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

Surrati, Amal; Linforth, Robert; Fisk, Ian D.; Sottile, Virginie; Kim, Dong-Hyun

doi:10.1039/c6an00170j

Cited by 29 publications

(38 citation statements)

References 49 publications

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“…Utilization of metabolomic profiles as biomarkers of MSC differentiation was also suggested by Jang et al, who used nuclear magnetic resonance spectroscopy to monitor chondrogenesis [14]. Surrati et al [35] monitored osteogenesis of mouse MSCs with the use of dexamethasone by performing metabolomics of the culture medium, finding increased TCA cycle and glycerol derivatives in the medium. Nevertheless, their approach is limited by the fact that extracellular metabolites provide only a rough approximation of intracellular metabolism [36].…”

Section: Discussionmentioning

confidence: 99%

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Klontzas

Vernardis

Heliotis

et al. 2017

Stem Cells and Development

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Mesenchymal stem cells (MSCs) of fetal origin, such as umbilical cord blood MSCs (UCB MSCs), have emerged as a promising cell source for musculoskeletal tissue regeneration because of their higher proliferation potential, lack of donor site morbidity, and their off-the-shelf potential. MSCs differentiated toward the osteogenic lineage exhibit a specific metabolic phenotype characterized by reliance to oxidative phosphorylation for energy production and reduced glycolytic rates. Currently, limited information exists on the metabolic transitions at different stages of the osteogenic process after osteoinduction with different agents. Herein, the osteoinduction efficiency of BMP-2 and dexamethasone on UCB MSCs was assessed using gas chromatography–mass spectrometry (GC-MS) metabolomics analysis, revealing metabolic discrepancies at 7, 14, and 21 days of induction. Whereas both agents when administered individually were able to induce collagen I, osteocalcin, and osteonectin expression, BMP-2 was less effective than dexamethasone in promoting alkaline phosphatase expression. The metabolomics analysis revealed that each agent induced distinct metabolic alterations, including changes in amino acid pools, glutaminolysis, one-carbon metabolism, glycolysis, and tricarboxylic acid cycle. Importantly, we showed that in vitro-differentiated UCB MSCs acquire a metabolic physiology similar to primary osteoblasts when induced with dexamethasone but not with BMP-2, highlighting the fact that metabolomics analysis is sensitive enough to reveal potential differences in the osteogenic efficiency and can be used as a quality control assay for evaluating the osteogenic process.

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Section: Discussionmentioning

confidence: 99%

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Klontzas

Vernardis

Heliotis

et al. 2017

Stem Cells and Development

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“…2). Six replicates were deemed to be valid for this type of cell linebased studies (Alazzo et al 2019;Surrati et al 2016). This separation indicated a distinctive metabolic signature of M1, M2 and M0 macrophages.…”

Section: Feature Selection and Identification Of Potential Biomarker mentioning

confidence: 99%

“…Liquid chromatography (LC)-mass spectrometry (MS)based metabolomics approach is a powerful tool for quantifying metabolites as well as for identifying known and unknown compounds in biological samples (Surrati et al 2016). Therefore, this technique has been used widely for disease diagnostic biomarker discovery (Jansson et al 2009) as well as evaluation of drug toxicity (Sun et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Metabolic characterisation of THP-1 macrophage polarisation using LC–MS-based metabolite profiling

et al. 2020

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Introduction Macrophages constitute a heterogeneous population of functionally distinct cells involved in several physiological and pathological processes. They display remarkable plasticity by changing their phenotype and function in response to environmental cues representing a spectrum of different functional phenotypes. The so-called M1 and M2 macrophages are often considered as representative of pro-and anti-inflammatory ends of such spectrum. Metabolomics approach is a powerful tool providing important chemical information about the cellular phenotype of living systems, and the changes in their metabolic pathways in response to various perturbations. Objectives This study aimed to characterise M1 and M2 phenotypes in THP-1 macrophages in order to identify characteristic metabolites of each polarisation state. Methods Herein, untargeted liquid chromatography (LC)-mass spectrometry (MS)-based metabolite profiling was applied to characterise the metabolic profile of M1-like and M2-like THP-1 macrophages. Results The results showed that M1 and M2 macrophages have distinct metabolic profiles. Sphingolipid and pyrimidine metabolism was significantly changed in M1 macrophages whereas arginine, proline, alanine, aspartate and glutamate metabolism was significantly altered in M2 macrophages. Conclusion This study represents successful application of LC-MS metabolomics approach to characterise M1 and M2 macrophages providing functional readouts that show unique metabolic signature for each phenotype. These data could contribute to a better understanding of M1 and M2 functional properties and could pave the way for developing new therapeutics targeting different immune diseases.

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“…Metabolites in the samples (5 mL, 4 C) were separated on a ZIC-pHILIC column (4.6 Â 150 mm, 5 mm particle size, Merck SeQuant, Watford, UK) in which the column was maintained at a ow rate of 300 mL min À1 at a temperature of 45 C for 24 min as previously described. 23 The gradient started with 20% A (20 mM ammonium carbonate in water) and 80% of B (acetonitrile) and increased to 95% A over 15 min, then the composition was returned to its initial conditions in 2 min and the column was re-equilibrated for 7 min. The MS was operated in ESI+ and ESIÀ switching acquisition modes for LC-MS proling of the samples and in data-dependent MS/MS (ddMS/MS) for identi-cation for the analysis of the QC samples.…”

Section: Analytical Methodologies: Lc-ms For Metabolite Proling and mentioning

confidence: 99%

Metabolic characterisation ofMagnetospirillum gryphiswaldenseMSR-1 using LC-MS-based metabolite profiling

et al. 2020

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

Cited by 29 publications

References 49 publications

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Metabolic characterisation of THP-1 macrophage polarisation using LC–MS-based metabolite profiling

Metabolic characterisation ofMagnetospirillum gryphiswaldenseMSR-1 using LC-MS-based metabolite profiling

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