Single Cell Flux Balance Analysis (scFBA) Service
scFBA was first proposed by Damiani and collaborators. It is a novel computational framework to characterize the metabolism of heterogeneous cancer cells by translating single-cell transcriptomes into single-cell fluxomes. Future investigations of intratumor heterogeneity may use omics data from single cells collected from cancer biopsies to pave the way to personalized models of cancer metabolism.
Fig. 1 Graphical representation of scFBA. (Damiani, 2019)
Published Data
| Paper Title | Integration of single-cell RNA-seq data into population models to characterize cancer metabolism |
| Journal | PLOS Computational Biology |
| IF | 4.475 |
| Published | 2019 |
| Abstract | Dimiani et al. showed that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. |
| Result |
Fig 3 reports the results of the hierarchical clustering analysis (distance metric: euclidean), for transcripts (left column) and fluxes (middle column), respectively for the two primary tumors and the other 3 datasets under study.
LCPT45 dataset. Clustergram (distance metric: Euclidean) of the transcripts of the metabolic gene included in the metabolic network (left) and of the metabolic fluxes predicted by scFBA (middle). Right panel: elbow analysis comparing cluster errors for k∈{1, ∙∙∙, 20} (k-means clustering) in both transcripts (blue) and fluxes (green). Same information as in A for the BC04 dataset. |
Fig.2 Clustering of transcripts vs. fluxes. (Damiani, 2019)
At Creative Biolabs, we offer scFBA for a better interpretation for your single cell heterogeneous data. If you have any requirements, please feel free to contact us for further communication about your project.
Reference
- Damiani, C.; et al. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS Computational Biology. 2019, 15(2): e1006733.
