1932

Abstract

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-genet-021920-110200
2020-11-23
2024-12-13
Loading full text...

Full text loading...

/deliver/fulltext/genet/54/1/annurev-genet-021920-110200.html?itemId=/content/journals/10.1146/annurev-genet-021920-110200&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Argelaguet R, Clark SJ, Mohammed H, Stapel LC, Krueger C et al. 2019. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576:7787487–91
    [Google Scholar]
  2. 2. 
    Avilion AA, Nicolis SK, Pevny LH, Perez L, Vivian N, Lovell-Badge R 2003. Multipotent cell lineages in early mouse development depend on SOX2 function. Genes Dev 17:1126–40
    [Google Scholar]
  3. 3. 
    Beccari L, Moris N, Girgin M, Turner DA, Baillie-Johnson P et al. 2018. Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids. Nature 562:7726272–76
    [Google Scholar]
  4. 4. 
    Benzer S. 1953. Induced synthesis of enzymes in bacteria analyzed at the cellular level. Biochim. Biophys. Acta 11:3383–95
    [Google Scholar]
  5. 5. 
    Bessonnard S, De Mot L, Gonze D, Barriol M, Dennis C et al. 2014. Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network. Development 141:193637–48
    [Google Scholar]
  6. 6. 
    Biase FH, Cao X, Zhong S 2014. Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing. Genome Res 24:111787–96
    [Google Scholar]
  7. 7. 
    Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:111093–95
    [Google Scholar]
  8. 8. 
    Burton A, Muller J, Tu S, Padilla-Longoria P, Guccione E, Torres-Padilla M-E 2013. Single-cell profiling of epigenetic modifiers identifies PRDM14 as an inducer of cell fate in the mammalian embryo. Cell Rep 5:3687–701
    [Google Scholar]
  9. 9. 
    Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM et al. 2019. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:7745496–502
    [Google Scholar]
  10. 10. 
    Chazaud C, Yamanaka Y, Pawson T, Rossant J 2006. Early lineage segregation between epiblast and primitive endoderm in mouse blastocysts through the Grb2-MAPK pathway. Dev. Cell 10:5615–24
    [Google Scholar]
  11. 11. 
    Chen J, Suo S, Tam PP, Han J-DJ, Peng G, Jing N 2017. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12:3566–80
    [Google Scholar]
  12. 12. 
    Chen Q, Shi J, Tao Y, Zernicka-Goetz M 2018. Tracing the origin of heterogeneity and symmetry breaking in the early mammalian embryo. Nat. Commun. 9:11819
    [Google Scholar]
  13. 13. 
    Cheng S, Pei Y, He L, Peng G, Reinius B et al. 2019. Single-cell RNA-seq reveals cellular heterogeneity of pluripotency transition and X chromosome dynamics during early mouse development. Cell Rep 26:102593–607.e3
    [Google Scholar]
  14. 14. 
    Chickarmane V, Peterson C. 2008. A computational model for understanding stem cell, trophectoderm and endoderm lineage determination. PLOS ONE 3:10e3478
    [Google Scholar]
  15. 15. 
    Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C 2006. Transcriptional dynamics of the embryonic stem cell switch. PLOS Comput. Biol. 2:9e123
    [Google Scholar]
  16. 16. 
    Cho H, Rockne RC. 2019. Mathematical modeling with single-cell sequencing data. bioRxiv 710640. https://doi.org/10.1101/710640
    [Crossref]
  17. 17. 
    Clark SJ, Argelaguet R, Kapourani C-A, Stubbs TM, Lee HJ et al. 2018. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9:1781
    [Google Scholar]
  18. 18. 
    Collombet S, van Oevelen C, Sardina Ortega JL, Abou-Jaoudé W, Di Stefano B et al. 2017. Logical modeling of lymphoid and myeloid cell specification and transdifferentiation. PNAS 114:235792–99
    [Google Scholar]
  19. 19. 
    Colomé-Tatché M, Theis FJ. 2018. Statistical single cell multi-omics integration. Curr. Opin. Syst. Biol. 7:54–59
    [Google Scholar]
  20. 20. 
    De Caluwé J, Tosenberger A, Gonze D, Dupont G 2019. Signalling-modulated gene regulatory networks in early mammalian development. J. Theor. Biol. 463:56–66
    [Google Scholar]
  21. 21. 
    De Mot L, Gonze D, Bessonnard S, Chazaud C, Goldbeter A, Dupont G 2016. Cell fate specification based on tristability in the inner cell mass of mouse blastocysts. Biophys. J. 110:3710–22
    [Google Scholar]
  22. 22. 
    Deng Q, Ramsköld D, Reinius B, Sandberg R 2014. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343:6167193–96
    [Google Scholar]
  23. 23. 
    Dietrich J-E, Hiiragi T. 2007. Stochastic patterning in the mouse pre-implantation embryo. Development 134:234219–31
    [Google Scholar]
  24. 24. 
    Efremova M, Teichmann SA. 2020. Computational methods for single-cell omics across modalities. Nat. Methods 17:114–17
    [Google Scholar]
  25. 25. 
    Eldar A, Elowitz MB. 2010. Functional roles for noise in genetic circuits. Nature 467:7312167–73
    [Google Scholar]
  26. 26. 
    Eling N, Morgan MD, Marioni JC 2019. Challenges in measuring and understanding biological noise. Nat. Rev. Genet. 20:9536–48
    [Google Scholar]
  27. 27. 
    Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA 2018. Correcting the mean-variance dependency for differential variability testing using single-cell RNA sequencing data. Cell Syst 7:3284–94.e12
    [Google Scholar]
  28. 28. 
    Elsasser WM. 1984. Outline of a theory of cellular heterogeneity. PNAS 81:165126–29
    [Google Scholar]
  29. 29. 
    Fan Z, Chen R, Chen X 2020. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res 48:D1D233–37
    [Google Scholar]
  30. 30. 
    Fischer SC, Corujo-Simon E, Lilao-Garzon J, Stelzer EHK, Muñoz-Descalzo S 2020. The transition from local to global patterns governs the differentiation of mouse blastocysts. PLOS ONE 15:5e0233030
    [Google Scholar]
  31. 31. 
    Frankenberg S, Gerbe F, Bessonnard S, Belville C, Pouchin P et al. 2011. Primitive endoderm differentiates via a three-step mechanism involving Nanog and RTK signaling. Dev. Cell 21:61005–13
    [Google Scholar]
  32. 32. 
    Frias-Aldeguer J, Kip M, Vivié J, Li L, Alemany A et al. 2020. Embryonic signals perpetuate polar-like trophoblast stem cells and pattern the blastocyst axis. bioRxiv 510362. https://doi.org/10.1101/510362
    [Crossref]
  33. 33. 
    Furusawa C, Kaneko K. 2012. A dynamical-systems view of stem cell biology. Science 338:6104215–17
    [Google Scholar]
  34. 34. 
    Garcia-Ojalvo J, Martinez Arias A 2012. Towards a statistical mechanics of cell fate decisions. Curr. Opin. Genet. Dev. 22:6619–26
    [Google Scholar]
  35. 35. 
    Goolam M, Scialdone A, Graham SJL, Macaulay IC, Jedrusik A et al. 2016. Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 165:161–74
    [Google Scholar]
  36. 36. 
    Graf T, Enver T. 2009. Forcing cells to change lineages. Nature 462:7273587–94
    [Google Scholar]
  37. 37. 
    Graham SJL, Zernicka-Goetz M. 2016. The acquisition of cell fate in mouse development: How do cells first become heterogeneous. ? Curr. Top. Dev. Biol. 117:671–95
    [Google Scholar]
  38. 38. 
    Guo G, Huss M, Tong GQ, Wang C, Li Sun L et al. 2010. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18:4675–85
    [Google Scholar]
  39. 39. 
    Harrison SE, Sozen B, Christodoulou N, Kyprianou C, Zernicka-Goetz M 2017. Assembly of embryonic and extraembryonic stem cells to mimic embryogenesis in vitro. Science 356:6334eaal1810
    [Google Scholar]
  40. 40. 
    Holmes WR, Reyes de Mochel NS, Wang Q, Du H, Peng T et al. 2017. Gene expression noise enhances robust organization of the early mammalian blastocyst. PLOS Comput. Biol. 13:1e1005320
    [Google Scholar]
  41. 41. 
    Honda H, Motosugi N, Nagai T, Tanemura M, Hiiragi T 2008. Computer simulation of emerging asymmetry in the mouse blastocyst. Development 135:81407–14
    [Google Scholar]
  42. 42. 
    Hu KH, Eichorst JP, McGinnis CS, Patterson DM, Chow ED et al. 2020. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17:83343
    [Google Scholar]
  43. 43. 
    Huang S. 2009. Non-genetic heterogeneity of cells in development: more than just noise. Development 136:233853–62
    [Google Scholar]
  44. 44. 
    Huang S, Eichler G, Bar-Yam Y, Ingber DE 2005. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94:12128701
    [Google Scholar]
  45. 45. 
    Huang S, Guo Y-P, May G, Enver T 2007. Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305:2695–713
    [Google Scholar]
  46. 46. 
    Hupalowska A, Jedrusik A, Zhu M, Bedford MT, Glover DM, Zernicka-Goetz M 2018. CARM1 and paraspeckles regulate pre-implantation mouse embryo development. Cell 175:71902–16.e13
    [Google Scholar]
  47. 47. 
    Johnson MH, Ziomek CA. 1981. The foundation of two distinct cell lineages within the mouse morula. Cell 24:171–80
    [Google Scholar]
  48. 48. 
    Kang M, Garg V, Hadjantonakis A-K 2017. Lineage establishment and progression within the inner cell mass of the mouse blastocyst requires FGFR1 and FGFR2. Dev. Cell 41:5496–510.e5
    [Google Scholar]
  49. 49. 
    Kang M, Piliszek A, Artus J, Hadjantonakis A-K 2013. FGF4 is required for lineage restriction and salt-and-pepper distribution of primitive endoderm factors but not their initial expression in the mouse. Development 140:2267–79
    [Google Scholar]
  50. 50. 
    Kime C, Kiyonari H, Ohtsuka S, Kohbayashi E, Asahi M et al. 2019. Induced 2C expression and implantation-competent blastocyst-like cysts from primed pluripotent stem cells. Stem Cell Rep 13:3485–98
    [Google Scholar]
  51. 51. 
    Ko MS, Nakauchi H, Takahashi N 1990. The dose dependence of glucocorticoid-inducible gene expression results from changes in the number of transcriptionally active templates. EMBO J 9:92835–42
    [Google Scholar]
  52. 52. 
    Korotkevich E, Niwayama R, Courtois A, Friese S, Berger N et al. 2017. The apical domain is required and sufficient for the first lineage segregation in the mouse embryo. Dev. Cell 40:3235–47.e7
    [Google Scholar]
  53. 53. 
    Koutsourakis M, Langeveld A, Patient R, Beddington R, Grosveld F 1999. The transcription factor GATA6 is essential for early extraembryonic development. Development 126:9723–32
    [Google Scholar]
  54. 54. 
    Krawchuk D, Honma-Yamanaka N, Anani S, Yamanaka Y 2013. FGF4 is a limiting factor controlling the proportions of primitive endoderm and epiblast in the ICM of the mouse blastocyst. Dev. Biol. 384:165–71
    [Google Scholar]
  55. 55. 
    Krupa M, Mazur E, Szczepańska K, Filimonow K, Maleszewski M, Suwińska A 2014. Allocation of inner cells to epiblast versus primitive endoderm in the mouse embryo is biased but not determined by the round of asymmetric divisions (8→16- and 16→32-cells). Dev. Biol. 385:1136–48
    [Google Scholar]
  56. 56. 
    Krupinski P, Chickarmane V, Peterson C 2011. Simulating the mammalian blastocyst–molecular and mechanical interactions pattern the embryo. PLOS Comput. Biol. 7:5e1001128
    [Google Scholar]
  57. 57. 
    Liebisch T, Drusko A, Mathew B, Stelzer EHK, Fischer SC, Matthäus F 2019. Cell fate clusters in ICM organoids arise from cell fate heredity & division - a modelling approach. bioRxiv 698928. https://doi.org/10.1101/698928
    [Crossref]
  58. 58. 
    Liu R, Chen P, Aihara K, Chen L 2015. Identifying early-warning signals of critical transitions with strong noise by dynamical network markers. Sci. Rep. 5:17501
    [Google Scholar]
  59. 59. 
    Liu Y, Yang M, Deng Y, Su G, Guo CC et al. 2019. High-spatial-resolution multi-omics atlas sequencing of mouse embryos via deterministic barcoding in tissue. bioRxiv 788992. https://doi.org/10.1101/788992
    [Crossref]
  60. 60. 
    Lorthongpanich C, Doris TPY, Limviphuvadh V, Knowles BB, Solter D 2012. Developmental fate and lineage commitment of singled mouse blastomeres. Development 139:203722–31
    [Google Scholar]
  61. 61. 
    Martinez Arias A, Nichols J, Schröter C 2013. A molecular basis for developmental plasticity in early mammalian embryos. Development 140:173499–510
    [Google Scholar]
  62. 62. 
    Mathew B, Muñoz-Descalzo S, Corujo-Simon E, Schröter C, Stelzer EHK, Fischer SC 2019. Mouse ICM organoids reveal three-dimensional cell fate clustering. Biophys. J. 116:1127–41
    [Google Scholar]
  63. 63. 
    McDole K, Guignard L, Amat F, Berger A, Malandain G et al. 2018. In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175:3859–76.e33
    [Google Scholar]
  64. 64. 
    Menchero S, Rollan I, Lopez-Izquierdo A, Andreu MJ, Sainz de Aja J et al. 2019. Transitions in cell potency during early mouse development are driven by Notch. eLife 8:e42930
    [Google Scholar]
  65. 65. 
    Method of the Year 2019: Single-cell multimodal omics 2020. Nat. Methods 17:11
    [Google Scholar]
  66. 66. 
    Metzger JJ, Simunovic M, Brivanlou AH 2018. Synthetic embryology: controlling geometry to model early mammalian development. Curr. Opin. Genet. Dev. 52:86–91
    [Google Scholar]
  67. 67. 
    Mitsui K, Tokuzawa Y, Itoh H, Segawa K, Murakami M et al. 2003. The homeoprotein Nanog is required for maintenance of pluripotency in mouse epiblast and ES cells. Cell 113:5631–42
    [Google Scholar]
  68. 68. 
    Mohammed H, Hernando-Herraez I, Savino A, Scialdone A, Macaulay I et al. 2017. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep 20:51215–28
    [Google Scholar]
  69. 69. 
    Mojtahedi M, Skupin A, Zhou J, Castaño IG, Leong-Quong RYY et al. 2016. Cell fate decision as high-dimensional critical state transition. PLOS Biol 14:12e2000640
    [Google Scholar]
  70. 70. 
    Molotkov A, Mazot P, Brewer JR, Cinalli RM, Soriano P 2017. Distinct requirements for FGFR1 and FGFR2 in primitive endoderm development and exit from pluripotency. Dev. Cell 41:5511–26.e4
    [Google Scholar]
  71. 71. 
    Morgani SM, Metzger JJ, Nichols J, Siggia ED, Hadjantonakis A-K 2018. Micropattern differentiation of mouse pluripotent stem cells recapitulates embryo regionalized cell fate patterning. eLife 7:e32839
    [Google Scholar]
  72. 72. 
    Moris N, Anlas K, van den Brink SC, Alemany A, Schröder J et al. 2020. An in vitro model of early anteroposterior organization during human development. Nature 582:410–15
    [Google Scholar]
  73. 73. 
    Moris N, Edri S, Seyres D, Kulkarni R, Domingues AF et al. 2018. Histone acetyltransferase KAT2A stabilizes pluripotency with control of transcriptional heterogeneity. Stem Cells 36:121828–38
    [Google Scholar]
  74. 74. 
    Morris SA, Graham SJL, Jedrusik A, Zernicka-Goetz M 2013. The differential response to Fgf signalling in cells internalized at different times influences lineage segregation in preimplantation mouse embryos. Open Biol 3:11130104
    [Google Scholar]
  75. 75. 
    Morris SA, Teo RTY, Li H, Robson P, Glover DM, Zernicka-Goetz M 2010. Origin and formation of the first two distinct cell types of the inner cell mass in the mouse embryo. PNAS 107:146364–69
    [Google Scholar]
  76. 76. 
    Nichols J, Silva J, Roode M, Smith A 2009. Suppression of Erk signalling promotes ground state pluripotency in the mouse embryo. Development 136:193215–22
    [Google Scholar]
  77. 77. 
    Nichols J, Zevnik B, Anastassiadis K, Niwa H, Klewe-Nebenius D et al. 1998. Formation of pluripotent stem cells in the mammalian embryo depends on the POU transcription factor Oct4. Cell 95:3379–91
    [Google Scholar]
  78. 78. 
    Nissen SB, Perera M, Gonzalez JM, Morgani SM, Jensen MH et al. 2017. Four simple rules that are sufficient to generate the mammalian blastocyst. PLOS Biol 15:7e2000737
    [Google Scholar]
  79. 79. 
    Niwayama R, Moghe P, Liu Y-J, Fabrèges D, Buchholz F et al. 2019. A tug-of-war between cell shape and polarity controls division orientation to ensure robust patterning in the mouse blastocyst. Dev. Cell 51:5564–74.e6
    [Google Scholar]
  80. 80. 
    Nowotschin S, Setty M, Kuo Y-Y, Liu V, Garg V et al. 2019. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569:7756361–67
    [Google Scholar]
  81. 81. 
    Ohnishi Y, Huber W, Tsumura A, Kang M, Xenopoulos P et al. 2014. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16:127–37
    [Google Scholar]
  82. 82. 
    Pelkmans L. 2012. Using cell-to-cell variability–a new era in molecular biology. Science 336:6080425–26
    [Google Scholar]
  83. 83. 
    Perea-Gomez A, Camus A, Moreau A, Grieve K, Moneron G et al. 2004. Initiation of gastrulation in the mouse embryo is preceded by an apparent shift in the orientation of the anterior-posterior axis. Curr. Biol. 14:3197–207
    [Google Scholar]
  84. 84. 
    Pijuan-Sala B, Griffiths JA, Guibentif C, Hiscock TW, Jawaid W et al. 2019. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566:490–95
    [Google Scholar]
  85. 85. 
    Piras V, Tomita M, Selvarajoo K 2014. Transcriptome-wide variability in single embryonic development cells. Sci. Rep. 4:7137
    [Google Scholar]
  86. 86. 
    Plachta N, Bollenbach T, Pease S, Fraser SE, Pantazis P 2011. Oct4 kinetics predict cell lineage patterning in the early mammalian embryo. Nat. Cell Biol. 13:2117–23
    [Google Scholar]
  87. 87. 
    Ptashne M. 2004. A Genetic Switch: Phage Lambda Revisited Cold Spring Harbor, NY: Cold Spring Harb. Lab. Press. , 3rd ed..
    [Google Scholar]
  88. 88. 
    Raina D, Stanoev A, Bahadori A, Protzek M, Koseska A, Schröter C 2020. Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell fates in embryonic stem cells. bioRxiv 2020.02.14.949701. https://doi.org/10.1101/2020.02.14.949701
    [Crossref]
  89. 89. 
    Raj A, van Oudenaarden A 2008. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135:2216–26
    [Google Scholar]
  90. 90. 
    Richard A, Boullu L, Herbach U, Bonnafoux A, Morin V et al. 2016. Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process. PLOS Biol 14:12e1002585
    [Google Scholar]
  91. 91. 
    Rivron NC, Frias-Aldeguer J, Vrij EJ, Boisset J-C, Korving J et al. 2018. Blastocyst-like structures generated solely from stem cells. Nature 557:7703106–11
    [Google Scholar]
  92. 92. 
    Roberts RM, Katayama M, Magnuson SR, Falduto MT, Torres KEO 2011. Transcript profiling of individual twin blastomeres derived by splitting two-cell stage murine embryos. Biol. Reprod. 84:3487–94
    [Google Scholar]
  93. 93. 
    Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E et al. 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363:64341463–67
    [Google Scholar]
  94. 94. 
    Rossant J. 2018. Genetic control of early cell lineages in the mammalian embryo. Annu. Rev. Genet. 52:185–201
    [Google Scholar]
  95. 95. 
    Rubin H. 1990. The significance of biological heterogeneity. Cancer Metastasis Rev 9:11–20
    [Google Scholar]
  96. 96. 
    Saiz N, Mora-Bitria L, Rahman S, George H, Herder JP et al. 2020. Growth-factor-mediated coupling between lineage size and cell fate choice underlies robustness of mammalian development. eLife 9:e56079
    [Google Scholar]
  97. 97. 
    Saiz N, Williams KM, Seshan VE, Hadjantonakis A-K 2016. Asynchronous fate decisions by single cells collectively ensure consistent lineage composition in the mouse blastocyst. Nat. Commun. 7:13463
    [Google Scholar]
  98. 98. 
    Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR et al. 2009. Early-warning signals for critical transitions. Nature 461:726053–59
    [Google Scholar]
  99. 99. 
    Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W et al. 2012. Anticipating critical transitions. Science 338:6105344–48
    [Google Scholar]
  100. 100. 
    Schröter C, Rué P, Mackenzie JP, Martinez Arias A 2015. FGF/MAPK signaling sets the switching threshold of a bistable circuit controlling cell fate decisions in embryonic stem cells. Development 142:244205–16
    [Google Scholar]
  101. 101. 
    Scialdone A, Tanaka Y, Jawaid W, Moignard V, Wilson NK et al. 2016. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535:7611289–93
    [Google Scholar]
  102. 102. 
    Semrau S, Goldmann JE, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A 2017. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat. Commun. 8:11096
    [Google Scholar]
  103. 103. 
    Shahbazi MN, Siggia ED, Zernicka-Goetz M 2019. Self-organization of stem cells into embryos: a window on early mammalian development. Science 364:6444948–51
    [Google Scholar]
  104. 104. 
    Shahbazi MN, Zernicka-Goetz M. 2018. Deconstructing and reconstructing the mouse and human early embryo. Nat. Cell Biol. 20:8878–87
    [Google Scholar]
  105. 105. 
    Shi J, Chen Q, Li X, Zheng X, Zhang Y et al. 2015. Dynamic transcriptional symmetry-breaking in pre-implantation mammalian embryo development revealed by single-cell RNA-seq. Development 142:203468–77
    [Google Scholar]
  106. 106. 
    Simunovic M, Brivanlou AH. 2017. Embryoids, organoids and gastruloids: new approaches to understanding embryogenesis. Development 144:6976–85
    [Google Scholar]
  107. 107. 
    Snijder B, Pelkmans L. 2011. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12:2119–25
    [Google Scholar]
  108. 108. 
    Sozen B, Amadei G, Cox A, Wang R, Na E et al. 2018. Self-assembly of embryonic and two extra-embryonic stem cell types into gastrulating embryo-like structures. Nat. Cell Biol. 20:8979–89
    [Google Scholar]
  109. 109. 
    Stanoev A, Schröter C, Koseska A 2019. Robustness and timing of cellular differentiation through population-based symmetry breaking. bioRxiv 578898. https://doi.org/10.1101/578898
    [Crossref]
  110. 110. 
    Strnad P, Gunther S, Reichmann J, Krzic U, Balazs B et al. 2016. Inverted light-sheet microscope for imaging mouse pre-implantation development. Nat. Methods 13:2139–42
    [Google Scholar]
  111. 111. 
    Strumpf D, Mao C-A, Yamanaka Y, Ralston A, Chawengsaksophak K et al. 2005. Cdx2 is required for correct cell fate specification and differentiation of trophectoderm in the mouse blastocyst. Development 132:92093–102
    [Google Scholar]
  112. 112. 
    Takaoka K, Hamada H. 2014. Origin of cellular asymmetries in the pre-implantation mouse embryo: a hypothesis. Philos. Trans. R. Soc. B 369:165720130536
    [Google Scholar]
  113. 113. 
    Takaoka K, Yamamoto M, Shiratori H, Meno C, Rossant J et al. 2006. The mouse embryo autonomously acquires anterior-posterior polarity at implantation. Dev. Cell 10:4451–59
    [Google Scholar]
  114. 114. 
    Tarkowski AK, Wróblewska J. 1967. Development of blastomeres of mouse eggs isolated at the 4- and 8-cell stage. J. Embryol. Exp. Morphol. 18:1155–80
    [Google Scholar]
  115. 115. 
    Teschendorff AE, Enver T. 2017. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nat. Commun. 8:15599
    [Google Scholar]
  116. 116. 
    Thomas R, Kaufman M. 2001. Multistationarity, the basis of cell differentiation and memory. I. Structural conditions of multistationarity and other nontrivial behavior. Chaos 11:1170–79
    [Google Scholar]
  117. 117. 
    Thomas R, Kaufman M. 2001. Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. Chaos 11:1180–95
    [Google Scholar]
  118. 118. 
    Torres-Padilla M-E, Parfitt D-E, Kouzarides T, Zernicka-Goetz M 2007. Histone arginine methylation regulates pluripotency in the early mouse embryo. Nature 445:7124214–18
    [Google Scholar]
  119. 119. 
    Tosenberger A, Gonze D, Bessonnard S, Cohen-Tannoudji M, Chazaud C, Dupont G 2017. A multiscale model of early cell lineage specification including cell division. NPJ Syst. Biol. Appl. 3:16
    [Google Scholar]
  120. 120. 
    Tsuchiya M, Giuliani A, Hashimoto M, Erenpreisa J, Yoshikawa K 2016. Self-organizing global gene expression regulated through criticality: mechanism of the cell-fate change. PLOS ONE 11:12e0167912
    [Google Scholar]
  121. 121. 
    van den Brink SC, Alemany A, van Batenburg V, Moris N, Blotenburg M et al. 2020. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582:405–9
    [Google Scholar]
  122. 122. 
    Vianello S, Lutolf MP. 2019. Understanding the mechanobiology of early mammalian development through bioengineered models. Dev. Cell 48:6751–63
    [Google Scholar]
  123. 123. 
    Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L et al. 2019. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16:10987–90
    [Google Scholar]
  124. 124. 
    Vrij EJ, Scholte, op Reimer Y, Aldeguer JF, Guerreiro IM, Kind J et al. 2019. Chemically-defined induction of a primitive endoderm and epiblast-like niche supports post-implantation progression from blastoids. bioRxiv 510396. https://doi.org/10.1101/510396
    [Crossref]
  125. 125. 
    Wang J, Wang L, Feng G, Wang Y, Li Y et al. 2018. Asymmetric expression of LincGET biases cell fate in two-cell mouse embryos. Cell 175:71887–901.e18
    [Google Scholar]
  126. 126. 
    Wassef M, Rodilla V, Teissandier A, Zeitouni B, Gruel N et al. 2015. Impaired PRC2 activity promotes transcriptional instability and favors breast tumorigenesis. Genes Dev 29:242547–62
    [Google Scholar]
  127. 127. 
    Wennekamp S, Mesecke S, Nédélec F, Hiiragi T 2013. A self-organization framework for symmetry breaking in the mammalian embryo. Nat. Rev. Mol. Cell Biol. 14:7452–59
    [Google Scholar]
  128. 128. 
    White MD, Angiolini JF, Alvarez YD, Kaur G, Zhao ZW et al. 2016. Long-lived binding of Sox2 to DNA predicts cell fate in the four-cell mouse embryo. Cell 165:175–87
    [Google Scholar]
  129. 129. 
    Wu G, Gentile L, Fuchikami T, Sutter J, Psathaki K et al. 2010. Initiation of trophectoderm lineage specification in mouse embryos is independent of Cdx2. Development 137:244159–69
    [Google Scholar]
  130. 130. 
    Xenopoulos P, Kang M, Puliafito A, Di Talia S, Hadjantonakis A-K 2015. Heterogeneities in Nanog expression drive stable commitment to pluripotency in the mouse blastocyst. Cell Rep 10:91508–20
    [Google Scholar]
  131. 131. 
    Yamanaka Y, Lanner F, Rossant J 2010. FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst. Development 137:5715–24
    [Google Scholar]
  132. 132. 
    Zhang HT, Hiiragi T. 2018. Symmetry breaking in the mammalian embryo. Annu. Rev. Cell Dev. Biol. 34:405–26
    [Google Scholar]
  133. 133. 
    Zhang S, Chen T, Chen N, Gao D, Shi B et al. 2019. Implantation initiation of self-assembled embryo-like structures generated using three types of mouse blastocyst-derived stem cells. Nat. Commun. 10:1496
    [Google Scholar]
  134. 134. 
    Zhou JX, Aliyu MDS, Aurell E, Huang S 2012. Quasi-potential landscape in complex multi-stable systems. J. R. Soc. Interface 9:773539–53
    [Google Scholar]
  135. 135. 
    Zhu C, Preissl S, Ren B 2020. Single-cell multimodal omics: the power of many. Nat. Methods 17:111–14
    [Google Scholar]
/content/journals/10.1146/annurev-genet-021920-110200
Loading
/content/journals/10.1146/annurev-genet-021920-110200
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error