Lipidomics, en route to accurate quantitation

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry (MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes. The metabolomes of cells, tissues, and organisms comprise a variety of molecules including lipids, amino acids, sugars, organic acids, and so on. Metabolomics mainly focus on the hydrophilic classes, while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes. The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases, but system-level understanding is largely lacking, which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies. While scientists are continuously striving to develop high-coverage omics approaches, integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation. Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape, enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology, facilitating the study of interconnection between lipids and other metabolites in disease progression. In this review, we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health. We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.

High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults

OBJECTIVE

Comprehensive assessment of serum lipidomic aberrations before type 2 diabetes mellitus (T2DM) onset has remained lacking in Han Chinese. We evaluated changes in lipid coregulation antecedent to T2DM and identified novel lipid predictors for T2DM in individuals with normal glucose regulation (NGR).

RESEARCH DESIGN AND METHODS

In the discovery study, we tested 667 baseline serum lipids in subjects with incident diabetes and propensity score-matched control subjects (n = 200) from a prospective cohort comprising 3,821 Chinese adults with NGR. In the validation study, we tested 250 lipids in subjects with incident diabetes and matched control subjects (n = 724) from a pooled validation cohort of 14,651 individuals with NGR covering five geographical regions across China. Differential correlation network analyses revealed perturbed lipid coregulation antecedent to diabetes. The predictive value of a serum lipid panel independent of serum triglycerides and 2-h postload glucose was also evaluated.

RESULTS

At the level of false-discovery rate <0.05, 38 lipids, including triacylglycerols (TAGs), lyso-phosphatidylinositols, phosphatidylcholines, polyunsaturated fatty acid (PUFA)-plasmalogen phosphatidylethanolamines (PUFA-PEps), and cholesteryl esters, were significantly associated with T2DM risk in the discovery and validation cohorts. A preliminary study found most of the lipid predictors were also significantly associated with the risk of prediabetes. Differential correlation network analysis revealed that perturbations in intraclass (i.e., non-PUFA-TAG and PUFA-TAGs) and interclass (i.e., TAGs and PUFA-PEps) lipid coregulation preexisted before diabetes onset. Our lipid panel further improved prediction of incident diabetes over conventional clinical indices.

CONCLUSIONS

These findings revealed novel changes in lipid coregulation existing before diabetes onset and expanded the current panel of serum lipid predictors for T2DM in normoglycemic Chinese individuals.

Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values

Background

Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.

Results

LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.

Conclusions

LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2803-8) contains supplementary material, which is available to authorized users.

Retention time bracketing for targeted sphingolipidomics by liquid chromatography-tandem mass spectrometry

Aim: In complex biological matrixes, many sphingolipids are present with multiple reaction monitoring traces or lack of standard for verification, potentially leading to inaccurate identification and quantitation. Results/methodology: Based on these retention times of available standards, we devised a retention time bracketing approach to identify and predict sphingolipids of the same homologous series. Excellent concordance of predicted and observed retention times (<0.1 min) of sphingolipids were demonstrated. We also showed that many odd- and/or short-chain sphingolipids, commonly used as internal standards, are present in biological matrices including human serum, peritoneal fluid and cells. Conclusion: A retention time table, and a list of appropriate standards are presented, which are expected to be useful resources in targeted sphingolipidomics.

A Data Science-Based Analysis Points at Distinct Patterns of Lipid Mediator Plasma Concentrations in Patients With Dementia

Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.

Quantification of Lipids: Model, Reality, and Compromise

Lipids are key molecules in various biological processes, thus their quantification is a crucial point in a lot of studies and should be taken into account in lipidomics development. This family is complex and presents a very large diversity of structures, so analyzing and quantifying all this diversity is a real challenge. In this review, the different techniques to analyze lipids will be presented: from nuclear magnetic resonance (NMR) to mass spectrometry (with and without chromatography) including universal detectors. First of all, the state of the art of quantification, with the definitions of terms and protocol standardization, will be presented with quantitative lipidomics in mind, and then technical considerations and limitations of analytical chemistry's tools, such as NMR, mass spectrometry and universal detectors, will be discussed, particularly in terms of absolute quantification.

Intact Cell Lipidomics Reveal Changes to the Ratio of Cardiolipins to Phosphatidylinositols in Response to Kanamycin in HeLa and Primary Cells

Antimicrobial resistance is a major threat the world is currently facing. Development of new antibiotics and the assessment of their toxicity represent important challenges. Current methods for addressing antibiotic toxicity rely on measuring mitochondrial damage using ATP and/or membrane potential as a readout. In this study, we propose an alternative readout looking at changes in the lipidome on intact and unprocessed cells by matrix-assisted laser desorption ionization mass spectrometry. As a proof of principle, we evaluated the impact of known antibiotics (levofloxacin, ethambutol, and kanamycin) on the lipidome of HeLa cells and mouse bone marrow-derived macrophages. Our methodology revealed that clinically relevant concentrations of kanamycin alter the ratio of cardiolipins to phosphatidylinositols. Unexpectedly, only kanamycin had this effect even though all antibiotics used in this study led to a decrease in the maximal mitochondrial respiratory capacity. Altogether, we report that intact cell-targeted lipidomics can be used as a qualitative method to rapidly assess the toxicity of aminoglycosides in HeLa and primary cells. Moreover, these results demonstrate there is no direct correlation between the ratio of cardiolipins to phosphatidylinositols and the maximal mitochondrial respiratory capacity.

Contemporary lipidomic analytics: opportunities and pitfalls

Recent advances in analytical techniques have greatly enhanced the depth of coverage, however lipidomic studies are still restricted to analysing only a subset of known lipids. Numerous complementary techniques are used for investigation of cellular lipidomes, including mass spectrometry (MS), nuclear magnetic resonance and vibrational spectroscopy. The development in electrospray ionization (ESI) MS has accelerated lipidomics research in the past two decades and represents one of the most widely used technique. The versatility of ESI-MS systems allows development of methods to detect and quantify a large diversity of lipid species and classes. However, highly targeted and specific approaches can preclude global analysis of many lipid classes. Indeed, experimental procedures are generally optimised for the lipid species, or lipid class of interest. Therefore, careful consideration of experimental procedures is required for characterisation of biological lipidomes. The current review will describe the lipidomic approaches for considering tissue lipid physiology. Discussion of the main sequences in a lipidomics workflow will be presented, including preparation of samples, accurate quantitation of lipid species and statistical modelling.

NIST lipidomics workflow questionnaire: an assessment of community-wide methodologies and perspectives

INTRODUCTION

Efforts to harmonize lipidomic methodologies have been limited within the community. Here, we aimed to capitalize on the recent National Institute of Standards and Technology lipidomics interlaboratory comparison exercise by implementing a questionnaire that assessed current methodologies, quantitation strategies, standard operating procedures (SOPs), and quality control activities employed by the lipidomics community.

OBJECTIVES

Lipidomics is a rapidly developing field with diverse applications. At present, there are no community-vetted methods to assess measurement comparability or data quality. Thus, a major impetus of this questionnaire was to profile current efforts, highlight areas of need, and establish future objectives in an effort to harmonize lipidomics workflows.

METHODS

The 54-question survey inquired about laboratory demographics, lipidomic methodologies and SOPs, analytical platforms, quantitation, reference materials, quality control procedures, and opinions regarding challenges existing within the community.

RESULTS

A total of 125 laboratories participated in the questionnaire. A broad overview of results highlighted a wide methodological diversity within current lipidomic workflows. The impact of this diversity on lipid measurement and quantitation is currently unknown and needs to be explored further. While some laboratories do incorporate SOPs and quality control activities, these concepts have not been fully embraced by the community. The top five perceived challenges within the lipidomics community were a lack of standardization amongst methods/protocols, lack of lipid standards, software/data handling and quantification, and over-reporting/false positives.

CONCLUSION

The questionnaire provided an overview of current lipidomics methodologies and further promoted the need for community-accepted guidelines and protocols. The questionnaire also served as a platform to help determine and prioritize metrological issues to be investigated.

An integrated method for direct interrogation of sphingolipid homeostasis in the heart and brain tissues of mice through postnatal development up to reproductive senescence

Development of rapid metabolomic methods poised for pathway discovery is expected to facilitate the identification of therapeutic candidates in the metabolomic approach to translational medicine. Using sphingolipid homeostasis as a prototype, we present herein an integrated method to facilitate a fast interrogation of altered sphingolipid (and phospholipid) metabolism associated with perturbed endolysosomal functions in mammalian systems. Constructed upon high performance liquid chromatography coupled to mass spectrometry, this method allows semi-quantitative measurements of more than 300 individual species within 20 min. The method was applied to investigate cardiac- and neural-specific developmental changes in sphingolipid regulation from the postnatal stage to reproductive senescence in mice, revealing that endogenous lysobisphosphatidic acids and specific complex glycosphingolipids are tightly co-regulated to foster concerted reductions in sphingolipid levels at distinct stages of postnatal development. Our lipidomic data suggest that such changing regulatory patterns in sphingolipid homeostasis is attributed to differential endolysosomal degradation of complex sphingolipids, which may be critical in ensuring efficient sphingolipid catabolism and organismal health at each stage of postnatal development.

Absolute quantitative lipidomics reveals lipidome-wide alterations in aging brain

INTRODUCTION

The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism.

OBJECTIVES

We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis.

METHODS

We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids.

RESULTS

Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased.

CONCLUSION

We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis.

Mass-spectrometry-based lipidomics

Lipids, which have a core function in energy storage, signalling and biofilm structures, play important roles in a variety of cellular processes because of the great diversity of their structural and physiochemical properties. Lipidomics is the large-scale profiling and quantification of biogenic lipid molecules, the comprehensive study of their pathways and the interpretation of their physiological significance based on analytical chemistry and statistical analysis. Lipidomics will not only provide insight into the physiological functions of lipid molecules but will also provide an approach to discovering important biomarkers for diagnosis or treatment of human diseases. Mass-spectrometry-based analytical techniques are currently the most widely used and most effective tools for lipid profiling and quantification. In this review, the field of mass-spectrometry-based lipidomics was discussed. Recent progress in all essential steps in lipidomics was carefully discussed in this review, including lipid extraction strategies, separation techniques and mass-spectrometry-based analytical and quantitative methods in lipidomics. We also focused on novel resolution strategies for difficult problems in determining C=C bond positions in lipidomics. Finally, new technologies that were developed in recent years including single-cell lipidomics, flux-based lipidomics and multiomics technologies were also reviewed.

LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics

The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

Sequestration of polyunsaturated fatty acids in membrane phospholipids of Caenorhabditis elegans dauer larva attenuates eicosanoid biosynthesis for prolonged survival

Mechanistic basis governing the extreme longevity and developmental quiescence of dauer juvenile, a "non-ageing" developmental variant of Caenorhabditis elegans, has remained largely obscure. Using a lipidomic approach comprising multiple reaction monitoring transitions specific to distinct fatty acyl moieties, we demonstrated that in comparison to other developmental stages, the membrane phospholipids of dauer larva contain a unique enrichment of polyunsaturated fatty acids (PUFAs). Esterified PUFAs in phospholipids exhibited temporal accumulation throughout the course of dauer endurance, followed by sharp reductions prior to termination of diapause. Reductions in esterified PUFAs were accompanied by concomitant increases in unbound PUFAs, as well as their corresponding downstream oxidized derivatives (i.e. eicosanoids). Global phospholipidomics has unveiled that PUFA sequestration in membrane phospholipids denotes an essential aspect of dauer dormancy, principally via suppression of eicosanoid production; and a failure to upkeep membrane lipid homeostasis is associated with termination of dauer endurance.

Metabolomics through the lens of precision cardiovascular medicine

Metabolomics, which targets at the extensive characterization and quantitation of global metabolites from both endogenous and exogenous sources, has emerged as a novel technological avenue to advance the field of precision medicine principally driven by genomics-oriented approaches. In particular, metabolomics has revealed the cardinal roles that the environment exerts in driving the progression of major diseases threatening public health. Herein, the existent and potential applications of metabolomics in two key areas of precision cardiovascular medicine will be critically discussed: 1) the use of metabolomics in unveiling novel disease biomarkers and pathological pathways; 2) the contribution of metabolomics in cardiovascular drug development. Major issues concerning the statistical handling of big data generated by metabolomics, as well as its interpretation, will be briefly addressed. Finally, the need for integration of various omics branches and adopting a multi-omics approach to precision medicine will be discussed.