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.
| Language | English |
|---|---|
| Article number | e1006733 |
| Pages | 1-25 |
| Number of pages | 25 |
| Journal | PLoS Computational Biology |
| Volume | 15 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 28 Feb 2019 |
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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 Journal › Article › Academic › peer-review
TY - JOUR
T1 - Integration of single-cell RNA-seq data into population models to characterize cancer metabolism
AU - Damiani, Chiara
AU - Maspero, Davide
AU - Di Filippo, Marzia
AU - Colombo, Riccardo
AU - Pescini, Dario
AU - Graudenzi, Alex
AU - Westerhoff, Hans Victor
AU - Alberghina, Lilia
AU - Vanoni, Marco
AU - Mauri, Giancarlo
PY - 2019/2/28
Y1 - 2019/2/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062825440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062825440&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1006733
DO - 10.1371/journal.pcbi.1006733
M3 - Article
VL - 15
SP - 1
EP - 25
JO - PLoS Computational Biology
T2 - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 2
M1 - e1006733
ER -