Analytical Methods And Applications Of Lipidomics In Disease Research

Abstract

With thousands of lipid species, lipids are essential metabolites with many important functions that are closely related to homeostatic processes and disease states in living organisms. Lipidomics is an emerging field that focuses on the lipid composition of biological systems and its relationship to phenotype and function. Various analytical platforms have been developed for lipidomic analysis, with the most commonly used analytical technique being mass spectrometry (MS), which allows for the comprehensive profiling of a wide range of lipids for the discovery of lipid biomarkers for disease diagnosis, prognosis, and pathogenesis. Based on this, this review provides an overview of the main analytical strategies of lipidomics and discusses its applications in different diseases.

Introduction of lipidomics

Lipids are important components of biological systems and have diverse roles in biological processes, not only maintaining membrane integrity, fluidity, and compartmentalization, but also participating in cellular signaling, energy storage, and metabolism [1]. Abnormalities in lipid metabolism have been found to be closely associated with the development and progression of human diseases, including obesity, diabetes, neurodegenerative disorders, and cancers [2-5]. Therefore, comprehensive profiling of lipid species and changes has become essential for understanding lipid metabolism in physiological and pathological conditions.

Lipidomics is a rapidly evolving field that aims to systematically characterize lipid structure, mass levels, cell functions, and interactions, and is widely used to study the dynamic changes of lipids in status of health or disease [6, 7]. The lipidome covers diverse classes of hydrophobic and amphiphilic molecules, including glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, fatty acids, prenol lipids, saccharolipids, and polyketides [8, 9]. The chemical diversity and wide concentration range of lipid molecules present analytical challenges. To address these challenges, various lipidomic analytical platforms have been developed, among which, mass spectrometry (MS) offers high sensitivity and throughput for the simultaneous detection of hundreds of lipids, and various lipid classes can be accurate identified and quantified on the basis of their mass-to-charge ratios and fragmentation patterns [10, 11]. Currently, due to its significant technical advantages, MS has become the most widely used detection technique in lipidomic research.

Notably, lipidomics has become an essential technology in biomedical research in recent years and has been widely applied to elucidate lipid dysregulation in various diseases of the body systems [12]. According to the published literature, lipidomics has identified potential lipid biomarkers for early diagnosis and prognosis in diseases of various systems, such as free fatty acid in human lung tissue for pulmonary hypertension, which may provide therapeutic strategies targeting lipid metabolism [13]. In addition, lipidomics has also been successfully applied in the investigation of lipid-related signaling pathways to reveal the mechanism of drug action and the pathology of diseases [14, 15].

Overall, a key advantage of lipidomics is the ability to measure hundreds of lipid molecules simultaneously from minimal sample amounts compared to traditional lipid analysis methods. The high coverage of lipid species and increasing standardization of lipid quantification have made lipidomics a powerful tool for basic and clinical research. Going forward, leveraging lipidomics with other omics data will provide deeper insights into lipid metabolic networks underlying human health and disease.

Lipidomic technologies

Lipidomics can be performed using a variety of techniques. Nowadays, with the development of lipidomics, more and more technologies are being used to identify lipids in different biological samples. For example, nuclear magnetic resonance, gas-chromatography tandem mass spectrometry (GC-MS), liquid-chromatography tandem mass spectrometry (LC-MS), and ion-mobility spectrometry are all commonly used analytical platforms for lipidomic research [16]. Among them, MS-based lipidomics have emerged as a powerful tool for lipid analysis due to its high sensitivity, specificity, and throughput [17]. The development of MS-based lipidomics can be traced back to the early 1990s, when the first MS-based lipid analysis was reported [18, 19]. Since then, a variety of MS-based lipidomic approaches have been developed and widely used in lipidomic research to identify and quantify various lipid classes [20-22]. MS can provide detailed information about lipid structure and composition by identifying and quantifying individual lipid species based on their mass-to-charge ratios and fragmentation patterns.

The workflow of MS-based lipidomics is shown in Fig. 1. MS-based lipidomics helps to explore specific lipid species that may be associated with diseases or biological processes, and can also be employed to investigate lipid-associated signaling pathways and to identify lipid biomarkers for various diseases. In recent years, MS has greatly facilitated lipidomic research and provided new insights into the role of lipids in health and disease. We believe that with the development of new MS technologies, lipid analysis will become more comprehensive and accurate.

Figure 1. A typical flow chart of MS-based lipidomic analysis. MS-based lipidomics typically involves sample preparation, lipid separation and MS analysis, and data processing. Subsequently, bioinformatic analysis is further utilized to explore the molecular mechanisms involved in physiological or pathological phenotypes.

Lipidomics in diseases

In recent years, lipidomics has been recognized as a useful tool to discover biomarkers for disease prediction, diagnosis, and prevention. Noteworthy, among all human tissues, the brain is very rich in lipids, accounting for about 50% of its dry weight [23]. Brain lipids are mainly composed of cholesterol, phospholipids, and sphingolipids, which play an essential role in maintaining the structure and physiological function of the brain. Thus, the application of lipidomics in brain science has attracted increasing attention. Currently, numerous published studies have reported lipidomic studies targeting central nervous system disorders such as stroke, Alzheimer's disease (AD), brain tumors, and migraine headaches [24]. For example, lipidomic analysis revealed that long-chain acylcarnitines were significantly altered in the ischemic penumbra and peripheral blood of transient middle cerebral artery occlusion mice and acute ischemic stroke patients, and that they accumulated and damaged neurons by inducing mitochondrial dysfunction in astrocytes [25]. In addition, as a disease of great concern, research on the pathogenic mechanisms and therapeutic targets of AD has been the focus of the neuroscience field. Lipidomic studies have determined that the levels of various lipids, such as fatty acids, continue to change in AD brains, implying that lipid metabolism disorders may be involved in the pathological process of AD, which could contribute to the identification of new biomarkers for AD diagnosis and prognosis [26].

Additionally, several studies have utilized lipidomics to uncover the relationship between lipid metabolism with metabolic disorders such as obesity, diabetes, and atherosclerosis [27-29]. For example, Wang et al. provided evidence that lysophosphatidylcholines could be used to distinguish obese adolescents from normal-weight people [27]. Other literature reported that changes in multiple lipid species such as lysophosphatidylcholines were associated with type 2 diabetes mellitus and carotid atherosclerosis [29, 30]. Besides, in cancer research, lipidomic analysis has identified potential lipid biomarkers for early detection and monitoring of treatment response [31, 32]. Overall, the above studies highlight that the characterization of lipid alterations through lipidomics can help to identify promising targets and thus discovery new therapeutic strategies.

Conclusion

Lipidomics has emerged as an important technique in biomedical research for the discovery of lipid markers of disease and the understanding of lipid biochemical mechanisms. Rapid advances in lipid analysis tools and computational methods have enabled comprehensive analysis of a wide range of lipids across large sample cohorts. Furthermore, lipidomics, in combination with other omics data, can provide a systems-level understanding of lipid networks. Future applications of lipidomics will expand lipid coverage and improve quantitative and translational studies, and are expected to advance precision diagnostics and therapies targeting lipid metabolism.

References

1. Sethi, S. and E. Brietzke, Recent advances in lipidomics: Analytical and clinical perspectives. Prostaglandins & other lipid mediators, 2017: p. 8-16.
2. Mir, S., et al., Population-based plasma lipidomics reveals developmental changes in metabolism and signatures of obesity risk: a mother-offspring cohort study. BMC medicine, 2022. 20(1): p. 242.
3. Cuperlovic-Culf, M. and A. Badhwar, Recent advances from metabolomics and lipidomics application in alzheimer's disease inspiring drug discovery. Expert opinion on drug discovery, 2020. 15(3): p. 319-331.
4. Wang, G., et al., Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Science translational medicine, 2022. 14(630): p. eabk2756.
5. Butler, L., et al., Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Advanced drug delivery reviews, 2020. 159: p. 245-293.
6. Han, X. and R. Gross, The foundations and development of lipidomics. Journal of lipid research, 2022. 63(2): p. 100164.
7. Wang, M., et al., Novel advances in shotgun lipidomics for biology and medicine. Progress in lipid research, 2016. 61: p. 83-108.
8. Murphy, S. and A. Nicolaou, Lipidomics applications in health, disease and nutrition research. Molecular nutrition & food research, 2013. 57(8): p. 1336-46.
9. Tabassum, R. and S. Ripatti, Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases. Cellular and molecular life sciences : CMLS, 2021. 78(6): p. 2565-2584.
10. Heiles, S., Advanced tandem mass spectrometry in metabolomics and lipidomics-methods and applications. Analytical and bioanalytical chemistry, 2021. 413(24): p. 5927-5948.
11. Lee, H. and T. Yokomizo, Applications of mass spectrometry-based targeted and non-targeted lipidomics. Biochemical and biophysical research communications, 2018. 504(3): p. 576-581.
12. B, A., et al., Lipidomics: An excellent tool for chronic disease detection. Current research in translational medicine, 2022. 70(4): p. 103346.
13. Bordag, N., et al., Lipidomics for diagnosis and prognosis of pulmonary hypertension. medRxiv : the preprint server for health sciences, 2023.
14. Zhang, Z.H., et al., An integrated lipidomics and metabolomics reveal nephroprotective effect and biochemical mechanism of Rheum officinale in chronic renal failure. Nature Publishing Group, 2016(1).
15. Sensen, et al., A plasma lipidomics strategy reveals perturbed lipid metabolic pathways and potential lipid biomarkers of human colorectal cancer. Journal of Chromatography B, 2017.
16. Baek, J., et al., Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nature reviews. Nephrology, 2022. 18(1): p. 38-55.
17. Salihovic, S., et al., Recent advances towards mass spectrometry-based clinical lipidomics. Current opinion in chemical biology, 2023. 76: p. 102370.
18. Wenk, M., Lipidomics: new tools and applications. Cell, 2010. 143(6): p. 888-95.
19. Han, X. and R. Gross, Electrospray ionization mass spectroscopic analysis of human erythrocyte plasma membrane phospholipids. Proceedings of the National Academy of Sciences of the United States of America, 1994. 91(22): p. 10635-9.
20. Han, X. and R. Gross, Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass spectrometry reviews, 2005. 24(3): p. 367-412.
21. Borisov, R., M. Matveeva, and V. Zaikin, Reactive Matrices for Analytical Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry. Critical reviews in analytical chemistry, 2023. 53(5): p. 1027-1043.
22. Lee, S., et al., Targeted lipidomics using electron capture atmospheric pressure chemical ionization mass spectrometry. Rapid communications in mass spectrometry : RCM, 2003. 17(19): p. 2168-76.
23. Yoon, J., et al., Brain lipidomics: From functional landscape to clinical significance. Science advances, 2022. 8(37): p. eadc9317.
24. Zhao, T., et al., Identification of Serum Biomarkers of Ischemic Stroke in a Hypertensive Population Based on Metabolomics and Lipidomics. Neuroscience, 2023. 533: p. 22-35.
25. Huang, X., et al., Lipidomic analysis identifies long-chain acylcarnitine as a target for ischemic stroke. Journal of advanced research, 2023.
26. Yin, F., Lipid metabolism and Alzheimer's disease: clinical evidence, mechanistic link and therapeutic promise. The FEBS journal, 2023. 290(6): p. 1420-1453.
27. Wang, Y., et al., Lipidomic Profile Revealed the Association of Plasma Lysophosphatidylcholines with Adolescent Obesity. BioMed research international, 2019. 2019: p. 1382418.
28. Wang, Y., et al., BMI and lipidomic biomarkers with risk of gestational diabetes in pregnant women. Obesity (Silver Spring, Md.), 2022. 30(10): p. 2044-2054.
29. Miao, G., et al., Longitudinal lipidomic signature of carotid atherosclerosis in American Indians: Findings from the Strong Heart Family Study. Atherosclerosis, 2023. 382: p. 117265.
30. Liu, J., et al., LC-MS-Based Lipidomic Analysis of Serum Samples from Patients with Type 2 Diabetes Mellitus (T2DM). Disease markers, 2022. 2022: p. 5559470.
31. Wolrab, D., et al., Lipidomic profiling of human serum enables detection of pancreatic cancer. Nature communications, 2022. 13(1): p. 124.
32. Agarwala, P., R. Aneja, and S. Kapoor, Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses. Medicinal research reviews, 2022. 42(2): p. 983-1018.

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