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

Nature Physics volume 20, pages 1659–1669 (2024)Cite this article

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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.

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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.

Author information

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

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Source Data Fig. 3

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Source Data Fig. 4

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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

Source data for the plots.

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. 20, 1659–1669 (2024). https://doi.org/10.1038/s41567-024-02600-2

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