Mass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS2 imaging (MS2I) method that can provide MS2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on‐tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t‐distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information.

Mass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS 2 imaging (MS 2 I) method that can provide MS 2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on-tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t-distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information.