Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Recording morphogen signals reveals mechanisms underlying gastruloid symmetry breaking

Nature Cell Biology (2024)Cite this article

Subjects

Abstract

Aggregates of stem cells can break symmetry and self-organize into embryo-like structures with complex morphologies and gene expression patterns. Mechanisms including reaction-diffusion Turing patterns and cell sorting have been proposed to explain symmetry breaking but distinguishing between these candidate mechanisms of self-organization requires identifying which early asymmetries evolve into subsequent tissue patterns and cell fates. Here we use synthetic ‘signal-recording’ gene circuits to trace the evolution of signalling patterns in gastruloids, three-dimensional stem cell aggregates that form an anterior–posterior axis and structures resembling the mammalian primitive streak and tailbud. We find that cell sorting rearranges patchy domains of Wnt activity into a single pole that defines the gastruloid anterior–posterior axis. We also trace the emergence of Wnt domains to earlier heterogeneity in Nodal activity even before Wnt activity is detectable. Our study defines a mechanism through which aggregates of stem cells can form a patterning axis even in the absence of external spatial cues.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Gastruloids self-organize a polarized posterior domain of Wnt activity.
Fig. 2: Tracing signalling activity in embryonic stem cells using signal-recorder gene circuits.
Fig. 3: Recorded Wnt signalling histories identify the critical time window for specifying the future position and fate of cells.
Fig. 4: Gastruloid polarization is driven by cell sorting.
Fig. 5: Sustained Wnt activity does not directly coordinate gastruloid cell sorting.
Fig. 6: Differential cadherin expression mediates changes in tissue mechanics and cell sorting in stem cell aggregates and gastruloids.
Fig. 7: Early heterogeneity in TGFβ signalling modulates the duration of Wnt signalling to define anterior and posterior tissue domains.

Similar content being viewed by others

Data availability

The scRNAseq data generated in this study are available through the NCBI Gene Expression Omnibus (accession number GSE274389). Plasmids used to construct signalling-reporter and signalling-recorder cell lines are available from Addgene (plasmids 225522–225531). Source data are provided with this paper. All other materials (for example, cell lines) are available on request.

Code availability

The scRNAseq data were processed and analysed using the publicly available Single-Cell Analysis in Python (scanpy) software package version 1.10 and gastruloid images were processed using custom MATLAB code (MATLAB 2024a). All code is available on the authors’ GitHub repository (https://github.com/toettchlab/McNamara2024).

References

  1. Bardot, E. S. & Hadjantonakis, A.-K. Mouse gastrulation: coordination of tissue patterning, specification and diversification of cell fate. Mech. Dev. 163, 103617 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gregor, T., Wieschaus, E. F., McGregor, A. P., Bialek, W. & Tank, D. W. Stability and nuclear dynamics of the bicoid morphogen gradient. Cell 130, 141–152 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lord, N. D., Carte, A. N., Abitua, P. B. & Schier, A. F. The pattern of nodal morphogen signaling is shaped by co-receptor expression. eLife 10, e54894 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Turner, D. A. et al. Anteroposterior polarity and elongation in the absence of extra-embryonic tissues and of spatially localised signalling in gastruloids: mammalian embryonic organoids. Development 144, 3894–3906 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Beccari, L. et al. Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids. Nature 562, 272–276 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Veenvliet, J. V. et al. Mouse embryonic stem cells self-organize into trunk-like structures with neural tube and somites. Science 370, eaba4937 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Miao, Y. et al. Reconstruction and deconstruction of human somitogenesis in vitro. Nature 614, 500–508 (2023).

    Article  CAS  PubMed  Google Scholar 

  8. Ishihara, K. & Tanaka, E. M. Spontaneous symmetry breaking and pattern formation of organoids. Curr. Opin. Syst. Biol. 11, 123–128 (2018).

    Article  Google Scholar 

  9. van den Brink, S. C. et al. Symmetry breaking, germ layer specification and axial organisation in aggregates of mouse embryonic stem cells. Development 141, 4231–4242 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Scoones, J. C., Banerjee, D. S. & Banerjee, S. Size-regulated symmetry breaking in reaction-diffusion models of developmental transitions. Cells 9, 1646 (2020).

    Article  CAS  Google Scholar 

  11. Sozen, B., Cornwall-Scoones, J. & Zernicka-Goetz, M. The dynamics of morphogenesis in stem cell-based embryology: novel insights for symmetry breaking. Dev. Biol. 474, 82–90 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Simunovic, M. et al. A 3D model of a human epiblast reveals BMP4-driven symmetry breaking. Nat. Cell Biol. 21, 900–910 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Sick, S., Reinker, S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction-diffusion mechanism. Science 314, 1447–1450 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Raspopovic, J., Marcon, L., Russo, L. & Sharpe, J. Digit patterning is controlled by a Bmp–Sox9–Wnt Turing network modulated by morphogen gradients. Science 345, 566–570 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Glover, J. D. et al. The developmental basis of fingerprint pattern formation and variation. Cell 186, 940–956 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Hashmi, A. et al. Cell-state transitions and collective cell movement generate an endoderm-like region in gastruloids. eLife 11, e59371 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Srivastava, V. et al. Configurational entropy is an intrinsic driver of tissue structural heterogeneity. Preprint at bioRxiv https://doi.org/10.1101/2023.07.01.546933 (2023).

  18. Shyer, A. E. et al. Emergent cellular self-organization and mechanosensation initiate follicle pattern in the avian skin. Science 357, 811–815 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Palmquist, K. H. et al. Reciprocal cell–ECM dynamics generate supracellular fluidity underlying spontaneous follicle patterning. Cell 185, 1960–1973 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Kondo, S., Watanabe, M. & Miyazawa, S. Studies of Turing pattern formation in zebrafish skin. Philos. Trans. R. Soc. A 379, 20200274 (2021).

    Article  CAS  Google Scholar 

  21. Korinek, V. et al. Constitutive transcriptional activation by a β-catenin–Tcf complex in APC−/− colon carcinoma. Science 275, 1784–1787 (1997).

    Article  CAS  PubMed  Google Scholar 

  22. Suppinger, S. et al. Multimodal characterization of murine gastruloid development. Cell Stem Cell https://doi.org/10.1016/j.stem.2023.04.018 (2023).

  23. Serup, P. et al. Partial promoter substitutions generating transcriptional sentinels of diverse signaling pathways in embryonic stem cells and mice. Dis. Model. Mech. 5, 956–966 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Morgani, S. M., Metzger, J. J., Nichols, J., Siggia, E. D. & Hadjantonakis, A.-K. Micropattern differentiation of mouse pluripotent stem cells recapitulates embryo regionalized cell fate patterning. eLife 7, e32839 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Grosswendt, S. et al. Epigenetic regulator function through mouse gastrulation. Nature 584, 102–108 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. van den Brink, S. C. et al. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582, 405–409 (2020).

    Article  PubMed  Google Scholar 

  27. Merle, M., Friedman, L., Chureau, C., Shoushtarizadeh, A. & Gregor, T. Precise and scalable self-organization in mammalian pseudo-embryos. Nat. Struct. Mol. Biol. 31, 896–902 (2024).

  28. Underhill, E. J. & Toettcher, J. E. Control of gastruloid patterning and morphogenesis by the Erk and Akt signaling pathways. Development 150, dev201663 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Anand, G. M. et al. Controlling organoid symmetry breaking uncovers an excitable system underlying human axial elongation. Cell 186, 497–512 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chal, J. & Pourquié, O. Making muscle: skeletal myogenesis in vivo and in vitro. Development 144, 2104–2122 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Wang, X. et al. The development of highly potent inhibitors for porcupine. J. Med. Chem. 56, 2700–2704 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Steinberg, M. S. On the mechanism of tissue reconstruction by dissociated cells. I. Population kinetics, differential adhesiveness, and the absence of directed migration. Proc. Natl Acad. Sci. USA 48, 1577–1582 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Toda, S., Blauch, L. R., Tang, S. K. Y., Morsut, L. & Lim, W. A. Programming self-organizing multicellular structures with synthetic cell–cell signaling. Science 361, 156–162 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Stevens, A. J. et al. Programming multicellular assembly with synthetic cell adhesion molecules. Nature 614, 144–152 (2023).

    Article  CAS  PubMed  Google Scholar 

  35. Tsai, T. Y.-C. et al. An adhesion code ensures robust pattern formation during tissue morphogenesis. Science 370, 113–116 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Redmer, T. et al. E‐cadherin is crucial for embryonic stem cell pluripotency and can replace OCT4 during somatic cell reprogramming. EMBO Rep. 12, 720–726 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Soncin, F. et al. E-cadherin acts as a regulator of transcripts associated with a wide range of cellular processes in mouse embryonic stem cells. PLoS ONE 6, e21463 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xu, P.-F., Houssin, N., Ferri-Lagneau, K. F., Thisse, B. & Thisse, C. Construction of a vertebrate embryo from two opposing morphogen gradients. Science 344, 87–89 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Massey, J. et al. Synergy with TGFβ ligands switches WNT pathway dynamics from transient to sustained during human pluripotent cell differentiation. Proc. Natl Acad. Sci. USA 116, 4989–4998 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Camacho-Aguilar, E., Yoon, S., Ortiz-Salazar, M. A. & Warmflash, A. Combinatorial interpretation of BMP and WNT controls the decision between primitive streak and extraembryonic fates. Cell Syst. 15, 445–461 (2024).

    Article  CAS  PubMed  Google Scholar 

  41. Mayran, A. et al. Cadherins modulate the self-organizing potential of gastruloids. Preprint at bioRxiv https://doi.org/10.1101/2023.11.22.568291 (2023).

  42. Dingare, C., Cao, D., Yang, J. J., Sozen, B. & Steventon, B. Mannose controls mesoderm specification and symmetry breaking in mouse gastruloids. Dev. Cell 59, 1523–1537 (2024).

  43. Arias, A. M., Marikawa, Y. & Moris, N. Gastruloids: pluripotent stem cell models of mammalian gastrulation and embryo engineering. Dev. Biol. 488, 35–46 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pinheiro, D., Kardos, R., Hannezo, É. & Heisenberg, C.-P. Morphogen gradient orchestrates pattern-preserving tissue morphogenesis via motility-driven unjamming. Nat. Phys. 18, 1482–1493 (2022).

    Article  CAS  Google Scholar 

  45. Schüle, K. M. et al. Eomes restricts Brachyury functions at the onset of mouse gastrulation. Dev. Cell 58, 1627–1642 (2023).

    Article  PubMed  Google Scholar 

  46. Anlas, K. & Trivedi, V. Studying evolution of the primary body axis in vivo and in vitro. eLife 10, e69066 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Petersen, C. P. & Reddien, P. W. Wnt signaling and the polarity of the primary body axis. Cell 139, 1056–1068 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Petersen, C. P. & Reddien, P. W. Polarized notum activation at wounds inhibits Wnt function to promote planarian head regeneration. Science 332, 852–855 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Feinberg, S., Roure, A., Piron, J. & Darras, S. Antero–posterior ectoderm patterning by canonical Wnt signaling during ascidian development. PLoS Genet. 15, e1008054 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang, R., Steele, R. E. & Collins, E.-M. S. Wnt signaling determines body axis polarity in regenerating Hydra tissue fragments. Dev. Biol. 467, 88–94 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Lovas, J. R. & Yuste, R. Dissociation and reaggregation of Hydra vulgaris for studies of self-organization. STAR Protoc. 3, 101504 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ferenc, J. et al. Mechanical oscillations orchestrate axial patterning through Wnt activation in Hydra. Sci. Adv. 7, eabj6897 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Amadei, G. et al. Embryo model completes gastrulation to neurulation and organogenesis. Nature 610, 143–153 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Tarazi, S. et al. Post-gastrulation synthetic embryos generated ex utero from mouse naive ESCs. Cell 185, 3290–3306 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bhattarai-Kline, S. et al. Recording gene expression order in DNA by CRISPR addition of retron barcodes. Nature 608, 217–225 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Choi, J. et al. A time-resolved, multi-symbol molecular recorder via sequential genome editing. Nature 608, 98–107 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Thielicke, W. & Sonntag, R. Particle image velocimetry for MATLAB: accuracy and enhanced algorithms in PIVlab. J. Open Res. Softw. 9, 12 (2021).

Download references

Acknowledgements

The authors thank all members of the Toettcher laboratory, particularly E. Underhill, for their insightful comments and suggestions. We also thank A. Martinez-Arias for helpful discussions and feedback on the manuscript, K. Zaret for sharing reporter plasmid constructs, A. Lin for advice on single-cell sequencing and the Lewis Sigler Institute Genomics Core Facility team (W. Wang, J. Miller and J. A. Volmar) for technical support. Figure illustrations in Figures 1a, 2a, 4a, 6a,d,f,i and 7a,i were created in part using BioRender. This work was supported by the Lewis Sigler Scholars programme and the NSF Center for the Physics of Biological Function (grant number PHY1734030) to H.M.M., NIH (grant numbers T32GM007388 and F31HD113443) to S.C.S., NIH (grant number R35GM138167) to B.A., and NSF RECODE (grant number 2134935), NIH (grant numbers U01DK127429 and R01GM144362) and a Vallee Scholars award to J.E.T.

Author information

Authors and Affiliations

  1. Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Harold M. McNamara, Britt Adamson & Michelle M. Chan

  2. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Sabrina C. Solley, Britt Adamson, Michelle M. Chan & Jared E. Toettcher

  3. Omenn–Darling Bioengineering Institute, Princeton University, Princeton, NJ, USA

    Jared E. Toettcher

Authors
  1. Harold M. McNamara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabrina C. Solley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Britt Adamson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michelle M. Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jared E. Toettcher
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: H.M.M., M.M.C. and J.E.T. Methodology: H.M.M., S.C.S., M.M.C. and J.E.T. Investigation: H.M.M. Funding: H.M.M. and J.E.T. Writing and editing: H.M.M. and J.E.T. Supervision: J.E.T. and B.A.

Corresponding authors

Correspondence to Harold M. McNamara or Jared E. Toettcher.

Ethics declarations

Competing interests

J.E.T. is a scientific advisor for Prolific Machines and Nereid Therapeutics. B.A. is an advisory board member with options for Arbor Biotechnologies and Tessera Therapeutics, and holds equity in Celsius Therapeutics. H.M.M. is a cofounder and scientific advisor for C16 Biosciences. The remaining authors declare no conflicts of interest.

Peer review

Peer review information

Nature Cell Biology thanks Takashi Hiiragi, Prisca Liberali and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Benchmarking the Wnt reporter during gastruloid development.

(A) Characterization of off-kinetics of destabilized iRFP reporter of Wnt activity (PTCF/LEF-iRFP-PEST). Shaded region indicates mean ± standard deviation. (B) Experimental protocol for gastruloid growth with ‘LIF only’ seed culture media. (C) Patterns of Wnt activity in gastruloids grown according to protocol in B. Wnt activity is heterogeneous both immediately before (t = 48 haa) and immediately after (t = 72 haa) CHIR treatment. Scale bar = 200 µm. (D) Representative images of Wnt activity patterns at different time points during polarization. Maximum-intensity projections are shown. Scale bar = 200 μm. (E) Representative data illustrating how single cells were gated for analysis from flow cytometry data (see Methods). (F) Definition of threshold to define Wnt-active versus Wnt-inactive cell populations (Fig. 1f) based on separation of activity distributions before (t = 48 haa) and after (t = 72 haa) CHIR stimulation. (G) Schematic of quantification of ΔCOM polarization metric (Fig. 1g).

Source data

Extended Data Fig. 2 Characterizing signal-recorder gene circuits.

(A) Recording efficiency in Wnt-Recorder cells depends on doxycycline concentration (2i + LIF medium, 24 h recording window). (B) Recording efficiency in Wnt-Recorder cells depends on the concentration of recombinant Wnt3a ligand (200 ng ml−1 doxycycline, 24 h recording window). Shaded regions in (A-B) indicate mean ± standard deviation (3 biological replicates per condition). (C) Flow cytometry histograms corresponding to recorder-ligand crosstalk measurement in Fig. 2e. Recording windows were 6 h for Wnt Recorder and Nodal Recorder, and 3 h for BMP recorder. All ligand concentrations (including dox) were 200 ng ml−1. (D) BMP-Recorder labelling is sensitive to BMP inhibition with LDN-193189.

Source data

Extended Data Fig. 3 Measuring positional information in signal-recorded gastruloids.

(A) Illustration of image processing pipeline. Maximum-intensity projections of gastruloids are segmented, skeletonized, and then binned to assign an A–P coordinate to each pixel (Methods). (B) Left: Example quantification of average A–P profiles of both GFP and DsRed expression within a single gastruloid (tdox = 114 h, tf = 144 h). Pixel intensities were separately normalized relative to maximum image intensities in each channel. Right: relative fraction of GFP labelling across the A–P axis within the same gastruloid (Methods). (C) Overlay of relative fraction of GFP labelling in individual gastruloids for the same recording condition, along with the mean profile for this condition. (D) Simultaneous measurement of the final Wnt activity pattern and recorded Wnt activity signal within the same gastruloid. Maximum-intensity projection is shown. Scale bar = 200 μm. (E) Kymograph of the data presented in Fig. 3b to visualize different Wnt dynamics corresponding to different spatial positions. Following the emergence of an anterior domain at tdox = 96 h, Wnt signalling becomes progressively more restricted to the posterior domain. (F) Integrated Wnt signalling activity between t = 96 and t = 134 h shows a linear ‘temporal gradient’ associated with A–P fate. Shaded regions indicate mean ± standard deviation as a function of A–P position.

Source data

Extended Data Fig. 4 Single-cell sequencing of 120 haa Wnt-recorded gastruloids.

(A) Comparison of gene expression between Leiden clusters identified in gastruloids at tseq = 120 h and annotated cell types from a reference atlas gene expression during mouse gastrulation2. Size of dots indicates the number of shared marker genes; colour indicates the binomial likelihood of having at least this many marker genes if compared to a random choice of marker genes (Methods). (B) Single-cell expression levels of reference genes associated with gastrulation and axial morphogenesis. (C) Illustration of reference gene organization across the anterior–posterior axis during axial elongation and somitogenesis. The organization of reference gene expression (Extended Data Fig. 4b) aligns with inferred histories of Wnt activity (Fig. 3c). (D) Final Wnt activity distribution across cells as measured by PTCF/LEF-rtTA expression. (E) Top: individual cells in dataset separated according to Wnt-recording condition. Bottom: embedding density of cells according to Wnt-recording condition. (F) Relative contributions of GFP-positive and GFP-negative cells to different Leiden clusters, normalized according to total cells recovered in both conditions (Methods).

Source data

Extended Data Fig. 5 Exploring Gastruloid reaggregation.

(A) Computer simulation of signal-recording and dissociation/reaggregation experiment in the case of a reaction-diffusion Turing patterning circuit without cell rearrangements. Cell states are labelled according to signalling activity during an intermediate state, then cells locations are randomly shuffled (simulating dissociation), and continued to evolve according to the same reaction-diffusion equations. (B) Quantification of final A–P profiles of recording simulations under reaction-diffusion assumptions, averaged over n = 100 independent trials. Because a ‘new’ pole forms following reaggregation, recorded cells remain evenly distributed across the A–P axis. (C) Representative imaging of cell-sorting polarization following bulk reaggregation. n = 20 of 48 samples recovered show a single pole of Wnt-recorded cells (left). n = 28 of 48 show at least two poles of Wnt activity (right). Smaller ‘satellite’ clusters were not quantified. Maximum-intensity projections are shown. Scale bar = 100 μm. (D) Quantification of A–P axis length for reaggregated samples by flow cytometric sorting vs “bulk” reaggregation by pipetting cells into wells. Error bars indicate mean ± standard deviation. (E) Analysis of differential motility in polarized gastruloids. Left, Middle images: beginning and endpoint images of interval used to calculate motility. Right: average motility map, calculated using particle image velocimetry (PIV) on the nuclear iRFP channel. Motility was calculated for each frame, and then averaged to generate average motility maps (Methods). (F) A–P motility profiles for each of three biological replicates, calculated by averaging motility maps along the A–P axis. (G) Overall average A–P motility profile for all samples analysed. Shaded area represents mean ± standard deviation at each A–P position.

Source data

Extended Data Fig. 6 Gene expression patterns in 96 haa Wnt-reporter gastruloids.

(A) Comparison of gene expression between Leiden clusters identified in gastruloids at tseq = 96 h and annotated cell types from a reference atlas gene expression during mouse gastrulation (Methods). Size of dots indicates the number of shared marker genes; colour indicates the binomial likelihood of having at least this many marker genes if compared to a random choice of marker genes (Methods). (B) Expression of signalling-associated genes within the scRNAseq dataset at tseq = 96 h. (C) Expression of retinoic acid biosynthesis enzyme Aldh1a2 in the Wnt-inactive cell population. Maximum-intensity projections show, scale bar = 50 µm. (D-E) Positive feedback is required to maintain Wnt activity following cessation of CHIR treatment at 72 haa. (D) Flow cytometry measurements of single cell Wnt activity levels in dissociated gastruloids at 96 haa. Treatment with the inhibitor of Wnt secretion IWP-2 from 72 haa-onwards converts majority of cells to the Wnt-off population. (E) Imaging of Wnt activity patterns of intact gastruloids at 96 haa, with and without IWP-2 treatment. Maximum-intensity projections are shown. Scale bar = 50 µm.

Source data

Extended Data Fig. 7 Role of cell adhesion in gastruloid self-organization.

(A) Single-cell differential expression of additional genes associated with cell–cell adhesion at 96 haa. (B) Doxycycline-dependent expression of Cdh1 within the iCdh1 cell line. Maximum-intensity projections shown, scale bar = 10 µm. (C) Doxycycline-dependent expression of Pcdh19 with the iPcdh19 cell line. Maximum-intensity projections shown, scale bar = 10 µm. Clones were chosen for similar expression levels upon induction. (D-E) Average radial concentrations of cell lines in mosaic spheroids with and without doxycycline treatment (related to Fig. 6h). N = 84 spheroids measured (Supplementary Table 3). Shaded regions indicate mean ± standard deviation. (F) Representative images of iCdh1/iPcdh19 mosaic spheroids. Maximum-intensity projections shown, scale bar = 100 µm. (G) Histogram of polarization alignment in fused gastruloids generated by fusing two wild-type spheroids, with and without doxycycline. (H) Quantification of average polarization alignment of fused spheroids in different conditions (related to Fig. 6l, Extended Data Fig. 7g). N = 58 gastruloids total (Supplementary Table 3). Conditions compared via two-sided t-test. P-values: p = 0.54, p = 0.00012. The asterisks (***) indicate p < 0.001.

Source data

Extended Data Fig. 8 Recording Nodal/BMP signalling during gastruloid self-organization.

(A) Measurement of Nodal activity (PAR8-mCherry-PEST) at different time points during gastruloid development = . Maximum-intensity projections are shown, scale bar = 50 µm. (B) Measurement BMP activity (PIBRE4-mCherry-PEST) at different time points during gastruloid development. Maximum-intensity projections are shown, scale bar = 50 µm. (C) Nodal activity levels in 48 haa gastruloids as a function of initial aggregate size. Nodal activity at 48 haa requires aggregation, and monotonically increases with aggregate size. Adherent cells were measured after 48 haa in equivalent media (N2B27) for comparison. (D) Recordings of early Nodal activity traced to tF = 120 haa. (E) Recordings of early BMP activity traced to tF = 120 haa. Shaded regions for C-D indicate standard deviation as a function of A–P position. N = 116 gastruloids measured total (Supplementary Table 3). (F) Representative image of spatial distribution of cell fates with recorded Nodal activity from 69-72 haa, imaged at 120 haa. Top: medial slice. Bottom: Maximum-intensity projection. Scale bar = 200 µm. (G) Representative images of cell fates with recorded BMP activity from 69-72 haa (top) and from 81-84 haa (bottom). Scale bar = 200 µm. Maximum-intensity projections are shown for both recording time points. (H) Simultaneous recording of early Nodal activity (recorded from 45-48 haa) and reporting of later instantaneous Wnt activity (imaged at 106 haa) in the same gastruloid using an engineered clonal cell line. Maximum-intensity projections of three separate representative gastruloids are shown. Scale bar = 100 µm. (I) Spatial distribution of early Nodal activity (recorded from 45-48 haa) traced to tf = 120 haa, plotted alongside final Wnt activity. Normalized profiles are shown for comparison. Shaded region represents mean ± standard error of the mean for Nodal recording, and mean ± standard deviation for Wnt reporting. Nodal recording data from (H), plotted without normalization. Recording density varies approximately twofold along the A–P axis. N = 15 gastruloids measured.

Source data

Extended Data Fig. 9 Perturbing Nodal/BMP signalling during gastruloid self-organization.

(A) Flow cytometry measurements of single-cell Wnt activity levels with and without Activin A pretreatment, measured at t = 72 h. (B) Flow cytometry measurements of single-cell Wnt activity levels with and without BMP4 pretreatment, measured at t = 72 h (left) and t = 96 h (right). (C) Dynamics of Nodal activity during gastruloid development, measured by flow cytometry of pooled and dissociated gastruloids. Substantial Nodal activity is detectable from 36-60, preceding CHIR stimulation at 48 and subsequently decaying to negligible levels by 72 h. (D) Nodal signalling levels in adherent cells treated for 48 h with different concentrations of recombinant Activin A (in N2B27). Dotted line is the mean gastruloid Nodal activity level at 48 haa under standard conditions (that is, initial aggregate of n = 200 cells) plotted for comparison. (E) Effect of a transient ActA treatment (100 ng ml−1, applied from 45-48 haa) on Wnt activity levels at 96. Transient ActA treatment continues to cause an enrichment in Wnt-off cells at 96 haa, even when duration of treatment is reduced. (F) Co-treatment of adherent Wnt reporter cells with BMP4 affects responses to Wnt3a treatment. Whereas moderate BMP4 (25 ng ml−1) treatment facilitated the Wnt response (middle), higher BMP4 (125 ng ml−1) treatment suppressed the Wnt response.

Source data

Extended Data Fig. 10 Phenotypic consequences of Nodal perturbation.

A-B). Quantification of anteroposterior morphology. (A) Gastruloid morphologies were segmented, binned according to nearest A–P axis coordinate (Methods, Extended Data Fig. 3), and then divided into anterior and posterior compartments based on the midline of the A–P axis (that is, without reference to Wnt activity). (B) Comparison of difference in area between anterior and posterior compartments under different Activin treatment conditions (that is, for samples analysed in Fig. 7g, h). 100 ng ml−1 Activin A treatment causes a morphological enlargement of the anterior compartment. N = 42 gastruloids measured (Supplementary Table 3). Statistical comparisons made via two-sided t-test; two asterisks (**) indicate p ≤ 0.01. P-values were: control and 10 ng ml−1 Activin, p = 0.84; control and 100 ng ml−1 Activin, p = 0.010; 10 ng ml−1 Activin and 100 ng ml−1 Activin, p = 0.0091. (C) Recording in the Nodal Recording cells is abolished with treatment with the Nodal inhibitor SB-431542. Activity was recorded for 3 h in 100 ng ml−1 doxycycline. (D) Endogenous early Nodal activity in gastruloids is abolished by Nodal inhibition from 24-48 haa. (Left) Nodal activity was recorded from 45-48 haa, with 100 ng ml−1 doxycycline, and measured at 68 haa. (Right) Early Nodal inhibition (from 24-48 haa) also abolished the formation of Wnt activity patches during symmetry breaking at 96 haa.

Source data

Supplementary information

Reporting Summary

Supplementary Tables 1–3

Supplementary Table 1. Plasmids used in this study. Supplementary Table 2. Cell lines used in this study. Supplementary Table 3. Number of gastruloids analysed for each experiment.

Supplementary Video 1

Live imaging of cellular rearrangements during formation and coalescence of Wnt-correlated domains in one gastruloid, separated by fluorescent channel. Scale bar, 100 µm.

Supplementary Video 2

Live imaging of cellular rearrangements during formation and coalescence of Wnt-correlated domains in multiple gastruloids. Arrows indicate emergence of a Wnt-excluded anterior domain. Scale bar, 100 µm.

Supplementary Video 3

Live imaging of cellular rearrangements during formation and coalescence of Wnt-correlated domains in multiple gastruloids, without matrigel embedding. Scale bar, 100 µm.

Supplementary Video 4

Live imaging of cell motility (imaged with an H2B–iRFP nuclear marker) following gastruloid polarization. Scale bar, 100 µm.

Source data

Source Data

Numerical source data for Figs. 1–7 and Extended Data Figs. 1–10.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McNamara, H.M., Solley, S.C., Adamson, B. et al. Recording morphogen signals reveals mechanisms underlying gastruloid symmetry breaking. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01521-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41556-024-01521-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing