NMR Metabolomics for Stem Cell type discrimination.
Franca CastiglioneMonica FerroEvangelos MavroudakisRosalia PellitteriPatrizia BossolascoDamiano ZaccheoMassimo MorbidelliVincenzo SilaniAndrea MeleDavide MoscatelliLidia CovaPublished in: Scientific reports (2017)
Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells (SCs) during development, ageing, pathological onset and progression. We derived and cultured selected subpopulations of rodent fetal, postnatal, adult Neural SCs (NSCs) and postnatal glial progenitors, Olfactory Ensheathing Cells (OECs), respectively from the subventricular zone (SVZ) and the olfactory bulb (OB). Cell lysates were analyzed by proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy leading to metabolites identification and quantitation. Subsequent multivariate analysis of NMR data by Principal Component Analysis (PCA), and Partial Least Square Discriminant Analysis (PLS-DA) allowed data reduction and cluster analysis. This strategy ensures the definition of specific features in the metabolic content of phenotypically similar SCs sharing a common developmental origin. The metabolic fingerprints for selective metabolites or for the whole spectra demonstrated enhanced peculiarities among cell types. The key result of our work is a neat divergence between OECs and the remaining NSC cells. We also show that statistically significant differences for selective metabolites characterizes NSCs of different ages. Finally, the retrived metabolome in cell cultures correlates to the physiological SC features, thus allowing an integrated bioengineering approach for biologic fingerprints able to dissect the (neural) SC molecular specificities.
Keyphrases
- stem cells
- magnetic resonance
- cell therapy
- single cell
- ms ms
- induced apoptosis
- rheumatoid arthritis
- high resolution
- preterm infants
- cell cycle arrest
- mass spectrometry
- computed tomography
- cell proliferation
- neuropathic pain
- young adults
- oxidative stress
- cell death
- machine learning
- data analysis
- signaling pathway
- liquid chromatography
- pluripotent stem cells