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Optogenetic generation of leader cells reveals a force–velocity relation for collective cell migration

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Abstract

During development, wound healing and cancer invasion, migrating cell clusters feature highly protrusive leader cells at their front. Leader cells are thought to pull and direct their cohort of followers, but whether their local action is enough to guide the entire cluster, or if a global mechanical organization is needed, remains controversial. Here we show that the effectiveness of the leader–follower organization is proportional to the asymmetry of traction and tension within cell clusters. By combining hydrogel micropatterning and optogenetic activation, we generate highly protrusive leaders at the edge of minimal cell clusters. We find that the induced leader can robustly drag one follower but not larger groups. By measuring traction forces and tension propagation in clusters of increasing size, we establish a quantitative relationship between group velocity and the asymmetry of the traction and tension profiles. Modelling motile clusters as active polar fluids, we explain this force–velocity relationship in terms of asymmetries in the active traction profile. Our results challenge the notion of autonomous leader cells, showing that collective cell migration requires global mechanical organization within the cluster.

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Fig. 1: Optogenetic control of lamellipodium formation in cell trains.
Fig. 2: Leader cell migratory efficiency decreases with number of followers.
Fig. 3: Asymmetric traction and tension profiles drive the migration of cell trains.
Fig. 4: Asymmetric traction profiles in 2D cell groups.
Fig. 5: An active polar fluid model explains how traction asymmetries drive migration.

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Data availability

The full datasets that support the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

Analysis procedures and codes are available via GitHub under a GPL-3.0 license at https://github.com/xt-prc-lab/Rossetti_et_al_2024_Nature_Physics. All other codes are available from the corresponding authors on reasonable request.

References

  1. Schaller, V., Weber, C., Semmrich, C., Frey, E. & Bausch, A. R. Polar patterns of driven filaments. Nature 467, 73–77 (2010).

    Article  ADS  Google Scholar 

  2. Boudet, J. F. et al. From collections of independent, mindless robots to flexible, mobile, and directional superstructures. Sci. Robot. 6, eabd0272 (2021).

    Article  Google Scholar 

  3. Silverberg, J. L., Bierbaum, M., Sethna, J. P. & Cohen, I. Collective motion of humans in mosh and circle pits at heavy metal concerts. Phys. Rev. Lett. 110, 228701 (2013).

    Article  ADS  Google Scholar 

  4. Suzuki, R., Weber, C. A., Frey, E. & Bausch, A. R. Polar pattern formation in driven filament systems requires non-binary particle collisions. Nat. Phys. 11, 839–843 (2015).

    Article  Google Scholar 

  5. Ben-Jacob, E., Cohen, I. & Levine, H. Cooperative self-organization of microorganisms. Adv. Phys. 49, 395–554 (2000).

    Article  ADS  Google Scholar 

  6. Gómez-Nava, L., Bon, R. & Peruani, F. Intermittent collective motion in sheep results from alternating the role of leader and follower. Nat. Phys. 18, 1494–1501 (2022).

    Article  Google Scholar 

  7. Yllanes, D., Leoni, M. & Marchetti, M. C. How many dissenters does it take to disorder a flock? New J. Phys. 19, 103026 (2017).

    Article  ADS  Google Scholar 

  8. Pearce, D. J. G. & Giomi, L. Linear response to leadership, effective temperature, and decision making in flocks. Phys. Rev. E 94, 022612 (2016).

    Article  ADS  Google Scholar 

  9. Pinkoviezky, I., Couzin, I. D. & Gov, N. S. Collective conflict resolution in groups on the move. Phys. Rev. E 97, 032304 (2018).

    Article  ADS  Google Scholar 

  10. Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).

    Article  ADS  Google Scholar 

  11. Nagy, M., Ákos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).

    Article  ADS  Google Scholar 

  12. Omelchenko, T., Vasiliev, J. M., Gelfand, I. M., Feder, H. H. & Bonder, E. M. Rho-dependent formation of epithelial ‘leader’ cells during wound healing. Proc. Natl Acad. Sci. USA 100, 10788–10793 (2003).

    Article  ADS  Google Scholar 

  13. Poujade, M. et al. Collective migration of an epithelial monolayer in response to a model wound. Proc. Natl Acad. Sci. USA 104, 15988–15993 (2007).

    Article  ADS  Google Scholar 

  14. Khalil, A. A. & Friedl, P. Determinants of leader cells in collective cell migration. Integr. Biol. 2, 568 (2010).

    Article  Google Scholar 

  15. Mayor, R. & Etienne-Manneville, S. The front and rear of collective cell migration. Nat. Rev. Mol. Cell Biol. 17, 97–109 (2016).

    Article  Google Scholar 

  16. Theveneau, E. & Linker, C. Leaders in collective migration: are front cells really endowed with a particular set of skills? F1000Res. 6, 1899 (2017).

    Article  Google Scholar 

  17. Yang, Y. & Levine, H. Leader-cell-driven epithelial sheet fingering. Phys. Biol. 17, 046003 (2020).

    Article  Google Scholar 

  18. 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  Google Scholar 

  19. Camley, B. A. & Rappel, W.-J. Physical models of collective cell motility: from cell to tissue. J. Phys. Appl. Phys. 50, 113002 (2017).

    Article  ADS  Google Scholar 

  20. Martinson, W. D. et al. Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration. eLife 12, e83792 (2023).

    Article  Google Scholar 

  21. Kozyrska, K. et al. p53 directs leader cell behavior, migration, and clearance during epithelial repair. Science 375, eabl8876 (2022).

    Article  Google Scholar 

  22. Yamaguchi, N., Mizutani, T., Kawabata, K. & Haga, H. Leader cells regulate collective cell migration via Rac activation in the downstream signaling of integrin β1 and PI3K. Sci. Rep. 5, 7656 (2015).

    Article  ADS  Google Scholar 

  23. Reffay, M. et al. Interplay of RhoA and mechanical forces in collective cell migration driven by leader cells. Nat. Cell Biol. 16, 217–223 (2014).

    Article  Google Scholar 

  24. Hino, N. et al. A feedback loop between lamellipodial extension and HGF-ERK signaling specifies leader cells during collective cell migration. Dev. Cell 57, 2290–2304.e7 (2022).

    Article  Google Scholar 

  25. Vishwakarma, M. et al. Mechanical interactions among followers determine the emergence of leaders in migrating epithelial cell collectives. Nat. Commun. 9, 3469 (2018).

    Article  ADS  Google Scholar 

  26. Law, R. A. et al. Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement. Sci. Adv. 9, eabq6480 (2023).

    Article  ADS  Google Scholar 

  27. Cai, D. et al. Mechanical feedback through E-cadherin promotes direction sensing during collective cell migration. Cell 157, 1146–1159 (2014).

    Article  Google Scholar 

  28. Vishwakarma, M., Spatz, J. P. & Das, T. Mechanobiology of leader–follower dynamics in epithelial cell migration. Curr. Opin. Cell Biol. 66, 97–103 (2020).

    Article  Google Scholar 

  29. Trepat, X. et al. Physical forces during collective cell migration. Nat. Phys. 5, 426–430 (2009).

    Article  Google Scholar 

  30. Caussinus, E., Colombelli, J. & Affolter, M. Tip-cell migration controls stalk-cell intercalation during Drosophila tracheal tube elongation. Curr. Biol. 18, 1727–1734 (2008).

    Article  Google Scholar 

  31. Arima, S. et al. Angiogenic morphogenesis driven by dynamic and heterogeneous collective endothelial cell movement. Development 138, 4763–4776 (2011).

    Article  Google Scholar 

  32. Wang, X., He, L., Wu, Y. I., Hahn, K. M. & Montell, D. J. Light-mediated activation reveals a key role for Rac in collective guidance of cell movement in vivo. Nat. Cell Biol. 12, 591–597 (2010).

    Article  Google Scholar 

  33. Labernadie, A. et al. A mechanically active heterotypic E-cadherin/N-cadherin adhesion enables fibroblasts to drive cancer cell invasion. Nat. Cell Biol. 19, 224–237 (2017).

    Article  Google Scholar 

  34. Cheung, K. J., Gabrielson, E., Werb, Z. & Ewald, A. J. Collective invasion in breast cancer requires a conserved basal epithelial program. Cell 155, 1639–1651 (2013).

    Article  Google Scholar 

  35. Vilchez Mercedes, S. A. et al. Decoding leader cells in collective cancer invasion. Nat. Rev. Cancer 21, 592–604 (2021).

    Article  Google Scholar 

  36. Machacek, M. et al. Coordination of Rho GTPase activities during cell protrusion. Nature 461, 99–103 (2009).

    Article  ADS  Google Scholar 

  37. de Beco, S. et al. Optogenetic dissection of Rac1 and Cdc42 gradient shaping. Nat. Commun. 9, 4816 (2018).

    Article  ADS  Google Scholar 

  38. Drozdowski, O. M., Ziebert, F. & Schwarz, U. S. Optogenetic control of migration of contractile cells predicted by an active gel model. Commun. Phys. 6, 158 (2023).

    Article  Google Scholar 

  39. Valon, L. et al. Predictive spatiotemporal manipulation of signaling perturbations using optogenetics. Biophys. J. 109, 1785–1797 (2015).

    Article  ADS  Google Scholar 

  40. Kennedy, M. J. et al. Rapid blue-light–mediated induction of protein interactions in living cells. Nat. Methods 7, 973–975 (2010).

    Article  Google Scholar 

  41. Valon, L., Marín-Llauradó, A., Wyatt, T., Charras, G. & Trepat, X. Optogenetic control of cellular forces and mechanotransduction. Nat. Commun. 8, 14396 (2017).

    Article  ADS  Google Scholar 

  42. Roca-Cusachs, P., Conte, V. & Trepat, X. Quantifying forces in cell biology. Nat. Cell Biol. 19, 742–751 (2017).

    Article  Google Scholar 

  43. Hennig, K. et al. Stick-slip dynamics of cell adhesion triggers spontaneous symmetry breaking and directional migration of mesenchymal cells on one-dimensional lines. Sci. Adv. 6, eaau5670 (2020).

    Article  ADS  Google Scholar 

  44. Butler, J. P., Tolić-Nørrelykke, I. M., Fabry, B. & Fredberg, J. J. Traction fields, moments, and strain energy that cells exert on their surroundings. Am. J. Physiol. Cell Physiol. 282, C595–C605 (2002).

    Article  Google Scholar 

  45. Tanimoto, H. & Sano, M. A simple force-motion relation for migrating cells revealed by multipole analysis of traction stress. Biophys. J. 106, 16–25 (2014).

    Article  ADS  Google Scholar 

  46. Delanoë-Ayari, H., Rieu, J. P. & Sano, M. 4D traction force microscopy reveals asymmetric cortical forces in migrating Dictyostelium cells. Phys. Rev. Lett. 105, 248103 (2010).

    Article  ADS  Google Scholar 

  47. Costa, G. et al. Asymmetric division coordinates collective cell migration in angiogenesis. Nat. Cell Biol. 18, 1292–1301 (2016).

    Article  Google Scholar 

  48. Hayashi, S. & Dong, B. Shape and geometry control of the Drosophila tracheal tubule. Dev. Growth Differ. 59, 4–11 (2017).

    Article  Google Scholar 

  49. Weigelin, B., Bakker, G.-J. & Friedl, P. Intravital third harmonic generation microscopy of collective melanoma cell invasion. IntraVital 1, 32–43 (2012).

    Article  Google Scholar 

  50. Alert, R. & Trepat, X. Physical models of collective cell migration. Annu. Rev. Condens. Matter Phys. 11, 77–101 (2020).

    Article  ADS  Google Scholar 

  51. Pérez-González, C. et al. Active wetting of epithelial tissues. Nat. Phys. 15, 79–88 (2019).

    Article  Google Scholar 

  52. Alert, R., Blanch-Mercader, C. & Casademunt, J. Active fingering instability in tissue spreading. Phys. Rev. Lett. 122, 088104 (2019).

    Article  ADS  Google Scholar 

  53. Blanch-Mercader, C. et al. Effective viscosity and dynamics of spreading epithelia: a solvable model. Soft Matter 13, 1235–1243 (2017).

    Article  ADS  Google Scholar 

  54. Delanoë-Ayari, H., Bouchonville, N., Courçon, M. & Nicolas, A. Linear correlation between active and resistive stresses provides information on force generation and stress transmission in adherent cells. Phys. Rev. Lett. 129, 098101 (2022).

    Article  ADS  Google Scholar 

  55. Brückner, D. B. et al. Stochastic nonlinear dynamics of confined cell migration in two-state systems. Nat. Phys. 15, 595–601 (2019).

    Article  Google Scholar 

  56. Chan, C. E. & Odde, D. J. Traction dynamics of filopodia on compliant substrates. Science 322, 1687–1691 (2008).

    Article  ADS  Google Scholar 

  57. Bangasser, B. L. et al. Shifting the optimal stiffness for cell migration. Nat. Commun. 8, 15313 (2017).

    Article  ADS  Google Scholar 

  58. Bergert, M. et al. Force transmission during adhesion-independent migration. Nat. Cell Biol. 17, 524–529 (2015).

    Article  Google Scholar 

  59. Sakamoto, R., Izri, Z., Shimamoto, Y., Miyazaki, M. & Maeda, Y. T. Geometric trade-off between contractile force and viscous drag determines the actomyosin-based motility of a cell-sized droplet. Proc. Natl Acad. Sci. USA 119, e2121147119 (2022).

    Article  Google Scholar 

  60. Godeau, A. L. et al. 3D single cell migration driven by temporal correlation between oscillating force dipoles. eLife 11, e71032 (2022).

    Article  Google Scholar 

  61. Carlsson, A. E. Mechanisms of cell propulsion by active stresses. New J. Phys. 13, 073009 (2011).

    Article  ADS  Google Scholar 

  62. Amiri, B., Heyn, J. C. J., Schreiber, C., Rädler, J. O. & Falcke, M. On multistability and constitutive relations of cell motion on fibronectin lanes. Biophys. J. 122, 753–766 (2023).

    Article  ADS  Google Scholar 

  63. Basan, M., Elgeti, J., Hannezo, E., Rappel, W.-J. & Levine, H. Alignment of cellular motility forces with tissue flow as a mechanism for efficient wound healing. Proc. Natl Acad. Sci. USA 110, 2452–2459 (2013).

    Article  ADS  Google Scholar 

  64. Ron, J. E. et al. Polarization and motility of one-dimensional multi-cellular trains. Biophys. J. 122, 4598–4613 (2023).

    Article  ADS  Google Scholar 

  65. Camley, B. A. Collective gradient sensing and chemotaxis: modeling and recent developments. J. Phys. Condens. Matter 30, 223001 (2018).

    Article  ADS  Google Scholar 

  66. Ruppel, A. et al. Force propagation between epithelial cells depends on active coupling and mechano-structural polarization. eLife 12, e83588 (2023).

    Article  Google Scholar 

  67. George, M., Bullo, F. & Campàs, O. Connecting individual to collective cell migration. Sci. Rep. 7, 9720 (2017).

    Article  ADS  Google Scholar 

  68. Zimmermann, J., Camley, B. A., Rappel, W.-J. & Levine, H. Contact inhibition of locomotion determines cell–cell and cell–substrate forces in tissues. Proc. Natl Acad. Sci. USA 113, 2660–2665 (2016).

    Article  ADS  Google Scholar 

  69. Boutillon, A. et al. Guidance by followers ensures long-range coordination of cell migration through α-catenin mechanoperception. Dev. Cell 57, 1529–1544.e5 (2022).

    Article  Google Scholar 

  70. Campanale, J. P. & Montell, D. J. Who’s really in charge: diverse follower cell behaviors in collective cell migration. Curr. Opin. Cell Biol. 81, 102160 (2023).

    Article  Google Scholar 

  71. Alert, R. & Casademunt, J. Role of substrate stiffness in tissue spreading: wetting transition and tissue durotaxis. Langmuir 35, 7571–7577 (2019).

    Article  Google Scholar 

  72. Serra-Picamal, X. et al. Mechanical waves during tissue expansion. Nat. Phys. 8, 628–634 (2012).

    Article  Google Scholar 

  73. Serra-Picamal, X., Conte, V., Sunyer, R., Muñoz, J. J. & Trepat, X. in Methods in Cell Biology Vol. 125 (Elsevier, 2015).

  74. Tambe, D. T. et al. Collective cell guidance by cooperative intercellular forces. Nat. Mater. 10, 469–475 (2011).

    Article  ADS  Google Scholar 

  75. Tambe, D. T. et al. Monolayer stress microscopy: limitations, artifacts, and accuracy of recovered intercellular stresses. PLoS ONE 8, e55172 (2013).

    Article  ADS  Google Scholar 

  76. Borau, C. Sankey flow chart. MATLAB Central File Exchange https://www.mathworks.com/matlabcentral/fileexchange/101516-sankey-flow-chart (2022).

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Acknowledgements

We thank all the members of our groups for their discussions and support. We thank S. Usieto and M. Purciolas for technical assistance. We also thank C. Tucker (Department of Pharmacology, University of Colorado) and S. D. Beco (Institut Jacques Monod) for sharing the plasmids used in this work. We thank P. Silberzan (Institut Curie) for sharing the experimental data with us. Finally, we thank I. Andreu, M. Matejčić and A. Beedle for discussions and feedback on the manuscript. This paper was funded by the Generalitat de Catalunya (AGAUR SGR-2017-01602 to X.T.; the CERCA Programme and ‘ICREA Academia’ awards to P.R.-C.); the Spanish Ministry for Science and Innovation MICCINN/FEDER (PID2021-128635NB-I00, MCIN/AEI/10.13039/501100011033 and ‘ERDF-EU A way of making Europe’ to X.T.; PID2019-110298GB-I00 to P.R.-C.); European Research Council (101097753 to P.R.-C. and Adv-883739 to X.T.); Fundació la Marató de TV3 (project 201903-30-31-32 to X.T.); European Commission (H2020-FETPROACT-01-2016-731957 to P.R.-C. and X.T.); the European Union’s Horizon 2020 research and innovation programme (under the Marie Skłodowska-Curie grant agreement ID 796883 to L.R.); La Caixa Foundation (LCF/PR/HR20/52400004 to P.R.-C. and X.T.); IBEC is recipient of a Severo Ochoa Award of Excellence from MINECO.

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Authors and Affiliations

  1. Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain

    Leone Rossetti, Steffen Grosser, Juan Francisco Abenza, Pere Roca-Cusachs & Xavier Trepat

  2. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

    Juan Francisco Abenza

  3. Department of Developmental and Stem Cell Biology, Institut Pasteur, CNRS UMR 3738, Paris, France

    Léo Valon & Xavier Trepat

  4. Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain

    Pere Roca-Cusachs & Xavier Trepat

  5. Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

    Ricard Alert

  6. Centre for Systems Biology Dresden, Dresden, Germany

    Ricard Alert

  7. Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany

    Ricard Alert

  8. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Xavier Trepat

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  1. Leone Rossetti
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Contributions

L.R., L.V. and X.T. conceived the project. L.V. designed and performed the preliminary experiments. L.R. and J.F.A. designed and performed the experiments. L.R. and S.G. analysed the data. J.F.A. and P.R.-C. contributed to the technical expertise, materials and discussion. R.A. developed the model. L.R., R.A. and X.T. wrote the manuscript. All authors revised the completed manuscript.

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Correspondence to Leone Rossetti, Ricard Alert or Xavier Trepat.

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Extended data

Extended Data Fig. 1 Photoactivation induces lamellipodium growth.

(a) (i) Segmentation of a lamellipodium before (green) and after photoactivation (lilac, cyan is the overlap), (ii) membrane fluorescence of lamellipodium after photoactivation, (iii) focal adhesions after photoactivation, (iv) a naturally occurring lamellipodium, (v) focal adhesions in the naturally occurring lamellipodium (scale bar 10 µm). Similar results were obtained in n=10 cell trains from 3 independent experiments. (b) Scheme of how lamellipodium area is calculated. The area of the top half of the cell is averaged during 1 h prior to (A) and following (A*) photoactivation (these same time intervals are considered also for control cells). The change of lamellipodium size is calculated as the difference between A* and A. (c) Lamellipodium area for trains of different lengths, comparing control cases and photoactivated trains. Photoactivation induces lamellipodium growth in nearly all cases. Statistical significance quantified by a two-sided Wilcoxon rank sum test, ** indicates p<0.01. Box plots showing first quartile, median and third quartile. Range includes all data points. Whiskers extend to first adjacent value within 1.5 x inter-quartile range. Full p-values in Supplementary Table 1. (d) Lamellipodium growth of photoactivated trains undergoing directed motion compared with other trains. Lamellipodium growth is not significantly different between these two subpopulations. Statistical significance quantified by a two-sided Wilcoxon rank sum test. Box plots showing first quartile, median and third quartile. Range includes all data points. Whiskers extend to first adjacent value within 1.5 x inter-quartile range.

Source data

Extended Data Fig. 2 Cell train and edge motion.

(a) Directed migration in control trains. In absence of photoactivation there is an equal probability for upwards (directed) or downwards (antidirected) migration. (b) Effect of photoactivation on edge motion, compared with control cases. The bars show the percentage of photoactivated edges that have significant velocities in the direction of photoactivation (magenta), in the opposing direction (green), or that have non-significant velocities (grey). For all values of Nc more than 50% of the cell trains have edge velocities biased in the direction of the induced lamellipodium. In the control case, both directions are equally probable. For increasing values of Nc the photoactivated edges total sample sizes are n=49, n=61, n=42, n=33, respectively, and for the control edges total sample sizes are n=38, n=31, n=23, n=15.

Source data

Extended Data Fig. 3 Photoactivated cell trains that undergo directed migration are not undergoing directed migration prior to photoactivation.

Motion of cell trains of different lengths before and during photoactivation. For each value of Nc the column on the left shows the type of migration of the trains that will be photoactivated, while the column on the right shows the type of migration of the same trains during photoactivation (same data as Fig. 2e). The stripes connecting the columns show how cell trains changed their migration type due to photoactivation. Tph stands for the time at which photoactivation starts and t is time.

Extended Data Fig. 4 Effect of photoactivation on traction forces.

Average profiles of the longitudinal component of the traction for cell trains undergoing coherent motion (red) and for other trains (grey). The top row shows only photoactivated trains, the bottom row non-photoactivated trains. Shaded regions along the curves show the standard error of the mean. Averages are over different cell trains; number of cell trains n is indicated on the plot.

Source data

Extended Data Fig. 5 Role of non-uniform friction on train motion.

(a-c) Predicted profiles of velocity (a), total traction (b), and tension (c) for different values of the relative strength of friction non-uniformity, \(\Delta \xi /{\xi }_{0}\), varied from 0% to 40%. Insets help to visualize the small effect of friction non-uniformity. The theory with non-uniform friction is explained in Section D of the Supplementary Note. (d) Centre-of-mass velocity as a function of the decay-length asymmetry of the active tractions, as in Fig. 5o, shown for different values of the relative friction non-uniformity. The effects of friction non-uniformity are small in all cases. In all panels, we chose \({\zeta }_{+}={\zeta }_{-}\), \({\ell }_{-}=0.4\,L\), and \(\lambda =10{L}\). In panels a-c, we chose \({\ell }_{+}/{\ell }_{-}=1.25\).

Extended Data Fig. 6 Fits of the predicted traction profiles to the experimental data.

(a) Model (blue curves) and experimental (red and grey curves) average longitudinal tractions for cell trains undergoing coherent motion (top row, red curves) and for other trains (bottom row, grey curves). In the top row, dashed green lines show the level of friction force, as also reported in the bottom left plot of panel (b). (b) Parameter values of the model (equation (1)) obtained from the fits. Error bars are confidence intervals derived from the fits (see Methods).

Source data

Supplementary information

Supplementary Information

Supplementary Notes A–D, Table 1 and captions for Videos 1–5.

Reporting Summary

Supplementary Video 1

Effect of photoactivation on a cell train with Nc = 1. Composite of bright-field and CIBN-GFP-CAAX fluorescence excited at 488 nm. Scale bar, 20 µm.

Supplementary Video 2

Effect of photoactivation on a cell train with Nc = 2. Composite of bright-field and CIBN-GFP-CAAX fluorescence excited at 488 nm. Scale bar, 20 µm.

Supplementary Video 3

Effect of photoactivation on a cell train with Nc = 3. Composite of bright-field and CIBN-GFP-CAAX fluorescence excited at 488 nm. Scale bar, 20 µm.

Supplementary Video 4

Effect of photoactivation on a cell train with Nc = 4. Composite of bright-field and CIBN-GFP-CAAX fluorescence excited at 488 nm. Scale bar, 20 µm.

Supplementary Video 5

Effect of photoactivation on a cell island. Composite of bright-field and CIBN-GFP-CAAX fluorescence excited at 488 nm. Scale bar, 20 µm.

Source data

Source Data Fig. 2

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Source Data Fig. 5

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 4

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Source Data Extended Data Fig. 6

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Rossetti, L., Grosser, S., Abenza, J.F. et al. Optogenetic generation of leader cells reveals a force–velocity relation for collective cell migration. Nat. Phys. (2024). https://doi.org/10.1038/s41567-024-02600-2

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