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

Research output: Contribution to JournalArticleAcademicpeer-review

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.

LanguageEnglish
Article numbere1006733
Pages1-25
Number of pages25
JournalPLoS Computational Biology
Volume15
Issue number2
DOIs
Publication statusPublished - 28 Feb 2019

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Population Model
RNA
Metabolism
cancer
Cancer
metabolism
Cells
Fluxes
neoplasms
metabolite
Cell
Metabolites
Population
drug resistance
Neoplasms
cells
MATLAB
Cell Population
Metabolic Networks and Pathways
biochemical pathways

Cite this

Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., ... Mauri, G. (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Computational Biology, 15(2), 1-25. [e1006733]. https://doi.org/10.1371/journal.pcbi.1006733
Damiani, Chiara ; Maspero, Davide ; Di Filippo, Marzia ; Colombo, Riccardo ; Pescini, Dario ; Graudenzi, Alex ; Westerhoff, Hans Victor ; Alberghina, Lilia ; Vanoni, Marco ; Mauri, Giancarlo. / Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. In: PLoS Computational Biology. 2019 ; Vol. 15, No. 2. pp. 1-25.
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Damiani, C, Maspero, D, Di Filippo, M, Colombo, R, Pescini, D, Graudenzi, A, Westerhoff, HV, Alberghina, L, Vanoni, M & Mauri, G 2019, 'Integration of single-cell RNA-seq data into population models to characterize cancer metabolism', PLoS Computational Biology, vol. 15, no. 2, e1006733, pp. 1-25. https://doi.org/10.1371/journal.pcbi.1006733

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. / Damiani, Chiara; Maspero, Davide; Di Filippo, Marzia; Colombo, Riccardo; Pescini, Dario; Graudenzi, Alex; Westerhoff, Hans Victor; Alberghina, Lilia; Vanoni, Marco; Mauri, Giancarlo.

In: PLoS Computational Biology, Vol. 15, No. 2, e1006733, 28.02.2019, p. 1-25.

Research output: Contribution to JournalArticleAcademicpeer-review

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Damiani C, Maspero D, Di Filippo M, Colombo R, Pescini D, Graudenzi A et al. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Computational Biology. 2019 Feb 28;15(2):1-25. e1006733. https://doi.org/10.1371/journal.pcbi.1006733