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:

Self-organized intracellular twisters

Nature Physics volume 20, pages 666–674 (2024)Cite this article

Subjects

Abstract

Life in complex systems, such as cities and organisms, comes to a standstill when global coordination of mass, energy and information flows is disrupted. Global coordination is no less important in single cells, especially in large oocytes and newly formed embryos, which commonly use fast fluid flows for dynamic reorganization of their cytoplasm. These cytoplasmic streaming flows have been proposed to spontaneously arise from hydrodynamic interactions among cortically anchored microtubules loaded with cargo-carrying molecular motors. Here, we combine modelling and simulation with live imaging to investigate such flows in the Drosophila oocyte. Using a fast, accurate and scalable numerical approach to investigate fluid–structure interactions of thousands of flexible fibres, we demonstrate the robust emergence and evolution of cell-spanning vortices—or twisters—in three-dimensional cellular geometries. These twister flows, dominated by a near-rigid-body rotation with secondary toroidal components, reproduce the variety of experimental observations. In cells, these flows are probably involved in rapid mixing and transport of ooplasmic components.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Hydrodynamic interactions of motor-loaded microtubules generate intracellular flows.
Fig. 2: Twisters are a combination of a strong vortical flow and a weak bitoroidal flow.
Fig. 3: Surface microtubule orientation and cytoplasmic velocity fields.
Fig. 4: The structure of the streaming flow is robust.

Similar content being viewed by others

Data availability

Simulational and experimental data sets generated during the current study are available from the corresponding author on reasonable request.

Code availability

A publicly available and elaborated version of the SkellySim codebase used to generate the simulations is available at https://github.com/flatironinstitute/SkellySim.

References

  1. Corti, B. Osservazioni microscopiche sulla tremella e sulla circolazione del fluido in una pianta acquajuola (Rocchi, 1774).

  2. Yi, K. et al. Dynamic maintenance of asymmetric meiotic spindle position through Arp2/3-complex-driven cytoplasmic streaming in mouse oocytes. Nat. Cell Biol. 13, 1252–1258 (2011).

    Article  Google Scholar 

  3. Almonacid, M. et al. Active diffusion positions the nucleus in mouse oocytes. Nat. Cell Biol. 17, 470–479 (2015).

    Article  Google Scholar 

  4. Deneke, V. E. et al. Self-organized nuclear positioning synchronizes the cell cycle in Drosophila embryos. Cell 177, 925–941 (2019).

    Article  Google Scholar 

  5. Glotzer, J. B., Saffrich, R., Glotzer, M. & Ephrussi, A. Cytoplasmic flows localize injected oskar RNA in Drosophila oocytes. Curr. Biol. 7, 326–337 (1997).

    Article  Google Scholar 

  6. van de Meent, J.-W., Tuval, I. & Goldstein, R. E. Nature’s microfluidic transporter: rotational cytoplasmic streaming at high Péclet numbers. Phys. Rev. Lett. 101, 178102 (2008).

    Article  ADS  Google Scholar 

  7. Hird, S. N. & White, J. G. Cortical and cytoplasmic flow polarity in early embryonic cells of Caenorhabditis elegans. J. Cell Biol. 121, 1343–1355 (1993).

    Article  Google Scholar 

  8. Emmons, S. et al. Cappuccino, a Drosophila maternal effect gene required for polarity of the egg and embryo, is related to the vertebrate limb deformity locus. Genes Dev. 9, 2482–2494 (1995).

    Article  Google Scholar 

  9. Trong, P. K., Doerflinger, H., Dunkel, J., St Johnston, D. & Goldstein, R. E. Cortical microtubule nucleation can organise the cytoskeleton of Drosophila oocytes to define the anteroposterior axis. eLife 4, e06088 (2015).

    Article  Google Scholar 

  10. Gross, P. et al. Guiding self-organized pattern formation in cell polarity establishment. Nat. Phys. 15, 293–300 (2019).

    Article  Google Scholar 

  11. Goldstein, R. E. & van de Meent, J.-W. A physical perspective on cytoplasmic streaming. Interface Focus 5, 20150030 (2015).

    Article  Google Scholar 

  12. Lu, W. & Gelfand, V. I. Go with the flow–bulk transport by molecular motors. J. Cell Sci. 136, jcs260300 (2023).

    Article  Google Scholar 

  13. Shamipour, S., Caballero-Mancebo, S. & Heisenberg, C.-P. Cytoplasm’s got moves. Dev. Cell 56, 213–226 (2021).

    Article  Google Scholar 

  14. Woodhouse, F. G. & Goldstein, R. E. Cytoplasmic streaming in plant cells emerges naturally by microfilament self-organization. Proc. Natl Acad. Sci. 110, 14132–14137 (2013).

    Article  ADS  Google Scholar 

  15. Quinlan, M. E. Cytoplasmic streaming in the Drosophila oocyte. Annu. Rev. Cell Dev. Biol. 32, 173–195 (2016).

    Article  Google Scholar 

  16. Becalska, A. N. & Gavis, E. R. Lighting up mRNA localization in Drosophila oogenesis. Development 136, 2493–2503 (2009).

    Article  Google Scholar 

  17. Gutzeit, H. & Koppa, R. Time-lapse film analysis of cytoplasmic streaming during late oogenesis of Drosophila. Development 67, 101–111 (1982).

    Article  Google Scholar 

  18. Monteith, C. E. et al. A mechanism for cytoplasmic streaming: kinesin-driven alignment of microtubules and fast fluid flows. Biophys. J. 110, 2053–2065 (2016).

    Article  ADS  Google Scholar 

  19. Lu, W., Winding, M., Lakonishok, M., Wildonger, J. & Gelfand, V. I. Microtubule–microtubule sliding by kinesin-1 is essential for normal cytoplasmic streaming in Drosophila oocytes. Proc. Natl Acad. Sci. 113, E4995–E5004 (2016).

    Article  ADS  Google Scholar 

  20. Palacios, I. M. & Johnston, D. S. Kinesin light chain-independent function of the kinesin heavy chain in cytoplasmic streaming and posterior localization in the Drosophila oocyte. Development 129, 5473–5485 (2002).

    Article  Google Scholar 

  21. Serbus, L. R., Cha, B. J., Theurkauf, W. E. & Saxton, W. M. Dynein and the actin cytoskeleton control kinesin-driven cytoplasmic streaming in Drosophila oocytes. Development 132, 3743–52 (2005).

    Article  Google Scholar 

  22. Ganguly, S., Williams, L. S., Palacios, I. M. & Goldstein, R. E. Cytoplasmic streaming in Drosophila oocytes varies with kinesin activity and correlates with the microtubule cytoskeleton architecture. Proc. Natl Acad. Sci. USA 109, 15109–15114 (2012).

    Article  ADS  Google Scholar 

  23. Ravichandran, A. et al. Chronology of motor-mediated microtubule streaming. eLife 8, e39694 (2019).

    Article  Google Scholar 

  24. Stein, D. B., De Canio, G., Lauga, E., Shelley, M. J. & Goldstein, R. E. Swirling instability of the microtubule cytoskeleton. Phys. Rev. Lett. 126, 028103 (2021).

    Article  ADS  Google Scholar 

  25. Gittes, F., Mickey, B., Nettleton, J. & Howard, J. Flexural rigidity of microtubules and actin filaments measured from thermal fluctuations in shape. J. Cell Biol. 120, 923–934 (1993).

    Article  Google Scholar 

  26. Shelley, M. J. The dynamics of microtubule/motor-protein assemblies in biology and physics. Annu. Rev. Fluid Mech. 48, 487–506 (2016).

    Article  ADS  MathSciNet  Google Scholar 

  27. Keller, J. B. & Rubinow, S. I. Slender-body theory for slow viscous flow. J. Fluid Mech. 75, 705–714 (1976).

    Article  ADS  Google Scholar 

  28. Tornberg, A.-K. & Shelley, M. J. Simulating the dynamics and interactions of flexible fibers in stokes flows. J. Comput. Phys. 196, 8–40 (2004).

    Article  ADS  MathSciNet  Google Scholar 

  29. Nazockdast, E., Rahimian, A., Zorin, D. & Shelley, M. A fast platform for simulating semi-flexible fiber suspensions applied to cell mechanics. J. Comput. Phys. 329, 173–209 (2017).

    Article  ADS  MathSciNet  Google Scholar 

  30. Elgeti, J. & Gompper, G. Emergence of metachronal waves in cilia arrays. Proc. Natl Acad. Sci. USA 110, 4470–4475 (2013).

    Article  ADS  Google Scholar 

  31. Chakrabarti, B., Fürthauer, S. & Shelley, M. J. A multiscale biophysical model gives quantized metachronal waves in a lattice of beating cilia. Proc. Natl Acad. Sci. USA 119, e2113539119 (2022).

    Article  Google Scholar 

  32. SkellySim cellular dynamics package. GitHub https://github.com/flatironinstitute/SkellySim (2023).

  33. Kanale, A. V., Ling, F., Guo, H., Fürthauer, S. & Kanso, E. Spontaneous phase coordination and fluid pumping in model ciliary carpets. Proc. Natl Acad. Sci. 119, e2214413119 (2022).

    Article  Google Scholar 

  34. Theurkauf, W. E. Premature microtubule-dependent cytoplasmic streaming in cappuccino and spire mutant oocytes. Science 265, 2093–2096 (1994).

    Article  ADS  Google Scholar 

  35. Dahlgaard, K., Raposo, A. A., Niccoli, T. & St Johnston, D. Capu and Spire assemble a cytoplasmic actin mesh that maintains microtubule organization in the Drosophila oocyte. Dev. Cell 13, 539–553 (2007).

    Article  Google Scholar 

  36. Isele-Holder, R. E., Jäger, J., Saggiorato, G., Elgeti, J. & Gompper, G. Dynamics of self-propelled filaments pushing a load. Soft Matter 12, 8495–8505 (2016).

    Article  ADS  Google Scholar 

  37. De Canio, G., Lauga, E. & Goldstein, R. E. Spontaneous oscillations of elastic filaments induced by molecular motors. J. R. Soc. Interface 14, 20170491 (2017).

    Article  Google Scholar 

  38. Ling, F., Guo, H. & Kanso, E. Instability-driven oscillations of elastic microfilaments. J. R. Soc. Interface 15, 20180594 (2018).

    Article  Google Scholar 

  39. Stone, H., Nadim, A. & Strogatz, S. H. Chaotic streamlines inside drops immersed in steady stokes flows. J. Fluid Mech. 232, 629–646 (1991).

    Article  ADS  MathSciNet  Google Scholar 

  40. Williams, L. S., Ganguly, S., Loiseau, P., Ng, B. F. & Palacios, I. M. The auto-inhibitory domain and ATP-independent microtubule-binding region of Kinesin heavy chain are major functional domains for transport in the Drosophila germline. Development 141, 176–186 (2014).

    Article  Google Scholar 

  41. Brendza, K. M., Rose, D. J., Gilbert, S. P. & Saxton, W. M. Lethal kinesin mutations reveal amino acids important for atpase activation and structural coupling. J. Biol. Chem. 274, 31506–31514 (1999).

    Article  Google Scholar 

  42. Loiseau, P., Davies, T., Williams, L. S., Mishima, M. & Palacios, I. M. Drosophila PAT1 is required for kinesin-1 to transport cargo and to maximize its motility. Development 137, 2763–2772 (2010).

    Article  Google Scholar 

  43. Theurkauf, W. E., Smiley, S., Wong, M. L. & Alberts, B. M. Reorganization of the cytoskeleton during Drosophila oogenesis: implications for axis specification and intercellular transport. Development 115, 923–936 (1992).

    Article  Google Scholar 

  44. Nashchekin, D., Fernandes, A. R. & St Johnston, D. Patronin/shot cortical foci assemble the noncentrosomal microtubule array that specifies the Drosophila anterior-posterior axis. Dev. Cell 38, 61–72 (2016).

    Article  Google Scholar 

  45. Lu, W. et al. Competition between kinesin-1 and myosin-v defines Drosophila posterior determination. eLife 9, e54216 (2020).

    Article  Google Scholar 

  46. Manseau, L. J. & Schüpbach, T. cappuccino and spire: two unique maternal-effect loci required for both the anteroposterior and dorsoventral patterns of the Drosophila embryo. Genes Dev. 3, 1437–1452 (1989).

    Article  Google Scholar 

  47. Schonbaum, C. P., Perrino, J. J. & Mahowald, A. P. Regulation of the vitellogenin receptor during Drosophila melanogaster oogenesis. Mol. Biol. Cell 11, 511–521 (2000).

    Article  Google Scholar 

  48. Greengard, L. & Rokhlin, V. A fast algorithm for particle simulations. J. Comput. Phys. 73, 325–348 (1987).

    Article  ADS  MathSciNet  Google Scholar 

  49. Stoddard, M. C. et al. Avian egg shape: form, function, and evolution. Science 356, 1249–1254 (2017).

    Article  ADS  Google Scholar 

  50. Lu, W. et al. Ooplasmic flow cooperates with transport and anchorage in Drosophila oocyte posterior determination. J. Cell Biol. 217, 3497–3511 (2018).

    Article  Google Scholar 

  51. Lu, W., Lakonishok, M. & Gelfand, V. I. Gatekeeper function for short stop at the ring canals of the Drosophila ovary. Curr. Biol. 31, 3207–3220.e4 (2021).

    Article  Google Scholar 

  52. Spracklen, A. J., Fagan, T. N., Lovander, K. E. & Tootle, T. L. The pros and cons of common actin labeling tools for visualizing actin dynamics during Drosophila oogenesis. Dev. Biol. 393, 209–226 (2014).

    Article  Google Scholar 

  53. Grieder, N. C., De Cuevas, M. & Spradling, A. C. The fusome organizes the microtubule network during oocyte differentiation in Drosophila. Development 127, 4253–4264 (2000).

    Article  Google Scholar 

  54. Kass, M., Witkin, A. & Terzopoulos, D. Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988).

    Article  Google Scholar 

  55. Farhadifar, R. & Needleman, D. in Mitosis: Methods and Protocols (ed. Sharp, D.) Ch. 3 (Springer, 2014).

  56. Zuiderveld, K. in Graphics Gems IV (ed. Heckbert, P. S.) 474–485 (1994).

  57. Willert, C. E. & Gharib, M. Digital particle image velocimetry. Exp. Fluids 10, 181–193 (1991).

    Article  Google Scholar 

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

  59. Jain, A. K. & Farrokhnia, F. Unsupervised texture segmentation using gabor filters. Pattern Recognit. 24, 1167–1186 (1991).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank B. Chakraborti, J.I. Alsous, E. Gavis and R. Goldstein for extensive and useful discussions and A. Farhadifar for generously sharing his Blender expertise. We acknowledge support from National Institutes of Health grant nos. R01GM134204 (S.Y.S.) and R35GM131752 (V.I.G.) and National Science Foundation grant no. DMR-2004469 (M.J.S.). Stocks obtained from the Bloomington Drosophila Stock Center, supported by National Institutes of Health grant no. P40OD018537, were used in this study. The computations in this work were performed at facilities supported by the Scientific Computing Core at the Flatiron Institute, a division of the Simons Foundation.

Author information

Author notes

  1. These authors contributed equally: Sayantan Dutta, Reza Farhadifar.

Authors and Affiliations

  1. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, US

    Sayantan Dutta & Stanislav Y. Shvartsman

  2. Center for Computational Biology, Flatiron Institute, New York, NY, US

    Sayantan Dutta, Reza Farhadifar, Gokberk Kabacaoğlu, Robert Blackwell, David B. Stein, Stanislav Y. Shvartsman & Michael J. Shelley

  3. Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, US

    Wen Lu, Margot Lakonishok & Vladimir I. Gelfand

  4. Department of Molecular Biology and Lewis Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, US

    Stanislav Y. Shvartsman

  5. Courant Institute of Mathematical Sciences, New York University, New York, NY, US

    Michael J. Shelley

Authors
  1. Sayantan Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Farhadifar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gokberk Kabacaoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert Blackwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David B. Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Margot Lakonishok
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vladimir I. Gelfand
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stanislav Y. Shvartsman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael J. Shelley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.J.S., S.Y.S. and V.I.G. designed the research. S.D., R.F., G.K. and R.B. contributed to simulation software development and simulation data analysis. W.L. and M.L. designed and performed the experiments. S.D., R.F. and M.J.S. developed the image processing software and analysis of experimental data. S.D., R.F., S.Y.S. and M.J.S. prepared the manuscript. All authors contributed to its editing.

Corresponding author

Correspondence to Michael J. Shelley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Physics thanks Camille Duprat, Gerhard Gompper 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.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Figs. 1–9.

Reporting Summary

Supplementary Video 1

Time course of configurations of microtubules anchored to the interior surface of a sphere in a cut-away view for a simulation with parameters \(\bar{\rho }=5\) and \(\bar{\sigma }=90\) (case I). The timestamp shows time normalized to the relaxation time of a single microtubule.

Supplementary Video 2

Time course of 2D projection of velocity field for a simulation with parameters \(\bar{\rho }=5\) and \(\bar{\sigma }=90\) (case I). The bordering circle represents the boundary of the sphere (case I). The timestamp shows time normalized to the relaxation time of a single microtubule.

Supplementary Video 3

Time course of configurations of microtubules anchored to the interior surface of a sphere in a cut-away view for a simulation with parameters \(\bar{\rho }=15\) and \(\bar{\sigma }=45\) (case II). The timestamp shows time normalized to the relaxation time of a single microtubule.

Supplementary Video 4

Time course of configurations of microtubules anchored to the interior surface of a sphere in a cut-away view for a simulation with parameters \(\bar{\rho }=15\) and \(\bar{\sigma }=45\) (case II). The timestamp shows time normalized to the relaxation time of a single microtubule.

Supplementary Video 5

Live video of a cross-section of a Drosophila oocyte obtained by a brightfield microscope. Scale bar, 50 μm. The timestamp is in units of minutes.

Supplementary Video 6

Time course of the polar order parameter P for a simulation with parameters \(\bar{\rho }=15\) and \(\bar{\sigma }=45\). Left and right views face two diametrically opposite defect-like structures formed in long time. The timestamp shows time normalized to the relaxation time of a single microtubule.

Supplementary Video 7

360∘ rotation of two snapshots at early time (t = 0.025τr, t = 0.035τr) and one snapshot at long time (t = 0.5τr) of a simulation with parameters \(\bar{\rho }=15\) and \(\bar{\sigma }=45\). τr is the relaxation time of a single microtubule. Surface polarity vectors pi corresponding to each microtubule are represented as arrows and superposed on the surface. The colour of the surface represents the polar order parameter P.

Supplementary Video 8

Time course of configurations of microtubules anchored to the interior surface for a cell shape similar to that of an oocyte, shown in two cut-away views. The simulation parameters are \(\bar{\rho }=15\) and \(\bar{\sigma }=45\). The timestamp shows time normalized to the relaxation time of a single microtubule.

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

Dutta, S., Farhadifar, R., Lu, W. et al. Self-organized intracellular twisters. Nat. Phys. 20, 666–674 (2024). https://doi.org/10.1038/s41567-023-02372-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41567-023-02372-1

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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