Structural isomerism in monounsaturated fatty acids (MUFAs) presents a persistent challenge in lipidomics due to limited chromatographic resolution and indistinguishable mass spectral profiles. Moreover, many methods that overcome these limitations require specialized instruments or complex photochemistry typically not accessible in for those using standard lipidomics core facilities. We report a streamlined LC-MS/MS method that integrates N-(4-aminomethylphenyl)pyridinium (AMP)-based charge-switch derivatization with meta-chloroperoxybenzoic acid (mCPBA) epoxidation to enable rapid and sensitive profiling of MUFA double bond positional isomers. Charge-switching enables enhanced detection of fatty acids while epoxidation improves chromatographic resolution and produces diagnostic fragmentation patterns upon collision-induced dissociation (CID), enabling precise localization of double bond positions. The optimized workflow achieves baseline separation of 16:1, 18:1, and 20:1 MUFA isomers within a 20-minute gradient, with reproducible retention times and consistent epoxide yields. The method also supports partial resolution of polyunsaturated fatty acids (PUFA) and cis/trans isomers. Application to prostate cancer cell lines revealed distinct MUFA isomer profiles associated with aberrant fatty acid desaturase 2 (FADS2) activity, including elevated n-10 and n-12 isomers in tumorigenic lines. These results align with prior studies using other isomer-resolving techniques, validating the method’s analytical performance. This accessible and robust strategy expands the toolkit for lipidomics research, providing a method that is compatible with conventional LC-MS/MS workflows, positioning it as a practical solution for high-resolution isomer analysis in complex biological samples

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Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.

Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.

Analyst, 2025, Accepted Manuscript DOI: 10.1039/D5AN00851D, Communication Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Melanie J. Bailey, Johanna von Gerichten, Kyle D.G. Saunders, Matt Spick Single-cell lipidomics holds tremendous promise for understanding a wide range of pathological conditions involving heterogenous cell populations, including infection, cancer, diabetes, and cardiovascular disease and yet its widespread adoption has... The content of this RSS Feed (c) The Royal Society of Chemistry

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This Scientific Data Collection welcomes descriptions of any technique relevant to lipidomics research, including mass spectrometry, NMR, theoretical studies, ...

Arrays of silicon nanoneedles are used to generate molecular replicas of live brain tissue for longitudinal spatial lipidomic classification via desorption electrospray ionization mass spectrometry im...

Avocados are a valuable source of lipids with potential applications in food, nutraceutical, and cosmetic products, all requiring food-grade and environmentally friendly extraction methods. Though o...

Authentication of the halalness of beef and pork products (sausages, nuggets, and corned beef) is important. PCR (polymerase chain reaction) is a method for authenticating halalness with good accuracy...

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Osteomyelitis occurs when Staphylococcus aureus invades the bone microenvironment, resulting in a bo

Scientific Reports - Uncovering metabolic dysregulation in schizophrenia and cannabis use disorder through untargeted plasma lipidomics