Orbitrap Illustration

Untargeted Lipidomics

Lipidomics, a branch of metabolomics, is a systems-based study of all lipid molecules (>30,000 individual species) within a biological system, tissue, fluid or cell. Lipids are the main structural components of biological membranes, a major form of energy storage in living cells, but are also well-known mediators of cell signaling. The lipidome is highly complex, consisting of eight major categories, over 80 major classes, 300 sub-classes, and thousands of lipid species[1] spanning a wide range of concentrations. In order to understand cellular physiology and pathophysiology, comprehensive identification and precise quantification of lipids is crucial in lipidomics research.[2]

Recent advances in high-performance liquid chromatography-mass spectrometry (HPLC-MS) platforms allow for rapid and sensitive detection of a variety of lipid species. One of the primary strategies undertaken in MS-based workflows is referred to as untargeted or discovery lipidomics. This strategy involves unbiased qualitative and quantitative analysis of a lipidome by analyzing an entire lipid extract by either LC-MS or direct infusion-MS without prerequisite targets.

An untargeted lipidomics approach makes it possible to analyze several hundreds to thousands of individual lipid species that may be valuable to assess an individual’s health status. Such detailed lipid profiles are very useful for assessing medical risks, monitoring and diagnosing patient treatments and are the basis for the concept of personalized medicine. Applications of untargeted lipidomics include agro science; biomarkers; Alzheimer's, atherosclerosis, cardiovascular, cancer, diabetes and obesity disease research; clinical diagnostics; drug discovery; food safety; neonatal screening; nutrition; plant science and systems biology.
 

1 Fahy, E. et al. LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 2009, 50, S9-S14. doi: 10.1194/jlr.R800095-JLR200.

2 Watson, A.D. Lipidomics: a global approach to lipid analysis in biological systems. J. Lipid Res. 2006, 47, 2101


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Workflow Overview of Untargeted Lipidomics using HRAM HPLC-MSn Approach


Improved lipidomic technologies greatly enhance the knowledge gained about lipid functions at the individual species level. Thin layer chromatography (TLC), the classical standard in lipid analysis, is cheap and fast, but it is very limited when it comes to identification issues below the level of lipid classes. Mass spectrometry (MS) determines the accurate molecular weight and reveals detailed structural information of lipid mixtures with high sensitivity and specificity. Due to its sensitivity and selectivity MS has become the method of choice for qualitative and quantitative lipidomic analysis.
High resolution accurate mass (HR/AM) HPLC-MSn approaches are often used to separate many overlapping isomeric or isobaric molecular ions in order to simultaneously identify and quantify the thousands of cellular lipid molecular species and their interactions with other lipids, proteins, and other metabolites. The identification of each lipid species is carried out by using the HR/AM MS/MS and/or MSn data and the simultaneous quantitation of identified lipids is carried out by using accurate mass of each lipid within a +/- 5 ppm mass tolerance window.

It is critical that the lipidomics LC-MSn platform offers high resolving power on both HPLC separation and MS detection to resolve the many isobaric and isomeric species from biological lipid extracts. It is also critical that the LC/MS platform offers fast effective MS/MS scan speed and excellent mass accuracy to identify and quantify as many of the lipid species from the lipid extracts. The LC/MS platform also needs to offer high sensitivity and wide dynamic range for both MS and MS/MS in order to detect and quantify both low abundance and high abundance lipid species. The high resolving power offered by the Orbitrap analyzer in combination with HR/AM enables a wide range of lipid identification and precise quantitation from complex biological samples. Dedicated software is also needed for high throughput lipid identification and quantification.