Mass Spectrometry Imaging Reveals a Gradient of Cancer-like Metabolic States in the Vicinity of Cancer Not Seen in Morphometric Margins from Microscopy
journal contributionposted on 02.03.2021, 22:31 authored by Michael Woolman, Lauren Katz, Georgia Gopinath, Taira Kiyota, Claudia M. Kuzan-Fischer, Isabelle Ferry, Mark Zaidi, Kaitlyn Peters, Ahmed Aman, Trevor McKee, Fred Fu, Siham Amara-Belgadi, Craig Daniels, Brad G. Wouters, James T. Rutka, Howard J. Ginsberg, Chris McIntosh, Arash Zarrine-Afsar
Spatially resolved ambient mass spectrometry imaging methods have gained popularity to characterize cancer sites and their borders using molecular changes in the lipidome. This utility, however, is predicated on metabolic homogeneity at the border, which would create a sharp molecular transition at the morphometric borders. We subjected murine models of human medulloblastoma brain cancer to mass spectrometry imaging, a technique that provides a direct readout of tissue molecular content in a spatially resolved manner. We discovered a distance-dependent gradient of cancer-like lipid molecule profiles in the brain tissue within 1.2 mm of the cancer border, suggesting that a cancer-like state progresses beyond the histologic border, into the healthy tissue. The results were further corroborated using orthogonal liquid chromatography and mass spectrometry (LC-MS) analysis of selected tissue regions subjected to laser capture microdissection. LC-MS/MS analysis for robust identification of the affected molecules implied changes in a number of different lipid classes, some of which are metabolized from the essential docosahexaenoic fatty acid (DHA) present in the interstitial fluid. Metabolic molecular borders are thus not as sharp as morphometric borders, and mass spectrometry imaging can reveal molecular nuances not observed with microscopy. Caution must be exercised in interpreting multimodal imaging results stipulated on a coincidental relationship between metabolic and morphometric borders of cancer, at least within animal models used in preclinical research.