Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids
Lipidomics in biomedical research-practical considerations☆
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.
Transparency Document
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.
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This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.