A subset of metabolomics, lipidomics is defined as the large-scale study of the structures, functions, pathways, and networks of cellular lipids--the lipidome--present in biological systems. Broadly defined, lipids are hydrophobic or amphipathic small molecules derived all or in part from two main building blocks: ketoacyl and isoprene functional groups. Lipids exhibit enormous structural diversity by virtue of their biosynthesis from wide and varied combinations of their building blocks. While not all permutations are experimentally detected, lipids comprise a vast, informative subset of the metabolome.

Extraction and Separation: Lipid extraction strategies typically rely on the high solubility of nonpolar hydrocarbon chains in organic solvents such as hexane, chloroform, and methyl-tert-butyl-ether (MTBE). Extraction strategies must be developed and optimized according to the tissue and class of lipids under investigation. Separation strategies generally start with some sort of liquid chromatography (LC) and may incorporate more than one technique, depending again on the goals of the analysis. Normal- and reverse-phase high performance or ultra-performance LC are common, whereas for more comprehensive coverage in untargeted lipidomics, hydrophilic interaction LC and nano-flow LC have delivered exceptional results.

Detection: Detection and measurement of lipids is usually accomplished with mass spectrometry fitted with ionizing sources that reduce the amount of fragmentation of the molecules. Ionizing sources used in lipidomics include electrospray (ESI), electrospray plus desorption (DESI), matrix-assisted laser desorption (MALDI), and atmospheric pressure chemical (APCI). The specific mass spectrometry platform used will, like with metabolomics, depend on the information sought.

Untargeted versus Targeted: Untargeted lipidomics has been generally thought of as a hypothesis-generating, global “look-and-see” analysis. Untargeted lipidomics is a powerful discovery platform. Targeted lipidomics, an hypothesis-driven approach, becomes the method of choice for validation and quantification of specific features or molecule classes.

Informatics: Untargeted lipidomics generates large datasets that remain challenging to analyze and interpret. Nevertheless, we at Nelson Scientific Labs have developed reliable workflows for extracting features from these data. Building predictive models from combined datasets is an area of particular interest for our team.

Nelson Scientific Labs provides comprehensive lipidomic services, from study design all the way to interpreting and reporting your findings.

Contact us today to find out how we may help you with your lipidomics project!