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Liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC–HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min. LC–HRMS is used for metabolomics studies in the biomedical and environmental sciences. MetaboAnalyst ( metaboanalyst.ca ) can be used to address challenges in data processing, statistical analysis, functional interpretation and multi-omics integration.

Oxidation–reduction (redox) reactions are central to the existence of life. Reactive species of oxygen, nitrogen and sulfur mediate redox control of a wide range of essential cellular processes. Yet, excessive levels of oxidants are associated with ageing and many diseases, including cardiological and neurodegenerative diseases, and cancer. Hence, maintaining the fine-tuned steady-state balance of reactive species production and removal is essential. Here, we discuss new insights into the dynamic maintenance of redox homeostasis (that is, redox homeodynamics) and the principles underlying biological redox organization, termed the ‘redox code’. We survey how redox changes result in stress responses by hormesis mechanisms, and how the lifelong cumulative exposure to environmental agents, termed the ‘exposome’, is communicated to cells through redox signals. Better understanding of the molecular and cellular basis of redox biology will guide novel redox medicine approaches aimed at preventing and treating diseases associated with disturbed redox regulation.Oxidation–reduction (redox) reactions involving reactive oxygen, nitrogen and sulfur species are vital for life, but excessive oxidant levels contribute to ageing and diseases. This Review explores cellular dynamics of redox homeostasis, such as responses to oxidative and reductive stresses and intracellular and intercellular redox communication pathways.

Metastasis formation is the major cause of death in most patients with cancer. Despite extensive research, targeting metastatic seeding and colonization is still an unresolved challenge. Only recently, attention has been drawn to the fact that metastasizing cancer cells selectively and dynamically adapt their metabolism at every step during the metastatic cascade. Moreover, many metastases display different metabolic traits compared with the tumours from which they originate, enabling survival and growth in the new environment. Consequently, the stage-dependent metabolic traits may provide therapeutic windows for preventing or reducing metastasis, and targeting the new metabolic traits arising in established metastases may allow their eradication.This Review describes the metabolic rewiring that occurs in cancer cells transitioning through the metastatic cascade and discusses the evidence for metabolically distinct features of primary tumours and metastases.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment. Several bottlenecks exist in metabolomics data analysis. Here, the authors present MetaboAnalystR 4.0 as a unified workflow for LC-MS untargeted metabolomics. It highlights significant improvements in LC-MS2 spectral processing and functional analysis, providing an end-to-end computational pipeline.

Traditional views of cellular metabolism imply that it is passively adapted to meet the demands of the cell. It is becoming increasingly clear, however, that metabolites do more than simply supply the substrates for biological processes; they also provide critical signals, either through effects on metabolic pathways or via modulation of other regulatory proteins. Recent investigation has also uncovered novel roles for several metabolites that expand their signalling influence to processes outside metabolism, including nutrient sensing and storage, embryonic development, cell survival and differentiation, and immune activation and cytokine secretion. Together, these studies suggest that, in contrast to the prevailing notion, the biochemistry of a cell is frequently governed by its underlying metabolism rather than vice versa. This important shift in perspective places common metabolites as key regulators of cell phenotype and behaviour. Yet the signalling metabolites, and the cognate targets and transducers through which they signal, are only beginning to be uncovered. In this Review, we discuss the emerging links between metabolism and cellular behaviour. We hope this will inspire further dissection of the mechanisms through which metabolic pathways and intermediates modulate cell function and will suggest possible drug targets for diseases linked to metabolic deregulation.Metabolites are generally viewed as intermediates or products of metabolism. However, many metabolites are also signalling molecules that regulate metabolic reactions and other processes in development, homeostasis and disease. As such, metabolites can confer adaptive responses to environmental changes.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics-derived data.This Perspective, from a large group of metabolomics experts, provides best practices and simplified reporting guidelines for practitioners of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics.

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated “tumor-normal interface” region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level. The spatial signature of metabolic remodeling in tumours remains to be explored. Here, the integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics allows the visualisation of metabolic heterogeneity in gastric cancer.

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100–300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics. Unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, the authors develop knowledge-guided multi-layer networking (KGMN) to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics.

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets. Genome-wide association studies comprising 1,091 metabolites and 309 metabolite ratios in 8,299 individuals from the Canadian Longitudinal Study on Aging provide insights into the genetic architecture of metabolites and their role in human diseases.

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously. In a study involving more than 100,000 individuals in the UK Biobank, a neural network model trained on metabolomic data can predict disease risk for over 20 conditions and adds predictive information over clinical variables for eight common diseases.