Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Chiara Damiani*, Davide Maspero, Marzia Di Filippo, Riccardo Colombo, Dario Pescini, Alex Graudenzi, Hans Victor Westerhoff, Lilia Alberghina, Marco Vanoni, Giancarlo Mauri

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We 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. The scFBA suite of MATLAB functions is available at BIMIB-DISCo/scFBA, as well as the case study datasets.

Original languageEnglish
Article numbere1006733
Pages (from-to)1-25
Number of pages25
JournalPLoS Computational Biology
Issue number2
Publication statusPublished - 28 Feb 2019


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