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Issue 12, 2016
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Non-destructive characterisation of mesenchymal stem cell differentiation using LC-MS-based metabolite footprinting

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Abstract

Bone regeneration is a complex biological process where major cellular changes take place to support the osteogenic differentiation of mesenchymal bone progenitors. To characterise these biological changes and better understand the pathways regulating the formation of mature bone cells, the metabolic profile of mesenchymal stem cell (MSC) differentiation in vitro has been assessed non-invasively during osteogenic (OS) treatment using a footprinting technique. Liquid chromatography (LC)-mass spectrometry (MS)-based metabolite profiling of the culture medium was carried out in parallel to mineral deposition and alkaline phosphatase activity which are two hallmarks of osteogenesis in vitro. Metabolic profiles of spent culture media with a combination of univariate and multivariate analyses investigated concentration changes of extracellular metabolites and nutrients linked to the presence of MSCs in culture media. This non-invasive LC-MS-based analytical approach revealed significant metabolic changes between the media from control and OS-treated cells showing distinct effects of MSC differentiation on the environmental footprint of the cells in different conditions (control vs. OS treatment). A subset of compounds was directly linked to the osteogenic time-course of differentiation, and represent interesting metabolite candidates as non-invasive biomarkers for characterising the differentiation of MSCs in a culture medium.

Graphical abstract: Non-destructive characterisation of mesenchymal stem cell differentiation using LC-MS-based metabolite footprinting

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Publication details

The article was received on 23 Jan 2016, accepted on 18 Apr 2016 and first published on 18 Apr 2016


Article type: Paper
DOI: 10.1039/C6AN00170J
Citation: Analyst, 2016,141, 3776-3787
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    Non-destructive characterisation of mesenchymal stem cell differentiation using LC-MS-based metabolite footprinting

    A. Surrati, R. Linforth, I. D. Fisk, V. Sottile and D. Kim, Analyst, 2016, 141, 3776
    DOI: 10.1039/C6AN00170J

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