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Rigshospitalet - a part of Copenhagen University Hospital
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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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  1. Novel nuances of human brown fat

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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.

Original languageEnglish
JournalAdipocyte
Volume10
Issue number1
Pages (from-to)621-630
Number of pages10
ISSN2162-3945
DOIs
Publication statusPublished - Dec 2021

    Research areas

  • Adipogenesis, Adipose Tissue, Cell Differentiation, Cells, Cultured, Deep Learning, Kinetics, Stromal Cells, adipose-derived stem cells, deep learning, stem cells, differentiation, machine learning

ID: 72156016