Metabolomic applications of HILIC-LC-MS

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

Dilute and shoot approach for toxicology testing

Toxicology testing is performed in clinical settings, forensic settings, and for controlling doping. Drug screening is a toxicology test to determine if drugs are present in biological samples. The most common specimen type for drug testing is urine, as drugs and/or their metabolites are often more concentrated in the urine, extending the detection window of drugs. The dilute-and-shoot method is a simple procedure used in toxicology testing, where a sample is diluted before being directly injected into the liquid chromatography-mass spectrometry (LC-MS) system. This method is easy, quick, and cost-saving, and can be used for protein-poor liquid specimens such as urine. Thus, it is reasonable and attractive for busy toxicology laboratories to combine the dilute-and-shoot method with high-resolution hyphenated-MS for urine drug screening. This method has several disadvantages, including a suboptimal detection capability for certain analytes, as well as interference from co-eluting matrix components called matrix effects, in which co-eluting matrix molecules alter the ionization efficiency of the analyte molecules at the ionization source in LC-MS, altering (mostly reducing) the analyte detection capability. The matrix effect testing is essential for the validation of LC-MS-based assays. A reasonable approach to addressing these undesirable effects would be to minimize these components. The most straightforward approach is to reduce the amounts of matrix components by using a higher dilution of the specimen and a lower volume for specimen injection. Optimization of the chromatographic separation is another reasonable approach for reducing co-eluting matrix components with the analyte.

Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma

The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.

Scout triggered multiple reaction monitoring mass spectrometry for the rapid transfer of large multiplexed targeted methods in metabolomics

The field of metabolomics based on mass spectrometry has grown considerably in recent years due to the need to detect and, above all, quantify a very large number of metabolites, simultaneously. Up to now, targeted multiplexed analysis on complex samples by Liquid Chromatography coupled with tandem Mass Spectrometry (LC-MS/MS) has relied almost exclusively on compound detection based on absolute retention times, as in the Scheduled-MRM (sMRM) approach. Those methods turn out to be poorly transferable from one instrument to another and result in a time-consuming and tedious method development involving a significant number of critical parameters that need specific re-optimisation. To address this challenge, we introduce a novel acquisition mode called scout-triggered MRM (stMRM). In stMRM, a marker transition is used to trigger MS analysis for a group of dependent target analytes. These marker transitions are strategically distributed throughout the chromatographic run, and the dependent analytes are associated based on their retention times. The result is a targeted assay that remains robust even in the presence of retention time shifts. A 3 to 5-fold increase in the number of detected transitions associated to plasma metabolites was obtained when transferring from a direct application of a published sMRM to a stMRM method. This significant improvement highlights the universal applicability of the stMRM method, as it can be implemented on any LC system without the need for extensive method development. We subsequently illustrate the robustness of stMRM in modified chromatographic elution conditions. Despite a large change in metabolite's selectivity, the multiplexed assay successfully recovered 70% of the monitored transitions when consequently modifying the gradient method. These findings demonstrate the versatility and adaptability of stMRM, opening new avenues for the development of highly multiplexed LC-MS/MS methods in metabolomics. These methods are characterized by their analytical transparency and straightforward implementation using existing literature data.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

Motivation

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical.

Results

We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform.

Availability and implementation

MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Miniaturized Two-Dimensional Heart Cutting for LC-MS-Based Metabolomics

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics usually combines hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) chromatography to cover a wide range of metabolomes, requiring both significant sample consumption and analysis time for separate workflows. We developed an integrated workflow enabling the coverage of both polar and nonpolar metabolites with only one injection of the sample for each ionization mode using heart-cutting trapping to combine HILIC and RP separations. This approach enables the trapping of some compounds eluted from the first chromatographic dimension for separation later in the second dimension. In our case, we applied heart-cutting to non-retained metabolites in the first dimension. For that purpose, two independent miniaturized one-dimensional HILIC and RP methods were developed by optimizing the chromatographic and ionization conditions using columns with an inner diameter of 1 mm. They were then merged into one two-dimensional micro LC-MS method by optimization of the trapping conditions. Equilibration of the HILIC column during elution on the RP column and vice versa reduced the overall analysis time, and the multidimensionality allows us to avoid signal measurements during the solvent front. To demonstrate the benefits of this approach to metabolomics, it was applied to the analysis of the human plasma standard reference material SRM 1950, enabling the detection of hundreds of metabolites without the significant loss of some of them while requiring an injection volume of only 0.5 μL.

The effect of root hairs on exudate composition: a comparative non-targeted metabolomics approach

Root exudation is a major pathway of organic carbon input into soils. It affects soil physical properties, element solubility as well as speciation, and impacts the microbial community in the rhizosphere. Root exudates contain a large number of primary and secondary plant metabolites, and the amount and composition are highly variable depending on plant species and developmental stage. Detailed information about exudate composition will allow for a better understanding of exudate-driven rhizosphere processes and their feedback loops. Although non-targeted metabolomics by high-resolution mass spectrometry is an established tool to characterize root exudate composition, the extent and depth of the information obtained depends strongly on the analytical approach applied. Here, two genotypes of Zea mays L., differing in root hair development, were used to compare six mass spectrometric approaches for the analysis of root exudates. Reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography combined with time-of-flight mass spectrometry (LC-TOF-MS), as well as direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR-MS), were applied with positive and negative ionization mode. By using the same statistical workflow, the six approaches resulted in different numbers of detected molecular features, ranging from 176 to 889, with a fraction of 48 to 69% of significant features (fold change between the two genotypes of > 2 and p-value < 0.05). All approaches revealed the same trend between genotypes, namely up-regulation of most metabolites in the root hair defective mutant (rth3). These results were in agreement with the higher total carbon and nitrogen exudation rate of the rth3-mutant as compared to the corresponding wild-type maize (WT). However, only a small fraction of features were commonly found across the different analytical approaches (20-79 features, 13-31% of the rth3-mutant up-regulated molecular formulas), highlighting the need for different mass spectrometric approaches to obtain a more comprehensive view into the composition of root exudates. In summary, 111 rth3-mutant up-regulated compounds (92 different molecular formulas) were detected with at least two different analytical approaches, while no WT up-regulated compound was found by both, LC-TOF-MS and DI-FT-ICR-MS. Zea mays L. exudate features obtained with multiple analytical approaches in our study were matched against the metabolome database of Zea mays L. (KEGG) and revealed 49 putative metabolites based on their molecular formula.

Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes

Advances in technology and software have radically expanded the scope of metabolomics studies and allow us to monitor a broad transect of central carbon metabolism in routine studies. These increasingly sophisticated tools have shown that many human diseases are modulated by microbial metabolism. Despite this, it remains surprisingly difficult to move beyond these statistical associations and identify the specific molecular mechanisms that link dysbiosis to the progression of human disease. This difficulty stems from both the biological intricacies of host-microbiome dynamics as well as the analytical complexities inherent to microbiome metabolism research. The primary objective of this review is to examine the experimental and computational tools that can provide insights into the molecular mechanisms at work in host-microbiome interactions and to highlight the undeveloped frontiers that are currently holding back microbiome research from fully leveraging the benefits of modern metabolomics.

Comprehending Cardiac Dysfunction by Oxidative Stress: Untargeted Metabolomics of In Vitro Samples

Cardiovascular diseases (CVDs) are noncommunicable diseases known for their complex etiology and high mortality rate. Oxidative stress (OS), a condition in which the release of free radical exceeds endogenous antioxidant capacity, is pivotal in CVC, such as myocardial infarction, ischemia/reperfusion, and heart failure. Due to the lack of information about the implications of OS on cardiovascular conditions, several methodologies have been applied to investigate the causes and consequences, and to find new ways of diagnosis and treatment as well. In the present study, cardiac dysfunction was evaluated by analyzing cells’ alterations with untargeted metabolomics, after simulation of an oxidative stress condition using hydrogen peroxide (H₂O₂) in H9c2 myocytes. Optimizations of H₂O₂ concentration, cell exposure, and cell recovery times were performed through MTT assays. Intracellular metabolites were analyzed right after the oxidative stress (oxidative stress group) and after 48 h of cell recovery (recovery group) by ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) in positive and negative ESI ionization mode. Significant alterations were found in pathways such as “alanine, aspartate and glutamate metabolism”, “glycolysis”, and “glutathione metabolism”, mostly with increased metabolites (upregulated). Furthermore, our results indicated that the LC-MS method is effective for studying metabolism in cardiomyocytes and generated excellent fit (R²Y > 0.987) and predictability (Q² > 0.84) values.

Optimized integration of metabolomics and lipidomics reveals brain region-specific changes of oxidative stress and neuroinflammation in type 1 diabetic mice with cognitive decline

INTRODUCTION

Type 1 diabetes (T1D) causes cognitive decline and has been associated with brain metabolic disorders, but its potential molecular mechanisms remain unclear.

OBJECTIVES

The purpose of this study was to explore the molecular mechanisms underlying T1D-induced cognitive impairment using metabolomics and lipidomics.

METHODS

We developed an optimized integration approach of metabolomics and lipidomics for brain tissue based on UPLC-Q-TOF-MS and analyzed a comprehensive characterization of metabolite and lipid profiles in the hippocampus and frontal cortex of T1D male mice with cognitive decline (T1DCD) and age-matched control (CONT) mice.

RESULTS

The results show that T1DCD mice had brain metabolic disorders in a region-specific manner relative to CONT mice, and the frontal cortex exhibited a higher lipid peroxidation than the hippocampus in T1DCD mice. Based on metabolic changes, we found that microglia was activated under diabetic condition and thereby promoted oxidative stress and neuroinflammation, leading to neuronal injury, and this event was more pronounced in the frontal cortex than the hippocampus.

CONCLUSION

Our results suggest that brain region-specific shifts in oxidative stress and neuroinflammation may contribute to diabetic cognitive decline, and the frontal cortex could be the more vulnerable brain region than the hippocampus.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites

Metabolomics studies rely on the availability of suitable analytical platforms to determine a vast collection of chemically diverse metabolites in complex biospecimens. Liquid chromatography-mass spectrometry operated under reversed-phase conditions is the most commonly used platform in metabolomics, which offers extensive coverage for nonpolar and moderately polar compounds. However, complementary techniques are required to obtain adequate separation of polar and ionic metabolites, which are involved in several fundamental metabolic pathways. This chapter focuses on the main mass-spectrometry-based analytical platforms used to determine polar and/or ionizable compounds in metabolomics (GC-MS, HILIC-MS, CE-MS, IPC-MS, and IC-MS). Rather than comprehensively describing recent applications related to GC-MS, HILIC-MS, and CE-MS, which have been covered in a regular basis in the literature, a brief discussion focused on basic principles, main strengths, limitations, as well as future trends is presented in this chapter, and only key applications with the purpose of illustrating important analytical aspects of each platform are highlighted. On the other hand, due to the relative novelty of IPC-MS and IC-MS in the metabolomics field, a thorough compilation of applications for these two techniques is presented here.

Ion Chromatography with Mass Spectrometry for Metabolomic Analysis

Ion chromatography (IC) represents an important technique for separation of charged and polar compounds. Traditionally, IC is often used for the analysis of small inorganic ions. Due to the development of eluent suppression technology that allows continuous online desalting and conversion of high-salt eluents into pure water, IC has been coupled with mass spectrometry (MS) for the analysis of more diverse range of anionic and cationic analytes. Recent studies have demonstrated that IC-MS is a powerful technique with exquisite detection sensitivity, high reproducibility, and quantitative capability for metabolomic analysis. In this chapter, we provide a brief overview of IC principles and IC-MS for metabolomic analysis.

Compound Sophorae Decoction: treating ulcerative colitis by affecting multiple metabolic pathways

Ulcerative colitis (UC) is a chronic refractory non-specific intestinal inflammatory disease that is difficult to be cured. The discovery of new ulcerative colitis-related metabolite biomarkers may help further understand UC and facilitate early diagnosis. It may also provide a basis for explaining the mechanism of drug action in the treatment of UC. Compound Sophorae Decoction (CSD) is an empirical formula used in the clinical treatment of UC. Although it is known to be efficacious, its mechanism of action in the treatment of UC is unclear. The purpose of this study was to investigate the changes in endogenous substances in UC rats and the effects of CSD on metabolic pathways using the metabonomics approach. Metabolomics studies in rats with UC and normal rats were performed using LC-MS/MS. Rats with UC induced using TNBS enema were used as the study models. Metabolic profiling and pathway analysis of biomarkers was performed using statistical and pathway enrichment analyses. 36 screened potential biomarkers were found to be significantly different between the UC and the normal groups; it was also found that CSD could modulate the levels of these potential biomarkers. CSD was found to be efficacious in UC by regulating multiple metabolic pathways.

Metabolic profiling for water-soluble metabolites in patients with schizophrenia and healthy controls in a Chinese population: A case-control study

Objectives: Objective measures integrated with clinical symptoms may improve early prevention and detection of schizophrenia. Herein we aim to evaluate potential water-soluble metabolic biomarkers in schizophrenia.Methods: We recruited adults with schizophrenia (n = 113) who had not received pharmacological treatment for at least 1 month prior to enrollment and 111 age- and sex-matched healthy subjects from Weifang, Shandong province, China. All serum samples were analysed using liquid chromatography-tandem mass spectrometry coupled with a hydrophilic interaction liquid chromatography column.Results: Eleven metabolites, namely carnitines (oleoylcarnitine, l-palmitoylcarnitine, 9-decenoylcarnitine and 2-trans,4-cis-decadienoylcarnitine), polar lipids (lysophosphatidylcholine (LPC)(P-16:0), LPC (16:0), LPC (15:0) and LPC(14:0)), amino acids (taurine and l-arginine), and organic acid (2,5-dichloro-4-oxohex-2-enedioate), separated the patients and healthy controls. Compared with healthy controls, taurine, l-palmitoylcarnitine and oleoylcarnitine levels were higher, whereas the remaining eight metabolites were lower in patients with schizophrenia. A combination of four metabolites, i.e., oleoylcarnitine, 9-decenoylcarnitine, LPC (15:0) and LPC (14:0), provided the most robust between-group separation.Conclusions: This study appears to distinguish between groups of patients and controls, which should be considered as a contribution to putative potential biomarkers. The water-soluble metabolites were determined to be significantly different between the groups in the current study, and were primarily related to cellular bioenergetics, notably oxidative stress.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

INTRODUCTION

We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.

OBJECTIVES

We aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.

METHODS

Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis.

RESULTS

We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples.

CONCLUSIONS

Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.

Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art

Liquid chromatography (LC) hyphenated to mass spectrometry is currently the most widely used means of determining metabolic phenotypes via both untargeted and targeted analysis. At present a range of analytical separations, including reversed-phase, hydrophilic interaction and ion-pair LC are employed to maximise metabolome coverage with ultra (high) performance liquid chromatography (UHPLC) increasingly displacing conventional high performance liquid chromatography because of the need for short analysis times and high peak capacity in such applications. However, it is widely recognized that these methodologies do not entirely solve the problems facing researchers trying to perform comprehensive metabolic phenotyping and in addition to these "routine" approaches there are continuing investigations of alternative separation methods including 2-dimensional/multi column approaches. These involve either new stationary phases or multidimensional combinations of the more conventional materials currently used, as well as application of miniaturization or "new" approaches such as supercritical HP and UHP- chromatographic separations. There is also a considerable amount of interest in the combination of chromatographic and ion mobility separations, with the latter providing both an increase in resolution and the potential to provide additional structural information via the determination of molecular collision cross section data. However, key problems remain to be solved including ensuring quality, comparability across different laboratories and the ever present difficulty of identifying unknowns.

Association between admission plasma 2-oxoglutarate levels and short-term outcomes in patients with acute heart failure: a prospective cohort study

Background

2-oxoglutarate (2OG), an intermediate metabolite in the tricarboxylic acid cycle, has been found to associate with chronic heart failure (HF), but its effect on short-term adverse outcomes in patients with acute HF (AHF) is uncertain.

Methods

This prospective cohort study included 411 consecutive hospitalized patients with AHF. During hospitalization, fasting plasma samples were collected within the first 24 h of admission. Plasma 2OG levels were measured by hydrophilic interaction liquid chromatography-liquid chromatography tandem mass spectrometry (HILIC-LC/MS/MS). All participants were followed up for six months. Multiple logistic regression was used to determine the odds ratio (OR) and 95% confidence interval (CI) for primary outcomes.

Results

The AHF cohort consisted of HF with preserved ejection fraction (EF) (64.7%), mid-range EF (16.1%), and reduced EF (19.2%), the mean age was 65 (±13) years, and 65.2% were male. Participants were divided into two groups based on median 2OG levels (μg/ml): low group (< 6.0, n = 205) and high group (≥6.0, n = 206). There was a relatively modest correlation between 2OG and N-terminal pro B-type natriuretic peptide (NT-proBNP) levels (r = 0.25; p < 0.001). After adjusting for age, sex, and body mass index, we found that the progression of the NYHA classification was associated with a gradual increase in plasma 2OG levels (p for trend< 0.001). After six months of follow-up, 76 (18.5%) events were identified. A high baseline 2OG level was positively associated with a short-term rehospitalization and all-cause mortality (OR: 2.2, 95% CI 1.3–3.7, p = 0.003), even after adjusting for NT-proBNP and estimated glomerular filtration rate (eGFR) (OR: 1.9, 95% CI 1.1–3.4, p = 0.032). After a similar multivariable adjustment, the OR was 1.4 (95% CI 1.1–1.7, p = 0.018) for a per-SD increase in 2OG level.

Conclusions

High baseline 2OG levels are associated with adverse short-term outcomes in patients with AHF independent of NT-proBNP and eGFR. Hence plasma 2OG measurements may be helpful for risk stratification and treatment monitoring in AHF.

Trial registration

ChiCTR-ROC-17011240. Registered 25 April 2017.

Serum metabolic profiling using small molecular water-soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre-post-treatment design

AIM

We sought to compare alterations in serum bioenergetic markers within a well-characterized sample of adults with schizophrenia at baseline and after 8 weeks of pharmacological treatment with the hypothesis that treatment would be associated with significant changes in bioenergetic markers given the role of bioenergetic dysfunction in schizophrenia.

METHODS

We recruited adults with schizophrenia (n = 122) who had not received pharmacological treatment for at least 1 month prior to enrollment, including drug-naïve (i.e., first-episode) participants and treatment non-adherent participants. Pre- and post-treatment serum samples were analyzed using liquid chromatography-tandem mass spectrometry.

RESULTS

Metabolites with the greatest change, when comparing pre- and post-treatment levels, were identified revealing 14 water-soluble metabolites of interest. The composition of these metabolites was: amino acids (n = 6), carnitines (n = 4), polar lipids (n = 3), and organic acid (n = 1). All amino acids and lysophosphatidylcholines (LysoPC) were increased, while the four carnitines - oleoylcarnitine, L-palmitoylcarnitine, linoleyl carnitine, and L-acetylcarnitine - were decreased post-treatment. Of these metabolite biomarkers, six - oleoylcarnitine, linoleyl carnitine, L-acetylcarnitine, LysoPC(15:0), D-glutamic acid, and L-arginine - were identified as having most consistently and predictably changed after 8 weeks of treatment.

CONCLUSION

The current study identified several bioenergetic markers that consistently change with pharmacological treatment. These bioenergetic changes may provide further insights into the pathophysiology of schizophrenia along with furthering our understanding of the mechanisms subserving both the effects (e.g., antipsychotic effects) and side-effects (e.g., metabolic syndrome) of antipsychotics.

Quantification of Low-Abundant Phosphorylated Carbohydrates Using HILIC-QqQ-MS/MS

Phosphorylated carbohydrates are central metabolites involved in key plant metabolic pathways, such as glycolysis and central carbon metabolism. Such pathways influence plant growth, development, and stress responses to environmental changes, and ultimately, reflect the plant's energy status. The high polarity of these metabolites, the variety of isomeric structures (e.g., glucose-1-phosphate (G1P)/fructose-6-phosphate (F6P)/mannose-6-phosphate (M6P)/G6P, sucrose-6-phosphate (S6P)/T6P), and rapid metabolic turnover makes their analysis particularly challenging. In this chapter, we describe the use of a set of known phosphorylated carbohydrates to develop and validate a hydrophilic interaction chromatography (HILIC) triple quadrupole (QqQ) tandem mass spectrometry (MS/MS) method in the highly sensitive and selective multiple reaction monitoring (MRM) mode for the target analysis of G1P, F6P, M6P, G6P, S6P, T6P, and the sugar nucleotide uridine 5-diphospho-glucose (UDPG). We present detailed information regarding HILIC column chemistry and practical considerations when coupling it with a QqQ-MS system.

Global omics strategies to investigate the effect of cyclodextrin nanoparticles on MCF-7 breast cancer cells

Cyclodextrins (CD) are natural macrocyclic oligosaccharides linked by α(1,4) glycosidic bonds. Hydrophobic cavity of CDs are able to incorporate small molecules, ions, macromolecules which makes them excellent delegates for forming nanoparticulate carriers upon chemical modification to render amphiphilicity to CDs. In this study, blank 6OCaproβCD nanoparticle was prepared and administered to MCF-7 breast cancer cells. The effects of these nanoparticles on the cells were investigated in depth through biochemical and proteomic tests following 48 h of incubation. Proteomics studies revealed that apoptosis-related protein levels of hnRNP and CBX1 were increased while HDGF was not affected supporting the idea that 6OCaproβCD nanoparticles prevent cell proliferation. Gene expression studies were generally in correlation with protein levels since gene expression was significantly stimulated while protein levels were lower compared to the control group suggesting that a post-transcriptional modification must have occurred. Furthermore, 6OCaproβCD was observed to not trigger multidrug resistance as proved with RT-PCR that effectuates another exquisite characteristic of 6OCaproβCD nanoparticle as carrier of chemotherapeutic drugs. Metabolomic pathways of CD effect on MCF7 cells were elucidated with HMDB as serine biosynthesis, transmembrane transport of small molecules, metabolism of steroid hormones, estrogen biosynthesis and phospholipid biosynthesis. In conclusion, 6OCaproβCD is a promising nanoparticulate carrier for chemotherapeutic drugs with intrinsic apoptotic effect to be employed in treatment of breast cancer and further studies should be conducted in order to comprehend the exact mechanism of action.

Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics

Existing hydrophilic interaction liquid chromatography (HILIC) methods, considered individually, each exhibit poor chromatographic performance for a substantial fraction of polar metabolites. In addition to limiting metabolome coverage, such deficiencies also complicate automated data processing. Here we show that some of these analytical challenges can be addressed for the ZIC-pHILIC, a zwitterionic stationary phase commonly used in metabolomics, with the addition of trace levels of phosphate. Specifically, micromolar phosphate extended metabolome coverage by hundreds of credentialed features, improved peak shapes, and reduced peak-detection errors during informatic processing. Although the addition of high levels of phosphate (millimolar) as a HILIC mobile phase buffer has been explored previously, such concentrations interfere with mass spectrometric (MS) detection. We show that using phosphate as a trace additive at micromolar concentrations improves analysis by electrospray MS, increasing signal for a diverse set of polar standards. Given the small amount of phosphate needed, comparable chromatographic improvements were also achieved by direct addition of phosphate to the sample during reconstitution. Our results suggest that defects in ZIC-pHILIC performance are predominantly driven by electrostatic interactions, which can be modulated by phosphate. These findings constitute both a methodological improvement for untargeted metabolomics and an advance in our understanding of the mechanisms limiting HILIC coverage.

Quantification of nucleotides and their sugar conjugates in biological samples: Purposes, instruments and applications

Nucleotides and their sugar conjugates are fundamental molecules in life, participating in processes of DNA/RNA composition, cell wall build-up, glycosylation reactions, and signal conduction. Therefore, the quantification of these compounds in biological samples significantly benefits the understanding of their functions. However, nucleotides and nucleotide sugars are extremely hydrophilic, causing bad retention and peak symmetry on regular C18 chromatographic columns. To solve this problem, ion-pair (IP) chromatography, ion-exchange (IE) chromatography and hydrophilic interaction chromatography (HILIC) were applied, of which differentiated mechanisms were utilized to increase the retention of the analytes on the stationary phases. IP-HPLC and HILIC were convenient for coupling with many kinds of detectors (ultraviolet, UV or mass spectrometry, MS). Combining these two kinds of techniques, the advantages of better separation and retention were increased, while disadvantages like irreversible adsorption by stationary phases were greatly decreased. Due to the high concentrations of nonvolatile buffer salts used, IE-HPLC was not suitable for MS detectors. Protein precipitation and solid phase extraction were the common methods for sample treatment in the analysis of nucleotides and nucleotide sugars. By carefully optimizing the LCUV or LCMS conditions, high sensitivities could be achieved, and the methods could be applied to the analysis of many kinds of biological samples (cells, tissues, plants, bacteria, etc.). Developing new analyzing techniques may help the utilization of nucleotides and nucleotide sugars in the diagnosis and therapy of diseases.

Best-Matched Internal Standard Normalization in Liquid Chromatography-Mass Spectrometry Metabolomics Applied to Environmental Samples

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization .

Retention and effective diffusion of model metabolites on porous graphitic carbon

The study of metabolites in biological samples is of high interest for a wide range of biological and pharmaceutical applications. Reversed phase liquid chromatography is a common technique used for the separation of metabolites, but it provides little retention for polar metabolites. An alternative to C18 bonded phases, porous graphitic carbon has the ability to provide significant retention for both non-polar and polar analytes. The goal of this work is to study the retention and effective diffusion properties of porous graphitic carbon, to see if it is suitable for the wide injection bands and long run times associated with long, packed capillary-scale separations. The retention of a set of standard metabolites was studied for both stationary phases over a wide range of mobile phase conditions. This data showed that porous graphitic carbon benefits from significantly increased retention (often >100 fold) under initial gradient conditions for these metabolites, suggesting much improved ability to focus a wide injection band at the column inlet. The effective diffusion properties of these columns were studied using peak-parking experiments with the standard metabolites under a wide range of retention conditions. Under the high retention conditions, which can be associated with retention after injection loading for gradient separations, Deff/Dm∼0.1 for both the C18-bonded and porous graphitic carbon columns. As C18 bonded particles are widely, and successfully utilized for long gradient separations without issue of increasing peak width from longitudinal diffusion, this suggests that porous graphitic carbon should be amenable for long runtime gradient separations as well.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase.

Comprehensive metabonomic analysis of heart tissue from isoproterenol-induced myocardial infarction rat based on reversed-phase and hydrophilic interaction chromatography coupled to mass spectrometry

We aim to describe the metabonomic characteristics of myocardial infarction rats. High-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was utilized to develop a metabonomic method of the heart homogenates of myocardial infarction rats. Hydrophilic interaction chromatography allows the analysis of high polar metabolites, providing complementary information to reversed-phase liquid chromatography. We combined reversed phase and hydrophilic interaction chromatographic separations to analyze 18 samples, ten from myocardial infarction rat hearts and eight from normal rat hearts. A total of 16 potential biomarkers in rat heart tissue were screened out, primarily related to oxidative stress, nitric oxide damage, taurine, and hypotaurine metabolism and sphingolipid metabolism. This research showed that a comprehensive metabonomic study is a useful tool to reveal the underlying mechanism of myocardial infarction.

A robust, single-injection method for targeted, broad-spectrum plasma metabolomics

BACKGROUND

Metabolomics is a powerful emerging technology for studying the systems biology and chemistry of health and disease. Current targeted methods are often limited by the number of analytes that can be measured, and/or require multiple injections.

METHODS

We developed a single-injection, targeted broad-spectrum plasma metabolomic method on a SCIEX Qtrap 5500 LC-ESI-MS/MS platform. Analytical validation was conducted for the reproducibility, linearity, carryover and blood collection tube effects. The method was also clinically validated for its potential utility in the diagnosis of chronic fatigue syndrome (CFS) using a cohort of 22 males CFS and 18 age- and sex-matched controls.

RESULTS

Optimization of LC conditions and MS/MS parameters enabled the measurement of 610 key metabolites from 63 biochemical pathways and 95 stable isotope standards in a 45-minute HILIC method using a single injection without sacrificing sensitivity. The total imprecision (CV_total) of peak area was 12% for both the control and CFS pools. The 8 metabolites selected in our previous study (PMID: 27573827) performed well in a clinical validation analysis even when the case and control samples were analyzed 1.5 years later on a different instrument by a different investigator, yielding a diagnostic accuracy of 95% (95% CI 85-100%) measured by the area under the ROC curve.

CONCLUSIONS

A reliable and reproducible, broad-spectrum, targeted metabolomic method was developed, capable of measuring over 600 metabolites in plasma in a single injection. The method might be a useful tool in helping the diagnosis of CFS or other complex diseases.

Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS

Blood plasma is the most popularly used sample matrix for metabolite profiling studies, which aim to achieve global metabolite profiling and biomarker discovery. However, most of the current studies on plasma metabolite profiling focused on either the polar metabolites or lipids. In this study, a comprehensive analysis approach based on HILIC-FTMS was developed to concurrently examine polar metabolites and lipids. The HILIC-FTMS method was developed using mixed standards of polar metabolites and lipids, the separation efficiency of which is better in HILIC mode than in C5 and C18 reversed phase (RP) chromatography. This method exhibits good reproducibility in retention times (CVs < 3.43%) and high mass accuracy (<3.5 ppm). In addition, we found MeOH/ACN/Acetone (1:1:1, v/v/v) as extraction cocktail could achieve desirable gathering of demanded extracts from plasma samples. We further integrated the MeOH/ACN/Acetone extraction with the HILIC-FTMS method for metabolite profiling and smoking-related biomarker discovery in human plasma samples. Heavy smokers could be successfully distinguished from non smokers by univariate and multivariate statistical analysis of the profiling data, and 62 biomarkers for cigarette smoke were found. These results indicate that our concurrent analysis approach could be potentially used for clinical biomarker discovery, metabolite-based diagnosis, etc.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

Monolithic stationary phases with incorporated fumed silica nanoparticles. Part I. Polymethacrylate-based monolithic column with incorporated bare fumed silica nanoparticles for hydrophilic interaction liquid chromatography

Fumed silica nanoparticles (FSNPs), were incorporated for the first time into a polymethacrylate monolithic column containing glyceryl monomethacrylate (GMM) and ethylene dimethacrylate (EDMA) in order to develop a new monolithic column for hydrophilic interaction high performance liquid chromatography (HILIC). When compared to poly(GMM-EDMA) monolithic column without FSNPs, the same monolithic column with incorporated FSNPs yielded important effects on HILIC separations. The effects of monomers and FSNPs content of the polymerization mixture on the performance of the monolithic column were examined in details, and the optimized stationary phase was investigated over a wide range of mobile phase composition with polar acidic, weakly basic and neutral analytes including hydroxy benzoic acids, nucleotides, nucleosides, dimethylformamide, formamide and thiourea. The retention of these analytes was mainly controlled by hydrophilic interactions with the FSNPs and electrostatic repulsion from the negatively charged silica surface in the case of hydroxy benzoic acids and nucleotides. The electrostatic repulsion was minimized by decreasing the pH of the aqueous component of the mobile phase, which in turn enhanced the retention of acidic solutes. Nucleotides were best separated using step gradient elution at decreasing pH as well as ACN concentration in the mobile phase. Improved peak shape and faster analysis of nucleosides were attained by a fast linear gradient elution with a shallow decrease in the ACN content of the ACN-rich mobile phase. The run-to-run and column-to-column reproducibility were satisfactory. The percent relative standard deviations (%RSDs) for the retention times of tested solutes were lower than 2.5% under isocratic conditions and lower than 3.5 under gradient conditions.

Specific interactions of leucine with disaccharides by electrospray ionization mass spectrometry: application to rapid differentiation of disaccharide isomers in combination with statistical analysis

The identification of carbohydrate isomers, including mono units, linkage positions and anomeric configurations, remains an arduous subject. In this study, the natural amino acid leucine (Leu) was found to specifically interact with cellobiose (Cello) to form a series of potassium adducts as [Cello + Leu + K](+), [Cello + 2Leu + K](+), and [2Cello + Leu + K](+) in the gas phase using mass spectrometry. By using CID-MS/MS, these complexes produced specific fragmentation patterns from the sugar backbone cleavage instead of non-covalent interactions. Moreover, their fragment distributions were dependent on the ratios of Cello-to-Leu in the complexes and the fragmentation pathways of potassium-cationized disaccharides (Dis) were remarkably changed with leucine binding. It should be pointed out that the ternary complex [2Cello + AA + K](+) was unique for leucine among all the twenty natural amino acids. The [2Dis + Leu + K](+) complex produced the most informative fragments by tandem mass spectrometry, which was successfully applied for rapid and efficient discrimination of twelve glucose-containing disaccharide isomers in combination with statistical analyses including PCA and OPLS-DA. The methodology developed here not only provides a novel analytical approach for the differentiation of disaccharide isomers, but also brings new sight towards the interactions of amino acids with disaccharides.

Some factors that can lead to poor peak shape in hydrophilic interaction chromatography, and possibilities for their remediation

Some factors which present difficulties for obtaining good peak shape in hydrophilic interaction chromatography (HILIC) were studied. The effect of injection solvent composition and volume was systematically investigated using a selection of weak and stronger basic compounds on a hybrid bare silica phase. Increasing the mismatch between the injection solvent (range 95-0% ACNv/v) and the mobile phase (maintained at 95% ACNv/v) gave increasing deterioration in peak shape. With the 2.1mm ID columns used, injections in the mobile phase of increasing volume (1-20 μL) gave poorer peak shape, but the magnitude of the effect was considerably smaller than that of solvent mismatch over this range. Some solute structural features such as galloyl (trihydroxy benzene), catechol (benzene diol) and phosphate (in nucleotides) gave serious peak tailing, attributed to interactions with metals in the stationary phase or the chromatographic hardware. These undesirable effects can be moderated by including complexing agents in the mobile phase, by changing the stationary phase chemistry, or by altering the mobile phase pH.