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Bringing the Benefits of 4D-Lipidomics Research to the Clinic

Blue and red lipid bilayer.
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Lipids are a diverse group of organic compounds that are integral to cellular structure and signaling, with remarkable utility in determining tissue pathology and the diagnosis of disease phenotypes.

 

By gaining a better understanding of the lipid composition of cells, tissues and biofluids, we can further our understanding of the metabolic mechanisms underpinning human health and disease. However, the large-scale study of lipids in complex biological systems, known as lipidomics, is only beginning to reveal its potential to influence clinical practice.

 

The selective measurement and accurate annotation (identification) of lipids present in biological samples remains a challenge for modern bioanalytical chemistry to solve, owing to close structural similarities among a vast array of distinct lipid species.1 Advances in analytical techniques are driving new developments in lipidomics, with exciting new methodologies starting to bring the technology to more mainstream use and setting the foundations for future research.

 

Although relatively new as an omics science compared to the more established disciplines of genomics and proteomics, lipidomics is rapidly evolving, driven by its potential to bring clinical researchers new insights in the study of major societal challenges in health and disease including cancer, cardiovascular disorders, neurodegenerative disease and metabolic disorders.

 

Utilizing TIMS technology for enhanced lipid separation


Lipidomics studies aim to comprehensively quantify and identify lipids in biological samples to drive better understanding, detection and treatment of disease phenotypes. These studies often employ powerful technologies, like mass spectrometry (MS) and liquid chromatography (LC), to differentiate hundreds of lipids and selectively measure them in complex mixtures.

 

However, strong structural similarities among lipids can challenge even the most powerful separation techniques. Lipids that share the same mass and charge can vary widely in their bioactivity, making separation and annotation a resource-intensive process which is challenging but necessary. Hence, techniques such as MS and LC must be used effectively to be able to differentiate between different lipid types accurately.

 

Augmenting MS-based lipid analysis with trapped ion mobility spectrometry (TIMS) adds a new dimension to the molecular separation of lipids. TIMS separates ionized lipids propelled by a gas flow based on their collisional cross section (CCS) values using an electric field to trap and selectively elute them.2

 

The combination of TIMS with time-of-flight (TOF) MS allows researchers to collect highly selective lipid measurement data complete with both CCS and mass-to-charge (m/z) molecular descriptors to aid in accurate lipid annotation. This approach is particularly helpful for the analysis of lipids that cannot be resolved by high-resolution MS alone.  

 

Exploring the lipidome at the speed of PASEF


When combined with both quadrupole and TOF mass analyzers, TIMS technology enables the parallel accumulation–serial fragmentation (PASEF) of ionized lipids for highly sensitive quantification and MS/MS based characterization in a single measurement cycle. PASEF uses TIMS to accumulate incoming precursor ions while previously trapped ions are mobility separated and fragmented to generate characteristic MS/MS spectra that encode a lipid’s constituent parts and identity.

 

This process of ion trapping efficiently utilizes the available ionized lipids to maximize measurement sensitivity while the mobility separation prior to fragmentation allows for highly efficient fragmentation of eluting species. Because of this, MS/MS spectral acquisition speeds of up to 300 Hz can be achieved, enabling deep lipidome characterization with short analysis times.3

 

When combined with LC, TIMS and PASEF enable four-dimensional (4D) analysis of the lipidome, combining LC retention time, CCS, m/z and MS/MS data for simultaneous and selective lipid quantification with accurate lipid annotation.4 Moreover, different PASEF data acquisition modes including data dependent analysis (DDA), data independent analysis (DIA) and parallel reaction monitoring (PRM) are being evaluated to pave the way for more efficient and standardized workflows in the future.5

 

Accurate and automated lipid annotation


The interpretation of lipidomics data can be severely hampered by incomplete and inaccurate annotations. Therefore, using the data from hundreds of measured lipid species and annotating new ones can be a daunting task fraught with ambiguity and uncertainty.

 

Lipidomics experts are able to assign identities to detected species on the basis of their MS/MS patterns by employing a ruleset for data interpretation that reveals the structural components of each lipid (for example, its polar head group and fatty-acyl chains). However, this process can be prohibitively laborious if conducted manually, and mistakes can easily be made which influence the interpretation of downstream results.

 

Determining the appropriate level of specificity or ambiguity at which a lipid should be annotated based on the data available can require a skillset and experience beyond the immediate reach of many analytical, clinical and interdisciplinary research labs. Therefore, to make the technique more accessible without undermining the validity of reported results, a need exists for software solutions that harness this expert knowledge and deploy it in an automated yet accurate manner.

 

Such MS/MS “rule-based” annotation is programmable as an augment to conventional m/z and isotope ratio-based annotation, but such solutions may lack the flexibility and intelligence to skillfully navigate less-than-perfect “chimeric” MS/MS data – spectra containing the mixed information from two or more distinct but co-isolated precursor lipid species – resulting in poor annotation confidence.

 

While rule-based lipid annotation should make the multiple possible interpretations transparent and navigable, TIMS and PASEF can be used to minimize the incidence of chimeric MS/MS spectra in the first place. By separating coeluting isomeric and isobaric lipid species, the automated application of rule-based annotation to complex lipidomics data is simplified.

 

To increase lipid annotation even further, the observed CCS values provided by TIMS are assigned to each lipid precursor and can be matched against in silico predicted CCS values for validation of the m/z, isotope ratio and MS/MS rule-based annotation, completing the high confidence lipid annotation workflow under full automation. Together, these advances make the analysis of lipids in complex biological samples conveniently accessible to lipidomics experts and novices alike.

 

A blueprint for clinical lipidomics


Leading researchers are now driving the development of 4D-lipidomics for the purposes of clinical and translational research aimed at positively impacting patient outcomes. A recent study4 describes the reproducible quantification and stringent annotation of hundreds of lipid species from human blood products using a combination of automated lipid extraction, microflow reversed-phase LC-TIMS-MS and PASEF data acquisition.

 

The authors’ focus on measurement precision and accurate quantification is key for the suitability of 4D-lipidomics for clinical profiling, as well as for the future of inter-laboratory studies and population screening efforts. Furthermore, accurate feature annotation in complex matrices is reinforced as a prerequisite for downstream interpretation of resulting data.

 

Their approach has been recently applied to the study of lipid dysregulation in the most common hematological malignancy worldwide, diffuse large B-cell lymphoma (DLBCL).6 Here, 4D-lipidomics was used to determine novel global lipidome changes in plasma between untreated women with DLBCL and disease-free controls, augmenting the targeted analysis of bioactive plasma lipids known to be involved in inflammation response and immunity. Together, these approaches revealed candidate diagnostic lipid signatures fit for prospective validation in larger DLBCL cohorts and also revealed key metabolic and signaling pathways affected by the disease, pointing towards potential targets for improved disease management.

  

Moving toward a deeper understanding and management of disease


Lipidomics is proving its worth in helping to understand the essential mechanistic aspects of the biology of major diseases. The recent inclusion of lipid markers in clinical practice for patient stratification of cardiovascular disease risk is a clear indication of the impact that lipidomics can have on the meaningful improvement of patient health outcomes.

 

As more researchers look to the world of lipid analysis for the discovery of new biomarkers and pathways to target in therapeutic drug development, technologies such as TIMS and the 4D-lipidomics workflow are well-positioned to enhance the reliability and reproducibility of results while making the field more generally accessible. With increased adoption, further lipidomics research is set to deliver real-life patient outcomes as the data generated progresses into clinical use, paving the way to advances in personalized medicine.

 

 

About the author: 

 

Matthew R. Lewis, PhD, is vice-president of metabolomics and lipidomics at Bruker Daltonics. For the past 25 years, Matthew has pursued his interests in better understanding biochemical processes and improving the analytical tools and techniques used to measure them. At Imperial College London he served as the chief operating officer of the UK's National Phenome Centre and lead for the academic section of Bioanalytical Chemistry in the Faculty of Medicine. Here, he worked to advance the understanding of human disease phenotypes using advanced bioanalytical and data analysis techniques for metabolic profiling at a previously unprecedented scale. Matthew transitioned to industry in 2022 to more directly further the development of research-enabling solutions through his role at Bruker Life Sciences Mass Spectrometry. He takes great pleasure in extensive engagement in interdisciplinary team science and large-scale scientific collaborations, supporting advancements in the fields of metabolomics and lipidomics.


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2. Michelmann K, Silveira JA, Ridgeway ME, Park MA. Fundamentals of trapped ion mobility spectrometry. J Am Soc Mass Spectrom. 2015;26(1):14–24. doi: 10.1007/s13361-014-0999-4

3. Merciai F, Musella S, Sommella E, Bertamino A, D’Ursi AM, Campiglia P. Development and application of a fast ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry method for untargeted lipidomics. J Chromatogr A. 2022;1673:463124. doi: 10.1016/j.chroma.2022.463124

4. Lerner R, Baker D, Schwitter C, et al. Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples. Nat Commun. 2023;14:937. doi: 10.1038/s41467-023-36520-1

5. Rudt E, Feldhaus M, Margraf CG, et al. Comparison of data-dependent acquisition, data-independent acquisition, and parallel reaction monitoring in trapped ion mobility spectrometry-time-of-flight tandem mass spectrometry-based lipidomics. Anal Chem. 2023;95(25):94889496. doi: 10.1021/acs.analchem.3c00440

6. Masnikosa R, Pirić D, Post JM, et al. Disturbed plasma lipidomic profiles in females with diffuse large B-cell lymphoma: a pilot study. Cancers. 2023;15(14):3653. doi: 10.3390/cancers15143653