Human stem cell metabolomics; headspace volatile gas analysis as an indicator of self-renewal and differentiation status

Abstract

Self-renewal and an ability to differentiate are key hallmarks of stem cells. Pluripotent stem cells (PSC) have the capacity to differentiate into all cell types found within the three germ layers; ectoderm, endoderm, mesoderm. Multipotent stem cells including mesenchymal stem cells (MSC) customarily differentiate into cell types representative of one germ layer only.

Metabolomics focusses on characterising the low molecular weight organic compounds which are by-products of protein-protein interactions and metabolic enzymatic processes; volatile organic compounds (VOCs) emitted from or consumed by cells can be correlated with metabolic status. Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) has been used to identify and discriminate between metabolites (VOCs) from both undifferentiated and differentiated stem cells in specific cell culture oxygen conditions (air (21% O2) and physiological oxygen (2% O2)).

The suitability of SIFT-MS for detecting and identifying VOCs from hPSCs was evaluated with SIFT-MS spectral data analysed via OPLS-DA. hPSCs cultured in 2% O2 displayed a distinct metabolic profile to those cultured in 21% O2. Metabolite markers differing between culture conditions for undifferentiated hPSCs included ethanol and acetaldehyde. Inhibition of ethanol/acetaldehyde conversion enzymes revealed mechanistic control of ethanol and acetaldehyde levels linked to environmental oxygen. hPSC differentiation-linked differences in metabolic profiles included immediate reductions in acetaldehyde and DMS/ethanethiol levels upon onset of differentiation.

For hMSCs, OPLS-DA score plots indicated that hMSCs cultured in a controlled, hermetic, workstation maintained at 2% O2 were distinct to those cultured in either 2% O2 or 21% O2. The VOC profile of hMSCs varied with oxygen condition and degree of differentiation during osteogenic differentiation.

In summary, this thesis demonstrates that SIFT-MS can detect and discriminate VOC profiles in two distinct stem cell populations. These profiles are sensitive to oxygen and differentiation status and therefore provide a potential valuable tool for non-invasive explorations of stem cell biology.