Keller, R. Shaping the vertebrate body plan by polarized embryonic cell movements. Science 298, 1950–1954 (2002).
Zhu, M. & Zernicka-Goetz, M. Principles of self-organization of the mammalian embryo. Cell 183, 1467–1478 (2020).
Liu, Y. et al. Morphogenesis beyond in vivo. Nat. Rev. Phys. 6, 28–44 (2024).
Gilmour, D., Rembold, M. & Leptin, M. From morphogen to morphogenesis and back. Nature 541, 311–320 (2017).
Keller, R. Physical biology returns to morphogenesis. Science 338, 201–203 (2012).
Trepat, X. & Sahai, E. Mesoscale physical principles of collective cell organization. Nat. Phys. 14, 671–682 (2018).
Stern, T., Shvartsman, S. Y. & Wieschaus, E. F. Deconstructing gastrulation at single-cell resolution. Curr. Biol. 32, 1861–1868 (2022).
Mongera, A. et al. A fluid-to-solid jamming transition underlies vertebrate body axis elongation. Nature 561, 401–405 (2018).
Zenker, J. et al. Expanding actin rings zipper the mouse embryo for blastocyst formation. Cell 173, 776–791 (2018).
Lim, H. Y. G. et al. Keratins are asymmetrically inherited fate determinants in the mammalian embryo. Nature 585, 404–409 (2020).
Rozbicki, E. et al. Myosin-ii-mediated cell shape changes and cell intercalation contribute to primitive streak formation. Nat. Cell Biol. 17, 397–408 (2015).
Ershov, D. et al. Trackmate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nat. Methods 19, 829–832 (2022).
Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat. Methods 19, 1634–1641 (2022).
Huijben, T. A. P. M. et al. inTRACKtive: a web-based tool for interactive cell tracking visualization. Nat. Methods 22, 2229–2231 (2025).
Toulany, N. et al. Uncovering developmental time and tempo using deep learning. Nat. Methods 20, 2000–2010 (2023).
Mitchell, N. P. & Cislo, D. J. Tubular: tracking in toto deformations of dynamic tissues via constrained maps. Nat. Methods 20, 1980–1988 (2023).
Noll, N., Streichan, S. J. & Shraiman, B. I. Variational method for image-based inference of internal stress in epithelial tissues. Phys. Rev. X 10, 011072 (2020).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Jumper, J. et al. Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021).
Pope, K. L. & Harris, T. J. C. Control of cell flattening and junctional remodeling during squamous epithelial morphogenesis in Drosophila. Development https://doi.org/10.1242/dev.019802 (2008).
Serra, M., Streichan, S., Chuai, M., Weijer, C. J. & Mahadevan, L. Dynamic morphoskeletons in development. Proc. Natl Acad. Sci. USA 117, 11444–11449 (2020).
Butler, L. C. et al. Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension. Nat. Cell Biol. 11, 859–864 (2009).
Irvine, K. D. & Wieschaus, E. Cell intercalation during Drosophila germband extension and its regulation by pair-rule segmentation genes. Development 120, 827–841 (1994).
Foe, V. E. Mitotic domains reveal early commitment of cells in Drosophila embryos. Development 107, 1–22 (1989).
Sweeton, D., Parks, S., Costa, M. & Wieschaus, E. Gastrulation in Drosophila: the formation of the ventral furrow and posterior midgut invaginations. Development 112, 775–789 (1991).
Campàs, O., Noordstra, I. & Yap, A. S. Adherens junctions as molecular regulators of emergent tissue mechanics. Nat. Rev. Mol. Cell Biol. 25, 252–269 (2024).
Bi, D., Lopez, J. H., Schwarz, J. M. & Manning, M. L. A density-independent rigidity transition in biological tissues. Nat. Phys. 11, 1074–1079 (2015).
Kim, S., Pochitaloff, M., Stooke-Vaughan, G. A. & Campàs, O. Embryonic tissues as active foams. Nat. Phys. 17, 859–866 (2021).
Mongera, A. et al. Mechanics of the cellular microenvironment as probed by cells in vivo during zebrafish presomitic mesoderm differentiation. Nat. Mater. 22, 135–143 (2023).
Bertet, C., Sulak, L. & Lecuit, T. Myosin-dependent junction remodelling controls planar cell intercalation and axis elongation. Nature 429, 667–671 (2004).
Blanchard, G. B. et al. Tissue tectonics: morphogenetic strain rates, cell shape change and intercalation. Nat. Methods 6, 458–464 (2009).
Rauzi, M., Verant, P., Lecuit, T. & Lenne, P.-F. Nature and anisotropy of cortical forces orienting Drosophila tissue morphogenesis. Nat. Cell Biol. 10, 1401–1410 (2008).
Wang, X. et al. Anisotropy links cell shapes to tissue flow during convergent extension. Proc. Natl Acad. Sci. USA 117, 13541–13551 (2020).
Etournay, R. et al. Interplay of cell dynamics and epithelial tension during morphogenesis of the Drosophila pupal wing. Elife 4, e07090 (2015).
Herrera-Perez, R. M., Cupo, C., Allan, C., Dagle, A. B. & Kasza, K. E. Tissue flows are tuned by actomyosin-dependent mechanics in developing embryos. PRX Life 1, 013004 (2023).
Cupo, C., Allan, C., Ailiani, V. & Kasza, K. E. Signatures of structural disorder in the developing Drosophila germband epithelium. PRX Life 2, 043004 (2024).
Walck-Shannon, E. & Hardin, J. Cell intercalation from top to bottom. Nat. Rev. Mol. Cell Biol. 15, 34–48 (2014).
Brauns, F., Claussen, N. H., Lefebvre, M. F., Wieschaus, E. F. & Shraiman, B. I. The geometric basis of epithelial convergent extension. Elife https://doi.org/10.7554/eLife.95521.3 (2024).
Campàs, O. et al. Quantifying cell-generated mechanical forces within living embryonic tissues. Nat. Methods 11, 183–189 (2014).
Di Talia, S. & Wieschaus, E. F. Short-term integration of cdc25 dynamics controls mitotic entry during Drosophila gastrulation. Dev. Cell 22, 763–774 (2012).
Ferree, P. L. Temporal Regulation of Cell Divisions in the Embryo of Drosophila melanogaster. PhD thesis, Duke Univ. (2022).
Claussen, N. H., Brauns, F. & Shraiman, B. I. A geometric-tension-dynamics model of epithelial convergent extension. Proc. Natl Acad. Sci. USA 121, e2321928121 (2024).
Yamamoto, T., Cockburn, K., Greco, V. & Kawaguchi, K. Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks. PLoS Comput. Biol. 18, e1010477 (2022).
Yang, H. et al. Learning collective cell migratory dynamics from a static snapshot with graph neural networks. PRX Life 2, 043010 (2024).
Supekar, R. et al. Learning hydrodynamic equations for active matter from particle simulations and experiments. Proc. Natl Acad. Sci. USA 120, e2206994120 (2023).
Lefebvre, M. F. et al. Learning a conserved mechanism for early neuroectoderm morphogenesis. Preprint at bioRxiv https://doi.org/10.1101/2023.12.22.573058 (2023).
LaChance, J., Suh, K., Clausen, J. & Cohen, D. J. Learning the rules of collective cell migration using deep attention networks. PLoS Comput. Biol. 18, e1009293 (2022).
Brückner, D. B., Ronceray, P. & Broedersz, C. P. Inferring the dynamics of underdamped stochastic systems. Phys. Rev. Lett. 125, 058103 (2020).
Frishman, A. & Ronceray, P. Learning force fields from stochastic trajectories. Phys. Rev. X 10, 021009 (2020).
Angelini, T. E. et al. Glass-like dynamics of collective cell migration. Proc. Natl Acad. Sci. USA 108, 4714–4719 (2011).
Atia, L. et al. Geometric constraints during epithelial jamming. Nat. Phys. 14, 613–620 (2018).
Brandstätter, T. et al. Curvature induces active velocity waves in rotating spherical tissues. Nat. Commun. 14, 1643 (2023).
Tang, W. et al. Topology and nuclear size determine cell packing on growing lung spheroids. Phys. Rev. X 15, 011067 (2025).
Hu, Y. et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat. Methods 21, 267–278 (2024).
Lecuit, T. & Lenne, P.-F. Cell surface mechanics and the control of cell shape, tissue patterns and morphogenesis. Nat. Rev. Mol. Cell Biol. 8, 633–644 (2007).
Rozman, J., Krajnc, M. & Ziherl, P. Collective cell mechanics of epithelial shells with organoid-like morphologies. Nat. Commun. 11, 3805 (2020).
Wang, X., Cupo, C. M., Ostvar, S., Countryman, A. D. & Kasza, K. E. E-cadherin tunes tissue mechanical behavior before and during morphogenetic tissue flows. Curr. Biol. https://doi.org/10.1016/j.cub.2024.06.038 (2024).
Thomson, D. W. On Growth and Form (Cambridge Univ. Press, 1917).
Firmin, J. et al. Mechanics of human embryo compaction. Nature 629, 646–651 (2024).
Martin, A. C., Kaschube, M. & Wieschaus, E. F. Pulsed contractions of an actin–myosin network drive apical constriction. Nature 457, 495–499 (2009).
Yevick, H. G., Miller, P. W., Dunkel, J. & Martin, A. C. Structural redundancy in supracellular actomyosin networks enables robust tissue folding. Dev. Cell 50, 586–598 (2019).
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Kipf, T.N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations 2017 https://openreview.net/pdf?id=SJU4ayYgl (ICLR, 2017).
Kipf, T. N. & Welling, M. Variational graph auto-encoders. In Bayesian Deep Learning Workshop https://www.bayesiandeeplearning.org/2016/papers/BDL_16.pdf (2016).
Corso, G., Cavalleri, L., Beaini, D., Liò, P. & Veličković, P. Principal neighbourhood aggregation for graph nets. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://proceedings.neurips.cc/paper/2020/file/99cad265a1768cc2dd013f0e740300ae-Paper.pdf (2020).
Veličković, P. et al. Graph attention networks. In International Conference on Learning Representations 2018 https://openreview.net/forum?id=rJXMpikCZ (ICLR, 2018).
Shi, Y. et al. Masked label prediction: unified message passing model for semi-supervised classification. In Proc. Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) https://www.ijcai.org/proceedings/2021/0214.pdf (IJCAI, 2021).
Bapst, V. et al. Unveiling the predictive power of static structure in glassy systems. Nat. Phys. 16, 448–454 (2020).
Yang, Z. & Buehler, M. J. Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks. NPJ Comput. Mater. 8, 198 (2022).
Pineda, J. et al. Geometric deep learning reveals the spatiotemporal features of microscopic motion. Nat. Mach. Intell. 5, 71–82 (2023).
Viñas, R. et al. Hypergraph factorization for multi-tissue gene expression imputation. Nat. Mach. Intell. 5, 739–753 (2023).
Baker, E. A. G., Schapiro, D., Dumitrascu, B., Vickovic, S. & Regev, A. In silico tissue generation and power analysis for spatial omics. Nat. Methods 20, 424–431 (2023).
Cubuk, E. D. et al. Identifying structural flow defects in disordered solids using machine-learning methods. Phys. Rev. Lett. 114, 108001 (2015).
Cubuk, E. D. et al. Structure-property relationships from universal signatures of plasticity in disordered solids. Science 358, 1033–1037 (2017).
Jung, G. et al. Roadmap on machine learning glassy dynamics. Nat. Rev. Phys. 7, 91–104 (2025).
Mitchell, N. P. et al. Morphodynamic atlas for Drosophila development. Preprint at bioRxiv https://doi.org/10.1101/2022.05.26.493584 (2022).
Zhou, B., Khosla, A., Lapedriza, A. Oliva, A. & Torralba, A. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf (IEEE, 2016).
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Lohoff, T. et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40, 74–85 (2022).
Hallou, A., He, R., Simons, B. D. & Dumitrascu, B. A computational pipeline for spatial mechano-transcriptomics. Nat. Methods https://doi.org/10.1038/s41592-025-02618-1 (2025).
Martin, A. C., Gelbart, M., Fernandez-Gonzalez, R., Kaschube, M. & Wieschaus, E. F. Integration of contractile forces during tissue invagination. J. Cell Biol. 188, 735–749 (2010).
Zallen, J. A. & Wieschaus, E. Patterned gene expression directs bipolar planar polarity in Drosophila. Dev. Cell 6, 343–355 (2004).
Popović, M., Druelle, V., Dye, N. A., Jülicher, F. & Wyart, M. Inferring the flow properties of epithelial tissues from their geometry. N. J. Phys. 23, 033004 (2021).
Falk, M. L. & Langer, J. S. Deformation and failure of amorphous, solidlike materials. Annu. Rev. Condens. Matter Phys. 2, 353–373 (2011).
Richard, D., Elgailani, A., Vandembroucq, D., Manning, M. L. & Maloney, C. E. Mechanical excitation and marginal triggering during avalanches in sheared amorphous solids. Phys. Rev. E 107, 034902 (2023).
Huang, J., Cochran, J. O., Fielding, S. M., Marchetti, M. C. & Bi, D. Shear-driven solidification and nonlinear elasticity in epithelial tissues. Phys. Rev. Lett. 128, 178001 (2022).
Rados, T. et al. Tissue-like multicellular development triggered by mechanical compression in archaea. Science 388, 109–115 (2025).
Shah, H. et al. Life-cycle-coupled evolution of mitosis in close relatives of animals. Nature 630, 116–122 (2024).
Park, J.-A. et al. Unjamming and cell shape in the asthmatic airway epithelium. Nat. Mater. 14, 1040–1048 (2015).
Yang, H. et al. Configurational fingerprints of multicellular living systems. Proc. Natl Acad. Sci. USA 118, e2109168118 (2021).
Hörl, D. et al. Bigstitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16, 870–874 (2019).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Yang, H. et al. Zenodo https://doi.org/10.5281/zenodo.17605530 (2025).