Untargeted Metabolite Mapping in 3D Cell Culture Models Using High Spectral Resolution FT-ICR Mass Spectrometry Imaging.
Lulu H TuckerGregory R HammRebecca J E SargeantRichard J A GoodwinC L Logan MackayColin J CampbellDavid J ClarkePublished in: Analytical chemistry (2019)
Multicellular tumor spheroids (MTS) are a well-established model system for drug development and are a valuable in vitro research tool for use prior to employing animal models. These 3D-cell cultures are thought to display chemical gradients of oxygen and nutrients throughout their structure, giving rise to distinct microenvironments in radial layers, thus, mimicking the pathophysiological environment of a tumor. Little is known about the localized distributions of metabolites within these microenvironments. To address this, here we utilize high spectral resolution Fourier-transform ion cyclotron resonance (FT-ICR), MALDI mass spectrometry imaging (MSI) to image the distribution of endogenous metabolites in breast cancer MCF-7 spheroids. We show that known specific metabolite markers (adenosine phosphates and glutathione) indicate that the central region of these cell culture models experiences increased hypoxic and oxidative stress. By using discriminatory analysis, we have identified which m/z values localize toward the outer proliferative or central hypoxic regions of an MTS. Elemental formulae were assigned with sub-ppm mass accuracy, allowing metabolite assignment. Using this information, we have mapped these metabolites back to distinct pathways to improve our understanding of the molecular environment and biochemistry of these tumor models.
Keyphrases
- mass spectrometry
- high resolution
- liquid chromatography
- ms ms
- oxidative stress
- single molecule
- optical coherence tomography
- gas chromatography
- high performance liquid chromatography
- single cell
- high resolution mass spectrometry
- stem cells
- heavy metals
- cell therapy
- signaling pathway
- protein kinase
- ischemia reperfusion injury
- risk assessment
- heat shock
- young adults
- data analysis