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Lipidomics – A Niche but Rapidly Growing Field
Article

Lipidomics – A Niche but Rapidly Growing Field

Lipidomics – A Niche but Rapidly Growing Field
Article

Lipidomics – A Niche but Rapidly Growing Field

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Although researchers have been working on lipid biochemistry for many decades, lipidomics is a more recent, newly emerging discipline – with potential applications for diagnosing and preventing disease.

“Lipidomics is the attempt to comprehensively analyze all of the lipids in a biological system,” says Albert Koulman, Head of the Core Metabolomics and Lipidomics Laboratory at the University of Cambridge.

Spawning from the broader field of metabolomics, it involves the systems-level analysis of lipid metabolism in human physiology and disease.

“You can use it for biomarker discovery – so looking for specific changes indicative of either a disease or healthy state,” says Koulman. “Or you can use it to look at how biological systems respond to a challenge to try and understand the underlying metabolic mechanisms.”

Recent advances in technologies are accelerating progress in this field. Lipid profiling is already enabling more accurate studies into diet and health – and holds promise for the development of clinical biomarkers that can help diagnose a disease many years before the onset of symptoms.

No "one size fits all" approach

Lipids are a hugely diverse group of molecules, making comprehensive profiling a challenge. The most standard approach for their identification and quantification is liquid chromatography (LC) followed by mass spectrometry.

“Although this works well for looking at specific lipid groups, it isn’t ideal for carrying out a complete lipid profile of a sample due to the range of polarities within the lipid pool,” says Koulman. Another approach is direct-infusion mass spectrometry (DIMS), which entirely removes the need for chromatographic separation.

“A huge advantage is because it’s so fast, we can screen large sets of samples in a very short timeframe,” says Koulman. Another option is mass spectrometry imaging, which has the added benefit of allowing researchers to build a detailed picture of where hundreds of specific lipids are localized within a tissue sample.

“That’s very powerful information if you’re trying to understand the biology behind what’s driving the differences in metabolism,” says Koulman.

But one of the biggest challenges is the lack of standardization across the field, making it difficult to integrate datasets across different laboratories. And even within one laboratory, using only technique is not enough to be certain that a change is real.

“If you measure a thousand lipids in a platform, it’s very likely that the method won’t be measuring many of these in the most optimal way,” says Koulman. “So we always validate with a different technique.” Large-scale initiatives, such as the LIPID MAPs® Lipidomic Gateway, have been set up to help standardize the field, providing centralized resources such as databases, tools and protocols.

Building up our knowledge base

Due to advances in the sensitivity of mass spectrometry, researchers are now able to detect ever-increasing numbers of lipids in a sample.

“Ten years ago, we could probably see a maximum of a few hundred lipid molecules in blood – but now, we can see in the thousands,” says Min Kim, Postdoctoral Researcher at King’s College London.

But our knowledge about many of these molecules lags behind – as the relevant functional studies often haven’t yet been done.

“We’re seeing lipids that people have never seen before – there are new molecules being discovered every year,” says Kim.

Building up a systems-level knowledge of lipid metabolism is also hugely challenging. Current bioinformatics approaches are usually based on simple ideas – such as the relationship between a substrate, an enzyme and a product. But these principles aren’t easily applied to lipids.

“Lipids are quite strange molecules in many ways,” explains Koulman. “A phospholipid can either be a signaling compound, part of the cell membrane – or used to traffic specific fatty acids around the body.”

So there is a huge need to develop new bioinformatics approaches that are specifically designed to piece together lipid pathways and networks.

Improving studies into diet and nutrition

One promising application for lipidomics is to improve epidemiology studies looking at the links between lifestyle factors and disease risk. For instance, much of our current dietary advice is based on studies asking people what they have eaten – such as food frequency questionnaires or diaries. But these can be notoriously misleading.

“Even when carried out in the most careful way, those approaches are always very subjective,” says Koulman. “But by measuring the levels of lipids in the circulation, it becomes possible to assess a person’s diet in a more objective way – and then see if there’s a link between diet and disease risk.” For example, one recent study suggests that the higher the levels of lipid biomarkers of dairy fat consumption in the circulation, the lower a person’s risk of type 2 diabetes.

“Based on our results, I think we may need to be more careful about the very blunt advice to stay away from saturated fats – as certain types may actually have a beneficial role for our health,” says Koulman.

And applying lipidomics techniques for the analysis of dried blood spots is making it easier to carry out large studies into infant nutrition and health outcomes.

“Originally we were quite sceptical as when the blood goes onto paper you will get hydrolysis and oxidation that can upset the lipid profile,” explains Koulman. “But we’ve done extensive validation studies showing that actually, you can still get relevant information.”

The team have since carried out studies on samples from large birth cohorts – such as the Cambridge Baby Growth Study – identifying differences in lipid profiles between formula-fed and breastfed infants that can be used as nutritional biomarkers.

Predicting disease

Lipid profiling could also raise a red flag for the development of common late-onset diseases many years before the onset of symptoms. For instance, as the brain is nearly 60 per cent fat, many researchers are exploring lipidomic markers for the early diagnosis of neurodegenerative conditions, such as Alzheimer’s disease.

“If you have a disease of the brain and get shrinkage of the brain –most likely it’s a loss of lipids,” says Cristina Legido-Quigley, Head of Systems Medicine in Steno and Kings College London. “So we’re looking to identify lipid-based biomarkers that can help diagnose the disease earlier.” The team has recently identified a panel of metabolites that include primary fatty amides in plasma that could be used as a diagnostic panel. They are now carrying out a large-scale trial involving thousands of patients with mild cognitive impairment to explore their value as predictive biomarkers for Alzheimer’s disease.

A bright future for lipidomics

The field of lipidomics, although still in its infancy, is set to play a crucial role in advancing our understanding of health and disease.

“I think it can be used much more – there are a lot of pathologies that have a component that people aren’t really looking at,” says Koulman. “For instance, there seems to be a strong link between lipid metabolism and mental health, but we don’t understand anything about this yet.”

The discovery of lipid-based biomarkers may also help shape more personalized lifestyle advice to help reduce the risk of disease. And the development of sensitive new tests that can diagnose a disease earlier, together with effective new interventions – such as exercise, diet or drugs – could help prevent the onset of a person’s symptoms.

“I predict that within 10 to 20 years, lipidomics will be part of a clinical test to diagnose disease,” says Legido-Quigley.

Meet The Author
Alison Halliday, PhD
Alison Halliday, PhD
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