Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models

• Published in: Frontiers in chemistry Vol. 11; p. 1332816
• Main Authors: Ma, Xin, Botros, Andro, Yun, Sylvia R, Park, Eun Young, Kim, Olga, Park, Soojin, Pham, Thu-Huyen, Chen, Ruihong, Palaniappan, Murugesan, Matzuk, Martin M, Kim, Jaeyeon, Fernández, Facundo M
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 08-01-2024
• Subjects: biomarkers; biomarkers mass spectrometry imaging; Chemistry; high-grade serous ovarian cancer; lipidomics; mass spectrometry imaging; matrix-assisted laser desorption/ionization; mass spectrometry imaging; biomarkers mass spectrometry imaging; high-grade serous ovarian cancer; matrix-assisted laser desorption/ionization; biomarkers; lipidomics
• ISSN: 2296-2646; 2296-2646
• DOI: 10.3389/fchem.2023.1332816

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (
= 4) and triple mutant mouse models (
= 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (
= 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.