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A New Atlas for Lipid Analysis

 

By: Carol A. Rouzer, VICB Communications
Published: March 18, 2019

 

A new report provides an important foundation for the use of ion mobility-mass spectrometry in lipidomics research.    

 

Lipids comprise important biomolecules that play a role in membrane structure and function, cell signaling, and energy storage. The structural diversity of lipids is impressive, ranging from the very nonpolar triglycerides and cholesteryl esters to the more polar glycerophospholipids, sphingolipids, and glycolipids. In addition to these variations based on class, further diversity can result from differences in acyl chain length and degree of saturation for nearly all lipids, and head group in the case of the polar lipids. Lipid structure can have a profound effect on lipid function, spurring increasing interest in lipidomics, the relatively new analytical field that aims to comprehensively characterize and quantify all lipids within a biological sample. Most lipidomics approaches rely on mass spectrometry (MS) for species detection and identification; however, to isolate isobaric lipid species present in complex biological samples, MS must be coupled with other separation techniques. To that end, Vanderbilt Institute of Chemical Biology member John McLean and his laboratory are exploring the use of ion mobility-MS (IM-MS) to address the challenges of lipidomics. They now report comprehensive data outlining the use of IM-MS to characterize individual species in complex mixtures of polar lipids [K. L. Leaptrot, et al. Nat. Comm., (2019) 10, 985].

 

IM-MS is based on the separation of analytes in the gas phase as they travel through a drift tube in the presence of an inert gas. Extensive studies in the McLean lab have demonstrated that different classes of molecules exhibit distinct behavior in the drift tube as a result of their molecular structure and packing efficiency. During IM-MS, the mobility of the ionized gaseous analyte through the drift tube provides a measure of its two-dimensional collision cross section (CCS), and the MS analysis provides information on the ion's mass-to-charge ratio and fragmentation pattern. In the current study, the researchers carried out an exhaustive analysis of the relationship between lipid structure and gas phase conformation as revealed by CCS. From their data, they developed a quantitative tool to correlate mass with CCS for seven classes of lipids (Figure 1), thereby providing a mechanism for efficient IM-MS-based lipid identification.

 


FIGURE 1. Structures of the lipids analyzed in this work. The general structure is given for the major categories of glycerophospholipids and sphingolipids. In each case, R, R1, and R2 refer to hydrocarbon chains of various lengths and/or degrees of unsaturation, and X refers to the head groups that define each class within the category as indicated. Figure reproduced under the Creative Commons Attribution 4.0 International License 4.0 from K. L. Leaptrot, et al. Nat. Comm., (2019) 10, 985.

 

 

For their initial analyses, the researchers obtained commercial standards of glycerophospholipids including phosphatidylethanolamine (PE), phosphatidylcholine (PC), and phosphatidylserine (PS), and sphingolipids including sphingomyelin (SM) and cerebroside (GlcCer). Phosphatidic acid (PA) was also present in the glycerophospholipid standards, and ceramide (Cer) was included in the GlcCer sample, providing a total of seven lipid classes for study. They subjected the standards to IM-MS, and used the resulting MS data (exact mass determinations) to identify each lipid according to the total number of carbons and double bonds in the acyl chains. They obtained 456 CCS values for 217 distinct lipids. In general, they found that glycerophospholipids containing longer chains also exhibited a larger number of double bonds, and that PE and PS tended to have more sites of unsaturation than did PC or PA. In general sphingolipids displayed fewer double bonds and exhibited a wider range of chain lengths than glycerophospholipids (Figure 2).


 

FIGURE 2. Summary of the structural characteristics of lipids analyzed in the study. The main graph is a plot of the total number of carbon atoms in the acyl chains of each lipid versus the number of double bonds in those chains. Each hexagon indicates that at least one lipid having those characteristics was present in the mixture, and the colored wedges inside of the hexagons indicate the class of lipids exhibiting those characteristics. The bar graph along the left side provides the relative proportion of each lipid class having the indicated number of sites of unsaturation, and the bar graph along the top provides the relative proportion of each lipid class having the indicated number of atoms in the side chains. The region colored in pink corresponds to sphingolipids, whereas the blue region corresponds to glycerophospholipids. Figure reproduced under the Creative Commons Attribution 4.0 International License 4.0 from K. L. Leaptrot, et al. Nat. Comm., (2019) 10, 985.

 

 

A positive and distinct correlation between mobility and mass was evident for each category of lipids (Figure 3a). In general, the CCS values for sphingolipids were 2 - 6% larger than for glycerophospholipids of comparable mass. For glycerophospholipids, CCS increased approximately 0.15 - 0.18 Å2 per mass unit, whereas the increase for sphingolipids was 0.19 Å2 per mass unit. Ionization of the lipids occurred through complexation with a positive ion (such as Na+ or H+) or deprotonation or complexation with a negative ion (such as Cl-). Thus, the researchers conducted MS analyses in both positive- and negative-ion modes. It was not unusual for them to detect a lipid species in multiple forms (i.e., sodiated and protonated), and the nature of the charge carrier had some influence on the CCS of the species. Nevertheless the investigators found that the two most important factors contributing to change in CCS with mass were acyl chain length and degree of unsaturation, with unsaturation exerting a four-fold higher influence than chain length. The researchers also found that they could use their data to develop mathematical relationships correlating these parameters to CCS and that these could then be used to differentiate lipids from different classes having the same mass (Figure 3b).

 

 

FIGURE 3. a) Relationship between lipid mass and drift tube CCS using a nitrogen atmosphere. Each symbol represents a detected lipid species, and the colors indicate the class of the lipid. (b) Structures of the various lipids identified with a mass of ~810 Da [gray rectangle in (a)]. Note that it would be difficult to distinguish these lipids on the basis of mass alone, but they can be differentiated on the basis of their distinct CCS values. Figure reproduced under the Creative Commons Attribution 4.0 International License 4.0 from K. L. Leaptrot, et al. Nat. Comm., (2019) 10, 985.

 

 

The researchers tested their approach using mixtures of lipids containing species from all 7 classes (Figure 4). In many cases, MS data alone sufficed for species identification using exact mass. However, in some cases, identification was difficult due to the presence of multiple species of very similar mass. In those cases, the investigators used the exact mass data to predict all possible species that could correspond to that signal. They then predicted the CCS behavior of each of those species based on the mathematical relationships they had developed. In the vast majority of cases, the CCS predictions enabled them to confidently identify each species in the spectrum.

 

 

 

FIGURE 4. Results of IM-MS analysis of a complex lipid mixture. (a) Chromatogram showing signals detected plotted on a mass (m/z) versus CCS basis. The positions of different classes of lipids are indicated. (b) Enlargement of the area in (a) outlined in the dotted line. Despite their very similar masses, three distinct lipids, including a PS, PC, and GlcCer, can be distinguished by the combination of mass and CCS. Figure reproduced under the Creative Commons Attribution 4.0 International License 4.0 from K. L. Leaptrot, et al. Nat. Comm., (2019) 10, 985.

 

 

These studies confirm the potential of IM-MS for lipidomics analysis of complex biological samples and provide the foundation for its application by other laboratories. We look forward to seeing the results of future studies in which IM-MS is applied to the analysis of lipids from a wide range of cells and tissues under varied conditions of health and disease.

 

 

 

View Nature Communications article: Ion mobility conformational lipid atlas for high confidence lipidomics

 

 

 

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