Space-resolved metabolomics using mass spectrometry imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics.

Ovarian cancer (OC) is one of the deadliest cancers among women with no effective screening tools available, especially for early-stage diagnosis. Due to the lack of symptoms at its early stage, only a small fraction of OCs is diagnosed for effective treatment. Furthermore, the detailed mechanism of OC progression and metastasis remains unclear. Herein, space-resolved lipid and N-glycan profiling of ovarian cancer tissues collected from two mouse models were investigated using matrix-assisted laser desorption/ionization (MALDI) MSI. In this talk, I will discuss spatial distributions and alterations of key lipids and N-glycans in OC mouse tissues, and their correlations with OC development and metastasis. Selected lipid and N-glycan features were used to develop multivariate statistical models for differentiation of OC tissues from healthy control tissues, providing basis for early-stage OC diagnosis.

I will also introduce my current work on developing a new laser based MSI platform powered by a triboelectric nanogenerator (TENG), which can induce lipid C=C bond epoxidation for in-depth lipid identification in MSI experiments.