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Spatial profiling of early primate gastrulation in utero

Nature volume 609, pages 136–143 (2022)Cite this article

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Abstract

Gastrulation controls the emergence of cellular diversity and axis patterning in the early embryo. In mammals, this transformation is orchestrated by dynamic signalling centres at the interface of embryonic and extraembryonic tissues1,2,3. Elucidating the molecular framework of axis formation in vivo is fundamental for our understanding of human development4,5,6 and to advance stem-cell-based regenerative approaches7. Here we illuminate early gastrulation of marmoset embryos in utero using spatial transcriptomics and stem-cell-based embryo models. Gaussian process regression-based 3D transcriptomes delineate the emergence of the anterior visceral endoderm, which is hallmarked by conserved (HHEX, LEFTY2, LHX1) and primate-specific (POSTN, SDC4, FZD5) factors. WNT signalling spatially coordinates the formation of the primitive streak in the embryonic disc and is counteracted by SFRP1 and SFRP2 to sustain pluripotency in the anterior domain. Amnion specification occurs at the boundaries of the embryonic disc through ID1, ID2 and ID3 in response to BMP signalling, providing a developmental rationale for amnion differentiation of primate pluripotent stem cells (PSCs). Spatial identity mapping demonstrates that primed marmoset PSCs exhibit the highest similarity to the anterior embryonic disc, whereas naive PSCs resemble the preimplantation epiblast. Our 3D transcriptome models reveal the molecular code of lineage specification in the primate embryo and provide an in vivo reference to decipher human development.

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Fig. 1: Spatial profiling of marmoset embryogenesis.
Fig. 2: Virtual reconstruction of marmoset embryos during gastrulation.
Fig. 3: 3D transcriptomes and stem-cell-based embryo models delineate body axis formation.
Fig. 4: Spatial identity mapping of in vitro cultured cells.

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Data availability

Access to marmoset 3D transcriptomes in virtual cross-sections from zygote to gastrula is available online (http://131.111.33.80/marmoset3D/). Please allow 5–10 s to load the transcriptome data at the start. STEP and single-cell RNA-seq data are available at the ArrayExpress and Gene Expression Omnibus repositories under accession numbers E-MTAB-9367 (spatial embryo profiling of primate gastrulation), E-MTAB-9349 (primed and naive marmoset pluripotent stem cells as a model for primate development), E-MTAB-9388 (http://www.human-gastrula.net) 9, GSE136447 (ref. 12), GSE74767 (ref. 25), GSE76267 (ref. 39), E-MTAB-6819 (ref. 43) and GSE134571 (ref. 16). 3D embryo reconstructions are available through Morphosource under accession numbers: CS5 (000419264), early CS6 (000419284), late CS6 (000419272) and CS7 (000419278). Source data are provided with this paper.

Code availability

The code used is available at GitHub (https://github.com/Boroviak-Lab/SpatialModelling).

References

  1. Tam, P. P. & Loebel, D. A. Gene function in mouse embryogenesis: get set for gastrulation. Nat. Rev. Genet. 8, 368–381 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Rossant, J. & Tam, P. P. L. L. Blastocyst lineage formation, early embryonic asymmetries and axis patterning in the mouse. Development 136, 701–713 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. 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  CAS  PubMed  Google Scholar 

  4. Rossant, J. & Tam, P. P. L. Exploring early human embryo development. Science 360, 1075–1076 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Boroviak, T. & Nichols, J. Primate embryogenesis predicts the hallmarks of human naïve pluripotency. Development 144, 175–186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Simunovic, M. & Brivanlou, A. H. Embryoids, organoids and gastruloids: New approaches to understanding embryogenesis. Development 144, 976–985 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tabar, V. & Studer, L. Pluripotent stem cells in regenerative medicine: challenges and recent progress. Nat. Rev. Genet. 15, 82–92 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ross, C. & Boroviak, T. E. Origin and function of the yolk sac in primate embryogenesis. Nat. Commun. 11, 3760 (2020) .

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  9. Tyser, R. C. V. et al. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Deglincerti, A. et al. Self-organization of the in vitro attached human embryo. Nature 533, 251–254 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Shahbazi, M. N. et al. Self-organization of the human embryo in the absence of maternal tissues. Nat. Cell Biol. 18, 700–708 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xiang, L. et al. A developmental landscape of 3D-cultured human pre-gastrulation embryos. Nature 577, 537–542 (2019).

  13. Ma, H. et al. In vitro culture of cynomolgus monkey embryos beyond early gastrulation. Science 366, eaax7890 (2019).

  14. Niu, Y. et al. Dissecting primate early post-implantation development using long-term in vitro embryo culture. Science 366, eaaw5754 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Shao, Y. et al. Self-organized amniogenesis by human pluripotent stem cells in a biomimetic implantation-like niche. Nat. Mater. 16, 419–425 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Zheng, Y. et al. Controlled modelling of human epiblast and amnion development using stem cells. Nature 573, 421–425 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shao, Y. et al. A pluripotent stem cell-based model for post-implantation human amniotic sac development. Nat. Commun. 8, 208 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Warmflash, A., Sorre, B., Etoc, F., Siggia, E. D. & Brivanlou, A. H. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat. Methods 11, 847–854 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Martyn, I., Siggia, E. D. & Brivanlou, A. H. Mapping cell migrations and fates in a gastruloid model to the human primitive streak. Development 146, dev179564 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sato, K. et al. Generation of a nonhuman primate model of severe combined immunodeficiency using highly efficient genome editing. Cell Stem Cell 19, 127–138 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Okano, H., Hikishima, K., Iriki, A. & Sasaki, E. The common marmoset as a novel animal model system for biomedical and neuroscience research applications. Semin. Fetal Neonatal Med. 17, 336–340 (2012).

    Article  PubMed  Google Scholar 

  22. Sasaki, E. et al. Generation of transgenic non-human primates with germline transmission. Nature 459, 523–527 (2009).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Boroviak, T. et al. Single cell transcriptome analysis of human, marmoset and mouse embryos reveals common and divergent features of preimplantation development. Development 145, dev167833 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nakamura, T. et al. A developmental coordinate of pluripotency among mice, monkeys and humans. Nature 537, 57–62 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Rasmussen, C. E. & Williams, C. K. I. in Gaussian Processes for Machine Learning Ch. 2 (MIT Press, 2006).

  27. Matheron, G. Krigeage d’un panneau rectangulaire par sa périphérie. Note Géostatistique 28 (1960).

  28. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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).

  30. Peng, G. et al. Molecular architecture of lineage allocation and tissue organization in early mouse embryo. Nature 572, 528–532 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Rivera-Pérez, J. A. & Magnuson, T. Primitive streak formation in mice is preceded by localized activation of Brachyury and Wnt3. Dev. Biol. 288, 363–371 (2005).

    Article  PubMed  Google Scholar 

  32. Perea-Gomez, A. et al. Nodal antaginists in the anterior visceral endoderm prevent the formation of multiple primitive streaks. Dev. Cell 3, 745–756 (2002).

    Article  CAS  PubMed  Google Scholar 

  33. Molè, M. A. et al. A single cell characterisation of human embryogenesis identifies pluripotency transitions and putative anterior hypoblast centre. Nat. Commun. 12, 3679 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  34. Wamaitha, S. E. et al. IGF1-mediated human embryonic stem cell self-renewal recapitulates the embryonic niche. Nat. Commun. 11, 764 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zorzan, I. et al. The transcriptional regulator ZNF398 mediates pluripotency and epithelial character downstream of TGF-beta in human PSCs Nat. Commun. 11, 2364 (2020).

  36. Nakaki, F. & Saitou, M. PRDM14: a unique regulator for pluripotency and epigenetic reprogramming. Trends Biochem. Sci. 39, 289–298 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Irie, N. et al. SOX17 is a critical specifier of human primordial germ cell fate. Cell 160, 253–268 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kobayashi, T. & Surani, M. A. On the origin of the human germline. Development 145, dev150433 (2018).

    Article  PubMed  Google Scholar 

  39. Sasaki, K. et al. The germ cell fate of cynomolgus monkeys is specified in the nascent amnion. Dev. Cell 39, 169–185 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Tewary, M. et al. A stepwise model of reaction-diffusion and positional information governs self-organized human peri-gastrulation-like patterning. Development https://doi.org/10.1242/dev.149658 (2017).

  41. Yang, R. et al. Amnion signals are essential for mesoderm formation in primates. Nat. Commun. 12, 5126 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hollnagel, A., Oehlmann, V., Heymer, J., Rüther, U. & Nordheim, A. Id genes are direct targets of bone morphogenetic protein induction in embryonic stem cells. J. Biol. Chem. 274, 19838–19845 (1999).

    Article  CAS  PubMed  Google Scholar 

  43. Messmer, T. et al. Transcriptional heterogeneity in naive and primed human pluripotent stem cells at single-cell resolution. Cell Rep. 26, 815–824 (2019).

  44. Guo, G. et al. Human naive epiblast cells possess unrestricted lineage potential. Cell Stem Cell https://doi.org/10.1016/j.stem.2021.02.025 (2021).

  45. Dong, C. et al. Derivation of trophoblast stem cells from naïve human pluripotent stem cells. eLife 9, e52504 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Linneberg-Agerholm, M. et al. Naïve human pluripotent stem cells respond to Wnt, Nodal and LIF signalling to produce expandable naïve extra-embryonic endoderm. Development 146, dev180620 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. O’Rahilly, R. & Müller, F. Developmental stages in human embryos: revised and new measurements. Cells Tissues Organs 192, 73–84 (2010).

    Article  PubMed  Google Scholar 

  48. Harlow, C. R., Hearn, J. P. & Hodges, J. K. Ovulation in the marmoset monkey: endocrinology, prediction and detection. J. Endocrinol. 103, 17–24 (1984).

    Article  CAS  PubMed  Google Scholar 

  49. 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).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  50. Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Krueger, F. Trim Galore. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/trim galore/ (2016).

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Nakamura, T. et al. Single-cell transcriptome of early embryos and cultured embryonic stem cells of cynomolgus monkeys. Sci. Data 4, 170067 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fernandez, A. et al. The WNT receptor FZD7 is required for maintenance of the pluripotent state in human embryonic stem cells. Proc. Natl Acad. Sci. USA 111, 1409–1414 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Okae, H. et al. Derivation of human trophoblast stem cells. Cell Stem Cell 22, 50–63 e6 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  58. O’Hagan, A. Curve fitting and optimal design for prediction. J. R. Stat. Soc. Ser. B https://doi.org/10.1111/j.2517-6161.1978.tb01643.x (1978).

  59. Rasmussen, C. E. & Nickisch, H. Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010).

  60. Kalaitzis, A. A. & Lawrence, N. D. A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression. BMC Bioinform. 12, 180 (2011).

    Article  Google Scholar 

  61. Jeffreys, H. Some tests of significance, treated by the theory of probability. Math. Proc. Cambridge Philos. Soc. https://doi.org/10.1017/S030500410001330X (1935).

  62. Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. https://doi.org/10.1080/01621459.1995.10476572 (1995).

  63. Adewumi, O. et al. Characterization of human embryonic stem cell lines by the International Stem Cell Initiative. Nat. Biotechnol. 25, 803–816 (2007). 2007 257.

    Article  CAS  PubMed  Google Scholar 

  64. Tewary, M. et al. High-throughput micropatterning platform reveals Nodal-dependent bisection of peri-gastrulation–associated versus preneurulation-associated fate patterning. PLoS Biol. 17, e3000081 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Vickers, A. et al. Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation. Stem Cell Rep. 16, 2628–2641 (2021).

    Article  CAS  Google Scholar 

  66. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Thomson, J. A., Kalishman, J. & Hearn, J. P. Nonsurgical uterine stage preimplantation embryo collection from the common marmoset. J. Med. Primatol. 23, 333–336 (1994).

    Article  CAS  PubMed  Google Scholar 

  68. Boroviak, T. et al. Lineage-specific profiling delineates the emergence and progression of naive pluripotency in mammalian embryogenesis. Dev. Cell 35, 366–382 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Carbon, S. et al. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  CAS  Google Scholar 

  71. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009); https://doi.org/10.1007/978-0-387-98141-3

  72. Brunson, J. ggalluvial: layered grammar for alluvial plots. J. Open Source Softw. https://doi.org/10.21105/joss.02017 (2020).

  73. Bergmann, S., Penfold, C., Slatery, E. and Boroviak, T. E. Spatial embryo profiling (STEP) of primate implantation stages. Protocol Exchange https://doi.org/10.21203/rs.3.pex-1899/v1 (2022).

  74. Hertig, A. On the preimplantation stages of the human ovum: a description of four normal and four abnormal specimens ranging from the second to the fifth day of development. Contrib. Embryol. 35, 119–220 (1954).

  75. Hertig, A. T. & Rock, J. Two human ova of the pre-villous stage, having an ovulation age of about eleven and twelve days respectively. Contrib. Embryol. 29, 127–156 (1941).

    Google Scholar 

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Acknowledgements

We thank A. Roberts and the staff at the Cambridge marmoset research facility for providing marmoset neonates; the members of Endocrinology Laboratory of the German Primate Center for hormone analyses and the members of the Platform Degenerative Diseases for assistance; and S. Li, C. Munger, M. Schindler, I. Ampartzidis, G. Jowett, C. Kyprianou and G. Burton for contributing discussions and comments on the manuscript. P. Humphreys and D. Clements for supporting imaging; A. Surani and J. Gurdon for providing access to the computational facilities at the Gurdon Institute; C. Bradshaw for help with high-performance computing; and X. Zhao and R. Hamilton for advice and bioinformatics support. This research is supported by the Wellcome Trust (WT RG89228), the Centre for Trophoblast Research, the Isaac Newton Trust and JSPS KAKENHI (15H02360, 19H05759). E.S. is supported by a Wellcome Trust PhD studentship (WT108438/C/15/Z).

Author information

Author notes

  1. These authors contributed equally: Sophie Bergmann, Christopher A. Penfold, Erin Slatery

Authors and Affiliations

  1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Sophie Bergmann, Christopher A. Penfold, Erin Slatery, Dylan Siriwardena, Stanley E. Strawbridge & Thorsten E. Boroviak

  2. Centre for Trophoblast Research, University of Cambridge, Cambridge, UK

    Sophie Bergmann, Christopher A. Penfold, Erin Slatery, Dylan Siriwardena, Stephen Clark, Wolf Reik & Thorsten E. Boroviak

  3. Jeffrey Cheah Biomedical Centre, Wellcome Trust–Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK

    Sophie Bergmann, Christopher A. Penfold, Erin Slatery, Dylan Siriwardena, Stanley E. Strawbridge & Thorsten E. Boroviak

  4. Wellcome Trust–Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK

    Christopher A. Penfold

  5. Research Platform Degenerative Diseases, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany

    Charis Drummer & Rüdiger Behr

  6. DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany

    Charis Drummer & Rüdiger Behr

  7. Epigenetics Programme, Babraham Institute, Cambridge, UK

    Stephen Clark & Wolf Reik

  8. Department of Marmoset Biology and Medicine, Central Institute for Experimental Animals, Kawasaki, Japan

    Keiko Kishimoto & Erika Sasaki

  9. Centre for Stem Cells and Regenerative Medicine, King’s College London, Guy’s Hospital, London, UK

    Alice Vickers & Mukul Tewary

  10. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Timo N. Kohler & Florian Hollfelder

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Contributions

T.E.B., S.B., E. Slatery, C.A.P. and D.S. designed and conducted experiments and wrote the manuscript, C.D. set up timed marmoset matings and extracted uteri, T.N.K. performed staining and imaging of PSCs, C.A.P., E. Slatery and D.S. performed bioinformatics, S.B., D.S., S.E.S. and E. Slatery established virtual reconstructions, C.A.P. pioneered GPR to model 3D transcriptomes, K.K. and E. Sasaki provided marmoset preimplantation embryos. R.B. provided marmoset postimplantation embryos, A.V. and M.T. provided expertise and produced micropattern plates, F.H., W.R. and S.C. provided expertise and facilities for imaging and high-throughput RNA-seq library preparation, T.E.B. conceived, coordinated and supervised the project.

Corresponding author

Correspondence to Thorsten E. Boroviak.

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Nature thanks Jianping Fu, Patrick Tam and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Overview of the STEP method 1.

Cryosectioning of the pregnant uterus: Pregnant marmoset uteri were extracted, embedded in O.C.T. and snap-frozen. To provide the best possible RNA quality, the tissue was processed unfixed for laser capture microdissection (LCM)-mediated Smart-Seq2 sequencing. 2. Stereological immunofluorescence imaging: Immunostainings for established lineage markers were performed for every other to every third section containing the implanted embryo. Tile-scan images were generated tile-scan with a confocal or apotome microscope. 12-µm-thick cryosections were catalogued in ascending order, which was used to determine the Z-coordinate of LCM-transcriptomes. 3. LCM assisted sample collection: Every other to every third section was processed for LCM sample collection and all LCM-processed sections were subjected to immunofluorescence (IF) with lineage markers afterwards. For transcriptome sample acquisition, a photo was taken of the section. Then, one to three cells in a region of interest were selected using the LCM software Zeiss PALM and cut out by the laser. In a second step, a pulse laser catapulted the sample into a collection tube with lysis buffer. Then, a second picture was taken of the section at the matching position with the sample removed and matched to the collection tube and image file. Each STEP-transcriptome was assigned an individual ID and lineage identity based on the original location within the embryo cryosection, i.e. the cut-out location of the LCM-sample. 4. Sample annotation: Collected LCM-samples were subjected to the Smart-Seq2 protocol and sequenced to an average depth of > 2 million 150bp paired reads. LCM-sample lineage identity was assigned based on the position within the embryo. Sample annotation was performed manually, side-by-side with phase contrast images acquired during sample collection and the confocal image with lineage markers (e.g. PDGFRA, OTX2, SOX2) of the same section. In addition, annotations were guided by the density and orientation of DAPI-labelled nuclei, which allowed us to discriminate between neighbouring tissues. We refined annotations by integration of lineage marker expression from immunofluorescence stainings or STEP-transcriptome data. Samples with more than one lineage signature were annotated as mixed and removed from downstream analysis. 5. Image registration and lineage segmentation: Images were aligned by image registration in Fiji, whereby each image was registered to the DAPI channel of the previous image. Next, nuclei were segmented into individual objects using Cell Profiler. For lineage segmentation, the segmented nuclei were assigned lineages based on lineage marker immunostaining (e.g. POU5F1 to demarcate the EmDisc and Amnion, PDGFRA for ExMes, VE and SYS, TFAP2C for trophoblast, Amnion and PGCs), and taking into account known anatomical features of the embryo (e.g. EmDisc resides in-between VE and Amnion). 6. Transcriptome coordinate integration: The X and Y coordinates of the annotated transcriptomes were compiled into MATLAB matrices. 7.Virtual 3D embryo reconstruction: We generated primary surfaces in MATLAB by triangulation (see Methods). In a second step, embryonic and extraembryonic surfaces were smoothened in Blender, an open-source 3D modelling and animation software. 8. Gaussian Process Regression (GPR) over LCM samples: LCM spatial transcriptome sample coordinates were integrated into the 3D embryo models and continuous expression patterns between discrete LCM samples were inferred using Gaussian Process Regression (see Methods). Since expression patterns may be discontinuous across tissue types, we inferred an independent GPR model for each tissue at each stage. 9. GPR spatial transcriptome models and virtual cross sections: Final smooth GPR gene expression patterns could be displayed in 3D on embryo models. Defined coordinates were used to extract expression patterns in each lineage for visualization of virtual cross sections. Scale bars represent 100 µm. CS, Carnegie stage; O.C.T, Optimal cutting temperature compound (used to mount uteri); EmDisc, Embryonic disc; Am, Amnion; SYS, Secondary Yolk Sac; VE, Visceral Endoderm; Tb, Trophoblast; ExMes, Extraembryonic mesoderm; PGCs, Primordial Germ Cells.

Extended Data Fig. 2 Staging of marmoset postimplantation embryos.

a, Staging of marmoset embryos based on listed hallmarks allowed us to stage blastocysts (Carnegie Stage (CS) 3), CS5, CS6 early and late, and CS7 embryos. Middle panel: illustrative cross section of embryo models for each stage with stage-specific differences in (1) secondary yolk sac, (2) Am, (3) stalk, and (4) ExMes formation indicated. Bottom panel: Representative images from human embryos at corresponding Carnegie Stages. CS3 reprinted from74, CS5, CS6 late, and CS7 from47, and CS6 early from75. b, Principal component analysis of marmoset development of CS5–7 integrated by stage based on whole transcriptome (>20,000 genes). c, Immunofluorescence stainings of marmoset embryo sections. Scale bars represent 100 µm. EmDisc, Embryonic disc; Am, Amnion; SYS, Secondary Yolk Sac; VE, Visceral Endoderm; Tb, trophoblast; ExMes, Extraembryonic mesoderm; PGCs, Primordial Germ Cells.

Extended Data Fig. 3 Gaussian Process Regression models of spatial transcriptomics in mouse and marmoset embryos.

a, Spatial modelling of mouse postimplantation embryos based on published mouse embryo expression data shown for representative genes markers of anterior/posterior (A/P) epiblast (Epi), primitive streak (PS) and anterior primitive streak (APS). Each kernel on corn plots represents a micro-dissected and sequenced section of the mouse embryo from ref. 4,5. Corn plots were transformed into spatial models to match anterior-posterior and left-right axis. Gaussian process regression allowed visualization of gene expression gradients, which were compared to in-situ hybridization of postimplantation mouse embryos for validation of gaussian process regression approach. Source publication indicated next to individual images. b, Projection of shots on 3D virtual reconstructions. LCM-samples projected as black dots on 3D virtual models of each developmental stage used for downstream gene expression analysis. EmDisc, Embryonic Disc; VE, Visceral Endoderm; ExMes, Extraembryonic Mesoderm; A, Anterior; P, Posterior. c-d, Spatial embryo profiling LCM-sample lineage assignment examples. LCM-samples that were spatially close were assigned to amnion or ExMes based on PDGFRA expression. PDGFRA immunostaining of the CS6 early embryo demonstrates that amnion (nuclei adjacent to the amniotic cavity) is PDGFRA-negative (highlighted in inset below). Raw PDGFRA feature counts of individual LCM-samples overlaid on DAPI recapitulate immunostaining pattern, showing high expression in ExMes samples and no expression in amnion samples. LCM-samples from other lineages, that showed mixed lineage identity, or did not pass QC are not displayed. Arrowheads indicate PDGFRA-negative amnion. Cross-section of 3D-model (right) represents gaussian process regression-based modelling of all lineages in CS6 early embryo, recapitulating specific PDGFRA expression pattern observed by immunofluorescence and raw transcriptome data. e–h, Spatial embryo profiling processing example. Representative example of a CS6 section processed by spatial embryo profiling and immunofluorescence staining for SOX2 (e) and OTX2 (g). Gaussian process regression-based modelling of EmDisc and VE for SOX2 recapitulates the anterior EmDisc expression pattern observed by immunofluorescence and raw transcriptome data (f), and recapitulated anterior expression of OTX2 recapitulates the anterior VE expression pattern observed by immunofluorescence and raw transcriptome data (h). Upper panel: Relative mRNA levels for gene expression across the model. Lower panel: mRNA expression changes along anterior-posterior axis (dashed line, anterior EmDisc (red, A) to posterior EmDisc (blue, P), anterior VE (yellow, A) to posterior VE (green, P)) change along A-P axis quantified by Bayesian factor (BF).

Extended Data Fig. 4 Expression gradients and signalling environment in marmoset gastrulation.

a, Posterior markers depicted in Gaussian process regression-based 3D models of CS5–7 EmDisc and VE. Upper panels: Relative mRNA levels across the model. Lower panels: mRNA expression change along EmDisc anterior-posterior axis (indicated by dashed line; anterior (red, A) to posterior (blue, P)), quantified by Bayes Factor (BF) (relates to Fig. 3a). b, BMP signalling-related gene expression depicted in CS5–7 model cross sections. Schematic (bottom) summarizes BMP signalling pathways in the context of amnion differentiation from EmDisc boundaries in CS5 and 6. c, WNT signalling genes shown in CS5–7 in model cross sections. Schematic summarizes WNT signalling patterning in the CS6 EmDisc during gastrulation. d, RTK-related gene expression depicted in CS5–7 model cross sections displays VE is the primary source of IGF1, low expression of FGFs involved in mouse gastrulation (FGF8, FGF5), and presence of FGF4 and intracellular FGFs (FGF12, FGF13). e, RTK-related gene expression depicted in EmDisc and VE in CS5 and 6 3D models. Schematic summarizes PDGFA and VEGFA in the CS6 embryo. f, IHH signalling-related gene expression shown in CS5–7 in model cross sections. Schematic summarizes proposed paracrine IHH signalling pathways. g, Representative anterior pluripotency genes depicted in CS5–7 EmDisc and VE 3D models (relates to Fig. 3e). h, Corn plots of matched pluripotency genes in the gastrulating mouse embryo at E6.0, E6.5, and E7.05. Each kernel represents the average transcriptome of micro-dissected, spatially-defined sections of mouse embryos (relates to Fig. 3c). i, Early (NANOG, PRDM1, POU5F1, KLF4) and late (DAZL, MAEL, PRAME) PGC marker and enriched signalling components (FGF4, WNT8A) expression in PGCs, depicted in CS6 model cross sections. EmDisc, Embryonic Disc; SYS, Secondary Yolk Sac; VE, Visceral Endoderm; ExMes, Extraembryonic Mesoderm; Am, Amnion; Tb, Trophoblast.

Extended Data Fig. 5 Amnion segregation from the embryonic disc.

a, PCA of EmDisc and Am based on the top 5000 most variable genes, PC1 = 20.7%, PC2 = 13.6%. b, Marker expression delineates the divergence of EmDisc and Am. Genes enriched in EmDisc and Am are marked in blue and purple, respectively; preimplantation genes are depicted in green. Stream plot track width is scaled to relative expression normalized to the mean across all stages displayed. c, Heatmap of expression of differentially expressed genes (DEG) in embryonic and extraembryonic lineages displayed in (a, b). Representative genes (left panel) and key gene ontology (GO) enrichment analysis (right panel) are shown. Genes shown in heatmap from Seurat function FindAllMarkers (minimum percent 50%, minimum log fold change 0.25) and filtered by adjusted p-value < 0.05. d–i, Virtual cross-sections of 3D-transcriptomes at CS5, 6 and 7 depicting mRNA levels of representative genes for (d) pluripotency factor expression in the nascent Am, (e) Am-Tb shared genes, (f) Am-ExMes shared genes, (g) Am-specific genes, (h) epithelial genes, (i) ECM-related genes. Categories indicated on the left of each panel. EmDisc, embryonic disc; Am, amnion.

Extended Data Fig. 6 Endogenous WNT is required for posterior patterning in marmoset 2D-gastruloids.

a–c, pSMAD1/5 (phosphorylated SMAD 1/5) activity in marmoset 2D-gastruloids detected by immunostaining at day 2 (a) or day 4 (b) compared to human 2D-gastruloids under conventional conditions at day 2 (c). Micropatterned colonies were treated with self-renewal conditions (10 ng ml−1 FGF + 20 ng ml−1 Activin A), conventional gastruloid conditions (10 ng ml−1 FGF + 20 ng ml−1 Activin A + 50 ng ml−1 BMP4) or WNT modulatory conditions (10 ng ml−1 FGF + 20 ng ml−1 Activin A + 3 µM IWP-2 or 10 ng ml−1 FGF + 20 ng ml−1 Activin A + 3 µM CHIR99021). Representative maximum projection of immunofluorescence images (left). Quantification plots (right) mean +/− SEM across a minimum of 10 gastruloids across 2 wells. F/A = FGF/Activin A. pSMAD1/5 gradient indicates that in the marmoset system, similar to the human, FGF/ActivinA/BMP4 induce a graded response to BMP signalling at day 2, with the highest signalling in the outermost ring of the colony. d, Molecular characterization of 2D-gastruloids. Representative immunofluorescence images of gastruloids differentiated in conventional gastruloid conditions (10 ng ml−1 FGF + 20 ng ml−1 Activin A + 50 ng ml−1 BMP4) for 2 days. Quantification plots (bottom) display mean +/− SEM across a minimum of 10 gastruloids across 2 experiments. Anterior domain (A, SOX2+, TBXT−, TFAP2C−), posterior domain (P, TBXT high, CDX2 heterogeneous, SOX17 sparse, LEF1 sparse), and amnion domain (Am, TFAP2C high, SOX2−) demarcated. ISL1 and TFAP2A are observed heterogeneously predominantly in the amnion region. PDGFRA expression is low, indicating lack of mature mesoderm. e, Schematic of lineage identities present in 2D-gastruloids and marker patterns that define each region. f, WNT-associated anterior/posterior patterning phenotypes of 2D-gastruloids. Representative immunofluorescence images of gastruloids differentiated in conditions listed at left for 2 days. Quantification plots (bottom) mean +/− SEM across a minimum of 10 gastruloids across 2 wells. g, WNT-associated amniogenesis phenotypes of 2D-gastruloids. Representative immunofluorescence images of gastruloids differentiated in conditions listed at left for 2 days. CDX2 expression is lost upon 3 μM IWP2 treatment, but TFAP2C and TFAP2A remain expressed. 3 μM CHIR treatment (WNT agonist) leads to low CDX2 expression but does not support upregulation of TFAP2C or TFAP2A. Quantification plots (bottom) mean +/− SEM across a minimum of 10 gastruloids across 2 wells. h, No evident change in expression profiles associated with Hedgehog signalling manipulation in 2D-gastruloids. Representative immunofluorescence images of gastruloids differentiated in conditions listed at left for 2 days. Exogenous Indian Hedgehog (IHH, 200 ng ml−1) did not lead to loss of pluripotency under FGF/Activin self-renewal conditions, or change expression patterns under conventional gastruloids conditions. Inhibition of Hedgehog signalling with Cyclopamine (5 μM) also did not lead to evident changes in expression patterns. Quantification plots (bottom) mean +/− SEM across a minimum of 10 gastruloids across 2 wells.

Extended Data Fig. 7 Functional analysis of genes regulating embryo patterning in marmoset 2D-gastruloids.

a, siRNA knockdown efficiency. Representative immunofluorescence images (left) and quantification of mean fluorescence intensity (right) 24 h following transfection with siRNA against POU5F1, SOX2, or NANOG. Comparisons to siGFP (green fluorescent protein) control conducted with two-tailed Mann-Whitney test (****, p < 0.0001). siRNA: small interfering RNA. b, Schematic of siRNA screening approach. cmPSCs (common marmoset pluripotent stem cells) were seeded in micropatterned 96-well plates on day −1 and transfected overnight with siRNA. On day 0, media was changed to gastruloid induction media (10 ng ml−1 FGF + 20 ng ml−1 Activin A + 50 ng ml−1 BMP4). 2D-gastruloids were fixed after 72 h and stained to assess pattern formation. c, Comparison of siGFP phenotype to in vivo EmDisc patterns. Representative maximum projection immunofluorescence image of siGFP-treated gastruloids differentiated in conventional gastruloid conditions (10 ng ml−1 FGF + 20 ng ml−1 Activin A + 50 ng ml−1 BMP4) for 3 days (left). 2D-gastruloid log expression patterns normalized to maximum intensity plotted for individual channels (SOX2, TBXT, TFAP2C) side by side with virtual embryo pattern of CS6 EmDisc expression patterns generated by Gaussian process regression of anterior-posterior axis expression. Quantification plot (right) shows mean +/− SEM across a minimum of 10 gastruloids with anterior domain (A, SOX2+, TBXT−, TFAP2C−), posterior domain (P, TBXT high), and amnion domain (Am, TFAP2C high, SOX2−) demarcated. d–f. siRNA knockdown phenotypes of pluripotency factors (siPOU5F1, siNANOG, siSOX2), BMP-related genes (siID1/2/3, siTBX3) and WNT-related genes (siSFRP1, siSFRP2, siSFRP1/2). For each siRNA, a representative maximum projection immunofluorescence is shown (left). Representative expression patterns are plotted for individual channels (SOX2, TBXT, TFAP2C) adjacent to quantification of the percent of nuclei positive for each marker per gastruloid (centre). Comparison conducted with two-tailed Mann-Whitney test (ns: not significant; p < 0.0332 (*); p < 0.0021 (**); p < 0.0002,(***); p < 0.1; p < 0.0001 (****)). Quantification plot (right) shows mean +/− SEM across a minimum of 10 gastruloids across 2 wells. SOX2 = green, TFAP2C = red, TBXT = grey. siGFP patterns plotted for comparison in reduced opacity and dashed line with control anterior domain (A, SOX2+, TBXT−, TFAP2C−), posterior domain (P, TBXT high), and amnion domain (Am, TFAP2C high, SOX2−) demarcated. cmPSCs, common marmoset PSCs.

Source data

Extended Data Fig. 8 Modelling of the marmoset embryonic disc by 3D-culture in vitro.

a, Schematic overview of interphase culture. To model self-organization of conventional cmPSCs (common marmoset pluripotent stem cells) in 3D culture, cmPSCs were seeded on a 100% Matrigel base overlaid with 1% Matrigel dissolved in N2B27 media supplemented with signalling factors. Interphase culture was amenable to probing signalling requirements that promote anterior embryonic disc-like pluripotency or differentiation into the germ layers of the gastrulating embryo. All experiments were performed in two different cell lines (cell line 1 (New4f), N = 2 and cell line 2 (New2f), N = 2) showing consistent results. b, Heatmap of marker genes used for molecular characterization of interphase culture structures. Relative mRNA levels were centred and scaled across all marmoset in vivo samples. c, Summary schematic of NODAL and FGF signalling in the marmoset embryo. The visceral endoderm is the primary source of NODAL and IGF1 in the marmoset embryo, while the EmDisc expresses low levels of FGF4. Relative mRNA expression gradients summarized in CS6 cross section. d, Time series brightfield images of interphase culture with FGF (100 ng ml−1) and Activin A (20 ng ml−1). FGF/Activin A culture provides a signalling environment that mimics high NODAL and IGF1 from the VE. Structures formed columnar epithelial cysts, reminiscent of the embryonic disc. Structures first open a lumen at day 3 and expand up to day 6. e–f, Molecular characterization of EmDisc-like structures at day 4. Representative maximum projection images from immunostaining at day 4 for pluripotency (SOX2), early gastrulation (TBXT), amnion (TFAP2C, TFAP2A), endoderm (SOX17) or mesoderm (CDX2) markers. EmDisc-like structures homogenously expressed SOX2, with heterogeneous low expression of TBXT, SOX17 and TFAP2C indicative of priming toward gastrulation and rare emergence of endoderm. Pluripotent EmDisc-like structures support a role for FGF and Activin/NODAL signalling in promoting pluripotency in the EmDisc. g, Summary schematic of canonical WNT signalling in the marmoset embryo. The posterior EmDisc, stalk and PGCs express WNT3. mRNA expression gradients summarized in CS6 cross section (left). Time series brightfield images of interphase culture with FGF and Activin A + CHIR (CHIR99021, WNT agonist) (right). The emergence of differentiated populations was evident at day 4. h–i, Molecular characterization of WNT-treated EmDisc-like structures at day 4. Representative maximum projection images from immunostaining at day 4 from staining for pluripotency (SOX2), early gastrulation (TBXT), amnion (TFAP2C, TFAP2A), endoderm (SOX17) or mesoderm (CDX2) markers. Structures exhibited loss of SOX2 expression and upregulation of TBXT and SOX17 in comparison to FGF/Activin A culture, consistent with differentiation into amnion, endoderm, and mesoderm populations. j, Summary schematic of WNT inhibition in the marmoset embryo. The VE and Amnion express canonical WNT inhibitor DKK1. mRNA expression gradients summarized in CS6 cross section (left). Time series brightfield images of interphase culture with FGF and Activin A + IWP-2 (right). Similar to FGF/Activin A culture, structures first open a lumen at day 3 and expand up to day 6. k-l, Molecular characterization of WNT-inhibited EmDisc-like structures at day 4. Representative maximum projection images from immunostaining at day 4 from staining for pluripotency (SOX2), early gastrulation (TBXT), amnion (TFAP2C, TFAP2A), endoderm (SOX17) or mesoderm (CDX2) markers. EmDisc-like structures homogenously expressed SOX2 and downregulated TBXT and SOX17 in comparison to FGF/Activin A culture, consistent with a role for WNT inhibition in preserving pluripotency in the EmDisc. m, Summary schematic of BMP signalling in the marmoset embryo. The ExMes, amnion, and PGCs are sources of BMP4 in the embryo. mRNA expression gradients summarized in CS6 cross section (left). Time series brightfield images of interphase culture with FGF and Activin A + BMP4 (right). The emergence of disorganized, differentiated populations was evident at day 4. n-o, Molecular characterization of BMP-treated EmDisc-like structures at day 4. Representative maximum projection images from immunostaining at day 4 from staining for pluripotency (SOX2), early gastrulation (TBXT), amnion (TFAP2C, TFAP2A), endoderm (SOX17) or mesoderm (CDX2) markers. Structures exhibited loss of SOX2 expression and upregulation of TFAP2C, TFAP2A, CDX2 and SOX17 in comparison to FGF/Activin A culture, consistent with a mixed amnion and posteriorized primitive streak-like fate. p, Summary schematic of BMP inhibition in the marmoset embryo. The VE expresses BMP inhibitor NOGGIN in the embryo. mRNA expression gradients summarized in CS6 cross section (left). Time series brightfield images of interphase culture with FGF and Activin A + BMP4 (right). Similar to FGF/Activin A culture, structures first open a lumen at day 3 and form homogenous spheroids. q–r, Molecular characterization of BMP-inhibited EmDisc-like structures at day 4. Representative maximum projection images from immunostaining at day 4 from staining for pluripotency (SOX2), early gastrulation (TBXT), amnion (TFAP2C, TFAP2A), endoderm (SOX17) or mesoderm (CDX2) markers. Scale bars represent 100 µm. EmDisc-like structures homogenously expressed SOX2 and downregulated TBXT, SOX17 and TFAP2C in comparison to FGF/Activin A culture. This is consistent with a role for BMP inhibition in preserving pluripotency in the EmDisc and inhibiting amnion differentiation. cmPSCs, common marmoset PSCs.

Extended Data Fig. 9 Cross-species analysis of primate mesoderm differentiation in vivo.

a, Alignment of EmDisc-derived postimplantation lineages in marmoset and human. Visualization based on alignment of embryo in vivo and in vitro datasets of pre- to postimplantation cynomolgus monkey25, in vitro-cultured human12, in vivo human CS79, preimplantation marmoset (ref. 24 and this study), and postimplantation marmoset embryo data (this study) were aligned. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. b, UMAP plots of marmoset CS5–7 or human late CS7 lineages showing normalized log expression of marker genes from9. Pluripotent EmDisc (Embryonic Disc): SOX2, CDH1; Primitive Streak: CDH1, FST, TBXT; Nasc Mes (Nascent mesoderm): TBXT, CDH2, Emergent Mesoderm: OTX2, LHX1; Advanced Mesoderm: FOXF1, HAND1, BMP4, GATA6; Amnion: VTCN1, HAND1, BMP4; Endoderm subcluster also indicated by co-expression of CDH2, OTX2, GATA6. c, Unbiased clustering of gastrulation stage lineages represented in UMAP in (a) resolves 9 clusters by shared nearest neighbour clustering: Pluripotent EmDisc (Embryonic Disc), PS early (Primitive Streak early), PS late (Primitive Streak late), PS advanced (Primitive Streak advanced), Endoderm, Nasc Mes (Nascent Mesoderm), Em Mes (Emergent Mesoderm), Adv Mes (Advanced Mesoderm), PGCs (Primordial Germ Cells). d, Bifurcation of endoderm and mesoderm from the primitive streak represented in a diffusion map. Human CS7 shows an additional route from primitive streak to advanced mesoderm through emergent mesoderm. Alignment includes data plotted in (c) with amnion and PGC clusters excluded where cluster identity is given by colour. The first 2 diffusion components are shown (dim 1, dim 2). e, Diffusion maps of marmoset CS5–7 or human late CS7 lineages showing normalized log expression of marker genes for primitive streak (TBXT), endoderm (FOXA2), pan-mesoderm (SNAI2, CDH2), emergent mesoderm (OTX2) or nascent mesoderm (FOXF1). f–g, Human vs. marmoset scatterplots of primitive streak vs. nascent mesoderm (f) or nascent mesoderm vs. endoderm (g). Highlighted quadrants show human-marmoset conserved markers for each lineage, whereas white quadrants show species-specific expression patterns. Gene names for transcription factors, ligands, and extracellular matrix molecules are labelled.

Extended Data Fig. 10 Spatial identity mapping of human in vitro models.

a, Alignment of naive and primed human and marmoset PSCs with in vivo marmoset data. Marmoset in vivo datasets of pre- to postimplantation development and naive/primed PSCs and human naive/primed PSCs43 were aligned. Visualization of aligned datasets by principal component analysis shows separation of preimplantation (on the left) and postimplantation (on the right) samples, with marmoset PSCs sitting between pre- and post-implantation. Human PSCs are plotted overlayed with transparent marmoset embryo data. Naive human cells mapped earlier on PC1 than naive marmoset cells and showed greater heterogeneity in mapping to ICM, Hypoblast, and Tb. Primed human cells mapped closely to the marmoset EmDisc. PCA, Principal component analysis; PSCs, pluripotent stem cells. ICM, inner cell mass; Hyp, hypoblast; SYS, Secondary Yolk Sac; VE, Visceral Endoderm; ExMes, Extraembryonic Mesoderm; EmDisc, Embryonic Disc; Am, Amnion; Tb, Trophoblast. b, Expression of extraembryonic markers in marmoset in vivo data (left) and human naive and primed PSCs (right) represented in aligned PCAs from (a) showing integrated log expression of trophoblast markers (JAM2), endoderm markers (EOMES, GATA6) and ICM/preimplantation blastocyst marker (ESRRB). Turquoise lines indicate location of naive cells, blue lines indicate primed cells on the PCA. c, Alignment of human microfluidic embryonic-like sac model to marmoset STEP data. Smart-seq2 profiling of marmoset in vivo lineages (EmDisc, PGC, Amnion, and Stalk populations) was aligned to 10x sequencing profiling of microfluidic embryonic-like sac model16. Visualization of aligned datasets by principal component analysis shows separation hPSC, mesoderm, PGCLCs, and AMLCs. PCA, Principal component analysis; PSC, human pluripotent stem cell; hPGCLC, human primordial germ cell-like cell, hAMLC; human amnion-like cell; hMELC1/2, human mesoderm-like cell population 1 or 2. d, Unbiased Clustering represented in UMAP in (c) resolves 5 clusters by shared nearest neighbour clustering in marmoset data. Four clusters aligned to subpopulation of the human microfluidic embryonic-like sac model and were annotated EmDisc, PGC, Amnion, and Mesoderm. A fifth subcluster in marmoset data contained cells from the gastrulating EmDisc, PGCs, and amnion and was therefore annotated PS-like (primitive streak-like). e, Pearson’s correlation of marmoset and human microfluidic embryonic-like sac model of clusters identified in (d). f–h. Spatial identity mapping of human microfluidic embryonic-like sac model. Subpopulations of the human microfluidic embryonic-like sac model were mapped to the marmoset EmDisc, PGC, Stalk, and Amnion. Spatial identity displayed in the orientations described in (f) for hPS Cs (g), which mapped to the anterior EmDisc and hAMLCs (h) which mapped most strongly to the posterior amnion. cmPSCs, common marmoset PSCs; hPSCs, human PSCs.

Supplementary information

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Supplementary Figs. 1–12

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Supplementary Table 1

Overview of transcriptome samples. A list and description of samples profiled by Smart-seq2 in the current study, related to Ext. Data Figure 2.

Supplementary Table 2

Average gene expression for marmoset lineages and in vitro cells. Average gene expression (counts per 10,000) for all samples profiled. Sample type labelled by acronym; full sample descriptions can be found in Supplementary Table 1.

Supplementary Table 3

Anterior and Posterior genes. Genes enriched in the anterior or posterior compartments in the EmDisc at CS5, CS6, and CS7 or VE at CS5 and CS6. BF: Bayes factor, Delta: difference in gene expression between anterior and posterior. Average gene expression (counts per 10,000) reprinted from Supplementary Table 2.

Supplementary Video 1

Overview of virtual embryo reconstruction, zygote to gastrulation. Virtual overview of preimplantation stages profiled by conventional single cell sequencing and 3D-reconstructed postimplantation embryo stages profiled by STEP.

Supplementary Video 2

Virtual reconstruction of Carnegie stage 5 embryo. Animated overview of pipeline for lineage segmentation, virtual stacking, surface generation and 3D model reconstruction. Complete 3D model exhibits hallmark Carnegie stage 5 features, including a nascent amniotic cavity, a thick embryonic disc and a small secondary yolk sac protrusion from the visceral endoderm. Representative immunofluorescence stained for DAPI (white), POU5F1 (green) and TFAP2C (red).

Supplementary Video 3

Virtual reconstruction of Carnegie stage 6 (late) embryo. Animated overview of pipeline for lineage segmentation, virtual stacking, surface generation and 3D model reconstruction. Complete 3D model exhibits hallmark Carnegie stage 6 features, including an enlarged amniotic cavity, the emergence of the secondary yolk sac cavity, extraembryonic mesoderm that surrounds the embryo, and evidence of primitive streak formation through stalk formation connecting the posterior embryonic disc to the trophoblast. Representative immunofluorescence stained for DAPI (blue), PDGFRA (red), SOX2 (green), and OTX2 (white).

Supplementary Video 4

Virtual reconstruction of Carnegie stage 7 embryo. Animated overview of pipeline for lineage segmentation, virtual stacking, surface generation and 3D model reconstruction. Complete 3D model exhibits hallmark Carnegie stage 7 features, including massively expanded amnion and secondary yolk sac cavities and rotation of the embryonic disc to fall perpendicular to the mesenchymal stalk. Representative immunofluorescence stained for DAPI (blue) and TFAP2C (red).

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Bergmann, S., Penfold, C.A., Slatery, E. et al. Spatial profiling of early primate gastrulation in utero. Nature 609, 136–143 (2022). https://doi.org/10.1038/s41586-022-04953-1

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