ReviewNature2026
Metabolomics across scales: from single cells to population studies.
Review in Nature, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Reprogramming immunometabolism: linking nutrient sensing, cell death, and therapeutic innovation.Immunologic research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics has matured into a powerful approach for probing metabolism, offering readouts that closely reflect cellular and organismal function in health and disease. Here we highlight two rapidly advancing frontiers: single-cell metabolomics and population-scale metabolomics. Single-cell metabolomics resolves the metabolic states of individual cells, uncovering cell-to-cell heterogeneity and spatial organization within tissues. Population-scale profiling profiles metabolites across large cohorts, enabling the discovery of markers of disease, environmental exposures and genetic variation. Although these approaches operate at different scales, they face shared challenges-including metabolite identification, quantification and multimodal data integration-and offer common advantages, such as the ability to capture non-genetic influences on phenotype and to scale to high throughput. We propose that continued advances in scalability will bring these domains together, enabling the construction of comprehensive metabolic atlases that chart cellular and interindividual variation and provide training data for foundation models of metabolism. By integrating cellular and population-level insights, single-cell and population-scale metabolomics promise to advance our understanding of metabolism across biology, medicine and pharmacology.
Indexed as
Identifiers
41951964What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.