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

Mass spectrometric characterization of aminophospholipids containing N-(2-hydroxyethyl)glycine in kombu algae extracts

RATIONALE

1,2-Diacyl-sn-glycero-3-phospho-O-[N-(2-hydroxyethyl)glycines] (PHEGs) are a class of rare aminophospholipids found specifically in brown algae, including kombu seaweed. Despite their potential importance in algal physiology, a comprehensive mass spectrometry (MS) characterization, useful to understand their biological behaviour, is still lacking.

METHODS

To establish the structural regiochemical features of PHEGs, we employed hydrophilic interaction liquid chromatography (HILIC). Following separation, the isolated band of PHEGs was analyzed using MS techniques. This included multistage tandem MS experiments, performed in both positive and negative electrospray ionization modes at low and high resolution.

RESULTS

By comparing MS/MS and MS3 spectra acquired in negative ion mode, the regiochemical rules for PHEG identification were established. The most abundant PHEG species in kombu seaweed, from both Laminaria ochroleuca (European Atlantic) and Laminaria longissima (Japan), was identified as PHEG 20:4/20:4. Less abundant species included PHEG 20:4/20:5 and hydroxylated forms of both PHEG 20:4/20:4 (i.e. 40:8;O) and 20:4/20:5 (40:9;O). The presence of a lyso PHEG 20:4 was consistently detected but at very low levels.

CONCLUSIONS

This study employed MS analysis to elucidate the regiochemical patterns of PHEGs in kombu seaweed. We identified PHEG 20:4/20:4 as the dominant species, along with several less abundant variants, including hydroxylated forms. These findings provide valuable insights into the potential roles and metabolism of PHEGs in brown algae, paving the way for further investigation into their biological functions.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Structural Elucidation of Intact Rough-Type Lipopolysaccharides using Field Asymmetric Ion Mobility Spectrometry and Kendrick Mass Defect Plots

Lipopolysaccharide (LPS) is a hallmark virulence factor of Gram-negative bacteria. It is a complex, structurally heterogeneous mixture due to variations in number, type, and position of its simplest units: fatty acids and monosaccharides. Thus, LPS structural characterization by traditional mass spectrometry (MS) methods is challenging. Here, we describe the benefits of field asymmetric ion mobility spectrometry (FAIMS) for analysis of intact R-type lipopolysaccharide complex mixture (lipooligosaccharide; LOS). Structural characterization was performed using Escherichia coli J5 (Rc mutant) LOS, a TLR4 agonist widely used in glycoconjugate vaccine research. FAIMS gas phase fractionation improved the (S/N) ratio and number of detected LOS species. Additionally, FAIMS allowed the separation of overlapping isobars facilitating their tandem MS characterization and unequivocal structural assignments. In addition to FAIMS gas phase fractionation benefits, extra sorting of the structurally related LOS molecules was further accomplished using Kendrick mass defect (KMD) plots. Notably, a custom KMD base unit of [NaH] created a highly organized KMD plot that allowed identification of interesting and novel structural differences across the different LOS ion families; i.e., ions with different acylation degrees, oligosaccharides composition, and chemical modifications. Defining the composition of a single LOS ion by tandem MS along with the organized KMD plot structural network was sufficient to deduce the composition of 179 LOS species out of 321 species present in the mixture. The combination of FAIMS and KMD plots allowed in-depth characterization of the complex LOS mixture and uncovered a wealth of novel information about its structural variations.

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

Untargeted lipidomic profiling of grapes highlights the importance of modified lipid species beyond the traditional compound classes

The aim of this paper is to provide a detailed characterisation of grape lipidome. To achieve this objective, it starts by describing a pipeline implemented in R software to allow the semi-automatic annotation of the detected lipid species. It also provides an extensive description of the different properties of each molecule (such as retention time dependencies, mass accuracy, adduct formation and fragmentation patterns), which allowed the annotations to be made more accurately. Most annotated lipids in the grape samples were (lyso)glycerophospholipids and glycerolipids, although a few free fatty acids, hydroxyceramides and sitosterol esters were also observed. The proposed pipeline also allowed the identification of a series of methylated glycerophosphates never previously observed in grapes. The current results highlight the importance of expanding chemical analyses beyond the classical lipid categories.

Global research trends and hotspots analysis of hallux valgus: A bibliometric analysis from 2004 to 2021

Background

Hallux valgus (HV) is a common foot and ankle surgery disease. The correction of HV deformity relies on a highly challenging surgical treatment. Thus, widely adopted evidence-based clinical guidelines are still needed to guide the selection of the most appropriate interventions. Recently, the study of HV has been growing and scholars are increasingly paying particular attention to this area. However, bibliometric literature remains lacking. Therefore, this study aims to reveal the hotspots and future research trends in HV via bibliometric analysis to fill this knowledge gap.

Methods

Literature related to HV from 2004 to 2021 was retrieved from the Science Citation Index Expanded (SCI-expanded) of the Web of Science Core Collection (WoSCC). Quantitative and qualitative analyses of scientific data are performed using software such as CiteSpace, R-bibliometrix, and VOSviewer.

Results

A total of 1,904 records were identified for analysis. The United States had the most number of published articles and total citations. Thus, the United States has made an essential contribution to the field of HV. Meanwhile, La Trobe University in Australia was the most productive institution. Menz HB and Foot & Ankle International were the most influential authors and the most popular journals among researchers, respectively. In addition, “older people,” “chevron osteotomy,” “Lapidus,” and “hallux rigidus” have always been the hotspots of attention. Changes and developments in the surgery of HV have gained researchers' interest. Future research trends are more focused on “radiographic measurement,” “recurrence,” “outcome,” “rotation,” “pronation,” and “minimally invasive surgery.” Thus, focusing on these subject directions can facilitate academic progress and provide the possibility of better treatments for HV.

Conclusion

This study summarizes the hotspots and trends in the field of HV from 2004 to 2021, which will provide researchers with an updated view of essential information and somehow guide future research.

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Differential Kendrick's Plots as an Innovative Tool for Lipidomics in Complex Samples: Comparison of Liquid Chromatography and Infusion-Based Methods to Sample Differential Study

Lipidomics has developed rapidly over the past decade. Nontargeted lipidomics from biological samples remains a challenge due to the high structural diversity, the concentration range of lipids, and the complexity of biological samples. We introduce here the use of differential Kendrick's plots as a rapid visualization tool for a qualitative nontargeted analysis of lipids categories and classes from data generated by either liquid chromatography-mass spectrometry (LC-MS) or direct infusion (nESI-MS). Each lipid class is easily identified by comparison with the theoretical Kendrick plot pattern constructed from exact mass measurements and by using MSKendrickFilter, an in-house Python software. The lipids are identified with the LIPID MAPS database. In addition, in LC-MS, the software based on the Kendrick plots returns the retention time from all the lipids belonging to the same series. Lipid extracts from a yeast (Saccharomyces cerevisiae) are used as a model. An on/off case comparing Kendrick plots from two cell lines (prostate cancer cell lines treated or not with a DGAT2 inhibition) clearly shows the effect of the inhibition. Our study demonstrates the good performance of direct infusion as a fast qualitative screening method as well as for the analysis of chromatograms. A fast screening semiquantitative approach is also possible, while the targeted mode remains the golden standard for precise quantitative analysis.

Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis

Phospholipids are small but critical lipids in milk. Conventional lipidomics is a powerful method for the analysis of lipids in milk. Although the number of lipidomics software has drastically increased over the past five years, reducing false positives and obtaining structurally accurate annotations of phospholipids remain a significant challenge. In this study, we developed a rapid and accurate method for measuring a wide spectrum of phospholipids in milk. The developed approach that employed information-dependent acquisition (IDA) mode and feature-based molecular networking has exhibited better performance on data processing and lipid annotation when compared with sequential window acquisition of all theoretical mass spectra (SWATH) and MS-DIAL. This validated method was further evaluated using three kinds of sheep milk. A total of 150 phospholipids were identified, including rarely reported phospholipids containing ethers or vinyl ethers. The result indicated that phospholipids could be used as potential markers to distinguish sheep milk from different varieties and origins. The experimental and computational methods provide a rapid and reliable method for the investigation of phospholipids in milk.

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.

Imaging lipids in biological samples with surface-assisted laser desorption/ionization mass spectrometry: A concise review of the last decade

Knowing the spatial location of the lipid species present in biological samples is of paramount importance for the elucidation of pathological and physiological processes. In this context, mass spectrometry imaging (MSI) has emerged as a powerful technology allowing the visualization of the spatial distributions of biomolecules, including lipids, in complex biological samples. Among the different ionization methods available, the emerging surface-assisted laser desorption/ionization (SALDI) MSI offers unique capabilities for the study of lipids. This review describes the specific advantages of SALDI-MSI for lipid analysis, including the ability to perform analyses in both ionization modes with the same nanosubstrate, the detection of lipids characterized by low ionization efficiency in MALDI-MS, and the possibilities of surface modification to improve the detection of lipids. The complementarity of SALDI and MALDI-MSI is also discussed. Finally, this review presents data processing strategies applied in SALDI-MSI of lipids, as well as examples of applications of SALDI-MSI in biomedical lipidomics.

Normalized Mass Maps in Three-Dimensional Space Combining Kendrick-like Values with Chromatographic Separation for Enhanced Data Deconvolution

The incorporation of retention-time information into a fully rotatable and interactive three-dimensional (3D), "Kendrick-like" normalized mass map (NMM) using a single software platform is reported. Surprising discoveries were made about the elution pattern of block ethoxylate-propoxylate oligomers (ca. 2800 Da) in the supercritical fluid after combined SFC-Orbitrap FTMS analysis. The 3D NMM also facilitated identification of impurities using interactive graphics tools within the map. By selecting map glyphs, associated reconstructed ion chromatograms were automatically generated. Last, since Kendrick and Kendrick-like mapping (NMM) are chemical-formula-based, incorporating retention time in 3D space allows the possibility of resolving isomers in the map.

Fast Evaluation of Herbal Substance Class Composition by Relative Mass Defect Plots

A holistic, nontargeted mass spectrometric analysis of any herbal material and preparation is intimately connected to fast chemical profiling and visualization of secondary plant metabolite classes or single compounds. High-resolution mass spectral data enable a broad variety of analytical possibilities. Often a fast and comprehensive overview on compound classes (phytochemical profiling) is needed before single-substance considerations. We present a fast approach for the initial characterization and substance class profiling using relative mass defect plots for the visualization of herbal compositions. From a dataset of 1160 common plant metabolites that represent a varied mixture of molecular classes in polarity, glycosylation, and alkylation, manually annotated for substance classes, the relative mass defects were calculated using theoretical molecular masses. For the calculation of the relative mass defect, a new approach incorporating two correction functions to obtain correct relative mass defect results also for large hydrocarbons, and a multitude of polyhalogenated molecules was developed. Using the Khachyan algorithm, elliptical areas clustering substance classes within the relative mass defect plots were calculated. The resulting novel relative mass defect plots provide a quick way of two-dimensional substance class mapping directly from high-resolution mass spectral data and may be considered as a unique fingerprint for herbals, part of them or herbal preparations. We show that adding the retention time as a third dimension improves the resolution power of the two-dimensional relative mass defect plot and offers the possibility for a more detailed substance class mapping.

Digging deeper - A new data mining workflow for improved processing and interpretation of high resolution GC-Q-TOF MS data in archaeological research

Gas chromatography-mass spectrometry profiling is the most established method for the analysis of organic residues, particularly lipids, from archaeological contexts. This technique allows the decryption of hidden chemical information associated with archaeological artefacts, such as ceramic pottery fragments. The molecular and isotopic compositions of such residues can be used to reconstruct past resource use, and hence address major questions relating to patterns of subsistence, diet and ritual practices in the past. A targeted data analysis approach, based on previous findings reported in the literature is common but greatly depends on the investigator’s prior knowledge of specific compound classes and their mass spectrometric behaviour, and poses the risk of missing unknown, potentially diagnostic compounds. Organic residues from post-prehistoric archaeological samples often lead to highly complex chromatograms, which makes manual chromatogram inspection very tedious and time consuming, especially for large datasets. This poses a significant limitation regarding the scale and interpretative scopes of such projects. Therefore, we have developed a non-targeted data mining workflow to extract a higher number of known and unknown compounds from the raw data to reduce investigator’s bias and to vastly accelerate overall analysis time. The workflow covers all steps from raw data handling, feature selection, and compound identification up to statistical interpretation.

Analysis of artificially oxidized cardiolipins and monolyso-cardiolipins via liquid chromatography/high-resolution mass spectrometry and Kendrick mass defect plots after hydrophilic interaction liquid chromatography based sample preparation

RATIONALE

Cardiolipins (CL) are a special lipid class which plays a main role in energy metabolism in mitochondria and is involved in apoptosis. In contrast to other glycerophospholipids, they contain four fatty acyl residues which results in a high structural diversity. Oxidation, for example by reactive oxygen species, or lyso forms such as monolyso-CL (MLCL), increases this diversity. Mass spectrometric analysis and computational identification of CL, MLCL and their oxidation products is therefore a challenging task.

METHODS

In order to distinguish CL, MLCL and their oxidation products, a liquid chromatography/tandem mass spectrometry (LC/MS/MS) method was developed. A hydrophilic interaction liquid chromatography (HILIC)-based solid-phase extraction (SPE) clean-up approach was developed for CL enrichment. Graphical analysis of CL, MLCL and their oxidation products was carried out by a three-dimensional Kendrick mass defect (3D-KMD) plot module, as well as a refined lipid search module of the open-source metabolomics data mining software MZmine 2.

RESULTS

The HILIC-based SPE clean-up enabled complete separation of polar and nonpolar lipid classes. A yeast (Saccharomyces cerevisiae) lipid extract, which was artificially oxidized by means of the Fenton reaction, was analyzed by the developed LC/MS/MS method. CL species with differences in chain length and degree of unsaturation have been separated by high-performance liquid chromatography (HPLC). In total 66 CL, MLCL and oxidized species have been identified utilizing 3D-KMD plots in combination with database matching using MZmine 2. For further characterization of annotated species, MS/MS experiments have been utilized.

CONCLUSIONS

3D-KMD plots capturing chromatographic and high-resolution mass spectrometry data have been successfully used for graphical identification of CL, MLCL as well as their oxidized species. Therefore, we chose multiple KMD bases such as hydrogen and oxygen to visualize the degree of unsaturation and oxidation capturing chromatographic data by means of a color-coded paint scale as the third dimension. In combination with database matching, the analysis of low concentrated lipid species in complex samples has been significantly improved.

A clean-up strategy for identification of circulating endogenous short peptides in human plasma by zwitterionic hydrophilic liquid chromatography and untargeted peptidomics identification

Short peptides, namely di- tri- and tetra peptides, have been proven to play an important diagnostic role in several diseases. Therefore, the development of an analytical approach for their detection and identification is nowadays an important research goal. This paper describes an analytical procedure able to overcome the issues of short peptide isolation, clean-up and identification in plasma samples. Four different protocols were compared and tested to maximize both recovery and total number of identifications of short circulating plasma endogenous peptides. The purified peptides, coming from the four different tested protocols, were separated by zwitterionic hydrophilic liquid chromatography coupled to high-resolution mass spectrometry with the purpose of accomplishing an untargeted investigation based on suspect screening for short peptides in plasma. In particular, the use of Phree™ Phospholipid removal cartridge in combination with a purification step by solid phase extraction on a graphitized carbon black sorbent allowed the identification of the largest number of amino acid sequences (91 short peptides). The clean-up procedure allowed to tackle the issue of the low abundance of such peptides and their suppression during mass-spectrometric analysis. The results indicated that sample preparation is therefore fundamental for short peptide analysis in plasma samples.

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.