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.

  • Opinion
  • Published:

Transition states and cell fate decisions in epigenetic landscapes

Nature Reviews Genetics volume 17, pages 693–703 (2016)Cite this article

Subjects

Abstract

Waddington's epigenetic landscape is an abstract metaphor frequently used to represent the relationship between gene activity and cell fates during development. Over the past few years, it has become a useful framework for interpreting results from single-cell transcriptomics experiments. It has led to the proposal that, during fate transitions, cells experience smooth, continuous progressions of global transcriptional activity, which can be captured by (pseudo)temporal dynamics. Here, focusing strictly on the fate decision events, we suggest an alternative view: that fate transitions occur in a discontinuous, stochastic manner whereby signals modulate the probability of the transition events.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Waddington's epigenetic landscape and modern representations.
Figure 2: Continuous and discrete analysis of cell fate decisions from single-cell gene expression data.
Figure 3: The transition state.

Similar content being viewed by others

References

  1. Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977).

    CAS  PubMed  Google Scholar 

  2. Nishida, H. Specification of embryonic axis and mosaic development in ascidians. Dev. Dyn. 233, 1177–1193 (2005).

    CAS  PubMed  Google Scholar 

  3. Davidson, E. H. The Regulatory Genome: Gene Regulatory Networks in Development and Evolution (Academic Press, 2010).

    Google Scholar 

  4. Levine, M. & Davidson, E. H. Gene regulatory networks for development. Proc. Natl Acad. Sci. USA 102, 4936–4942 (2005).

    CAS  PubMed  Google Scholar 

  5. Mathis, L. & Nicolas, J. F. Cellular patterning of the vertebrate embryo. Trends Genet. 18, 627–635 (2002).

    CAS  PubMed  Google Scholar 

  6. Davidson, E. H. et al. A genomic regulatory network for development. Science 295, 1669–1678 (2002).

    CAS  PubMed  Google Scholar 

  7. Stathopoulos, A. & Levine, M. Genomic regulatory networks and animal development. Dev. Cell 9, 449–462 (2005).

    CAS  PubMed  Google Scholar 

  8. Kamminga, L. M. et al. Autonomous behavior of hematopoietic stem cells. Exp. Hematol. 28, 1451–1459 (2000).

    CAS  PubMed  Google Scholar 

  9. Luer, K. & Technau, G. M. Single cell cultures of Drosophila neuroectodermal and mesectodermal central nervous system progenitors reveal different degrees of developmental autonomy. Neural Dev. 4, 30 (2009).

    PubMed  PubMed Central  Google Scholar 

  10. Keller, G. Embryonic stem cell differentiation: emergence of a new era in biology and medicine. Genes Dev. 19, 1129–1155 (2005).

    CAS  PubMed  Google Scholar 

  11. Loebel, D. A. F., Watson, C. M., De Young, R. A. & Tam, P. P. L. Lineage choice and differentiation in mouse embryos and embryonic stem cells. Dev. Biol. 264, 1–14 (2003).

    CAS  PubMed  Google Scholar 

  12. Hayward, P., Kalmar, T. & Martinez-Arias, A. Wnt/Notch signalling and information processing during development. Development 135, 411–424 (2008).

    CAS  PubMed  Google Scholar 

  13. Waddington, C. H. Canalization of development and the inheritance of acquired characteristics. Nature 3811, 563–565 (1942).

    Google Scholar 

  14. Waddington, C. H. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology (Allen & Unwin, 1957).

    Google Scholar 

  15. Allen, M. Compelled by the diagram: thinking through C. H. Waddington's epigenetic landscape. Contemporaneity 4, 119–142 (2015).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  17. Kauffman, S. A. The Origins of Order: Self Organization and Selection in Evolution (Oxford Univ. Press, 1993).

    Google Scholar 

  18. Huang, S., Eichler, G., Bar-Yam, Y. & Ingber, D. E. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94, 128701 (2005).

    PubMed  Google Scholar 

  19. Huang, S. The molecular and mathematical basis of Waddington's epigenetic landscape:a framework for post-Darwinian biology? BioEssays 34, 149–157 (2012).

    CAS  PubMed  Google Scholar 

  20. Wang, J., Xu, L., Wang, E. & Huang, S. The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation. Biophys. J. 99, 29–39 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Trott, J., Hayashi, K., Surani, A., Babu, M. M. & Martinez-Arias, A. Dissecting ensemble networks in ES cell populations reveals micro-heterogeneity underlying pluripotency. Mol. Biosyst. 8, 744–752 (2012).

    CAS  PubMed  Google Scholar 

  22. Marr, C., Zhou, J. X. & Huang, S. Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots. Curr. Opin. Biotechnol. 39, 207–214 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Jaeger, J., Manu & Reinitz, J. Drosophila blastoderm patterning. Curr. Opin. Genet. Dev. 22, 533–541 (2012).

    CAS  PubMed  Google Scholar 

  24. Ingham, P. W. The molecular genetics of embryonic pattern formation in Drosophila. Nature 335, 25–34 (1988).

    CAS  PubMed  Google Scholar 

  25. Ferguson, E. L., Sternberg, P. W. & Horvitz, H. R. A genetic pathway for the specification of the vulval cell lineages of Caenorhabditis elegans. Nature 326, 259–267 (1987).

    CAS  PubMed  Google Scholar 

  26. Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits (CRC Press, 2006).

    Google Scholar 

  27. Vermeirssen, V. et al. Transcription factor modularity in a gene-centered C. elegans core neuronal protein–DNA interaction network. Genome Res. 17, 1061–1071 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Arda, H. E. et al. Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network. Mol. Syst. Biol. 6, 367 (2010).

    PubMed  PubMed Central  Google Scholar 

  29. MacNeil, L. T. & Walhout, A. J. M. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res. 21, 645–657 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  32. Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007).

    CAS  PubMed  Google Scholar 

  33. Edgar, B. A., Odell, G. M. & Schubiger, G. A genetic switch, based on negative regulation, sharpens stripes in Drosophila embryos. Dev. Genet. 10, 124–142 (1989).

    CAS  PubMed  Google Scholar 

  34. Wang, L. et al. Bistable switches control memory and plasticity in cellular differentiation. Proc. Natl Acad. Sci. USA 106, 6638–6643 (2009).

    CAS  PubMed  Google Scholar 

  35. Bouldin, C. M. et al. Wnt signaling and tbx16 form a bistable switch to commit bipotential progenitors to mesoderm. Development 142, 2499–2507 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Bhattacharya, S., Zhang, Q. & Andersen, M. E. A deterministic map of Waddington's epigenetic landscape for cell fate specification. BMC Syst. Biol. 5, 85 (2011).

    PubMed  PubMed Central  Google Scholar 

  37. Verd, B., Crombach, A. & Jaeger, J. Classification of transient behaviours in a time-dependent toggle switch model. BMC Syst. Biol. 8, 43 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. Huang, S., Guo, Y. P., May, G. & Enver, T. Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695–713 (2007).

    CAS  PubMed  Google Scholar 

  39. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Schröter, C., Rué, P., Mackenzie, J. P. & Martinez-Arias, A. FGF/MAPK signaling sets the switching threshold of a mutual repressor circuit controlling cell fate decisions in ES cells. Development 142, 4205–4216 (2015).

    PubMed  PubMed Central  Google Scholar 

  42. Süel, G. M., Garcia-Ojalvo, J., Liberman, L. M. & Elowitz, M. B. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440, 545–550 (2006).

    PubMed  Google Scholar 

  43. Kalmar, T. et al. Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol. 7, e1000149 (2009).

    PubMed  PubMed Central  Google Scholar 

  44. Martinez-Arias, A. & Brickman, J. M. Gene expression heterogeneities in embryonic stem cell populations: origin and function. Curr. Opin. Cell Biol. 23, 650–656 (2011).

    CAS  PubMed  Google Scholar 

  45. Martinez-Arias, A. & Hayward, P. Filtering transcriptional noise during development: concepts and mechanisms. Nat. Rev. Genet. 7, 34–44 (2006).

    Google Scholar 

  46. Ahrends, R. et al. Controlling low rates of cell differentiation through noise and ultrahigh feedback. Science 344, 1384–1389 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).

    PubMed  Google Scholar 

  48. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Google Scholar 

  49. Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler's guide to cytometry. Trends Immunol. 33, 323–332 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Jaitin, D. A., Keren-Shaul, H., Elefant, N. & Amit, I. Each cell counts: hematopoiesis and immunity research in the era of single cell genomics. Semin. Immunol. 27, 67–71 (2015).

    CAS  PubMed  Google Scholar 

  51. Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    CAS  PubMed  Google Scholar 

  53. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    PubMed  Google Scholar 

  54. Chambers, I. et al. Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230–1234 (2007).

    CAS  PubMed  Google Scholar 

  55. Singer, Z. S. et al. Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol. Cell 55, 319–331 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Abranches, E. et al. Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency. Development 141, 2770–2779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    PubMed  Google Scholar 

  58. Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

    CAS  PubMed  Google Scholar 

  59. Iwasaki, H. & Akashi, K. Hematopoietic developmental pathways: on cellular basis. Oncogene 26, 6687–6696 (2007).

    CAS  PubMed  Google Scholar 

  60. Doulatov, S., Notta, F., Laurenti, E. & Dick, J. E. Hematopoiesis: a human perspective. Cell Stem Cell 10, 120–136 (2012).

    CAS  PubMed  Google Scholar 

  61. Drissen, R. et al. Distinct myeloid progenitor-differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17, 666–676 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    CAS  PubMed  Google Scholar 

  63. Takano, H., Ema, H., Sudo, K. & Nakauchi, H. Asymmetric division and lineage commitment at the level of hematopoietic stem cells: inference from differentiation in daughter cell and granddaughter cell pairs. J. Exp. Med. 199, 295–302 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Pronk, C. J. et al. Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1, 428–442 (2007).

    CAS  PubMed  Google Scholar 

  65. Arinobu, Y. et al. Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 1, 416–427 (2007).

    CAS  PubMed  Google Scholar 

  66. Akashi, K., Traver, D., Miyamoto, T. & Weissman, I. L. A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193–197 (2000).

    CAS  PubMed  Google Scholar 

  67. Teles, J. et al. Transcriptional regulation of lineage commitment — a stochastic model of cell fate decisions. PLoS Comput. Biol. 9, e1003197 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Bendall, Sean, C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Ocone, A., Haghverdi, L., Mueller, N. S. & Theis, F. J. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31, i89–i96 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Hu, M. et al. Multilineage gene expression precedes commitment in the hemopoietic system. Genes Dev. 11, 774–785 (1997).

    CAS  PubMed  Google Scholar 

  73. Goolam, M. et al. Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 165, 61–74 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Brunskill, E. W. et al. Single cell dissection of early kidney development: multilineage priming. Development 141, 3093–3101 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Miyamoto, T. et al. Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. Dev. Cell 3, 137–147 (2002).

    CAS  PubMed  Google Scholar 

  76. Laslo, P. et al. Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126, 755–766 (2006).

    CAS  PubMed  Google Scholar 

  77. Buganim, Y. et al. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150, 1209–1222 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Piras, V., Tomita, M. & Selvarajoo, K. Transcriptome-wide variability in single embryonic development cells. Sci. Rep. 4, 7137 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Nair, G., Abranches, E., Guedes, A. M., Henrique, D. & Raj, A. Heterogeneous lineage marker expression in naive embryonic stem cells is mostly due to spontaneous differentiation. Sci. Rep. 5, 13339 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Kumar, R. M. et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Pina, C. et al. Inferring rules of lineage commitment in haematopoiesis. Nat. Cell Biol. 14, 287–294 (2012).

    CAS  PubMed  Google Scholar 

  82. Muñoz Descalzo, S., Rué, P., Garcia-Ojalvo, J. & Martinez-Arias, A. Correlations between the levels of Oct4 and Nanog as a signature for naïve pluripotency in mouse embryonic stem cells. Stem Cells 30, 2683–2691 (2012).

    PubMed  Google Scholar 

  83. Garcia-Ojalvo, J. & Martinez-Arias, A. Towards a statistical mechanics of cell fate decisions. Curr. Opin. Genet. Dev. 22, 619–626 (2012).

    CAS  PubMed  Google Scholar 

  84. Cross, M. A. & Enver, T. The lineage commitment of haemopoietic progenitor cells. Curr. Opin. Genet. Dev. 7, 609–613 (1997).

    CAS  PubMed  Google Scholar 

  85. Mitschka, S. et al. Co-existence of intact stemness and priming of neural differentiation programs in mES cells lacking Trim71. Sci. Rep. 5, 11126 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Munoz-Descalzo, S., de Navascues, J. & Martinez-Arias, A. Wnt–Notch signalling: an integrated mechanism regulating transitions between cell states. Bioessays 34, 110–118 (2012).

    CAS  PubMed  Google Scholar 

  87. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Kutejova, E., Sasai, N., Shah, A., Gouti, M. & Briscoe, J. Neural progenitors adopt specific identities by directly repressing all alternative progenitor transcriptional programs. Dev. Cell 36, 639–653 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Pina, C. et al. Single-cell network analysis identifies DDIT3 as a Nodal lineage regulator in hematopoiesis. Cell Rep. 11, 1503–1510.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Laidler, K. J. & King, M. C. Development of transition-state theory. J. Phys. Chem. 87, 2657–2664 (1983).

    CAS  Google Scholar 

  91. Trott, J. & Martinez-Arias, A. Single cell lineage analysis of mouse embryonic stem cells at the exit from pluripotency. Biol. Open 2, 1049–1056 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).

    PubMed  Google Scholar 

  93. Turner, D. A. et al. Wnt/beta-catenin and FGF signalling direct the specification and maintenance of a neuromesodermal axial progenitor in ensembles of mouse embryonic stem cells. Development 141, 4243–4253 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Ferrell, J. E. Jr Bistability, bifurcations, and Waddington's epigenetic landscape. Curr. Biol. 22, R458–R466 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Kalkan, T. & Smith, A. Mapping the route from naive pluripotency to lineage specification. Phil. Trans. R. Soc. B 369, 20130540 (2014).

    PubMed  Google Scholar 

  96. Nichols, J. & Smith, A. Pluripotency in the embryo and in culture. Cold Spring Harb. Perspect. Biol. 4, a008128 (2012).

    PubMed  PubMed Central  Google Scholar 

  97. Rue, P. & Martinez-Arias, A. Cell dynamics and gene expression control in tissue homeostasis and development. Mol. Syst. Biol. 11, 792 (2015).

    PubMed  PubMed Central  Google Scholar 

  98. Balazsi, G., van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  100. 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).

    CAS  PubMed  Google Scholar 

  101. Morikawa, M., Koinuma, D., Miyazono, K. & Heldin, C. H. Genome-wide mechanisms of Smad binding. Oncogene 32, 1609–1615 (2013).

    CAS  PubMed  Google Scholar 

  102. Schmierer, B. & Hill, C. S. TGFβ–SMAD signal transduction: molecular specificity and functional flexibility. Nat. Rev. Mol. Cell Biol. 8, 970–982 (2007).

    CAS  PubMed  Google Scholar 

  103. Ohnishi, Y. et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16, 27–37 (2014).

    CAS  PubMed  Google Scholar 

  104. Plusa, B., Piliszek, A., Frankenberg, S., Artus, J. & Hadjantonakis, A. K. Distinct sequential cell behaviours direct primitive endoderm formation in the mouse blastocyst. Development 135, 3081–3091 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Frankenberg, S. et al. Primitive endoderm differentiates via a three-step mechanism involving Nanog and RTK signaling. Dev. Cell 21, 1005–1013 (2011).

    CAS  PubMed  Google Scholar 

  106. Bessonnard, S. et al. Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network. Development 141, 3637–3648 (2014).

    CAS  PubMed  Google Scholar 

  107. De Mot, L. et al. Cell fate specification based on tristability in the inner cell mass of mouse blastocysts. Biophys. J. 110, 710–722 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Strogatz, S. H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Westview Press, 1994).

    Google Scholar 

  109. Cox, A. M. A. & Cox, F. T. in Handbook of Data Visualization (eds Chen, C. et al.) 315–347 (Springer, 2008).

    Google Scholar 

  110. Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Machine Learn. Res. 9, 85 (2008).

    Google Scholar 

  111. Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmon. Analysis 21, 5–30 (2006).

    Google Scholar 

  112. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912 (1999).

    CAS  PubMed  Google Scholar 

  113. Törönen, P., Kolehmainen, M., Wong, G. & Castrén, E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451, 142–146 (1999).

    PubMed  Google Scholar 

  114. Huang, W., Cao, X., Biase, F. H., Yu, P. & Zhong, S. Time-variant clustering model for understanding cell fate decisions. Proc. Natl Acad. Sci. USA 111, E4797–E4806 (2014).

    CAS  PubMed  Google Scholar 

  115. Park, J., Ogunnaike, B., Schwaber, J. & Vadigepalli, R. Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability. Prog. Biophys. Mol. Biol. 117, 87–98 (2015).

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Naomi Moris and Alfonso Martinez Arias are at the Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK.,

    Naomi Moris & Alfonso Martinez Arias

  2. Cristina Pina is at the Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK.,

    Cristina Pina

Authors
  1. Naomi Moris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Pina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfonso Martinez Arias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alfonso Martinez Arias.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Glossary

Bifurcation theory

A branch of mathematics associated with dynamical systems that accounts for the evolution of a physical or biological system according to a control parameter.

Cell fate

The developmental destination of a cell if left undisturbed in its environment. The fate of a cell is more restricted than its potential.

Cell states

The transcriptional output of a gene regulatory network, with a variable degree of stability; development is characterized by sequences of cell states that culminate in specific fates.

Cellular potential

Biologically, potentials represent the range of fates into which a cell can develop. It is reduced during development and is obscured in, for example, lineage-tracing experiments, which only reveal fates. In physics, potential can be described as the ability to do work and represents an amount of energy stored for that purpose. In both biology and physics, it represents an ability to do something.

Dynamical systems

Systems defined by a number of related variables that evolve in time according to certain rules. A gene regulatory network is an example of a dynamical system in which the variables are the transcription factors that represent the nodes.

Epistasis analysis

A genetic technique in which analysis of the phenotype of double mutants allows an ordering of the temporal activity of the wild-type products of the mutated genes. This works best, and often only, in linear processes.

Gene expression heterogeneity

Variability in the expression of a gene or a group of genes across a population at single-cell resolution.

Gene regulatory networks

(GRNs). GRNs represent units of interacting proteins that are functionally constrained by defined regulatory relationships. These interactions provide a structure and determine an output in the form of a pattern of gene expression. GRNs are usually represented by nodes (proteins) and edges (their interactions).

Genetic programmes

Temporally ordered interactions between proteins, usually transcription factors, associated with the emergence of cell types.

Macrostate

A notion derived from statistical mechanics that defines the macroscopic state of a system (for example, a particular volume or temperature) and, in the case of a biological system, a functional state. Importantly, a macrostate can be observed and measured.

Microstate

A notion derived from statistical mechanics that defines a configuration of the elements that are associated with a particular macrostate of the system: for example, a molecular configuration associated with a particular volume or temperature. Any given macrostate may be associated with many different microstates. We surmise that gene expression profiles can be related to microstates in a biological context. These are often inferred.

Phase space

A geometrical representation of the possible states of a dynamical system as a function of the value of its variables. A simple example is the states of water in terms of pressure, temperature and volume. In a cell state, the 'phenotype' is represented by the levels of expression of the genes that are active in that state.

Pseudotime

A notion derived from the analysis of single-cell transcriptomes in a cell population that allows the ordering of individual cells based on minimal differences of their transcriptomes. It has an implicit assumption that the resulting order reflects a smooth and continuous change in the state of the cell and aims to relate this change to changes in gene expression.

Transition state

An intermediate state during cell fate decisions in which a cell exhibits a mixed identity between two or more states, which often represents the state of origin (that is, the initial state the cell is in) and that of destination (that is, the identity that the cell is adopting). It is highly unstable and reversible.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moris, N., Pina, C. & Arias, A. Transition states and cell fate decisions in epigenetic landscapes. Nat Rev Genet 17, 693–703 (2016). https://doi.org/10.1038/nrg.2016.98

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg.2016.98

This article is cited by

Search

Advanced 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