Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics

Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.

Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID)

SUMMARY

The analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here, we present PIRAMID (Program for Integration and Rapid Analysis of Mass Isotopomer Distributions), a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface-driven program to automate the extraction of isotopic information from mass spectrometry (MS) datasets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e. 2H, 13C, 15N, 18O, 34S).

DATA AVAILABILITY AND IMPLEMENTATION

MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available. All the data presented in this publication are available under the "Help_menu" folder of the PIRAMID software.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

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.

BRAF activation by metabolic stress promotes glycolysis sensitizing NRASQ61-mutated melanomas to targeted therapy

NRAS-mutated melanoma lacks a specific line of treatment. Metabolic reprogramming is considered a novel target to control cancer; however, NRAS-oncogene contribution to this cancer hallmark is mostly unknown. Here, we show that NRAS^Q61-mutated melanomas specific metabolic settings mediate cell sensitivity to sorafenib upon metabolic stress. Mechanistically, these cells are dependent on glucose metabolism, in which glucose deprivation promotes a switch from CRAF to BRAF signaling. This scenario contributes to cell survival and sustains glucose metabolism through BRAF-mediated phosphorylation of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-2/3 (PFKFB2/PFKFB3). In turn, this favors the allosteric activation of phosphofructokinase-1 (PFK1), generating a feedback loop that couples glycolytic flux and the RAS signaling pathway. An in vivo treatment of NRAS^Q61 mutant melanomas, including patient-derived xenografts, with 2-deoxy-D-glucose (2-DG) and sorafenib effectively inhibits tumor growth. Thus, we provide evidence for NRAS-oncogene contributions to metabolic rewiring and a proof-of-principle for the treatment of NRAS^Q61-mutated melanoma combining metabolic stress (glycolysis inhibitors) and previously approved drugs, such as sorafenib.

An Arabidopsis GCMS chemical ionization technique to quantify adaptive responses in central metabolism

We present a methodology to survey central metabolism in 13CO2-labeled Arabidopsis (Arabidopsis thaliana) rosettes by ammonia positive chemical ionization-gas chromatography-mass spectrometry. This technique preserves the molecular ion cluster of methyloxime/trimethylsilyl-derivatized analytes up to 1 kDa, providing unambiguous nominal mass assignment of >200 central metabolites and 13C incorporation rates into a subset of 111 from the tricarboxylic acid (TCA) cycle, photorespiratory pathway, amino acid metabolism, shikimate pathway, and lipid and sugar metabolism. In short-term labeling assays, we observed plateau labeling of ∼35% for intermediates of the photorespiratory cycle except for glyoxylate, which reached only ∼4% labeling and was also present at molar concentrations several fold lower than other photorespiratory intermediates. This suggests photorespiratory flux may involve alternate intermediate pools besides the generally accepted route through glyoxylate. Untargeted scans showed that in illuminated leaves, noncyclic TCA cycle flux and citrate export to the cytosol revert to a cyclic flux mode following methyl jasmonate (MJ) treatment. MJ also caused a block in the photorespiratory transamination of glyoxylate to glycine. Salicylic acid treatment induced the opposite effects in both cases, indicating the antagonistic relationship of these defense signaling hormones is preserved at the metabolome level. We provide complete chemical ionization spectra for 203 Arabidopsis metabolites from central metabolism, which uniformly feature the unfragmented pseudomolecular ion as the base peak. This unbiased, soft ionization technique is a powerful screening tool to identify adaptive metabolic trends in photosynthetic tissue and represents an important advance in methodology to measure plant metabolic flux.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites

In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.