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Lipidomics is the large-scale study of pathways and networks of cellular lipids. The lipidome describes the complete lipid profile within a cell, a tissue or a body fluid and is a subset of the metabolome (lipids, proteins/amino-acids, sugars and nucleic acids). Lipidomics involves the identification and quantification of thousands of cellular lipid molecular species and their interactions with other lipids, proteins, and metabolites. Lipidomics is a relatively new field that has been driven by rapid advances in mass spectrometry and computational methods. Lipids are recognized to play an important role in many metabolic diseases such as Obesity, Type II Diabetes, Cardiovascular Disease (e.g.  Atherosclerosis, Stroke and Hypertension).

Lipids are a diverse and ubiquitous group of compounds which have many key biological functions, such as acting as structural components of cell membranes, serving as energy storage sources and participating in signaling pathways. Lipids are broadly defined as hydrophobic small molecules that originate entirely or in part from ketoacyl and isoprene groups. The huge structural diversity found in lipids arises from the biosynthesis of various combinations of these building blocks.

Most methods of lipid extraction and isolation from biological samples exploit the high solubility of hydrocarbon chains in organic solvents. Given the diversity in lipid classes, it is not possible to accommodate all classes with a common extraction method. The traditional procedure uses chloroform/methanol-based protocols that include phase partitioning into the organic layer. These protocols work relatively well for a wide variety of physiologically relevant lipids but they have to be adapted for complex lipid chemistries and low-abundance and labile lipid metabolites.

High-performance liquid chromatography (HPLC) is extensively used in lipidomic analysis to separate lipids prior to mass analysis. Normal Phase HPLC effectively separates glycerophospholipids on the basis of headgroup polarity, whereas Reversed Phase HPLC effectively separates fatty acids such as eicosanoids on the basis of chain length, degree of unsaturation and substitution. For untargeted lipidomic studies it is common to use both Normal and Reversed Phase HPLC approaches for increased coverage of lipidome. This can be further improved with ultra-performance (UPLC) columns. U-HPLC  allows for a significantly higher resolution of separation of complex lipids generating increased peak capacity and sensitivity.

The progress of modern lipidomics has been greatly accelerated by the development of soft ionization techniques for mass spectrometry such as electrospray ionization (ESI), desorption electrospray ionization (DESI), and matrix-assisted laser desorption/ionization (MALDI) in particular. Soft ionization avoids extensive fragmentation enabling for  comprehensive detection of an entire range of lipids within a complex mixture that can be correlated to experimental conditions or disease state. ESI-MS depends on the formation of gaseous ions from polar, thermally labile and mostly non-volatile molecules and is particularly suitable for a variety of lipids. Various ESI-MS methods have been developed for analysis of different classes, subclasses, and individual lipid species from biological extracts. The major advantages of ESI-MS are high accuracy, sensitivity, reproducibility, and the applicability of the technique to complex solutions without prior derivatization.

A major challenge for lipidomics, as for any -omics discipline, is related to the handling of the enormous amounts of data. Chromatographic and MS data collection requires substantial efforts in spectral alignment and statistical evaluation of fluctuations in signal intensities. Such variations have a multitude of origins, including biological variations, sample handling and analytical accuracy. Several replicates are normally required for reliable determination of lipid levels in complex mixtures. A number of software packages have been developed by various companies and research groups to analyze data generated by MS profiling of metabolites, including lipids. The data processing for differential profiling usually proceed through several stages, including input file manipulation, spectral filtering, peak detection, chromatographic alignment, normalization, visualization, and data export. Some software packages include multivariate statistical analysis (for example, principal component analysis) and these will be helpful for the identification of correlations in lipid metabolites that are associated with a physiological phenotype, in particular for the development of lipid-based biomarkers.

Beside data (quality) analysis, software packages are used to  construct metabolic maps from data on lipid structures and lipid-related protein and genes. Some lipid pathways are extremely complex. The development of searchable and interactive databases of lipids and lipid-related genes/proteins is an  important reference resource for the lipidomics community. Integration of these databases with MS and other experimental data, as well as with metabolic networks offers an opportunity to develop therapeutic strategies that prevent or reverse pathological states involving dysfunction of lipid-related processes.

For further information on these assays and our standard experimental set-up, please download the CellMade Mass Spectrometry Assays – Technical Information and General Instructions leaflet.


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