Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Glucose 6 phosphate dehydrogenase overexpression rescues the loss of cognition in the double transgenic APP/PS1 mouse model of Alzheimer's disease

Mice models of Alzheimer's disease (APP/PS1) typically experience cognitive decline with age. G6PD overexpressing mice (G6PD-Tg) exhibit better protection from age-associated functional decline including improvements in metabolic and muscle functions as well as reduced frailty compared to their wild-type counterparts. Importantly G6PD-Tg mice show diminished accumulation of DNA oxidation in the brain at different ages in both males and females. To further explore the potential benefits of modulating the G6PD activity in neurodegenerative diseases, triple transgenic mice (3xTg G6PD) were generated, overexpressing APP, PSEN1, and G6PD genes. The cognitive decline characteristic of APP/PS1 mice was prevented in 3xTg G6PD mice, despite similar amyloid-β (Aβ) levels in the hippocampus. This challenges the dominant hypothesis in Alzheimer's disease (AD) etiology and the majority of therapeutic efforts in the field, based on the notion that Aβ is pivotal in cognitive preservation. Notably, the antioxidant properties of G6PD led to a decrease in oxidative stress parameters, such as improved GSH/GSSG and GSH/CysSSG ratios, without major changes in oxidative damage markers. Additionally, metabolic changes in 3xTg G6PD mice increased brain energy status, countering the hypometabolism observed in Alzheimer's models. Remarkably, a higher respiratory exchange ratio suggested increased carbohydrate utilization. The relative failures of Aβ-targeted clinical trials have raised significant skepticism on the amyloid cascade hypothesis and whether the development of Alzheimer's drugs has followed the correct path. Our findings highlight the significance of targeting glucose-metabolizing enzymes rather than solely focusing on Aβ in Alzheimer's research, advocating for a deeper exploration of glucose metabolism's role in cognitive preservation.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Application of the Hydrophilic Interaction Liquid Chromatography (HILIC-MS) Novel Protocol to Study the Metabolic Heterogeneity of Glioblastoma Cells

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites-polaromics.

Comparisons of different extraction methods and solvents for saliva samples

INTRODUCTION

Changes in the categories and concentrations of salivary metabolites may be closely related to oral, intestinal or systemic diseases. To study salivary metabolites, the first analytical step is to extract them from saliva samples as much as possible, while reducing interferences to a minimum. Frequently used extraction methods are protein precipitation (PPT), liquid-liquid extraction (LLE) and solid-phase extraction (SPE), with various organic solvents. The types and quantities of metabolites extracted with different methods may vary greatly, but few studies have systematically evaluated them.

OBJECTIVES

This study aimed to select the most suitable methods and solvents for the extraction of saliva according to different analytical targets.

METHODS

An untargeted metabolomics approach based on liquid chromatography-mass spectrometry was applied to obtain the raw data. The numbers of metabolites, repeatability of the data and intensities of mass spectrometry signals were used as evaluation criteria.

RESULTS

PPT resulted in the highest coverage. Among the PPT solvents, acetonitrile displayed the best repeatability and the highest coverage, while acetone resulted in the best signal intensities for the extracted compounds. LLE with the mixture of chloroform and methanol was the most suitable for the extraction of small hydrophobic compounds.

CONCLUSION

PPT with acetonitrile or acetone was recommended for untargeted analysis, while LLE with the mixture of chloroform and methanol was recommended for small hydrophobic compounds.

Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS)

In metabolomic analysis, one of the most commonly used techniques to support the detection sensitivity and quantitation of mass spectrometry is combining it with liquid chromatography. Recently, we developed a method that enables comprehensive single-run measurement of hydrophilic metabolites using unified-hydrophilic interaction/anion exchange liquid chromatography/high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) with a polymer-based mixed amines column (Gelpack GL-HilicAex). However, the importance of stationary phase functional groups and mobile phase conditions for the separation mechanisms and sensitive detection in unified-HILIC/AEX/HRMS is not yet fully understood. This study aimed to understand the importance of the mobile and stationary phases in unified-HILIC/AEX/HRMS. Two different alkali-resistant polymer-based amines-modified columns (Gelpack GL-HilicAex, primary, secondary, tertiary, and quaternary amine-modified polyglycerol dimethacrylate gel; Asahipak NH2P-50 2D, secondary amine-modified polyvinyl alcohol gel) and two eluents (acetonitrile and ammonium bicarbonate solution, pH 9.8) were used for comparative validation. A comparison of mobile phase conditions using both columns confirmed that the two-step separation from HILIC to AEX characteristic of unified-HILIC/AEX requires a linear gradient condition from acetonitrile to nearly 50% water and AEX with up to 40 mM bicarbonate ions. We found that when alkali-resistant hydrophilic polymer packing materials are modified with amines, unified-HILIC/AEX separation can be reproduced if at least one secondary amine associated with the amine series is present in the stationary phase. Furthermore, the difference in sensitivity in the HILIC and AEX modes owing to the different columns indicates the need for further improvements in the mobile phase composition and stationary phase.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Widely targeted metabolomics unveils baicalin-induced hippocampal metabolic alternations in a rat model of chronic unpredictable mild stress

Baicalin has various neuroprotective effects in models of nervous system disease. Our study has shown baicalin could alleviate depressive-like behaviors in a neuroendocrine mouse model. But the systematic metabolic characteristic and particular targets of baicalin in regulating depressive behaviors have never been investigated. Therefore, this study aims to reveal the hippocampal metabolic profiling of chronic unpredictable mild stress (CUMS) induced depressive rats and the potential metabolic variations after baicalin treatment. We first used the sucrose preference test and open field test to access the antidepressant effects of baicalin. Then, metabolites of the hippocampus after baicalin therapy were monitored by widely-targeted metabolomics based on ultra-performance liquid chromatography-tandem mass spectrometry technology. Finally, the potential mechanism associated with neurogenesis obtained from metabolomics was verified by immunohistochemistry. The results showed that baicalin(40,80 mg/kg) could significantly alleviate depressive behaviors induced by CUMS as demonstrated by an increase in sucrose preference and movement distance and stand-up times in open field test. In the metabolomic analysis, a total of 733 metabolites were identified after baicalin treatment including 15 differential metabolites such as organic acid and its derivatives, heterocyclic compounds, fatty acid, bile acids, amino acid and its metabolites, and so on. Enrichment for differential metabolites showed that the differential metabolites might be involved in the process of folate and cofactor biosynthesis, cholesterol metabolism, primary bile acid biosynthesis, tyrosine metabolism and dopaminergic synapse. Moreover, immunohistochemical analysis confirmed baicalin could facilitate hippocampal neurogenesis of depressive rats in CUMS model. These results suggested baicalin might exert antidepressant effects through regulating the differential metabolites which might play a crucial role in inhibiting oxidative stress and improving neurogenesis. Our findings wish to discover the potential mechanism of baicalin on depression from the metabolomics perspective and promote its clinical application.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Effect of providing citrus pulp-integrated diet on fecal microbiota and serum and fecal metabolome shifts in crossbred pigs

The study aimed to assess the impact of dehydrated citrus pulp (DCP) on growth performance, fecal characteristics, fecal bacterial composition (based on 16S rRNA analysis), and fecal and serum metabolomic profiles in crossbred pigs. 80 finishing pigs Duroc × (Landrace × Large White) were fed either a control diet (C) or a diet with 240 g/kg DCP (T) for six weeks. Including DCP in diets tended to decrease feed intake, increased (p < 0.05) the concentrations of acetic and heptanoic acids and decreased (p < 0.05) fecal butyric and branched-chain fatty acid concentrations in feces. Animals fed DCP exhibited a lower abundance of the genera Clostridium and Romboutsia, while Lachnospira significantly increased. Orthogonal partial least squares discriminant analysis plotted a clear separation of fecal and serum metabolites between groups. The main discriminant fecal metabolites were associated with bacterial protein fermentation and were downregulated in T-fed pigs. In serum, DCP supplementation upregulated metabolites related to protein and fatty acids metabolism. In conclusion, the addition of DCP as an environmentally friendly source of nutrients in pig diets, resulted in modifications of fecal bacterial composition, fermentation patterns, and overall pig metabolism, suggesting improvements in protein metabolism and gut health.

Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis

Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.

A comparison of hydrophilic interaction liquid chromatography and capillary electrophoresis for the metabolomics analysis of human serum

Cationic, anionic, zwitterionic and, partially polar metabolites are very important constituents of blood serum. Several of these metabolites underpin the core metabolism of cells (e.g., Krebs cycle, urea cycle, proteins synthesis, etc.), while others might be considered ancillary but still important to grasp the status of any organism through blood serum analysis. Due to its wide chemical diversity, modern metabolomics analysis of serum is still struggling to provide a complete and comprehensive picture of the polar metabolome, due to the limitations of each specific analytical method. In this study, two metabolomics-based analytical methods using the most successful techniques for polar compounds separation in human serum samples, namely hydrophilic interaction liquid chromatography (HILIC) and capillary electrophoresis (CE), are evaluated, both coupled to a high-resolution time-of-flight mass spectrometer via electrospray ionization (ESI-Q-TOF-MS). The performance of the two methods have been compared using five terms of comparison, three of which are specific to metabolomics, such as (1) compounds' detectability (2) Pezzatti score (Pezzatti et al. 2018), (3) intra-day precision (repeatability), (4) ease of automatic analysis of the data (through a common deconvolution alignment and extrapolation software, MS-DIAL, and (5) time & cost analysis. From this study, HILIC-MS proved to be a better tool for polar metabolome analysis, while CE-MS helped identify some interesting variables that gave it interest in completing metabolome coverage in metabolomics studies. Finally, in this framework, MS-DIAL demonstrates for the first time its ability to process CE data for metabolomics, although it is not designed for it.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

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.

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.

Metabolomics in viral hepatitis: advances and review

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Changes of gut microbiota and tricarboxylic acid metabolites may be helpful in early diagnosis of necrotizing enterocolitis: A pilot study

Purpose

We aimed to explore the value of gut microbiota and tricarboxylic acid (TCA) metabolites in early diagnosis of necrotizing enterocolitis (NEC) among infants with abdominal manifestations.

Methods

Thirty-two preterm infants with abdominal manifestations at gestational age ≤ 34 weeks were included in the study and were divided into non-NEC (n = 16) and NEC (n = 16) groups. Faecal samples were collected when the infants were enrolled. The gut microbiota was analysed with high-throughput sequencing, and TCA metabolites were measured with multiple reaction monitoring (MRM) targeted metabolomics. Receiver operating characteristic (ROC) curves were generated to explore the predictive value of the obtained data.

Results

There was no significant difference in alpha diversity or beta diversity between the two groups (p &gt; 0.05). At the phylum level, Proteobacteria increased, and Actinomycetota decreased in the NEC group (p &lt; 0.05). At the genus level, Bifidobacterium and Lactobacillaceae decreased significantly, and at the species level, unclassified Staphylococcus, Lactobacillaceae and Bifidobacterium animalis subsp. lactis decreased in the NEC group (p &lt; 0.05). Further Linear discriminant analysis effect sizes (LEfSe) analysis showed that the change in Proteobacteria at the phylum level and Lactobacillaceae and Bifidobacterium at the genus level scored higher than 4. The concentrations of succinate, L-malic acid and oxaloacetate in the NEC group significantly increased (p &lt; 0.05), and the areas under the ROC curve for these metabolites were 0.6641, 0.7617, and 0.7344, respectively.

Conclusion

Decreased unclassified Staphylococcus, Lactobacillaceae and Bifidobacterium animalis subsp. lactis at the species level as well as the increase in the contents of some TCA metabolites, including succinate, L-malic acid and oxaloacetate, have potential value for the early diagnosis of NEC.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

HILIC-MS for Untargeted Profiling of the Free Glycation Product Diversity

Glycation products produced by the non-enzymatic reaction between reducing carbohydrates and amino compounds have received increasing attention in both food- and health-related research. Although liquid chromatography mass spectrometry (LC-MS) methods for analyzing glycation products already exist, only a few common advanced glycation end products (AGEs) are usually covered by quantitative methods. Untargeted methods for comprehensively analyzing glycation products are still lacking. The aim of this study was to establish a method for simultaneously characterizing a wide range of free glycation products using the untargeted metabolomics approach. In this study, Maillard model systems consisting of a multitude of heterogeneous free glycation products were chosen for systematic method optimization, rather than using a limited number of standard compounds. Three types of hydrophilic interaction liquid chromatography (HILIC) columns (zwitterionic, bare silica, and amide) were tested due to their good retention for polar compounds. The zwitterionic columns showed better performance than the other two types of columns in terms of the detected feature numbers and detected free glycation products. Two zwitterionic columns were selected for further mobile phase optimization. For both columns, the neutral mobile phase provided better peak separation, whereas the acidic condition provided a higher quality of chromatographic peak shapes. The ZIC-cHILIC column operating under acidic conditions offered the best potential to discover glycation products in terms of providing good peak shapes and maintaining comparable compound coverage. Finally, the optimized HILIC-MS method can detect 70% of free glycation product features despite interference from the complex endogenous metabolites from biological matrices, which showed great application potential for glycation research and can help discover new biologically important glycation products.

The metabolomic differential plasma profile between dialysates. Pursuing to understand the mechanisms of citrate dialysate clinical benefits

Background: Currently, bicarbonate-based dialysate needs a buffer to prevent precipitation of bicarbonate salts with the bivalent cations, and acetate at 3-4 mmol/L is the most used. However, citrate is being postulated as a preferred option because of its association with better clinical results by poorly understood mechanisms. In that sense, this hypothesis-generating study aims to identify potential metabolites that could biologically explain these improvements found in patients using citrate dialysate. Methods: A unicentric, cross-over, prospective untargeted metabolomics study was designed to analyze the differences between two dialysates only differing in their buffer, one containing 4 mmol/L of acetate (AD) and the other 1 mmol/L of citrate (CD). Blood samples were collected in four moments (i.e., pre-, mid-, post-, and 30-min-post-dialysis) and analyzed in an untargeted metabolomics approach based on UPLC-Q-ToF mass spectrometry. Results: The 31 most discriminant metabolomic variables from the plasma samples of the 21 participants screened by their potential clinical implications show that, after dialysis with CD, some uremic toxins appear to be better cleared, the lysine degradation pathway is affected, and branched-chain amino acids post-dialysis levels are 9-10 times higher than with AD; and, on its part, dialysis with AD affects acylcarnitine clearance. Conclusion: Although most metabolic changes seen in this study could be attributable to the dialysis treatment itself, this study successfully identifies some metabolic variables that differ between CD and AD, which raise new hypotheses that may unveil the mechanisms involved in the clinical improvements observed with citrate in future research.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

LipidMS 3.0: an R-package and a web-based tool for LC-MS/MS data processing and lipid annotation

MOTIVATION

LipidMS was initially envisioned to use fragmentation rules and data-independent acquisition (DIA) for lipid annotation. However, data-dependent acquisition (DDA) remains the most widespread acquisition mode for untargeted LC-MS/MS-based lipidomics. Here we present LipidMS 3.0, an R package that not only adds DDA and new lipid classes to its pipeline, but also the required functionalities to cover the whole data analysis workflow from pre-processing (i.e., peak-peaking, alignment and grouping) to lipid annotation.

RESULTS

We applied the new workflow in the data analysis of a commercial human serum pool spiked with 68 representative lipid standards acquired in full scan, DDA and DIA modes. When focusing on the detected lipid standard features and total identified lipids, LipidMS 3.0 data pre-processing performance is similar to XCMS, whereas it complements the annotations returned by MS-DIAL, providing a higher level of structural information and a lower number of incorrect annotations. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users, the workflow was also implemented as a web-based application.

AVAILABILITY

The LipidMS R-package is freely available at https://CRAN.R-project.org/package=LipidMS and as a website at http://www.lipidms.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Low-dose carbon monoxide suppresses metastatic progression of disseminated cancer cells

Low-dose carbon monoxide (CO) is under investigation in clinical trials to treat non-cancerous diseases and has excellent safety profiles. Due to the early detection and cancer awareness, increasing cancer patients are diagnosed at early stages and potentially curative surgical resection can be done. However, many patients ultimately experience recurrence. Here, we evaluate the therapeutic effect of CO on cancer metastatic progression. We show that 250 ppm CO inhibits migration of multiple types of cancer cell lines including breast, pancreatic, colon, prostate, liver, and lung cancer and reduces the ability to adhere to fibronectin. We demonstrate that in mouse models, 250 ppm inhaled CO inhibits lung metastasis of breast cancer and liver metastasis of pancreatic cancer. Moreover, low-dose CO suppresses recurrence and increases survival after surgical removal of primary pancreatic cancer in mice. Mechanistically, low-dose CO blocks transcription of heme importers, leading to diminished intracellular heme levels and a heme-regulated enzyme, cytochrome P4501B1 (CYP1B1). Either supplementing heme or overexpressing CYP1B1 reverses the anti-migration effect of low-dose CO. Taken together, low-dose CO therapy inhibits cell migration, reduces adhesion to fibronectin, prevents disseminated cancer cells from expanding into gross metastases, and improves survival in pre-clinical mouse models of metastasis.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

The underlying rationality of Chinese medicine herb pair Coptis chinensis and Dolomiaea souliei: From the perspective of metabolomics and intestinal function

ETHNOPHARMACOLOGICAL RELEVANCE

The combination of Coptis chinensis (RC) and Dolomiaea souliei (VR) has long been used as a classic herb pair for the treatment of gastrointestinal diseases, but the underlying mechanisms remain unknown.

MATERIALS AND METHODS

In this study, the rationality of evidence-based RC and VR combination was explored from the perspective of metabolism, gut microbiota and gastrointestinal function.

RESULTS

After 5 weeks treatment, VR extracts (700 mg/kg) and RC alkaloids (800 mg/kg) showed no toxic effect on mice. However, RC administration significantly decreased the body weight of mice. Gastric emptying, gastrointestinal motility function and the absorption of FITC dextran were retarded in the mice of RC group, taking RC along with low dose VR (RC-VRL) and high dose VR (RC-VRH) reversed the impaired gastrointestinal function caused by RC. RC administration significantly increased villus height/crypt depth value. Notably, VR administration increased the number of crypts in mice ileum and reduced villus height/crypt depth value in VR and RC combination group. RC treatment significantly increased the expression of occludin compared to NC group; RC-VRL treatment reversed this tendency. While, VR administration increased ZO1 expression by 99.4% compared to NC mice. As for gut microbiota, RC gavage decreased the gut microbiota diversity, but gut microbiota in VR group was similar to NC group, and VR and RC combination increased gut microbiota diversity. RC administration obviously increased the proportion of Akkermansia muciniphila, Bacteroides thetaiotaomicron, Parabacteroides distasonis, and Escherichia coli, compared to NC mice. VR treatment increased the richness of Bacteroides thetaiotaomicron, Parabacteroides distasonis. RC-VRL and RC-VRH treatment dose-dependently increased the richness of Rikenellaceae RC9, Lactobacillus, and decreased the abundance of Psychrobacter, Bacteroides and Ruminococcus in mice. Serum metabolomic analysis revealed that RC gavage significantly down regulated 76 metabolites and up regulated 31 metabolites. VR treatment significantly down regulated 30 metabolites and up regulated 12 metabolites. Weight loss caused by RC may attribute to the elevated methylxanthine level in mice. The potential adverse effects caused by high dose RC intake may partially alleviate by high serum contents of adenosine, inosine and urolithin A resulted from VR coadministration.

CONCLUSION

VR may alleviate RC caused "fluid retention" via normalizing gastrointestinal function, gut microbiota and modulating the perturbed metabolism.

Simultaneous Analysis of the Metabolome and Lipidome Using Polarity Partition Two-Dimensional Liquid Chromatography-Mass Spectrometry

Comprehensive metabolic profiling is a considerable challenge for systems biology since the metabolites in biological samples have significant polarity differences. A heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method-based polarity partition was established to analyze both the metabolome and lipidome in a single run. Based on the polarity partition strategy, metabolites with high polarity were retained and separated by one-dimensional hydrophilic chromatography, while low- and medium-polarity lipids were collected into a sample loop and injected into two-dimensional reversed-phase chromatography for separation. A simple online dilution strategy realized the online coupling of the 2D-LC-MS, which effectively solved band broadening and peak distortion caused by solvent incompatibility. Moreover, a dual gradient elution procedure was introduced to further broaden the coverage of low-polarity lipids. The metabolites' log P values, which this 2D-LC-MS method could analyze, ranged from -8.79 to 26.86. The feasibility of the 2D-LC-MS system was demonstrated by simultaneous analysis of the metabolome and lipidome in rat plasma related to depression. A total of 319 metabolites were determined within 40 min, including organic acids, nucleosides, carbohydrate derivatives, amino acids, lipids, and other organic compounds. Finally, 44 depression-related differential metabolites were screened. Compared with conventional LC-MS-based methods, the 2D-LC method covered over 99% of features obtained by two conventional methods. In addition, the selectivity and resolution of the hydrophilic metabolites were improved, and the matrix effects of the hydrophobic metabolites were reduced in the developed method. The results indicated that the established 2D-LC system is a powerful tool for comprehensive metabolomics studies.

Beyond Sperm and Male Accessory Gland Proteins: Exploring Insect Reproductive Metabolomes

Insect seminal fluid, the non-sperm component of the ejaculate, comprises a variegated set of molecules, including, but not limited to, lipids, proteins, carbohydrates, salts, hormones, nucleic acids, and vitamins. The identity and functional role of seminal fluid proteins (SFPs) have been widely investigated, in multiple species. However, most of the other small molecules in insect ejaculates remain uncharacterized. Metabolomics is currently adopted to deepen our understanding of complex biological processes and in the last 15years has been applied to answer different physiological questions. Technological advances in high-throughput methods for metabolite identification such as mass spectrometry and nuclear magnetic resonance (NMR) are now coupled to an expanded bioinformatics toolbox for large-scale data analysis. These improvements allow for the processing of smaller-sized samples and for the identification of hundreds to thousands of metabolites, not only in Drosophila melanogaster but also in disease vectors, animal, and agricultural pests. In this review, we provide an overview of the studies that adopted metabolomics-based approaches in insects, with a particular focus on the reproductive tract (RT) of both sexes and the ejaculate. Progress in the field of metabolomics will contribute not only to achieve a deeper understanding of the composition of insect ejaculates and how they are affected by endogenous and exogenous factors, but also to provide increasingly powerful tools to decipher the identity and molecular interactions between males and females during and after mating.

Assessment of Biphasic Extraction Methods of Mouse Fecal Metabolites for Liquid Chromatography-Mass Spectrometry-Based Metabolomic Studies

With the increasing knowledge about the important roles of gut microbiota on the biological system, a systematic strategy to profile the fecal metabolome is urgently needed. Thus, an unbiased, efficient, and reproducible fecal metabolite extraction protocol needs to be established; however, the effect of biphasic extraction methods for the fecal samples remains unclear. In this study, five different methods were assessed in the extraction of polar and non-polar metabolites for the liquid chromatography-mass spectrometry (LC-MS)-based mouse fecal metabolomic study. First, the detection coverage of two extraction systems, the Bligh and Dyer extraction method (M1, chloroform/methanol/water, 2/2/1.8) and Matyash method (M2, methyl tert-butyl ether (MTBE)/methanol/water, 10/3/2.5), was compared; then, MTBE/methanol/water system with different solvent ratios (M3, 2.6/2.0/2.4; M4, 4.5/1/2.5; and M5, 3/2.5/2.5) were further evaluated. The results showed that M2 showed higher detection coverage than M1. For the MTBE/methanol/water system with different solvent ratios, M3 showed the largest detection coverage based on peak numbers and numbers of putatively annotated metabolites, while M4 presented the least overlap between two phases, higher peak intensities of metabolites, and superior reproducibility. Based on the above evidence, M4 was recommended for the biphasic extraction of fecal metabolites in the LC-MS-based mouse fecal metabolomic study.

A Robust Method for Sample Preparation of Gastrointestinal Stromal Tumour for LC/MS Untargeted Metabolomics

Gastrointestinal stromal tumour has already been well explored at the genome level; however, little is known about metabolic processes occurring in the sarcoma. Sample preparation is a crucial step in untargeted metabolomics workflow, highly affecting the metabolome coverage and the quality of the results. In this study, four liquid-liquid extraction methods for the isolation of endogenous compounds from gastrointestinal stromal tumours were compared and evaluated. The protocols covered two-step or stepwise extraction with methyl-tert-butyl ether (MTBE) or dichloromethane. The extracts were subjected to LC-MS analysis by the application of reversed-phase and hydrophilic interaction liquid chromatography to enable the separation and detection of both polar and nonpolar analytes. The extraction methods were compared in terms of efficiency (total number of detected metabolites) and reproducibility. The method was based on the stepwise extraction with MTBE, methanol, and water proved to be the most reproducible, and thus, its robustness to fluctuations in experimental conditions was assessed employing Plackett–Burman design and hierarchical modelling. While most studied factors had no effect on the metabolite abundance, the highest coefficient value was observed for the volume of MTBE added during extraction. Herein, we demonstrate the application and the feasibility of the selected protocol for the analysis of gastrointestinal stromal tumour samples. The method selected could be considered as a reference for the best characterization of underlying molecular changes associated with complex tissue extracts of GIST.

Tumor Microenvironment-Derived Metabolites: A Guide to Find New Metabolic Therapeutic Targets and Biomarkers

Metabolic reprogramming is a hallmark of cancer that enables cancer cells to grow, proliferate and survive. This metabolic rewiring is intrinsically regulated by mutations in oncogenes and tumor suppressors, but also extrinsically by tumor microenvironment factors (nutrient and oxygen availability, cell-to-cell interactions, cytokines, hormones, etc.). Intriguingly, only a few cancers are driven by mutations in metabolic genes, which lead metabolites with oncogenic properties (i.e., oncometabolites) to accumulate. In the last decade, there has been rekindled interest in understanding how dysregulated metabolism and its crosstalk with various cell types in the tumor microenvironment not only sustains biosynthesis and energy production for cancer cells, but also contributes to immune escape. An assessment of dysregulated intratumor metabolism has long since been exploited for cancer diagnosis, monitoring and therapy, as exemplified by 18F-2-deoxyglucose positron emission tomography imaging. However, the efficient delivery of precision medicine demands less invasive, cheaper and faster technologies to precisely predict and monitor therapy response. The metabolomic analysis of tumor and/or microenvironment-derived metabolites in readily accessible biological samples is likely to play an important role in this sense. Here, we review altered cancer metabolism and its crosstalk with the tumor microenvironment to focus on energy and biomass sources, oncometabolites and the production of immunosuppressive metabolites. We provide an overview of current pharmacological approaches targeting such dysregulated metabolic landscapes and noninvasive approaches to characterize cancer metabolism for diagnosis, therapy and efficacy assessment.

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.