Bioinformatics and computational methods for lipidomics

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Expanding the Molecular Disturbances of Lipoproteins in Cardiometabolic Diseases: Lessons from Lipidomics

The increasing global burden of cardiometabolic diseases highlights the urgent clinical need for better personalized prediction and intervention strategies. Early diagnosis and prevention could greatly reduce the enormous socio-economic burden posed by these states. Plasma lipids including total cholesterol, triglycerides, HDL-C, and LDL-C have been at the center stage of the prediction and prevention strategies for cardiovascular disease; however, the bulk of cardiovascular disease events cannot be explained sufficiently by these lipid parameters. The shift from traditional serum lipid measurements that are poorly descriptive of the total serum lipidomic profile to comprehensive lipid profiling is an urgent need, since a wealth of metabolic information is currently underutilized in the clinical setting. The tremendous advances in the field of lipidomics in the last two decades has facilitated the research efforts to unravel the lipid dysregulation in cardiometabolic diseases, enabling the understanding of the underlying pathophysiological mechanisms and identification of predictive biomarkers beyond traditional lipids. This review presents an overview of the application of lipidomics in the study of serum lipoproteins in cardiometabolic diseases. Integrating the emerging multiomics with lipidomics holds great potential in moving toward this goal.

Chemical derivatization strategy for mass spectrometry-based lipidomics

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

The foundations and development of lipidomics

For over a century, the importance of lipid metabolism in biology was recognized but difficult to mechanistically understand due to the lack of sensitive and robust technologies for identification and quantification of lipid molecular species. The enabling technological breakthroughs emerged in the 1980s with the development of soft ionization methods (Electrospray Ionization and Matrix Assisted Laser Desorption/Ionization) that could identify and quantify intact individual lipid molecular species. These soft ionization technologies laid the foundations for what was to be later named the field of lipidomics. Further innovative advances in multistage fragmentation, dramatic improvements in resolution and mass accuracy, and multiplexed sample analysis fueled the early growth of lipidomics through the early 1990s. The field exponentially grew through the use of a variety of strategic approaches, which included direct infusion, chromatographic separation, and charge-switch derivatization, which facilitated access to the low abundance species of the lipidome. In this Thematic Review, we provide a broad perspective of the foundations, enabling advances, and predicted future directions of growth of the lipidomics field.

Overview of Lipidomic Analysis of Triglyceride Molecular Species in Biological Lipid Extracts

Triglyceride (TG) is a class of neutral lipids, which functions as an energy storage depot and is important for cellular growth, metabolism, and function. The composition and content of TG molecular species are crucial factors for nutritional aspects in food chemistry and are directly associated with several diseases, including atherosclerosis, diabetes, obesity, stroke, etc. As a result of the complexities of aliphatic moieties and their different connections/locations to the glycerol backbone in TG molecules, accurate identification of individual TG molecular species and quantitative assessment of TG composition and content are particularly challenging, even at the current stage of lipidomics development. Herein, methods developed for analysis of TG species, such as liquid chromatography-mass spectrometry with a variety of columns and different mass spectrometric techniques, shotgun lipidomics approaches, and ion-mobility-based analysis, are reviewed. Moreover, the potential limitations of the methods are discussed. It is our sincere hope that the overviews and discussions can provide some insights for researchers to select an appropriate approach for TG analysis and can serve as the basis for those who would like to establish a methodology for TG analysis or develop a new method when novel tools become available. Biologically accurate analysis of TG species with an enabling method should lead us toward improving the nutritional quality, revealing the effects of TG on diseases, and uncovering the underlying biochemical mechanisms related to these diseases.

Dynamic binning peak detection and assessment of various lipidomics liquid chromatography-mass spectrometry pre-processing platforms

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics generates large datasets that need to be interpreted using high-performance data pre-processing tools such as XCMS, mzMine, and Progenesis. These pre-processing tools rely heavily on accurate peak detection, which depends on proper setting of the peak detection mass tolerance (PDMT). The PDMT is usually set with a fixed value in either ppm or Da units. However, this fixed value may result in duplicates or missed peak detection and inaccurate peak quantification. To improve the accuracy of peak detection, we developed the dynamic binning method, which considers peak broadening described by the physics of ion separation and sets the PDMT dynamically in function of m/z. In our method, the PDMT is proportional to (mz)² for Fourier-transform ion cyclotron resonance (FTICR), to (mz)^1.5 for Orbitrap and to m/z for Quadrupole time-of-flight (Q-TOF), and is a constant for Quadrupole mass analyzer. The dynamic binning method was implemented in XCMS [1,2], and the adopted source code is available in GitHub at https://github.com/xiaodfeng/DynamicXCMS. We have compared the performance of the XCMS implemented dynamic binning with different popular lipidomics pre-processing tools to find differential compounds. We generated set samples with 43 lipid internal standards that were differentially spiked to aliquots of one human plasma lipid sample using Orbitrap LC-MS/MS. The performance of various pipelines using matched parameter sets was quantified by a quality score system that reflects the ability of a pre-processing pipeline to detect differential peaks spiked at various concentrations. The quality score indicated that our dynamic binning method improves the quantification performance of XCMS (maximum p-value 9.8·10^-3 of two-sample Wilcoxon test) over its original implementation. We also showed that the XCMS with dynamic binning found differential spiked-in lipids better or with similar performance as mzMine and Progenesis do.

Site-Specific Lipidomic Signatures of Sea Lettuce (Ulva spp., Chlorophyta) Hold the Potential to Trace Their Geographic Origin

The wild harvest and aquaculture of Ulva spp. has deserved growing attention in Europe. However, the impact of geographical origin on the biochemical composition of different species and/or strains is yet to be described in detail. Hence, the present study aimed to detect the variability of the lipidome of different species and/or strains of Ulva originating from different geographic locations. We hypothesized that lipidomic signatures can be used to trace the geographic origin post-harvesting of these valuable green seaweeds. Ulva spp. was sampled from eight distinct ecosystems along the Atlantic Iberian coast and Ulva rigida was sourced from an aquaculture farm operating a land-based integrated production site. Results showed significant differences in the lipidomic profile displayed by Ulva spp. originating from different locations, namely, due to different levels of polyunsaturated betaine lipids and galactolipids; saturated betaine lipids and sulfolipids; and some phospholipid species. Overall, a set of 25 site-specific molecular lipid species provide a unique lipidomic signature for authentication and geographic origin certification of Ulva species. Present findings highlight the potential of lipidome plasticity as a proxy to fight fraudulent practices, but also to ensure quality control and prospect biomass for target bioactive compounds.

Metabolomics Profiling As a Diagnostic Tool in Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a complex disease with a multifaceted pathophysiology. Impairment of energy metabolism is a key component of secondary insults. This phenomenon is a consequence of multiple potential mechanisms including diffusion hypoxia, mitochondrial failure, and increased energy needs due to systemic trauma responses, seizures, or spreading depolarization. The degree of disturbance in brain metabolism is affected by treatment interventions and reflected in clinical patient outcome. Hence, monitoring of these secondary events in peripheral blood will provide a window into the pathophysiological course of severe TBI. New methods for assessing perturbation of brain metabolism are needed in order to monitor on-going pathophysiological processes and thus facilitate targeted interventions and predict outcome. Circulating metabolites in peripheral blood may serve as sensitive markers of pathological processes in TBI. The levels of these small molecules in blood are less dependent on the integrity of the blood–brain barrier as compared to protein biomarkers. We have recently characterized a specific metabolic profile in serum that is associated with both initial severity and patient outcome of TBI. We found that two medium-chain fatty acids, octanoic and decanoic acids, as well as several sugar derivatives are significantly associated with the severity of TBI. The top ranking peripheral blood metabolites were also highly correlated with their levels in cerebral microdialyzates. Based on the metabolite profile upon admission, we have been able to develop a model that accurately predicts patient outcome. Moreover, metabolomics profiling improved the performance of the well-established clinical prognostication model. In this review, we discuss metabolomics profiling in patients with severe TBI. We present arguments in support of the need for further development and validation of circulating biomarkers of cerebral metabolism and for their use in assessing patients with severe TBI.

LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets

Lipids are dynamic constituents of biological systems, rapidly responding to any changes in physiological conditions. Thus, there is a large interest in lipid-derived markers for diagnostic and prognostic applications, especially in translational and systems medicine research. As lipid identification remains a bottleneck of modern untargeted lipidomics, we developed LipidHunter, a new open source software for the high-throughput identification of phospholipids in data acquired by LC-MS and shotgun experiments. LipidHunter resembles a workflow of manual spectra annotation. Lipid identification is based on MS/MS data analysis in accordance with defined fragmentation rules for each phospholipid (PL) class. The software tool matches product and neutral loss signals obtained by collision-induced dissociation to a user-defined white list of fatty acid residues and PL class-specific fragments. The identified signals are tested against elemental composition and bulk identification provided via LIPID MAPS search. Furthermore, LipidHunter provides information-rich tabular and graphical reports allowing to trace back key identification steps and perform data quality control. Thereby, 202 discrete lipid species were identified in lipid extracts from rat primary cardiomyocytes treated with a peroxynitrite donor. Their relative quantification allowed the monitoring of dynamic reconfiguration of the cellular lipidome in response to mild nitroxidative stress. LipidHunter is available free for download at https://bitbucket.org/SysMedOs/lipidhunter .

Dynamic lipid profile of hyperlipidemia mice

Biomarkers of serum fatty acids in hyperlipidemia need to be elucidated. 90 SPF KM male mice were randomly divided into 18 groups (n=5/group), control groups, and high fat diet (HFD) groups at 9 time points. On day 7, 10, 15, 18, 21, 24, 28, 31, and 35, the mice were sacrificed; blood was collected into tubes from the eyes, serum samples for clinical biochemistry assays and gas chromatography-mass spectroscopy were attained after centrifugation, and the contents of serum fatty acids were detected with GC-MS. Sections of livers were taken and stored in formalin solution for histological assessments. No species differences existed in all these groups. The contents of C16:1, C18:1, C22:6 were significantly different between HFD groups and the corresponding controls; meanwhile, the proportion of fatty acids, especially the monounsaturated degree, the polyunsaturated degree, changed significantly and regularly (P<0.05). Thus the three unsaturated fatty acids C16:1, C18:1, C22:6 and the monounsaturated/polyunsaturated unsaturated degrees may be as potential biomarkers of hyperlipidemia.

Lipidomics in biomedical research-practical considerations

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.

Plant lipidomics at the crossroads: From technology to biology driven science

The identification and quantification of lipids from plant tissues have become commonplace and many researchers now incorporate lipidomics approaches into their experimental studies. Plant lipidomics research continues to involve technological developments such as those in mass spectrometry imaging, but in large part, lipidomics approaches have matured to the point of being accessible to the novice. Here we review some important considerations for those planning to apply plant lipidomics to their biological questions, and offer suggestions for appropriate tools and practices. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Approaches to Study Phosphatases

Phosphatases play key roles in normal physiology and diseases. Studying phosphatases has been both essential and challenging, and the application of conventional genetic and biochemical methods has led to crucial but still limited understanding of their mechanisms, substrates, and exclusive functions within highly intricate networks. With the advances in technologies such as cellular imaging and molecular and chemical biology in terms of sensitive tools and methods, the phosphatase field has thrived in the past years and has set new insights for cell signaling studies and for therapeutic development. In this review, we give an overview of the existing interdisciplinary tools for phosphatases, give examples on how they have been applied to increase our understanding of these enzymes, and suggest how they-and other tools yet barely used in the phosphatase field-might be adapted to address future questions and challenges.

Lipidomic analyses in epidemiology

Clinical lipid measurements have been the mainstay of risk assessment for chronic disease since the Framingham study commenced over 60 years ago. Thousands of subsequent epidemiological studies have provided much insight into the relationship between plasma lipid profiles, health and disease. However, the human lipidome consists of thousands of individual lipid species, and current lipidomic technology presents us with an unprecedented opportunity to measure lipid phenotypes, representing genomic, metabolic, diet and lifestyle-related exposures, in large epidemiological studies. The number of epidemiological studies using lipidomic profiling is increasing and has the potential to provide improved biological and clinical insight into human disease. In this review, we discuss current lipidomic technologies, epidemiological studies using these technologies and the statistical approaches used in the analysis of the resulting data. We highlight the potential of integrating genomic and lipidomic datasets and discuss the future opportunities and challenges in this emerging field.

Novel advances in shotgun lipidomics for biology and medicine

The field of lipidomics, as coined in 2003, has made profound advances and been rapidly expanded. The mass spectrometry-based strategies of this analytical methodology-oriented research discipline for lipid analysis are largely fallen into three categories: direct infusion-based shotgun lipidomics, liquid chromatography-mass spectrometry-based platforms, and matrix-assisted laser desorption/ionization mass spectrometry-based approaches (particularly in imagining lipid distribution in tissues or cells). This review focuses on shotgun lipidomics. After briefly introducing its fundamentals, the major materials of this article cover its recent advances. These include the novel methods of lipid extraction, novel shotgun lipidomics strategies for identification and quantification of previously hardly accessible lipid classes and molecular species including isomers, and novel tools for processing and interpretation of lipidomics data. Representative applications of advanced shotgun lipidomics for biological and biomedical research are also presented in this review. We believe that with these novel advances in shotgun lipidomics, this approach for lipid analysis should become more comprehensive and high throughput, thereby greatly accelerating the lipidomics field to substantiate the aberrant lipid metabolism, signaling, trafficking, and homeostasis under pathological conditions and their underpinning biochemical mechanisms.

LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database) is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level.

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.

Cholesterol, sphingolipids, and glycolipids: what do we know about their role in raft-like membranes?

Lipids rafts are considered to be functional nanoscale membrane domains enriched in cholesterol and sphingolipids, characteristic in particular of the external leaflet of cell membranes. Lipids, together with membrane-associated proteins, are therefore considered to form nanoscale units with potential specific functions. Although the understanding of the structure of rafts in living cells is quite limited, the possible functions of rafts are widely discussed in the literature, highlighting their importance in cellular functions. In this review, we discuss the understanding of rafts that has emerged based on recent atomistic and coarse-grained molecular dynamics simulation studies on the key lipid raft components, which include cholesterol, sphingolipids, glycolipids, and the proteins interacting with these classes of lipids. The simulation results are compared to experiments when possible.

Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Mycobacterial Lipidomics

Lipidomics is a distinct subspecialty of metabolomics concerned with hydrophobic molecules that organize into membranes. Most of the lipid classes present in Mycobacterium tuberculosis are found only in Actinobacteria and show extreme structural diversity. This article highlights the conceptual basis and the practical challenges associated with the mass spectrometry–based lipidomic study of M. tuberculosis to solve basic questions about the virulence of this lipid-laden organism.

Predicting glycerophosphoinositol identities in lipidomic datasets using VaLID (Visualization and Phospholipid Identification)--an online bioinformatic search engine

The capacity to predict and visualize all theoretically possible glycerophospholipid molecular identities present in lipidomic datasets is currently limited. To address this issue, we expanded the search-engine and compositional databases of the online Visualization and Phospholipid Identification (VaLID) bioinformatic tool to include the glycerophosphoinositol superfamily. VaLID v1.0.0 originally allowed exact and average mass libraries of 736,584 individual species from eight phospholipid classes: glycerophosphates, glyceropyrophosphates, glycerophosphocholines, glycerophosphoethanolamines, glycerophosphoglycerols, glycerophosphoglycerophosphates, glycerophosphoserines, and cytidine 5'-diphosphate 1,2-diacyl-sn-glycerols to be searched for any mass to charge value (with adjustable tolerance levels) under a variety of mass spectrometry conditions. Here, we describe an update that now includes all possible glycerophosphoinositols, glycerophosphoinositol monophosphates, glycerophosphoinositol bisphosphates, and glycerophosphoinositol trisphosphates. This update expands the total number of lipid species represented in the VaLID v2.0.0 database to 1,473,168 phospholipids. Each phospholipid can be generated in skeletal representation. A subset of species curated by the Canadian Institutes of Health Research Training Program in Neurodegenerative Lipidomics (CTPNL) team is provided as an array of high-resolution structures. VaLID is freely available and responds to all users through the CTPNL resources web site.

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.

Nutritional lipidomics: molecular metabolism, analytics, and diagnostics

The field of lipidomics is providing nutritional science a more comprehensive view of lipid intermediates. Lipidomics research takes advantage of the increase in accuracy and sensitivity of mass detection of MS with new bioinformatics toolsets to characterize the structures and abundances of complex lipids. Yet, translating lipidomics to practice via nutritional interventions is still in its infancy. No single instrumentation platform is able to solve the varying analytical challenges of the different molecular lipid species. Biochemical pathways of lipid metabolism remain incomplete and the tools to map lipid compositional data to pathways are still being assembled. Biology itself is dauntingly complex and simply separating biological structures remains a key challenge to lipidomics. Nonetheless, the strategy of combining tandem analytical methods to perform the sensitive, high-throughput, quantitative, and comprehensive analysis of lipid metabolites of very large numbers of molecules is poised to drive the field forward rapidly. Among the next steps for nutrition to understand the changes in structures, compositions, and function of lipid biomolecules in response to diet is to describe their distribution within discrete functional compartments lipoproteins. Additionally, lipidomics must tackle the task of assigning the functions of lipids as signaling molecules, nutrient sensors, and intermediates of metabolic pathways.

Simulation of triacylglycerol ion profiles: bioinformatics for interpretation of triacylglycerol biosynthesis

Although the synthesis pathways of intracellular triacylglycerol (TAG) species have been well elucidated, assessment of the contribution of an individual pathway to TAG pools in different mammalian organs, particularly under pathophysiological conditions, is difficult, although not impossible. Herein, we developed and validated a novel bioinformatic approach to assess the differential contributions of the known pathways to TAG pools through simulation of TAG ion profiles determined by shotgun lipidomics. This powerful approach was applied to determine such contributions in mouse heart, liver, and skeletal muscle and to examine the changes of these pathways in mouse liver induced after treatment with a high-fat diet. It was clearly demonstrated that assessment of the altered TAG biosynthesis pathways under pathophysiological conditions can be readily achieved through simulation of lipidomics data. Collectively, this new development should greatly facilitate our understanding of the biochemical mechanisms underpinning TAG accumulation at the states of obesity and lipotoxicity.

Metabolomics in the studies of islet autoimmunity and type 1 diabetes

The metabolome is sensitive to genetic and environmental factors contributing to complex diseases such as type 1 diabetes (T1D). Metabolomics is the study of biochemical and physiological processes involving metabolites. It is therefore one of the key platforms for the discovery and study of pathophysiological phenomena leading to T1D and the development of T1D-associated complications. Although the application of metabolomics in T1D research is still rare, metabolomic research has already advanced across the full spectrum, from disease progression to the development of diabetic complications. Metabolomic studies in T1D have contributed to an improved etiopathogenic understanding and demonstrated their potential in the clinic. For example, metabolomic data from recent T1D studies suggest that a specific metabolic profile, or metabotype, precedes islet autoimmunity and the development of overt T1D. These early metabolic changes are attributed to many biochemical pathways, thus suggesting a systemic change in metabolism which may be inborn. Based on this evidence, the role of the metabolome in the progression to T1D is therefore to facilitate specific biochemical processes associated with T1D, and to contribute to the development of a vulnerable state in which disease is more likely to be triggered. This may have important implications for the understanding of T1D pathophysiology and early disease detection and prevention.

Lipidomic profiling of model organisms and the world's major pathogens

Lipidomics is a subspecialty of metabolomics that focuses on water insoluble metabolites that form membrane barriers. Most lipidomic databases catalog lipids from common model organisms, like humans or Escherichia coli. However, model organisms' lipid profiles show surprisingly little overlap with those of specialized pathogens, creating the need for organism-specific lipidomic databases. Here we review rapid progress in lipidomic platform development with regard to chromatography, detection and bioinformatics. We emphasize new methods of comparative lipidomics, which use aligned datasets to identify lipids changed after introducing a biological variable. These new methods provide an unprecedented ability to broadly and quantitatively describe lipidic change during biological processes and identify changed lipids with low error rates.

Statistical analysis of the processes controlling choline and ethanolamine glycerophospholipid molecular species composition

The regulation and maintenance of the cellular lipidome through biosynthetic, remodeling, and catabolic mechanisms are critical for biological homeostasis during development, health and disease. These complex mechanisms control the architectures of lipid molecular species, which have diverse yet highly regulated fatty acid chains at both the sn1 and sn2 positions. Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) serve as the predominant biophysical scaffolds in membranes, acting as reservoirs for potent lipid signals and regulating numerous enzymatic processes. Here we report the first rigorous computational dissection of the mechanisms influencing PC and PE molecular architectures from high-throughput shotgun lipidomic data. Using novel statistical approaches, we have analyzed multidimensional mass spectrometry-based shotgun lipidomic data from developmental mouse heart and mature mouse heart, lung, brain, and liver tissues. We show that in PC and PE, sn1 and sn2 positions are largely independent, though for low abundance species regulatory processes may interact with both the sn1 and sn2 chain simultaneously, leading to cooperative effects. Chains with similar biochemical properties appear to be remodeled similarly. We also see that sn2 positions are more regulated than sn1, and that PC exhibits stronger cooperative effects than PE. A key aspect of our work is a novel statistically rigorous approach to determine cooperativity based on a modified Fisher's exact test using Markov Chain Monte Carlo sampling. This computational approach provides a novel tool for developing mechanistic insight into lipidomic regulation.

Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses

Since our last comprehensive review on multi-dimensional mass spectrometry-based shotgun lipidomics (Mass Spectrom. Rev. 24 (2005), 367), many new developments in the field of lipidomics have occurred. These developments include new strategies and refinements for shotgun lipidomic approaches that use direct infusion, including novel fragmentation strategies, identification of multiple new informative dimensions for mass spectrometric interrogation, and the development of new bioinformatic approaches for enhanced identification and quantitation of the individual molecular constituents that comprise each cell's lipidome. Concurrently, advances in liquid chromatography-based platforms and novel strategies for quantitative matrix-assisted laser desorption/ionization mass spectrometry for lipidomic analyses have been developed. Through the synergistic use of this repertoire of new mass spectrometric approaches, the power and scope of lipidomics has been greatly expanded to accelerate progress toward the comprehensive understanding of the pleiotropic roles of lipids in biological systems.

Analysis of major phospholipid species and ergosterol in fermenting industrial yeast strains using atmospheric pressure ionization ion-trap mass spectrometry

Knowledge of the individual lipid species that are associated with ethanol tolerance in Saccharomyces cerevisiae is necessary to understand potential mechanisms of how this organism uses these molecules to mitigate the toxic effects of ethanol. Three industrial yeast strains with varying degrees of ethanol tolerance were examined utilizing normal phase high-performance liquid chromatography and atmospheric pressure ionization-ion-trap mass spectrometry methods to quantitatively determine phospholipid and ergosterol levels at numerous fermentation time points. Both high and low Brix fermentations were performed to assess the sugar utilization capabilities of the strains. The results indicated that the strain with the most robust fermentation characteristics had the highest phosphatidylinositol levels and lowest phosphatidylcholine levels. Examination of the phospholipid structural data from tandem MS experiments indicated that the levels of several phospholipid species were unique to the slowest fermenting strain. The relation of ergosterol and other phospholipids to ethanol tolerance is also discussed.

Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry

Recent advances in mass spectrometry approaches to the analysis of lipids include the ability to incorporate both lipid class identification with lipid structural information for increased characterization capabilities. The detailed examination of lipids and their biosynthetic and biochemical pathways made possible by novel instrumental and bioinformatics approaches is advancing research in fundamental cellular and medical studies. Recently, high-throughput structural analysis has been demonstrated through the use of rapid gas-phase separation on the basis of the ion mobility (IM) analytical technique combined with mass spectrometry (IM-MS). While IM-MS has been extensively utilized in biochemical research for peptide, protein and small molecule analysis, the role of IM-MS in lipid research is still an active area of development. In this review of lipid-based IM-MS research, we begin with an overview of three contemporary IM techniques which show great promise in being applied towards the analysis of lipids. Fundamental concepts regarding the integration of IM-MS are reviewed with emphasis on the applications of IM-MS towards simplifying and enhancing complex biological sample analysis. Finally, several recent IM-MS lipid studies are highlighted and the future prospects of IM-MS for integrated omics studies and enhanced spatial profiling through imaging IM-MS are briefly described.

Shotgun lipidomics on high resolution mass spectrometers

Despite their compositional complexity, lipidomes comprise a large number of isobaric species that cannot be distinguished by conventional low resolution mass spectrometry and therefore in-depth MS/MS analysis was required for their accurate quantification. Here we argue that the progress in high resolution mass spectrometry is changing the concept of lipidome characterization. Because exact masses of isobaric species belonging to different lipid classes are not necessarily identical, they can now be distinguished and directly quantified in total lipid extracts. By streamlining and simplifying the molecular characterization of lipidomes, high resolution mass spectrometry has developed into a generic tool for cell biology and molecular medicine.

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.

A mathematical model for the determination of steady-state cardiolipin remodeling mechanisms using lipidomic data

Technical advances in lipidomic analysis have generated tremendous amounts of quantitative lipid molecular species data, whose value has not been fully explored. We describe a novel computational method to infer mechanisms of de novo lipid synthesis and remodeling from lipidomic data. We focus on the mitochondrial-specific lipid cardiolipin (CL), a polyglycerol phospholipid with four acyl chains. The lengths and degree of unsaturation of these acyl chains vary across CL molecules, and regulation of these differences is important for mitochondrial energy metabolism. We developed a novel mathematical approach to determine mechanisms controlling the steady-state distribution of acyl chain combinations in CL . We analyzed mitochondrial lipids from 18 types of steady-state samples, each with at least 3 replicates, from mouse brain, heart, lung, liver, tumor cells, and tumors grown in vitro. Using a mathematical model for the CL remodeling mechanisms and a maximum likelihood approach to infer parameters, we found that for most samples the four chain positions have an independent and identical distribution, indicating they are remodeled by the same processes. Furthermore, for most brain samples and liver, the distribution of acyl chains is well-fit by a simple linear combination of the pools of acyl chains in phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG). This suggests that headgroup chemistry is the key determinant of acyl donation into CL, with chain length/saturation less important. This canonical remodeling behavior appears damaged in some tumor samples, which display a consistent excess of CL molecules having particular masses. For heart and lung, the “proportional incorporation” assumption is not adequate to explain the CL distribution, suggesting additional acyl CoA-dependent remodeling that is chain-type specific. Our findings indicate that CL remodeling processes can be described by a small set of quantitative relationships, and that bioinformatic approaches can help determine these processes from high-throughput lipidomic data.

Multifaceted nature of membrane microdomains in colorectal cancer

Membrane microdomains or lipid rafts are known to be highly dynamic and to act as selective signal transduction mediators that facilitate interactions between the cell’s external and internal environments. Lipid rafts play an important mediating role in the biology of cancer: they have been found in almost all existing experimental cancer models, including colorectal cancer (CRC), and play key regulatory roles in cell migration, metastasis, cell survival and tumor progression. This paper explores the current state of knowledge in this field by highlighting some of the pioneering and recent lipid raft studies performed on different CRC cell lines and human tissue samples. From this literature review, it becomes clear that membrane microdomains appear to be implicated in all key intracellular signaling pathways for lipid metabolism, drug resistance, cell adhesion, cell death, cell proliferation and many other processes in CRC. All signal transduction pathways seem to originate directly from those peculiar lipid islands, thereby orchestrating the colon cancer cells’ state and fate. As confirmed by recent animal and preclinical studies in different CRC models, continuing to unravel the structure and function of lipid rafts - including their associated complex signaling pathways - will likely bring us one step closer to better monitoring and treating of colon cancer patients.

Lipidomics: coming to grips with lipid diversity

Although lipids are biomolecules with seemingly simple chemical structures, the molecular composition of the cellular lipidome is complex and, currently, poorly understood. The exact mechanisms of how compositional complexity affects cell homeostasis and its regulation also remain unclear. This emerging field is developing sensitive mass spectrometry technologies for the quantitative characterization of the lipidome. Here, we argue that lipidomics will become an essential tool kit in cell and developmental biology, molecular medicine and nutrition.

Desorption electrospray ionization imaging mass spectrometry of lipids in rat spinal cord

Imaging mass spectrometry allows for the direct investigation of tissue samples to identify specific biological compounds and determine their spatial distributions. Desorption electrospray ionization (DESI) mass spectrometry has been used for the imaging and analysis of rat spinal cord cross sections. Glycerophospholipids and sphingolipids, as well as fatty acids, were detected in both the negative and positive ion modes and identified through tandem mass spectrometry (MS/MS) product ion scans using collision-induced dissociation and accurate mass measurements. Differences in the relative abundances of lipids and free fatty acids were present between white and gray matter areas in both the negative and positive ion modes. DESI-MS images of the corresponding ions allow the determination of their spatial distributions within a cross section of the rat spinal cord, by scanning the DESI probe across the entire sample surface. Glycerophospholipids and sphingolipids were mostly detected in the white matter, while the free fatty acids were present in the gray matter. These results show parallels with reported distributions of lipids in studies of rat brain. This suggests that the spatial intensity distribution reflects relative concentration differences of the lipid and fatty acid compounds in the spinal cord tissue. The "butterfly" shape of the gray matter in the spinal cord cross section was resolved in the corresponding ion images, indicating that a lateral resolution of better than 200 mum was achieved. The selected ion images of lipids are directly correlated with anatomic features on the spinal cord corresponding to the white and the gray matter.

Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics

Cardiolipin is a class of mitochondrial specific phospholipid, which is intricately involved in mitochondrial functionality. Differences in cardiolipin species exist in a variety of tissues and diseases. It has been demonstrated that the cardiolipin profile is a key modulator of the functions of many mitochondrial proteins. However, the chemical mechanism(s) leading to normal and/or pathological distribution of cardiolipin species remain elusive. Herein, we describe a novel approach for investigating the molecular mechanism of cardiolipin remodeling through a dynamic simulation. This approach applied data from shotgun lipidomic analyses of the heart, liver, brain, and lung mitochondrial lipidomes to model cardiolipin remodeling, including relative content, regiospecificity, and isomeric composition of cardiolipin species. Generated cardiolipin profiles were nearly identical to those determined by shotgun lipidomics. Importantly, the simulated isomeric compositions of cardiolipin species were further substantiated through product ion analysis. Finally, unique enzymatic activities involved in cardiolipin remodeling were assessed from the parameters used in the dynamic simulation of cardiolipin profiles. Collectively, we described, verified, and demonstrated a novel approach by integrating both lipidomic analysis and dynamic simulation to study cardiolipin biology. We believe this study provides a foundation to investigate cardiolipin metabolism and bioenergetic homeostasis in normal and disease states.

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.