Triboelectric Ambient Mass Spectrometry Imaging of Renal Cell Carcinomas

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Principal Investigator: FM Fernández – Triboelectric Ambient Mass Spectrometry Imaging of Renal Cell Carcinomas Understanding complex chemical and biological alterations in cancer requires detailed knowledge of the molecular composition of cancer tissues and the changes in these alterations over time, and following interventions. Mass spectrometry imaging (MSI) is the tool of choice for probing thin tissue sections when femto- to attomole sensitivity is required with simultaneous exquisite specificity. In this project it is proposed to develop a proof-of-concept MSI ion source based on a triboelectric nanogenerator (TENG), and benchmark it’s performance against standard MSI techniques such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) using de-identified clear cell renal carcinoma tissue sections. These are lipid-rich cancer tumors where lipid metabolism plays a major role in disease development. TENG, when coupled to MS, have shown higher sensitivity than standard nanoelectrospray ionization, particularly for difficult to ionize, low polarity lipids and metabolites. TENG can also be used to yield structural information about important molecules such as lipids, by enlarging the TENG electrode area, which allows to carry out controlled gas-phase ion molecule reactions that yield diagnostic fragmentation patterns. Tissue sections to be examined by TENG MSI will be selected from the biobank maintained by Prof. John Petros, a long- standing collaborator at Emory University. The TENG MSI ion source will be coupled to an ion mobility- mass spectrometer to enable distinguishing lipid isobars during imaging experiments, in collaboration with the instrument vendor (Waters). Co-registration of TENG images with MALDI and DESI images will be conducted with algorithms developed with collaborators at Georgia Tech (Kemp). Improvements to the TENG MSI ion source will be achieved using a symbolic regression approach that will enable the simultaneous optimization of several quantitative performance metrics such as spatial resolution, overall sensitivity, the number of detected spectral features, the number of lipid/metabolite chemical classes detected, and the number of oxidized lipids. Overall, this project will develop an MSI technology that will become an invaluable tool for investigating lipid-rich tissues of importance in cancer research.