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  • Letter
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Topological defects in epithelia govern cell death and extrusion

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Abstract

Epithelial tissues (epithelia) remove excess cells through extrusion, preventing the accumulation of unnecessary or pathological cells. The extrusion process can be triggered by apoptotic signalling1, oncogenic transformation2,3 and overcrowding of cells4,5,6. Despite the important linkage of cell extrusion to developmental7, homeostatic5 and pathological processes2,8 such as cancer metastasis, its underlying mechanism and connections to the intrinsic mechanics of the epithelium are largely unexplored. We approach this problem by modelling the epithelium as an active nematic liquid crystal (that has a long range directional order), and comparing numerical simulations to strain rate and stress measurements within monolayers of MDCK (Madin Darby canine kidney) cells. Here we show that apoptotic cell extrusion is provoked by singularities in cell alignments9,10 in the form of comet-shaped topological defects. We find a universal correlation between extrusion sites and positions of nematic defects in the cell orientation field in different epithelium types. The results confirm the active nematic nature of epithelia, and demonstrate that defect-induced isotropic stresses are the primary precursors of mechanotransductive responses in cells, including YAP (Yes-associated protein) transcription factor activity11, caspase-3-mediated cell death, and extrusions. Importantly, the defect-driven extrusion mechanism depends on intercellular junctions, because the weakening of cell–cell interactions in an α-catenin knockdown monolayer reduces the defect size and increases both the number of defects and extrusion rates, as is also predicted by our model. We further demonstrate the ability to control extrusion hotspots by geometrically inducing defects through microcontact printing of patterned monolayers. On the basis of these results, we propose a mechanism for apoptotic cell extrusion: spontaneously formed topological defects in epithelia govern cell fate. This will be important in predicting extrusion hotspots and dynamics in vivo, with potential applications to tissue regeneration and the suppression of metastasis. Moreover, we anticipate that the analogy between the epithelium and active nematic liquid crystals will trigger further investigations of the link between cellular processes and the material properties of epithelia.

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Figure 1: Extrusion correlates with singularities in cell orientation in the epithelia.
Figure 2: MDCK WT epithelia behave as 2D, extensile, active nematic liquid crystals.
Figure 3: Compressive stresses at +1/2 defects trigger both cell extrusion and YAP mechanosensitive response and can be modulated by cell–cell junction strength.
Figure 4: Topologically induced +1/2 defects can control extrusion hotspots.

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Change history

  • 01 June 2017

    The Reviewer Information section was corrected.

References

  1. Rosenblatt, J., Raff, M. C. & Cramer, L. P. An epithelial cell destined for apoptosis signals its neighbors to extrude it by an actin-and myosin-dependent mechanism. Curr. Biol. 11, 1847–1857 (2001)

    Article  CAS  Google Scholar 

  2. Hogan, C. et al. Characterization of the interface between normal and transformed epithelial cells. Nat. Cell Biol. 11, 460–467 (2009)

    Article  CAS  Google Scholar 

  3. Wagstaff, L. et al. Mechanical cell competition kills cells via induction of lethal p53 levels. Nat. Commun. 7, 11373 (2016)

    Article  ADS  CAS  Google Scholar 

  4. Marinari, E. et al. Live-cell delamination counterbalances epithelial growth to limit tissue overcrowding. Nature 484, 542–545 (2012)

    Article  ADS  CAS  Google Scholar 

  5. Eisenhoffer, G. T. et al. Crowding induces live cell extrusion to maintain homeostatic cell numbers in epithelia. Nature 484, 546–549 (2012)

    Article  ADS  CAS  Google Scholar 

  6. Levayer, R., Dupont, C. & Moreno, E. Tissue crowding induces caspase-dependent competition for space. Curr. Biol. 26, 670–677 (2016)

    Article  CAS  Google Scholar 

  7. Toyama, Y., Peralta, X. G., Wells, A. R., Kiehart, D. P. & Edwards, G. S. Apoptotic force and tissue dynamics during Drosophila embryogenesis. Science 321, 1683–1686 (2008)

    Article  ADS  CAS  Google Scholar 

  8. Slattum, G. M. & Rosenblatt, J. Tumour cell invasion: an emerging role for basal epithelial cell extrusion. Nat. Rev. Cancer 14, 495–501 (2014)

    Article  CAS  Google Scholar 

  9. Kemkemer, R., Teichgräber, V., Schrank-Kaufmann, S., Kaufmann, D. & Gruler, H. Nematic order-disorder state transition in a liquid crystal analogue formed by oriented and migrating amoeboid cells. Eur. Phys. J. E 3, 101–110 (2000)

    Article  CAS  Google Scholar 

  10. Duclos, G., Erlenkämper, C., Joanny, J.-F. & Silberzan, P. Topological defects in confined populations of spindle-shaped cells. Nat. Phys. 13, 58–62 (2016)

    Article  Google Scholar 

  11. Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–183 (2011)

    Article  CAS  Google Scholar 

  12. de Gennes, P.-G &. Prost, J. The Physics of Liquid Crystals Ch. 4 (Oxford Univ. Press, 1995)

  13. Hirst, L. S. et al. Morphology transition in lipid vesicles due to in-plane order and topological defects. Proc. Natl Acad. Sci. USA 110, 3242–3247 (2013)

    Article  ADS  CAS  Google Scholar 

  14. Sanchez, T., Chen, D. T., DeCamp, S. J., Heymann, M. & Dogic, Z. Spontaneous motion in hierarchically assembled active matter. Nature 491, 431–434 (2012)

    Article  ADS  CAS  Google Scholar 

  15. Schaller, V. & Bausch, A. R. Topological defects and density fluctuations in collectively moving systems. Proc. Natl Acad. Sci. USA 110, 4488–4493 (2013)

    Article  ADS  MathSciNet  Google Scholar 

  16. Doostmohammadi, A., Thampi, S. P. & Yeomans, J. M. Defect-mediated morphologies in growing cell colonies. Phys. Rev. Lett. 117, 048102 (2016)

    Article  ADS  Google Scholar 

  17. Vedula, S. R. K. et al. Epithelial bridges maintain tissue integrity during collective cell migration. Nat. Mater. 13, 87–96 (2014)

    Article  ADS  CAS  Google Scholar 

  18. Vedula, S. R. K. et al. Emerging modes of collective cell migration induced by geometrical constraints. Proc. Natl Acad. Sci. USA 109, 12974–12979 (2012)

    Article  ADS  CAS  Google Scholar 

  19. Doostmohammadi, A. et al. Celebrating Soft Matter’s 10th anniversary: cell division: a source of active stress in cellular monolayers. Soft Matter 11, 7328–7336 (2015)

    Article  ADS  CAS  Google Scholar 

  20. Drescher, K., Dunkel, J., Cisneros, L. H., Ganguly, S. & Goldstein, R. E. Fluid dynamics and noise in bacterial cell–cell and cell–surface scattering. Proc. Natl Acad. Sci. USA 108, 10940–10945 (2011)

    Article  ADS  CAS  Google Scholar 

  21. Guasto, J. S., Rusconi, R. & Stocker, R. Fluid mechanics of planktonic microorganisms. Annu. Rev. Fluid Mech. 44, 373–400 (2012)

    Article  ADS  MathSciNet  Google Scholar 

  22. Rossen, N. S., Tarp, J. M., Mathiesen, J., Jensen, M. H. & Oddershede, L. B. Long-range ordered vorticity patterns in living tissue induced by cell division. Nat. Commun. 5, 5720 (2014)

    Article  ADS  CAS  Google Scholar 

  23. Prost, J., Jülicher, F. & Joanny, J. Active gel physics. Nat. Phys. 11, 111–117 (2015)

    Article  CAS  Google Scholar 

  24. Nier, V. et al. Inference of internal stress in a cell monolayer. Biophys. J. 110, 1625–1635 (2016)

    Article  ADS  CAS  Google Scholar 

  25. Aragona, M. et al. A mechanical checkpoint controls multicellular growth through YAP/TAZ regulation by actin-processing factors. Cell 154, 1047–1059 (2013)

    Article  CAS  Google Scholar 

  26. Dong, J. et al. Elucidation of a universal size-control mechanism in Drosophila and mammals. Cell 130, 1120–1133 (2007)

    Article  CAS  Google Scholar 

  27. Chiba, T. et al. MDCK cells expressing constitutively active Yes-associated protein (YAP) undergo apical extrusion depending on neighboring cell status. Sci. Rep. 6, 28383 (2016)

    Article  ADS  CAS  Google Scholar 

  28. Yonemura, S., Wada, Y., Watanabe, T., Nagafuchi, A. & Shibata, M. α-Catenin as a tension transducer that induces adherens junction development. Nat. Cell Biol. 12, 533–542 (2010)

    Article  CAS  Google Scholar 

  29. Doostmohammadi, A., Adamer, M. F., Thampi, S. P. & Yeomans, J. M. Stabilization of active matter by flow-vortex lattices and defect ordering. Nat. Commun. 7, 10557 (2016)

    Article  ADS  CAS  Google Scholar 

  30. Kuipers, D. et al. Epithelial repair is a two-stage process driven first by dying cells and then by their neighbours. J. Cell Sci. 127, 1229–1241 (2014)

    Article  CAS  Google Scholar 

  31. Kocgozlu, L. et al. Epithelial cell packing induces distinct modes of cell extrusions. Curr. Biol. 26(21), 2942–2950 (2016)

    Article  Google Scholar 

  32. Campàs, O. et al. Quantifying cell-generated mechanical forces within living embryonic tissues. Nat. Methods 11, 183–189 (2014); corrigendum 11, 349 (2014)

    Article  Google Scholar 

  33. Tambe, D. T. et al. Monolayer stress microscopy: limitations, artefacts, and accuracy of recovered intercellular stresses. PLoS One 8, e55172 (2013); correction 9, 1 (2014)

    Article  ADS  CAS  Google Scholar 

  34. Moussus, M. et al. Intracellular stresses in patterned cell assemblies. Soft Matter 10, 2414–2423 (2014); comment 10, 2414 (2014)

    Article  ADS  CAS  Google Scholar 

  35. Vedula, S. R. K. et al. Microfabricated environments to study collective cell behaviors. Methods Cell Biol. 120, 235–252 (2014)

    Article  Google Scholar 

  36. Brugués, A. et al. Forces driving epithelial wound healing. Nat. Phys. 10, 683–690 (2014)

    Article  Google Scholar 

  37. Martiel, J.-L. et al. Measurement of cell traction forces with ImageJ. Methods Cell Biol. 125, 269–287 (2015)

    Article  CAS  Google Scholar 

  38. Rezakhaniha, R. et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomech. Model. Mechanobiol. 11, 461–473 (2012)

    Article  CAS  Google Scholar 

  39. Huterer, D. & Vachaspati, T. Distribution of singularities in the cosmic microwave background polarization. Phys. Rev. D 72, 043004 (2005)

    Article  ADS  Google Scholar 

  40. Chalfoun, J. et al. FogBank: a single cell segmentation across multiple cell lines and image modalities. BMC Bioinformatics 15, 431 (2014)

    Article  Google Scholar 

  41. Thielicke, W. & Stamhuis, E. J. PIVlab–Towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB. J. Open Res. Softw. 2, e30 (2014)

    Article  Google Scholar 

  42. Blow, M. L., Thampi, S. P. & Yeomans, J. M. Biphasic, lyotropic, active nematics. Phys. Rev. Lett. 113, 248303 (2014)

    Article  ADS  Google Scholar 

  43. Giomi, L., Mahadevan, L., Chakraborty, B. & Hagan, M. Excitable patterns in active nematics. Phys. Rev. Lett. 106, 218101 (2011)

    Article  ADS  CAS  Google Scholar 

  44. Thampi, S. P., Golestanian, R. & Yeomans, J. M. Instabilities and topological defects in active nematics. Europhys. Lett. 105, 18001 (2014)

    Article  ADS  Google Scholar 

  45. Marenduzzo, D., Orlandini, E., Cates, M. E. & Yeomans, J. M. Steady-state hydrodynamic instabilities of active liquid crystals: hybrid lattice Boltzmann simulations. Phys. Rev. E 76, 031921 (2007)

    Article  ADS  CAS  Google Scholar 

  46. Thampi, S. P., Golestanian, R. & Yeomans, J. M. Vorticity, defects and correlations in active turbulence. Phil. Trans. R. Soc. A 372, 20130366 (2014)

    Article  ADS  Google Scholar 

  47. Bittig, T., Wartlick, O., Kicheva, A., González-Gaitán, M. & Jülicher, F. Dynamics of anisotropic tissue growth. New J. Phys. 10, 063001 (2008)

    Article  ADS  Google Scholar 

  48. Volfson, D., Cookson, S., Hasty, J. & Tsimring, L. S. Biomechanical ordering of dense cell populations. Proc. Natl Acad. Sci. USA 105, 15346–15351 (2008)

    Article  ADS  Google Scholar 

  49. Larson, R. G. The Structure and Rheology of Complex Fluids Vol. 33 (Oxford Univ. Press, 1999)

  50. Edwards, S. A. & Yeomans, J. M. Spontaneous flow states in active nematics: a unified picture. Europhys. Lett. 85, 18008 (2009)

    Article  ADS  Google Scholar 

  51. Bittig, T., Wartlick, O., González-Gaitán, M. & Jülicher, F. Quantification of growth asymmetries in developing epithelia. Eur. Phys. J. E 30, 93–99 (2009)

    Article  CAS  Google Scholar 

  52. Basan, M., Joanny, J.-F., Prost, J. & Risler, T. Undulation instability of epithelial tissues. Phys. Rev. Lett. 106, 158101 (2011)

    Article  ADS  Google Scholar 

  53. Delarue, M. et al. Mechanical control of cell flow in multicellular spheroids. Phys. Rev. Lett. 110, 138103 (2013)

    Article  ADS  Google Scholar 

  54. Denniston, C., Marenduzzo, D., Orlandini, E. & Yeomans, J. M. Lattice Boltzmann algorithm for three-dimensional liquid–crystal hydrodynamics. Phil. Trans. R. Soc. A 362, 1745–1754 (2004)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  55. Fielding, S., Marenduzzo, D. & Cates, M. E. Nonlinear dynamics and rheology of active fluids: simulations in two dimensions. Phys. Rev. E 83, 041910 (2011)

    Article  ADS  CAS  Google Scholar 

  56. von Toussaint, U. Bayesian inference in physics. Rev. Mod. Phys. 83, 943–999 (2011)

    Article  ADS  Google Scholar 

  57. Kaipio, J. & Somersalo, E. Statistical and Computational Inverse Problems Vol. 160 (Springer Science & Business Media, 2006)

  58. Hansen, P. C. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev. 34, 561–580 (1992)

    Article  MathSciNet  Google Scholar 

  59. du Roure, O . et al. Force mapping in epithelial cell migration. Proc. Natl Acad. Sci. USA 102, 2390–2395 (2005); correction 102, 14122 (2005)

    Article  ADS  CAS  Google Scholar 

  60. Cochet-Escartin, O., Ranft, J., Silberzan, P. & Marcq, P. Border forces and friction control epithelial closure dynamics. Biophys. J. 106, 65–73 (2014)

    Article  ADS  CAS  Google Scholar 

  61. Harris, A. R. et al. Characterizing the mechanics of cultured cell monolayers. Proc. Natl Acad. Sci. USA 109, 16449–16454 (2012)

    Article  ADS  CAS  Google Scholar 

  62. Jenkins, A. D. & Dysthe, K. B. The effective film viscosity coefficients of a thin floating fluid layer. J. Fluid Mech. 344, 335–337 (1997)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  63. Hecht, F. New development in FreeFem++. J. Numer. Math. 20, 251–266 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

We thank G. Peyret, S. Jain, R.-M. Mège and group members from MBI for discussions, as well as I. Yow, A. P. Le, S. Begnaud and A. Kehren for experimental assistance, and S. Begnaud for providing the YAP nucleus-to-cytoplasm ratio quantification algorithm. We also thank the MBI Microfabrication core, the MBI Microscopy core and the MBI Science Communication core for support. W. J. Nelson and M. Sudol are thanked for their gifts of MDCK cell lines and YAP antibody, respectively. This work was supported by the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC grant agreements 617233 (B.L.) and 291234 (J.M.Y.), and by the Mechanobiology Institute. T.B.S. was supported by an NGS scholarship.

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Authors and Affiliations

Authors

Contributions

T.B.S., A.D., J.M.Y. and B.L. designed research, T.B.S., L.K. and Y.T. performed experiments, A.D. and S.T. implemented the numerical method and did the in silico simulations, T.B.S., A.D., V.N. and P.M. contributed new reagents, modelling and computational tools, T.B.S., A.D., J.M.Y. and B.L. wrote the paper, and C.T.L., J.M.Y. and B.L. supervised the project. All authors read the manuscript and commented on it.

Corresponding authors

Correspondence to Chwee Teck Lim, Julia M. Yeomans or Benoit Ladoux.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks A. Bausch, D. Discher and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Further characterization of cell, monolayer and extrusion–defect correlation properties.

a, b, Time evolution of cell aspect ratio (a) and cell area (b) in a confluent MDCK epithelium. Data for each time point are binned over a duration of 120 min. From lowest to highest time points, n = 5,101, 5,537, 5,772, 6,549, 6,572, 6,876, 6,593 and 6,831 cells. c, Time evolution of nematic measure (averaged local order parameter, S) of corresponding epithelium (Methods), n = 294 data points for each bar. d, MDCK monolayer in circular confinement (left). Red lines (represented again as black lines in middle) show local cell orientation, colour coded at right. Scale bar, 100 μm. e, Number of closest +1/2 defects per unit area as function of re normalized such that area underneath the density curve sums to 1. n = 30 different random point sets. See Methods. f, Left, diagram of determination of correlation between −1/2 defects and extrusions: distance, re, of each extrusion to its closest −1/2 defect in preceding frame is measured, and the number of these defects per unit area as function of re is normalized (right). See Methods. n = 50 (MDCK WT) extrusions from 4 independent movies in 3 independent experiments; n = 61 (MDCK, mytomycin-c) extrusions from 3 independent movies in 2 independent experiments; n = 85 (MCF10A) extrusions in 2 independent movies; n = 79 (HaCaT) extrusions in 2 independent movies. g, Similar to e, but is the normalized density curve between random points and −1/2 defects. h, A measure of correlation strength between extrusions and defects: ratio of density curve values at re = 10 μm to that at re = 120 μm (first and last points in respective curves, Fig. 1f and Extended Data Fig. 1f). i, Measure of correlation strength for eventual extrusion points and +1/2 defects as function of +1/2 defect distributions at each time point (WT-MDCK). All data represented as mean ± s.e.m.

Extended Data Figure 2 Further examination of active nematic properties and the correlation of extrusion with defects in the epithelium under different conditions.

a, Velocity field around +1/2 defect in contractile, active nematic liquid crystal simulation. Director configuration around the defect is same as Fig. 2a b, Average velocity field around +1/2 defect for mytomycin C and blebbistatin treated MDCK. n = 2,003 (mytomycin C) defects from 3 independent movies in 2 independent experiments; n = 3,061 (blebbistatin) defects from 3 independent movies. +1/2 defect has same orientation and position as in Fig. 2a. c, Total defect areal density evolution in simulation, activity parameter decreased at simulation time t = 0, then increased at t = 5. d, Average total defect density for WT MDCK (‘Non-treated’) and blebbistatin (10 μM and 50 μM) treated MDCK. n = 314 frames from 4 independent movies in 3 independent experiments (WT); n = 155 frames from 3 independent movies (blebbistatin 10 μM); n = 26 frames from 4 independent movies (blebbistatin, 50 μM). t-test, ***P < 0.001. e, Left, number of closest +1/2 and −1/2 defects per unit area as function of distance, re, measured from respective extrusions (Ext, +1/2) and (Ext, −1/2), in 10 μM blebbistatin treated monolayer. Similar curves also plotted for +1/2 and −1/2 defects measured from random points (Random, +1/2) and (Random, −1/2). Each density curve is normalized such that area under each curve sums to 1. n = 78 extrusions in 3 independent movies; n = 30 different random point sets. Right, ratio of density curve values at re = 10 μm to re = 120 μm for the curves (Ext, +1/2) and (Ext, −1/2) on the left. All data represented as mean ± s.e.m.

Extended Data Figure 3 Further investigation of the relation between local cell density, compressive stress, defects and extrusions, and the role of caspase-3 activation.

a, Number of closest high cell density spots per unit area as function of radius, r, measured from extrusion points in monolayer (Ext, High cell density). Similar curve plotted for high cell density spots measured from random points (Random, High cell density). n = 50 (MDCK WT) extrusions from 4 independent movies in 3 independent experiments; n = 30 different random point sets. b, Normalized number of closest defects per unit area as function of distance, re, against spots with top 5% of all compressive stresses in simulation domain. See Methods for calculation of simulation stress. c, Normalized number of closest extrusions per unit area as a function of distance against +1/2 defects, grouped by magnitude of compressive stress at head regions of defects. Extrusions are within 40 min after the frame of defect. From lowest (least negative) to highest compressive isotropic stress (most negative), n = 331, 215, 180 and 72 defects in 2 independent experiments. All density curves in a, b and c are normalized such that area under the curve sums to 1. d, Typical example of extrusion event, showing configuration of +1/2 defect before and after extrusion. The same neighbour cell before (t < 0 min) and after (t > 0 min) extrusion is outlined with same colour. e, Left, same data set as Fig. 3b, showing average isotropic stress evolution around cells experiencing negative (compressive) stress values during extrusion initiation at t = 0 min, before and after extrusion (n = 32 extrusions in 2 independent experiments). A t-test was performed for each time point against a normal distribution centred at zero, *P < 0.0001. Right, scatter plot of same data. f, Typical example of extrusion event with caspase-3 activation (yellow arrowhead) (t = 0 min). Top panel, nematic directors (red lines) overlaid on monolayer. Bottom panel, corresponding caspase-3 signalling. g, Average extrusion rate in non-drug-treated (ND) and caspase-3 inhibited MDCK. n = 8 (ND) independent movies in 3 independent experiments; n = 7 (caspase-3 inhibited) independent movies in 2 independent experiments. ks-test, ***P < 0.001. All data represented as mean ± s.e.m.

Extended Data Figure 4 Order parameter and extrusion–defect correlation for α-catKD MDCK, and simulations comparing small and large nematic bending elasticity.

a, Average local order parameter, S, for WT and α-catKD MDCK epithelium. n = 3 (WT) independent movies in 2 independent experiments; n = 3 (α-catKD) independent movies. t-test, ***P < 0.001. b, Left, number of closest +1/2 and −1/2 defects per unit area as a function of distance, re, measured from respective extrusions (Ext, +1/2) and (Ext, −1/2) in α-catKD MDCK. Similar curves plotted for +1/2 and −1/2 defects measured from random points (Random, +1/2) and (Random, −1/2). Each density curve is normalized such that area under each curve sums to 1. n = 56 extrusions in 3 independent movies; n = 30 different random point sets. Right, ratio of density curve values at re = 10 μm to re = 120 μm, for the curves (Ext, +1/2) and (Ext, −1/2) on the left. c, d, Simulations comparing size of defects (c) and total defect areal density (d) for small and large nematic bending elasticity, K. K is 0.02 and 0.08 for c, and 0.04 and 0.08 for d. All data represented as mean ± s.e.m.

Extended Data Figure 5 Further analysis of topologically induced defects and extrusions.

a, Maps of normalized number of +1/2 (left column) and −1/2 (right column) defects per unit area in star (top row) and circle (bottom row) epithelium confinements (Methods). n = 6,738 (+1/2) defects and n = 5,083 (−1/2) defects from 12 independent movies in 2 independent experiments (star); n = 5,389 (+1/2) defects and n = 4,858 (−1/2) defects from 8 independent movies in 3 independent experiments (circle). b, Top, number of closest +1/2 and −1/2 defects per unit area as function of distance, re, measured from respective extrusions (Ext, +1/2) and (Ext, −1/2) in star-shaped monolayer. Similar curves plotted for +1/2 and −1/2 defects measured from random points (Random, +1/2) and (Random, −1/2). Each density curve is normalized such that area under each curve sums to 1. n = 145 extrusions from 12 independent movies in 2 independent experiments; n = 30 different random point sets. Bottom, ratio of density curve values at re = 10 μm to re = 120 μm, for the curves (Ext, +1/2) and (Ext, −1/2) on the top. c, Average velocity field around +1/2 defect in caspase-3 inhibited monolayer. n = 2,993 defects from 7 independent movies in 2 independent experiments. Defect has same orientation and position as in Fig. 2a. d, Isotropic stress measured around cells just before extrusion (t = 0 min is time of extrusion). >70% (<30%) of cells experienced negative (positive respectively) stress, denoted as Group 1 (Group 2 respectively). n = 44 total number of extrusions in 2 independent movies. ks-test, ***P < 0.001. All data represented as mean ± s.e.m.

Extended Data Figure 6 Automated nematic director detection and robustness study.

a, 5 step process of automated nematic director detection. Step 1, phase contrast image of monolayer is obtained. Step 2, details of image are smoothed. Step 3, local orientation of cells are obtained using OrientationJ. Step 4, local contrast is applied to identify cell body regions. Step 5, nematic directors are obtained. b, Example of defect detection in a given nematic director field using the winding number approach (left, red for +1/2 defect, blue for −1/2 defect) and the diffusive charge approach (right, yellow for +1/2 defect, blue for −1/2 defect). c, Number of stable defects detected as a function of window size. A bigger window size allows the orientation of more cells to be averaged for the direction of a nematic. Averaging is done over n = 50 frames of a monolayer movie, for each window size analysis. ks-test, **P < 0.01, ***P < 0.001. Data are represented as mean ± s.e.m.

Extended Data Figure 7 Epithelium can be modelled as a 2D incompressible material.

a, Instantaneous (for every 10 min frame interval) velocity divergence field (colour coded) in circularly confined epithelium for one time point. b, Temporal average of velocity divergence field in circularly confined epithelium (averaged over about 20 h or 128 images consecutively).

Extended Data Figure 8 Bayesian inference stress method (BISM) and robustness study.

a, Diagram of inference algorithm. b–d, Plots of inferred stress versus simulated stress for each component, in kPa μm. Red line, bisector y = x; blue dots, 3N × 3N stress, σwhole for the whole system; red dots, N × N stress, σcentral for the central region; black circles, stresses obtained less than 2 μm from the boundary of the central region, N = 50 points. eg, Pressure, and hj, shear stress fields in kPa μm for the whole system: from left to right are shown exact values, σnum; inferred values obtained for the whole monolayer, σwhole; and inferred values obtained for the central region, σcentral. Black dashed box represents the central region.

Supplementary information

Defect generation, cell flow and extrusion in a monolayer

A +1/2 defect appears within -200 to -160 min, flows to the top left corner of the image, and finally induces an extrusion (bright spot) at 0 min. From left to right: Phase contrast images of the process; Velocity vectors overlaid on the monolayer, color-coded by the speed; Nematic directors (red) overlaid on the monolayer; Nematic directors (black) and the (blue) arrow situated at the defect core, pointing in the defect’s tail-to-head direction. (MP4 5462 kb)

Number of defects is proportional to activity in simulation

A simulation of extensile, active nematic liquid crystal showing the nematic directors (black) and defect cores (blue for +1/2 defect and green for -1/2 defect). The simulation starts at high activity and then reduced to half at the 13th frame, before being increased again at the 26th frame. (MP4 3642 kb)

Blebbistatin and washout experiment

50 μM blebbistatin is added at the start of the video to reduce acto-myosin activity in the monolayer, then washed out at 800 min. (Top left) Phase contrast image of the monolayer. (Top right) Nematic directors (red) overlaid on the monolayer. (Bottom left) Nematic directors in black. (Bottom right) Defect positions in the monolayer, blue for +1/2 defect, green for -1/2 defect. (MP4 6097 kb)

Evolution of isotropic stress heatmap around a +1/2 defect and extrusion, comparing WT and α-catenin knockdown monolayer

(Top panel) A +1/2 defect in a WT monolayer, flowing to the top left corner of the image and inducing an extrusion (bright spot) at 0 min. From left to right: Phase contrast images of the process; Nematic directors (red) overlaid on the monolayer, and the (cyan) arrow situated at the defect core, pointing in the defect’s tail-to-head direction; The isotropic stress heatmap evolution. (Bottom panel) The corresponding situation in a α-catenin knockdown monolayer. (MP4 4285 kb)

Extrusion and defect distribution in a star and circle shaped monolayer

Evolution of an epithelium confined in certain shapes by micro-contact printing, showing the extrusion and defect spatial distributions. (Top panel, left) Phase contrast images of a star shaped confined monolayer, with red arrowheads pointing to the occurrence of extrusions. (Top panel, right) Nematic directors (red) overlaid on the monolayer; Nematic directors (black) with +1/2 defects (blue) and -1/2 defect (green). (Bottom panel) The corresponding images for a circularly confined monolayer. (MP4 10545 kb)

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Saw, T., Doostmohammadi, A., Nier, V. et al. Topological defects in epithelia govern cell death and extrusion. Nature 544, 212–216 (2017). https://doi.org/10.1038/nature21718

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