Research on Metabolomics and Lipidomics

Research on Metabolomics and Lipidomics ...

Lucy Woods, PhD, is a professor in neuroscience.

metabolites play a significant role in cellular biochemistry, especially in those who study sugars, nucleotides, and amino acids. However, we are only beginning to understand the many aspects of metabolites' involvement in health and disease. Until recently, metabolites have been subjected to extensive profiling, namely, metabolomics research. Activating our understanding of disease pathogenesis and enabling the discovery of diagnostic biomarkers, we have a profound connection with these findings.

metabolomics and emerging subdisciplines, such as lipidomics, are recapitulating the development of other omics disciplines, such as genomics and proteomics. Signs of progress include significant applications in translational and clinical research. Ultimately, metabolomics and lipidomics will lead to the realization of precision medicine.

Molecular biology, and medicine are all connected to isomer metabolites that have structural variations. A common approach in metabolomics research is either untargeted or global metabolite profiling. This approach is being used to reshape our understanding of cell biology, physiology, and medicine. However, metabolic pathways require that isomer metabolites that have structural variations. This is because the presence of isomer metabolites makes complex identification and precise interpretation very difficult.

Mass spectrometry (MS) is a major component in metabolomics because of its capability to quantitatively measure thousands of metabolites from small amounts of biological material. However, even high-performance MS instruments struggle to analyze separate isomers due to the chemical similarities of isomer metabolites. Because isomers can have the same mass, charge, and physical properties, separation and identification are a time- and resource-intensive process.

The metabolic phenotype is extremely sensitive; a persons metabolic phenotype is influenced by environmental factors and has significant variation. Data can also be more confusing because metabolites are the products of downstream biological transformations of molecules in a similar mass range, which requires greater sensitivity to obtain broad metabolites and relative concentrations.

Ion mobility spectrometry (IMS) has long been used for post-ionization separation and structural characterization of biomolecules, and its association with MS has given researchers the tools to distinguish molecules in a third dimension, utilizing collisional cross section values. Consequently, the use of IMS-MS in untargeted metabolomics has now uncovered its potential potential.

A new approach is being developed to better understand the biological relevance of metabolites in health and disease. Since it's commercialization in 2016, trapped ion mobility spectrometry (TIMS)-MS has led to significant improvements in biomolecule separation and characterization. Its higher mobility resolving capability allows the separation of molecules with differences in their CCS values so little that traditional IMS-MS cannot distinguish them. In addition, the novel scan mode, called parallel accumulationserial fragmentation (PASEF), multiplie

New developments in lipid biochemistry have been made in the past thanks to the rise of metabolomics. Lipids are small molecules that have different physical and chemical properties, and the abundance of lipids and different lipid classes is crucial to metabolic regulation. lipidomics is becoming a prominent source of collecting and measuring data within the metabolome. Because the high isomeric content of lipids and the varying abundance of lipids in typical samples such as plasma extracts, is required. An analytical equipment with a high

In a recent study, a workflow was developed to simplify the annotation and validation of lipid isomers.2 This workflow is called 4D-Lipidomics, and it is compatible with the PASEF-powered TIMS. The study described how the lipid content of a NIST SRM 1950 extract from reference plasma (Sigma-Aldrich, Germany) was studied in the 4D-Lipidomics workflow, which includes reversed-phase liquid chromatography (RP-LC), and the

The SRM 1950 lipid extract was analyzed with different chromatographic run times (20, 10, and 5 minutes) to investigate the increased MS/MS quality and peak capacity provided by PASEF.

The maximum number of unique identified lipids was observed with 20-min gradient times, and even when the runtime was reduced to 5 mins, 75 percent (271) of the annotated lipids with a CV of less than 20% were still detected (Figure 1B). CCS values are independent of liquid chromatography and can be reliably used for compound identification (Figure 1C).

The results reveal the high MS/MS coverage that can be achieved using PASEF and how automatically acquired CCS values increase confidence in annotations and compound identification. These results set the stage for deep lipidomics profiling. Methods such as 4D-Lipidomics can achieve high throughput to enable studies that require a high turnover. These are for example clinical research.

Molecular alterations at the metabolome level reflect disturbances in previous biological cascades, bridging the gap between the genome and phenotype. Changing metabolomics may be beneficial in determining the way in which chemical compounds are used, and elaborating a deeper understanding of the effects of infection.

Current metabolomics research focuses on a variety of challenging diseases, including Alzheimers disease and cancer, and scientists are studying further into metabolomics to investigate how the cells in our body behave and what this might be like as we seek for a future of personalized medicine.

Advances in MS technology such as TIMS bring researchers a step closer to a future in which personalized medicine is perceived to be within reach. For more information, please visit

At Bruker Daltonics, Lucy Woods, a worldwide business unit manager for metabolomics and lipidomics, is available online. Website:

References 1.Meier F, Brunner A-D, Kock S, and et al. LCMS-179, 2021. 3.Tounta V, Liu Y, Cheyne A, Larrouy-Maumus, G. Metabolomics in infectious diseases and drug discovery. Mol. Omics 2021; 17(3): 376393.

You may also like: