Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations

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. Computational models hold the promise to bridge this gap, by estimating fluxes across metabolic pathways. Yet they currently portray the average behavior of intermixed subpopulations, masking their inherent heterogeneity known to hinder cancer diagnosis and treatment. If complemented with the information on single-cell transcriptome, now enabled by RNA sequencing (scRNA-seq), metabolic models of cancer populations are expected to empower the characterization of the mechanisms behind metabolic heterogeneity. To this aim, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate sc-transcriptomes into single-cell fluxomes. We show that the integration of scRNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients, into a multi-scale stoichiometric model of cancer population: 1) significantly 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. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.