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Single-cell transcriptional profiling: a window into embryonic cell-type specification

Nature Reviews Molecular Cell Biology volume 19, pages 399–412 (2018)Cite this article

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Abstract

During mammalian embryonic development, a single fertilized egg cell will proliferate and differentiate into all the cell lineages and cell types that eventually form the adult organism. Cell lineage diversification involves repeated cell fate choices that ultimately occur at the level of the individual cell rather than at the cell-population level. Improvements in single-cell technologies are transforming our understanding of mammalian development, not only by overcoming the limitations presented by the extremely low cell numbers of early embryos but also by enabling the study of cell fate specification in greater detail. In this Review, we first discuss recent advances in single-cell transcriptomics and imaging and provide a brief outline of current bioinformatics methods available to analyse the resulting data. We then discuss how these techniques have contributed to our understanding of pre-implantation and early post-implantation development and of in vitro pluripotency. Finally, we overview the current challenges facing single-cell research and highlight the latest advances and potential future avenues.

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Fig. 1: Single-cell transcriptomics.
Fig. 2: Cell fate specification during mouse embryonic development.
Fig. 3: Mesoderm differentiation during gastrulation.
Fig. 4: Pluripotent stem cells to understand cell states.

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References

  1. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Herzenberg, L. A. et al. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 48, 1819–1827 (2002).

    PubMed  CAS  Google Scholar 

  3. Brady, G., Barbara, M. & Iscove, N. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol. Cell. Biol. 2, 17–23 (1990).

    CAS  Google Scholar 

  4. Higuchi, R., Dollinger, G., Walsh, P. S. & Griffith, R. Simultaneous Amplification and detection of specific DNA sequences. Nat. Biotechnol. 10, 413–417 (1992).

    Article  CAS  Google Scholar 

  5. Higuchi, R., Fockler, C., Dollinger, G. & Watson, R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Nat. Biotechnol. 11, 1026–1030 (1993).

    Article  CAS  Google Scholar 

  6. Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675–685 (2010).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  9. Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42–e42 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  PubMed  CAS  Google Scholar 

  13. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  PubMed  CAS  Google Scholar 

  14. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013). This report presents the Smart-seq2 protocol for scRNA-seq.

    Article  PubMed  CAS  Google Scholar 

  16. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). This article presents Drop-seq, a droplet-based protocol that performs scRNA-seq.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Ibarra-Soria, X. et al. Defining murine organogenesis at single cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation. Nat. Cell Biol. 20, 127–134 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  22. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Kolodziejczyk, A. A. et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17, 471–485 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Maaten, L. J. P. van der & Hinton, G. E. Visualizing high-dimensional data using t-SNE. J. Machine Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  27. Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

    Article  PubMed  CAS  Google Scholar 

  29. Scialdone, A. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016). This study characterizes the E6.5 epiblast, the Flk1+ mesodermal progenitor population and the differentiation path towards blood during gastrulation using the Smart-seq2 protocol.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Brunskill, E. W. et al. A gene expression atlas of early craniofacial development. Dev. Biol. 391, 133–146 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. DeLaughter, D. M. et al. Single-cell resolution of temporal gene expression during heart development. Dev. Cell 39, 480–490 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Petropoulos, S. et al. Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 165, 1012–1026 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Singer, Z. S. et al. Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol. Cell 55, 319–331 (2014). This study describes the expression dynamics of Nanog in mESCs, which includes transcription bursts.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    Article  PubMed  CAS  Google Scholar 

  41. Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc 10, 442–458 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Haim, L., Zipor, G., Aronov, S. & Gerst, J. E. A genomic integration method to visualize localization of endogenous mRNAs in living yeast. Nat. Methods 4, 409–412 (2007).

    PubMed  CAS  Google Scholar 

  43. Hocine, S., Raymond, P., Zenklusen, D., Chao, J. A. & Singer, R. H. Single-molecule analysis of gene expression using two-color RNA labeling in live yeast. Nat. Methods 10, 119–121 (2013).

    Article  PubMed  CAS  Google Scholar 

  44. Graf, T. & Enver, T. Forcing cells to change lineages. Nature 462, 587–594 (2009).

    Article  PubMed  CAS  Google Scholar 

  45. Hoppe, P. S. et al. Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature 535, 299–302 (2016).

    Article  PubMed  CAS  Google Scholar 

  46. Strumpf, D. et al. Cdx2 is required for correct cell fate specification and differentiation of trophectoderm in the mouse blastocyst. Development 132, 2093–2102 (2005).

    Article  PubMed  CAS  Google Scholar 

  47. Palmieri, S. L., Peter, W., Hess, H. & Schöler, H. R. Oct-4 Transcription factor is differentially expressed in the mouse embryo during establishment of the first two extraembryonic cell lineages involved in implantation. Dev. Biol. 166, 259–267 (1994).

    Article  PubMed  CAS  Google Scholar 

  48. 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  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  50. 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  PubMed  CAS  Google Scholar 

  51. Schrode, N., Saiz, N., Di Talia, S. & Hadjantonakis, A.-K. GATA6 levels modulate primitive endoderm cell fate choice and timing in the mouse blastocyst. Dev. Cell 29, 454–467 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Dietrich, J.-E. & Hiiragi, T. Stochastic patterning in the mouse pre-implantation embryo. Development 134, 4219–4231 (2007).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Alarcón, V. B. & Marikawa, Y. Deviation of the blastocyst axis from the first cleavage plane does not affect the quality of mouse postimplantation development. Biol. Reprod. 69, 1208–1212 (2003).

    Article  PubMed  CAS  Google Scholar 

  55. Hiiragi, T. & Solter, D. First cleavage plane of the mouse egg is not predetermined but defined by the topology of the two apposing pronuclei. Nature 430, 360–364 (2004).

    Article  PubMed  CAS  Google Scholar 

  56. Motosugi, N., Bauer, T., Polanski, Z., Solter, D. & Hiiragi, T. Polarity of the mouse embryo is established at blastocyst and is not prepatterned. Genes Dev. 19, 1081–1092 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Anani, S., Bhat, S., Honma-Yamanaka, N., Krawchuk, D. & Yamanaka, Y. Initiation of Hippo signaling is linked to polarity rather than to cell position in the pre-implantation mouse embryo. Development 141, 2813–2824 (2014).

    Article  PubMed  CAS  Google Scholar 

  58. Bischoff, M., Parfitt, D.-E. & Zernicka-Goetz, M. Formation of the embryonic-abembryonic axis of the mouse blastocyst: relationships between orientation of early cleavage divisions and pattern of symmetric/asymmetric divisions. Development 135, 953–962 (2008).

    Article  PubMed  CAS  Google Scholar 

  59. Johnson, M. H. & Ziomek, C. A. The foundation of two distinct cell lineages within the mouse morula. Cell 24, 71–80 (1981).

    Article  PubMed  CAS  Google Scholar 

  60. Korotkevich, E. et al. The apical domain is required and sufficient for the first lineage segregation in the mouse embryo. Dev. Cell 40, 235–247.e7 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Piotrowska, K. & Zernicka-Goetz, M. Role for sperm in spatial patterning of the early mouse embryo. Nature 409, 517–521 (2001).

    Article  PubMed  CAS  Google Scholar 

  62. Plachta, N., Bollenbach, T., Pease, S., Fraser, S. E. & Pantazis, P. Oct4 kinetics predict cell lineage patterning in the early mammalian embryo. Nat. Cell Biol. 13, 117–123 (2011).

    Article  PubMed  CAS  Google Scholar 

  63. Tabansky, I. et al. Developmental bias in cleavage-stage mouse blastomeres. Curr. Biol. 23, 21–31 (2013).

    Article  PubMed  CAS  Google Scholar 

  64. Torres-Padilla, M.-E., Parfitt, D.-E., Kouzarides, T. & Zernicka-Goetz, M. Histone arginine methylation regulates pluripotency in the early mouse embryo. Nature 445, 214–218 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Zernicka-Goetz, M. Development: do mouse embryos play dice? Curr. Biol. 23, R15–R17 (2013).

    Article  PubMed  CAS  Google Scholar 

  66. Biase, F., Cao, X. & Zhong, S. Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing. Genome Res. 24, 1787–1796 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Shi, J. et al. Dynamic transcriptional symmetry-breaking in pre-implantation mammalian embryo development revealed by single-cell RNA-seq. Development 142, 3468–3477 (2015).

    Article  PubMed  CAS  Google Scholar 

  68. Flach, G., Johnson, M. H., Braude, P. R., Taylor, R. A. & Bolton, V. N. The transition from maternal to embryonic control in the 2-cell mouse embryo. EMBO J. 1, 681–686 (1982).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Blakeley, P. et al. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Development 142, 3151–3165 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Braude, P., Bolton, V. & Moore, S. Human gene expression first occurs between the four- and eight-cell stages of preimplantation development. Nature 332, 459–461 (1988).

    Article  PubMed  CAS  Google Scholar 

  71. Niakan, K. K. & Eggan, K. Analysis of human embryos from zygote to blastocyst reveals distinct gene expression patterns relative to the mouse. Dev. Biol. 375, 54–64 (2013).

    Article  PubMed  CAS  Google Scholar 

  72. Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Maître, J.-L. et al. Asymmetric division of contractile domains couples cell positioning and fate specification. Nature 536, 344 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Maître, J.-L. Mechanics of blastocyst morphogenesis. Biol. Cell 109, 323–338 (2017).

    Article  PubMed  Google Scholar 

  75. Chan, C. J., Heisenberg, C.-P. & Hiiragi, T. Coordination of morphogenesis and cell-fate specification in development. Curr. Biol. 27, R1024–R1035 (2017).

    Article  PubMed  CAS  Google Scholar 

  76. Chazaud, C., Yamanaka, Y., Pawson, T. & Rossant, J. Early lineage segregation between epiblast and primitive endoderm in mouse blastocysts through the Grb2-MAPK pathway. Dev. Cell 10, 615–624 (2006).

    Article  PubMed  CAS  Google Scholar 

  77. Morris, S. A. et al. Origin and formation of the first two distinct cell types of the inner cell mass in the mouse embryo. Proc. Natl Acad. Sci. USA 107, 6364–6369 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Morris, S. A., Graham, S. J. L., Jedrusik, A. & Zernicka-Goetz, M. The differential response to Fgf signalling in cells internalized at different times influences lineage segregation in preimplantation mouse embryos. Open Biol. 3, 130104 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Yamanaka, Y., Lanner, F. & Rossant, J. FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst. Development 137, 715–724 (2010).

    Article  PubMed  CAS  Google Scholar 

  80. 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). Using single-cell transcriptomics, this study identifies Fgf4 as one of the first genes to be differentially expressed within the ICM. Evaluation of Fgf4 mutant embryos with scRNA-seq shows that Fgf4 −/− cells are arrested before the decision between epiblast and primitive endoderm occurs.

    Article  PubMed  CAS  Google Scholar 

  81. Xenopoulos, P., Kang, M., Puliafito, A., Di Talia, S. & Hadjantonakis, A.-K. Heterogeneities in Nanog expression drive stable commitment to pluripotency in the mouse blastocyst. Cell Rep. 10, 1508–1520 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  83. Molotkov, A., Mazot, P., Brewer, J. R., Cinalli, R. M. & Soriano, P. Distinct requirements for FGFR1 and FGFR2 in primitive endoderm development and exit from pluripotency. Dev. Cell 41, 511–526.e4 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  84. Kang, M., Garg, V. & Hadjantonakis, A.-K. Lineage establishment and progression within the inner cell mass of the mouse blastocyst requires FGFR1 and FGFR2. Dev. Cell 41, 496–510.e5 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  85. Arnold, S. J. & Robertson, E. J. Making a commitment: cell lineage allocation and axis patterning in the early mouse embryo. Nat. Rev. Mol. Cell Biol. 10, 91–103 (2009).

    Article  PubMed  CAS  Google Scholar 

  86. Sutherland, A. E. Tissue morphodynamics shaping the early mouse embryo. Semin. Cell Dev. Biol. 55, 89–98 (2016).

    Article  PubMed  Google Scholar 

  87. Tam, P. P. L. & Behringer, R. R. Mouse gastrulation: the formation of a mammalian body plan. Mech. Dev. 68, 3–25 (1997).

    Article  PubMed  CAS  Google Scholar 

  88. Tam, P. P., Parameswaran, M., Kinder, S. J. & Weinberger, R. P. The allocation of epiblast cells to the embryonic heart and other mesodermal lineages: the role of ingression and tissue movement during gastrulation. Development 124, 1631–1642 (1997).

    PubMed  CAS  Google Scholar 

  89. Wen, J. et al. Single-cell analysis reveals lineage segregation in early post-implantation mouse embryos. J. Biol. Chem. 292, 9840–9854 (2017). Using single-cell transcriptional profiling, this study reveals the existence of a population of mesendodermal cells as early as E5.5, potentially one of the earliest populations after the exit from epiblast.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Rodaway, A. & Patient, R. Mesendoderm: an ancient germ layer? Cell 105, 169–172 (2001).

    Article  PubMed  CAS  Google Scholar 

  91. Tada, S. et al. Characterization of mesendoderm: a diverging point of the definitive endoderm and mesoderm in embryonic stem cell differentiation culture. Development 132, 4363–4374 (2005).

    Article  PubMed  CAS  Google Scholar 

  92. Ema, M. et al. Combinatorial effects of Flk1 and Tal1 on vascular and hematopoietic development in the mouse. Genes Dev. 17, 380–393 (2003).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Motoike, T., Markham, D. W., Rossant, J. & Sato, T. N. Evidence for novel fate of Flk1 + progenitor: contribution to muscle lineage. Genes 35, 153–159 (2003).

    Article  Google Scholar 

  94. Yamashita, J. et al. Flk1-positive cells derived from embryonic stem cells serve as vascular progenitors. Nature 408, 92–96 (2000).

    Article  PubMed  CAS  Google Scholar 

  95. Ferdous, A. et al. Nkx2–5 transactivates the Ets-related protein 71 gene and specifies an endothelial/endocardial fate in the developing embryo. Proc. Natl Acad. Sci. USA 106, 814–819 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Rasmussen, T. L. et al. ER71 directs mesodermal fate decisions during embryogenesis. Development 138, 4801–4812 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Gong, W. et al. Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis. Nat. Commun. 8, 14362 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Saga, Y. et al. MesP1 is expressed in the heart precursor cells and required for the formation of a single heart tube. Development 126, 3437–3447 (1999).

    PubMed  CAS  Google Scholar 

  99. Devine, W. P., Wythe, J. D., George, M., Koshiba-Takeuchi, K. & Bruneau, B. G. Early patterning and specification of cardiac progenitors in gastrulating mesoderm. eLife 3, e03848 (2014).

    Article  PubMed Central  CAS  Google Scholar 

  100. Lescroart, F. et al. Early lineage restriction in temporally distinct populations of Mesp1 progenitors during mammalian heart development. Nat. Cell Biol. 16, 829–840 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  101. Evans, M. J. & Kaufman, M. H. Establishment in culture of pluripotential cells from mouse embryos. Nature 292, 154–156 (1981).

    Article  PubMed  CAS  Google Scholar 

  102. Weinberger, L., Ayyash, M., Novershtern, N. & Hanna, J. H. Dynamic stem cell states: naive to primed pluripotency in rodents and humans. Nat. Rev. Mol. Cell Biol. 17, 155–169 (2016).

    Article  PubMed  CAS  Google Scholar 

  103. Guo, G. et al. Serum-based culture conditions provoke gene expression variability in mouse embryonic stem cells as revealed by single-cell analysis. Cell Rep. 14, 956–965 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. 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).

    Article  PubMed  CAS  Google Scholar 

  105. Filipczyk, A. et al. Network plasticity of pluripotency transcription factors in embryonic stem cells. Nat. Cell Biol. 17, 1235–1246 (2015).

    Article  PubMed  CAS  Google Scholar 

  106. Cannon, D., Corrigan, A. M., Miermont, A., McDonel, P. & Chubb, J. R. Multiple cell and population-level interactions with mouse embryonic stem cell heterogeneity. Development 142, 2840–2849 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Macfarlan, T. S. et al. Embryonic stem cell potency fluctuates with endogenous retrovirus activity. Nature 487, 57–63 (2012). Using single-cell imaging, this study identifies 2C-like cells, a rare subpopulation within mESCs that resembles the in vivo 2-cell stage.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Zalzman, M. et al. Zscan4 regulates telomere elongation and genomic stability in ES cells. Nature 464, 858–863 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Eckersley-Maslin, M. A. et al. MERVL/Zscan4 network activation results in transient genome-wide DNA demethylation of mESCs. Cell Rep. 17, 179–192 (2016). This study shows similarities between the in vivo 2-cell state and 2 C-like cells at the transcriptional level. Furthermore, 2 C-like cells have open chromatin and hypomethylated DNA, both characteristics of the in vivo 2-cell stage.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. 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  PubMed  CAS  Google Scholar 

  112. Takahashi, K. & Yamanaka, S. A decade of transcription factor-mediated reprogramming to pluripotency. Nat. Rev. Mol. Cell Biol. 17, 183–193 (2016).

    Article  PubMed  CAS  Google Scholar 

  113. Lujan, E. et al. Early reprogramming regulators identified by prospective isolation and mass cytometry. Nature 521, 352–356 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. O’Malley, J. et al. High-resolution analysis with novel cell-surface markers identifies routes to iPS cells. Nature 499, 88–91 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Chung, K.-M. et al. Single cell analysis reveals the stochastic phase of reprogramming to pluripotency is an ordered probabilistic process. PLoS ONE 9, e95304 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. P. A. Continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16, 323–337 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Kim, D. H. et al. Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming. Cell Stem Cell 16, 88–101 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Polo, J. M. et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 151, 1617–1632 (2012). Transcriptional analyses define the reprogramming towards iPSC as a two-step process, where DNA methylation changes occur late in reprogramming.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Smith, Z. D., Nachman, I., Regev, A. & Meissner, A. Dynamic single-cell imaging of direct reprogramming reveals an early specifying event. Nat. Biotechnol. 28, 521–526 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Apostolou, E. & Hochedlinger, K. Chromatin dynamics during cellular reprogramming. Nature 502, 462–471 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Pasque, V. et al. X chromosome reactivation dynamics reveal stages of reprogramming to pluripotency. Cell 159, 1681–1697 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    Article  PubMed  CAS  Google Scholar 

  124. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  125. Espina, V. et al. Laser-capture microdissection. Nat. Protoc. 1, 586–603 (2006).

    Article  PubMed  CAS  Google Scholar 

  126. Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).

    Article  PubMed  CAS  Google Scholar 

  127. Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016). This study presents a spatial transcriptome map of the E7.0 epiblast. This was achieved using laser capture microdissection and profiling pools of 20 cells by scRNA-seq while keeping track of their original locations.

    Article  PubMed  CAS  Google Scholar 

  128. Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018). This study reports a combined method to obtain the transcriptome, chromatin accessibility and DNA methylation states of individual cells.

    Google Scholar 

  129. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    Article  PubMed  CAS  Google Scholar 

  130. Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). This study presents a proof-of-principle experiment of lineage tracking at single-cell resolution. Using a genomic barcode harbouring unique CRISPR–Cas9 target sites in a fertilized zebrafish egg gives rise to the accumulation of thousands of uniquely edited barcodes in the offspring cells of the adult fish, enabling the authors to infer lineage relationships between all adult cells.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

B.P.-S. is funded by the Wellcome Trust 4-Year PhD programme in Stem Cell Biology and Medicine and the University of Cambridge, UK. C.G. is funded by the Swedish Research Council. Research in the Göttgens laboratory is supported by programme grants from the Wellcome Trust, CRUK and Bloodwise and by a Wellcome Strategic Award to study cell fate decisions during gastrulation (105031/D/14/Z). The authors also gratefully acknowledge core support funding from the Wellcome Trust to the Wellcome–Medical Research Council Cambridge Stem Cell Institute.

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  1. Wellcome and MRC Cambridge Stem Cell Institute and the Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

    Blanca Pijuan-Sala, Carolina Guibentif & Berthold Göttgens

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  1. Blanca Pijuan-Sala
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  2. Carolina Guibentif
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  3. Berthold Göttgens
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B.P.-S. researched data for the article and wrote the article. C.G. contributed substantially to the discussion of the content. B.P.-S., C.G. and B.G. reviewed and edited the manuscript before submission.

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Correspondence to Berthold Göttgens.

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Glossary

Fluorescence-activated cell sorting

Flow cytometry method to analyse and sort single cells based on the expression of cell-surface markers.

Microfluidics systems

Automated technologies based on the use of microminiaturized devices for mixing and manipulating low fluid volumes aiming to achieve multiplexing and high-throughput yields.

Blastocyst

Embryonic stage composed of inner cell mass cells, a fluid-filled cavity called blastocoel and outer trophectoderm cells.

Epiblast

Group of cells derived from the inner cell mass that will give rise to the embryo proper.

Primitive endoderm

Group of cells derived from the inner cell mass that contribute to extra-embryonic tissues such as the yolk sac.

Unique molecular identifiers

Short sequences that uniquely tag individual RNA molecules.

Dimensionality-reduction approaches

Methods used in high-dimensional datasets, where each gene represents a dimension, to reduce the number of dimensions and elicit the visualization of the data set structure in a two-dimensional or three-dimensional plot.

Gastrulation

Embryonic process, following implantation, where epiblast cells become specified into the three germ layers (ectoderm, mesoderm and endoderm).

Inner cell mass

(ICM). Group of cells located inside the blastocyst that will give rise to the primitive endoderm and the epiblast.

Trophectoderm

Group of cells located on the outer part of the blastocyst that will become the supportive extra-embryonic tissues, such as the placenta.

Blastomeres

The cells resulting from the first divisions of the fertilized egg.

Maternal-to-zygotic transition

Process occurring shortly after fertilization, where maternal RNA and proteins are degraded and the zygotic genome is activated and produces RNA and proteins.

Bimodal distribution

In gene expression analyses, a gene being either (a) highly expressed or (b) not or lowly expressed, with small numbers of cells displaying intermediate levels. Cells can thus be divided into two subpopulations based on the expression levels of that particular gene.

Salt-and-pepper

Group of inner cell mass cells with heterogeneous expression of epiblast and primitive endoderm markers, where cells expressing more epiblast markers are intermingled with cells expressing more primitive endoderm markers.

Morula

Early embryonic stage where the embryo is composed of a symmetric ball of morphologically similar cells.

Primitive streak

Morphological structure at the posterior side of the embryo formed by the accumulation of cells. It is where epiblast cells will ingress to become mesoderm or endoderm.

Mesendodermal progenitor

A cell that can give rise to either mesoderm or endoderm.

Yolk sac

Extra-embryonic tissue that originates from the primitive endoderm.

Boolean algorithm

Qualitative algorithm based on the Boolean (binary) logic, where only two values are accepted. In gene regulatory networks, one value will be active and the other one inactive.

Unimodal distribution

In gene expression analyses, unimodal distribution refers to a gene being mostly expressed at intermediate levels.

X-chromosome reactivation

Process where the inactivated X chromosome in mammalian female cells becomes reactivated.

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Pijuan-Sala, B., Guibentif, C. & Göttgens, B. Single-cell transcriptional profiling: a window into embryonic cell-type specification. Nat Rev Mol Cell Biol 19, 399–412 (2018). https://doi.org/10.1038/s41580-018-0002-5

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