Separation strategies for untargeted metabolomics

Responses of the blood acid-base balance and blood plasma metabolomics of broiler chickens after change to diets with high free amino acid levels

Free amino acids (AA) are needed to fulfill the AA requirements of broiler chickens in diets low in CP. This study investigated whether the acid-base balance and the blood plasma metabolome are affected immediately after a change to diets with high free AA levels. Male broiler chickens received a starter diet with 164 g CP/kg and 80 g soy protein isolate/kg until d 7 post-hatch. From this day on, birds were offered a diet almost identical to the starter diet (0FAA) or 2 diets with 50% (50FAA) or 100% (100FAA) of the digestible AA from soy protein isolate substituted with free AA. Blood was sampled to determine the acid-base status and for untargeted metabolomics analysis on d 0, 1, 2, 4, 7, and 14 and d 1, 7, and 14 after diet change, respectively (n = 14 birds/treatment). Compared to 0FAA, blood pH was decreased on d 4 and 7 for 100FAA and on d 4 for 50FAA (P ≤ 0.019). On d 4, 7, and 14, bicarbonate, base excess, and total carbon dioxide were lower for 100FAA than for 0FAA (P ≤ 0.006). The partial pressure of carbon dioxide was higher for 50FAA than for 0FAA on d 4 (P = 0.047). Compared to 0FAA, chloride was higher for 100FAA on d 1, 2, 4, 7, and 14, and for 50FAA on d 1, 2, and 4 (P ≤ 0.030). In the metabolomics assay, 602, 463, and 302 metabolites were affected by treatment on d 1, 7, and 14, respectively (P < 0.050), but they did not indicate that metabolic pathways were affected. Flavonoids were the most consistently affected category of metabolites. The results indicated a metabolic acidosis for 100FAA from d 4 to 7 and a respiratory acidosis for 50FAA on d 4 after diet change. These types of acidosis were compensated later on in the experiment. The metabolomics analysis did not indicate that high free AA inclusion affected metabolic pathways.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

Detection and analysis of chiral molecules as disease biomarkers

The chirality of small metabolic molecules is important in controlling physiological processes and indicating the health status of humans. Abnormal enantiomeric ratios of chiral molecules in biofluids and tissues occur in many diseases, including cancers and kidney and brain diseases. Thus, chiral small molecules are promising biomarkers for disease diagnosis, prognosis, adverse drug-effect monitoring, pharmacodynamic studies and personalized medicine. However, it remains difficult to achieve cost-effective and reliable analysis of small chiral molecules in clinical procedures, in part owing to their large variety and low concentration. In this Review, we describe current and emerging techniques that detect and quantify small-molecule enantiomers and their biological importance.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Direct introduction MALDI FTICR MS based on dried droplet deposition applied to non-targeted metabolomics on Pisum Sativum root exudates

Non-targeted metabolomic approaches based on direct introduction (DI) through a soft ionization source are nowadays used for large-scale analysis and wide cover-up of metabolites in complex matrices. When coupled with ultra-high-resolution Fourier-Transform ion cyclotron resonance (FTICR MS), DI is generally performed through electrospray (ESI), which, despite the great analytical throughput, can suffer of matrix effects due to residual salts or charge competitors. In alternative, matrix assisted laser desorption ionization (MALDI) coupled with FTICR MS offers relatively high salt tolerance but it is mainly used for imaging of small molecule within biological tissues. In this study, we report a systematic evaluation on the performance of direct introduction ESI and MALDI coupled with FTICR MS applied to the analysis of root exudates (RE), a complex mixture of metabolites released from plant root tips and containing a relatively high salt concentration. Classic dried droplet deposition followed by screening of best matrices and ratio allowed the selection of high ranked conditions for non-targeted metabolomics on RE. Optimization of MALDI parameters led to improved reproducibility and precision. A RE desalted sample was used for comparison on ionization efficiency of the two sources and ion enhancement at high salinity was highlighted in MALDI by spiking desalted solution with inorganic salts. Application of a true lyophilized RE sample exhibited the complementarity of the two sources and the ability of MALDI in the detection of undisclosed metabolites suffering of matrix effects in ESI mode.

Mass Spectrometry-Based Untargeted Metabolomics and Lipidomics Platforms to Analyze Cell Culture Extracts

Metabolites represent the most downstream level of the cellular organization. Hence, an in vitro untargeted metabolomics approach is extremely valuable to deepen the understanding of how endogenous metabolites in cells are altered under a given biological condition. This chapter describes a robust liquid chromatography-high-resolution mass spectrometry-based metabolomics and lipidomics platform applied to cell culture extracts. The analytical workflow includes an optimized sample preparation procedure to cover a wide range of metabolites using liquid-liquid extraction and validated instrumental operation procedures with the implementation of comprehensive quality assurance and quality control measures to ensure high reproducibility. The lipidomics platform is based on reversed-phase liquid chromatography for the separation of slightly polar to apolar metabolites and covers a broad range of lipid classes, while the metabolomics platform makes use of two hydrophilic interaction liquid chromatography methods for the separation of polar metabolites, such as organic acids, amino acids, and sugars. The chapter focuses on the analysis of cultured HepaRG cells that are derived from a human hepatocellular carcinoma; however, the sample preparation and analytical platforms can easily be adapted for other types of cells.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Systematic Investigation of LC Miniaturization to Increase Sensitivity in Wide-Target LC-MS-Based Trace Bioanalysis of Small Molecules

Covering a wide spectrum of molecules is essential for global metabolome assessment. While metabolomics assays are most frequently carried out in microbore LC-MS analysis, reducing the size of the analytical platform has proven its ability to boost sensitivity for specific -omics applications. In this study, we elaborate the impact of LC miniaturization on exploratory small-molecule LC-MS analysis, focusing on chromatographic properties with critical impact on peak picking and statistical analysis. We have assessed a panel of small molecules comprising endogenous metabolites and environmental contaminants covering three flow regimes—analytical, micro-, and nano-flow. Miniaturization to the micro-flow regime yields moderately increased sensitivity as compared to the nano setup, where median sensitivity gains around 80-fold are observed in protein-precipitated blood plasma extract. This gain resulting in higher coverage at low µg/L concentrations is compound dependent. At the same time, the nano-LC-high-resolution mass spectrometry (HRMS) approach reduces the investigated chemical space as a consequence of the trap-and-elute nano-LC platform. Finally, while all three setups show excellent retention time stabilities, rapid gradients jeopardize the peak area repeatability of the nano-LC setup. Micro-LC offers the best compromise between improving signal intensity and metabolome coverage, despite the fact that only incremental gains can be achieved. Hence, we recommend using micro-LC for wide-target small-molecule trace bioanalysis and global metabolomics of abundant samples.

Green Chemistry Preservation and Extraction of Biospecimens for Multi-omic Analyses

The Environmental Protection Agency's definition of "Green Chemistry" is "the design of chemical products and processes that reduces or eliminates the use or generation of hazardous substances. Green chemistry applies across the life cycle of a chemical product, including its design, manufacture, use, and ultimate disposal." Conventional omic tissue extraction procedures use solvents that are toxic and carcinogenic, such as chloroform and methyl-tert-butyl ether for lipidomics, or caustic chaotropic solutions for genomics and transcriptomics, such as guanidine or urea. A common preservation solution for pathology is formaldehyde, which is a carcinogen. Use of acetonitrile as a universal biospecimen preservation and extraction solvent will reduce these hazardous wastes, because it is less toxic and more environmentally friendly than the conventional solvents used in biorepository and biospecimen research. A new extraction method never applied to multi-omic, system biology research, called cold-induced phase separation (CIPS), uses freezing point temperatures to induce a phase separation of acetonitrile-water mixtures. Also, the CO₂ exposure during CIPS will acidify the water precipitating DNA out of aqueous phase. The resulting phase separation brings hydrophobic lipids to the top acetonitrile fraction that is easily decanted from the bottom aqueous fraction, especially when the water is frozen. This CIPS acetonitrile extract contains the lipidome (lipids), the bottom aqueous fraction is sampled to obtain the transcriptome (RNA) fraction, and the remaining water and pellet is extracted with 60% acetonitrile to isolate the metabolome (<1 kD polar molecules). Finally, steps 4 and 5 use a TRIzol™ liquid-liquid extraction SOP of the pellet to isolate the genome (DNA) and proteome (proteins). This chapter details the multi-omic sequential extraction SOP and potential problems associated with each of the 5 steps, with steps 2, 4, and 5 still requiring validation. The metabolomic and lipidomic extraction efficiencies using the CIPS SOP is compared to conventional solvent extraction SOPs and is analyzed by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS), respectively. Acetonitrile biospecimen preservation combined with the CIPS multi-omic extraction SOP is green chemistry technology that will eliminate the generation of the hazardous substances associated with biospecimen processing and permits separation and safe disposal of acetonitrile avoiding environmental contamination.

Application of omics in hypertension and resistant hypertension

Hypertension is a major modifiable risk factor that affects the global health burden. Despite the availability of multiple antihypertensive drugs, blood pressure is often not optimally controlled. The prevalence of true resistant hypertension in treated hypertensive patients is ~2-20%, and these patients are at higher risk for adverse events and poor clinical outcomes. Therefore, an in-depth dissection of the pathophysiological mechanisms of hypertension and resistant hypertension is needed to identify more effective targets for regulating blood pressure. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, can accurately present the characteristics of organisms at varying molecular levels. Integrative omics can further reveal the network of interactions between molecular levels and provide a complete dynamic view of the organism. In this review, we describe the applications, progress, and challenges of omics technologies in hypertension. Specifically, we discuss the application of omics in resistant hypertension. We believe that omics approaches will produce a better understanding of the pathogenesis of hypertension and resistant hypertension and improve diagnostic and therapeutic strategies, thus increasing rates of blood pressure control and reducing the public health burden of hypertension.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-¹³C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their ¹²C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Urine metabolic profiling of dementia rats with vital energy deficiency using ultra-high-performance liquid chromatography coupled with an orbitrap mass spectrometer

Dementia is a chronic and multifactor-induced neurodegenerative disorder that occurs frequently in the elderly with weak constitution and insufficient vital energy. However, the relationship between vital energy deficiency and the occurrence and development of dementia is still unclear. In this study, a rat model of dementia with vital energy deficiency was established through intraperitoneal injection with d-galactose and AlCl₃ and combined with exhaustive swimming. Changes in the dementia with vital energy deficiency rat model were assessed by examining behaviors, hippocampal histopathological and biochemical parameters, and serum biochemical parameters. Urine metabolomics based on ultra-high-performance liquid chromatography coupled with an orbitrap mass spectrometer was also used to discover endogenous metabolic profile and disease-related biomarkers and investigate the potential mechanism of dementia with vital energy deficiency. Among the 31 potential biomarkers that were identified, nine involved metabolic pathways. The four main types were phenylalanine, tyrosine and tryptophan metabolism, taurine and hypotaurine metabolism, and citrate cycle and pyrimidine metabolism. The pathogenesis of dementia with vital energy deficiency is mainly neurotoxin accumulation and body aging that leads to oxidative stress injury and loss of neuronal protective substances. Vital energy deficiency inhibits the body's energy metabolism and eventually leads to aggravate the dementia.

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.

Improving data quality in liquid chromatography-mass spectrometry metabolomics of human urine

Signal variation is a common drawback in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS), mainly due to the complexity of biological matrices and reduced sample preparation, which results in the accumulation of sample components in the column and the ion source. Here we propose a simple, easy to implement approach to improve data quality in untargeted metabolomics by LC-MS. This approach involves the use of a divert valve to direct the column effluent to waste at the beginning of the chromatographic run and during column cleanup and equilibration, in combination with longer column cleanups in between injections. Our approach was tested using urine samples collected from patients after renal transplantation. Analytical responses were contrasted before and after introducing these modifications by analyzing a batch of untargeted metabolomics data. A significant improvement in peak area repeatability was observed for the quality controls, with relative standard deviations (RSDs) for several metabolites decreasing from ∼60% to ∼10% when our approach was introduced. Similarly, RSDs of peak areas for internal standards improved from ∼40% to ∼10%. Furthermore, calibrant solutions were more consistent after introducing these modifications when comparing peak areas of solutions injected at the beginning and the end of each analytical sequence. Therefore, we recommend the use of a divert valve and extended column cleanup as a powerful strategy to improve data quality in untargeted metabolomics, especially for very complex types of samples where minimum sample preparation is required, such as in this untargeted metabolomics study with urine from renal transplanted patients.

Antioxidant Profiling of Ginger via Reaction Flow Chromatography

Reaction flow (RF) chromatography is a powerful and efficient approach that utilizes conventional high-performance liquid chromatography (HPLC)–ultraviolet (UV)–visible detection. This technique exploits a novel column end-fitting and an extra HPLC pump that delivers a reagent specific for selective detection, in particular the antioxidant profiling of natural products. This study employed RF for the first time to identify antioxidants in a commercial ginger sample. This demonstrated the previously validated assay's ease and power to extract information about the natural product's antioxidant properties. Due to the simplicity involved with data analysis and peak matching process, the following information was revealed between the chemical and antioxidant profiles: three of the strongest antioxidant activity peaks in the ginger sample (593 nm) did not correlate with the three most abundant chemical profile peaks (UV absorbance at 254 and 280 nm); the ratio of seven antioxidant peaks may be potentially used for food authenticity purposes, and future research should target these peaks for the early discovery of novel antioxidants sourced in ginger. Utilization of this previously validated assay provided the resolution of numerous peaks in the ginger extract and information associated with their antioxidant attributes and chemical abundance. This approach is more informative than total antioxidant assays that lack compound specificity information. Furthermore, it is superior to mass spectrometric (MS) assays that cannot evaluate each compound's antioxidant strength, and does not involve the expense involved in the acquisition and maintenance of the MS detection hardware, and does not require the high level of expertise needed to conduct the MS data analysis.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

UPLC-Q-TOF/MS based Untargeted Metabolite and Lipid Analysis on Premature Ovarian Insufficiency Plasma Samples

Background:

Metabolomics is one of the main areas to understand cellular process at molecular level by analyzing metabolites. In recent years metabolomics has emerged as a key tool to understand molecular basis of diseases, to find diagnostic and prognostic biomarkers and develop new treatment opportunities and drug molecules.

Objective:

In this study, untargeted metabolite and lipid analysis were performed to identify potential biomarkers on premature ovarian insufficiency plasma samples. 43 POI subject plasma samples were compared with 32 healthy subject plasma samples.

Methods:

Plasma samples were pooled and extracted using chloroform:methanol:water (3:3:1 v/v/v) mixture. Agilent 6530 LC/MS Q-TOF instrument equipped with ESI source was used for analysis. A C18 column (Agilent Zorbax 1.8 μM, 50 x 2.1 mm) was used for separation of the metabolites and lipids. XCMS, an “R software” based freeware program, was used for peak picking, grouping and comparing the findings. Isotopologue Parameter Optimization (IPO) software was used to optimize XCMS parameters. The analytical methodology and data mining process were validated according to the literature.

Results:

83 metabolite peaks and 213 lipid peaks were found to be in semi-quantitatively and statistically different (fold change >1.5, p <0.05) between the POI plasma samples and control subjects.

Conclusion:

According to the results, two groups were successfully separated through principal component analysis. Among the peaks, phenyl alanine, decanoyl-L-carnitine, 1-palmitoyl lysophosphatidylcholine and PC(O-16:0/2:0) were identified through auto MS/MS and matched with human metabolome database and proposed as plasma biomarker for POI and monitoring the patients in treatment period.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Chromatographic Profiling with Machine Learning Discriminates the Maturity Grades of Nicotiana tabacum L. Leaves

Nicotiana tabacum L. (NTL) is an important agricultural and economical crop. Its maturity is one of the key factors affecting its quality. Traditionally, maturity is discriminated visually by humans, which is subjective and empirical. In this study, we concentrated on detecting as many compounds as possible in NTL leaves from different maturity grades using ultra-performance liquid chromatography ion trap time-of-flight mass spectrometry (UPLC-IT-TOF/MS). Then, the low-dimensional embedding of LC-MS dataset by t-distributed stochastic neighbor embedding (t-SNE) clearly showed the separation of the leaves from different maturity grades. The discriminant models between different maturity grades were established using orthogonal partial least squares discriminant analysis (OPLS-DA). The quality metrics of the models are R2Y = 0.939 and Q2 = 0.742 (unripe and ripe), R2Y = 0.900 and Q2 = 0.847 (overripe and ripe), and R2Y = 0.972 and Q2 = 0.930 (overripe and unripe). The differential metabolites were screened by their variable importance in projection (VIP) and p-Values. The existing tandem mass spectrometry library of plant metabolites, the user-defined library of structures, and MS-FINDER were combined to identify these metabolites. A total of 49 compounds were identified, including 12 amines, 14 lipids, 10 phenols, and 13 others. The results can be used to discriminate the maturity grades of the leaves and ensure their quality.

A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing

Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.

Virgin olive oil metabolomics: A review

Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges regarding the issues of food safety, quality, and authenticity in a more comprehensive way. Virgin olive oil (VOO) is a key product of the Mediterranean diet, with a globalized consumer interest as it may be associated with various nutritional and health benefits. Despite the strict legislation to protect this high added-value agricultural commodity and offer guarantees to consumers and honest producers, there are still analytical issues needing to be further addressed. Thus, this review aims to present the efforts made using targeted and untargeted metabolomics approaches, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry-based techniques (mainly LC/GC-MS) combined with multivariate statistical analysis. Case-studies focusing on geographical/varietal classification and detection of adulteration are discussed with regards to the identification of possible markers. The advantages and limitations of each of the aforementioned techniques applied to VOO analysis are also highlighted.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

An Analytical System for Single-Cell Metabolomics of Typical Mammalian Cells Based on Highly Sensitive Nano-Liquid Chromatography Tandem Mass Spectrometry

The rapid development of next-generation sequencing techniques has enabled single-cell genomic and transcriptomic analyses, which have revealed the importance of heterogeneity in biological systems. However, analytical methods to accurately identify and quantify comprehensive metabolites from single mammalian cells with a typical diameter of 10-20 μm are still in the process of development. The aim of this study was to develop a single-cell metabolomic analytical system based on highly sensitive nano-liquid chromatography tandem mass spectrometry (nano-LC-MS/MS) with multiple reaction monitoring. A packed nano-LC column (3-μm particle-size pentafluorophenylpropyl Discovery HSF5 of dimensions 100 μm i.d.×180 mm) was prepared using a slurry technique. The optimized nano-LC-MS/MS method showed 3-132-fold (average value, 26-fold) greater sensitivity than semimicro-LC-MS/MS, and the detection limits for several hydrophilic metabolites, including amino acids and nucleic acid related metabolites were in the sub-fmol range. By combining live single-cell sampling and nano-LC-MS/MS, we successfully detected 18 relatively abundant hydrophilic metabolites (16 amino acids and 2 nucleic acid related metabolites) from single HeLa cells (n=22). Based on single-cell metabolic profiles, the 22 HeLa cells were classified into three distinct subclasses, suggesting differences in metabolic function in cultured HeLa cell populations. Our single-cell metabolomic analytical system represents a potentially useful tool for in-depth studies focused on cell metabolism and heterogeneity.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

LC-MS-Based Metabolomic Approach Revealed the Significantly Different Metabolic Profiles of Five Commercial Truffle Species

Truffles are ascomycetous ectomycorrhizal fungi that have elevated status in the culinary field due to their unique aroma and taste as well as their nutritional value and potential biological activities. Tuber melanosporum, T. indicum, T. panzhihuanense, T. sinoaestivum, and T. pseudoexcavatum are five commercial truffle species mainly distributed in Europe or China. In this study, an untargeted metabolomics technology based on an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was applied to analyze the metabolic profiles and variations among these five truffle species. In our results, a total of 2376 metabolites were identified under positive ion mode, of which 1282 had significantly differential amounts and covered 110 pathways or metabolisms. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) revealed a clear separation from each of these five truffles, indicating a significantly different metabolic profile among them, with the biggest difference between T. melanosporum and the other four truffles. The differential metabolites covered various chemical categories, and a detailed analysis was performed for nine metabolic categories, including amino acids, saccharides and nucleosides, organic acids, alkaloids, flavonoids, carnitines, phenols and alcohols, esters, and sulfur compounds. For each of the nine categories, most of metabolites predominantly accumulated in T. melanosporum compared with the other four truffles. Meanwhile, there were significant differences of the average ion intensity in each category among the five truffles, e.g., higher amounts of amino acids was detected in T. panzhihuanense and T. pseudoexcavatum; T. indicum contained significantly more carnitines, while there were more alkaloids in T. melanosporum. Additionally, some metabolites with biological activities were discussed for each category, such as acetyl-L-carnitine, adenine, neobavaisoflavone, and anandamide. Generally, this study may provide the valuable information regarding the variation of the metabolic composition of these five commercial truffle species, and the biological significance of these metabolites was uncovered to explore the metabolic mechanisms of truffles, which would be helpful for further research on the compounds and potential biological functions in truffles that have not yet been investigated.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

PURPOSE OF THE REVIEW

Osteoarthritis (OA) is a multifactorial and progressive disease affecting whole synovial joint. The extract pathogenic mechanisms and diagnostic biomarkers of OA remain unclear. In this article, we review the studies related to metabolomics of OA, discuss the biomarkers as a tool for early OA diagnosis. Furthermore, we examine the major studies on the application of metabolomics methodology in the complex context of OA and create a bridge from findings in basic science to their clinical utility.

RECENT FINDINGS

Recently, the tissue metabolomics signature permits a view into transitional phases between the healthy and OA joint. Both nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry-based metabolomics approaches have been used to interrogate the metabolic alterations that may indicate the complex progression of OA. Specifically, studies on alterations pertaining to lipids, glucose, and amino acid metabolism have aided in the understanding of the complex pathogenesis of OA. The discovery of identified metabolites could be important for diagnosis and staging of OA, as well as for the assessment of efficacy of new drugs.

Evaluation of an untargeted nano-liquid chromatography-mass spectrometry approach to expand coverage of low molecular weight dissolved organic matter in Arctic soil

Characterizing low molecular weight (LMW) dissolved organic matter (DOM) in soils and evaluating the availability of this labile pool is critical to understanding the underlying mechanisms that control carbon storage or release across terrestrial systems. However, due to wide-ranging physicochemical diversity, characterizing this complex mixture of small molecules and how it varies across space remains an analytical challenge. Here, we evaluate an untargeted approach to detect qualitative and relative-quantitative variations in LMW DOM with depth using water extracts from a soil core from the Alaskan Arctic, a unique system that contains nearly half the Earth's terrestrial carbon and is rapidly warming due to climate change. We combined reversed-phase and hydrophilic interaction liquid chromatography, and nano-electrospray ionization coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization mode. The optimized conditions were sensitive, robust, highly complementary, and enabled detection and putative annotations of a wide range of compounds (e.g. amino acids, plant/microbial metabolites, sugars, lipids, peptides). Furthermore, multivariate statistical analyses revealed subtle but consistent and significant variations with depth. Thus, this platform is useful not only for characterizing LMW DOM, but also for quantifying relative variations in LMW DOM availability across space, revealing hotspots of biogeochemical activity for further evaluation.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Metabolomic and glycomic findings in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a stressor-related disorder that develops in a subset of individuals exposed to a traumatic experience. Factors associated with vulnerability to PTSD are still not fully understood. PTSD is frequently comorbid with various psychiatric and somatic disorders, moderate response to treatment and remission rates. The term "theranostics" combines diagnosis, prognosis, and therapy and offers targeted therapy based on specific analyses. Theranostics, combined with novel techniques and approaches called "omics", which integrate genomics, transcriptomic, proteomics and metabolomics, might improve knowledge about biological underpinning of PTSD, and offer novel therapeutic strategies. The focus of this review is on metabolomic and glycomic data in PTSD. Metabolomics evaluates changes in the metabolome of an organism by exploring the set of small molecules (metabolites), while glycomics studies the glycome, a complete repertoire of glycan structures with their functional roles in biological systems. Both metabolome and glycome reflect the physiological and pathological conditions in individuals. Only a few studies evaluated metabolic and glycomic changes in patients with PTSD. The metabolomics studies in PTSD patients uncovered different metabolites that might be associated with psychopathological alterations in PTSD. The glycomics study in PTSD patients determined nine N-glycan structures and found accelerated and premature aging in traumatized subjects and subjects with PTSD based on a GlycoAge index. Therefore, further larger studies and replications are needed. Better understanding of the biological basis of PTSD, including metabolomic and glycomic data, and their integration with other "omics" approaches, might identify new molecular targets and might provide improved therapeutic approaches.

A Protocol to Compare Methods for Untargeted Metabolomics

There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).

Diapause-associated changes in the lipid and metabolite profiles of the Asian tiger mosquito, Aedes albopictus

Diapause is an alternative life-history strategy that allows organisms to enter developmental arrest in anticipation of unfavorable conditions. Diapause is widespread among insects and plays a key role in enhancing overwinter survival as well as defining the seasonal and geographic distributions of populations. Next-generation sequencing has greatly advanced our understanding of the transcriptional basis for this crucial adaptation but less is known about the regulation of embryonic diapause physiology at the metabolite level. Here, we characterized the lipid and metabolite profiles of embryonic diapause in the Asian tiger mosquito, Aedes albopictus We used an untargeted approach to capture the relative abundance of 250 lipids and 241 metabolites. We observed adjustments associated with increased energy storage, including an accumulation of lipids, the formation of larger lipid droplets and increased lipogenesis, as well as metabolite shifts suggesting reduced energy utilization. We also found changes in neuroregulatory- and insulin-associated metabolites with potential roles in diapause regulation. Finally, we detected a group of unidentified, diapause-specific metabolites which have physical properties similar to those of steroids/steroid derivatives and may be associated with the ecdysteroidal regulation of embryonic diapause in A.albopictus Together, these results deepen our understanding of the metabolic regulation of embryonic diapause and identify key targets for future investigations.

Isotope Labeling-Assisted Evaluation of Hydrophilic and Hydrophobic Liquid Chromatograph-Mass Spectrometry for Metabolomics Profiling

High throughput untargeted metabolomics usually relies on complementary liquid chromatography-mass spectrometry (LC-MS) methods to expand the coverage of diverse metabolites, but the integration of those methods is not fully characterized. We systematically investigated the performance of hydrophilic interaction liquid chromatography (HILIC)-MS and nanoflow reverse-phase liquid chromatography (nRPLC)-MS under 8 LC-MS settings, varying stationary phases (HILIC and C18), mobile phases (acidic and basic pH), and MS ionization modes (positive and negative). Whereas nRPLC-MS optimization was previously reported, we found in HILIC-MS (2.1 mm × 150 mm) that the optimal performance was achieved in a 90 min gradient with 100 μL/min flow rate by loading metabolite extracts from 2 mg of cell/tissue samples. Since peak features were highly compromised by contaminants, we used stable isotope labeled yeast to enhance formula identification for comparing different LC-MS conditions. The 8 LC-MS settings enabled the detection of a total of 1050 formulas, among which 78%, 73%, and 62% formulas were recovered by the best combination of 4, 3, and 2 LC-MS settings, respectively. Moreover, these yeast samples were harvested in the presence or absence of nitrogen starvation, enabling quantitative comparisons of altered formulas and metabolite structures, followed by validation with selected synthetic metabolites. The results revealed that nitrogen starvation downregulated amino acid components but upregulated uridine-related metabolism. In summary, this study introduces a thorough evaluation of hydrophilicity and hydrophobicity based LC-MS and provides information for selecting complementary settings to balance throughput and efficiency during metabolomics experiments.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

High-Resolution Metabolomics Assessment of Military Personnel: Evaluating Analytical Strategies for Chemical Detection

OBJECTIVE

The aim of this study was to maximize detection of serum metabolites with high-resolution metabolomics (HRM).

METHODS

Department of Defense Serum Repository (DoDSR) samples were analyzed using ultrahigh resolution mass spectrometry with three complementary chromatographic phases and four ionization modes. Chemical coverage was evaluated by number of ions detected and accurate mass matches to a human metabolomics database.

RESULTS

Individual HRM platforms provided accurate mass matches for up to 58% of the KEGG metabolite database. Combining two analytical methods increased matches to 72% and included metabolites in most major human metabolic pathways and chemical classes. Detection and feature quality varied by analytical configuration.

CONCLUSIONS

Dual chromatography HRM with positive and negative electrospray ionization provides an effective generalized method for metabolic assessment of military personnel.

Metabolomics and Lipidomics of Ischemic Stroke

Ischemic stroke is a sudden loss of brain function due to the reduction of blood flow. Brain tissues cease to function with subsequent activation of the ischemic cascade. Metabolomics and lipidomics are modern disciplines that characterize the metabolites and lipid components of a biological system, respectively. Because the pathogenesis of ischemic stroke is heterogeneous and multifactorial, it is crucial to establish comprehensive metabolomic and lipidomic approaches to elucidate these alterations in this disease. Fortunately, metabolomic and lipidomic studies have the distinct advantages of identifying tissue/mechanism-specific biomarkers, predicting treatment and clinical outcome, and improving our understanding of the pathophysiologic basis of disease states. Therefore, recent applications of these analytical approaches in the early diagnosis of ischemic stroke were discussed. In addition, the emerging roles of metabolomics and lipidomics on ischemic stroke were summarized, in order to gain new insights into the mechanisms underlying ischemic stroke and in the search for novel metabolite biomarkers and their related pathways.

Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome

Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C₈ column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C₈ RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics. Graphical abstract Analysis of semipolar and nonpolar metabolites necessitates two reversed-phase chromatography (RPLC) methods, which extend metabolome coverage more than fivefold for untargeted profiling. HILIC hydrophilic interaction liquid chromatography.

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis

Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms. Because they intimately utilize and often rewire host metabolism, viruses are an excellent choice to study by metabolomics techniques. Studies of the effects of viruses on metabolism during replication in vitro and infection in animal models or human subjects have provided novel insights into these networks and provided new targets for therapy and biomarker development. Identifying the common metabolic pathways utilized by viruses has the potential to reveal those that can be targeted by broad-spectrum antiviral and vaccine approaches.

Metabolomic profiles of current cigarette smokers

Smoking-related biomarkers for lung cancer and other diseases are needed to enhance early detection strategies and to provide a science base for tobacco product regulation. An untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF MS) totaling 957 assays was used in a novel experimental design where 105 current smokers smoked two cigarettes 1 h apart. Blood was collected immediately before and after each cigarette allowing for within-subject replication. Dynamic changes of the metabolomic profiles from smokers' four blood samples were observed and biomarkers affected by cigarette smoking were identified. Thirty-one metabolites were definitively shown to be affected by acute effect of cigarette smoking, uniquely including menthol-glucuronide, the reduction of glutamate, oleamide, and 13 glycerophospholipids. This first time identification of a menthol metabolite in smokers' blood serves as proof-of-principle for using metabolomics to identify new tobacco-exposure biomarkers, and also provides new opportunities in studying menthol-containing tobacco products in humans. Gender and race differences also were observed. Network analysis revealed 12 molecules involved in cancer, notably inhibition of cAMP. These novel tobacco-related biomarkers provide new insights to the effects of smoking which may be important in carcinogenesis but not previously linked with tobacco-related diseases. © 2016 Wiley Periodicals, Inc.

Cerebral Metabolic Profiling of Hypothermic Circulatory Arrest with and Without Antegrade Selective Cerebral Perfusion: Evidence from Nontargeted Tissue Metabolomics in a Rabbit Model

Background:

Antegrade selective cerebral perfusion (ASCP) is regarded to perform cerebral protection during the thoracic aorta surgery as an adjunctive technique to deep hypothermic circulatory arrest (DHCA). However, brain metabolism profile after ASCP has not been systematically investigated by metabolomics technology.

Methods:

To clarify the metabolomics profiling of ASCP, 12 New Zealand white rabbits were randomly assigned into 60 min DHCA with (DHCA+ASCP [DA] group, n = 6) and without (DHCA [D] group, n = 6) ASCP according to the random number table. ASCP was conducted by cannulation on the right subclavian artery and cross-clamping of the innominate artery. Rabbits were sacrificed 60 min after weaning off cardiopulmonary bypass. The metabolic features of the cerebral cortex were analyzed by a nontargeted metabolic profiling strategy based on gas chromatography-mass spectrometry. Variable importance projection values exceeding 1.0 were selected as potentially changed metabolites, and then Student's t-test was applied to test for statistical significance between the two groups.

Results:

Metabolic profiling of brain was distinctive significantly between the two groups (Q²Y = 0.88 for partial least squares-DA model). In comparing to group D, 62 definable metabolites were varied significantly after ASCP, which were mainly related to amino acid metabolism, carbohydrate metabolism, and lipid metabolism. Kyoto Encyclopedia of Genes and Genomes analysis revealed that metabolic pathways after DHCA with ASCP were mainly involved in the activated glycolytic pathway, subdued anaerobic metabolism, and oxidative stress. In addition, L-kynurenine (P = 0.0019), 5-methoxyindole-3-acetic acid (P = 0.0499), and 5-hydroxyindole-3-acetic acid (P = 0.0495) in tryptophan metabolism pathways were decreased, and citrulline (P = 0.0158) in urea cycle was increased in group DA comparing to group D.

Conclusions:

The present study applied metabolomics analysis to identify the cerebral metabolic profiling in rabbits with ASCP, and the results may shed new lights that cerebral metabolism is better preserved by ASCP compared with DHCA alone.