ArticleNature communications2025
Dynamic single-cell metabolomics reveals cell-cell interaction between tumor cells and macrophages.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Probiotic extracellular vesicles reprogram macrophage immunometabolism: From gut crosstalk to host health.Gut microbes · 2026Review
- Resetting immunometabolic set points in autoimmune disease.Journal of translational autoimmunity · 2026Review
- Metabolic immune checkpoints (MICs) in cancer: from molecular mechanisms to challenges and opportunities in clinical translation.Signal transduction and targeted therapy · 2026Review
- Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response.Bulletin of mathematical biology · 2026Article
- AGCLD: an adaptive graph contrastive learning method with denoising for spatial domain identification.Briefings in bioinformatics · 2026Article
- Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.Journal of food and drug analysis · 2026Review
- Polyploid cancer cells surviving cisplatin reallocate central carbon sources to fuel antioxidant metabolism for survival.Molecular metabolism · 2026Article
- Leveraging Macrophage Metabolic Reprogramming for Enhanced Anti-Tumor Immunity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Research progress in single‑cell omics technologies for kidney disease (Review).International journal of molecular medicine · 2026Review
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Designing Microbe-Semiconductor Interfaces for Semibiological Photosynthesis.Chemical reviews · 2026Review
- Single-Cell Metabolic Profiling in a Glioblastoma Coculture Model Using AP-MALDI-Based Mass Spectrometry Imaging.Analytical chemistry · 2026Article
- Review
- Metabolic engineering of SLC38A2 reprograms glutamine utilization and enhances CAR-macrophage antitumor function in solid tumors.Cancer biology & medicine · 2026Article
- Extracellular Vesicles Associated Metabolites as Intercellular Signalling Mediators in Disease and Therapy.Metabolites · 2026Review
- Single-cell thiol profiling enabled by live-cell labeling reveals metabolic heterogeneity in ferroptosis.Nature communications · 2026Article
- Identification of immunosuppressive neutrophils using multi-omics: why functional testing remains key.Frontiers in immunology · 2026Review
- Revisiting macrophage metabolic reprogramming in metabolic dysfunction-associated steatotic liver disease: mechanisms, regulation, and therapeutic breakthroughs.Frontiers in immunology · 2026Review
- Future Research Directions on Mycobacterium tuberculosis Proteins.Advances in experimental medicine and biology · 2026Review
- Reprogramming the immunosuppressive breast cancer microenvironment: integrating cellular, metabolic, and stromal targets for rational immunotherapy.Frontiers in immunology · 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
6 authors.
Funding
Abstract
Single-cell metabolomics reveals cell heterogeneity and elucidates intracellular molecular mechanisms. However, general concentration measurement of metabolites can only provide a static delineation of metabolomics, lacking the metabolic activity information of biological pathways. Herein, we develop a universal system for dynamic metabolomics by stable isotope tracing at the single-cell level. This system comprises a high-throughput single-cell data acquisition platform and an untargeted isotope tracing data processing platform, providing an integrated workflow for dynamic metabolomics of single cells. This system enables the global activity profiling and flow analysis of interlaced metabolic networks at the single-cell level and reveals heterogeneous metabolic activities among single cells. The significance of activity profiling is underscored by a 2-deoxyglucose inhibition model, demonstrating delicate metabolic alteration within single cells which cannot reflected by concentration analysis. Significantly, the system combined with a neural network model enables the metabolomic profiling of direct co-cultured tumor cells and macrophages. This reveals intricate cell-cell interaction mechanisms within the tumor microenvironment and firstly identifies versatile polarization subtypes of tumor-associated macrophages based on their metabolic signatures, which is in line with the renewed diversity atlas of macrophages from single-cell RNA-sequencing. The developed system facilitates a comprehensive understanding single-cell metabolomics from both static and dynamic perspectives.
Indexed as
Identifiers
What 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.