What Is Lipidomics?

Define of Lipidomics

Lipids are essential metabolites that have many crucial cellular functions and can provide a direct readout of cellular metabolic status. The total lipid content in a cell is called a lipidome. Lipidomics is the study of lipidomes using the principles and techniques of analytical chemistry, and emerged in 2003 as an approach to study the metabolism of the cellular lipidome.The analytical power of, and new developments in, mass spectrometry (MS) have accelerated this emerging discipline.

Lipidomics enables us to study cellular metabolism by quantifying the changes of individual lipid classes, subclasses and molecular species that reflect metabolic differences. As the pathways and networks of lipid metabolism have been extensively studied, any changes in lipid amounts can simultaneously reveal variations in several enzymatic levels, activities and/or gene expression patterns.

There are two main principal approaches in lipidomics, the targeted and the untargeted approach. The targeted approach focuses on a limited number of defined lipids in order to accurately determine their relative abundances. In contrast, the un-targeted approach screens lipid species within a sample without preselection, resulting in high number of mass signals.

Fig1. Lipidomics for studying metabolismFig1. Lipidomics for studying metabolism (Han, X, 2016)

Lipid Classes and Structures

Lipids are a diverse group of biomolecules that play crucial roles in various physiological processes, including energy storage, membrane structure, and cell signaling. They can be broadly classified into several classes based on their chemical structure and function. The main lipid classes include fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterols, and prenols.

Fatty Acids: Fatty acids are long-chain hydrocarbons with a carboxylic acid group at one end. They can be saturated or unsaturated depending on the presence of double bonds in their carbon chains. Saturated fatty acids like palmitic acid have no double bonds, while unsaturated fatty acids like oleic acid have one or more double bonds.

Glycerolipids: Glycerolipids consist of a glycerol backbone esterified with one, two, or three fatty acid molecules. The most common form is triglycerides, which serve as a primary storage form of energy in adipose tissue. Monoacylglycerols and diacylglycerols are also important intermediates in lipid metabolism.

Glycerophospholipids: Glycerophospholipids are a major component of cell membranes, consisting of a glycerol backbone, two fatty acid chains, a phosphate group, and a polar head group. Phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine are examples of glycerophospholipids with diverse roles in membrane structure and function.

Sphingolipids: Sphingolipids contain a sphingosine or sphingoid base backbone instead of glycerol. They are abundant in neural tissues and play essential roles in cell recognition, signaling, and membrane integrity. Examples include ceramides, sphingomyelins, and glycosphingolipids like cerebrosides and gangliosides.

Sterols: Sterols are a subgroup of steroids with a hydroxyl group at the C-3 position. Cholesterol is the most well-known sterol, found in animal cell membranes and serving as a precursor for steroid hormones and bile acids. Sterols contribute to membrane fluidity and stability.

Prenols: Prenols, also known as terpenoids, are derived from isoprene units and have diverse functions, including serving as precursors for hormones, vitamins, and pigments. Coenzyme Q10 and vitamin E are examples of prenols with antioxidant properties.

Lipid Metabolism

Lipid metabolism encompasses the complex biochemical processes involved in the synthesis, storage, breakdown, and utilization of lipids within the body. Lipids, including triglycerides, phospholipids, and cholesterol, serve essential roles in cellular structure, energy storage, insulation, and signaling. The regulation of lipid metabolism is tightly controlled to maintain homeostasis and meet the dynamic metabolic demands of the organism.

At the core of lipid metabolism is the balance between lipogenesis, the synthesis of lipids, and lipolysis, the breakdown of lipids. Lipogenesis primarily occurs in the liver, adipose tissue, and lactating mammary glands, where excess carbohydrates and proteins are converted into fatty acids and then esterified into triglycerides for storage. Lipolysis, on the other hand, releases fatty acids from stored triglycerides, providing a crucial energy source during fasting or periods of increased energy demand.

Cholesterol metabolism is another vital aspect of lipid metabolism, with cholesterol serving as a precursor for steroid hormones, bile acids, and cellular membranes. The liver plays a central role in cholesterol homeostasis by synthesizing cholesterol and regulating its uptake, storage, and excretion. Disruption of cholesterol metabolism can lead to dyslipidemia, a major risk factor for cardiovascular diseases such as atherosclerosis.

Lipid metabolism is intricately linked with other metabolic pathways, including carbohydrate and protein metabolism, through shared intermediates and regulatory mechanisms. Hormones such as insulin, glucagon, and leptin exert significant control over lipid metabolism, modulating enzyme activity, gene expression, and substrate availability in response to nutritional status and energy requirements.

Integration of lipidomics analysis with studies of lipid metabolism offers a holistic view of lipid homeostasis and its functional implications. This interdisciplinary approach allows researchers to identify biomarkers of disease, elucidate lipid-mediated signaling pathways, and uncover novel therapeutic targets for metabolic disorders, cardiovascular diseases, cancer, and neurodegenerative conditions.

How Does Lipidomics Work?

A typical workflow for lipidomic analysis of biological samples includes sample preparation, mass spectrometry-based analysis (i.e., MS data acquisition), and data processing.

Fig2. A typical workflow of lipidomic analysis of biological samplesFig2. A typical workflow of lipidomic analysis of biological samples (Züllig, T.; et al, 2020)

Sample processing

The first and most vital step in sample processing is the sampling itself. If samples are not immediately processed or flash frozen, many enzymatic and chemical processes will continue, eventually metabolizing the lipids. Therefore, it is highly advisable to process samples as quickly as possible, or, if this is not possible, to store them at least at −80 °C. Since lipids are generally prone to oxidization and hydrolysis, it is advisable to restrict the storage of samples to a period that is as short as possible, even at −80 °C.

  • Sample homogenization

This step becomes very important when working on tissue samples or cells. Homogenization ensures that lipids from all parts of a piece of tissue are equally accessible to extraction solvents.

  • Liquid–liquid extraction

The sample preparation technique most widely used in lipidomics is liquid–liquid extraction. The Folch protocol and the Bligh and Dyer protocol both rely on a ternary mixture of chloroform, methanol, and water.

  • Solid-phase extraction

Solid-phase extraction (SPE) is a very specific sample preparation technique used in lipidomics that yields highly enriched samples with little contamination. It can be a highly valuable method if only few precious samples need to be analyzed with a very high coverage of lipid species.

  • Derivatization

Derivatization serves four purposes in lipidomics: it can be used to (1) increase ionization efficiency, (2) introduce a selective fragment that can even be used in precursor ion or neutral loss scans, (3) mask functional groups (e.g., phosphates) that may stick to steel surfaces when they are transferred to the mass spectrometer, and (4) introduce an isotopic label for differential quantitation.

Mass spectrometry-based analysis

Shot-gun lipidomics directly introduces lipids and detects the lipids by MS, with separation of lipids achieved solely on the basis of mass-to-charge ratio. Creative Proteomics uses a new shotgun lipidomics platform was established by combining an Orbitrap Fusion mass spectrometer with a TriVersa Nanomate nanoelectrospray ion source.

Although shot-gun lipidomics can answer many of the needs associated with sample lipid profiling, some workflows do require chromatographic separation before MS analysis. In these cases, Creative Proteomics would choose a column that is suitable for the separation of your lipids of interest and use ultrahigh-performance liquid/gas chromatography equipped with different mass spectrometry systems to analyze the lipidomics.

MALDI-imaging mass spectrometry (MALDI-IMS) is a two-dimensional MALDI-MS technique used to visualize the spatial distribution of biomolecules without extraction, purification, separation, or labeling of biological samples. This technique can reveal the distribution of hundreds of ion signals in a single measurement and also helps in understanding the cellular profile of the biological system. MALDI-IMS has already revealed the characteristic distribution of several kinds of lipids in various tissues. The versatility of MALDI-IMS has opened a new frontier in several fields, especially in lipidomics.

Data processing

Mass-spectrometric lipidomic approaches generate large amounts of mass spectrometric data, which have to be analyzed by biostatistical methods. Guidelines for lipidomics studies were established and validated to standardize the corresponding lipidomic data, aiming at comparability of the results.

Applications of Lipidomics

Lipid profiling in disease research

Lipid profiling, a fundamental aspect of lipidomics, has emerged as a powerful tool in disease research, offering comprehensive insights into the intricate role of lipids in various pathological conditions. Through meticulous analysis of lipid composition and abundance in biological samples, researchers can unravel the complex interplay between lipids and disease pathogenesis.

At the heart of lipid profiling lies the ability to discern subtle changes in lipid species and their distribution across different cellular compartments and biological matrices. By comparing lipid profiles between healthy and diseased states, researchers can identify characteristic lipid signatures associated with specific diseases, ranging from cancer and cardiovascular disorders to neurodegenerative conditions and metabolic syndromes.

This detailed characterization of lipid alterations provides valuable clues about underlying disease mechanisms and potential therapeutic targets. For instance, lipidomic studies have elucidated the dysregulation of lipid metabolism pathways, such as increased de novo lipogenesis or altered lipid signaling, in various disease contexts. Furthermore, lipid profiling enables the identification of lipid biomarkers—specific lipid species or lipid ratios—that can serve as diagnostic indicators, prognostic markers, or predictors of treatment response.

Lipid biomarkers for disease diagnosis and prognosis

Lipid biomarkers have emerged as pivotal elements in disease diagnosis and prognosis, providing invaluable insights into underlying pathophysiological mechanisms. Derived from meticulous lipidomic analyses, they serve as discerning indicators of disease states, enabling clinicians to detect diseases at their incipient stages and tailor therapeutic strategies accordingly.

Central to their utility is their ability to reflect dynamic perturbations within lipid metabolism, indicative of pathological processes. These alterations are often rooted in dysregulated lipid biosynthesis pathways, oxidative stress-induced lipid modifications, or aberrant lipid signaling cascades, contributing uniquely to disease etiology.

In the diagnostic realm, lipid biomarkers offer unparalleled advantages, serving as minimally invasive or non-invasive tools for early disease detection. Lipidomic profiling of plasma or serum constituents has unveiled disease-specific lipid signatures across diverse maladies, empowering routine clinical assessments with enhanced diagnostic precision.

Lipid biomarkers assume critical roles in prognostic endeavors, furnishing clinicians with actionable insights into disease trajectory and patient outcomes. By tracking longitudinal changes in lipid profiles or discerning lipidomic responses to therapeutic interventions, clinicians gain the ability to tailor treatment strategies to individual patient needs.

Beyond their diagnostic and prognostic prowess, lipid biomarkers constitute cornerstones of personalized medicine paradigms, holding promise in optimizing treatment selection, dose titration, and therapeutic efficacy while minimizing treatment-associated adversities.

Lipid metabolism and its role in metabolic disorders

Lipid metabolism intricately orchestrates the etiology and progression of metabolic disorders, comprising a spectrum of conditions such as obesity, type 2 diabetes, dyslipidemia, and cardiovascular diseases. The dysregulation within lipid metabolic pathways disrupts the delicate equilibrium governing lipid uptake, synthesis, storage, and utilization, precipitating pathological deviations in lipid homeostasis that underpin metabolic dysfunction.

Central to lipid metabolism is the assimilation and sequestration of dietary lipids within adipose tissue. Upon chronic caloric excess and sedentarism, adipocyte hypertrophy and hyperplasia ensue, outstripping the adipose tissue's storage capacity. This adipocyte dysfunction fosters a pro-inflammatory milieu and ectopic lipid deposition within non-adipose tissues, notably the liver, muscle, and pancreas.

Perturbations in lipid biosynthesis pathways, notably de novo lipogenesis and cholesterol synthesis, exacerbate lipid accumulation and metabolic derangement. Heightened de novo lipogenesis, potentiated by dietary carbohydrates and insulin, engenders augmented fatty acid and triglyceride synthesis, fostering hepatic steatosis and insulin resistance. Similarly, dysregulated cholesterol biosynthesis and hampered cholesterol efflux pathways promote atherogenic lipid profiles, predisposing individuals to cardiovascular morbidities.

Disruptions in lipid metabolism precipitate perturbations in lipid signaling cascades governing glucose homeostasis, insulin sensitivity, and inflammation. Lipid-derived metabolites, such as ceramides, diacylglycerols, and fatty acyl-CoAs, impede insulin signaling pathways, incite pro-inflammatory cascades, and evoke cellular stress responses, exacerbating insulin resistance and impairing pancreatic β-cell function.

In addition to their role as signaling molecules, lipids serve as vital substrates for mitochondrial β-oxidation and oxidative phosphorylation. Compromised mitochondrial function and oxidative metabolism in metabolic disorders culminate in substrate overflow, accumulation of cytotoxic lipid intermediates, and mitochondrial dysfunction, thereby exacerbating metabolic dysregulation and insulin resistance.

Lipidomics in drug discovery and development

In drug discovery, lipidomics can help identify potential new drug targets. By analyzing the lipidome of a disease condition, researchers can pinpoint abnormal lipid metabolism that may be at the root of the disease. These abnormal processes can then be targeted for drug intervention. Additionally, the lipidome can provide insight into how a potential drug might interact with the body and what side effects it may cause.

In drug development, lipidomics plays a crucial role in biomarker discovery and validation. Biomarkers are indicators of the normal or abnormal functioning of the body and can be used to measure the progress of a disease or the effects of a drug. Understanding the complete lipid profile of a biological system can help identify potential biomarkers for specific diseases.

An important aspect of lipidomics in drug discovery and development is personalized or precision medicine. Given that lipid profiles can vary significantly among individuals, assessing the lipidome of a person can provide valuable insights into the individual's disease risk and potential response to a specific therapy. This personalized approach could significantly enhance the success rate of drug therapy and reduce potential side effects.

Moreover, lipidomics can help understand the underlying mechanisms of drug resistance, especially in diseases like cancer where drug resistance often develops. By investigating the changes in lipid metabolism in drug-resistant cells, new strategies to overcome drug resistance can be developed.

Despite these benefits, there are still challenges in implementing lipidomics in drug discovery and development. Analyzing thousands of lipids in a sample is a complex task, requiring advanced technologies and computational tools for lipid identification, quantification, and data interpretation.

Hotspots of Lipidomics

  • Structural characterization of known lipid classes and subclasses as well as the identification of novel lipid classes and molecular species;
  • Development of quantitative methods for analysis of high attomole to femtomole levels of lipids in cells, tissues, or biological fluids;
  • Network analysis that clarifies metabolic adaptation in health and disease and biomarker analysis that facilitates diagnosis of disease states and determination of treatment efficacy;
  • Tissue mapping of altered lipid distribution present in complex organs;
  • Bioinformatics approaches for the automated high-throughput analysis of data, including identification of lipid species through database searches, automated quantification of individual molecular species, and molecular modeling with lipidomics data.

What is The Difference Between Lipidomics and Metabolomics?

FocusComprehensive analysis of lipids: identification, quantification, characterization, and dynamics.Study of small molecules (metabolites) and their pathways.
Target MoleculesLipids: fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, etc.Metabolites: sugars, amino acids, nucleotides, organic acids, etc.
TechniquesMass spectrometry, chromatography, NMR spectroscopy, lipid imaging.Mass spectrometry, chromatography, NMR spectroscopy.
Biological RoleEmphasizes lipid function, metabolism, and lipid-related diseases.Broadly examines cellular metabolism, disease states, and biomarker discovery.
ApplicationsInvestigating lipid-related diseases, drug development, personalized medicine.Studying metabolic changes in diseases, clinical diagnostics, precision medicine.
ScopeLipid composition, dynamics, and metabolic pathways.Overall metabolic profiles, cellular metabolism, and pathways.
ExamplesPhospholipids, sphingolipids, triglycerides, cholesterol.Glucose, amino acids, organic acids, nucleotides.


  1. Han, X. Lipidomics for studying metabolism. Nat Rev Endocrinol. 2016, 12, 668–679.
  2. Züllig, T.; et al. Lipidomics from sample preparation to data analysis: a primer. Anal Bioanal Chem. 2020, 412, 2191–2209.
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