Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

Sriram Chandrasekaran, Jin Zhang, Zhen Sun, Li Zhang, Christian A. Ross, Yu Chung Huang, John M. Asara, Hu Li, George Q. Daley, James J. Collins

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions.

Original languageEnglish (US)
Pages (from-to)2965-2977
Number of pages13
JournalCell reports
Issue number10
StatePublished - Dec 5 2017


  • cell fate
  • genome-scale modeling
  • histone methylation
  • metabolic network
  • metabolism
  • naive (ground) state
  • pluripotency
  • primed state
  • stem cell biology
  • systems biology

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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