Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions

Two-dimensional ion chromatography tandem-mass spectrometric (2D-IC-MS/MS) method for the analysis of phosphorus compounds in soil

Investigations into soil organic phosphorus (Po) dynamics are instrumental in understanding the transformations and processes responsible for ecosystem productivity. However, quantitative analysis of Po in a soil environment is extremely challenging due to low target analyte concentrations and matrix interferences with chromatography and analysis. Consequently, a two-dimensional ion chromatography-tandem mass spectrometric (2D-IC-MS/MS) method was developed to estimate soil Po concentrations. The first dimension diverted early eluting anions to waste while preconcentrating P compounds in a trap column, followed by chromatographic separation and detection in the second dimension. Detection was done using a mass spectrometer, and quantification was performed using the multiple reaction monitoring scan (MRM) method. The linear range of the studied P compounds, mostly nucleotides, was 0.05-50 ng/mL. Most P compounds were detected and quantified in calcareous subsoil samples in the concentration ranges 0.70-51.78 ng/g. The developed method achieved chromatographic separation that allowed unambiguous identification of isobars/isomers and isotopologues contributing to interferences in MS detection. However, improvements to the extraction method and post-clean-up procedures are required due to the complexity of soil extract composition, extreme matrix effect and/or loss of analyte during preconcentration. The method is ideal for simultaneously analyzing P compounds from environmental samples to elucidate key components of the soil P dynamics.

Genetic factors shaping the plasma lipidome and the relations to cardiometabolic risk in children and adolescents

BACKGROUND

Lipid species are emerging as biomarkers for cardiometabolic risk in both adults and children. The genetic regulation of lipid species and their impact on cardiometabolic risk during early life remain unexplored.

METHODS

Using mass spectrometry-based lipidomics, we measured 227 plasma lipid species in 1149 children and adolescents (44.8% boys) with a median age of 11.2 years. We performed genome-wide association analyses to identify genetic variants influencing lipid species. Colocalisation and Mendelian randomisation (MR) analyses were performed to infer causality between lipid species and cardiometabolic outcomes.

FINDINGS

We identified 37 genome-wide significant loci for 52 lipid species, nine of which are previously unreported. Colocalisation analyses revealed that seven lipid loci shared genetic variants associated with adult cardiometabolic outcomes. One-sample MR analysis identified positive causal associations between ceramides and liver enzymes, sphingomyelins and hemoglobin A1c (HbA1c), and phosphatidylethanolamines and high-sensitivity C-reactive protein in children and adolescents. Two-sample MR using adult-based summary statistics showed consistent direction of associations and indicated additional causal links, specifically between ceramides and elevated HbA1c levels, and phosphatidylinositols with elevated liver enzymes.

INTERPRETATION

These findings highlight the potential long-term implications of plasma lipid genetic determinants on cardiometabolic risk.

FUNDING

Novo Nordisk Foundation, The Innovation Fund Denmark, The Danish Heart Foundation, EU Horizon, and LundbeckFonden.

Thermometer Ions, Internal Energies, and In-Source Fragmentation in Ambient Ionization

Ionization and fragmentation are at the core of mass spectrometry. But they are not necessarily separated in space, as in-source fragmentation can also occur. Here, we survey the literature published since our 2005 review on the internal energy and fragmentation in electrospray ionization sources. We present new thermometer molecules to diagnose and quantify source heating, provide tables of recommended threshold (E0) and appearance energies (Eapp) for the survival yield method, and attempt to compare the softness of a variety of ambient pressure ionization sources. The droplet size distribution and desolvation dynamics play a major role: lower average internal energies are obtained when the ions remain protected by a solvation shell and spend less time nakedly exposed to activating conditions in the transfer interface. Methods based on small droplet formation without charging can thus be softer than electrospray. New dielectric barrier discharge sources can gas-phase ionize small molecules while conferring barely more internal energy than electrospray ionization. However, the tuning of the entire source interface often has an even greater influence on ion internal energies and fragmentation than on the ionization process itself. We hope that this review will facilitate further research to control and standardize in-source ion activation conditions, and to ensure the transferability of data and research results in mass spectrometry.

Highly reliable LC-MS lipidomics database for efficient human plasma profiling based on NIST SRM 1950

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS)-based methods have become the gold standard methodology for the comprehensive profiling of the human plasma lipidome. However, both the complexity of lipid chemistry and LC-HRMS-associated data pose challenges to the characterization of this biological matrix. In accordance with the current consensus of quality requirements for LC-HRMS lipidomics data, we aimed to characterize the NIST® Standard Reference Material for Human Plasma (SRM 1950) using an LC-ESI(+/-)-MS method compatible with high-throughput lipidome profiling. We generated a highly curated lipid database with increased coverage, quality, and consistency, including additional quality assurance procedures involving adduct formation, within-method m/z evaluation, retention behavior of species within lipid chain isomers, and expert-driven resolution of isomeric and isobaric interferences. As a proof-of-concept, we showed the utility of our in-house LC-MS lipidomic database -consisting of 592 lipid entries- for the fast, comprehensive, and reliable lipidomic profiling of the human plasma from healthy human volunteers. We are confident that the implementation of this robust resource and methodology will have a significant impact by reducing data redundancy and the current delays and bottlenecks in untargeted plasma lipidomic studies.

In-Depth Analysis of Molecular Network Based on Liquid Chromatography Coupled with Tandem Mass Spectrometry in Natural Products: Importance of Redundant Nodes Discovery

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC-MS/MS presents a challenge for data processing and interpretation, and the LC-MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Obscurity of chlorophyll tails - Is chlorophyll with farnesyl tail incorporated into PSII complexes?

Chlorophyll is essential in photosynthesis, converting sunlight into chemical energy in plants, algae, and certain bacteria. Its structure, featuring a porphyrin ring enclosing a central magnesium ion, varies in forms like chlorophyll a, b, c, d, and f, allowing light absorption at a broader spectrum. With a 20-carbon phytyl tail (except for chlorophyll c), chlorophyll is anchored to proteins. Previous findings suggested the presence of chlorophyll with a modified farnesyl tail in thermophilic cyanobacteria Thermosynechoccocus vestitus. In our Arabidopsis thaliana PSII cryo-EM map, specific chlorophylls showed incomplete phytyl tails, suggesting potential farnesyl modifications. However, further high-resolution mass spectrometry (HRMS) analysis in A. thaliana and T. vestitus did not confirm the presence of any farnesyl tails. Instead, we propose the truncated tails in PSII models may result from binding pocket flexibility rather than actual modifications.

OptiMS: An Accessible Program for Automating Mass Spectrometry Parameter Optimization and Configuration

Mass spectrometers have an enormous number of user-changeable parameters that drastically affect the observed mass spectrum. Using optimal parameters can significantly improve mass spectrometric data by increasing signal stability and signal-to-noise ratio, which decreases the limit of detection, thus revealing previously unobservable species. However, ascertaining optimal parameters is time-consuming, tedious, and made further challenging by the fact that parameters can act dependently on each other. Consequently, suboptimal parameters are frequently used during characterization, reducing the quality of results. OptiMS, an open-source, cross-platform program, was developed to simplify, accelerate, and more accurately determine optimal mass spectrometer parameters for a given system. It addresses common difficulties associated with existing software such as slow performance, high costs, and limited functionality. OptiMS efficacy was demonstrated through its application to multiple systems, quickly and successfully optimizing instrument parameters unassisted to maximize a user-defined metric, such as the intensity of a particular analyte. Additionally, among other features, OptiMS allows running of a sequence of predefined parameter configurations, reducing the workload of users wishing to obtain mass spectra under multiple sets of conditions.

Deep graph convolutional network for small-molecule retention time prediction

The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-based molecule identification. Recent research shows that graph neural networks (GNNs) outperform traditional machine learning algorithms in RT prediction. However, all of these models use relatively shallow GNNs. This study for the first time investigates how depth affects GNNs' performance on RT prediction. The results demonstrate that a notable improvement can be achieved by pushing the depth of GNNs to 16 layers by the adoption of residual connection. Additionally, we also find that graph convolutional network (GCN) model benefits from the edge information. The developed deep graph convolutional network, DeepGCN-RT, significantly outperforms the previous state-of-the-art method and achieves the lowest mean absolute percentage error (MAPE) of 3.3% and the lowest mean absolute error (MAE) of 26.55 s on the SMRT test set. We also finetune DeepGCN-RT on seven datasets with various chromatographic conditions. The mean MAE of the seven datasets largely decreases 30% compared to previous state-of-the-art method. On the RIKEN-PlaSMA dataset, we also test the effectiveness of DeepGCN-RT in assisting molecular structure identification. By 30% lessening the number of potential structures, DeepGCN-RT is able to improve top-1 accuracy by about 11%.

Development of a targeted hydrophilic interaction liquid chromatography-tandem mass spectrometry based lipidomics platform applied to a coronavirus disease severity study

The importance of lipids seen in studies of metabolism, cancer, the recent COVID-19 pandemic and other diseases has brought the field of lipidomics to the forefront of clinical research. Quantitative and comprehensive analysis is required to understand biological interactions among lipid species. However, lipidomic analysis is often challenging due to the various compositional structures, diverse physicochemical properties, and wide dynamic range of concentrations of lipids in biological systems. To study the comprehensive lipidome, a hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS)-based screening method with 1200 lipid features across 19 (sub)classes, including both nonpolar and polar lipids, has been developed. HILIC-MS/MS was selected due to its class separation property and fatty acyl chain level information. 3D models of class chromatographic retention behavior were established and evaluations of cross-class and within-class interferences were performed to avoid over-reporting these features. This targeted HILIC-MS/MS method was fully validated, with acceptable analytical parameters in terms of linearity, precision, reproducibility, and recovery. The accurate quantitation of 608 lipid species in the SRM 1950 NIST plasma was achieved using multi-internal standards per class and post-hoc correction, extending current databases by providing lipid concentrations resolved at fatty acyl chain level. The overall correlation coefficients (R2) of measured concentrations with values from literature range from 0.64 to 0.84. The applicability of the developed targeted lipidomics method was demonstrated by discovering 520 differential lipid features related to COVID-19 severity. This high coverage and targeted approach will aid in future investigations of the lipidome in various disease contexts.

Age-related changes in circadian regulation of the human plasma lipidome

Aging alters the amplitude and phase of centrally regulated circadian rhythms. Here we evaluate whether peripheral circadian rhythmicity in the plasma lipidome is altered by aging through retrospective lipidomics analysis on plasma samples collected in 24 healthy individuals (9 females; mean ± SD age: 40.9 ± 18.2 years) including 12 younger (4 females, 23.5 ± 3.9 years) and 12 middle-aged older, (5 females, 58.3 ± 4.2 years) individuals every 3 h throughout a 27-h constant routine (CR) protocol, which allows separating evoked changes from endogenously generated oscillations in physiology. Cosinor regression shows circadian rhythmicity in 25% of lipids in both groups. On average, the older group has a ~14% lower amplitude and a ~2.1 h earlier acrophase of the lipid circadian rhythms (both, p ≤ 0.001). Additionally, more rhythmic circadian lipids have a significant linear component in addition to the sinusoidal across the 27-h CR in the older group (44/56) compared to the younger group (18/58, p < 0.0001). Results from individual-level data are consistent with group-average results. Results indicate that prevalence of endogenous circadian rhythms of the human plasma lipidome is preserved with healthy aging into middle-age, but significant changes in rhythmicity include a reduction in amplitude, earlier acrophase, and an altered temporal relationship between central and lipid rhythms.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

Ultra-high performance liquid chromatography and gas chromatography coupled to tandem mass spectrometry for the analysis of 32 pyrethroid pesticides in fruits and vegetables: A comparative study

In this study, multi-residue analysis methods for 32 pyrethroids in fruit and vegetable samples were established in both GC-MS/MS and UHPLC-MS/MS. The parameters that affecting the ionization efficiencies of pyrethroids in UHPLC-ESI-MS/MS, including ion source temperature, in-source fragmentation, and mobile phase conditions were thoroughly investigated to guarantee better performance. These two techniques were comprehensively compared in terms of recovery, LOQ, linearity, and matrix effects. In general, UHPLC-MS/MS was found suitable for more pesticides than GC-MS/MS. Lower LOQs were obtained for most of the selected pyrethroids in UHPLC-MS/MS. Similar results were obtained in terms of recoveries and RSDs for the validated pesticides in fortification experiments. A total of 136 real samples were analyzed by both techniques, obtaining similar results. The results suggest that UHPLC-MS/MS offers a suitable alternative to GC-MS/MS in the routine analysis of pyrethroids in fruits and vegetables.

Widespread occurrence of in-source fragmentation in the analysis of natural compounds by liquid chromatography-electrospray ionization mass spectrometry

RATIONALE

The in-source fragmentation (ISF) of analyte or co-eluting substances produces unintentional fragment ions, which hampers identification and quantification by liquid chromatography-mass spectrometry (LC/MS). Natural compounds derived from plants also contain fragile moieties that may undergo ISF. However, the characteristics of ISF of natural compounds in LC/MS are still unclear.

METHODS

The ISF behavior of 214 natural compounds was assayed in LC with Q/orbitrap MS in electrospray ionization (ESI) mode and the extent of ISF was evaluated.

RESULTS

Up to 82% of tested compounds underwent ISF and half of the tested natural compounds that contain more than one fragile moiety underwent successive and severe ISF to generate serial structurally related ISF products. The major ISF-altering moieties for natural compounds were hydroxyl, lactone, glycosyl and ether, resulting in neutral loss of H₂ O or CO, deglycosylation or cleavage of ether bond, respectively. Some compounds such as terpenoids underwent severe ISF and less than 1% parent form can be observed. For natural compounds, ISF products with similar structures are more likely to cause interference in analysis because the ISF products may share identical mass-to-charge ratio and similar MS² fragmentation patterns with precursor ions of the homologs in plants. Furthermore, severe ISF may cause a false negative in the identification of the parent form.

CONCLUSIONS

In summary, ISF was a highly frequent phenomenon for analysis of natural compounds by LC/ESI-MS, and extensive and successive ISF of natural products may cause misannotation and misidentification with homologs in plants. The study should raise awareness of ISF interference during the analysis of natural compounds.

Clinical Blood Metabogram: Application to Overweight and Obese Patients

Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual's metabolic status in disease.

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

In-source fragmentation of nucleosides in electrospray ionization towards more sensitive and accurate nucleoside analysis

Nucleosides have been found to suffer in-source fragmentation (ISF) in electrospray ionization mass spectrometry, which leads to reduced sensitivity and ambiguous identification. In this work, a combination of theoretical calculations and nuclear magnetic resonance analysis revealed the key role of protonation at N3 near the glycosidic bond during ISF. Therefore, an ultrasensitive liquid chromatography-tandem mass spectrometry system for 5-formylcytosine detection was developed with 300 fold signal enhancement. Also, we established a MS1-only platform for nucleoside profiling and successfully identified sixteen nucleosides in the total RNA of MCF-7 cells. Taking ISF into account, we can realize analysis with higher sensitivity and less ambiguity, not only for nucleosides, but for other molecules with similar protonation and fragmentation behaviors.

Mass Spectrometric Blood Metabogram: Acquisition, Characterization, and Prospects for Application

In metabolomics, many metabolites are measured simultaneously in a single run. Such analytical performance opens up prospects for clinical laboratory diagnostics. In this work, a mass spectrometric metabogram was developed as a simplified and clinically applicable way of measuring the blood plasma metabolome. To develop the metabogram, blood plasma samples from healthy male volunteers (n = 48) of approximately the same age, direct infusion mass spectrometry (DIMS) of the low molecular fraction of samples, and principal component analysis (PCA) of the mass spectra were used. The seven components of the metabogram defined by PCA, which cover ~70% of blood plasma metabolome variability, were characterized using a metabolite set enrichment analysis (MSEA) and clinical test results of participating volunteers. It has been established that the components of the metabogram are functionally related groups of the blood metabolome associated with regulation, lipid-carbohydrate, and lipid-amine blood components, eicosanoids, lipid intake into the organism, and liver function thereby providing a lot of clinically relevant information. Therefore, metabogram provides the possibility to apply the metabolomics performance in the clinic. The features of the metabogram are also discussed in comparison with the thin-layer chromatography and with the analysis of blood metabolome by liquid chromatography combined with mass spectrometry.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

Metabolite Profiling of Tartary Buckwheat Extracts in Rats Following Co-Administration of Ethanol Using UFLC-Q-Orbitrap High-Resolution Mass Spectrometry

Tartary buckwheat, a gluten-free pseudocereal, has received considerable attention owing to its unique nutritional ingredients and beneficial health effects such as anti-tumor, anti-oxidation, anti-inflammation and hepatoprotective activities. Pharmacokinetic and metabolite profiling have been preliminarily assessed for Tartary buckwheat extracts. However, its metabolites have not yet been characterized in vivo after co-administration with ethanol when Tartary buckwheat extracts are used for the treatment of alcoholic liver disease. In this paper, a Q-Exactive orbitrap high-resolution mass spectrometer was employed to identify the metabolites of Tartary buckwheat extracts in rat biological samples. Compared with previous metabolite profiling results, a total of 26 novel metabolites were found in rat biological samples, including 11, 10, 2 and 5 novel metabolites in rat plasma, bile, urine and feces, respectively, after oral co-administration of 240 mg/kg Tartary buckwheat extracts with ethanol (42%, v/v). The major metabolic pathways of the constituents in Tartary buckwheat extracts involved hydroxylation, methylation, glucuronidation, acetylation and sulfation. Quercetin and its metabolites may be the pharmacological material basis of Tartary buckwheat for the protective effect against alcoholic liver injury. The research enriched in vivo metabolite profiling of Tartary buckwheat extracts, which provided experimental data for a comprehensive understanding and rational use of Tartary buckwheat against alcoholic liver disease.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

Maximizing MS/MS Acquisition for Lipidomics Using Capillary Separation and Orbitrap Tribrid Mass Spectrometer

Liquid chromatography-mass spectrometry (LC-MS) is a typical strategy for lipidomics analysis. Although capillary LC-MS is a common analytical technique for proteomics analysis, its application to lipidomics has been limited. In this study, we aim at improving lipid identifications achieved in a single LC-MS analysis by a 3-fold approach: capillary LC and nanoelectrospray for enhanced ionization, ion trap for higher sensitivity tandem MS, and parallelization of mass analyzers for increased speed of acquisition on an Orbitrap hybrid system. By applying the methods to a complex lipid mixture of human plasma, we identified and performed relative quantification on over 1500 lipids within a 60 min capillary LC-MS analysis.

Characterization of degradation products of carrageenan by LC-QTOF/MS with a hypothetical database

Carrageenan (CGN) belongs to the sulfated polysaccharides family that is commonly used in the food industry. For oligosaccharide analysis, a liquid chromatography quadrupole time-of-flight/mass spectrometry strategy was developed using a hypothetical database. There are 2100 structures in the developed hypothetical κ-CGN database. To eliminate false-positive results, three approaches were used, including size exclusion chromatography with mass spectrometry, which differentiates the loss of sulfated groups caused by the hydrolysis process or the ionization process. Profiling of acidic hydrolysis products of κ-CGN was found that after 12 h of HCl cultivation, the κ-CGN was hydrolyzed to oligosaccharides lower than the degree of polymerization 10, breaking the α-1,3-glycoside linkage and producing even-numbered oligosaccharides. Another finding was that the pH at which acidic hydrolysis is terminated affects the generation of even and odd oligosaccharides. Peeling reaction occurs at the reduction end 4-linked-3,6-anhydrous-d-galactose when adjusted to alkaline conditions, thus generating odd oligosaccharides.

RECOGNITION AND AVOIDANCE OF ION SOURCE-GENERATED ARTIFACTS IN LIPIDOMICS ANALYSIS

Lipid research is attracting more and more attention as various key roles and novel biological functions of lipids have been demonstrated and discovered in the organism. Mass spectrometry (MS)-based lipidomics approaches are the most powerful and effective tools for analysis of cellular lipidomes with very high sensitivity and specificity. However, the artifacts generated from in-source fragmentation are always present in all kinds of ion sources, even soft ionization techniques (i.e., electrospray ionization and matrix-assisted laser desorption/ionization [MALDI]). These artifacts can cause many problems for lipidomics, especially when the fragment ions correspond to/are isomeric species of other endogenous lipid species in complex biological samples. These commonly observed artifacts could lead to misannotation, false identification, and consequently, incorrect attribution of phenotypes, and will have negative impact on any MS-based lipidomics research including but not limited to biomarker discovery, drug development, etc. Liquid chromatography-MS, shotgun lipidomics, and MALDI-MS imaging are three representative lipidomics approaches in which ion source-generated artifacts are all manifested and are comprehensively summarized in this article. The strategies on how to avoid/reduce the artifacts of in-source fragmentation on lipidomics analysis are also discussed in detail. We believe that with the recognition and avoidance of ion source-generated artifacts, MS-based lipidomics approaches will provide better accuracy on comprehensive analysis of biological samples and will make greater contribution to the research on metabolism and translational/precision medicine (collectively termed functional lipidomics). © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

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.

Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow

High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CH₃COO]^-, [M + CD₃COO]^-, and [M - CH₃]^- coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.

Recommendations for good practice in MS-based lipidomics

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

Soil Organic Matter Characterization by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation

The biogeochemical cycling of soil organic matter (SOM) plays a central role in regulating soil health, water quality, carbon storage, and greenhouse gas emissions. Thus, many studies have been conducted to reveal how anthropogenic and climate variables affect carbon sequestration and nutrient cycling. Among the analytical techniques used to better understand the speciation and transformation of SOM, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is the only technique that has sufficient mass resolving power to separate and accurately assign elemental compositions to individual SOM molecules. The global increase in the application of FTICR MS to address SOM complexity has highlighted the many challenges and opportunities associated with SOM sample preparation, FTICR MS analysis, and mass spectral interpretation. Here, we provide a critical review of recent strategies for SOM characterization by FTICR MS with emphasis on SOM sample collection, preparation, analysis, and data interpretation. Data processing and visualization methods are presented with suggested workflows that detail the considerations needed for the application of molecular information derived from FTICR MS. Finally, we highlight current research gaps, biases, and future directions needed to improve our understanding of organic matter chemistry and cycling within terrestrial ecosystems.

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS²) spectra of their precursor ions, and (3) sharing similar MS² fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS² spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).

Remodeling of Arctic char (Salvelinus alpinus) lipidome under a stimulated scenario of Arctic warming

Arctic warming associated with global climate change poses a significant threat to populations of wildlife in the Arctic. Since lipids play a vital role in adaptation of organisms to variations in temperature, high-resolution mass-spectrometry-based lipidomics can provide insights into adaptive responses of organisms to a warmer environment in the Arctic and help to illustrate potential novel roles of lipids in the process of thermal adaption. In this study, we studied an ecologically and economically important species-Arctic char (Salvelinus alpinus)-with a detailed multi-tissue analysis of the lipidome in response to chronic shifts in temperature using a validated lipidomics workflow. In addition, dynamic alterations in the hepatic lipidome during the time course of shifts in temperature were also characterized. Our results showed that early life stages of Arctic char were more susceptible to variations in temperature. One-year-old Arctic char responded to chronic increases in temperature with coordinated regulation of lipids, including headgroup-specific remodeling of acyl chains in glycerophospholipids (GP) and extensive alterations in composition of lipids in membranes, such as less lyso-GPs, and more ether-GPs and sphingomyelin. Glycerolipids (e.g., triacylglycerol, TG) also participated in adaptive responses of the lipidome of Arctic char. Eight-week-old Arctic char exhibited rapid adaptive alterations of the hepatic lipidome to stepwise decreases in temperature while showing blunted responses to gradual increases in temperature, implying an inability to adapt rapidly to warmer environments. Three common phosphatidylethanolamines (PEs) (PE 36:6|PE 16:1_20:5, PE 38:7|PE 16:1_22:6, and PE 40:7|PE 18:1_22:6) were finally identified as candidate lipid biomarkers for temperature shifts via machine learning approach. Overall, this work provides additional information to a better understanding of underlying regulatory mechanisms of the lipidome of Arctic organisms in the face of near-future warming.

Obesity-Related Changes in Human Plasma Lipidome Determined by the Lipidyzer Platform

Obesity is an increasing public health concern both in the developed and developing countries. Previous studies have demonstrated that considerable alterations in lipid metabolism and consequently marked changes in lipid profile are associated with the onset and progression of obesity-related complications. To characterize the full spectrum of obesity-induced changes in lipid metabolism, direct infusion tandem mass spectrometry analysis is the most promising approach. To better understand which of the many lipid species are the most strongly associated with obesity, the aim of our work was to measure and profile plasma lipids in normal (n = 57), overweight (n = 31), and obese (n = 48) individuals randomly selected from samples of Hungarian general and Roma populations by using the targeted quantitative lipidomics platform, the Lipidyzer. Principal component and stepwise regression analyses were used to identify the most significant clusters and species of lipids by increasing body mass index (BMI). From the 18 clusters identified four key lipid species (PE P-16:0/20:3, TG 20:4_33:1, TG 22:6_36:4, TG 18:3_33:0) showed a strong significant positive and three others (Hex-Cer 18:1;O2/22:0, LPC 18:2, PC 18:1_18:1) significant negative association with BMI. Compared to individual lipid species alone, the lipid species ratio (LSR) we introduced showed an extremely strong, at least 9 orders of magnitude stronger, association with BMI. The LSR can be used as a sensitive and predictive indicator to monitor obesity-related alterations in human plasma and control the effectiveness of treatment of obesity associated non-communicable diseases.

Microdroplet Impact-Induced Spray Ionization Mass Spectrometry (MISI MS) for Online Reaction Monitoring and Bacteria Discrimination

Microdroplet impact-induced spray ionization (MISI) is demonstrated involving the impact of microdroplets produced from a paper and their impact on another, leading to the ionization of analytes deposited on the latter. This cascaded process is more advantageous in comparison to standard spray ionization as it performs reactions and ionization simultaneously in the absence of high voltage directly applied on the sample. In MISI, we apply direct current (DC) potential only to the terminal paper, used as the primary ion source. Charge transfer due to microdroplet/ion deposition on the flowing analyte solution on the second surface generates secondary charged microdroplets from it carrying the analytes, which ionize and get detected by a mass spectrometer. In this way, up to three cascaded spray sources could be assembled in series. We show the detection of small molecules and proteins in such ionization events. MISI provides a method to understand chemical reactions by droplet impact. The C-C bond formation reactions catalyzed by palladium and alkali metal ion encapsulation using crown ether were studied as our model reactions. To demonstrate the application of our ion source in a bioanalytical context, we studied the noninvasive in situ discrimination of bacteria samples under ambient conditions.

Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation

Capillary electrophoresis coupled to mass spectrometry is a power tool in untargeted metabolomics studies to analyze charged and polar compounds. However, identification is a challenge due to the variability of migration times and the lack of MS/MS spectra in CE-TOF-MS, the type of instruments most frequently employed. We present here a CE-MS search platform incorporated in CEU Mass Mediator to annotate metabolites with a confidence level L2. For its the development we analyzed 226 compounds using two fragmentor voltages: 100 and 200 V. The information obtained, such as relative migration times (RMT) and in-source fragments, were incorporated into the platform. In addition, we validated the CE-MS search functionality using different types of biological samples such as plasma samples (human, rat, and rabbit), mouse macrophages, and human urine. The RMT tolerance percentage for the search of metabolites has been determined, establishing 5% for all compounds, except for the compounds migrating in the electro-osmotic flow, for which the tolerance should be of 10%. It has also been demonstrated the robustness of the in-source fragmentation, which makes possible the annotation of compounds by means of their fragmentation pattern. As an example, 3-methylhistidine and 1-methilhistidine, whose RMT are very close, have been annotated. Studies of the fragmentation mechanisms of acyl-L-carnitines have shown that in-source fragmentation follows the general fragmentation rules and is a suitable alternative to MS/MS.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

Establishing an untargeted-to-MRM liquid chromatography-mass spectrometry method for discriminating reconstituted milk from ultra-high temperature milk

The counterfeit use of reconstituted milk to simulate fresh milk in some countries is largely driven by profiteering; hence, establishing a robust market-surveillance method is an important objective. In this study, an untargeted metabolomics approach that uses ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was established to discover biomarkers that can be used to classify UHT and reconstituted milk. Furthermore, 36 positive- and 24 negative-ionization mode features were selected as candidate markers to establish a UPLC-tandem mass spectrometry (UPLC-MS/MS) multiple reaction monitoring (MRM) method. The support vector machine (SVM) method was used to process the responses of the selected potential markers, and 100% classifiability was observed. The marker metabolites could be divided into three categories by hierarchical clustering analysis: peptides, lipids, and nucleic acids. The results suggest that the untargeted-to-MRM metabolomics method is an effective tool for distinguishing between UHT and reconstituted milk.

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.

Avoiding H/D Scrambling with Minimal Ion Transmission Loss for HDX-MS/MS-ETD Analysis on a High-Resolution Q-TOF Mass Spectrometer

Hydrogen/deuterium exchange monitored by mass spectrometry (HDX-MS) enables the study of protein dynamics by measuring the time-resolved deuterium incorporation into a protein incubated in D₂O. Using electron-based fragmentation in the gas phase it is possible to measure deuterium uptake at single-residue resolution. However, a prerequisite for this approach is that the solution-phase labeling is conserved in the gas phase prior to precursor fragmentation. It is therefore essential to reduce or even avoid intramolecular hydrogen/deuterium migration, which causes randomization of the deuterium labels along the peptide (hydrogen scrambling). Here, we describe an optimization strategy for reducing scrambling to a negligible level while minimizing the impact on sensitivity on a high-resolution Q-TOF equipped with ETD and an electrospray ionization interface consisting of a glass transfer capillary followed by a dual ion funnel. In our strategy we narrowed down the optimization to two accelerating potentials, and we defined the optimization of these in a simple rule by accounting for their interdependency in relation to scrambling and transmission efficiency. Using this rule, we were able to reduce scrambling from 75% to below 5% on average using the highly scrambling-sensitive quadruply charged P1 peptide scrambling probe resulting in a minor 33% transmission loss. To demonstrate the applicability of this approach, we probe the dynamics of certain regions in cytochrome c.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Evaluation of Lipid In-Source Fragmentation on Different Orbitrap-based Mass Spectrometers

Natural lipidomes represent a complex mixture of lipid molecular species with a variety of biological and signaling functions. Modern mass spectrometry (MS)-based analytical platforms are often used to resolve the complexity of natural lipidomes. The quantitative transfer of lipid molecular species in the gas phase during the electrospray ionization required for MS analysis might be challenged by lipid in-source fragmentation (ISF) hampering their accurate identification and quantification. Here we evaluated the effect of transmission radio frequency (RF) levels and ion transfer temperatures (ITTs) on the analysis of four different lipids (ceramide, cholesteryl ester, phosphatidylethanolamine, and triacylglyceride) ionized in positive ion mode on three different Orbitrap-based platforms. ITT and RF levels were ramped in a systematic way to determine the best settings, allowing the most sensitive detection accompanied by the lowest ISF of a lipid. The extent of the ISF was shown to depend on the configurations of the transmission devices (S-lens vs letterbox/ion funnel) at defined RF and ITT levels for each studied lipid class. We provide here the recommendations for reducing the extent of lipid ISF without a significant loss in sensitivity for Q Exactive HF, Q Exactive HF-X, and Orbitrap Fusion Lumos platforms.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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.

Lipidomics: Current state of the art in a fast moving field

Lipids are essential for all facets of life. They play three major roles: energy metabolism, structural, and signaling. They are dynamic molecules strongly influenced by endogenous and exogenous factors including genetics, diet, age, lifestyle, drugs, disease and inflammation. As precision medicine starts to become mainstream, there is a huge burgeoning interest in lipids and their potential to act as unique biomarkers or prognostic indicators. Lipids comprise a large component of all metabolites (around one-third), and our expanding knowledge about their dynamic behavior is fueling the hope that mapping their regulatory biochemical pathways on a systems level will revolutionize our ability to prevent, diagnose, and stratify major human diseases. Up to now, clinical lipid measurements have consisted primarily of total cholesterol or triglycerides, as a measure for cardiovascular risk and response to lipid lowering drugs. Nowadays, we are able to measure thousands of individual lipids that make up the lipidome. nuclear magnetic resonance spectrometry (NMR) metabolomics is also being increasingly used in large cohort studies where it can report on total levels of selected lipid classes, and relative levels of fatty acid saturation. To support the application of lipidomics research, LIPID MAPS was established in 2003, and since then has gone on to become the go-to resource for several lipid databases, lipid drawing tools, data deposition, and more recently lipidomics informatics tools, and a lipid biochemistry encyclopedia, LipidWeb. Alongside this, the recently established Lipidomics Standards Initiative plays a key role in standardization of lipidomics methodologies. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Analytical and Computational Methods > Analytical Methods.

Comprehensive lipidomics of mouse plasma using class-specific surrogate calibrants and SWATH acquisition for large-scale lipid quantification in untargeted analysis

Lipidomics has gained rising attention in recent years. Several strategies for lipidomic profiling have been developed, with targeted analysis of selected lipid species, typically utilized for lipid quantification by low-resolution triple quadrupole MS/MS, and untargeted analysis by high-resolution MS instruments, focusing on hypothesis generation for prognostic, diagnostic and/or disease-relevant biomarker discovery. The latter methodologies generally yield relative quantification data with limited inter-assay comparability. In this work we aimed to combine untargeted analysis and absolute quantification to enhance data quality and to obtain independent results for optimum comparability to previous studies or database entries. For the lipidomic analysis of mouse plasma, RP-UHPLC hyphenated to a high-resolution quadrupole TOF mass spectrometer in comprehensive data-independent SWATH acquisition mode was employed. This way, quantifiable data on the MS and the MS/MS level were recorded, which increases assay specificity and quantitative performance. Due to the lack of an appropriate blank matrix for untargeted lipidomics, we herein established a sophisticated strategy for lipid class-specific calibration with stable isotope labeled standards (surrogate calibrants). LLOQs were in the range between 10 and 50 ng mL^-1 for LPC, LPE, PI, PS, PG, SM, PC, PE, DAG) or 100-700 ng mL^-1 (MAG, TAG), except for cholesterol and CE (1-20 μg mL^-1). Acceptable values for accuracy and precision well below ±15% bias were reached for the majority of surrogate calibrants. However, to achieve sufficient accuracy for target lipids, response factors to corresponding surrogate calibrants are required. An approach to estimate response factors via a standard reference material (NIST SRM 1950) was therefore conducted. Furthermore, a useful workflow for post-acquisition re-calibration, involving response factor determination and iteratively built libraries, is suggested. In comparison to single-point calibration, the presented surrogate calibrant method was shown to yield results with improved accuracy that are largely in accordance with standard addition. Quantitative results of real samples (high-fat diet vs control diet) were then compared to two previously published dietary mouse plasma studies that provided absolute lipid levels and showed similar trends.