Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.
Adenocarcinoma of Lung
/ genetics
Algorithms
Breast Neoplasms
/ genetics
Computational Biology
/ methods
Computer Simulation
Female
Gene Expression Profiling
/ methods
Genetics, Population
/ methods
Humans
Male
Metabolic Networks and Pathways
Neoplasms
/ genetics
RNA
/ genetics
Sequence Analysis, RNA
/ methods
Single-Cell Analysis
/ methods
Software
Transcriptome
/ genetics
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
02 2019
02 2019
Historique:
received:
20
06
2018
accepted:
22
12
2018
revised:
12
03
2019
pubmed:
1
3
2019
medline:
29
3
2019
entrez:
1
3
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 30818329
doi: 10.1371/journal.pcbi.1006733
pii: PCOMPBIOL-D-18-01038
pmc: PMC6413955
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1006733Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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