Mass Spectrometry Imaging Reveals a Gradient of Cancer-like Metabolic States in the Vicinity of Cancer Not Seen in Morphometric Margins from Microscopy.

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.

Dual Laser and Desorption Electrospray Ionization Mass Spectrometry Imaging Using the Same Interface.

For a more comprehensive characterization of molecular heterogeneities of matter, multimodal mass spectrometry imaging must be developed to take advantage of the complementarity of information available through different ionization mechanisms. We report the design, implementation, and performance validation of a laser desorption imaging interface composed of add-on components that adapt a commercial Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging interface for dual imaging of Picosecond Infrared Laser Mass Spectrometry (PIRL-MS) with DESI-MS. The interface utilizes hardware elements and data analysis pipelines already established for DESI-MS imaging, and was further validated in cancer margin assessments using human medulloblastoma cancers. The PIRL-MS images were robust and reproducible across multiple experimental runs on independently prepared xenograft tumors, and could be segmented into cancer and healthy regions in concordance with pathology using a variety of supervised and unsupervised clustering methods. The spectral quality and complexity obtained with this interface were examined with infiltrating and noninfiltrating tumors, and were comparable to other mass spectrometry analysis interfaces. The average PIRL-MS spectra from spatially resolved images could be used for robust cancer m/z model building to classify medulloblastoma cancer from healthy tissue without any misclassifications, an observation that held true over close to 70 sampling data points. While the unsupervised spectral analysis methods suggested a slight suppression of signal in the phospholipid range compared to the hand-held configuration, these changes were insufficient to hamper utility in cancer margin assessment with spatially resolved data obtained with our interface. Dual PIRL-MS and DESI-MS imaging of consecutive sections, as suggested by multivariate loading plots, revealed highly complementary molecular information with m/z values identifiable with one desorption method sufficient to reveal cancer regions being absent in another, further emphasizing the need for effective hardware and software interfaces for dual mass spectrometry imaging.