Lipidomics Offers Insights Into Human Health and Disease

Lipids are complex, water-insoluble organic biomolecules that are essential for life, playing key roles in a variety of cellular processes.¹ In the field of lipidomics, scientists use advanced analytical chemistry techniques to untangle the relationships between lipids and human health, from developing diagnostic tools to examining the effects of food choices on our wellbeing. This article delves into the details of lipidomics, from analysis to applications.

What Is Lipidomics?

Lipidomics is the study of the lipidome, which is the complete set of cellular lipids present within a biological sample.² Lipidomics is also a category within the broader field of metabolomics.³

The lipidome is incredibly dynamic, responding to constant changes in environmental and physiological conditions, such as age, genetics, diet, and disease.⁴ Scientists have identified around 50,000 different molecular species of lipids, categorized into several major classes including phospholipids, triglycerides, fatty acids, sterols, and waxes.² In addition to making up the phospholipid bilayer of cell membranes, lipids are also involved in cellular signaling and energy storage, among other functions.¹

The complex and dynamic nature of the lipidome makes it difficult to study; the field of lipidomics emerged in 2003, yet only in the last decade have lipid profiling methods significantly improved.³ Lipidomics researchers now use advanced analytical chemistry techniques to identify, characterize, and quantify the vast range of molecular species that make up the lipidome and study their effects on human health and disease.⁴

Lipidomics Analysis

Scientists can perform lipidomics analyses on a wide array of biological samples, such as tissue samples or bodily fluids, plant materials, or food products.² There are several key steps in a lipidomics workflow.

Sample collection and preparation

After sample collection, researchers homogenize the sample and extract the lipids using either liquid-liquid extraction (LLE) or solid-phase extraction (SPE) methods.⁵

LLE

LLE involves partitioning lipids into an organic phase using a mixture of solvents such as chloroform, water, and methanol.⁵ Scientists extract lipids in the chloroform phase of the mixture, leaving other biomolecules in the water/methanol phase.⁵ This approach is generally used for global lipidomics profiling, otherwise known as untargeted lipidomics or shotgun lipidomics, in which scientists investigate the entire range of lipids and identify unknown species.⁵

SPE

For targeted lipidomics analysis, researchers use SPE, which involves a solid phase material in a cartridge or column that separates a specific class (or classes) of lipids from the rest of the sample.⁵ When scientists apply a sample to an SPE column, the solid-phase material absorbs the target group of lipids, while other sample components pass through.⁶ Researchers then collect the target lipids by washing the column with a lipid-soluble solvent, such as a chloroform and methanol mixture.⁶

Data acquisition

Scientists typically use mass spectrometry (MS) technology to collect data from their lipidomics samples. MS measures the mass-to-charge ratio of ions, allowing researchers to identify molecules.² For lipidomics investigations, scientists must first apply a charge to the lipids, usually via electrospray ionization.² MS can also be coupled with gas chromatography (GC-MS), liquid chromatography (LC-MS), or other methods, depending on the sample type.³ Nuclear magnetic resonance (NMR) spectroscopy can also be used for lipidomics.

For example, scientists can use MS-based approaches when they need to identify and quantify lipids at very low concentrations in a sample, and NMR when they want to determine the molecular structures of certain lipids.³ In spatial lipidomics, researchers determine the spatial heterogeneity of the lipid profile in frozen tissue sections using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS).⁷

While there is no single comprehensive method that can analyze the entire lipidome, the range of instruments and techniques currently available allows for more robust sample interrogation than was previously possible.²

Data processing and interpretation

Because raw lipidomics datasets contain a large number of artifacts, scientists need to clean up data considerably with informatics filters that minimize the false discovery rate during further analysis.⁸ Researchers then use commercial software, bioinformatics pipelines, and publicly available lipidomics databases to create a profile of all the lipids present in the sample.² Lipidomics analysis software, which is generally included with data collection instruments, can be used to identify and quantify lipid species.¹ Researchers often use bioinformatics tools to perform statistical and pathway analyses, and they increasingly employ machine learning models to analyze large and complex lipidomics datasets and translate their findings into clinical practice.^1,9

Lipidomics Applications

Scientists use lipidomics to understand human health and disease, examine dietary health effects, identify novel biomarkers, and study tumorigenesis. For example, cellular lipid metabolism is significantly elevated at various stages of tumor growth.⁹ A 2019 study of human plasma samples revealed lipid dysregulation signatures that were specific to different tumor types.¹⁰ Researchers are also using lipidomics to identify biomarkers that can detect cancer in early stages.⁹

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Additionally, scientists apply lipidomics to study metabolic diseases, such as type 2 diabetes and metabolic syndrome (MetS), the latter of which is becoming increasingly common and is linked to many comorbidities.⁹ In 2022, a study identified major alterations in the lipidomic profile of fecal samples collected from individuals with MetS compared to healthy individuals, laying the foundation for a non-invasive screening method for the condition.¹¹

Lipidomics has also helped scientists identify several lipid-related risk factors for Parkinson’s disease (PD), and it is now well-established that PD patients have a significantly altered serum lipid profile.¹² A recent study used machine learning to perform an untargeted lipidomics analysis of PD serum samples, discovering that the lipid profile was a better prediction tool for clinical outcomes in PD patients than age, sex, or genetic mutations.¹²