LC/MS Method for Comprehensive Analysis of Plasma Lipids

Importance of the Topic

Comprehensive profiling of plasma lipids by LC-MS supports biomarker discovery, metabolic phenotyping, nutritional studies, and quality control in clinical and research laboratories. Accurate lipid coverage enables deeper insight into disease mechanisms, nutritional responses, and pharmacological effects.

Objectives and Study Overview

This application note presents a streamlined workflow for the extraction, separation, and detection of complex lipid species in human blood plasma. The aim is to optimize mobile-phase conditions and instrument settings to detect over 270 unique lipid molecules across major classes within a rapid 15-minute injection-to-injection cycle.

Methodology and Instrumentation

Sample Preparation

• Liquid–liquid extraction using cold methanol, methyl tert-butyl ether (MTBE), and water in a bi-phasic system.
• Addition of odd-chain and deuterated internal standards covering key lipid classes.
• Evaporation of organic extracts and reconstitution in methanol/toluene with CUDA internal standard.

Chromatographic Separation

• Reversed-phase C18 column (100×2.1 mm, 1.8 µm) on Agilent 1290 Infinity LC.
• Binary gradient from acetonitrile/water to isopropanol/acetonitrile with 10 mM ammonium formate (positive mode) or ammonium acetate (negative mode), with 0.1% formic acid for enhanced ionization.
• Flow rate 0.6 mL/min, column at 60 °C, 15 min runtime.

Mass Spectrometry

• Agilent 6550 iFunnel Q-TOF MS with Jet Stream ESI source in positive and negative modes.
• Acquisition mass range m/z 100–1700 at 2 spectra/s using extended dynamic range (2 GHz).
• Source parameters optimized for drying gas, sheath gas, capillary and nozzle voltages, and fragmentor settings.

Results and Discussion

Using dedicated modifiers for each ionization polarity improved coverage and sensitivity. Positive mode (AJS+) with ammonium formate and formic acid detected 192 lipids, mainly cholesteryl esters, diacylglycerols, triacylglycerols, phosphatidylcholines, phosphatidylethanolamines, sphingomyelins, and ceramides. Negative mode (AJS–) with ammonium acetate yielded 158 additional lipids, including nonesterified fatty acids, phosphatidylinositols, and overlapping lysophospholipids. Combined analysis identified over 270 unique species. Signal intensities for key standards improved up to 300-fold when using optimized modifier systems.

Benefits and Practical Applications

This workflow offers

• High lipidome coverage in a single run without extensive sample prep.
• Fast throughput (15 min per injection) suitable for large cohorts.
• Semiquantitative data using class-specific internal standards.
• Flexible adaptation to clinical, nutritional, and pharmaceutical studies.

Future Trends and Opportunities

Integration of ion mobility, higher-resolution MS, and automated data processing will further enhance lipid identification and quantification. Streamlined software pipelines and expanded spectral libraries will support large-scale studies, biomarker validation, and personalized medicine applications.

Conclusion

The optimized LC–Q-TOF MS method with tailored mobile-phase modifiers and rapid gradient delivers broad, semi-targeted lipid profiling of plasma in under 15 minutes. Utilizing positive and negative ion modes extends coverage across twelve lipid classes, demonstrating robust performance for research and QA/QC environments.

References

1. Sandra K, Sandra P. Lipidomics from an analytical perspective. Curr Opin Chem Biol. 2013;17:847–853.
2. Cajka T, Fiehn O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt Chem. 2014;61:192–206.
3. Hyotylainen T, Ollila M. Optimizing the lipidomics workflow for clinical studies—practical considerations. Anal Bioanal Chem. 2015;407:4973–4993.
4. Matyash V, et al. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res. 2008;49:1137–1146.
5. Cajka T, Fiehn O. LC-MS-based lipidomics and automated identification of lipids using the LipidBlast in-silico MS/MS library. Methods Mol Biol. 2017;1609:149–170.
6. Cajka T, Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics. 2016;12:34.
7. Cajka T, et al. Using a lipidomics approach for nutritional phenotyping in response to a test meal containing gamma-linolenic acid. Metabolomics. 2016;12:127.
8. Kind T, et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10:755–758.