Qualitative analysis of phospholipids and their oxidised derivatives - used techniques and examples of their applications related to lipidomic research and food analysis

Phospholipids (PLs) are important biomolecules that not only constitute structural building blocks and scaffolds of cell and organelle membranes but also play a vital role in cell biochemistry and physiology. Moreover, dietary exogenous PLs are characterised by high nutritional value and other beneficial health effects, which are confirmed by numerous epidemiological studies. For this reason, PLs are of high interest in lipidomics that targets both the analysis of membrane lipid distribution as well as correlates composition of lipids with their effects on functioning of cells, tissues and organs. Lipidomic assessments follow-up the changes occurring in living organisms, such as free radical attack and oxidative modifications of the polyunsaturated fatty acids (PUFAs) build in PL structures. Oxidised PLs (oxPLs) can be generated exogenously and supplied to organisms with processed food or formed endogenously as a result of oxidative stress. Cellular and tissue oxPLs can be a biomarker predictive of the development of numerous diseases such as atherosclerosis or neuroinflammation. Therefore, suitable high-throughput analytical techniques, which enable comprehensive analysis of PL molecules in terms of the structure of hydrophilic group, fatty acid (FA) composition and oxidative modifications of FAs, have been currently developed. This review addresses all aspects of PL analysis, including lipid isolation, chromatographic separation of PL classes and species, as well as their detection. The bioinformatic tools that enable handling of a large amount of data generated during lipidomic analysis are also discussed. In addition, imaging techniques such as confocal microscopy and mass spectrometry imaging for analysis of cellular lipid maps, including membrane PLs, are presented.

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects

Lipid signaling has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid research. We used unsupervised machine-learning to analyze lipid marker serum concentrations, pursuing the hypothesis that for the most relevant markers the emerging data structures will coincide with the diagnosis of MS. Machine learning was implemented as emergent self-organizing feature maps (ESOM) combined with the U*-matrix visualization technique. The data space consisted of serum concentrations of three main classes of lipid markers comprising eicosanoids (d = 11 markers), ceramides (d = 10), and lyosophosphatidic acids (d = 6). They were analyzed in cohorts of MS patients (n = 102) and healthy subjects (n = 301). Clear data structures in the high-dimensional data space were observed in eicosanoid and ceramides serum concentrations whereas no clear structure could be found in lysophosphatidic acid concentrations. With ceramide concentrations, the structures that had emerged from unsupervised machine-learning almost completely overlapped with the known grouping of MS patients versus healthy subjects. This was only partly provided by eicosanoid serum concentrations. Thus, unsupervised machine-learning identified distinct data structures of bioactive lipid serum concentrations. These structures could be superimposed with the known grouping of MS patients versus healthy subjects, which was almost completely possible with ceramides. Therefore, based on the present analysis, ceramides are first-line candidates for further exploration as drug-gable targets or biomarkers in MS.

Comparative lipidomic study of urothelial cancer models: association with urothelial cancer cell invasiveness

A joint NMR/LC-MS approach allows to establish significant differences in the lipidoma of invasive urothelial carcinoma cells (T24) with respect to noninvasive urothelial cells (RT4).

Lipidomic analysis can distinguish between two morphologically similar strains of Nannochloropsis oceanica

The two morphologically similar microalgae NMBluh014 and NMBluh-X belong to two different strains of Nannochloropsis oceanica. They possess obviously different feeding effects on bivalves, but are indistinguishable by 18S rRNA and morphological features. In this work, lipidomic analysis followed by principal component analysis and orthogonal projections to latent structures discriminant analysis provided a clear distinction between these strains. Metabolites that definitively contribute to the classification were selected as potential biomarkers. The most important difference in polar lipids were sulfoquinovosyldiacylglycerol (containing 18:1/16:0 and 18:3/16:0) and monogalactosyldiacylglycerol (containing 18:3/16:3 and 20:5/14:0), which were detected only in NMBluh-X. Additionally, an exhaustive qualitative and quantitative profiling of the neutral lipid triacylglycerol (TAG) in the two strains was carried out. The predominant species of TAG containing 16:1/16:1/16:1 acyl groups was detected only in NMBluh-X with a content of ~93.67 ± 11.85 nmol · mg(-1) dry algae at the onset of stationary phase. Meanwhile, TAG containing 16:0/16:0/16:0 was the main TAG in NMBluh014 with a content of 40.25 ± 3.92 nmol · mg(-1) . These results provided the most straightforward evidence for differentiating the two species. The metabolomic profiling indicated that NMBluh-X underwent significant chemical and physiological changes during the growth process, whereas NMBluh014 did not show such noticeable time-dependent metabolite change. This study is the first using Ultra Performance Liquid Chromatography coupled with Electrospray ionization-Quadrupole-Time of Flight Mass Spectrometry (UPLC-Q-TOF-MS) for lipidomic profiling with multivariate statistical analysis to explore lipidomic differences of plesiomorphous microalgae. Our results demonstrate that lipidomic profiling is a valid chemotaxonomic tool in the study of microalgal systematics.

Differential network analysis with multiply imputed lipidomic data

The importance of lipids for cell function and health has been widely recognized, e.g., a disorder in the lipid composition of cells has been related to atherosclerosis caused cardiovascular disease (CVD). Lipidomics analyses are characterized by large yet not a huge number of mutually correlated variables measured and their associations to outcomes are potentially of a complex nature. Differential network analysis provides a formal statistical method capable of inferential analysis to examine differences in network structures of the lipids under two biological conditions. It also guides us to identify potential relationships requiring further biological investigation. We provide a recipe to conduct permutation test on association scores resulted from partial least square regression with multiple imputed lipidomic data from the LUdwigshafen RIsk and Cardiovascular Health (LURIC) study, particularly paying attention to the left-censored missing values typical for a wide range of data sets in life sciences. Left-censored missing values are low-level concentrations that are known to exist somewhere between zero and a lower limit of quantification. To make full use of the LURIC data with the missing values, we utilize state of the art multiple imputation techniques and propose solutions to the challenges that incomplete data sets bring to differential network analysis. The customized network analysis helps us to understand the complexities of the underlying biological processes by identifying lipids and lipid classes that interact with each other, and by recognizing the most important differentially expressed lipids between two subgroups of coronary artery disease (CAD) patients, the patients that had a fatal CVD event and the ones who remained stable during two year follow-up.

Lipidomics: new insight into kidney disease

Due to the incidence of type-2 diabetes and hypertension, chronic kidney disease (CKD) has emerged as a major public health problem worldwide. CKD results in premature death from accelerated cardiovascular disease and various other complications. Early detection, careful monitoring of renal function, and response to therapeutic intervention are critical for prevention of CKD progression and its complications. Unfortunately, traditional biomarkers of renal function are insufficiently sensitive or specific to detect early stages of disease when therapeutic intervention is most effective. Therefore, more sensitive biomarkers of kidney disease are needed for early diagnosis, monitoring, and effective treatment. CKD results in profound changes in lipid and lipoprotein metabolism that, in turn, contribute to progression of CKD and its cardiovascular complications. Lipids and lipid-derived metabolites play diverse and critically important roles in the structure and function of cells, tissues, and biofluids. Lipidomics is a branch of metabolomics, which encompasses the global study of lipids and their biologic function in health and disease including identification of biomarkers for diagnosis, prognosis, prevention, and therapeutic response for various diseases. This review summarizes recent developments in lipidomics and its application to various kidney diseases including chronic glomerulonephritis, IgA nephropathy, chronic renal failure, renal cell carcinoma, diabetic nephropathy, and acute renal failure in clinical and experimental research. Analytical technologies, data analysis, as well as currently known metabolic biomarkers of kidney diseases are addressed. Future perspectives and potential limitations of lipidomics are discussed.

Novel approach for analysis of bronchoalveolar lavage fluid (BALF) using HPLC-QTOF-MS-based lipidomics: lipid levels in asthmatics and corticosteroid-treated asthmatic patients

To better understand the respiratory lipid phenotypes of asthma, we developed a novel method for lipid profiling of bronchoalveolar lavage fluid (BALF) using HPLC-QTOF-MS with an internal spectral library and high-throughput lipid-identifying software. The method was applied to BALF from 38 asthmatic patients (18 patients with nonsteroid treated bronchial asthma [NSBA] and 20 patients with steroid treated bronchial asthma [SBA]) and 13 healthy subjects (NC). We identified 69 lipids, which were categorized into one of six lipid classes: lysophosphatidylcholine (LPC), phosphatidylcholine (PC), phosphatidylglycerol (PG), phosphatidylserine (PS), sphingomyelin (SM) and triglyceride (TG). Compared with the NC group, the individual quantity levels of the six classes of lipids were significantly higher in the NSBA subjects. In the SBA subjects, the PC, PG, PS, SM, and TG levels were similar to the levels observed in the NC group. Using differentially expressed lipid species (p value < 0.05, FDR < 0.1 and VIP score of PLS-DA > 1), 34 lipid biomarker candidates with high prediction performance between asthmatics and controls were identified (AUROC > 0.9). These novel findings revealed specific characteristics of lipid phenotypes in asthmatic patients and suggested the importance of future research on the relationship between lipid levels and asthma.

Lipidomics applications for discovering biomarkers of diseases in clinical chemistry

Lipids are the fundamental components of biological membranes as well as the metabolites of organisms. Lipids play diverse and important roles in biologicals. The lipid imbalance is closely associated with numerous human lifestyle-related diseases, such as atherosclerosis, obesity, diabetes, and Alzheimer's disease. Lipidomics or lipid profiling is a system-based study of all lipids aiming at comprehensive analysis of lipids in the biological system. Lipidomics has been accepted as a lipid-related research tool in lipid biochemistry, clinical biomarker discovery, disease diagnosis, and in understanding disease pathology. Lipidomics will not only provide insights into the specific functions of lipid species in health and disease, but will also identify potential biomarkers for establishing preventive or therapeutic programs for human diseases. This review presents an overview of lipidomics followed by in-depth discussion of its application to the study of human diseases, including extraction methods of lipids, analytical technologies, data analysis, and clinical research in cancer, neuropsychiatric disease, cardiovascular disease, kidney disease, and respiratory disease. We describe the current status of the identification of metabolic biomarkers in different diseases. We also discuss the lipidomics for the future perspectives and their potential problems. The application of lipidomics in clinical studies may provide new insights into lipid profiling and pathophysiological mechanisms.

Statistical agglomeration: peak summarization for direct infusion lipidomics

MOTIVATION

Quantification of lipids is a primary goal in lipidomics. In direct infusion/injection (or shotgun) lipidomics, accurate downstream identification and quantitation requires accurate summarization of repetitive peak measurements. Imprecise peak summarization multiplies downstream error by propagating into species identification and intensity estimation. To our knowledge, this is the first analysis of direct infusion peak summarization in the literature.

RESULTS

We present two novel peak summarization algorithms for direct infusion samples and compare them with an off-machine ad hoc summarization algorithm as well as with the propriety Xcalibur algorithm. Our statistical agglomeration algorithm reduces peakwise error by 38% mass/charge (m/z) and 44% (intensity) compared with the ad hoc method over three datasets. Pointwise error is reduced by 23% (m/z). Compared with Xcalibur, our statistical agglomeration algorithm produces 68% less m/z error and 51% less intensity error on average on two comparable datasets.

AVAILABILITY

The source code for Statistical Agglomeration and the datasets used are freely available for non-commercial purposes at https://github.com/optimusmoose/statistical_agglomeration. Modified Bin Aggolmeration is freely available in MSpire, an open source mass spectrometry package at https://github.com/princelab/mspire/.

Sum of the parts: mass spectrometry-based metabolomics

Metabolomics is a rapidly growing field of research used in the identification and quantification of the small molecule metabolites within an organism, thereby providing insights into cell metabolism and bioenergetics as well as processes important in clinical medicine, such as disposition of pharmaceutical compounds. It offers comprehensive information about thousands of low-molecular mass compounds (<1500 Da) that represent a wide range of pathways and intermediary metabolism. Because of its vast expansion in the past two decades, mass spectrometry has become an indispensable tool in "omic" analyses. The use of different ionization techniques such as the more traditional electrospray and matrix-assisted laser desorption, as well as recently popular desorption electrospray ionization, has allowed the analysis of a wide range of biomolecules (e.g., peptides, proteins, lipids, and sugars), and their imaging and analysis in the original sample environment in a workup free fashion. An overview of the current state of the methodology is given, as well as examples of application.

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake

Plant sterols (PS) are well known to reduce serum levels of total cholesterol and LDL-cholesterol. Lipidomics potentially provides detailed information on a wide range of individual serum lipid metabolites, which may further add to our understanding of the biological effects of PS. In this study, lipidomics analysis was applied to serum samples from a placebo-controlled, parallel human intervention study (n = 97) of 4-week consumption of two PS-enriched, yoghurt drinks differing in fat content (based on 0.1% vs. 1.5% dairy fat). A comprehensive data analysis strategy was developed and implemented to assess and compare effects of two different PS-treatments and placebo treatment. The combination of univariate and multivariate data analysis approaches allowed to show significant effects of PS intake on the serum lipidome, and helped to distinguish them from fat content and non-specific effects. The PS-enriched 0.1% dairy fat yoghurt drink had a stronger impact on the lipidome than the 1.5% dairy fat yoghurt drink, despite similar LDL-cholesterol lowering effects. The PS-enriched 0.1% dairy fat yoghurt drink reduced levels of several sphingomyelins which correlated well with the reduction in LDL-cholesterol and can be explained by co-localization of sphingomyelins and cholesterol on the surface of LDL lipoprotein. Statistically significant reductions in serum levels of two lysophosphatidylcholines (LPC(16:1), LPC(20:1)) and cholesteryl arachidonate may suggest reduced inflammation and atherogenic potential. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0384-2) contains supplementary material, which is available to authorized users.

Quantitative analysis of glycerophospholipids by LC-MS: acquisition, data handling, and interpretation

As technology expands what it is possible to accurately measure, so too the challenges faced by modern mass spectrometry applications expand. A high level of accuracy in lipid quantitation across thousands of chemical species simultaneously is demanded. While relative changes in lipid amounts with varying conditions may provide initial insights or point to novel targets, there are many questions that require determination of lipid analyte absolute quantitation. Glycerophospholipids present a significant challenge in this regard, given the headgroup diversity, large number of possible acyl chain combinations, and vast range of ionization efficiency of species. Lipidomic output is being used more often not just for profiling of the masses of species, but also for highly-targeted flux-based measurements which put additional burdens on the quantitation pipeline. These first two challenges bring into sharp focus the need for a robust lipidomics workflow including deisotoping, differentiation from background noise, use of multiple internal standards per lipid class, and the use of a scriptable environment in order to create maximum user flexibility and maintain metadata on the parameters of the data analysis as it occurs. As lipidomics technology develops and delivers more output on a larger number of analytes, so must the sophistication of statistical post-processing also continue to advance. High-dimensional data analysis methods involving clustering, lipid pathway analysis, and false discovery rate limitation are becoming standard practices in a maturing field.

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background

Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases.

Results

We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma.

Conclusions

The novel computational paradigm provides unique "fingerprints" by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Software tool for mining liquid chromatography/multi-stage mass spectrometry data for comprehensive glycerophospholipid profiling

Electrospray ionization mass spectrometry (ESI-MS) has emerged as an indispensable tool in the field of lipidomics. Despite the growing interest in lipid analysis, there are only a few software tools available for data evaluation, as compared for example to proteomics applications. This makes comprehensive lipid analysis a complex challenge. Thus, a computational tool for harnessing the raw data from liquid chromatography/mass spectrometry (LC/MS) experiments was developed in this study and is available from the authors on request. The Profiler-Merger-Viewer tool is a software package for automatic processing of raw-data from data-dependent experiments, measured by high-performance liquid chromatography hyphenated to electrospray ionization hybrid linear ion trap Fourier transform mass spectrometry (FTICR-MS and Orbitrap) in single and multi-stage mode. The software contains three parts: processing of the raw data by Profiler for lipid identification, summarizing of replicate measurements by Merger and visualization of all relevant data (chromatograms as well as mass spectra) for validation of the results by Viewer. The tool is easily accessible, since it is implemented in Java and uses Microsoft Excel (XLS) as output format. The motivation was to develop a tool which supports and accelerates the manual data evaluation (identification and relative quantification) significantly but does not make a complete data analysis within a black-box system. The software's mode of operation, usage and options will be demonstrated on the basis of a lipid extract of baker's yeast (S. cerevisiae). In this study, we focused on three important representatives of lipids: glycerophospholipids, lyso-glycerophospholipids and free fatty acids.

Advances in mass spectrometry for lipidomics

Recent expansion in research in the field of lipidomics has been driven by the development of new mass spectrometric tools and protocols for the identification and quantification of molecular lipids in complex matrices. Although there are similarities between the field of lipidomics and the allied field of mass spectrometry (e.g., proteomics), lipids present some unique advantages and challenges for mass spectrometric analysis. The application of electrospray ionization to crude lipid extracts without prior fractionation-the so-called shotgun approach-is one such example, as it has perhaps been more successfully applied in lipidomics than in any other discipline. Conversely, the diverse molecular structure of lipids means that collision-induced dissociation alone may be limited in providing unique descriptions of complex lipid structures, and the development of additional, complementary tools for ion activation and analysis is required to overcome these challenges. In this article, we discuss the state of the art in lipid mass spectrometry and highlight several areas in which current approaches are deficient and further innovation is required.