Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux
Abstract Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40457-w |
| _version_ | 1827710111952404480 |
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| author | Yuefan Huang Vakul Mohanty Merve Dede Kyle Tsai May Daher Li Li Katayoun Rezvani Ken Chen |
| author_facet | Yuefan Huang Vakul Mohanty Merve Dede Kyle Tsai May Daher Li Li Katayoun Rezvani Ken Chen |
| author_sort | Yuefan Huang |
| collection | DOAJ |
| description | Abstract Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types. |
| first_indexed | 2024-03-10T17:33:38Z |
| format | Article |
| id | doaj.art-6eae94f5d4744f51821d2e6c20abee2c |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:33:38Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-6eae94f5d4744f51821d2e6c20abee2c2023-11-20T09:57:03ZengNature PortfolioNature Communications2041-17232023-08-0114111610.1038/s41467-023-40457-wCharacterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFluxYuefan Huang0Vakul Mohanty1Merve Dede2Kyle Tsai3May Daher4Li Li5Katayoun Rezvani6Ken Chen7Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer CenterDepartment of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer CenterDepartment of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterAbstract Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.https://doi.org/10.1038/s41467-023-40457-w |
| spellingShingle | Yuefan Huang Vakul Mohanty Merve Dede Kyle Tsai May Daher Li Li Katayoun Rezvani Ken Chen Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux Nature Communications |
| title | Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux |
| title_full | Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux |
| title_fullStr | Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux |
| title_full_unstemmed | Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux |
| title_short | Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux |
| title_sort | characterizing cancer metabolism from bulk and single cell rna seq data using metaflux |
| url | https://doi.org/10.1038/s41467-023-40457-w |
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