Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Systems biology of stem cell fate and cellular reprogramming

Key Points

  • Stem cell differentiation and the maintenance of self-renewal are intrinsically complex processes that require the coordinated dynamic expression of hundreds of genes and proteins in precise response to external signalling cues.

  • As computational tools can help identify patterns and elucidate structure in complex datasets, they are now beginning to be used in stem cell research to better understand this complexity.

  • The representation of complex molecular regulatory interactions as networks is useful in conceptualizing this complexity. However, it is difficult to relate network architecture to cell behaviour in a quantitative way.

  • The collective behaviour of complex regulatory networks can be explored by using techniques from dynamical systems theory and analysing cell types associated with attractors of underlying regulatory networks.

  • Robust heterogeneity at the population level can arise from stochastic transitions between coexisting attractors driven by widespread molecular noise.

  • Cellular reprogramming corresponds to navigation through a complex noisy attractor landscape. Understanding the relationships between stochasticity and determinism in defining cell fate might help decipher the molecular regulatory mechanisms of cellular reprogramming.

Abstract

Stem cell differentiation and the maintenance of self-renewal are intrinsically complex processes requiring the coordinated dynamic expression of hundreds of genes and proteins in precise response to external signalling cues. Numerous recent reports have used both experimental and computational techniques to dissect this complexity. These reports suggest that the control of cell fate has both deterministic and stochastic elements: complex underlying regulatory networks define stable molecular 'attractor' states towards which individual cells are drawn over time, whereas stochastic fluctuations in gene and protein expression levels drive transitions between coexisting attractors, ensuring robustness at the population level.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Stem cell regulatory networks.
Figure 2: Cellular reprogramming as navigation through a complex attractor landscape.

Similar content being viewed by others

References

  1. Gage, F. Mammalian neural stem cells. Science 287, 1433–1438 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Wagers, A. & Weissman, I. Plasticity of adult stem cells. Cell 116, 639–648 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Pittenger, M. et al. Multilineage potential of adult human mesenchymal stem cells. Science 284, 143–161 (1999).

    Article  CAS  PubMed  Google Scholar 

  4. Weissman, I. L. Translating stem and progenitor cell biology to the clinic: barriers and opportunities. Science 287, 1442–1446 (2000).

    Article  CAS  PubMed  Google Scholar 

  5. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Takahashi, K. et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Xie, H., Ye, M., Feng, R. & Graf, T. Stepwise reprogramming of B cells into macrophages. Cell 117, 663–676 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Zhou, Q., Brown, J., Kanarek, A., Rajagopal, J. & Melton, D. A. In vivo reprogramming of adult pancreatic exocrine cells to β-cells. Nature 455, 627–632 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Hanna, J. et al. Treatment of sickle cell anemia mouse model with iPS cells generated from autologous skin. Science 318, 1920–1923 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Loh, Y. et al. The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nature Genet. 38, 431–440 (2006).

    Article  CAS  PubMed  Google Scholar 

  11. Boyer, L. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kim, J., Chu, J., Shen, X., Wang, J. & Orkin, S. An extended transcriptional network for pluripotency of embryonic stem cells. Cell 132, 1049–1061 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Avilion, A. A. et al. Multipotent cell lineages in early mouse development depend on SOX2 function. Genes Dev. 17, 126–140 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chambers, I. et al. Functional expression cloning of Nanog, a pluripotency sustaining factor in embryonic stem cells. Cell 113, 643–655 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Nichols, J. et al. Formation of pluripotent stem cells in the mammalian embryo depends on the POU transcription factor Oct4. Cell 95, 379–391 (1998).

    Article  CAS  PubMed  Google Scholar 

  16. Mitsui, K. et al. The homeoprotein nanog is required for maintenance of pluripotency in mouse epiblast and ES cells. Cell 113, 631–642 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Wang, J. et al. A protein interaction network for pluripotency of embryonic stem cells. Nature 444, 364–368 (2006). This study derives a high-confidence protein–protein interaction network in ES cells centred around the transcription factor NANOG.

    CAS  Google Scholar 

  18. Muller, F.-J. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455, 401–405 (2008). This study uses innovative computational techniques to derive an extended network for stem cell pluripotency.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Aderem, A. Systems biology: its practice and challenges. Cell 121, 511–513 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Kirschner, M. W. The meaning of systems biology. Cell 121, 503–504 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. Kitano, H. Systems biology: a brief overview. Science 295, 1662–1664 (2002).

    Article  CAS  PubMed  Google Scholar 

  23. Alon, U. An introduction to systems biology: design principles of biological circuits (Chapman & Hall/CRC, Boca Raton, 2007).

    Google Scholar 

  24. Sontag, E. Mathematical control theory (Springer, New York, 1998).

    Book  Google Scholar 

  25. Wiggins, S. Introduction to applied nonlinear dynamical systems and chaos (Springer, New York, 2003).

    Google Scholar 

  26. McQuarrie, D. & Allan, D. Statistical mechanics (University Science Books, Sausalito, 2000).

    Google Scholar 

  27. Lee, T. I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Ihmels, J. et al. Revealing modular organization in the yeast transcriptional network. Nature Genet. 31, 370–378 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Shen-Orr, S., Milo, R., Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet. 31, 64–68 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Isalan, M. et al. Evolvability and hierarchy in rewired bacterial gene networks. Nature 452, 840–845 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kidder, B. L., Yang, J. & Palmer, S. Stat3 and c-Myc genome-wide promoter occupancy in embryonic stem cells. PLoS ONE 3, e3932 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Chen, X. et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Spooncer, E. et al. Developmental fate determination and marker discovery in hematopoietic stem cell biology using proteomic fingerprinting. Mol. Cell. Proteomics 7, 573–581 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Hinsby, A. M., Olsen, J. V. & Mann, M. Tyrosine phosphoproteomics of fibroblast growth factor signaling: a role for insulin receptor substrate-4. J. Biol. Chem. 279, 46438–46447 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Harary, F. Graph theory (Westview, Boulder, 1994).

    Google Scholar 

  36. Tyson, J. J., Chen, K. & Novak, B. Network dynamics and cell physiology. Nature Rev. Mol. Cell Biol. 2, 908–916 (2001).

    Article  CAS  Google Scholar 

  37. Ma'ayan, A. et al. Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science 309, 1078–1083 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bromberg, K. D., Ma'ayan, A., Neves, S. R. & Iyengar, R. Design logic of a cannabinoid receptor signaling network that triggers neurite outgrowth. Science 320, 903–909 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Rual, J.-F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621–1625 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nature Genet. 37, 382–390 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Ma'ayan, A. Network integration and graph analysis in mammalian molecular systems biology. IET Syst. Biol. 2, 206–221 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Bansal, M., Belcastro, V., Ambesi-Impiombato, A. & di Bernardo, D. How to infer gene networks from expression profiles. Mol. Syst. Biol. 3, 78 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  44. D'haeseleer, P., Liang, S. & Somogyi, R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16, 707–726 (2000).

    Article  CAS  PubMed  Google Scholar 

  45. Berger, S., Posner, J. & Ma'ayan, A. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics 8, 372 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jiang, J. et al. A core Klf circuitry regulates self-renewal of embryonic stem cells. Nature Cell Biol. 10, 353–360 (2008).

    Article  PubMed  CAS  Google Scholar 

  48. Singh, S. K., Kagalwala, M. N., Parker-Thornburg, J., Adams, H. & Majumder, S. REST maintains self-renewal and pluripotency of embryonic stem cells. Nature 453, 223–227 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cole, M., Johnstone, S., Newman, J., Kagey, M. & Young, R. Tcf3 is an integral component of the core regulatory circuitry of embryonic stem cells. Genes Dev. 22, 746–755 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Liu, X. et al. Yamanaka factors critically regulate the developmental signaling network in mouse embryonic stem cells. Cell Res. 18, 1177–1189 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Marson, A. et al. Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 134, 521–533 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mackay, J. P., Sunde, M., Lowry, J. A., Crossley, M. & Matthews, J. M. Protein interactions: is seeing believing? Trends Biochem. Sci. 32, 530–531 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Ji, H., Vokes, S. A. & Wong, W. H. A comparative analysis of genome-wide chromatin immunoprecipitation data for mammalian transcription factors. Nucleic Acids Res. 34, e146 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Ulitsky, I. & Shamir, R. Identification of functional modules using network topology and high-throughput data. BMC Syst. Biol. 1, 8 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Mathur, D. et al. Analysis of the mouse embryonic stem cell regulatory networks obtained by ChIP-chip and ChIP-PET. Genome Biol. 9, R126 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Boyer, L. A. et al. Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature 441, 349–353 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Johnson, R. et al. REST regulates distinct transcriptional networks in embryonic and neural stem cells. PLoS Biol. 6, e256 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Hu, G. et al. A genome-wide RNAi screen identifies a new transcriptional module required for self-renewal. Genes Dev. 23, 837–848 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nature Methods 6, 83–90 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Ding, L. et al. A genome-scale RNAi screen for Oct4 modulators defines a role of the Paf1 complex for embryonic stem cell identity. Cell Stem Cell 4, 403–415 (2009).

    Article  CAS  PubMed  Google Scholar 

  62. Mogilner, A., Wollman, R. & Marshall, W. F. Quantitative modeling in cell biology: what is it good for? Dev. Cell 11, 279–287 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Wilkinson, D. J. Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Rev. Genet. 10, 122–133 (2009).

    Article  CAS  PubMed  Google Scholar 

  64. Hasty, J., McMillen, D., Isaacs, F. & Collins, J. J. Computational studies of gene regulatory networks: in numero molecular biology. Nature Rev. Genet. 2, 268–279 (2001).

    Article  CAS  PubMed  Google Scholar 

  65. Kauffman, S. A. The origins of order: self-organization and selection in evolution (Oxford Univ. Press, 1993).

    Google Scholar 

  66. Huang, A., Hu, L., Kauffman, S., Zhang, W. & Shmulevich, I. Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation. BMC Syst. Biol. 3, 20 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Bar-Yam, Y., Harmon, D. & de Bivort, B. Systems biology: attractors and democratic dynamics. Science 323, 1016–1017 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Chang, H., Oh, P., Ingber, D. & Huang, S. Multistable and multistep dynamics in neutrophil differentiation. BMC Cell Biol. 7, 11 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Huang, S. & Ingber, D. A non-genetic basis for cancer progression and metastasis: self-organizing attractors in cell regulatory networks. Breast Dis. 26, 27–54 (2007).

    Article  Google Scholar 

  70. Kramer, B. P. & Fussenegger, M. Hysteresis in a synthetic mammalian gene network. Proc. Natl Acad. Sci. USA 102, 9517–9522 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Brock, A., Chang, H. & Huang, S. Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours. Nature Rev. Genet. 10, 336–342 (2009).

    Article  CAS  PubMed  Google Scholar 

  72. Enver, T., Pera, M., Peterson, C. & Andrews, P. W. Stem cell states, fates, and the rules of attraction. Cell Stem Cell 4, 387–397 (2009).

    Article  CAS  PubMed  Google Scholar 

  73. Delbruck, M. in Unités biologiques douées de continuité génétique 33–35 (Editions du Centre National de la Recherche Scientifique, Paris, 1949).

    Google Scholar 

  74. Thomas, R. Laws for the dynamics of regulatory networks. Int. J. Dev. Biol. 42, 479–485 (1998).

    CAS  PubMed  Google Scholar 

  75. Waddington, C. Organisers & genes (Cambridge Univ. Press, Cambridge, UK, 1940).

    Google Scholar 

  76. Waddington, C. The strategy of the genes. (George Allen & Unwin, London,1957).

    Google Scholar 

  77. Waddington, C. H. in The Development of Animal Behavior: a Reader (eds Bolhuis, J. J. & Hogan, J. A.) 22 (Blackwell, Oxford, 1999).

    Google Scholar 

  78. Milnor, J. On the concept of attractor. Commun. Math. Phys. 99, 177–195 (1985).

    Article  Google Scholar 

  79. Strogatz, S. H. Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering (Westview, Boulder, 2000).

    Google Scholar 

  80. Kauffman, S. Homeostasis and differentiation in random genetic control networks. Nature 224, 177–178 (1969).

    Article  CAS  PubMed  Google Scholar 

  81. MacArthur, B. D., Please, C. P. & Oreffo, R. O. C. Stochasticity and the molecular mechanisms of induced pluripotency. PLoS ONE 3, e3086 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Chickarmane, V., Troein, C., Nuber, U. A., Sauro, H. M. & Peterson, C. Transcriptional dynamics of the embryonic stem cell switch. PLoS Comput. Biol. 2, e123 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Cinquin, O. & Demongeot, J. High-dimensional switches and the modelling of cellular differentiation. J. Theor. Biol. 233, 391–411 (2005).

    Article  CAS  PubMed  Google Scholar 

  84. Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008). This paper provides evidence of coexisting mammalian attractor states and switching between coexisting attractors at the single cell level owing to transcriptome-wide fluctuations in protein expression levels.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Huang, S., Eichler, G., Bar-Yam, Y. & Ingber, D. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94, 128701 (2005). This study provides the first experimental evidence that a mammalian cell type corresponds to a high-dimensional attractor of an underlying dynamical system.

    Article  PubMed  CAS  Google Scholar 

  86. Xiong, W. & Ferrell, J. E. A positive-feedback-based bistable “memory module” that governs a cell fate decision. Nature 426, 460–465 (2003).

    Article  CAS  PubMed  Google Scholar 

  87. Becskei, A., Séraphin, B. & Serrano, L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 2528–2535 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Ferrell, J. E. Jr & Machleder, E. M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280, 895–898 (1998).

    Article  CAS  PubMed  Google Scholar 

  89. Graf, T. & Stadtfeld, M. Heterogeneity of embryonic and adult stem cells. Cell Stem Cell 3, 480–483 (2008).

    Article  CAS  PubMed  Google Scholar 

  90. Ying, Q.-L. et al. The ground state of embryonic stem cell self-renewal. Nature 453, 519–523 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006).

    Article  CAS  PubMed  Google Scholar 

  92. Till, J. E., McCulloch, E. A. & Siminovitch, L. A stochastic model of stem cell proliferation, based on the growth of spleen colony-forming cells. Proc. Natl Acad. Sci. USA 51, 29–36 (1964).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Suda, T., Suda, J. & Ogawa, M. Disparate differentiation in mouse hemopoietic colonies derived from paired progenitors. Proc. Natl Acad. Sci. USA 81, 2520–2524 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Ogawa, M., Porter, P. & Nakahata, T. Renewal and commitment to differentiation of hemopoietic stem cells (an interpretive review). Blood 61, 823–829 (1983).

    Article  CAS  PubMed  Google Scholar 

  95. Suda, T., Suda, J. & Ogawa, M. Single-cell origin of mouse hemopoietic colonies expressing multiple lineages in variable combinations. Proc. Natl Acad. Sci. USA 80, 6689–6693 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. McAdams, H. H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl Acad. Sci. USA 94, 814–819 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Arias, A. M. & Hayward, P. Filtering transcriptional noise during development: concepts and mechanisms. Nature Rev. Genet. 7, 34–44 (2006).

    Article  CAS  PubMed  Google Scholar 

  99. Zwaka, T. P. Keeping the noise down in ES cells. Cell 127, 1301–1302 (2006).

    Article  CAS  PubMed  Google Scholar 

  100. Szutorisz, H., Georgiou, A., Tora, L. & Dillon, N. The proteasome restricts permissive transcription at tissue-specific gene loci in embryonic stem cells. Cell 127, 1375–1388 (2006).

    Article  CAS  PubMed  Google Scholar 

  101. Chi, A. S. & Bernstein, B. E. Developmental biology: pluripotent chromatin state. Science 323, 220–221 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Austin, D. W. et al. Gene network shaping of inherent noise spectra. Nature 439, 608–611 (2006).

    Article  CAS  PubMed  Google Scholar 

  103. Thattai, M. & van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA 98, 8614–8619 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kaern, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nature Rev. Genet. 6, 451–464 (2005).

    Article  CAS  PubMed  Google Scholar 

  105. Thattai, M. & van Oudenaarden, A. Stochastic gene expression in fluctuating environments. Genetics 167, 523–530 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Newman, J. R. S. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

    Article  CAS  PubMed  Google Scholar 

  107. Chambers, I. et al. Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230–1234 (2007). This study shows that NANOG expression fluctuates in ES cells and that it provides a temporary predisposition towards cell differentiation.

    Article  CAS  PubMed  Google Scholar 

  108. Feng, B. et al. Reprogramming of fibroblasts into induced pluripotent stem cells with orphan nuclear receptor Esrrb. Nature Cell Biol. 11, 197–203 (2009).

    Article  CAS  PubMed  Google Scholar 

  109. Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

    Article  CAS  PubMed  Google Scholar 

  110. Nakagawa, M. et al. Generation of induced pluripotent stem cells without Myc from mouse and human fibroblasts. Nature Biotechnol. 26, 101–106 (2008).

    Article  CAS  Google Scholar 

  111. Wernig, M., Meissner, A., Cassady, J. P. & Jaenisch, R. c-Myc is dispensable for direct reprogramming of mouse fibroblasts. Cell Stem Cell 2, 10–12 (2008).

    Article  CAS  PubMed  Google Scholar 

  112. Aoi, T. et al. Generation of pluripotent stem cells from adult mouse liver and stomach cells. Science 321, 699–702 (2008).

    Article  CAS  PubMed  Google Scholar 

  113. Park, I.-H. et al. Reprogramming of human somatic cells to pluripotency with defined factors. Nature 451, 141–146 (2008).

    Article  CAS  PubMed  Google Scholar 

  114. Kim, J. B. et al. Oct4-induced pluripotency in adult neural stem cells. Cell 136, 411–419 (2009).

    Article  CAS  PubMed  Google Scholar 

  115. Maherali, N. et al. directly reprogrammed fibroblasts show global epigenetic remodeling and widespread tissue contribution. Cell Stem Cell 1, 55–70 (2007).

    CAS  Google Scholar 

  116. Okita, K., Ichisaka, T. & Yamanaka, S. Generation of germline-competent induced pluripotent stem cells. Nature 448, 313–317 (2007).

    Article  CAS  PubMed  Google Scholar 

  117. Wernig, M. et al. In vitro reprogramming of fibroblasts into a pluripotent ES-cell-like state. Nature 448, 318–324 (2007).

    Article  CAS  PubMed  Google Scholar 

  118. Sridharan, R. et al. Role of the murine reprogramming factors in the induction of pluripotency. Cell 136, 364–377 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Mikkelsen, T. S. et al. Dissecting direct reprogramming through integrative genomic analysis. Nature 454, 49–55 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Jaenisch, R. & Young, R. Stem cells, the molecular circuitry of pluripotency and nuclear reprogramming. Cell 132, 567–582 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Hemberger, M., Dean, W. & Reik, W. Epigenetic dynamics of stem cells and cell lineage commitment: digging Waddington's canal. Nature Rev. Mol. Cell Biol. 10, 526–537 (2009).

    Article  CAS  Google Scholar 

  122. Ivanova, N. et al. Dissecting self-renewal in stem cells with RNA interference. Nature 442, 533–538 (2006).

    Article  CAS  PubMed  Google Scholar 

  123. Daheron, L. et al. LIF/STAT3 signaling fails to maintain self-renewal of human embryonic stem cells. Stem Cells 22, 770–778 (2004).

    Article  CAS  PubMed  Google Scholar 

  124. Nishimoto, M., Fukushima, A., Okuda, A. & Muramatsu, M. The gene for the embryonic stem cell coactivator UTF1 carries a regulatory element which selectively interacts with a complex composed of Oct-3/4 and Sox-2. Mol. Cell. Biol. 19, 5453–5465 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Yuan, H., Corbi, N., Basilico, C. & Dailey, L. Developmental-specific activity of the FGF-4 enhancer requires the synergistic action of Sox2 and Oct-3. Genes Dev. 9, 2635–2645 (1995).

    Article  CAS  PubMed  Google Scholar 

  126. Zhang, P. et al. Negative cross-talk between hematopoietic regulators: GATA proteins repress PU.1. Proc. Natl Acad. Sci. USA 96, 8705–8710 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. van den Berg, D. L. C. et al. Estrogen-related receptor β interacts with Oct4 To positively regulate Nanog gene expression. Mol. Cell. Biol. 28, 5986–5995 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Zhang, X., Zhang, J., Wang, T., Esteban, M. A. & Pei, D. Esrrb activates Oct4 transcription and sustains self-renewal and pluripotency in embryonic stem cells. J. Biol. Chem. 283, 35825–35833 (2008).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank J. Wang for supplying the NANOG interactome data used to create Fig. 1a and Y.-S. Ang for helping to compile the list of genes used to create the supplementary stem cell transcription network.

Author information

Authors and Affiliations

Authors

Related links

Related links

FURTHER INFORMATION

The Black Family Stem Cell Institute homepage

Integrated Stem Cell Molecular Interactions database

Glossary

Inner cell mass

Early cells in the embryo that generate all lineages of the mature organism but do not give rise to the placenta.

Blastocyst

The embryo before implantation, which contains at least two distinct cell types: the trophectoderm and the inner cell mass.

Feedback loop

A closed path in a network starting and ending at the same node and passing through intermediary nodes only once.

Feedforward loop

The union of two distinct paths in a network from a source node to a target node, passing through intermediary nodes only once.

Attractor

A stable balanced state of a dynamical system towards which nearby configurations are drawn over time. Attractors can be stationary states, limit cycles (oscillators) or even strange (chaotic).

ChIP-on-chip

A high-throughput chromatin immunoprecipitation (ChIP) procedure that is used to identify binding sites for a specific transcription factor or other DNA-binding protein in the entire genome.

ChIP-seq

A procedure similar to ChIP-on-chip except that instead of hybridizing isolated DNA fragments bound by the protein of interest with a microarray, the fragments are amplified, size-selected and directly sequenced using massively parallel signature sequencing (MPSS)-based deep sequencing techniques.

ChIP-PET

A procedure that is similar to ChIP-on-chip and ChIP-seq. In this case isolated DNA from the ChIP portion of the experiment is digested into 18-nucleotide-long fragments that are concatenated, tagged and sequenced (known as paired-end ditags (PETs)). The sequences of the PETs are then reassembled and compared with the genome to identify actual binding sites.

Reverse engineering

Inferring regulatory interactions from high-throughput datasets using computational and statistical inference techniques.

Induced pluripotent stem (iPS) cell

A type of pluripotent stem cell that can be produced by various adult somatic cell types by forced expression of certain combinations of key embryonic stem cell-associated transcription factors.

Epigenetic modifier

A substance that causes a change in gene expression without changing DNA sequence.

Molecular noise

Stochastic fluctuations in molecular expression levels originating from the inherent the indeterminism of molecular processes and the unpredictable variability of the extracellular environment.

Multi-stable system

A dynamical system that supports the existence of two or more coexisting attractors for some region of parameter space.

26S proteasome

Large multi-subunit protease complex that selectively degrades multiubiquitylated proteins. It contains a 20S particle that carries the catalytic activity and two regulatory 19S particles.

Rights and permissions

Reprints and permissions

About this article

Cite this article

MacArthur, B., Ma'ayan, A. & Lemischka, I. Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol 10, 672–681 (2009). https://doi.org/10.1038/nrm2766

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrm2766

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing