Lipid identification depends on data analysis

Lipids comprise a diverse group of water-insoluble organic compounds that have a variety of functions in living organisms, including as energy storage vessels, cell membrane components and hormones. Discovering all the many lipid species that exist in living organisms has been a major challenge for scientists. Now, however, a team of German and Canadian researchers may have made the task a little easier by developing a new data-dependent acquisition (DDA) method that can efficiently generate lipid profiles.

DDA involves creating and analysing multidimensional data files produced by different analytical instruments, such as a suite of mass spectrometers (MS). Its strength is that it allows a greater proportion of the rich array of data generated by MS analyses to be utilised for identifying biological compounds. The researchers, led by Andrej Shevchenko from the Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, have developed a DDA method that can be used to accurately identify lipids based on the data produced by a linked set of three quadrupole mass spectrometers.

The first MS scans the lipid sample, which might have been separated by chromatography although this isn't essential, to identify interesting precursor ions, which are then investigated in more detail by the second MS. Finally, the third MS breaks the precursor ions of interest into their component fragment ions. Spectral data on the precursor ions and fragment ions are then analysed by a software package called LipidInspector, which was specially created by a German software company known as Scionics Computer Innovations. LipidInspector uses the spectral data produced by the third MS to identify the fragment ions and then uses this information, together with the spectral data produced by the second MS, to help identify the precursor ions. All this information is then used to identify the original lipids in the sample.

Using this method, the researchers were able to identify 35 individual species of a type of lipid known as triacylglycerols in a lipid sample from the worm Caenorhabditis elegans. This kind of analysis generally takes less than 30 minutes.

However, the LipidInspector software allows the researchers to conduct even more complex analyses of the spectral data, involving Boolean scans similar to the kind used in internet search engines. For example, in order to identify members of a class of lipid known as phosphatidylethanolamines (PEs), Shevchenko and his team set the software to only highlight fragment ions that met four different criteria. When used to analyse the PEs in a sample of cow heart, this Boolean scan allowed the researchers to identify all the known species.

Finally, the researchers conducted a range of DDA analyses, some of which involved Boolean scans, to obtain a profile of all the lipid species (known as the lipidome) in C. elegans. This allowed them to identify a total of 90 glycerophospholipids, comprising mainly PEs and phosphatidylcholines, with an accuracy that was better than most other methods used for analysing lipidomes.

The researchers claim that their DDA method will be highly suitable for analysing the reams of data produced by the new generation of high-resolution, hybrid analytical instruments, such as the linear ion trap-orbitrap MS. They also envisage adapting the software to allow users to develop their own interpretation rules, in addition to Boolean scans, such that the same spectral data can be analysed in various different ways.

They conclude that "data-dependent acquisition and processing could become a powerful tool in lipidomics, expanding a limited palette of mass spectrometric methods currently available in the field".