Elsevier

Analytica Chimica Acta

Volume 1147, 22 February 2021, Pages 199-210
Analytica Chimica Acta

Review
High-coverage lipidomics for functional lipid and pathway analyses

https://doi.org/10.1016/j.aca.2020.11.024Get rights and content

Highlights

  • Recent decade witnessed a rapid expansion in lipidomics coverage.

  • Considerable challenges exist that impede comprehensive lipid pathway analyses.

  • DGCA reveals novel functional interactions between lipids.

  • Lipid pathway analysis transforms lipidomics into a hypothesis-generating tool.

Abstract

Rapid advances in front-end separation approaches and analytical technologies have accelerated the development of lipidomics, particularly in terms of increasing analytical coverage to encompass an expanding repertoire of lipids within a single analytical approach. Developments in lipid pathway analysis, however, have somewhat lingered behind, primarily due to (1) the lack of coherent alignment between lipid identifiers in common databases versus that generated from experiments, owing to the differing structural resolution of lipids at molecular level that is specific to the analytical approaches adopted by various laboratories; (2) the immense complexity of lipid metabolic relationships that may entail head group changes, fatty acyls modifications of various forms (e.g. elongation, desaturation, oxidation), as well as active remodeling that demands a multidimensional, panoramic view to take into account all possibilities in lipid pathway analyses. Herein, we discuss current efforts undertaken to address these challenges, as well as alternative form of “pathway analyses” that may be particularly useful for uncovering functional lipid interactions under different biological contexts. Consolidating lipid pathway analyses will be indispensable in facilitating the transition of lipidomics from its prior role of phenotype validation to a hypothesis-generating tool that uncovers novel molecular targets to drive downstream mechanistic pursuits under biomedical settings.

Introduction

Lipids, now widely-recognized as key biological molecules in vivo, execute important biochemical and/or biophysical roles in energy storage, energy mobilization, modulating membrane fluidity, membrane compartmentalization, as well as various trafficking and signaling events [[1], [2], [3]]. Aberrant lipid metabolism is implicated in the pathology of a plethora of human diseases, including diabetes, cardiovascular complications, steatohepatitis, neurodegenerative diseases and cancers [[4], [5], [6], [7]].

As reviewed elsewhere, lipid analysis has evolved tremendously over the past decades, from the application of traditional techniques such as thin layer chromatography (TLC) that only permit the detection of a rather limited set of lipid classes, to comprehensive interrogation of endogenous lipidomes at systems levels, i.e. lipidomics that covers hundreds or even thousands of individual species [8]. Lipidomics, as a relatively young member of the omics family, delivers a comprehensive snapshot of lipid profiles in a given biological context, providing vital clues for understanding the connection between lipids and phenotypes [4,9,10]. As lipidomics provides the closest readout to lipid-associated cellular phenotypes, it is being increasingly applied to study pathway perturbations in various biological and biomedical settings that implicate dysregulation in lipid metabolism [4,10,11]. Various lipids, such as ceramides, diacylglycerols, phospholipids, and oxysterols had been reported as biomarkers for a myriad of major human diseases, and a number of these discoveries were clinically translated to diagnostic tools at bedside [[12], [13], [14], [15], [16], [17]]. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) represent the mainstream techniques for the characterization and quantitation of lipids. While NMR could be considered as a standalone technique, various mass spectrometers have been widely used either directly (shotgun) or coupling with different chromatographic systems, such as liquid-chromatography (LC), gas-chromatography (GC) and supercritical fluid chromatography (SFC), to allow for extensive identification and quantification of diverse lipid classes [18]. According to data collected from the Web of Science, however, research published in the field of lipidomics since the year of 2003 had largely deployed MS, which accounted for approximately 95% of the recorded research under the search term “lipidomics”, while NMR application is less common (Fig. S1). Therefore, MS is still regarded as the primary technique for lipidomics to date. It has, however, remained challenging to execute comprehensive analysis of lipidomes with accurate quantitation primarily due to a few issues, such as lack of isotopic internal standards, and the vastly different dynamic ranges of lipid classes across different biological samples, etc, as previously discussed elsewhere [8].

Although lipidomics has advanced rapidly over the past fifteen years, both in terms of quantitation and biomarker elucidation, lipid pathway analysis has experienced a considerable lag mainly due to the complexity and diversity of lipids, as well as technical constraints in achieving sufficient analytical coverage of endogenous lipids that come in immense diversity. Herein, we summarize recent progress in lipidomics, with central emphasis on the importance of achieving sufficient analytical coverage to render biological pathway analysis. We also bring up the technical constraints that impede comprehensive lipid pathway analysis, as well as the significance of considering lipid metabolic pathways from a “panoramic” perspective, instead of circumscription to conventional, two-dimensional illustration in the analysis of lipid metabolic pathways.

Section snippets

Lipidomics approaches for extending analytical coverage

As extensively reviewed elsewhere, lipid analysis at the molecular level have been largely propelled by the rapid development of key instrumentations over the past 30 years, such as the MS [8,[19], [20], [21]]. Before 1990s, GC coupling with mass spectrometry (GC-MS) was probably the fundamental technique for analysis of lipids including sterols, glycerolipids, sphingolipids, fatty acids, as well as their derivatives including prostaglandins. A major drawback in earlier studies was that lipid

The transiting role of lipidomics from phenotype validation to hypothesis generation

As lipidomics confer both identification and (relative) quantification of a myriad of lipids at molecular level, it has, for a long time, been broadly and conventionally applied to present phenotype evidence and alterations (e.g. biomarkers) in various biological and biomedical studies [[87], [88], [89], [90]]. The issue of quantification (absolute versus relative) in lipidomics has been previously reviewed in details elsewhere [8]. In terms of phenotype characterization, for example, lipidomic

High-coverage lipidomics for multi-dimensional pathway analysis

While pathway analysis is becoming an essential part in the typical lipidomics workflow, considerable challenges prevail against its wide application in various biological and biomedical settings. In most pathway analyses, a critical initial step is identifier (ID) mapping against reference database IDs or compound synonyms. Such lipid records are available in many public reference databases. Some have been developed for chemicals, like CAS and PubChem; or for biologically relevant metabolites,

High-coverage lipidomics to uncover functional lipid modules

In the foregoing lipid pathway analyses, known and reported relationships or reactions between interacting lipids are prerequisites. In a way, this requirement forbids the discovery of novel, previously unknown functional interactions between lipids in a given biological context. Co-regulated genes often display similar patterns of gene expression, which translates to intense correlations between their gene expression levels [122]. In the same light, co-regulated lipids are expected to exhibit

Concluding remarks

Advances in both front-end separation approaches and analytical technologies, together with the ever-increasing repository of knowledge pertaining to lipid biology, have allowed lipidomics to significantly aggrandize its analytical coverage and encompass an expanding repertoire of lipids in a single analytical platform. High-coverage lipidomics expedites the application of data-driven pathway and functional modules analyses that allows scientists to probe into lipid homeostasis and metabolic

CRediT authorship contribution statement

Sin Man Lam: conceived the manuscript, wrote the manuscript. Zehua Wang: wrote the manuscript. Bowen Li: conceived the manuscript, wrote the manuscript. Guanghou Shui: conceived the manuscript, wrote the manuscript.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors thank Miss Xiaojie Liu for assistance with graphical illustrations. This work was financially supported by grants from the National Key R&D Program of China (2018YFA0506900, 2018YFA0800901), The Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12030211), National Natural Science Foundation of China (31671226, 31871194).

Professor Guanghou Shui received his PhD degree in 2004 from the National University of Singapore (NUS). Following this, he conducted his postdoctoral research in the Department of Biochemistry in 2004–2008 and worked at a senior research fellow at NUS in 2008–2012. He joined the Institute of Genetics and Developmental Biology in 2013. The major research interests of his laboratory are (1) developing advanced lipidomics/metabolomics tools to unravel the association between lipids and onset of

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    Professor Guanghou Shui received his PhD degree in 2004 from the National University of Singapore (NUS). Following this, he conducted his postdoctoral research in the Department of Biochemistry in 2004–2008 and worked at a senior research fellow at NUS in 2008–2012. He joined the Institute of Genetics and Developmental Biology in 2013. The major research interests of his laboratory are (1) developing advanced lipidomics/metabolomics tools to unravel the association between lipids and onset of diseases; (2) lipid biology in development, metabolic disorders and associated diseases. Guanghou is the coauthor of over 150 papers resulting in an h-index of 49 based on 8500 citations.

    Sin Man Lam graduated with a Ph.D. degree in Biochemistry from the National University of Singapore in 2013. She conducted postdoctoral research from 2013 to 2017 in the Institute of Genetics and Developmental Biology. She is now the chief technology officer of LipidALL Technologies and a visiting scientist at the Institute of Genetics and Developmental Biology. Her research interest lies in the translational application of omics approaches to elucidate metabolic regulation underlying biological and physiological processes, such as development and aging; as well as the use of omics-driven methodologies to interrogate metabolic dysregulation in major human diseases. She has coauthored over 60 papers in international journals.

    Zehua Wang received her bachelor degree in 2014 from Lanzhou University of China. From 2014 to present, she is a PhD candidate specializing in the area of lipidomics in the Institute of Genetics and Developmental Biology. She had published three papers in international journals including Redox biology, Cell Metabolism and The Journal of Cell Biology.

    Bowen Li received his master degree in 2011 from National University of Singapore. From 2008 to 2013, he worked in the Singapore Lipidomics Incubator of NUS, focusing on bioinformatics in lipidomics. Between 2013 and 2015, he studied biostatistics in School of Public Health, NUS. From 2015 to 2017 he worked in the Singapore National University Hospital as senior system analyst. He joined LipidALL Technologies in 2017. He has published over 10 articles in international journals including Diabetes Care, Metabolomics, MicrobiologyOpen, Scientific Reports, Journal of Genetics & Genomics, J. Cardiovasc. Surg, PLoS ONE, J. Anal. Test, Nat. Commun, etc.

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