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Updated: Jul 6, 2026

Arabidopsis thaliana Polar Glycerolipid Profiling by Thin Layer Chromatography TLC Coupled with Gas-Liquid Chromatography GLC
Published on: March 18, 2011
Meeting the analytical challenges of fatty-acid engineering: a comprehensive method for triacylglycerol
Daria Makeeva1, Susana Silvestre1, Johnathan A Napier1
1Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom.
Background:
The structural elucidation and quantification of complex triacylglycerol (TAG) mixtures remain a major challenge in lipidomics due to extensive isomeric and isobaric diversity arising from differences in fatty-acyl chain length, unsaturation, and positional arrangement. Genetically engineering oilseeds to produce long-chain omega-3 fatty acids further expands this complexity by introducing numerous novel and low-abundance TAG species. A comprehensive method to quantify a wide diversity of TAG is required [67].
Results:
To address these analytical demands, we developed a robust workflow using a Q-Exactive Orbitrap platform that integrates untargeted data dependent acquisition (DDA) with targeted parallel reaction monitoring (PRM) for accurate identification and quantification of TAG in genetically modified Camelina sativa engineered to synthesize EPA (20:5) and DHA (22:6). The combined DDA-PRM strategy, supported by retention-time validation, resolved 86% of isobaric TAG pairs and enabled confident assignment of 162 fully fatty-acid resolved TAG species. This is the highest number reported for any plant oil to date, including TAG containing non-native fatty-acids 22:6, 20:5, 22:5, 18:4, and 20:4 which were generated from the engineered enzyme activities. Quantitative accuracy was achieved using a response-factor approach that corrects for ionization efficiency differences, with validation against orthogonal GC-FID analysis [125].
Significance:
The method provides four orders of linear dynamic range, exceeding previous PRM-based lipidomics on Q-TOF instruments. Custom scripts for response-factor calculation and PRM scheduling improved throughput and scalability. This integrated workflow bridges discovery driven lipidomics and high-precision quantitation, offering a versatile and reliable platform for TAG profiling in applications spanning plant metabolic engineering, nutritional quality assessment, and food authenticity [58].

