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.     

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.     

脂质组学分析方法进展及其在癌症研究中的应用

脂质占人体内源性代谢物的一半以上,种类繁多,结构复杂,因而具有多种生物功能,与多种生命活动密切相关。脂质组学是代谢组学分支的新兴学科,它可以通过比较不同生理状态下脂质含量的变化,寻找代谢通路中关键的脂质生物标志物,最终揭示脂质在各种生命活动中的作用机制。随着质谱技术的进步,脂质组学在疾病脂类生物标志物的识别、疾病诊断、药物作用机制的研究等方面已展现出广泛的应用前景。本文主要就脂质组学近几年的分析方法进展及其在癌症中的最新应用进行了综述。     

Lipid metabolism in mycobacteria—Insights using mass spectrometry-based lipidomics

Diseases including tuberculosis and leprosy are caused by species of the Mycobacterium genus and are a huge burden on global health, aggravated by the emergence of drug resistant strains. Mycobacteria have a high lipid content and complex lipid profile including several unique classes of lipid. Recent years have seen a growth in research focused on lipid structures, metabolism and biological functions driven by advances in mass spectrometry techniques and instrumentation, particularly the use of electrospray ionization. Here we review the contributions of lipidomics towards the advancement of our knowledge of lipid metabolism in mycobacterial species.     

生物质谱分析法在脂质组学的应用

脂质与许多慢性病(如糖尿病、高血压)和精神系统疾病(如阿尔茨海默病)等有关。脂质组学是以现代生物技术为手段,对生物体中的全脂质进行定性和定量的一门新兴学科。目前,生物质谱分析法是对脂质谱进行分析和定量的最有效方法,国内对脂质组学的系统研究还比较匮乏。综述脂质组学的概念与分类,探究不同的生物样品前处理方法,系统介绍近几年国际上生物质谱分析法在脂质组学的应用,并对脂质组学的发展趋势进行展望。     

The evolution of lipidomics through space and time

Although the foundations of mass spectrometry-based lipidomics have been practiced for over 30 years, recent technological advances in ionization modalities in conjunction with robust increases in mass accuracy and resolution have greatly accelerated the emergence, growth and importance of the field of lipidomics. Moreover, advances in the separation sciences, bioinformatic strategies and the availability of robust databases have been synergistically integrated into modern lipidomic technologies leading to unprecedented improvements in the depth, penetrance and precision of lipidomic analyses and identification of their biological and mechanistic significance. The purpose of this "opinion" article is to briefly review the evolution of lipidomics, critique the platforms that have evolved and identify areas that are likely to emerge in the years to come. Through seamlessly integrating a rich repertoire of mass spectrometric, chemical and bioinformatic strategies, the chemical identities and quantities of tens of thousands to hundreds of thousands of different lipid molecular species and their metabolic alterations during physiologic or pathophysiologic perturbations can be obtained. Thus, the field of lipidomics which already has a distinguished history of exciting new discoveries in many disease states holds unparalleled potential to identify the pleiotropic roles of lipids in health and disease at the chemical level. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.     

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.     

Characterization and direct quantitation of cerebroside molecular species from lipid extracts by shotgun lipidomics

By using shotgun lipidomics based on the separation of lipid classes in the electrospray ion source (intrasource separation) and two-dimensional (2D) MS techniques (Han, X., and R. W. Gross. 2004. Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of the cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom. Rev. First published on June 18, 2004; doi: 10.1002/mas.20023, In press), individual molecular species of most major and many minor lipid classes can be quantitated directly from biological lipid extracts. Herein, we extended shotgun lipidomics to the characterization and quantitation of cerebroside molecular species in biological samples. By exploiting the differential fragmentation patterns of chlorine adducts using electrospray ionization (ESI) tandem mass spectrometry, hydroxy and nonhydroxy cerebroside species are readily identified. The hexose (either galactose or glucose) moiety of a cerebroside species can be distinguished by examination of the peak intensity ratio of its product ions at m/z 179 and 89 (i.e., 0.74 +/- 0.10 and 4.8 +/- 0.7 for galactose- and glucose-containing cerebroside species, respectively). Quantitation of cerebroside molecular species (as little as 10 fmol) from chloroform extracts of brain tissue samples was directly conducted by 2D ESI/MS after correction for differences in (13)C-isotopomer intensities. This method was demonstrated to have a greater than 1,000-fold linear dynamic range in the low concentration region; therefore, it should have a wide range of applications in studies of the cellular sphingolipid lipidome.     

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。     

Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

ABSTRACT: BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.     

Membrane composition and organization of Bacillus subtilis 168 and its genome-reduced derivative miniBacillus PG10

A form of lateral membrane compartmentalization in bacteria is represented by functional membrane microdomains (FMMs). FMMs are important for various cellular processes and offer application possibilities in microbial biotechnology. We designed a lipidomics method to directly measure relative abundances of lipids in detergent-resistant and detergent-sensitive membrane fractions of the model bacterium Bacillus subtilis 168 and the biotechnologically attractive miniBacillus PG10 strain. Our study supports previous work suggesting that cardiolipin and prenol lipids are enriched in FMMs of B. subtilis. Additionally, structural analysis of acyl chains of major phospholipids indicated that FMMs display increased order and thickness compared with the surrounding bilayer. Despite the 36% genome reduction, membrane and FMM integrity are largely preserved in miniBacillus PG10, as supported by analysis of membrane fluidity, flotillin distribution and gene expression data. The novel insights in FMM architecture reported here will contribute to further explore the biological significance of FMMs and the means by which FMMs can be exploited as heterologous production platforms. Moreover, our lipidomics method enables comparative FMM lipid profiling between different bacteria.     

Incorporating pathway information into boosting estimation of high-dimensional risk prediction models

Background  

There are several techniques for fitting risk prediction models to high-dimensional data, arising from microarrays. However, the biological knowledge about relations between genes is only rarely taken into account. One recent approach incorporates pathway information, available, e.g., from the KEGG database, by augmenting the penalty term in Lasso estimation for continuous response models.     

Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data

MOTIVATION: Discriminant analysis for high-dimensional and low-sample-sized data has become a hot research topic in bioinformatics, mainly motivated by its importance and challenge in applications to tumor classifications for high-dimensional microarray data. Two of the popular methods are the nearest shrunken centroids, also called predictive analysis of microarray (PAM), and shrunken centroids regularized discriminant analysis (SCRDA). Both methods are modifications to the classic linear discriminant analysis (LDA) in two aspects tailored to high-dimensional and low-sample-sized data: one is the regularization of the covariance matrix, and the other is variable selection through shrinkage. In spite of their usefulness, there are potential limitations with each method. The main concern is that both PAM and SCRDA are possibly too extreme: the covariance matrix in the former is restricted to be diagonal while in the latter there is barely any restriction. Based on the biology of gene functions and given the feature of the data, it may be beneficial to estimate the covariance matrix as an intermediate between the two; furthermore, more effective shrinkage schemes may be possible. RESULTS: We propose modified LDA methods to integrate biological knowledge of gene functions (or variable groups) into classification of microarray data. Instead of simply treating all the genes independently or imposing no restriction on the correlations among the genes, we group the genes according to their biological functions extracted from existing biological knowledge or data, and propose regularized covariance estimators that encourages between-group gene independence and within-group gene correlations while maintaining the flexibility of any general covariance structure. Furthermore, we propose a shrinkage scheme on groups of genes that tends to retain or remove a whole group of the genes altogether, in contrast to the standard shrinkage on individual genes. We show that one of the proposed methods performed better than PAM and SCRDA in a simulation study and several real data examples.     

Diurnal Regulation of Lipid Metabolism and Applications of Circadian Lipidomics

The circadian timing system plays a key role in orchestrating lipid metabolism. In concert with the solar cycle, the circadian system ensures that daily rhythms in lipid absorption, storage, and transport are temporally coordinated with rest-activity and feeding cycles. At the cellular level, genes involved in lipid synthesis and fatty acid oxidation are rhythmically activated and repressed by core clock proteins in a tissue-specific manner. Consequently, loss of clock gene function or misalignment of circadian rhythms with feeding cycles （e.g., in shift work） results in impaired lipid homeostasis. Herein, we review recent progress in circadian rhythms research using lipidomics, i.e., large-scale profiling of lipid metabolites, to characterize circadian-regulated lipid pathways in mammals. In mice, novel regulatory circuits involved in fatty acid metabolism have been identified in adipose tissue, liver, and muscle. Extensive diversity in circadian regulation of plasma lipids has also been revealed in humans using lipidomics and other metabolomics approaches. In future studies, lipidomics platforms will be increasingly used to better understand the effects of genetic variation, shift work, food intake, and drugs on circadian-regulated lipid pathways and metabolic health.     

Recent developments in data acquisition,treatment and analysis with ion mobility-mass spectrometry for lipidomics

Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focuses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols.     

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。     

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.     

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.     

Analysis of Lipid Experiments (ALEX): A Software Framework for Analysis of High-Resolution Shotgun Lipidomics Data

Global lipidomics analysis across large sample sizes produces high-content datasets that require dedicated software tools supporting lipid identification and quantification, efficient data management and lipidome visualization. Here we present a novel software-based platform for streamlined data processing, management and visualization of shotgun lipidomics data acquired using high-resolution Orbitrap mass spectrometry. The platform features the ALEX framework designed for automated identification and export of lipid species intensity directly from proprietary mass spectral data files, and an auxiliary workflow using database exploration tools for integration of sample information, computation of lipid abundance and lipidome visualization. A key feature of the platform is the organization of lipidomics data in ”database table format” which provides the user with an unsurpassed flexibility for rapid lipidome navigation using selected features within the dataset. To demonstrate the efficacy of the platform, we present a comparative neurolipidomics study of cerebellum, hippocampus and somatosensory barrel cortex (S1BF) from wild-type and knockout mice devoid of the putative lipid phosphate phosphatase PRG-1 (plasticity related gene-1). The presented framework is generic, extendable to processing and integration of other lipidomic data structures, can be interfaced with post-processing protocols supporting statistical testing and multivariate analysis, and can serve as an avenue for disseminating lipidomics data within the scientific community. The ALEX software is available at www.msLipidomics.info.