Cell Fate Decision as High-Dimensional Critical State Transition

PLoS Biology

Volumn 14, Issue 12, 2016, Pages

  Mojtahedi, Mitra ^a,b   Skupin, Alexander ^b,c   Zhou, Joseph ^b   Castaño, Ivan G ^a,d   Leong Quong, Rebecca Y Y ^a   Chang, Hannah ^e   Trachana, Kalliopi ^b   Giuliani, Alessandro ^f   Huang, Sui ^a,b  

^a UNIVERSITY OF CALGARY   (Canada)

^b INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

^c UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^d Grupo Biociencias Parque Explora   (Colombia)

^e 5AM Ventures ^*  (United States)

^f ISTITUTO SUPERIORE DI SANITÀ   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ERYTHROPOIETIN; GRANULOCYTE MACROPHAGE COLONY STIMULATING FACTOR; INTERLEUKIN 3; TRANSCRIPTION FACTOR GATA 1;

ANIMAL CELL; ARTICLE; AUTOMATED PATTERN RECOGNITION; BONE MARROW CELL; CELL DIFFERENTIATION; CELL FATE; CELL LINEAGE; CELL POPULATION; CONTROLLED STUDY; CORRELATION ANALYSIS; ERYTHROID CELL; FLOW CYTOMETRY; FLUORESCENCE ACTIVATED CELL SORTING; GENE EXPRESSION; MOUSE; MULTIPOTENT STEM CELL; NONHUMAN; POLYMERASE CHAIN REACTION; PREDICTION; PROTEIN EXPRESSION; QUANTITATIVE ANALYSIS; SIGNAL TRANSDUCTION; SINGLE CELL ANALYSIS; ANIMAL; GENE EXPRESSION PROFILING;

ANIMALS; CELL DIFFERENTIATION; CELL LINEAGE; GENE EXPRESSION PROFILING; SINGLE-CELL ANALYSIS;

EID: 85009096649     PISSN: 15449173     EISSN: 15457885     Source Type: Journal    
DOI: 10.1371/journal.pbio.2000640     Document Type: Article

References (48)

1. Macarthur BD, Ma’ayan A, Lemischka IR, Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol. 2009; 10:672–81. doi: 10.1038/nrm276619738627
2. Huang S, Cell lineage determination in state space: a systems view brings flexibility to dogmatic canonical rules. PLoS Biol. 2010; 8(e1000380):1–4.
3. Wray J, Kalkan T, Smith AG, The ground state of pluripotency. Biochem Soc Trans. 2010; 38:1027–1032. doi: 10.1042/BST038102720658998
4. Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S, Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008; 453:544–7. doi: 10.1038/nature0696518497826
5. Hough SR, Laslett AL, Grimmond SB, Kolle G, Pera MF, A continuum of cell states spans pluripotency and lineage commitment in human embryonic stem cells. PLoS ONE. 2009; 4(11):e7708. doi: 10.1371/journal.pone.000770819890402
6. Pina C, Fugazza C, Tipping AJ, Brown J, Soneji S, Teles J, et al. Inferring rules of lineage commitment in haematopoiesis. Nat Cell Biol. 2010; 14:287–94.
7. Kauffman S, Homeostasis and differentiation in random genetic control networks. Nature. 1969; 224(215): 177–178.
8. Huang S, Guo YP, May G, Enver T, Bifurcation dynamics of cell fate decision in bipotent progenitor cells. Dev Biol. 2007; 305:695–713. doi: 10.1016/j.ydbio.2007.02.03617412320
9. Kaplan D, Glass L, Understanding Nonlinear DynamicsNew York, Springer; 1995.
10. Perko L, Marsden JE, Sirovich L, Golubisky M, Differential Equations and Dynamical Systems Texts in Applied Mathematics. New York, Springer Book; 2006; 7:557.
11. Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, et al. Anticipating critical transitions. Science. 2012; 338:344–8. doi: 10.1126/science.122524423087241
12. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, et al. Early-warning signals for critical transitions. Nature. 2009; 461(7260):53–59. doi: 10.1038/nature0822719727193
13. Trefois C, Antony PM, Goncalves J, Skupin A, Balling R, Critical transitions in chronic disease: transferring concepts from ecology to systems medicine. Curr Opin Biotechnol. 2015; 34:48–55. doi: 10.1016/j.copbio.2014.11.02025498477
14. Huang S, Eichler G, Bar-Yam Y, Ingber DE, Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett. 2005; 94(12):128701. doi: 10.1103/PhysRevLett.94.12870115903968
15. Chickarmane V, Peterson C, A computational model for understanding stem cell, trophectoderm and endoderm lineage determination. PLoS ONE. 2008; 3:e3478. doi: 10.1371/journal.pone.000347818941526
16. Duff C, Smith-Miles K, Lopes L, Tian T, Mathematical modelling of stem cell differentiation: the PU.1-GATA-1 interaction. J Math Biol. 2011; 64(3):449–68. doi: 10.1007/s00285-011-0419-321461760
17. Ferrell JE, Bistability, bifurcations, and Waddington's epigenetic landscape. Current biology. 2012; CB 22:R458–466. doi: 10.1016/j.cub.2012.03.04522677291
18. Jolly MJ, Huang B, Lu M, Mani SA, Levine H, Ben-Jacob E, Towards elucidating the connection between epithelial-mesenchymal transitions and stemness. J R Soc Interface. 2014; 11:20140962. doi: 10.1098/rsif.2014.096225339690
19. Furusawa C., Kaneko k, A dynamical-systems view of stem cell biology. Science. 2012; 338(6104):215–217. doi: 10.1126/science.122431123066073
20. Pal M, Ghosh S, Bose I, Non-genetic heterogeneity, criticality and cell differentiation. Phys Biol. 2014; 12(1): 016001. doi: 10.1088/1478-3975/12/1/01600125429686
21. Chen L, Liu R, Liu ZP, Li M, Aihara K, Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep. 2012; 2:18–20.
22. Tsai S, Bartelmez S, Sitnicka E, Collins S, Lymphohematopoietic pro-genitors immortalized by a retroviral vector harboring a dominant-negative ret-inoic acid receptor can recapitulate lymphoid, myeloid, and erythroid develop-ment. Genes Dev. 1994; 8(23):2831–2841. 7995521
23. Paul F, Arkin Y, Giladi A, Adhemar Jaitin D, Kenigsberg E, Keren-Shaul H, et al. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. Cell. 2015; 163(7): 1663–1677. doi: 10.1016/j.cell.2015.11.01326627738
24. Ying QL, Wray J, Nichols J, Batlle-Morera L, Doble B, Woodgett J, et al. The ground state of embryonic stem cell self-renewal. Nature. 2008; 453(7194):519–23. doi: 10.1038/nature0696818497825
25. Grun D, Kester L, van Oudenaarden A, Validation of noise models for single-cell transcriptomics. Nature methods. 2014; 11(6):637–40. doi: 10.1038/nmeth.293024747814
26. Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014; 24(3):496–510. doi: 10.1101/gr.161034.11324299736
27. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME, Quantitative assessment of single-cell RNA-sequencing methods. Nature methods. 2014; 11(1):41–6. doi: 10.1038/nmeth.269424141493
28. Kim JK, Kolodziejczyk AA, Illicic T, Teichmann SA, Marioni JC, Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. 2015; 6:8687. doi: 10.1038/ncomms968726489834
29. Zhou JX, Aliyu MDS, Aurell E, Huang S, Quasi-potential landscape in complex multi—stable systems. J R Soc Interface. 2015; 12:1–15.
30. Waddington CH, Principles of Embryology. Allen Unwin Ltd. 1956.
31. Gorban AN, Smirnova EV, Tyukina T, Correlations, risk and crisis: From physiology to finance. Phys A Stat Mech. its Appl. 2010; 389:3193–3217.
32. 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. 2010; 99:29–39. doi: 10.1016/j.bpj.2010.03.05820655830
33. Giuliani A, Statistical Mechanics of Gene Expression Networks: Increasing Connectivity as a Response to Stressful Condition. Adv Syst Biol. 2014; 3:1–4.
34. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014; 509(7500):371–375. doi: 10.1038/nature1317324739965
35. Giuliani A, Zbilut JP, Conti F, Manetti C, Miccheli A, Invariant features of metabolic networks: a data analysis application on scaling properties of biochemical pathways. Physica A. 2014; 337(1–2):157–170.
36. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190):1396–1401. doi: 10.1126/science.125425724925914
37. Enver T, Heyworth CM, Dexter TM, Do stem cells play dice?Blood. 1998; 92: 358–41; discussion 352.
38. Enver T, Jacobsen SEW, Instructions writ in blood. Nature. 2009; 461.
39. Coffman RL, Reiner SL, Instruction, selection, or tampering with the odds?Science. 1999; 284:1283–1285. 10383307
40. Callard RE, Decision-making by the immune response. Immunol. Cell Biol. 2007; 85:300–305. doi: 10.1038/sj.icb.710006017471303
41. Veraart AJ, Faassen EJ, Dakos V, Van nes EH, Lurling M, Scheffer M, Corrigendum: Recovery rates reflect distance to a tipping point in a living system. Nature. 2012; 484:404–404.
42. Metcalf D, Stem cells, pre-progenitor cells and lineage-committed cells: Are our dogmas correct?Ann N Y Acad Sci. 1999; 872:289–304. 10372131
43. Ogawa M, Hemopoietic stem cells: stochastic differentiation and humoral control of prolifera-tion. Environ Health Perspect. 1989; 80:199–207. 2647480
44. Eichler GS, Huang S, Ingber DE, Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles. Bioinformatics. 2003; 22;19(17): 2321–2. 14630665
45. Metcalf D, Lineage commitment and maturation in hematopoietic cells: the case for extrinsic regulation. Blood. 1998; 92(2):345–347; discussion 352.
46. Marr C, Zhou JX, Huang S, Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots. Curr Opin Biotechnol. 2016; 39:207–214. doi: 10.1016/j.copbio.2016.04.01527152696
47. Zhou JX, Huang S, Understanding gene circuits at cell-fate branch points for rational cell reprogramming. Trends Genet. 2011; 27(2):55–62. doi: 10.1016/j.tig.2010.11.00221146896
48. Liu P, Shi J, Wang Y, Imperfect transcritical and pitchfork bifurcations. J Funct Anal. 2007; 251(2): 573–600.