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Scaling behaviour and control of nuclear wrinkling

Nature Physics volume 19, pages 1927–1935 (2023)Cite this article

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Abstract

The cell nucleus is enveloped by a complex membrane, whose wrinkling has been implicated in disease and cellular aging. The biophysical dynamics and spectral evolution of nuclear wrinkling during multicellular development remain poorly understood due to a lack of direct quantitative measurements. Here we characterize the onset and dynamics of nuclear wrinkling during egg development in the fruit fly when nurse cell nuclei increase in size and display stereotypical wrinkling behaviour. A spectral analysis of three-dimensional high-resolution live-imaging data from several hundred nuclei reveals a robust asymptotic power-law scaling of angular fluctuations consistent with renormalization and scaling predictions from a nonlinear elastic shell model. We further demonstrate that nuclear wrinkling can be reversed through osmotic shock and suppressed by microtubule disruption, providing tunable physical and biological control parameters for probing the mechanical properties of the nuclear envelope. Our findings advance the biophysical understanding of nuclear membrane fluctuations during early multicellular development.

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Fig. 1: Dynamic wrinkling of nurse cell’s NEs during Drosophila egg development.
Fig. 2: Fluctuating elastic shell theory predicts a scaling law with exponent of ~3 for the wrinkle power spectrum, in agreement with experiments.
Fig. 3: Perturbation experiments confirm the robustness of observed scaling laws and reveal NE wrinkling reversal mechanisms.

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Data availability

All data supporting the findings of this work are available within the paper and its Supplementary Information. Higher-resolution images, point cloud data, spherical harmonic processing codes and spreadsheets containing the experimental data points shown in the plots are available via Figshare at https://doi.org/10.6084/m9.figshare.23800287. Due to file-size limitations, the raw microscopy data are available from the corresponding authors upon request.

Code availability

The code used for numerical simulations is publicly available via GitHub at https://github.com/NicoRomeo/d3shell.

References

  1. Blees, M. K. et al. Graphene kirigami. Nature 524, 204–207 (2015).

    ADS  Google Scholar 

  2. Los, J. H., Fasolino, A. & Katsnelson, M. I. Mechanics of thermally fluctuating membranes. npj 2D Mater. Appl. 1, 9 (2017).

    Google Scholar 

  3. Yoo, J. & Aksimentiev, A. In situ structure and dynamics of DNA origami determined through molecular dynamics simulations. Proc. Natl Acad. Sci. USA 110, 20099–20104 (2013).

    ADS  Google Scholar 

  4. Kalukula, Y., Stephens, A. D., Lammerding, J. & Gabriele, S. Mechanics and functional consequences of nuclear deformations. Nat. Rev. Mol. Cell Biol. 23, 583–602 (2022).

    Google Scholar 

  5. Lomakin, A. J. et al. The nucleus acts as a ruler tailoring cell responses to spatial constraints. Science 370, eaba2894 (2020).

    Google Scholar 

  6. Almonacid, M. et al. Active fluctuations of the nuclear envelope shape the transcriptional dynamics in oocytes. Dev. Cell 51, 145–157.e10 (2019).

    Google Scholar 

  7. Biedzinski, S. et al. Microtubules control nuclear shape and gene expression during early stages of hematopoietic differentiation. EMBO J. 39, e103957 (2020).

    Google Scholar 

  8. Brochard, F. & Lennon, J. F. Frequency spectrum of the flicker phenomenon in erythrocytes. J. Phys. 36, 1035–1047 (1975).

    Google Scholar 

  9. Betz, T., Lenz, M., Joanny, J.-F. & Sykes, C. ATP-dependent mechanics of red blood cells. Proc. Natl Acad. Sci. USA 106, 15320–15325 (2009).

    ADS  Google Scholar 

  10. Bowick, M. J., Košmrlj, A., Nelson, D. R. & Sknepnek, R. Non-Hookean statistical mechanics of clamped graphene ribbons. Phys. Rev. B 95, 104109 (2017).

    ADS  Google Scholar 

  11. Kantsler, V., Segre, E. & Steinberg, V. Vesicle dynamics in time-dependent elongation flow: wrinkling instability. Phys. Rev. Lett. 99, 178102 (2007).

    ADS  Google Scholar 

  12. Kokot, G., Faizi, H. A., Pradillo, G. E., Snezhko, A. & Vlahovska, P. M. Spontaneous self-propulsion and nonequilibrium shape fluctuations of a droplet enclosing active particles. Commun. Phys. 5, 91 (2022).

    Google Scholar 

  13. Honerkamp-Smith, A. R., Woodhouse, F. G., Kantsler, V. & Goldstein, R. E. Membrane viscosity determined from shear-driven flow in giant vesicles. Phys. Rev. Lett. 111, 038103 (2013).

    ADS  Google Scholar 

  14. Ben-Isaac, E. et al. Effective temperature of red-blood-cell membrane fluctuations. Phys. Rev. Lett. 106, 238103 (2011).

    ADS  Google Scholar 

  15. Turlier, H. et al. Equilibrium physics breakdown reveals the active nature of red blood cell flickering. Nat. Phys. 12, 513–519 (2016).

    Google Scholar 

  16. Chu, F.-Y., Haley, S. C. & Zidovska, A. On the origin of shape fluctuations of the cell nucleus. Proc. Natl Acad. Sci. USA 114, 10338–10343 (2017).

    ADS  Google Scholar 

  17. Venturini, V. et al. The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior. Science 370, eaba2644 (2020).

    Google Scholar 

  18. Scaffidi, P. & Misteli, T. Lamin A-dependent nuclear defects in human aging. Science 312, 1059–1063 (2006).

    ADS  Google Scholar 

  19. Mounkes, L. C., Kozlov, S., Hernandez, L., Sullivan, T. & Stewart, C. L. A progeroid syndrome in mice is caused by defects in A-type lamins. Nature 423, 298–301 (2003).

    ADS  Google Scholar 

  20. Nelson, D., Piran, T. & Weinberg, S. Statistical Mechanics of Membranes and Surfaces—Proceedings of the 5th Jerusalem Winter School for Theoretical Physics (World Scientific, 1989).

  21. Košmrlj, A. & Nelson, D. R. Statistical mechanics of thin spherical shells. Phys. Rev. X 7, 011002 (2017).

    Google Scholar 

  22. Aebi, U., Cohn, J., Buhle, L. & Gerace, L. The nuclear lamina is a meshwork of intermediate-type filaments. Nature 323, 560–564 (1986).

    ADS  Google Scholar 

  23. Lammerding, J. et al. Lamin A/C deficiency causes defective nuclear mechanics and mechanotransduction. J. Clin. Investig. 113, 370–378 (2004).

    Google Scholar 

  24. Strambio-De-Castillia, C., Niepel, M. & Rout, M. P. The nuclear pore complex: bridging nuclear transport and gene regulation. Nat. Rev. Mol. Cell Biol. 11, 490–501 (2010).

    Google Scholar 

  25. Knockenhauer, K. E. & Schwartz, T. U. The nuclear pore complex as a flexible and dynamic gate. Cell 164, 1162–1171 (2016).

    Google Scholar 

  26. Hudson, A. M. & Cooley, L. Methods for studying oogenesis. Methods 68, 207–217 (2014).

    Google Scholar 

  27. King, R. C., Rubinson, A. C. & Smith, R. F. Oogenesis in adult Drosophila melanogaster. Growth 20, 121–157 (1956).

    Google Scholar 

  28. McLaughlin, J. M. & Bratu, D. P. Drosophila melanogaster oogenesis: an overview. in Drosophila Oogenesis, Methods in Molecular Biology Vol. 1328 (eds Bratu, D. P. & McNeil, G. P.) 1–20 (Springer, 2015).

  29. Bastock, R. & St Johnston, D. Drosophila oogenesis. Curr. Biol. 18, R1082–R1087 (2008).

    Google Scholar 

  30. Imran Alsous, J. et al. Dynamics of hydraulic and contractile wave-mediated fluid transport during Drosophila oogenesis. Proc. Natl Acad. Sci. USA 118, e2019749118 (2021).

    Google Scholar 

  31. Mahajan-Miklos, S. & Cooley, L. Intercellular cytoplasm transport during Drosophila oogenesis. Dev. Biol. 165, 336–351 (1994).

    Google Scholar 

  32. Lin, H. & Spradling, A. C. Germline stem cell division and egg chamber development in transplanted Drosophila germaria. Dev. Biol. 159, 140–152 (1993).

    Google Scholar 

  33. Tzur, A., Kafri, R., LeBleu, V. S., Lahav, G. & Kirschner, M. W. Cell growth and size homeostasis in proliferating animal cells. Science 325, 167–171 (2009).

    ADS  Google Scholar 

  34. Malhas, A. N. & Vaux, D. J. Nuclear Envelope Invaginations and Cancer 523–535 (Springer, 2014).

  35. Fricker, M., Hollinshead, M., White, N. & Vaux, D. Interphase nuclei of many mammalian cell types contain deep, dynamic, tubular membrane-bound invaginations of the nuclear envelope. J. Cell Biol. 136, 531–544 (1997).

    Google Scholar 

  36. Zilman, A. G. & Granek, R. Undulations and dynamic structure factor of membranes. Phys. Rev. Lett. 77, 4788–4791 (1996).

    ADS  Google Scholar 

  37. Andrejevic, J., Lee, L. M., Rubinstein, S. M. & Rycroft, C. H. A model for the fragmentation kinetics of crumpled thin sheets. Nat. Commun. 12, 1470 (2021).

    ADS  Google Scholar 

  38. Witten, T. A. Stress focusing in elastic sheets. Rev. Mod. Phys. 79, 643–675 (2007).

    ADS  MathSciNet  MATH  Google Scholar 

  39. Paulose, J., Vliegenthart, G. A., Gompper, G. & Nelson, D. R. Fluctuating shells under pressure. Proc. Natl Acad. Sci. USA 109, 19551–19556 (2012).

    ADS  Google Scholar 

  40. Guilak, F., Tedrow, J. R. & Burgkart, R. Viscoelastic properties of the cell nucleus. Biochem. Biophys. Res. Commun. 269, 781–786 (2000).

    Google Scholar 

  41. Funkhouser, C. M. et al. Mechanical model of blebbing in nuclear lamin meshworks. Proc. Natl Acad. Sci. USA 110, 3248–3253 (2013).

    ADS  Google Scholar 

  42. Kim, D.-H. et al. Volume regulation and shape bifurcation in the cell nucleus. J. Cell Sci. 128, 3375–3385 (2015).

    Google Scholar 

  43. Dahl, K. N., Kahn, S. M., Wilson, K. L. & Discher, D. E. The nuclear envelope lamina network has elasticity and a compressibility limit suggestive of a molecular shock absorber. J. Cell Sci. 117, 4779–4786 (2004).

    Google Scholar 

  44. Enyedi, B. & Niethammer, P. Nuclear membrane stretch and its role in mechanotransduction. Nucleus 8, 156–161 (2017).

    Google Scholar 

  45. Pécréaux, J., Döbereiner, H.-G., Prost, J., Joanny, J.-F. & Bassereau, P. Refined contour analysis of giant unilamellar vesicles. Eur. Phys. J. E 13, 277–290 (2004).

    Google Scholar 

  46. Landau, L. D. & Lifshitz, E. M. Theory of Elasticity. Number 7 in Course of Theoretical Physics (Elsevier, 2009).

  47. Novoselov, K. S. et al. Electric field effect in atomically thin carbon films. Science 306, 666–669 (2004).

    ADS  Google Scholar 

  48. Baumgarten, L. & Kierfeld, J. Buckling of thermally fluctuating spherical shells: parameter renormalization and thermally activated barrier crossing. Phys. Rev. E 97, 052801 (2018).

    ADS  Google Scholar 

  49. Schmidt, C. F. et al. Existence of a flat phase in red cell membrane skeletons. Science 259, 952–955 (1993).

    ADS  Google Scholar 

  50. Yalonetskaya, A., Mondragon, A. A., Hintze, Z. J., Holmes, S. & McCall, K. Nuclear degradation dynamics in a nonapoptotic programmed cell death. Cell Death Differ. 27, 711–724 (2020).

    Google Scholar 

  51. Düring, G., Josserand, C., Krstulovic, G. & Rica, S. Strong turbulence for vibrating plates: emergence of a Kolmogorov spectrum. Phys. Rev. Fluids 4, 064804 (2019).

    ADS  Google Scholar 

  52. Swift, J. et al. Nuclear lamin-A scales with tissue stiffness and enhances matrix-directed differentiation. Science 341, 1240104 (2013).

    Google Scholar 

  53. Schulze, S. R. et al. Molecular genetic analysis of the nested Drosophila melanogaster lamin C gene. Genetics 171, 185–196 (2005).

    Google Scholar 

  54. Riemer, D. et al. Expression of Drosophila lamin C is developmentally regulated: analogies with vertebrate A-type lamins. J. Cell Sci. 108, 3189–3198 (1995).

    Google Scholar 

  55. Agrawal, V., Pandey, V. & Mitra, D. Active buckling of pressurized spherical shells: Monte Carlo simulation. Preprint at https://arxiv.org/abs/2206.14172 (2022).

  56. Chakrabarti, B. et al. Flexible filaments buckle into helicoidal shapes in strong compressional flows. Nat. Phys. 16, 689–694 (2020).

    Google Scholar 

  57. Loubet, B., Seifert, U. & Lomholt, M. A. Effective tension and fluctuations in active membranes. Phys. Rev. E 85, 031913 (2012).

    ADS  Google Scholar 

  58. Vutukuri, H. R. et al. Active particles induce large shape deformations in giant lipid vesicles. Nature 586, 52–56 (2020).

    ADS  Google Scholar 

  59. Bausch, A. R. & Kroy, K. A bottom-up approach to cell mechanics. Nat. Phys. 2, 231–238 (2006).

    Google Scholar 

  60. Hampoelz, B. et al. Microtubule-induced nuclear envelope fluctuations control chromatin dynamics in Drosophila embryos. Development 138, 3377–3386 (2011).

    Google Scholar 

  61. Deviri, D. & Safran, S. A. Balance of osmotic pressures determines the nuclear-to-cytoplasmic volume ratio of the cell. Proc. Natl Acad. Sci. USA 119, e2118301119 (2022).

    Google Scholar 

  62. Lemiére, J., Real-Calderon, P., Holt, L. J., Fai, T. G. & Chang, F. Control of nuclear size by osmotic forces in Schizosaccharomyces pombe. eLife 11, e76075 (2022).

    Google Scholar 

  63. Cosgrove, B. D. et al. Nuclear envelope wrinkling predicts mesenchymal progenitor cell mechano-response in 2D and 3D microenvironments. Biomaterials 270, 120662 (2021).

    Google Scholar 

  64. Elosegui-Artola, A. et al. Force triggers YAP nuclear entry by regulating transport across nuclear pores. Cell 171, 1397–1410.e14 (2017).

    Google Scholar 

  65. Makhija, E., Jokhun, D. S. & Shivashankar, G. V. Nuclear deformability and telomere dynamics are regulated by cell geometric constraints. Proc. Natl Acad. Sci. USA 113, E32–E40 (2016).

    ADS  Google Scholar 

  66. Kelpsch, D. J., Groen, C. M., Fagan, T. N., Sudhir, S. & Tootle, T. L. Fascin regulates nuclear actin during Drosophila oogenesis. Mol. Biol. Cell 27, 2965–2979 (2016).

    Google Scholar 

  67. Dialynas, G., Speese, S., Budnik, V., Geyer, P. K. & Wallrath, L. L. The role of Drosophila lamin C in muscle function and gene expression. Development 137, 3067–3077 (2010).

    Google Scholar 

  68. Prasad, M. & Montell, D. J. Cellular and molecular mechanisms of border cell migration analyzed using time-lapse live-cell imaging. Dev. Cell 12, 997–1005 (2007).

    Google Scholar 

  69. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Google Scholar 

  70. Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).

    Google Scholar 

  71. Mietke, A. Dynamics of Active Surfaces. PhD thesis, Technische Univ. Dresden (2018).

  72. Burns, K. J., Vasil, G. M., Oishi, J. S., Lecoanet, D. & Brown, B. P. Dedalus: a flexible framework for numerical simulations with spectral methods. Phys. Rev. Research 2, 023068 (2020).

    ADS  Google Scholar 

  73. Lin, L. C.-L. & Brown, F. L. H. Brownian dynamics in Fourier space: membrane simulations over long length and time scales. Phys. Rev. Lett. 93, 256001 (2004).

    ADS  Google Scholar 

  74. Reuther, A. et al. Interactive supercomputing on 40,000 cores for machine learning and data analysis. In 2018 IEEE High Performance Extreme Computing Conference (HPEC) 1–6 (IEEE, 2018).

  75. Wieczorek, M. A. & Meschede, M. SHTools: tools for working with spherical harmonics. Geochem. Geophys. Geosyst. 19, 2574–2592 (2018).

    ADS  Google Scholar 

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Acknowledgements

We thank the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing high-performance computing resources that have contributed to the research results reported in this paper. We thank M. Kardar, R. D. Kamm, E. Folker, M. A. Collins and D. P. Holmes for helpful discussions. This work was supported by a MathWorks Science Fellowship (N.R.), NSF Award DMS-1952706 (J.D. and N.R.), Sloan Foundation Grant G-2021-16758 (J.D.), MIT Mathematics Robert E. Collins Distinguished Scholar Fund (J.D.), Feodor Lynen Research Fellowship from the Humboldt Foundation (J.F.T.), Jarve Fund MIT grant (A.C.M. and J.D.) and the National Institute of General Medical Sciences of the National Institutes of Health under award no. R01GM144115 (A.C.M). N.R. and J.F.T. acknowledge participation in the KITP online workshop ‘The Physics of Elastic Films: from Biological Membranes to Extreme Mechanics’ supported in part by the National Science Foundation under grant no. NSF PHY-1748958.

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Author notes

  1. These authors contributed equally: Jonathan A. Jackson, Nicolas Romeo.

Authors and Affiliations

  1. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan A. Jackson & Adam C. Martin

  2. Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA

    Jonathan A. Jackson

  3. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nicolas Romeo, Alexander Mietke, Keaton J. Burns, Jan F. Totz & Jörn Dunkel

  4. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nicolas Romeo

  5. School of Mathematics, University of Bristol, Bristol, UK

    Alexander Mietke

  6. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA

    Jasmin Imran Alsous

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Contributions

J.I.A., J.A.J., N.R., J.F.T., J.D. and A.C.M. conceived the project. J.I.A. and J.A.J. designed and conducted the experiments. N.R., K.J.B. and J.F.T. designed and implemented the numerical simulations. N.R. and A.M. performed the analytical calculations. J.A.J. and N.R. performed the image and data analyses. N.R., J.A.J., J.I.A. and J.D. wrote the original paper, with input from all authors. All authors revised the paper.

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Correspondence to Jörn Dunkel or Jasmin Imran Alsous.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Table 1.

Reporting Summary

Supplementary Video 1

Three z stacks of the Nup107 signal through individual nurse cell nuclei from egg chambers of time proxies 118, 157 and 216 (from left to right), showing the progression of NE shapes from unwrinkled (left) to heavily wrinkled (right) during development. Scale bar, 5 μm; 10 fps.

Supplementary Video 2

Two time-lapse images of the MIPs of two nurse cell nuclei each from egg chambers expressing Nup107::GFP, showing fluctuations in NE wrinkling over time. The first video shows a lower-resolution video acquired at 1 frame per 40 s (time proxy 160). The second video shows a higher-resolution video acquired at 1 frame every 5 min (time proxy 200). Scale bar, 10 μm; 10 fps; the second video has each frame duplicated four times for an effective rate of 2 fps.

Supplementary Video 3

The z stack through an egg chamber (time proxy 166) expressing Nup107::GFP (green) and stained with SPY555-tubulin to visualize the microtubules (magenta). The white box highlights a region corresponding to the zoomed-in z stack in the latter part of the video. Microtubules are present around the nurse cell nuclei, but their locations do not seem to correlate with the locations of NE wrinkles. The orange arrows point to surface fluctuations without a nearby tubulin signal; the white arrow, to surface fluctuations with a nearby tubulin signal; and the cyan arrow, to a tubulin signal near the undeformed surface. Scale bar, 20 μm; 15 fps.

Supplementary Video 4

Egg chamber (time proxy 172) expressing Nup107::GFP before and after the addition of 9 mg ml–1 colchicine to disrupt the microtubules. After drug addition, nurse cell’s NE wrinkles markedly decrease in amplitude over the next 30 min. This effect is not simply due to the addition of a fresh medium (Supplementary Video 5). The white box in the second and third frames indicates the rough border of the zoomed-in image shown for the rest of the video. Scale bar, 20 μm; 5 fps.

Supplementary Video 5

Egg chamber (time proxy 163) expressing Nup107::GFP before and after the addition of a fresh, identical culture medium, used as a control for colchicine addition. After medium addition, the NE wrinkles show no major change. The white box in the second and third frames indicates the rough border of the zoomed-in image shown for the rest of the video. Scale bar, 20 μm; 5 fps.

Supplementary Video 6

Two time-lapse images showing single optical sections from NEs (Nup107, green), cell membranes (magenta) and the motion of cytoplasmic components (reflected light, grey) in the egg chambers under normal conditions (no inhibitors added). The first time lapse is from an egg chamber of time proxy 190; the second is of time proxy 173. Scale bar, 20 μm; 30 fps.

Supplementary Video 7

Three reflection microscopy time-lapse images showing the effects of colchicine addition on intracellular motion. First time lapse: MIP through 4 μm near the top of a nucleus in one nurse cell; the NE appears as a large, bright object deforming and rotating alongside fluctuations of small objects in the cytoplasm (white dots). Alternating dark and light regions on the right side of the NE are fluctuating wrinkles. Second time lapse: projection through 8 μm of the same egg chamber after the addition of 9 mg ml–1 colchicine. The boxed nucleus corresponds to the nucleus shown in the first portion of the video. A reduction in NE roughness can be seen by the reduction in the linear patterns of the dark and light regions. Reduction in cytoplasmic motion becomes more easily apparent at around 10 min. Third time lapse: projection through 6.5 μm of a nucleus from a different egg chamber, more than 30 min after colchicine was added, to ensure that the slowing in the second time lapse did not result from photodamage. Here, too, the NE is smoother than expected given the egg chamber age, and small cytoplasmic objects are substantially less mobile. Scale bar, 10 μm; 20 fps.

Supplementary Video 8

Egg chamber (time proxy 178) expressing Nup107::GFP before and after the addition of 10 μg ml–1 cytochalasin D to disrupt F-actin. After drug addition, nurse cell’s NE wrinkles show no substantial change. The addition of DMSO alone also causes no major change (Supplementary Video 9). The white box in the second and third frames indicates the rough border of the zoomed-in image shown for the rest of the video. Scale bar, 20 μm; 5 fps.

Supplementary Video 9

Egg chamber (time proxy 170) expressing Nup107::GFP before and after the addition of DMSO, used as a control for cytochalasin D addition. After DMSO addition, the NE wrinkles show no major change. The white box in the second and third frames indicates the rough border of the zoomed-in image shown for the rest of the video. Scale bar, 20 μm; 5 fps.

Supplementary Video 10

Three z stacks through individual nurse cell nuclei (Supplementary Video 1) from egg chambers of time proxy 118, 157 and 216 (from left to right). The Nup107 signal is shown in grey following the background subtraction and Gaussian blur used to preprocess before segmentation; the segmentation output is shown in red. The rightmost nucleus is near the oldest of the nuclei segmented and hence effectively represents a lower bound on the accuracy achieved for segmentations used in reconstructions. Scale bar, 5 μm; 10 fps.

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Jackson, J.A., Romeo, N., Mietke, A. et al. Scaling behaviour and control of nuclear wrinkling. Nat. Phys. 19, 1927–1935 (2023). https://doi.org/10.1038/s41567-023-02216-y

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