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 PT., Munthe-Fog L., Rieneck K., Banch Clausen F., Rivera OB., Kannik Haastrup E.
Laboratory Study, published in Adipocyte (2021) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Adipocyte (2021)
- Country
- United States
- Reported sample size
- —
- Source database
- PubMed
- PMID
- 34747303
- PMCID
- PMC8632106
- DOI
- 10.1080/21623945.2021.2000696
- Citations
- 7
Abstract (original English)
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 d
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
How we grade evidenceBrowse all related research
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