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Separation anxiety: Choosing the best approach to quantify the lipidome

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Front-End Solutions for Lipid Analysis
Which method should you choose?

Lipids are a diverse group of compounds that serve many purposes. They are the basis for key biological entities including cell membranes, hormones, and lipoproteins and are involved in a variety of biological processes including cell signaling, inflammation, and energy storage. But because of their ubiquitous nature within biological systems, they can also be significant players when it comes to disease. Metabolic disorders, neurological disorders, cardiovascular disease, liver disease, and even cancer have all been linked to lipid irregularities. Thus, the characterization of the lipidome in general, as well as the study of specific lipids in detail, has become a major research focus. In particular, assays using mass spectrometry, or MS, have grown in popularity due to the high sensitivity, specificity, and throughput that MS provides and the ability to both identify and quantify the lipids measured.

However, the very thing that makes lipids so incredibly versatile is also the thing that makes them difficult to analyze, and that is their extreme diversity. Lipids are formed from the combinatorial attachment of fatty acids with head groups that can often result in the same mass. As a result, there is much isobaric and isomeric overlap between lipid species. Depending upon the ultimate goal of the research, MS methods can simply analyze lipid class distribution, characterize the complete lipid composition, or quantitate specific lipids in a more targeted fashion. With any of these approaches, method development is non-trivial. But several sample introduction approaches are now routinely used. We recently caught up with Martin Giera, Associate Professor and head of metabolomics at Leiden University Medical Centre to get his opinion on how to choose between these different approaches.

Infusion MS Approaches
The simplest method for analyzing lipids by MS is to employ a direct infusion or ‘shotgun’ approach. By removing the need for LC separation, infusion approaches aim to provide a simple and reproducible acquisition strategy that can be used for targeted or untargeted quantitative analysis. However, isobaric overlap within the lipidome is pronounced without upfront separation. Hence techniques like differential mobility spectrometry (DMS) such as the SCIEX SelexION® DMS device can improve infusion-based methods for broad quantitation of lipid classes.

The SelexION device provides an orthogonal separation prior to mass analysis and removes many instances of interference due to isobaric overlap. Giera describes SelexION as a “game-changer for infusion-based approaches”1 and has used infusion methods, especially with the SCIEX Lipidyzer™ Platform as a “broad quantitative screen” for a multitude of disease studies. He says: “To have a validated system to quantify phosphatidylcholines and phosphatidylethanolamines in serum and plasma has been of tremendous use in clinical lipidomics research- it’s our first pass approach when we receive clinical samples.”

Giera also describes other situations where this broad screen approach can provide useful quantitative information. For example, fatty acid composition analysis in drug and treatment studies provides extremely useful insight into which metabolic pathways are affected by the drug. Also, in studies of lipid droplets in non-alcoholic fatty liver disease (NAFLD), the ability to broadly quantify the distribution of neutral lipids, in particular triglycerides, provides valuable information into their use as drug targets for treatment.

Another infusion based approach used by researchers is the Infusion MS/MSALL workflow.2 Here, data is collected in an untargeted way on the TripleTOF® System by a sequential acquisition of TOF MS/MS scans for each of the precursors within the lipidome mass range (m/z 200 ~ 2250).

However, infusion approaches can still suffer from ion suppression which can hamper discovery-based approaches and any targeted approaches pursuing low abundant species such as signaling lipids. Additionally, isobaric overlap can still exist even with technologies such as SelexION and can become problematic in certain circumstances.

LC-MS Approaches
LC-MS approaches to lipidomics and targeted lipid quantitation aim to get around the issues of ion suppression and isobaric overlap with either online or offline front-end LC separation. In discovery approaches this greatly improves the possibility of finding novel differential lipid species. For targeted approaches it can enable the quantitation of lower level and more challenging analytes.

There are two main approaches for LC-MS analysis depending upon the goal of the study. Reverse phase LC typically separates by fatty acid chain chemistry and separates individual lipid compounds. However, because of the wide variety of chemical structures, it can be challenging to find one LC method that can resolve all lipid species. Conversely, HILIC approaches separate lipids by head group chemistry, and therefore by lipid class, making it amenable to broad panel screening as described in the recently developed SCIEX HILIC method.3 Although it doesn’t exhaustively separate all lipid species, this approach can give increased coverage of the lipidome relative to direct infusion due to reduced ion suppression and isobaric overlap.

Giera states that the effort involved in the development of specific LC-MS methods to answer specific biological questions needs to be weighed against the biological information it provides. “Sure you can develop complicated, multi-dimensional approaches, but not many people do this because it doesn’t add anything biology-wise. Sometimes when you have too much detail you lose the biological context of the data.” Giera goes on to clarify that specific LC-MS approaches are essential in some circumstances, such as the study of cholesterol biosynthesis which is important for understanding a variety of inherited and lifestyle-related disease pathophysiology. “In this case it’s extremely difficult [without a specific LC-MS approach] because the structural differences are very small so you absolutely need a retention time, MS, and MS/MS spectrum to confirm you are quantifying the right thing.”

Conclusion and Future Outlook
Coming back to the question of which strategy would work best for a given situation, it really comes down to the end justifying the means. The biological value of the information that can be gained from a particular approach determines the complexity of the method required to attain that information. The study of diseases is frequently hypothesis-driven based on a mechanistic and metabolic understanding of that disease and thus may warrant a very targeted approach. Even so, Giera sees an important role for simple infusion-based screening methods as a “first pass” in clinical sample sets to get a broad idea of lipid abundance and distribution before digging into particular lipids or fatty acid compositions using targeted methods.

Occasionally the aim of a study is the discovery of novel lipids that have a role in disease pathophysiology. In this case, larger datasets can be generated by more advanced LC-MS approaches where suppression and isobaric overlap have been minimized. “The potential of a HILIC class-based separation approach with a technique like Scanning SWATH acquisition is pretty exciting,” says Giera. These acquisition methods promise to dig even deeper into the lipidome in the search for novel species. Hopefully this will result in a better understanding of disease patho-physiology and generate novel drug targets and diagnostic strategies.

References

  1. SCIEX Technical Note: Differential Mobility Separation for Improving Lipidomic Analysis by Mass Spectrometry: SelexION® Technology on QTRAP® Systems.  https://sciex.com/Documents/tech%20notes/Lipid-class-separation-selexION-QTRAP.pdf
  2. SCIEX Technical Note: MS/MSALL Workflow with Optimized Automated Flow Injection Platform for Lipidomics Biomarker Discovery: Qualitative and Quantitative Discovery via Un-biased Un-targeted High Throughput Workflow.   https://sciex.com/Documents/tech%20notes/2019/Automated-FIA_MSMSall_TripleTOF6600.pdf
  3. SCIEX Technical Note: Achieve Broad Lipid Quantitation using a High-Throughput Targeted Lipidomics Method: LC-Based Approach for Lipid Class Separation and Quantitation on QTRAP® 6500+ System. https://sciex.com/x115304
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