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 https://github.com/ BIMIB-DISCo/scFBA, as well as the case study datasets.

Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., et al. (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS COMPUTATIONAL BIOLOGY, 15(2) [10.1371/journal.pcbi.1006733].

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

Damiani, C
;
Maspero, D;Di Filippo, M;Colombo, R;Pescini, D;Graudenzi, A;Alberghina, L;Vanoni, M;Mauri, G
2019

Abstract

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 https://github.com/ BIMIB-DISCo/scFBA, as well as the case study datasets.
Articolo in rivista - Articolo scientifico
RNASeq; metabolic modeling; inter-cell communication;
English
Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., et al. (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS COMPUTATIONAL BIOLOGY, 15(2) [10.1371/journal.pcbi.1006733].
Damiani, C; Maspero, D; Di Filippo, M; Colombo, R; Pescini, D; Graudenzi, A; Westerhoff, H; Alberghina, L; Vanoni, M; Mauri, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/222494
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