Innovations in Metabolomics Research

1 Listicle From discovering biomarkers and developing new drugs, to personalized medicine and more, metabolomics has a wide range of applications. The field is currently booming, with the global market forecast to grow from $2.69 billion to $7.22 billion over the 2024–2031 period.1 Metabolomics – the study of metabolites within cells, tissues, biological fluids and organisms – can be applied using a targeted or untargeted approach. Targeted methods are used to quantify predetermined metabolites, while untargeted methods will detect all measurable metabolites in the sample.2 The field has its challenges, including the high cost of instruments, and the complexity of data analysis can create a barrier to access. The diversity of molecules to be analyzed may also make it difficult to assess the whole metabolome using a single method.3 Metabolomics has become an invaluable tool for research settings, but translation into clinical settings has been slower. Innovation within the sector has enabled more sensitive detection,4 higher throughput testing5,6 and streamlined data analysis.7,8 In this listicle, we’ll explore the latest advances in metabolomics, from mass spectrometry (MS) to artificial intelligence (AI), and discuss the challenges and opportunities that lie ahead. Mass spectrometry in metabolomics MS remains the foundational tool of metabolomics research, with recent advancements leading to improved sensitivity and accuracy. High-resolution MS (HRMS) is a useful tool for resolving complex sample matrices, often using analyzers such as time of flight (TOF), Orbitrap and Fourier transform ion cyclotron resonance (FT-ICR). To enhance the structural information obtained, tandem MS (MS/MS) can be employed, featuring two or more analyzers, combining the strengths of each analyzer. Separately, in a bid to improve the accessibility of MS instruments, miniaturized mass spectrometers are being developed, opening new opportunities to take metabolomics to the point of need.4 Spatial metabolomics is an emerging field within omics research that uses MS imaging (MSI) technologies to map metabolites to cellular and subcellular locations without the need for labelling,9 offering a non-targeted, high-throughput and high-accuracy approach.5 MSI can provide insight into healthy tissue function and disease pathogenesis10 and has played a significant role in studies of cancer tissues, biomarker discovery efforts, cancer screening and drug discovery.11 In one study, MSI-based spatial metabolomics was combined in a multiomics approach with spatial transcriptomics to investigate gastric tumour microenvironments. This revealed insights into cancer-associated metabolic dependencies and immunometabolic alterations that could be targeted for cancer therapy.12 Innovations in Metabolomics Research Katie Minns, PhD INNOVATIONS IN METABOLOMICS RESEARCH 2 Listicle Single-cell metabolomics is a technique complementary to spatial metabolomics, allowing analysis of cell-to-cell variation, without necessarily being resolved in space.13 This approach, using MS, can be used to identify phenotypic heterogeneity among cell populations, and find subgroups of similar cells to trace the origin of drug resistance and disease progression, for example.14 Researchers have used single-cell metabolomics to observe the synergistic effect of two drugs overcoming resistance in cancer cells, shedding light on the mechanisms behind this and paving the way for more effective treatments.15 NMR spectroscopy applications in metabolomics While not as commonly used as MS, nuclear magnetic resonance (NMR) spectroscopy is an alternative technique that has several powerful advantages. NMR is a non-destructive technique with high reproducibility and minimal sample preparation required. However, it has lower sensitivity than MS and the technique is not optimal for targeted analysis.16 Enhancements in NMR instrumentation have occurred in recent years, not only in terms of sensitivity, but efforts have also been made to lower the barriers to owning or using NMR spectrometers. Data collection and processing has been simplified, and benchtop instruments with lower costs and footprints have been made available. NMR automation has also advanced, enabling high throughput metabolomic studies and the reduction of errors.6 However, while improvements in sensitivity have been made, low signal intensity remains a challenge for in vivo magnetic resonance spectroscopy due to the low concentration of biochemicals in tissue.17 NMR is highly suitable for metabolic flux and metabolic imaging studies, as it can perform analysis on living cells, tissues and organs, including for complex metabolite mixtures. NMR can be used to monitor cellular metabolism in patients’ cells, with potential for future personalized medicine applications. Metabolictargeted therapy is also emerging as a core research area in the search for new cancer treatments.17 While largely used in research settings, one application that NMR has been clinically approved for is measuring lipids and Apolipoprotein B for improved testing of atherosclerotic cardiovascular disease risk.18 Liquid and gas chromatography applications in metabolomics Liquid and gas chromatography (LC and GC) are currently the leading technologies for the separation of metabolites.19 When combined with MS for detection, LC-MS accounts for more than 70% of published metabolomics studies,6 rising to over 80% when GC-MS methods are included.16 Using LC and GC techniques to separate complex mixtures of metabolites greatly improves the capacity of MS to profile metabolites from a variety of biological samples. For additional separation power, two-dimensional (2D)-LC can be used for highly complex, non-volatile samples. The technique does come with its challenges, such as insufficient sensitivity for some applications, solvent mismatch problems and complexity of operation.20 Recent technological advancements in 2D-LC have increased throughput and detection sensitivity and mitigated solvent mismatches. New columns have become available that enable the separation of both polar and non-polar metabolites. The technique has proven to be a useful tool in both targeted and untargeted metabolomics.21 For example, one study developed a simple and rapid MS-based assay to determine steroid hormones in human plasma, suitable for a routine practice setting. For this purpose, a 2D-LC-MS/MS method was chosen, which allowed for minimal sample pre-treatment.22 INNOVATIONS IN METABOLOMICS RESEARCH 3 Listicle New approaches to data handling Metabolomic studies often result in large datasets, with complex relationships between many different metabolites. Analyzing these vast data sets and identifying biomarkers has recently become more simplified thanks to the development of AI and machine learning (ML). AI algorithms can now process raw MS data, detect chromatogram peaks and identify metabolites. They can provide deep analysis, helping to discover biomarkers, build predictive models, construct pathways and more.7 This saves time and makes the analysis more accessible and at a lower cost. Progress within ML techniques has enabled faster annotation of metabolites, as well as data visualization, interpretation and integration. Untargeted metabolomics studies typically only structurally annotate a subset of the data, but ML can now be used to assess structural properties and map molecules to mass spectral libraries.8 Meanwhile, the creation of metabolite databases, such as MassBank, The Human Metabolome Database and MetaCyc, has facilitated more extensive targeted methods.19 In one study, a ML algorithm was used to analyze lipidomic data and identify biomarkers for malignant brain gliomas, enabling a non-invasive diagnosis using high-throughput methods. This showed the potential to facilitate earlier detection and improved patient outcomes, compared to existing methods that rely on histological examination.23 Combining metabolomics data into a multiomics approach, with genomics, transcriptomics, proteomics or other omics data, requires sophisticated computational support. For example, one component of the analytical workflow may involve visualizing multiomics data in the context of biological processes and pathways.8 As the resolution and sensitivity of instruments increases, so will the complexity of the data that they produce. AI and ML techniques will need to be able to manage this data, while integrating other large omics datasets. The future of metabolomics There are challenges that remain within the field, such as the standardization of data and protocols, and the complexity of data analysis.24 There have been efforts to improve accessibility, by miniaturizing instruments to enable more point of care/point of need applications, while open-source software tools are helping with data analysis. With the inclusion of AI and ML, it is likely that the time to market for new drugs and crops will be shortened. As the metabolomics field continues to grow, more applications are likely to translate into the clinic for diagnostic tests, personalized treatments and monitoring. For example, patients could expect to have a metabolic fingerprint measured to track the onset and progression of cardiovascular disease.25 Meanwhile, new research into diabetic kidney disease aims to develop more sensitive diagnostics that can detect subtle metabolic alterations earlier, before the onset of kidney damage.26 Beyond healthcare, metabolomics is also gaining traction in environmental research and plant breeding. Metabolomics-assisted breeding is helping to select elite crop cultivars with improved stress tolerance, and there are hopes that metabolomics discoveries will help to ensure gene-edited crops are healthier and safer.27 And for those willing to tackle the most complex data analysis, multiomics approaches offer a powerful combination of techniques to achieve a more comprehensive picture. 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