ArticleProceedings of the National Academy of Sciences of the United States of America2023
Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- Key molecular networks underlying retinol's biphasic effects on cell proliferation through multi-omics integrative analysis using genome-scale metabolic modeling.Synthetic and systems biotechnology · 2027Article
- Novel CRISPR-Cas9 BAP1 knockout pre-clinical tumor model recapitulates human melanoma tumorigenesis and immune evolution.Communications biology · 2026Article
- Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Microglia cause HIV-induced transcriptional and metabolic changes in human neural organoids.Communications biology · 2026Article
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.NPJ systems biology and applications · 2026Article
- ZIP8 loss impairs macrophage-mediated phagolysosomal removal of bacteria and is overcome by butyrate supplementation.Communications biology · 2026Article
- Co-Metabolic Network Reveals the Metabolic Mechanism of Host-Microbiota Interplay in Colorectal Cancer.Metabolites · 2026Article
- A Closed Computational-Experimental Loop Identifies Metabolic Collapse at the Root of Macrophage Dysfunction due to Zinc Dyshomeostasis.bioRxiv : the preprint server for biology · 2026Article
- From Genome to Function: Metabolism-Wide Models of Lifecycle Stages of Parasites.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cholesterol sulfate replenishment rejuvenates aged hematopoietic stem cell phenomes.Theranostics · 2026Article
- Article
- Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.NPJ systems biology and applications · 2025Article
- Predicting metabolic preferences through transcriptomics: a data-driven approach to align metabolic signatures with gene expression profiles.Biochemistry and biophysics reports · 2025Article
- Metabolic network analysis of Crohn's disease reveals sex- and age-specific cellular phenotypes.Cell systems · 2025Article
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- A computational model elucidates the effects of oncogene-induced expression alterations on the energy metabolism of neuroblastoma.Scientific reports · 2025Article
- Genome-Scale Metabolic Modeling Identifies Synergistic Metabolites that Enhance 5-Fluorouracil Efficacy in Colon Cancer.Iranian biomedical journal · 2025Article
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Competition and cooperation: The plasticity of bacterial interactions across environments.PLoS computational biology · 2025Article
Corrections and comments
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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