Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
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).
Baker, S. A. & Rutter, J. Metabolites as signalling molecules. Nat. Rev. Mol. Cell Biol. 24, 355–374 (2023).
Zamboni, N., Saghatelian, A. & Patti, G. J. Defining the metabolome: size, flux, and regulation. Mol. Cell 58, 699–706 (2015).
Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018).
Bloszies, C. S. & Fiehn, O. Using untargeted metabolomics for detecting exposome compounds. Curr. Opin. Toxicol. 8, 87–92 (2018).
Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473–484 (2016).
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).
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).
Kirwan, J. A. Translating metabolomics into clinical practice. Nat. Rev. Bioeng. 1, 228–229 (2023).
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.
Ali, A. et al. Single-cell metabolomics by mass spectrometry: advances, challenges, and future applications. Trends Anal. Chem. 120, 115436 (2019).
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).
Petrova, B. & Guler, A. T. Recent developments in single-cell metabolomics by mass Spectrometry─A perspective. J. Proteome Res. 24, 1493–1518 (2025).
Hajjar, G. et al. Scaling-up metabolomics: current state and perspectives. Trends Anal. Chem. 167, 117225 (2023).
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).
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).
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).
Cao, J. et al. Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution. Cell Metab. 36, 209–221 (2024).
Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18, 799–805 (2021).
Capolupo, L. et al. Sphingolipids control dermal fibroblast heterogeneity. Science 376, eabh1623 (2022).
Wang, Z. et al. Integrative single-cell metabolomics and phenotypic profiling reveals metabolic heterogeneity of cellular oxidation and senescence. Nat. Commun. 16, 2740 (2025).
Cairns, J. L. et al. Mass-guided single-cell MALDI imaging of low-mass metabolites reveals cellular activation markers. Adv. Sci. 12, e2410506 (2025).
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.
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.
Mao, X. et al. Single-cell simultaneous metabolome and transcriptome profiling revealing metabolite-gene correlation network. Adv. Sci. 12, e2411276 (2025).
Kang, M. et al. Single-cell metabolome and RNA-seq multiplexing on single plant cells. Proc. Natl Acad. Sci. USA 122, e2512828122 (2025).
Samarah, L. Z. et al. Spatial metabolic gradients in the liver and small intestine. Nature 648, 182–190 (2025).
Christofk, H. et al. Metabolic heterogeneity in humans. Cell 187, 3821–3823 (2024).
Kim, J. & DeBerardinis, R. J. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 30, 434–446 (2019).
Demicco, M., Liu, X.-Z., Leithner, K. & Fendt, S.-M. Metabolic heterogeneity in cancer. Nat. Metab. 6, 18–38 (2024).
Ghosh-Choudhary, S., Liu, J. & Finkel, T. Metabolic regulation of cell fate and function. Trends Cell Biol. 30, 201–212 (2020).
Stegen, S. & Carmeliet, G. Metabolic regulation of skeletal cell fate and function. Nat. Rev. Endocrinol. 20, 399–413 (2024).
Zhang, H. et al. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. Npj Imaging 2, 20 (2024).
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.
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).
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).
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).
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).
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).
Wang, G. et al. Analyzing cell-type-specific dynamics of metabolism in kidney repair. Nat. Metab. 4, 1109–1118 (2022).
Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. Trends Cell Biol. 27, 863–875 (2017).
Rodríguez-Colman, M. J. et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 543, 424–427 (2017).
Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).
Di Virgilio, F. & Adinolfi, E. Extracellular purines, purinergic receptors and tumor growth. Oncogene 36, 293–303 (2017).
Loo, J. M. et al. Extracellular metabolic energetics can promote cancer progression. Cell 160, 393–406 (2015).
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).
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).
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).
Hansen, J. et al. A reference tissue atlas for the human kidney. Sci. Adv. 8, eabn4965 (2022).
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.
Molenaar, M. R. et al. Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth. Front. Mol. Biosci. 9, 1021889 (2022).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2021).
Yu, B. et al. The Consortium of Metabolomics Studies (COMETS): metabolomics in 47 prospective cohort studies. Am. J. Epidemiol. 188, 991–1012 (2019).
Begzati, A. et al. Plasma lipid metabolites, clinical glycemic predictors, and incident type 2 diabetes. Diabetes Care 48, 473–480 (2025).
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.
Zhang, S. et al. A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank. Nat. Commun. 15, 8081 (2024).
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).
Chu, X. et al. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol. 22, 198 (2021).
Bossi, E. et al. Revolutionizing blood collection: Innovations, applications, and the potential of microsampling technologies for monitoring metabolites and lipids. Metabolites 14, 46 (2024).
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).
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.
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).
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.
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).
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).
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).
Dmitrenko, A., Reid, M. & Zamboni, N. Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. Bioinformatics 39, btad096 (2023).
Dmitrenko, A., Reid, M. & Zamboni, N. A system suitability testing platform for untargeted, high-resolution mass spectrometry. Front. Mol. Biosci. 9, 1026184 (2022).
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).
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).
Wang, W. et al. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark. Res. 11, 66 (2023).
Murphy, R. M., Watt, M. J. & Febbraio, M. A. Metabolic communication during exercise. Nat. Metab. 2, 805–816 (2020).
Sieber, M. H. & Spradling, A. C. The role of metabolic states in development and disease. Curr. Opin. Genet. Dev. 45, 58–68 (2017).
Chi, H. Immunometabolism at the intersection of metabolic signaling, cell fate, and systems immunology. Cell. Mol. Immunol. 19, 299–302 (2022).
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).
Sen, P. et al. Metabolic alterations in immune cells associate with progression to type 1 diabetes. Diabetologia 63, 1017–1031 (2020).
Huang, H. et al. Decoding aging clocks: new insights from metabolomics. Cell Metab. 37, 34–58 (2025).
Kim, H.-H. & Dixit, V. D. Metabolic regulation of immunological aging. Nat. Aging 5, 1425–1440 (2025).
Sebastiani, P. et al. Metabolite signatures of chronological age, aging, survival, and longevity. Cell Rep. 43, 114913 (2024).
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).
Santos, A. et al. A knowledge graph to interpret clinical proteomics data. Nat. Biotechnol. 40, 692–702 (2022).
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).
Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the missing links among metabolites, microbes, and the host. Immunity 40, 824–832 (2014).
El Abiead, Y. et al. Discovery of metabolites prevails amid in-source fragmentation. Nat. Metab. 7, 435–437 (2025).
Heinonen, M., Shen, H., Zamboni, N. & Rousu, J. Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics 28, 2333–2341 (2012).
Wadie, B. et al. METASPACE-ML: context-specific metabolite annotation for imaging mass spectrometry using machine learning. Nat. Commun. 15, 9110 (2024).
Peets, P. et al. Chemical characteristics vectors map the chemical space of natural biomes from untargeted mass spectrometry data. J. Chemoinform. 17, 82 (2025).
Heininen, J. et al. Targeted and untargeted Amine metabolite quantitation in single cells with isobaric multiplexing. Chemistry 30, e202403278 (2024).
Orth, J. D., Thiele, I. & Palsson, B. Ø What is flux balance analysis?. Nat. Biotechnol. 28, 245–248 (2010).
Bartman, C. R., TeSlaa, T. & Rabinowitz, J. D. Quantitative flux analysis in mammals. Nat. Metab. 3, 896–908 (2021).
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).
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).
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).
Wegner, A., Meiser, J., Weindl, D. & Hiller, K. How metabolites modulate metabolic flux. Curr. Opin. Biotechnol. 34, 16–22 (2015).
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).
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.
Piazza, I. et al. A map of protein–metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018).
Farr, E. et al. MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. Brief. Bioinform. 25, bbae347 (2024).
Amara, A. et al. Networks and graphs discovery in metabolomics data analysis and interpretation. Front. Mol. Biosci. 9, 841373 (2022).
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).
Quake, S. R. A decade of molecular cell atlases. Trends Genet. 38, 805–810 (2022).
Guo, F. et al. Foundation models in bioinformatics. Natl Sci. Rev. 12, nwaf028 (2025).
Cui, H. et al. Towards multimodal foundation models in molecular cell biology. Nature 640, 623–633 (2025).
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).
Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024).

