Lipid and energy metabolism reprogramming is tightly connected with cancer and enables the possibility to differentiate malignant and intact tissues on the molecular level. Since cancer-related metabolic changes have multiple interconnections, it is required to implement the approaches of big data analysis to investigate metabolic alterations regardless of the natural variability. In this work, the ambient mass spectrometry molecular profiling was combined with conventional lipidomics pipeline to detect and identify key molecular alterations in human glial tumors.
Histologically annotated samples were provided by N.N. Burdenko National Scientific and Practical Center for Neurosurgery. More than 100 samples obtained from more than 30 patients were analyzed using Inline Cartridge Extraction approach developed for the mass spectrometry assisting in neurosurgery. For each sample, measurements were done twice - one part of a sample was investigated immediately after resection using Thermo LTQ XL. The other part of the sample was frozen and transferred into the scientific laboratory to perform high-resolution profiling with Thermo LTQ XL Orbitrap instrument. Since stereotaxic biopsy of brain tumors usually provide small amounts of sample for analysis, less than 10% of samples were analyzed by LC-MS/MS. Based on the results of histological examination, all data were classified, and sets of distinctive features were highlighted using non-tumor pathological tissues as a control. The analysis of the significant amount of samples analyzed by ambient mass spectrometry profiling allows refining lists of detected features while tandem mass spectrometry coupled with reversed-phase liquid chromatography was used for the identification of peaks, corresponding to these features.
The results of this study not only enhance the reliability of mass spectrometric approaches for intraoperative tumor tissue differentiation but shed light on the molecular mechanisms allowing such differentiation.