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Control of cell–cell forces and collective cell dynamics by the intercellular adhesome

Nature Cell Biology volume 17, pages 409–420 (2015)Cite this article

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Abstract

Dynamics of epithelial tissues determine key processes in development, tissue healing and cancer invasion. These processes are critically influenced by cell–cell adhesion forces. However, the identity of the proteins that resist and transmit forces at cell–cell junctions remains unclear, and how these proteins control tissue dynamics is largely unknown. Here we provide a systematic study of the interplay between cell–cell adhesion proteins, intercellular forces and epithelial tissue dynamics. We show that collective cellular responses to selective perturbations of the intercellular adhesome conform to three mechanical phenotypes. These phenotypes are controlled by different molecular modules and characterized by distinct relationships between cellular kinematics and intercellular forces. We show that these forces and their rates can be predicted by the concentrations of cadherins and catenins. Unexpectedly, we identified different mechanical roles for P-cadherin and E-cadherin; whereas P-cadherin predicts levels of intercellular force, E-cadherin predicts the rate at which intercellular force builds up.

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Figure 1: Intercellular cohesiveness increases with monolayer expansion.
Figure 2: As the monolayer increases its cohesiveness, cell migration slows down and physical forces build up.
Figure 3: Downregulation of adherens junctions alters monolayer dynamics.
Figure 4: Downregulation of tight junctions, desmosomes and gap junctions alters monolayer dynamics.
Figure 5: Cell monolayers with perturbed cell–cell adhesions exhibit distinct mechanical phenotypes.
Figure 6: Protein concentrations predict intercellular forces and their build-up rate.
Figure 7: Force applied to E-cadherin triggers reinforcement feedback loops whereas force applied to P-cadherin does not.
Figure 8: Vinculin is involved in mechanotransduction through P-cadherin and E-cadherin.

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Acknowledgements

We thank F. Supek, B. Lehner, A. Brugués and R. Vincent for discussions, R. Zaidel-Bar for sharing unpublished data, and E. Sahai for contributing reagents. This research was supported by the European Research Council (StG-242993 and CoG - 616480 to X.T.), the 7th European Community Framework Programme (PCIG10-GA-2011-303848 to P.R-C., PIRG-GA-2010-277166 to R.G., PIRG-GA-2010-268342 to M.S-P., FET Grant 317532 to M.S-P. and R.G.), the Spanish Ministerio de Economía y Comptetitividad (BFU2012-38146 to X.T., DPI2013-43727-R to J.J.M., FIS2010-18639 and FIS2013-47532-C3 to M.S-P. and R.G., BFU2011-23111 to P.R-C., Juan de la Cierva Fellowship JCI-2012-15123 to V.C.), the National Institutes of Health (R01HL107561 to X.T.), Fundació La Caixa, Fundació la Marató de TV3 (20133330 to P.R-C.), and the James S. McDonnell Foundation (R.G. and M.S-P.).

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Authors and Affiliations

  1. Institute for Bioengineering of Catalonia, Barcelona 08028, Spain

    Elsa Bazellières, Vito Conte, Alberto Elosegui-Artola, Xavier Serra-Picamal, María Bintanel-Morcillo, Pere Roca-Cusachs & Xavier Trepat

  2. Unitat de Biofísica i Bioenginyeria, Facultat de Medicina, Universitat de Barcelona, and CIBERES, Barcelona 08036, Spain

    Pere Roca-Cusachs & Xavier Trepat

  3. Department of Applied Mathematics III, Laboratori de Càlcul Numèric, Universitat Politècnica de Catalunya, Barcelona 08036, Spain

    José J. Muñoz

  4. Departament d’Enginyeria Química, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain

    Marta Sales-Pardo & Roger Guimerà

  5. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain

    Roger Guimerà & Xavier Trepat

Authors
  1. Elsa Bazellières
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Contributions

E.B. and X.T. conceived the study and designed experiments. E.B., M.B-M. and A.E-A. performed experiments. E.B., V.C. and A.E-A. analysed data. X.S-P. and P.R-C. developed data analysis tools. V.C. and J.J.M. developed computational mechanics tools. M.S-P. and R.G. performed unsupervised clustering analysis and LOOCV analysis. E.B., M.S-P., R.G. and X.T. wrote the manuscript. All authors discussed and interpreted results and commented on the manuscript.

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Integrated supplementary information

Supplementary Figure 5 mRNA and protein expression levels after siRNA transfections.

(a) Scheme illustrating the localization of all targeted proteins. (b) Levels of mRNA after 5 days of transfection. Data were quantified by RT-PCR. Data are presented as mean ± SEM (normalized to control cells). RT-PCR was run in triplicate. n = 12 samples pooled from 4 independent experiments for CT siRNA, Ncad siRNA, Pcad siRNA, DSC3 siRNA, αcat siRNA, p120 siRNA, LIMA1 siRNA, CLDN8 siRNA, JAM-A siRNA; n = 9 samples pooled from 3 independent experiments for βcat siRNA, DDR1 siRNA, VCL siRNA, CLDN1 siRNA, CLDN7 siRNA, ZO-1 siRNA, ZO-3 siRNA, PKP2 siRNA, JUP siRNA; n = 6 samples pooled from 2 independent experiments for CLDN4 siRNA, CX43 siRNA, OCLN siRNA. (c) Protein levels after 5 days of transfection. Data are presented as mean ± SEM. For each protein, n = 3 samples pooled from 3 independent transfections (normalized to control cells).

Supplementary Figure 6 Representative maps of monolayer mechanics for siRNA perturbations targeting E-, N-, and P-cadherin.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 7 Representative maps of monolayer mechanics for siRNA perturbations targeting α-catenin, β-catenin, and p120.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 8 Representative maps of monolayer mechanics for siRNA perturbations targeting DDR1, LIMA1, and VCL.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 9 Representative maps of monolayer mechanics for siRNA perturbations targeting JAM-A, OCLN, ZO-1, and ZO-3.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 10 Representative maps of monolayer mechanics for siRNA perturbations targeting claudins 1, 4, 7, and 8.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 11 Representative maps of monolayer mechanics for siRNA perturbations targeting desmocollin 3, plakophilin 2, plakoglobin, and connexin 43.

Data show phase contrast images (first column), velocities in the x direction (second column), traction forces in the x direction (third column), and monolayer tension (fourth column) for two time points (0h first row and 6h second row). Scale bar, 100 μm.

Supplementary Figure 12 Time evolution of monolayer mechanics for each siRNA perturbation.

Data are presented as mean ± SEM. n = 13 independent cell monolayers (CT siRNA), n = 3 independent cell monolayers (Ecad siRNA, βcat siRNA, JAM-A siRNA, ZO-3 siRNA, Ncad siRNA, LIMA1 siRNA, DDR1 siRNA, PKP2 siRNA), n = 4 independent cell monolayers (Pcad siRNA, αcat siRNA, p120 siRNA, CX43 siRNA, VCL siRNA, JUP siRNA), n = 5 independent cell monolayers (DSC3 siRNA, CLDN1 siRNA, CLDN8 siRNA), n = 6 independent monolayers (OCLN siRNA, CLDN4 siRNA), n = 7 independent cell monolayers (ZO-1 siRNA), n = 8 independent cell monolayers (CLDN7 siRNA); monolayers were assessed from 10 experiments (CT siRNA), 4 experiments (CLDN7 siRNA, CLDN4 siRNA), 3 experiments (αcat siRNA, CX43 siRNA, DSC3 siRNA), 2 experiments (Ecad siRNA, βcat siRNA, JAM-A siRNA, ZO-3 siRNA, Ncad siRNA, LIMA1 siRNA, DDR1 siRNA, PKP2 siRNA, Pcad siRNA, p120 siRNA, VCL siRNA, JUP siRNA, CLDN1 siRNA, CLDN8 siRNA, OCLN siRNA, ZO-1 siRNA).

Supplementary Table 1 List of all physical variables measured or computed in this study.
Full size table
Supplementary Table 2 List of all 1-protein models that predict physical properties with statistical significance (p < 0.05, leave-one-out cross-validation).
Full size table
Supplementary Table 3 List of all N-protein models that predict physical properties with statistical significance (p < 0.05, leave-one-out cross-validation).
Full size table
Supplementary Table 4 List of siRNAs used for this study with their corresponding Taqman probes.
Full size table

Supplementary information

Supplementary Information

Supplementary Information (PDF 5154 kb)

Expansion of a micropatterned monolayer of MCF10A cells.

Scale bar, 100 μm. (AVI 3111 kb)

Dynamics of an expanding cell monolayer of MCF10A cells.

Top: velocity field Vx overlaid on phase contrast images. Middle: traction force field Tx overlaid on phase contrast images. Bottom: monolayer tension σxx overlaid on phase contrast images. Scale bar, 100 μm. (AVI 3523 kb)

Time evolution of monolayer tension in response to siRNAs targeting cell-cell junctions.

Composition of representative experiments for the 21 siRNA pools and 3 controls. Each panel shows monolayer tension σxx overlaid on phase contrast images. Scale bar, 100 μm. (AVI 3648 kb)

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Bazellières, E., Conte, V., Elosegui-Artola, A. et al. Control of cell–cell forces and collective cell dynamics by the intercellular adhesome. Nat Cell Biol 17, 409–420 (2015). https://doi.org/10.1038/ncb3135

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