More than Cell Membranes: Detecting Lipids with Mass Spec

The field of lipid research, or lipidomics, has evolved significantly over the past 30 years. Detailed investigations into the structure and function of lipids, including their interactions with other lipids as well as proteins and metabolites, have revealed their involvement in a wealth of different cell functions and their exciting potential as novel disease biomarkers.

Mass spectrometry (MS) is one of the main techniques used to detect and measure this important group of biological macromolecules within different cellular “lipidomes.” This article will review the role of lipids in health and disease, the challenges of measuring lipids, as well as essential technologies and techniques being used in this fast-growing field.

A role for lipids in health and disease

“Lipidomics is playing a key role in revealing phenotypes and elucidating molecular processes involved in disease progression, as well as identifying lipid biomarkers that can be used in early detection of disease in the clinic,” says Sven Hackbusch, Senior Lab Applications Scientist, Chromatography and Mass Spectrometry at Thermo Fisher Scientific.

“Cellular and sub-cellular organelles are studied frequently to investigate membrane fluidity, budding, trafficking, and signaling in order to study disease states, genetic disorders, and other roles,” Mackenzie Pearson, Staff Apps Scientist at SCIEX, adds. These circulating lipids have been linked to many diseases, such as heart disease, diabetes, and inflammation, and are a very important part of lipidomics research.

“The fact that every tissue type has a unique lipid profile is also an important consideration for understanding cellular metabolism and the pathophysiology of human disease,” she says. These biomarkers, detected in the blood and cerebrospinal fluid, are sometimes called “free circulating lipids,” although most of these lipids are not actually “free” but are associated with proteins such as albumin, or with lipid LDL or HDL particles.

Pearson additionally highlights that lipid-based markers such as oxidized lipids (like isoprostanes) can be used as an indicator of overall oxidative stress, and that total triglycerides and cholesteryl esters can be predictive of cardiovascular risk although the field is continually searching for better lipid benchmarks for different disease states. Specialized pro-resolving mediators (SPMs), or oxidized omega-3 fatty acids, are also a key target, having been shown to resolve inflammation when added exogenously to cell culture models. One interesting application of lipidomics for disease prediction is a study from researchers at Georgetown University, who reported on a blood test that can predict the development of mild cognitive impairment (MCI) or Alzheimer’s disease (AD) in a currently healthy person within three years with 90% accuracy.¹

Jeff McDonald, Professor at the Center for Human Nutrition, UT Southwestern Medical Center, comments on one key discovery by the Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium.² In its study, the group detected elevated 25-hydroxycholesterol levels in an immortalized mouse macrophage when challenged with lipopolysaccharide (LPS). “This discovery has led to major advances in understanding the role of this molecule in modulating immunity,” he highlights. Another study concerning COVID-19 demonstrates the potential for lipidomics to provide useful, relevant biomarkers.³ MS-based lipidomics characterization was used to monitor cell lipid changes when infected with SARS-CoV-2. The researchers reported that some lipid classes were significantly elevated in HCoV-299E infected cells compared to non-infected cells.

Deciphering the lipidome with MS

“A significant amount of expertise is currently required on the scientist’s part to generate biological insight from their samples,” says Hackbusch. Although characterizing the lipid space with MS can have its challenges, it can be a lot more straightforward than techniques of the past.

“Gas chromatography- MS (GC-MS) was and still is a common technique to look at fatty acid changes in the lipidome,” Pearson explains, although she also states that it does not allow for the complete structural analysis of complex lipids. Lipid measurements are analytically very challenging to perform, agrees McDonald, who is also a member of the Lipidomics Standards Initiative, which aims to develop guidelines for this area of research. He adds that “the fact that there is so much disharmony in the lipidomic field and a lack of operational standards and guidelines is a critical issue we face today. The lack of rapid, reliable methods for the purpose of precision medicine is therefore a definite roadblock.” And when there can be up to a hundred species of lipid in a single sample, being able to get an accurate, sensitive, and reproducible read is essential.

Lipidomic analysis is almost exclusively performed based on MS. This includes low-resolution triple quadrupole MS for targeted quantification as well as high-resolution MS (HRMS) for structural analysis and biomarker discovery. Giorgis Isaac, Consulting Biomedical Research Scientist in Scientific Operations at Waters Corporation, emphasizes the value of MS: “MS can measure a wide array of lipid species in biological samples without the need for derivatization compared to thin layer chromatography (TLC) or GC-MS. Separation techniques at the front end and data processing (informatics) at the back end are also very helpful. Advances in LC separation techniques, such as ultrahigh-performance liquid chromatography and ultrahigh-performance supercritical fluid chromatography coupled with MS, can also shorten analysis times and improve peak capacity and resolution to increase lipid coverage.”

The rise in the utilization of ultra-high resolution accurate MS instrumentation has enabled researchers to gain more detailed insight into the lipidome, adds Hackbusch. Previously, correlations between phenotype and the lipidome could often only be drawn at the level of sum composition or lipid class. Now, molecular species can be determined with confidence in untargeted lipidomics experiments by combining information from full-spectrum analysis and fragmentation data.

Future lipidomics challenges and trends

Hackbusch explains two key challenges in lipidomics: diversity in lipid structures and their physical chemical properties, as well as the presence of isobaric and isomeric lipid species. “Solving these issues requires a) high resolving power both for the LC separation and MS analysis, and b) the need for high-quality fragmentation spectra for molecular species level identification.”

“Lipid complexity both in terms of number and diversity might lead to misidentification. Confidence in lipid identification can be improved with custom databases containing values from ion mobility MS measurements such as experimental retention time, fragment ion information, and collision cross-section,” says Isaac. Future trends, he explains, include the implementation of ion mobility and different approaches for detailed lipid structure characterization, as well as increasing the specificity of lipid identification in complex biological samples.

It seems that lipids still have a lot to teach us. Fortunately, the technology required for this challenging task is advancing quicker than ever before and is showing promising signs of having a significant future impact in the clinic, potentially contributing to improved human health.

“As the field makes advances to speed up confident lipid identification, with wider availability of standards and software improvements starting at data acquisition with real-time instrument decision-making, lipidomics will become more broadly accessible as a tool to clinical researchers,” concludes Hackbusch.

References

1. Mapstone, M., et al., 2014. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20(4): 415–418. 

2. Dennis, E.A., et al., 2010. A mouse macrophage lipidome. J Biol Chem 285(51):39976-85. 

3. Yan, B., et al., 2019. Characterization of the Lipidomic Profile of Human Coronavirus-Infected Cells: Implications for Lipid Metabolism Remodeling upon Coronavirus Replication. Viruses 6;11(1):73.