Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification

LipidA-IDER to Explore the Global Lipid A Repertoire of Drug-Resistant Gram-Negative Bacteria

With the global emergence of drug-resistant bacteria causing difficult-to-treat infections, there is an urgent need for a tool to facilitate studies on key virulence and antimicrobial resistant factors. Mass spectrometry (MS) has contributed substantially to the elucidation of the structure-function relationships of lipid A, the endotoxic component of lipopolysaccharide which also serves as an important protective barrier against antimicrobials. Here, we present LipidA-IDER, an automated structure annotation tool for system-level scale identification of lipid A from high-resolution tandem mass spectrometry (MS²) data. LipidA-IDER was validated against previously reported structures of lipid A in the reference bacteria, Escherichia coli and Pseudomonas aeruginosa. Using MS² data of variable quality, we demonstrated LipidA-IDER annotated lipid A with a performance of 71.2% specificity and 70.9% sensitivity, offering greater accuracy than existing lipidomics software. The organism-independent workflow was further applied to a panel of six bacterial species: E. coli and Gram-negative members of ESKAPE pathogens. A comprehensive atlas comprising 188 distinct lipid A species, including remodeling intermediates, was generated and can be integrated with software including MS-DIAL and Metabokit for identification and semiquantitation. Systematic comparison of a pair of polymyxin-sensitive and polymyxin-resistant Acinetobacter baumannii isolated from a human patient unraveled multiple key lipid A structural features of polymyxin resistance within a single analysis. Probing the lipid A landscape of bacteria using LipidA-IDER thus holds immense potential for advancing our understanding of the vast diversity and structural complexity of a key lipid virulence and antimicrobial-resistant factor. LipidA-IDER is freely available at https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER.

A software-assisted untargeted liquid chromatography-mass spectrometry method for lipidomic profiling of human plasma samples

INTRODUCTION

In this paper, an analytical pipeline designed for untargeted lipidomic profiling in human plasma is proposed. The analytical pipeline was developed for case-control studies nested in prospective cohorts.

METHODS

The procedure consisted of isopropanol protein precipitation followed by reverse phase liquid chromatography coupled to high resolution mass spectrometry and software-assisted data processing. The compounds are putatively annotated by matching experimental mass spectrometry data with spectral library data using LipidSearch software. The lipid profile of a pool of plasma samples from 10 healthy volunteers was detected in both positive and negative polarity modes. The impact of the chosen polarity on the number and quality of the lipid identification has been evaluated.

RESULTS

More than 1000 lipids from 12 different classes were detected, 1150 in positive mode and 273 in negative mode. Nearly half of them were unambiguously identified by the software in positive mode, and about one-third in negative mode. The method repeatability was assessed on the plasma pool samples by means of variance components analysis. The intra- and inter-assay precision was measured for 10 lipids chosen among the most abundant found within the different lipid classes. The intra-assay coefficients of variation ranged from 2.56% to 4.56% while intra- and inter-day coefficients of variance never exceeded the 15% benchmark adopted. The lipidomic profiles of the 10 healthy volunteers were also investigated.

DISCUSSION

This method detects a wide range of lipids and reports their degree of identification. It is particularly fit and well-designed for large case-control epidemiologic studies.

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction

MOTIVATION

Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).

RESULTS

We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.

AVAILABILITY AND IMPLEMENTATION

All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

The implication of lipid dysregulation in diseases, toxic exposure outcomes, and inflammation has brought great interest to lipidomic studies. However, lipids have proven to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have been implemented to separate lipid isomers as well as provide structural information and increased identification confidence. These data sets are however extremely large and complex, resulting in challenges for data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional lipid libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique lipids and is combined with adapted Skyline functions such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for enhanced selectivity. For comparison with other studies, this database was used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract. The same workflow was then utilized to assess plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify lipid-based patient prognostic and diagnostic markers.

The state of the art in plant lipidomics

Lipids are a group of compounds with diverse structures that perform several important functions in plants. To unravel and better understand their in vivo functions, plant biologists have been using various lipidomic technologies including liquid-chromatography (LC)-mass spectrometry (MS). However, there are still significant challenges in LC-MS based plant lipidomics, which need to be addressed. In this review, we provide an overview of the key developments in LC-MS based lipidomic approaches to detect and identify plant lipids with emphasis on areas that can be further improved. Given that the cellular lipidome is estimated to contain hundreds of thousands of lipids,^1,2 many of the lipid structures remain to be discovered. Furthermore, the plant lipidome is considered to be significantly more complex compared to that of mammals. Recent technical developments in mass spectrometry have made the detection of novel lipids possible; hence, approaches that can be used for plant lipid discovery are also discussed.

The biosynthesis of phospholipids is linked to the cell cycle in a model eukaryote

The structural challenges faced by eukaryotic cells through the cell cycle are key for understanding cell viability and proliferation. We tested the hypothesis that the biosynthesis of structural lipids is linked to the cell cycle. If true, this would suggest that the cell's structure is important for progress through and perhaps even control of the cell cycle. Lipidomics (31P NMR and MS), proteomics (Western immunoblotting) and transcriptomics (RT-qPCR) techniques were used to profile the lipid fraction and characterise aspects of its metabolism at seven stages of the cell cycle of the model eukaryote, Desmodesmus quadricauda. We found considerable, transient increases in the abundance of phosphatidylethanolamine during the G1 phase (+35%, ethanolamine phosphate cytidylyltransferase increased 2·5×) and phosphatidylglycerol (+100%, phosphatidylglycerol synthase increased 22×) over the G1/pre-replication phase boundary. The relative abundance of phosphatidylcholine fell by ~35% during the G1. N-Methyl transferases for the conversion of phosphatidylethanolamine into phosphatidylcholine were not found in the de novo transcriptome profile, though a choline phosphate transferase was found, suggesting that the Kennedy pathway is the principal route for the synthesis of PC. The fatty acid profiles of the four most abundant lipids suggested that these lipids were not generally converted between one another. This study shows for the first time that there are considerable changes in the biosynthesis of the three most abundant phospholipid classes in the normal cell cycle of D. quadricauda, by margins large enough to elicit changes to the physical properties of membranes.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Plasma-derived exosome-like vesicles are enriched in lyso-phospholipids and pass the blood-brain barrier

Exosomes are vesicles involved in intercellular communication. Their membrane structure and core content is largely dependent on the cell of origin. Exosomes have been investigated both for their biological roles and their possible use as disease biomarkers and drug carriers. These potential technological applications require the rigorous characterization of exosomal blood brain barrier permeability and a description of their lipid bilayer composition. To achieve these goals, we have established a 3D static blood brain barrier system based on existing systems for liposomes and a complementary LC-MS/MS and ³¹P nuclear magnetic resonance methodology for the analysis of purified human plasma-derived exosome-like vesicles. Results show that the isolated vesicles pass the blood brain barrier and are taken up in endothelial cells. The compositional analysis revealed that the isolated vesicles are enriched in lyso phospholipids and do not contain phosphatidylserine. These findings deviate significantly from the composition of exosomes originating from cell culture, and may reflect active removal by macrophages that respond to exposed phosphahtidylserine.

Multi-Omics Reveals Impact of Cysteine Feed Concentration and Resulting Redox Imbalance on Cellular Energy Metabolism and Specific Productivity in CHO Cell Bioprocessing

Chinese hamster ovary (CHO) cells are currently the primary host cell lines used in biotherapeutic manufacturing of monoclonal antibodies (mAbs) and other biopharmaceuticals. Cellular energy metabolism and endoplasmic reticulum (ER) stress are known to greatly impact cell growth, viability, and specific productivity of a biotherapeutic; but the molecular mechanisms are not fully understood. The authors previously employed multi-omics profiling to investigate the impact of a reduction in cysteine (Cys) feed concentration in a fed-batch process and found that disruption of the redox balance led to a substantial decline in cell viability and titer. Here, the multi-omics findings are expanded, and the impact redox imbalance has on ER stress, mitochondrial homeostasis, and lipid metabolism is explored. The reduced Cys feed activates the amino acid response (AAR), increases mitochondrial stress, and initiates gluconeogenesis. Multi-omics analysis reveals that together, ER stress and AAR signaling shift the cellular energy metabolism to rely primarily on anaplerotic reactions, consuming amino acids and producing lactate, to maintain energy generation. Furthermore, the pathways are demonstrated in which this shift in metabolism leads to a substantial decline in specific productivity and altered mAb glycosylation. Through this work, meaningful bioprocess markers and targets for genetic engineering are identified.

A lipidome atlas in MS-DIAL 4

We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.

Methods of the Analysis of Oxylipins in Biological Samples

Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.

Fast and Quantitative Phospholipidomic Analysis of SH-SY5Y Neuroblastoma Cell Cultures Using Liquid Chromatography-Tandem Mass Spectrometry and 31P Nuclear Magnetic Resonance

Global lipid analysis still lags behind proteomics with respect to the availability of databases, experimental protocols, and specialized software. Determining the lipidome of cellular model systems in common use is of particular importance, especially when research questions involve lipids directly. In Parkinson's disease research, there is a growing awareness for the role of the biological membrane, where individual lipids may contribute to provoking α-synuclein oligomerisation and fibrillation. We present an analysis of the whole cell and plasma membrane lipid isolates of a neuroblastoma cell line, SH-SY5Y, a commonly used model system for research on this and other neurodegenerative diseases. We have used two complementary lipidomics methods. The relative quantities of PC, PE, SMs, CL, PI, PG, and PS were determined by ³¹P NMR. Fatty acid chain composition and their relative abundances within each phospholipid group were evaluated by liquid chromatography-tandem mass spectrometry. For this part of the analysis, we have developed and made available a set of Matlab scripts, LipMat. Our approach allowed us to observe several deviations of lipid abundances when compared to published reports regarding phospholipid analysis of cell cultures or brain matter. The most striking was the high abundance of PC (54.7 ± 1.9%) and low abundance of PE (17.8 ± 4.8%) and SMs (2.7 ± 1.2%). In addition, the observed abundance of PS was smaller than expected (4.7 ± 2.7%), similar to the observed abundance of PG (4.5 ± 1.8%). The observed fatty acid chain distribution was similar to the whole brain content with some notable differences: a higher abundance of 16:1 PC FA (17.4 ± 3.4% in PC whole cell content), lower abundance of 22:6 PE FA (15.9 ± 2.2% in plasma membrane fraction), and a complete lack of 22:6 PS FA.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Hybrid Search: A Method for Identifying Metabolites Absent from Tandem Mass Spectrometry Libraries

Metabolomics has a critical need for better tools for mass spectral identification. Common metabolites may be identified by searching libraries of tandem mass spectra, which offers important advantages over other approaches to identification. But tandem libraries are not nearly complete enough to represent the full molecular diversity present in complex biological samples. We present a novel hybrid search method that can help identify metabolites not in the library by similarity to compounds that are. We call it "hybrid" searching because it combines conventional, direct peak matching with the logical equivalent of neutral-loss matching. A successful hybrid search requires the library to contain "cognates" of the unknown: similar compounds with a structural difference confined to a single region of the molecule, that does not substantially alter its fragmentation behavior. We demonstrate that the hybrid search is highly likely to find similar compounds under such circumstances.

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

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

Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow. Graphical Abstract.

Lipid Species Annotation at Double Bond Position Level with Custom Databases by Extension of the MZmine 2 Open-Source Software Package

In recent years, proprietary and open-source bioinformatics software tools have been developed for the identification of lipids in complex biological samples based on high-resolution mass spectrometry data. These existent software tools often rely on publicly available lipid databases, such as LIPID MAPS, which, in some cases, only contain a limited number of lipid species for a specific lipid class. Other software solutions implement their own lipid species databases, which are often confined regarding implemented lipid classes, such as phospholipids. To address these drawbacks, we provide an extension of the widely used open-source metabolomics software MZmine 2, which enables the annotation of detected chromatographic features as lipid species. The extension is designed for straightforward generation of a custom database for selected lipid classes. Furthermore, each lipid's sum formula of the created database can be rapidly modified to search for derivatization products, oxidation products, in-source fragments, or adducts. The versatility will be exemplified by a liquid chromatography-high resolution mass spectrometry data set with postcolumn Paternò-Büchi derivatization. The derivatization reaction was performed to pinpoint the double bond positions in diacylglyceryltrimethylhomoserine lipid species in a lipid extract of a green algae ( Chlamydomonas reinhardtii) sample. The developed Lipid Search module extension of MZmine 2 supports the identification of lipids as far as double bond position level.

Incurred Sample Reanalysis: Time to Change the Sample Size Calculation?

Reliable results of pharmacokinetic and toxicokinetic studies are vital for correct decision making during drug discovery and development. Thus, ensuring high quality of bioanalytical methods is of critical importance. Incurred sample reanalysis (ISR)-one of the tools used to validate a method-is included in the bioanalytical regulatory recommendations. The methodology of this test is well established, but the estimation of the sample size is still commented on and contested. We have applied the hypergeometric distribution to evaluate ISR test passing rates in different clinical study sizes. We have tested both fixed rates of the clinical samples-as currently recommended by FDA and EMA-and a fixed number of ISRs. Our study revealed that the passing rate using the current sample size calculation is related to the clinical study size. However, the passing rate is much less dependent on the clinical study size when a fixed number of ISRs is used. Thus, we suggest using a fixed number of ISRs, e.g., 30 samples, for all studies. We found the hypergeometric distribution to be an adequate model for the assessment of similarities in original and repeated data. This model may be further used to optimize the sample size needed for the ISR test as well as to bridge data from different methods. This paper provides a basis to re-consider current ISR recommendations and implement a more statistically rationalized and risk-controlled approach.

LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics

High resolution LC-MS untargeted lipidomics using data independent acquisition (DIA) has the potential to increase lipidome coverage, as it enables the continuous and unbiased acquisition of all eluting ions. However, the loss of the link between the precursor and the product ions combined with the high dimensionality of DIA data sets hinder accurate feature annotation. Here, we present LipidMS, an R package aimed to confidently identify lipid species in untargeted LC-DIA-MS. To this end, LipidMS combines a coelution score, which links precursor and fragment ions with fragmentation and intensity rules. Depending on the MS evidence reached by the identification function survey, LipidMS provides three levels of structural annotations: (i) "subclass level", e.g., PG(34:1); (ii) "fatty acyl level", e.g., PG(16:0_18:1); and (iii) "fatty acyl position level", e.g., PG(16:0/18:1). The comparison of LipidMS with freely available data dependent acquisition (DDA) and DIA identification tools showed that LipidMS provides significantly more accurate and structural informative lipid identifications. Finally, to exemplify the utility of LipidMS, we investigated the lipidomic serum profile of patients diagnosed with nonalcoholic steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty liver disease, a disorder underlying a strong lipid dysregulation. As previously published, a significant decrease in lysophosphatidylcholines, phosphatidylcholines and cholesterol esters and an increase in phosphatidylethanolamines were observed in NASH patients. Remarkably, LipidMS allowed the identification of a new set of lipids that may be used for NASH diagnosis. Altogether, LipidMS has been validated as a tool to assist lipid identification in the LC-DIA-MS untargeted analysis of complex biological samples.

Future Prospects of Spectral Clustering Approaches in Proteomics

In this article, current and future applications of spectral clustering are discussed in the context of mass spectrometry-based proteomics approaches. First of all, the main algorithms and tools that can currently be used to perform spectral clustering are introduced. In addition, its main applications and their use in current computational proteomics workflows are explained, including the generation of spectral libraries and spectral archives. Finally, possible future directions for spectral clustering, including its potential use to achieve a deeper coverage of the proteome and the discovery of novel post-translational modifications and single amino acid variants.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Three-dimensional Kendrick mass plots as a tool for graphical lipid identification

RATIONALE

The rising field of lipidomics strongly relies on the identification of lipids in complex matrices. Recent technical advances regarding liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) enable the mapping of the lipidome of an organism with short data acquisition times. However, interpretation and evaluation of resulting multidimensional datasets are challenging and this is still the bottleneck regarding overall analysis times.

METHODS

A novel adaption of Kendrick mass plot analysis is presented for a rapid and accurate analysis of lipids in complex matrices. Separation of lipids by their respective head group was achieved via hydrophilic interaction liquid chromatography (HILIC) coupled to HRMS. The resulting LC/HRMS datasets are processed to a list of chromatographically separated features by applying an optimized MZmine 2 workflow. All features are plotted in a three-dimensional Kendrick mass plot, which allows a fast identification of present lipid classes, based on equidistant features with fitting retention times and the same Kendrick mass defect. Suspected lipid classes are used for exact mass database matching to annotate features. A second three-dimensional Kendrick mass plot of annotated features of a single lipid class helps to reveal potential database mismatches, resulting in a curated list of identified lipid species.

RESULTS

The use of the novel adaption of the Kendrick mass plot has accelerated the identification of the relevant lipid species in the green alga Chlamydomonas reinhardtii. A total of 106 species were identified within the lipid classes: phosphatidylserine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol, and sulfoquinovosyldiacylglycerol.

CONCLUSIONS

This work shows how the addition of chromatographic information, i.e. the retention time, to a classical two-dimensional Kendrick mass plot enables rapid and accurate analysis of LC/HRMS datasets, exemplified on a green alga (C. reinhardtii) sample. Three-dimensional Kendrick mass plots have improved lipid class identification and fast spotting of falsely annotated lipid species.

Contemporary lipidomic analytics: opportunities and pitfalls

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

Lipidomics of CHO Cell Bioprocessing: Relation to Cell Growth and Specific Productivity of a Monoclonal Antibody

As the demand for biological therapeutic proteins rises, there is an increasing need for robust and highly efficient bioprocesses, specifically, maximizing protein production by controlling the cellular nutritional and metabolic needs. A comprehensive lipidomics analysis has been performed, for the first time, over the time course of CHO cells producing an IgG1 monoclonal antibody (mAb) with fed batch 5 L bioreactors. The dynamic nature and importance of the CHO lipidome, especially on cellular growth and specific productivity, is demonstrated. A robust LC-MS method using positive and negative mode ESI was developed for lipid identification and quantitation of 377 unique lipids. The analysis revealed large changes in lipid features between the different days in bioprocessing including accumulation of triacylglycerol (TG) and lysophospholipid species with depletion of diacylglycerol (DG) species. Exploring pathway analysis where the lipid data was combined with polar metabolites and transcriptomics (RNA sequencing) revealed differences in lipid metabolism between the various stages of cellular growth and highlighted the role of key features of lipid metabolism on cell growth and specific productivity. The study demonstrates the importance of lipidomics in the expanding role of 'Omics methodologies in gaining insight into cellular behavior during protein production in a fed batch bioprocess.

Lipidomics informatics for life-science

Lipidomics encompasses analytical approaches that aim to identify and quantify the complete set of lipids, defined as lipidome in a given cell, tissue or organism as well as their interactions with other molecules. The majority of lipidomics workflows is based on mass spectrometry and has been proven as a powerful tool in system biology in concert with other Omics disciplines. Unfortunately, bioinformatics infrastructures for this relatively young discipline are limited only to some specialists. Search engines, quantification algorithms, visualization tools and databases developed by the 'Lipidomics Informatics for Life-Science' (LIFS) partners will be restructured and standardized to provide broad access to these specialized bioinformatics pipelines. There are many medical challenges related to lipid metabolic alterations that will be fostered by capacity building suggested by LIFS. LIFS as member of the 'German Network for Bioinformatics' (de.NBI) node for 'Bioinformatics for Proteomics' (BioInfra.Prot) and will provide access to the described software as well as to tutorials and consulting services via a unified web-portal.

Evidence that Listeria innocua modulates its membrane's stored curvature elastic stress, but not fluidity, through the cell cycle

This paper reports that the abundances of endogenous cardiolipin and phosphatidylethanolamine halve during elongation of the Gram-positive bacterium Listeria innocua. The lyotropic phase behaviour of model lipid systems that describe these modulations in lipid composition indicate that the average stored curvature elastic stress of the membrane is reduced on elongation of the cell, while the fluidity appears to be maintained. These findings suggest that phospholipid metabolism is linked to the cell cycle and that changes in membrane composition can facilitate passage to the succeding stage of the cell cycle. This therefore suggests a means by which bacteria can manage the physical properties of their membranes through the cell cycle.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data

BACKGROUND

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.

RESULTS

We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.

CONCLUSIONS

LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets

Accurate and high-quality curation of lipidomic datasets generated from plasma, cells, or tissues is becoming essential for cell biology investigations and biomarker discovery for personalized medicine. However, a major challenge lies in removing artifacts otherwise mistakenly interpreted as real lipids from large mass spectrometry files (>60 K features), while retaining genuine ions in the dataset. This requires powerful informatics tools; however, available workflows have not been tailored specifically for lipidomics, particularly discovery research. We designed LipidFinder, an open-source Python workflow. An algorithm is included that optimizes analysis based on users' own data, and outputs are screened against online databases and categorized into LIPID MAPS classes. LipidFinder outperformed three widely used metabolomics packages using data from human platelets. We show a family of three 12-hydroxyeicosatetraenoic acid phosphoinositides (16:0/, 18:1/, 18:0/12-HETE-PI) generated by thrombin-activated platelets, indicating crosstalk between eicosanoid and phosphoinositide pathways in human cells. The software is available on GitHub (https://github.com/cjbrasher/LipidFinder), with full userguides.

Upon the Shoulders of Giants: Open-Source Hardware and Software in Analytical Chemistry

Isaac Newton famously observed that "if I have seen further it is by standing on the shoulders of giants." We propose that this sentiment is a powerful motivation for the "open-source" movement in scientific research, in which creators provide everything needed to replicate a given project online, as well as providing explicit permission for users to use, improve, and share it with others. Here, we write to introduce analytical chemists who are new to the open-source movement to best practices and concepts in this area and to survey the state of open-source research in analytical chemistry. We conclude by considering two examples of open-source projects from our own research group, with the hope that a description of the process, motivations, and results will provide a convincing argument about the benefits that this movement brings to both creators and users.

LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome

Lipid identification is a major bottleneck in high-throughput lipidomics studies. However, tools for the analysis of lipid tandem MS spectra are rather limited. While the comparison against spectra in reference libraries is one of the preferred methods, these libraries are far from being complete. In order to improve identification rates, the in silico fragmentation tool MetFrag was combined with Lipid Maps and lipid-class specific classifiers which calculate probabilities for lipid class assignments. The resulting LipidFrag workflow was trained and evaluated on different commercially available lipid standard materials, measured with data dependent UPLC-Q-ToF-MS/MS acquisition. The automatic analysis was compared against manual MS/MS spectra interpretation. With the lipid class specific models, identification of the true positives was improved especially for cases where candidate lipids from different lipid classes had similar MetFrag scores by removing up to 56% of false positive results. This LipidFrag approach was then applied to MS/MS spectra of lipid extracts of the nematode Caenorhabditis elegans. Fragments explained by LipidFrag match known fragmentation pathways, e.g., neutral losses of lipid headgroups and fatty acid side chain fragments. Based on prediction models trained on standard lipid materials, high probabilities for correct annotations were achieved, which makes LipidFrag a good choice for automated lipid data analysis and reliability testing of lipid identifications.

LipidPioneer : A Comprehensive User-Generated Exact Mass Template for Lipidomics

Lipidomics, the comprehensive measurement of lipid species in a biological system, has promising potential in biomarker discovery and disease etiology elucidation. Advances in chromatographic separation, mass spectrometric techniques, and novel substrate applications continue to expand the number of lipid species observed. The total number and type of lipid species detected in a given sample are generally indicative of the sample matrix examined (e.g., serum, plasma, cells, bacteria, tissue, etc.). Current exact mass lipid libraries are static and represent the most commonly analyzed matrices. It is common practice for users to manually curate their own lists of lipid species and adduct masses; however, this process is time-consuming. LipidPioneer, an interactive template, can be used to generate exact masses and molecular formulas of lipid species that may be encountered in the mass spectrometric analysis of lipid profiles. Over 60 lipid classes are present in the LipidPioneer template and include several unique lipid species, such as ether-linked lipids and lipid oxidation products. In the template, users can add any fatty acyl constituents without limitation in the number of carbons or degrees of unsaturation. LipidPioneer accepts naming using the lipid class level (sum composition) and the LIPID MAPS notation for fatty acyl structure level. In addition to lipid identification, user-generated lipid m/z values can be used to develop inclusion lists for targeted fragmentation experiments. Resulting lipid names and m/z values can be imported into software such as MZmine or Compound Discoverer to automate exact mass searching and isotopic pattern matching across experimental data. Graphical Abstract ᅟ.

On Mass Ambiguities in High-Resolution Shotgun Lipidomics

Mass-spectrometry-based lipidomics aims to identify as many lipid species as possible from complex biological samples. Due to the large combinatorial search space, unambiguous identification of lipid species is far from trivial. Mass ambiguities are common in direct-injection shotgun experiments, where an orthogonal separation (e.g., liquid chromatography) is missing. Using the rich information within available lipid databases, we generated a comprehensive rule set describing mass ambiguities, while taking into consideration the resolving power (and its decay) of different mass analyzers. Importantly, common adduct species and isotopic peaks are accounted for and are shown to play a major role, both for perfect mass overlaps due to identical sum formulas and resolvable mass overlaps. We identified known and hitherto unknown mass ambiguities in high- and ultrahigh resolution data, while also ranking lipid classes by their propensity to cause ambiguities. On the basis of this new set of ambiguity rules, guidelines and recommendations for experimentalists and software developers of what constitutes a solid lipid identification in both MS and MS/MS were suggested. For researchers new to the field, our results are a compact source of ambiguities which should be accounted for. These new findings also have implications for the selection of internal standards, peaks used for internal mass calibration, optimal choice of instrument resolution, and sample preparation, for example, in regard to adduct ion formation.

Untargeted Metabolomics Strategies-Challenges and Emerging Directions

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.

Major roles for minor bacterial lipids identified by mass spectrometry

Mass spectrometry of lipids, especially those isolated from bacteria, has ballooned over the past two decades, affirming in the process the complexity of the lipidome. With this has come the identification of new and interesting lipid structures. Here is an overview of several novel lipids, from both Gram-negative and Gram-positive bacteria with roles in health and disease, whose structural identification was facilitated using mass spectrometry. This article is part of a Special Issue entitled: Bacterial Lipids edited by Russell E. Bishop.