ArticleGenome research2021
A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers, 1 of them a synthesis that pooled it.
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Who cites it
148 citing papers in PubMed, 1 synthesis or guideline pooled it, 220 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Resetting immunometabolic set points in autoimmune disease.Journal of translational autoimmunity · 2026Review
- Integrated single-cell and metabolomic profiling identifies ovarian metabolic differences and endothelial heterogeneity associated with litter-size variation in Hu sheep.Journal of animal science and biotechnology · 2026Article
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Spatial transcriptomics reveals microenvironmental heterogeneity in osteoarthritic subchondral bone.Nature communications · 2026Article
- Article
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- Integrating single-nucleus RNA sequencing and gene perturbation reveals regulatory networks of intramuscular fat deposition in bovine skeletal muscle.Animal bioscience · 2026Article
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- APOC1⁺IFI30⁺ TAMs orchestrate colorectal cancer liver metastasis by coupling cholesterol biosynthesis with secretion of immunosuppressive factors.Cellular and molecular life sciences : CMLS · 2026Article
- Dissecting autonomous enzymatic variability in single cells.Nature communications · 2026Article
- Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.Experimental & molecular medicine · 2026Review
- APE1/Ref-1: multifunctional biology, selective inhibition, and the path to clinical translation.Expert opinion on therapeutic targets · 2026Review
- Integrative spatiotemporal transcriptomics identifies a liver metastasis-related initial cell population associated with the SEMA3A-NRP1 axis in pancreatic ductal adenocarcinoma.Journal of translational medicine · 2026Article
- Multi-omics reveals fibroblast subtypes with distinct roles in inflammatory bowel disease.Biology direct · 2026Article
- mFLIP: metabolic flux interval prediction.BMC bioinformatics · 2026Article
- Metabolic characterization of the tumor microenvironment orchestrates therapeutic strategies and clinical outcomes in pancreatic cancer.Nature communications · 2026Article
- A Perturb-seq screen guided by species divergence uncovers pathways for collateral artery formation.bioRxiv : the preprint server for biology · 2026Article
- Nanoparticle-Mediated Immunometabolic-Epigenetic Remodeling Enhances Schwann Cell-Macrophage Interaction for Sciatic Nerve Regeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Lactate-Driven IL8+ Tumor-Associated Macrophages Mediate Immunosuppression and Resistance to Immunotherapy in Clear-Cell Renal Cell Carcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
88 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.
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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.