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Issue 9, 2019
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Label-free density-based detection of adipocytes of bone marrow origin using magnetic levitation

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Abstract

Adipocyte hypertrophy and hyperplasia are important parameters in describing abnormalities in adipogenesis that are concomitant to diseases such as obesity, diabetes, anorexia nervosa and osteoporosis. Therefore, technical developments in the detection of adipocytes become an important driving factor in adipogenesis research. Current techniques such as optical microscopy and flow cytometry are available in detection and examination of adipocytes, driving cell- and molecular-based research of adipogenesis. Even though microscopy techniques are common and straightforward, they are restricted in terms of manipulation and separation of the cells. Flow cytometry is an alternative, but mature adipocytes are fragile and cannot withstand the flow process. Other separation methods usually require labeling of the cells or usage of microfluidic platforms that utilize fluids with different densities. Magnetic levitation is a novel label-free technology with the principle of movement of cells towards the lower magnetic field in a paramagnetic medium depending on their individual densities. In this study, we used a magnetic levitation device for density-based single cell detection of differentiated adipogenic cells in heterogeneous populations. Results showed that the magnetic levitation platform was sensitive to changes in the lipid content of mesenchymal stem cells committed to adipogenesis and it could be successfully used to detect the adipogenic differentiation of the cells.

Graphical abstract: Label-free density-based detection of adipocytes of bone marrow origin using magnetic levitation

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Publication details

The article was received on 26 Dec 2018, accepted on 10 Mar 2019 and first published on 11 Mar 2019


Article type: Paper
DOI: 10.1039/C8AN02503G
Citation: Analyst, 2019,144, 2942-2953

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    Label-free density-based detection of adipocytes of bone marrow origin using magnetic levitation

    O. Sarigil, M. Anil-Inevi, E. Yilmaz, G. Mese, H. C. Tekin and E. Ozcivici, Analyst, 2019, 144, 2942
    DOI: 10.1039/C8AN02503G

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