Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
Google Scholar
Wellen, K. E. & Thompson, C. B. A two-way street: reciprocal regulation of metabolism and signalling. Nat. Rev. Mol. Cell Biol. 13, 270–276 (2012).
Google Scholar
Baker, S. A. & Rutter, J. Metabolites as signalling molecules. Nat. Rev. Mol. Cell Biol. 24, 355–374 (2023).
Google Scholar
Zamboni, N., Saghatelian, A. & Patti, G. J. Defining the metabolome: size, flux, and regulation. Mol. Cell 58, 699–706 (2015).
Google Scholar
Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018).
Google Scholar
Bloszies, C. S. & Fiehn, O. Using untargeted metabolomics for detecting exposome compounds. Curr. Opin. Toxicol. 8, 87–92 (2018).
Google Scholar
Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473–484 (2016).
Google Scholar
Saigusa, D., Matsukawa, N., Hishinuma, E. & Koshiba, S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab. Pharmacokinet. 37, 100373 (2021).
Google Scholar
Pang, H. & Hu, Z. Metabolomics in drug research and development: the recent advances in technologies and applications. Acta Pharm. Sin. B 13, 3238–3251 (2023).
Google Scholar
Kirwan, J. A. Translating metabolomics into clinical practice. Nat. Rev. Bioeng. 1, 228–229 (2023).
Google Scholar
Schuhknecht, L. et al. A human metabolic map of pharmacological perturbations reveals drug modes of action. Nat. Biotechnol. 43, 1996–2008 (2025). A large-scale study focusing on how metabolomics can reveal the metabolic mode of action of drugs.
Google Scholar
Ali, A. et al. Single-cell metabolomics by mass spectrometry: advances, challenges, and future applications. Trends Anal. Chem. 120, 115436 (2019).
Google Scholar
Saunders, K. D. G., Lewis, H.-M., Beste, D. J. V., Cexus, O. & Bailey, M. J. Spatial single cell metabolomics: current challenges and future developments. Curr. Opin. Chem. Biol. 75, 102327 (2023).
Google Scholar
Petrova, B. & Guler, A. T. Recent developments in single-cell metabolomics by mass Spectrometry─A perspective. J. Proteome Res. 24, 1493–1518 (2025).
Google Scholar
Hajjar, G. et al. Scaling-up metabolomics: current state and perspectives. Trends Anal. Chem. 167, 117225 (2023).
Google Scholar
Plekhova, V., De Windt, K., De Spiegeleer, M., De Graeve, M. & Vanhaecke, L. Recent advances in high-throughput biofluid metabotyping by direct infusion and ambient ionization mass spectrometry. Trends Anal. Chem. 168, 117287 (2023).
Google Scholar
Young, R. S. E. et al. Subcellular mass spectrometry imaging of lipids and nucleotides using transmission geometry ambient laser desorption and plasma ionisation. Nat. Commun. 16, 9130 (2025).
Google Scholar
Castro, D. C., Chan-Andersen, P., Romanova, E. V. & Sweedler, J. V. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. Mass Spectrom. Rev. 43, 888–912 (2024).
Google Scholar
Cao, J. et al. Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution. Cell Metab. 36, 209–221 (2024).
Google Scholar
Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18, 799–805 (2021).
Google Scholar
Capolupo, L. et al. Sphingolipids control dermal fibroblast heterogeneity. Science 376, eabh1623 (2022).
Google Scholar
Wang, Z. et al. Integrative single-cell metabolomics and phenotypic profiling reveals metabolic heterogeneity of cellular oxidation and senescence. Nat. Commun. 16, 2740 (2025).
Google Scholar
Cairns, J. L. et al. Mass-guided single-cell MALDI imaging of low-mass metabolites reveals cellular activation markers. Adv. Sci. 12, e2410506 (2025).
Google Scholar
Hu, T. et al. Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology. Nat. Commun. 14, 8260 (2023). An integrative approach highlighting the presence of unique metabolic cell states within cell types.
Google Scholar
Nunes, J. B. et al. Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling. Nat. Methods 21, 1796–1800 (2024). A study demonstrating single-cell metabolic heterogeneity in tissue for cancer cells and immune cells.
Google Scholar
Mao, X. et al. Single-cell simultaneous metabolome and transcriptome profiling revealing metabolite-gene correlation network. Adv. Sci. 12, e2411276 (2025).
Google Scholar
Kang, M. et al. Single-cell metabolome and RNA-seq multiplexing on single plant cells. Proc. Natl Acad. Sci. USA 122, e2512828122 (2025).
Google Scholar
Samarah, L. Z. et al. Spatial metabolic gradients in the liver and small intestine. Nature 648, 182–190 (2025).
Google Scholar
Christofk, H. et al. Metabolic heterogeneity in humans. Cell 187, 3821–3823 (2024).
Google Scholar
Kim, J. & DeBerardinis, R. J. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 30, 434–446 (2019).
Google Scholar
Demicco, M., Liu, X.-Z., Leithner, K. & Fendt, S.-M. Metabolic heterogeneity in cancer. Nat. Metab. 6, 18–38 (2024).
Google Scholar
Ghosh-Choudhary, S., Liu, J. & Finkel, T. Metabolic regulation of cell fate and function. Trends Cell Biol. 30, 201–212 (2020).
Google Scholar
Stegen, S. & Carmeliet, G. Metabolic regulation of skeletal cell fate and function. Nat. Rev. Endocrinol. 20, 399–413 (2024).
Google Scholar
Zhang, H. et al. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. Npj Imaging 2, 20 (2024).
Google Scholar
Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. 42, 1046–1050 (2024). First paper demonstrating the feasibility of detecting both transcriptomes and metabolomes from the same tissue section.
Google Scholar
Colwell, N., Chen, D. & Yang, Z. Achieving single-cell resolution via desorption electrospray ionization mass spectrometry imaging (DESI-MSI). Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2024-826pr (2024).
Kavita, K. & Breaker, R. R. Discovering riboswitches: the past and the future. Trends Biochem. Sci 48, 119–141 (2023).
Google Scholar
Trefny, M. P., Kroemer, G., Zitvogel, L. & Kobold, S. Metabolites as agents and targets for cancer immunotherapy. Nat. Rev. Drug Discov. 24, 764–784 (2025).
Google Scholar
Kawalekar, O. U. et al. Distinct signaling of coreceptors regulates specific metabolism pathways and impacts memory development in CAR T cells. Immunity 44, 380–390 (2016).
Google Scholar
Hutton, A. & Meyer, J. G. Trajectory inference for single cell omics. Preprint at https://doi.org/10.48550/arXiv.2502.09354 (2025).
Jost, P. J., Weindl, D., Wunderling, K., Thiele, C. & Hasenauer, J. Pseudo-time trajectory of single-cell lipidomics: suggestion for experimental setup and computational analysis. Preprint at bioRxiv https://doi.org/10.1101/2025.04.11.648323 (2025).
Zhang, Y. et al. Dynamic single-cell metabolomics reveals cell-cell interaction between tumor cells and macrophages. Nat. Commun. 16, 4582 (2025).
Google Scholar
Buglakova, E. et al. Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer. Nat. Metab. 6, 1695–1711 (2024).
Google Scholar
Wang, G. et al. Analyzing cell-type-specific dynamics of metabolism in kidney repair. Nat. Metab. 4, 1109–1118 (2022).
Google Scholar
Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. Trends Cell Biol. 27, 863–875 (2017).
Google Scholar
Rodríguez-Colman, M. J. et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 543, 424–427 (2017).
Google Scholar
Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).
Google Scholar
Di Virgilio, F. & Adinolfi, E. Extracellular purines, purinergic receptors and tumor growth. Oncogene 36, 293–303 (2017).
Google Scholar
Loo, J. M. et al. Extracellular metabolic energetics can promote cancer progression. Cell 160, 393–406 (2015).
Google Scholar
Bamford, S. E. et al. High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning. J. Extracell. Biol. 2, e110 (2023).
Google Scholar
Maugrion, E. et al. Extracellular vesicles contribute to the difference in lipid composition between ovarian follicles of different size revealed by mass spectrometry imaging. Metabolites 13, 1001 (2023).
Google Scholar
Van de Sande, B. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 22, 496–520 (2023).
Google Scholar
Hansen, J. et al. A reference tissue atlas for the human kidney. Sci. Adv. 8, eabn4965 (2022).
Google Scholar
Delafiori, J. et al. HT SpaceM: a high-throughput and reproducible method for small-molecule single-cell metabolomics. Cell 188, 6028–6043 (2025). Paper demonstrating the detection of small-molecule metabolites from single cells at high throughput.
Google Scholar
Molenaar, M. R. et al. Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth. Front. Mol. Biosci. 9, 1021889 (2022).
Google Scholar
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2021).
Google Scholar
Yu, B. et al. The Consortium of Metabolomics Studies (COMETS): metabolomics in 47 prospective cohort studies. Am. J. Epidemiol. 188, 991–1012 (2019).
Google Scholar
Begzati, A. et al. Plasma lipid metabolites, clinical glycemic predictors, and incident type 2 diabetes. Diabetes Care 48, 473–480 (2025).
Google Scholar
Nightingale Health Biobank Collaborative Group. Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks. Nat. Commun. 15, 10092 (2024). The largest-scale metabolomics study so far, which, moreover, shows that metabolomics is more predictive than a polygenic score in predicting disease outcomes, even for non-metabolic diseases.
Google Scholar
Zhang, S. et al. A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank. Nat. Commun. 15, 8081 (2024).
Google Scholar
Wang, N. et al. Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program. Am. J. Hum. Genet. 122, 2720–2738 (2025).
Google Scholar
Chu, X. et al. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol. 22, 198 (2021).
Google Scholar
Bossi, E. et al. Revolutionizing blood collection: Innovations, applications, and the potential of microsampling technologies for monitoring metabolites and lipids. Metabolites 14, 46 (2024).
Google Scholar
Correia, M. S. P., Othman, A. & Zamboni, N. Fast, general-purpose metabolome analysis by mixed-mode liquid chromatography-mass spectrometry. Analyst 150, 4955–4961 (2025).
Google Scholar
Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011). Pioneering paper demonstrating the power of combining genetics with metabolomics to reveal genetic determinants of metabolic variation in human populations.
Google Scholar
Antcliffe, D. B. et al. Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome. Intensive Care Med. 51, 529–541 (2025).
Google Scholar
Buergel, T. et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 28, 2309–2320 (2022). The largest study at that time showing that metabolomics can predict multi-disease outcomes.
Google Scholar
Kohler, I., Hankemeier, T., van der Graaf, P. H., Knibbe, C. A. J. & van Hasselt, J. G. C. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur. J. Pharm. Sci. 109, S15–S21 (2017).
Google Scholar
Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).
Google Scholar
Broadhurst, D. et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14, 72 (2018).
Google Scholar
Dmitrenko, A., Reid, M. & Zamboni, N. Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. Bioinformatics 39, btad096 (2023).
Google Scholar
Dmitrenko, A., Reid, M. & Zamboni, N. A system suitability testing platform for untargeted, high-resolution mass spectrometry. Front. Mol. Biosci. 9, 1026184 (2022).
Google Scholar
Delabriere, A., Warmer, P., Brennsteiner, V. & Zamboni, N. SLAW: a scalable and self-optimizing processing workflow for untargeted LC–MS. Anal. Chem. 93, 15024–15032 (2021).
Google Scholar
Li, S., Siddiqa, A., Thapa, M., Chi, Y. & Zheng, S. Trackable and scalable LC–MS metabolomics data processing using asari. Nat. Commun. 14, 4113 (2023).
Google Scholar
Wang, W. et al. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark. Res. 11, 66 (2023).
Google Scholar
Murphy, R. M., Watt, M. J. & Febbraio, M. A. Metabolic communication during exercise. Nat. Metab. 2, 805–816 (2020).
Google Scholar
Sieber, M. H. & Spradling, A. C. The role of metabolic states in development and disease. Curr. Opin. Genet. Dev. 45, 58–68 (2017).
Google Scholar
Chi, H. Immunometabolism at the intersection of metabolic signaling, cell fate, and systems immunology. Cell. Mol. Immunol. 19, 299–302 (2022).
Google Scholar
Park, S. J., Park, M. J., Park, S., Lee, E.-S. & Lee, D. Y. Integrative metabolomics of plasma and PBMCs identifies distinctive metabolic signatures in Behçet’s disease. Arthritis Res. Ther. 25, 5 (2023).
Google Scholar
Sen, P. et al. Metabolic alterations in immune cells associate with progression to type 1 diabetes. Diabetologia 63, 1017–1031 (2020).
Google Scholar
Huang, H. et al. Decoding aging clocks: new insights from metabolomics. Cell Metab. 37, 34–58 (2025).
Google Scholar
Kim, H.-H. & Dixit, V. D. Metabolic regulation of immunological aging. Nat. Aging 5, 1425–1440 (2025).
Google Scholar
Sebastiani, P. et al. Metabolite signatures of chronological age, aging, survival, and longevity. Cell Rep. 43, 114913 (2024).
Google Scholar
Wadie, B., Molenaar, M. R., Vieira, L. M. & Alexandrov, T. Enrichment analysis for spatial and single-cell metabolomics accounting for molecular ambiguity. Bioinform. Adv. 5, vbaf100 (2025).
Google Scholar
Santos, A. et al. A knowledge graph to interpret clinical proteomics data. Nat. Biotechnol. 40, 692–702 (2022).
Google Scholar
Cordes, T., Michelucci, A. & Hiller, K. Itaconic acid: the surprising role of an industrial compound as a mammalian antimicrobial metabolite. Annu. Rev. Nutr. 35, 451–473 (2015).
Google Scholar
Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the missing links among metabolites, microbes, and the host. Immunity 40, 824–832 (2014).
Google Scholar
El Abiead, Y. et al. Discovery of metabolites prevails amid in-source fragmentation. Nat. Metab. 7, 435–437 (2025).
Google Scholar
Heinonen, M., Shen, H., Zamboni, N. & Rousu, J. Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics 28, 2333–2341 (2012).
Google Scholar
Wadie, B. et al. METASPACE-ML: context-specific metabolite annotation for imaging mass spectrometry using machine learning. Nat. Commun. 15, 9110 (2024).
Google Scholar
Peets, P. et al. Chemical characteristics vectors map the chemical space of natural biomes from untargeted mass spectrometry data. J. Chemoinform. 17, 82 (2025).
Google Scholar
Heininen, J. et al. Targeted and untargeted Amine metabolite quantitation in single cells with isobaric multiplexing. Chemistry 30, e202403278 (2024).
Google Scholar
Orth, J. D., Thiele, I. & Palsson, B. Ø What is flux balance analysis?. Nat. Biotechnol. 28, 245–248 (2010).
Google Scholar
Bartman, C. R., TeSlaa, T. & Rabinowitz, J. D. Quantitative flux analysis in mammals. Nat. Metab. 3, 896–908 (2021).
Google Scholar
Bartman, C. R., Faubert, B., Rabinowitz, J. D. & DeBerardinis, R. J. Metabolic pathway analysis using stable isotopes in patients with cancer. Nat. Rev. Cancer 23, 863–878 (2023).
Google Scholar
Lee, K. S., Su, X. & Huan, T. Metabolites are not genes—avoiding the misuse of pathway analysis in metabolomics. Nat. Metab. 7, 858–861 (2025).
Google Scholar
Badur, M. G. & Metallo, C. M. Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab. Eng. 45, 95–108 (2018).
Google Scholar
Wegner, A., Meiser, J., Weindl, D. & Hiller, K. How metabolites modulate metabolic flux. Curr. Opin. Biotechnol. 34, 16–22 (2015).
Google Scholar
Gruber, C. H., Noor, E., Buffing, M. F. & Sauer, U. Systematic identification of allosteric effectors in Escherichia coli metabolism. Proc. Natl. Acad. Sci. USA 122, e2423767122 (2025).
Google Scholar
Hicks, K. G. et al. Protein–metabolite interactomics of carbohydrate metabolism reveal regulation of lactate dehydrogenase. Science 379, 996–1003 (2023). Large-scale demonstration of the extent of previously unknown interactions between enzymes and metabolites, even across distant pathways.
Google Scholar
Piazza, I. et al. A map of protein–metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018).
Google Scholar
Farr, E. et al. MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. Brief. Bioinform. 25, bbae347 (2024).
Google Scholar
Amara, A. et al. Networks and graphs discovery in metabolomics data analysis and interpretation. Front. Mol. Biosci. 9, 841373 (2022).
Google Scholar
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the Human Cell Atlas on medicine. Nat. Med. 28, 2486–2496 (2022).
Google Scholar
Quake, S. R. A decade of molecular cell atlases. Trends Genet. 38, 805–810 (2022).
Google Scholar
Guo, F. et al. Foundation models in bioinformatics. Natl Sci. Rev. 12, nwaf028 (2025).
Google Scholar
Cui, H. et al. Towards multimodal foundation models in molecular cell biology. Nature 640, 623–633 (2025).
Google Scholar
Bushuiev, R. et al. Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02663-3 (2025).
Google Scholar
Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024).
Google Scholar
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