Lipidomics in biomedical research-practical considerations

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Highlights

  • Lipidomics is a key platform for the study of lipid metabolism in health and disease.

  • Multiple suitable techniques for the global measurement of molecular lipids exist.

  • New experimental protocols are needed to study lipidomes at physiological level.

  • Better in silico tools are needed to study lipids at the systems level.

Abstract

Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Introduction

Over the past decade, lipidomics emerged as a key approach aimed at elucidating lipid-related pathways and mechanisms underlying the cellular function. Additionally, due to the undeniable role of lipids in the maintenance of system homeostasis at the cellular as well as physiological levels, lipidomics also emerged as a powerful tool for identifying the key processes and related molecular signatures associated with progression to and protection from many diseases [1], [2]. Biological importance of lipids is not only due to their roles in biochemical and signaling pathways, but also due to them being part of lipid-containing ensembles such as cell membranes and lipid particles (e.g., lipoproteins and exosomes). In order to understand the roles of lipids in specific pathologies, it is therefore essential to adopt a systems approach which accounts for spatial and dynamic complexity of lipid metabolism [3].

Much of the debate concerning lipidomics applications in health and disease has been about the advantages or disadvantages of specific technologies for the measurement of lipids. However, the current methods such as based on shotgun mass spectrometry (MS) and liquid chromatography (LC)-MS, each with its own advantages and disadvantages, are all applicable for the studies of biological systems including in clinical settings, as long as they are properly optimized and their pro-s and con-s are understood.

Here we focus on selected practical considerations when choosing and developing lipidomics methods as well as highlight key considerations and challenges.

Section snippets

Sample preparation for lipidomic analysis

Regardless of the analytical method used for lipidomics data acquisition, lipids need to be extracted from the biological samples. Sample preparation has a major impact both on the quality as well as on the sample throughput in lipidomics [4]. Different types of sample matrices may also require different types of sample preparation protocols.

There are several studies comparing different sample preparation methods, yet the results have not always been consistent [5], [6], [7], [8]. Criteria for

Quantification of lipids – different approaches to lipidomics

The term ‘quantitative analysis’ is often used rather loosely in life sciences. Terms such as ‘quantitation’ and ‘absolute quantitation’ have been applied in the fields of proteomics and lipidomics, even though the analyses applied were strictly speaking semi-quantitative. For an analytical chemist, quantitative analysis means that each compound analyzed is quantified against a structurally equivalent compound which is used as an internal standard (i.e., isotope labeled standards each

Quality control

Strict quality control (QC) in lipidomics is crucial, particularly when a large number of samples are analyzed. In lipidomics, repeatability is one of the most important criteria to assure the reliability of the various analytical assays. It is essential that internal standards are used for normalization, the samples are prepared and analyzed in random order, and sufficient amount of different types of quality control samples and blanks are analyzed together with the samples (Table 1).

Lipidomics data processing and analysis

Data processing is a crucial step, which transforms raw lipidomics data into an interpretable dataset amenable to statistical and pathway analysis. Several tools are available for lipidomics data processing, both for shotgun and LC-MS approaches [17], [18]. In addition to the solutions developed by the research community, the MS instrument vendors have also started to offer increasingly sophisticated solutions for processing and analysis of lipidomics data. The choice of the data processing

Lipid identification

In most cases, the identification of lipids is generally not as challenging as e.g. for the polar metabolites, because lipids comprise common structural blocks that can be detected by MS/MS (or more broadly MSn) analysis. This makes lipid identification also amenable to automated computational approaches [26]. Current high-resolution MS instruments in most cases afford reliable identification of major lipids (apart from double bond and acyl chain position).

Much of the ambiguity concerning the

Modeling of lipid metabolism

Elucidation of lipid metabolism and its complex dynamics at physiological or cellular levels can only be achieved in dedicated experimental settings which are not feasible in most clinical lipidomics studies. Nevertheless, functional studies even if limited in sample size, such as by using stable isotope tracers or focusing on lipidomic profiling in isolated lipoprotein fractions [28], [29], [30], are highly important if one is to interpret lipidomic profiles in the physiological context.

One

Impact of lipidomics in clinical research

In clinical research, the main challenge is not in the technologies applied, but rather in identifying the problems where the identification of lipid-related markers may have an impact. Since lipid homeostasis is fundamental to maintain health, specific lipid disturbances may be an underlying link and shared across multiple co-morbid conditions behind specific diseases. Lipidomics may therefore be a valuable tool for identifying particularly vulnerable co-morbid sub-populations which would

Concluding remarks

Lipidomics led to many advances in the studies of health and disease over the past decade, providing valuable insights into the mechanisms and molecular signatures behind specific diseases and their co-morbidities. The field has matured to the point that the introduction of lipidomics into clinical practice is being considered and debated. While much of the discussion has so far focused on specific technologies for the measurement of lipids at the molecular level, the key challenges ahead lie

Conflict of interest statement

The authors declare that no conflict of interest exists.

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Acknowledgments

The authors thank Thomas Sparholt Dyrlund (Novo Nordisk A/S, Måløv, Denmark) for his contribution to the development of automated quality control system for lipidomics.

References (39)

  • G. Liebisch et al.

    Shorthand notation for lipid structures derived from mass spectrometry

    J. Lipid Res.

    (2013)
  • A. Kontush et al.

    Unraveling the complexities of the HDL lipidome

    J. Lipid Res.

    (2013)
  • J. Nielsen

    Systems biology of metabolism: a driver for developing personalized and precision medicine

    Cell Metab.

    (2017)
  • O. Quehenberger et al.

    Lipidomics reveals a remarkable diversity of lipids in human plasma

    J. Lipid Res.

    (2010)
  • M. Oresic

    Obesity and psychotic disorders: uncovering common mechanisms through metabolomics

    Dis. Model. Mech.

    (2012)
  • T. Hyotylainen et al.

    Bioanalytical techniques in nontargeted clinical lipidomics

    Bioanalysis

    (2016)
  • R.M. Pellegrino et al.

    LC/MS lipid profiling from human serum: a new method for global lipid extraction

    Anal. Bioanal. Chem.

    (2014)
  • M.H. Sarafian et al.

    Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography–mass spectrometry

    Anal. Chem.

    (2014)
  • E.G. Bligh et al.

    A rapid method of total lipid extraction and purification

    Can. J. Biochem. Physiol.

    (1959)
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    This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

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