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.     

鸟枪脂组学研究进展及其应用

继基因组学之后,针对各种代谢物的组学研究蓬勃兴起,鸟枪脂组学(shotgun lipidom ics)作为脂类研究的重要新兴手段,在创立和初期发展的过程中便已经展示出惊人的潜力,随着相关技术的进一步完善和发展,必将成为系统生物学的组成部分,在生物医学的研究和应用中发挥难以替代的重要作用。鸟枪脂组学利用质谱技术对全部或单一脂类及其相关分子进行系统分析,研究其改变对生物体所产生的作用并探讨其作用机制。传统脂类分析中的瓶颈问题在以电喷射离子质谱为基础的脂组学方法出现后获得了突破,使脂类分析进入高通量、高精度和高效能的时代。脂类在生物体内分布广泛、种类众多,并且与人类疾病密切相关。将脂组学分析方法运用于疾病相关的特异脂类标志物的发现并揭示其在疾病发生发展等复杂过程中的作用,可能为疾病的诊断治疗提供新的思路和策略。     

Green light for lipid fingerprinting

The use of targeted lipidomic approaches for the analysis of plant lipids has steadily increased during recent years. We review recent developments of these methods and suggest the introduction of discovery lipidomics as additional approach through a new workflow, lipid fingerprinting, that integrates the advantages of shotgun lipidomics (quantitative data) with LC-MS-based strategies (higher resolution and/or coverage). This article is part of a Special Issue entitled:BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.     

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.

     

The emergence of yeast lipidomics

The emerging field of lipidomics, driven by technological advances in lipid analysis, provides greatly enhanced opportunities to characterize, on a quantitative or semi-quantitative level, the entire spectrum of lipids, or lipidome, in specific cell types. When combined with advances in other high throughput technologies in genomics and proteomics, lipidomics offers the opportunity to analyze the unique roles of specific lipids in complex cellular processes such as signaling and membrane trafficking. The yeast system offers many advantages for such studies, including the relative simplicity of its lipidome as compared to mammalian cells, the relatively high proportion of structural and regulatory genes of lipid metabolism which have been assigned and the excellent tools for molecular genetic analysis that yeast affords. The current state of application of lipidomic approaches in yeast and the advantages and disadvantages of yeast for such studies are discussed in this report.     

Informatics and computational strategies for the study of lipids

The ability to translate vast amounts of information, as obtained from lipidomic analysis, into the knowledge and understanding of biological phenomena is an important challenge faced by the lipidomics community. While many of the informatics and computational tools from other domains such as bioinformatics and metabolomics are also applicable to lipidomics data processing and analysis, new solutions and strategies are needed for the studies of lipidomes at the systems level. This is due to enormous functional and structural diversity of lipids as well as because of their complex regulation at multiple spatial and temporal scales. In order to better understand the lipidomes at the physiological level, lipids need to be modeled not only at the level of biological pathways but also at the level of the biophysical systems they are part of, such as cellular membranes or lipoprotein particles. Herein the current state, recent advances and new opportunities in the field of lipid bioinformatics are reviewed.     

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.     

Lipidomics as a Principal Tool for Advancing Biomedical Research

Lipidomics,which targets at the construction of a comprehensive map of lipidome comprising the entire lipid pool within a cell or tissue,is currently emerging as an independent discipline at the interf...     

Strategies to Improve/Eliminate the Limitations in Shotgun Lipidomics

Direct infusion‐based shotgun lipidomics is one of the most powerful and useful tools in comprehensive analysis of lipid species from lipid extracts of various biological samples with high accuracy/precision. However, despite many advantages, the classical shotgun lipidomics suffers some general dogmas of limitations, such as ion suppression, ambiguous identification of isobaric/isomeric lipid species, and ion source–generated artifacts, restraining the applications in analysis of low‐abundance lipid species, particularly those less ionizable or isomers that yield almost identical fragmentation patterns. This article reviews the strategies (such as modifier addition, prefractionation, chemical derivatization, charge feature utilization) that have been employed to improve/eliminate these limitations in modern shotgun lipidomics approaches (e.g., high mass resolution mass spectrometry–based and multidimensional mass spectrometry–based shotgun lipidomics). Therefore, with the enhancement of these strategies for shotgun lipidomics, comprehensive analysis of lipid species including isomeric/isobaric species is achieved in a more accurate and effective manner, greatly substantiating the aberrant lipid metabolism, signaling trafficking, and homeostasis under pathological conditions.     

Informatics and computational strategies for the study of lipids

Recent advances in mass spectrometry (MS)-based techniques for lipidomic analysis have empowered us with the tools that afford studies of lipidomes at the systems level. However, these techniques pose a number of challenges for lipidomic raw data processing, lipid informatics, and the interpretation of lipidomic data in the context of lipid function and structure. Integration of lipidomic data with other systemic levels, such as genomic or proteomic, in the context of molecular pathways and biophysical processes provides a basis for the understanding of lipid function at the systems level. The present report, based on the limited literature, is an update on a young but rapidly emerging field of lipid informatics and related pathway reconstruction strategies.     

脂质组学在医药研究中的应用

脂质组学是对整体脂质进行系统分析的一门新兴学科,通过比较不同生理状态下脂代谢网络的变化,进而识别代谢调控中关键的脂生物标志物,最终揭示脂质在各种生命活动中的作用机制。电喷雾电离-质谱技术是脂质组学领域中最核心的研究手段,目前已能对各种脂质尤其是磷脂进行高分辨率、高灵敏度、高通量的分析。随着质谱技术的进步,脂质组学在疾病脂生物标志物的识别、疾病诊断、药物靶点及先导化合物的发现和药物作用机制的研究等方面已展现出广泛的应用前景。     

Proteomics moves from expression to turnover: update and future perspective

Proteomics is a rapidly developing discipline that seeks to understand the role of proteins in the wider biological context. In order to take a holistic view of a biological system, it is vital that we can elucidate the dynamics of the proteome. In this article, we have outlined the recent advances in experimental strategies for measuring protein synthesis and degradation on a proteome-wide scale. The application of mass spectrometry and non-mass spectrometric-based approaches in this field of research has been discussed. The article also explores the challenges associated with these types of analyses and the development of appropriate bioinformatic resources for interrogating the complex datasets that are generated.     

A matter of fat: an introduction to lipidomic profiling methods

In recent years, lipidomics or lipid profiling, an extension of metabolomics where the lipid complement of a cell, tissue or organism is measured, has been the recipient of increasing attention as a research tool in a range of diverse disciplines including physiology, lipid biochemistry, clinical biomarker discovery and pathology. The advancement of the field has been driven by the development of analytical technologies, and in particular advances in liquid chromatography mass spectrometry and chemometric methods. In this review, we give an overview of the current methods with which lipid profiling is being performed. The benefits and shortcomings of mass spectrometry both in the presence and absence of chromatographic separation techniques such as liquid-, gas- and thin layer chromatography are explored. Alone these techniques have their limitations but through a combination many of the disadvantages may be overcome providing a valuable analytical tool for a variety of disease processes.     

Lipidomics: when apocrypha becomes canonical

Lipidomics is a branch of the field of metabolomics. Although only about a decade since its inception, lipidomics has already had a major influence on the way in which questions about lipid metabolism and signaling are posed. The field is intertwined in the culture and rich history of mass spectrometry. Early work emphasized analytical issues such as limits of detection and numbers of molecular species quantitated in single injections. Increased sophistication in applications of lipidomic analysis and emerging technologies, such as imaging mass spectrometry, are facilitating the study of lipid metabolism and signaling species in cellular functions and human diseases. In the coming years we anticipate a richer understanding of how specific lipid species mediate complex biological processes and interconnections between cellular pathways that were thought to be disparate.     

脂质组学研究方法及其应用

脂质不仅是生物膜的骨架成分和能量贮存物质, 越来越多的证据表明, 脂质也参与细胞的许多重要功能。脂质组学是代谢组学的一个重要分支, 主要研究生物体内所有的脂质分子的特性以及它们在蛋白质表达和基因调控过程中的作用。脂质组学是依赖技术驱动的科学。近年来, 随着人们对脂质研究的重视, 脂质组学研究方法和策略有了突破性进展, 在动物上开发出的脂质组学分析方法已经扩展应用到植物上。该文重点介绍脂质组学的研究方法及其应用, 以期推动脂质组学,特别是植物脂质组学的进一步发展。     

Multi-dimensional mass spectrometry-based shotgun lipidomics and the altered lipids at the mild cognitive impairment stage of Alzheimer's disease

Multi-dimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) is a well-developed technology for global lipid analysis, which identifies and quantifies individual lipid molecular species directly from lipid extracts of biological samples. By using this technology, we have revealed three marked changes of lipids in brain samples of subjects with mild cognitive impairment of Alzheimer's disease including sulfatides, ceramides, and plasmalogens. Further studies using MDMS-SL lead us to the identification of the potential biochemical mechanisms responsible for the altered lipids at the disease state, which are thoroughly discussed in this minireview. Specifically, in studies to identify the causes responsible for sulfatide depletion at the mild cognitive impairment stage of Alzheimer's disease, we have found that apolipoprotein E is associated with sulfatide transport and mediates sulfatide homeostasis in the nervous system through lipoprotein metabolism pathways and that alterations in apolipoprotein E-mediated sulfatide trafficking can lead to sulfatide depletion in the brain. Collectively, the results obtained from lipidomic analyses of brain samples provide important insights into the biochemical mechanisms underlying the pathogenesis of Alzheimer's disease.     

Functional lipidomics: the roles of specialized lipids and lipid-protein interactions in modulating neuronal function

Lipids fulfill multiple specialized roles in neuronal function. In brain, the conduction of electrical impulses, synaptic function, and complex signaling pathways depend on the temporally and spatially coordinated interactions of specialized lipids (e.g., arachidonic acid and plasmalogens), proteins (e.g., ion channels, phospholipases and cyclooxygenases) and integrative lipid-protein interactions. Recent technical advances in mass spectrometry have allowed unparalled insight into the roles of lipids in neuronal function. Through shotgun lipidomics and multidimensional mass spectrometry, in conjunction with the identification of new classes of phospholipases (e.g., calcium dependent and calcium independent intracellular phospholipases), new roles for lipids in cerebral function have been accrued. This review summarizes the advances in our understanding of the types of lipids and phospholipases in the brain and the role of functional lipidomics in increasing our chemical understanding of complex neuronal processes.     

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.     

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles

MOTIVATION: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today. RESULTS: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.