Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance.
To remove this limitation, researchers from the University of Milan-Bicocca have developed single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. They show 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.
Graphical representation of scFBA
Extracellular fluxes and sc-transcriptomes are translated respectively into type 1 and 2 heterogeneous constraints imposed on a initially homogenous population of Ncells replicates of metabolic network A. The output is a heterogeneous set of flux patterns that may predict sc-fluxes.
Availability – The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.