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

PLoS Comput Biol. 2019 Feb 28;15(2):e1006733. doi: 10.1371/journal.pcbi.1006733. eCollection 2019 Feb.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • 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
  • Neoplasms / metabolism
  • RNA / genetics
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Software
  • Transcriptome / genetics

Substances

  • RNA

Grant support

This work is supported with FOE funds to SYSBIO Italian Centre of Systems Biology, from the Italian Ministry of Education, Universities and Research (MIUR, http://www.istruzione.it/) - within the Italian Roadmap for ESFRI Research Infrastructures. GM, LA, CD and MV received funding from FLAG-ERA grant ITFoC. HVW received EU (311815; 642691; 654248) and WOTRO (W01.65.324.00/4) funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.