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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.

Graphical representation of scFBA.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.

Clustering of transcripts vs. fluxes.Fig.2 Clustering of transcripts vs. fluxes. (Damiani, 2019)


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.

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

  1. 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.
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