630 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adipogenesis differentiation adipose-derived stem cells stem cells deep learning machine learning Diseases of the endocrine glands. Clinical endocrinology RC648-665 Cytology QH573-671 Physiology QP1-981
spellingShingle	adipogenesis differentiation adipose-derived stem cells stem cells deep learning machine learning Diseases of the endocrine glands. Clinical endocrinology RC648-665 Cytology QH573-671 Physiology QP1-981 Patrick Terrence Brooks Lea Munthe-Fog Klaus Rieneck Frederik Banch Clausen Olga Ballesteros Rivera Eva Kannik Haastrup Anne Fischer-Nielsen Jesper Dyrendom Svalgaard Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells
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.
format	article
author	Patrick Terrence Brooks Lea Munthe-Fog Klaus Rieneck Frederik Banch Clausen Olga Ballesteros Rivera Eva Kannik Haastrup Anne Fischer-Nielsen Jesper Dyrendom Svalgaard
author_facet	Patrick Terrence Brooks Lea Munthe-Fog Klaus Rieneck Frederik Banch Clausen Olga Ballesteros Rivera Eva Kannik Haastrup Anne Fischer-Nielsen Jesper Dyrendom Svalgaard
author_sort	Patrick Terrence Brooks
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_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_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_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
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/bff1e437b2dc439eaaae392b6254a39b
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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

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