Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

CD38-mediated metabolic reprogramming promotes the stability and suppressive function of regulatory T cells in tumor

In the tumor microenvironment (TME), regulatory T cells (Tregs) adapt their metabolism to thrive in low-glucose, high-lactate conditions, but the mechanisms remain unclear. Our study identifies CD38 as a key regulator of this adaptation by depleting nicotinamide adenine dinucleotide (oxidized form) (NAD+), redirecting lactate-derived pyruvate toward phosphoenolpyruvate and bypassing the tricarboxylic acid (TCA) cycle. This prevents accumulation of α-ketoglutarate, which destabilizes Tregs by inducing hypermethylation at the Foxp3 locus. Restoring NAD+ with nicotinamide mononucleotide reverses this adaptation, pushing Tregs back to the TCA cycle and reducing their suppressive function. In YUMM1.7 melanoma-bearing mice, small-molecule CD38 inhibition selectively destabilizes intratumoral Tregs, sparking robust antitumor immunity. These findings reveal that targeting the CD38-NAD+ axis disrupts Tregs metabolic adaptation and offers a strategy to enhance antitumor responses.

Clinical metabolomics in type 2 diabetes mellitus: from pathogenesis to biomarkers

As a multidimensional metabolic disorder, the disability and death rate of type 2 diabetes mellitus (T2DM) has increased over time. T2DM covers a wide range of pathological manifestations ranging from hyperglycemia to multi-organ failure, and it has the potential to evolve into acute complications, including ketosis and chronic complications such as peripheral neuropathy, retinopathy, and nephropathy. T2DM mainly occurs in microvascular and large vessels and thus it is restricted for the clinician to diagnose and prescribe. However, the pathological mechanism and clinical diagnosis are inadequate. High-throughput metabolomics, characterized by non-invasive diagnostic techniques to identify potential biomarkers and distinct stages of T2DM, has been increasingly recognized as a vigorous tool with latent capacity for clinical translation. The pathological stratification of T2DM can significantly reduce disability and mortality rates. By tracing the metabolome and associated pathways from impaired fasting blood glucose or impaired glucose tolerance to severe organ failure, the chief contributions of large, independent population-based cohorts are summarized herein. These results facilitate understanding the pathophysiology and mechanism and supports research in accurate diagnosis, risk prediction, curative effect, distinct stages, and prognosis judgment of T2DM.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data

MOTIVATION

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

RESULTS

In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.

AVAILABILITY AND IMPLEMENTATION

DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Lipidome isotope labelling of gut microbes (LILGM): A method of discovering endogenous microbial lipids

Lipidomics analysis of gut microbiome has become critical in recent surge of extensive human disease studies that investigate microbiome contributions. However, challenges remain in comprehending the origins of thousands of lipid species produced by the diverse microbes. Here, we proposed the development and utilization of a liquid chromatography-mass spectrometry-based approach, named lipidome isotope labelling of gut microbes (LILGM), which enables confident detection and identification of endogenous gut microbial lipidome via 13C/15N labeling strategy and high-resolution mass spectrometry. Our method leveraged in vitro microbial cultures and stable isotope-labeled 13C and 15N, allowing a reasonable degree of isotope incorporation into microbial lipids over short-term of inoculation. We then systematically detected the mass spectral patterns of 182 labeled lipid species by our in-house data analysis pipeline. Further bioinformatics analyses confidently identified biologically relevant microbial lipids from lipid classes such as diacylglycerols (DGs), fatty acids (FAs), phosphatidylglycerols (PGs), and phosphatidylethanolamines (PEs) that may have profound impacts to human physiology. Our study also demonstrated the application of LILGM by showcasing the confident detection of dysregulated microbial lipids post antibiotic perturbation. The debiased sparse partial correlation analysis provides insights into lipid metabolism intricacies. Overall, our method can provide unambiguous analyses to the endogenous microbial lipids in given biological context, and can also instantly reflect the lipidomic changes of gut microbes in response to environmental factors. We believe our LILGM approach has the potential to provide new body of knowledge by combining promising analytical approaches for sensitive and specific lipid detection to support functional microbiome studies.

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

Dual strategy for 13C-Metabolic flux analysis of central carbon and energy metabolism in Mammalian cells based on LC-isoMRM-MS

Central carbon and energy metabolism are the most concerned metabolic pathways in 13C-Metabolic flux analysis (13C-MFA). However, some α-keto acids, ribonucleoside triphosphate (NTPs) and deoxyribonucleoside triphosphate (dNTPs) involved in central carbon and energy metabolism pathways were unstable or reactive, leading to inaccurate metabolic flux analysis. To achieve accurate 13C-MFA of central carbon and energy metabolism, we proposed a dual strategy for the detection of 101 metabolites in glucose metabolism pathways. N-Methylphenylethylamine (MPEA) was utilized for derivatization of 4 carboxyl (α-keto acids) and 8 phosphate metabolites (NTPs and dNTPs). After derivatization, the MPEA derivatives were investigated to be stable for 4 weeks under 4 °C and detected with high intensity in ∼104 cells. On the other hand, we analyzed an additional 89 metabolites in central carbon and energy metabolic pathways were directly analyzed by liquid chromatography tandem mass spectrometry (LC-MRM-MS). The limit of detection (LODs) of our method were as low as 0.05 ng/mL and the linear range was at least two orders of magnitude with determination coefficient (R2) > 0.9701. The relative standard divisions (RSDs) of intra- and inter-day of 95% metabolites were below 20%. In addition, the isotope list of 82 detected metabolites in central carbon and energy metabolism were generated according to isotopologues and isotopomers for each metabolite resulting from 13C incorporation. Accurate assessment of mass isotopomer distributions (MIDs) of intracellular 13C-labeled metabolites was achieved in [U-13C]-glucose cultured HepG2 cells by our dual strategy. Finally, we performed MID analysis of 101 metabolites in central carbon and energy metabolism. Overall, this dual method is reproducible and robust for application on 13C-MFA and has a great potential for studying clinical isotope labeled samples.

Mitochondrial hyperfusion induces metabolic remodeling in lung endothelial cells by modifying the activities of electron transport chain complexes I and III

OBJECTIVE

Pulmonary hypertension (PH) is a progressive disease with vascular remodeling as a critical structural alteration. We have previously shown that metabolic reprogramming is an early initiating mechanism in animal models of PH. This metabolic dysregulation has been linked to remodeling the mitochondrial network to favor fission. However, whether the mitochondrial fission/fusion balance underlies the metabolic reprogramming found early in PH development is unknown.

METHODS

Utilizing a rat early model of PH, in conjunction with cultured pulmonary endothelial cells (PECs), we utilized metabolic flux assays, Seahorse Bioassays, measurements of electron transport chain (ETC) complex activity, fluorescent microscopy, and molecular approaches to investigate the link between the disruption of mitochondrial dynamics and the early metabolic changes that occur in PH.

RESULTS

We observed increased fusion mediators, including Mfn1, Mfn2, and Opa1, and unchanged fission mediators, including Drp1 and Fis1, in a two-week monocrotaline-induced PH animal model (early-stage PH). We were able to establish a connection between increases in fusion mediator Mfn1 and metabolic reprogramming. Using an adenoviral expression system to enhance Mfn1 levels in pulmonary endothelial cells and utilizing 13C-glucose labeled substrate, we found increased production of 13C lactate and decreased TCA cycle metabolites, revealing a Warburg phenotype. The use of a 13C5-glutamine substrate showed evidence that hyperfusion also induces oxidative carboxylation. The increase in glycolysis was linked to increased hypoxia-inducible factor 1α (HIF-1α) protein levels secondary to the disruption of cellular bioenergetics and higher levels of mitochondrial reactive oxygen species (mt-ROS). The elevation in mt-ROS correlated with attenuated ETC complexes I and III activities. Utilizing a mitochondrial-targeted antioxidant to suppress mt-ROS, limited HIF-1α protein levels, which reduced cellular glycolysis and reestablished mitochondrial membrane potential.

CONCLUSIONS

Our data connects mitochondrial fusion-mediated mt-ROS to the Warburg phenotype in early-stage PH development.

Novel Relationship between Mitofusin 2-Mediated Mitochondrial Hyperfusion, Metabolic Remodeling, and Glycolysis in Pulmonary Arterial Endothelial Cells

The disruption of mitochondrial dynamics has been identified in cardiovascular diseases, including pulmonary hypertension (PH), ischemia-reperfusion injury, heart failure, and cardiomyopathy. Mitofusin 2 (Mfn2) is abundantly expressed in heart and pulmonary vasculature cells at the outer mitochondrial membrane to modulate fusion. Previously, we have reported reduced levels of Mfn2 and fragmented mitochondria in pulmonary arterial endothelial cells (PAECs) isolated from a sheep model of PH induced by pulmonary over-circulation and restoring Mfn2 normalized mitochondrial function. In this study, we assessed the effect of increased expression of Mfn2 on mitochondrial metabolism, bioenergetics, reactive oxygen species production, and mitochondrial membrane potential in control PAECs. Using an adenoviral expression system to overexpress Mfn2 in PAECs and utilizing 13C labeled substrates, we assessed the levels of TCA cycle metabolites. We identified increased pyruvate and lactate production in cells, revealing a glycolytic phenotype (Warburg phenotype). Mfn2 overexpression decreased the mitochondrial ATP production rate, increased the rate of glycolytic ATP production, and disrupted mitochondrial bioenergetics. The increase in glycolysis was linked to increased hypoxia-inducible factor 1α (HIF-1α) protein levels, elevated mitochondrial reactive oxygen species (mt-ROS), and decreased mitochondrial membrane potential. Our data suggest that disrupting the mitochondrial fusion/fission balance to favor hyperfusion leads to a metabolic shift that promotes aerobic glycolysis. Thus, therapies designed to increase mitochondrial fusion should be approached with caution.

Quantification and isotope abundance determination of 13C labeled intracellular sugar metabolites with hydrophilic interaction liquid chromatography

Metabolic flux analysis (MFA) using stable isotope labeled tracers is a powerful tool to estimate fluxes through metabolic pathways. It finds applications in studying metabolic changes in diseases, regulation of cellular energetics, and novel strategies for metabolic engineering. Accurate and precise quantification of the concentration of metabolites and their labeling states is critical for correct MFA results. Utilizing an ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) system, an analytical method for simultaneously quantifying the concentration of sugar metabolites and their mass isotopologue distribution (MID) was developed. The method performs with good linearity and coefficient of determination (R2) > 0.99, while the detection limit ranged from 0.1 to 50 mg L-1. Seven sugar metabolites were detected in a labeled Brevibacterium flavum sample using the method. The detected quantities ranged from 6.15 to 3704.21 mg L-1, and 13C abundance was between 12.77% and 66.67% in the fermentation fluid and 16.28% and 91.93% in the bacterial body. Overall, the method is efficient, accurate, and suitable for analysis of labeled sugar metabolites in 13C MFA studies.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Development of Pseudo-targeted Profiling of Isotopic Metabolomics using Combined Platform of High Resolution Mass Spectrometry and Triple Quadrupole Mass Spectrometry with Application of 13C6-Glucose Tracing in HepG2 Cells

Isotope tracing assisted metabolic analysis is becoming a unique tool to understand metabolic regulation in cell biology and biomedical research. Targeted mass spectrometry analysis based on selected reaction monitoring (SRM) has been widely applied in isotope tracing experiment with the advantages of high sensitivity and broad linearity. However, its application for new pathway discovery is largely restrained by molecular coverage. To overcome this limitation, we describe a strategy called pseudo-targeted profiling of isotopic metabolomics (PtPIM) to expand the analysis of isotope labeled metabolites beyond the limit of known pathways and chemical standards. Pseudo-targeted metabolomics was first established with ion transitions and retention times transformed from high resolution (orbitrap) mass spectrometry. Isotope labeled MRM transitions were then generated according to chemical formulas of fragments, which were derived from accurate ion masses acquired by HRMS. An in-house software "PseudoIsoMRM" was developed to simulate isotope labeled ion transitions in batch mode and correct the interference of natural isotopologues. This PtPIM strategy was successfully applied to study ¹³C₆-glucose traced HepG2 cells. As 313 molecules determined as analysis targets, a total of 4104 ion transitions were simulated to monitor ¹³C labeled metabolites in positive-negative switching mode of QQQ mass spectrometer with minimum dwell time of 0.3 ms achieved. A total of 68 metabolites covering glycolysis, TCA cycle, nucleotide biosynthesis, one-carbon metabolism and related derivatives were found to be labeled (> 2%) in HepG2 cells. Active pentose phosphate pathway was observed with diverse labeling status of glycolysis intermediates. Meanwhile, our PtPIM strategy revealed that rotenone severely suppressed mitochondrial function e.g. oxidative phosphorylation and fatty acid beta-oxidation. In this case, anaerobic respiration became the major source of energy metabolism by producing abundant lactate. Conclusively, the simulation based PtPIM method demonstrates a strategy to broaden metabolite coverage in isotope tracing analysis independent of standard chemicals.

Loss of fatty acid degradation by astrocytic mitochondria triggers neuroinflammation and neurodegeneration

Astrocytes provide key neuronal support, and their phenotypic transformation is implicated in neurodegenerative diseases. Metabolically, astrocytes possess low mitochondrial oxidative phosphorylation (OxPhos) activity, but its pathophysiological role in neurodegeneration remains unclear. Here, we show that the brain critically depends on astrocytic OxPhos to degrade fatty acids (FAs) and maintain lipid homeostasis. Aberrant astrocytic OxPhos induces lipid droplet (LD) accumulation followed by neurodegeneration that recapitulates key features of Alzheimer's disease (AD), including synaptic loss, neuroinflammation, demyelination and cognitive impairment. Mechanistically, when FA load overwhelms astrocytic OxPhos capacity, elevated acetyl-CoA levels induce astrocyte reactivity by enhancing STAT3 acetylation and activation. Intercellularly, lipid-laden reactive astrocytes stimulate neuronal FA oxidation and oxidative stress, activate microglia through IL-3 signalling, and inhibit the biosynthesis of FAs and phospholipids required for myelin replenishment. Along with LD accumulation and impaired FA degradation manifested in an AD mouse model, we reveal a lipid-centric, AD-resembling mechanism by which astrocytic mitochondrial dysfunction progressively induces neuroinflammation and neurodegeneration.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line

The combination of high-resolution LC-MS untargeted metabolomics with stable isotope-resolved tracing is a promising approach for the global exploration of metabolic pathway activities. In our established workflow we combine targeted isotopologue feature extraction with the non-targeted X¹³CMS routine. Metabolites, detected by X¹³CMS as differentially labeled between two biological conditions are subsequently integrated into the original targeted library. This strategy enables monitoring of changes in known pathways as well as the discovery of hitherto unknown metabolic alterations. Here, we demonstrate this workflow in a PTEN (phosphatase and tensin homolog) null breast cancer cell line (MDA-MB-468) exploring metabolic pathway activities in the absence and presence of the selective PI3Kβ inhibitor AZD8186. Cells were fed with [U-¹³C] glucose and treated for 1, 3, 6, and 24 h with 0.5 µM AZD8186 or vehicle, extracted by an optimized sample preparation protocol and analyzed by LC-QTOF-MS. Untargeted differential tracing of labels revealed 286 isotope-enriched features that were significantly altered between control and treatment conditions, of which 19 features could be attributed to known compounds from targeted pathways. Other 11 features were unambiguously identified based on data-dependent MS/MS spectra and reference substances. Notably, only a minority of the significantly altered features (11 and 16, respectively) were identified when preprocessing of the same data set (treatment vs. control in 24 h unlabeled samples) was performed with tools commonly used for label-free (i.e. w/o isotopic tracer) non-targeted metabolomics experiments (Profinder´s batch recursive feature extraction and XCMS). The structurally identified metabolites were integrated into the existing targeted isotopologue feature extraction workflow to enable natural abundance correction, evaluation of assay performance and assessment of drug-induced changes in pathway activities. Label incorporation was highly reproducible for the majority of isotopologues in technical replicates with a RSD below 10%. Furthermore, inter-day repeatability of a second label experiment showed strong correlation (Pearson R² > 0.99) between tracer incorporation on different days. Finally, we could identify prominent pathway activity alterations upon PI3Kβ inhibition. Besides pathways in central metabolism, known to be changed our workflow revealed additional pathways, like pyrimidine metabolism or hexosamine pathway. All pathways identified represent key metabolic processes associated with cancer metabolism and therapy.

Isomer and Conformer-Specific Mass Distribution-Based Isotopic Shifts in High-Resolution Cyclic Ion Mobility Separations

Herein, we present the use of mass distribution-based isotopic shifts in high-resolution cyclic ion mobility spectrometry-mass spectrometry (cIMS-MS)-based separations to characterize various isomeric species as well as conformers. Specifically, by using the observed relative arrival time values for the isotopologues found in the isotopic envelope after long pathlength cIMS-MS separations, we were able to distinguish dibromoaniline, dichloroaniline, and quaternary ammonium salt isomers, as well as a pair of 25-hydroxyvitamin D3 conformers based on their respective mass distribution-based shifts. Our observed shifts were highly reproducible and broadly applied to the isotopologues of various atoms (i.e., Cl, Br, and C). Additionally, through a control experiment, we determined that such shifts are indeed pathlength-independent, thus demonstrating that our presented methodology could be readily extended to other high-resolution IMS-MS platforms. These results are the first characterization of conformers using mass distribution-based IMS-MS shifts, as well as the first use of a commercial cIMS-MS platform to characterize isomers via their mass distribution-based shifts. We anticipate that our methodology will have broad applicability for biological analytes and that mass distribution-based shifts could potentially act as an added dimension of analysis in existing IMS-MS workflows in omics-based research. Specifically, we envision that the development of a database of these mass distribution-based shifts could, for example, enable the identification of unknown metabolites in complex matrices.

The critical function of metabolic reprogramming in cancer metastasis

Cancer metastasis is the leading cause of cancer‐related death. It is a complex, inefficient, and multistep process related to poor prognosis and high mortality of patients. Increasing evidence has shown that metabolic programming is a recognized hallmarker of cancer, plays a critical role in cancer metastasis. Metabolism alterations of glucose, lipid, and amino acid provide cancer cells with energy and substances for biosynthesis, maintain biofunctions and significantly affect proliferation, invasion, and metastasis of cancer cells. Tumor microenvironment (TME) is a complex system formed by varieties of cellular and noncellular elements. Nontumor cells in TME also undergo metabolic reprogramming or respond to metabolites to promote migration and invasion of cancer cells. A comprehensive understanding of the regulatory mechanism in metastasis from the metabolic reprogramming aspect is required to develop new therapeutic strategies combatting cancer metastasis. This review illustrates the metabolic reprogramming and interaction of cancer cells and nontumor cells in the TME, and the development of treatment strategies targeting metabolism alterations.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Characterization and Determination of 13C-Labeled Nonessential Amino Acids in a 13C5-Glutamine Isotope Tracer Experiment with a Mass Spectrometry Strategy Combining Parallel Reaction Monitoring and Multiple Reaction Monitoring

Isotopic tracer, a powerful technique for metabolic pathway analysis, is currently widely applied in metabolic flux analysis. However, the qualitative and quantitative analyses of ¹³C-labeled metabolites pose great challenges, especially in complex biological sample matrices. Here, we present an integrated method for the qualitative and quantitative analyses of various isotopologues and isotopomers of ¹³C-labeled nonessential amino acids (NEAAs) in HepG2 cells incubated with ¹³C₅-glutamine (Gln) based on ultra-high-performance liquid chromatography (UHPLC) coupled with tandem mass spectrometry (MS). First, accurate mass-to-charge (m/z) values of protonated isotopologues and elution time of standards were simultaneously analyzed to characterize ¹³C-labeled NEAAs by high-resolution Orbitrap MS in the parallel reaction monitoring (PRM) mode. Second, isotopologues and isotopomers of ¹³C-labeled NEAAs were investigated in HepG2 cells incubated with ¹³C₅-Gln at different time points. Ultimately, a total of 66 multiple reaction monitoring (MRM) transitions were performed by UHPLC coupled with triple quadrupole MS. Among them, 29 MRM transitions were monitored for pure metabolites (unambiguously identified). The other 37 MRM transitions were monitored for mixtures with exactly identical MRM transitions and retention time. The application of targeted profiling of ¹³C-labeled NEAAs in the dynamic ¹³C-labeling experiment indicated that the concentration-time profiles of NEAAs were different from each other. The concentrations of most ¹³C-labeled Gln, Glu, Pro, and Asp altered after ¹³C₅-Gln incubation, indicating that Gln plays a fundamental role in the biosynthesis of Glu, Pro, and Asp. The proposed PRM-MRM combination mode LC-MS approach is expected to provide valuable insights into analyses of isotope-labeled metabolites in isotope tracer experiments.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a highly sensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) derivatization strategy that simultaneously targets carbonyl, carboxyl, and phosphoryl groups for targeted metabolomic analysis (HSD^ccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Enhanced Detection of Short-Chain Fatty Acids Using Gas Chromatography Mass Spectrometry

Short-chain fatty acids (SCFAs) are produced mainly by intestinal microbiota and play an important role in many host biological processes such as immune system development, glucose and energy homeostasis, and regulation of immune response and inflammation. In addition, they participate in the regulation of anorectic hormones, which have a role in appetite control, tumor suppression, and regulating the central and peripheral nervous systems. As such, there is great interest in monitoring levels of SCFAs in various biological samples. Due to the highly hydrophilic and volatile characteristics of SCFAs, optimizing extraction and sample preparation procedures is often a central component to further improve SCFA quantification. Here, we describe a rapid and highly sensitive analytical method for measuring SCFAs in human serum and feces. Briefly, SCFAs are protected by adding sodium hydroxide, followed by a one-step extraction (pH > 7). Then, SCFAs are quantified by gas chromatography coupled to mass spectrometry (GC-MS) after derivatization with N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA). This method demonstrates excellent sensitivity, linearity, and derivatization efficiency for simultaneous determination of 14 different SCFAs. Further, this validated method can be successfully applied to quantify SCFAs in micro-scale biological samples. In summary, we describe efficient and advanced sample preparation and detection procedures that are critically needed for monitoring SCFA concentrations in human biological samples. © 2021 Wiley Periodicals LLC. Basic Protocol: SCFA extraction and detection from fecal and serum samples with gas chromatography-mass spectrometry.

Metabolomics, metabolic flux analysis and cancer pharmacology

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics.

Capillary flow-based sample preparation system for metabolomic analysis of mammalian cells in suspension

Sample preparation methodology is critical to obtaining reliable data for studying endogenous metabolites. Dependable preparation techniques require separation of cells from culture media, quenching of enzymatic activity, and extraction of metabolites from the cells. Presented here is a simple, rapid, semi-automated metabolomic sample preparation technique for 20 μL samples of RAW 264.7 cells suspended in culture media. This method uses online filter-assisted electroporation-based cell lysis and chilled organic solvent extraction to prepare metabolomic samples from cells in suspension in 2 min. Experiments using an isotopically labeled adenosine triphosphate internal standard were carried out to ensure enzymatic quenching by monitoring the ratio of labeled adenosine diphosphate to adenosine triphosphate. Cells were metabolically labeled with ¹³C-glucose concurrent with sampling aliquots of the cell suspension over the course of 24 h. Incorporation of ¹³C into organic acid metabolites such as itaconate Cell lysates was analyzed by nano-reverse-phase liquid chromatography-mass spectrometry (nano-RP-LC-MS), showing incorporation of ¹³C into organic acid metabolites such as itaconate.