
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences

39231206
202405560
10.1073/pnas.2405560121
research-articleResearch ArticlevideoVideocell-bioCell Biologybiophys-physBiophysics and Computational Biology409
Biological Sciences
Cell Biology
Physical Sciences
Biophysics and Computational Biology
Regulation of intercellular viscosity by E-cadherin-dependent phosphorylation of EGFR in collective cell migration
Fu Chaoyu a 1
Dilasser Florian a 1
Lin Shao-Zhen b https://orcid.org/0000-0002-2719-5392

Karnat Marc b
Arora Aditya a
Rajendiran Harini a
Ong Hui Ting a https://orcid.org/0000-0002-3434-4683

Mui Hoon Brenda Nai c https://orcid.org/0000-0003-2823-1508

Phow Sound Wai a
Hirashima Tsuyoshi a https://orcid.org/0000-0001-7323-9627

Sheetz Michael a https://orcid.org/0000-0001-8747-3430

Rupprecht Jean-François b https://orcid.org/0000-0001-8904-5878

Tlili Sham d
Viasnoff Virgile virgile.viasnoff@cnrs.fr
a e 2
aMechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
bAix Marseille Univ, Université de Toulon, CNRS, Centre de Physique Theorique (UMR 7332), Turing Centre for Living systems, Marseille 13009, France
cDepartment of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
dAix Marseille Univ, Institut de Biologie du developpement de Marseille (UMR 7288), Turing Centre for Living systems, Marseille 13009, France
eCNRS International Research Lab 3639, Singapore 117411, Singapore
2To whom correspondence may be addressed. Email: virgile.viasnoff@cnrs.fr.
Edited by Denis Duboule, College de France, Paris, France; received March 18, 2024; accepted June 27, 2024

1C.F. and F.D. contributed equally to this work.

4 9 2024
10 9 2024
4 9 2024
121 37 e240556012118 3 2024
27 6 2024
Copyright © 2024 the Author(s). Published by PNAS.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

Significance

During collective migration of epithelial monolayers, cells undergo deformation to accommodate local mechanical constraints while maintaining cell–cell contact to ensure monolayer integrity. Single-cell deformability serves as a critical control parameter in determining collective flow dynamics. In this work, we unravel a mechanism through which cells self-regulate the intercellular viscosity of their cell–cell contacts, irrespective of mechanical tension. We demonstrate that the phosphorylation of epidermal growth factor receptor mediated by E-cadherin at cell–cell junctions modulates cortical actin dynamics, resulting in a “self-fluidization” of cell junctions. This mechanism plays a pivotal role in regulating collective migration modes.

Collective cell migration is crucial in various physiological processes, including wound healing, morphogenesis, and cancer metastasis. Adherens Junctions (AJs) play a pivotal role in regulating cell cohesion and migration dynamics during tissue remodeling. While the role and origin of the junctional mechanical tension at AJs have been extensively studied, the influence of the actin cortex structure and dynamics on junction plasticity remains incompletely understood. Moreover, the mechanisms underlying stress dissipation at junctions are not well elucidated. Here, we found that the ligand-independent phosphorylation of epithelial growth factor receptor (EGFR) downstream of de novo E-cadherin adhesion orchestrates a feedback loop, governing intercellular viscosity via the Rac pathway regulating actin dynamics. Our findings highlight how the E-cadherin-dependent EGFR activity controls the migration mode of collective cell movements independently of intercellular tension. This modulation of effective viscosity coordinates cellular movements within the expanding monolayer, inducing a transition from swirling to laminar flow patterns while maintaining a constant migration front speed. Additionally, we propose a vertex model with adjustable junctional viscosity, capable of replicating all observed cellular flow phenotypes experimentally.

intercellular viscosity
collective cell migration
EGFR
E-cadherin adhesion
Ministry of Education - Singapore (MOE) 501100001459 MOE2016-T3-1-002 Michael P. SheetzVirgile Viasnoff National Research Foundation Singapore (NRF) 501100001381 NRFI2018-07 Virgile Viasnoff Ministry of Education - Singapore (MOE) 501100001459 MOE2016-T3-1-002 Michael P. SheetzVirgile Viasnoff Agence Nationale de la Recherche (ANR) 501100001665 ANR-16-CONV-0001 Jean-François Rupprecht Agence Nationale de la Recherche (ANR) 501100001665 ANR-20-CE30-0023 Jean-François Rupprecht
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pmcCollective cell migration has garnered considerable attention in recent decades due to its involvement in fundamental physiological processes such as wound healing, morphogenesis, and cancer metastasis (1). Central to the orchestration of collective cell migration is the Adherens Junction (AJ), a pivotal structure regulating cell cohesion and migration dynamics during tissue remodeling (2). A growing body of research underscores that the mechanical properties of the junctional actomyosin cytoskeleton at AJs (encompassing myosin contractility and actin dynamics) are a governing factor for cell shape changes and the overall mechanics of the tissue (3).

It is well established that myosin activity drives cortical tension and plays a ubiquitous role in either shrinking or expanding junctions (4–9). However, our understanding of how the structure and dynamic properties of the actin cortex influence junction plasticity remains relatively limited. Similarly, the intricate orchestration of molecular mechanisms governing the mechanical properties of cortical actin remains incompletely understood. Moreover, it is widely acknowledged that the mechanosensitivity of junctions enables an adaptive response to mechanical loads (10–14). To maintain junction integrity under mechanical stress, cells can reinforce junctions to withstand stress and dissipate stress to prevent damage (15). Reinforcement of adherens junctions can occur through the induction of catch bonds in adhesion components or by increasing the number of adhesive complexes within junctions (16, 17). In contrast, the mechanisms underlying stress dissipation are less elucidated, although they are likely intertwined with the regulation of the viscoelastic properties of the actin cytoskeleton.

The results of this study uncover a multiscale mechanism in which E-cadherin-dependent phosphorylation of epithelial growth factor receptor (EGFR) governs the migration mode of collective cell movements, regardless of intercellular tension. Our findings demonstrate that within migrating Madin-Darby Canine Kidney (MDCK) monolayers, the modulation of cell–cell junctional deformation is governed by the effective viscosity regulated by Wave2 and Arp2/3 at the junctions, triggered by transient EGFR phosphorylation upon the engagement of new E-cadherin at the AJs. This modulation of effective viscosity plays a pivotal role in coordinating cellular movements within the expanding monolayer, inducing a transition from swirling to laminar flow patterns while maintaining a constant migration front speed. Furthermore, we elucidate the feedback loop mechanisms governing this phenomenon and propose a vertex model with adjustable junctional viscosity, capable of replicating all observed cellular flow phenotypes experimentally.

Results

Feedback Loop between Junction Elongation and EGFR Phosphorylation.

We tested the hypothesis of a feedback loop between cell junction elongation and E-cadherin-dependent phosphorylation of EGFR at AJs on MDCK cells with 2D migration in a culture dish. All experiments were conducted using both serum-free and serum-rich media, with consistently similar phenotypes observed, albeit more pronounced effects in serum-free conditions. All presented results pertain to serum-free conditions (Materials and Methods).

We first compared the migration of MDCK cells under control conditions and following selective inhibition of EGFR phosphorylation using Erlotinib at 1 μM. Quantification of cell movement on 2D or 1D line patterns (SI Appendix, Fig. S1 A and C) demonstrated that the migration of single isolated cells remained insensitive to EGFR inhibition. Consequently, we ruled out the possibility that EGFR inhibition directly altered cell–substrate interactions as well as their single-cell migration potential. In contrast, EGFR inhibition significantly impacted collective cell migration on 2D sparse islets and 1D line patterns (Fig. 1A and SI Appendix, Fig. S1 B and D and Movie S1). In the former case, EGFR inhibition markedly reduced the cellular swirling motion of cells by inhibiting EGFR phosphorylation. The velocity at which each contact changes length during migration (Materials and Methods), exhibited a twofold decrease upon inhibition (Fig. 1B). Thus, EGFR dephosphorylation reduced the dynamics of cell junction deformation.

Fig. 1. A positive feedback loop between apical EGFR phosphorylation and cell junction deformation. (A) Schematics of the experiment for cell migration with or without EGFR inhibition (Erlotinib at 1 μM). Representative patches of MDCK cells under control and EGFR-inhibited conditions including the tracking or individual cell trajectories. (Scale bar: 100 μm.) (B) Quantification of individual junction elongation velocities in the patches (mean ± s.d.) nCtrl = 36 junctions and nErlotinib = 29 junctions from 3 independent experiments, two-tailed unpaired t test, P< 0.0001. (C) Schematics of the experiment for cell arrest by dextran addition. Quantification of individual cell migration velocity 10 min after adding dextran with various molecular weights (mean ± s.d. N = 1,735 to 1,925 cells from 3 independent experiments.) (D) Western blot and its quantification of EGFR phosphorylated states (Y845) before and after cell arrest from 4 independent experiments, two-tailed unpaired t test, P= 0.029. (E) Experimental setup schematics (Left) and segmented contours quantification (Right) of cell mosaically expressing RUSH-EGFR before and after its release from the endoplasmic reticulum. (F) Quantifications of junction elongation velocities upon the release of EGFR, under control and pEGFR-inhibited conditions. nRush/Ctrl = 28 junctions and nRush/Erlotinib = 15 junctions from 3 independent experiments, two-tailed paired t test, PRush/Ctrl < 0.001, PRush/Erlotinib = 0.52. (G) Schematics of the physical induction of cell elongation around obstacles (Left). Images of cells encircling obstacles and in bulk regions including single cell tracking and apical localization of pEGFR-Y845 by immunostaining. (Scale bar: 50 μm.) (H) Quantifications of apical pEGFR-Y845 intensity around obstacles (mean ± s.d. nBulk = 50 junctions and nObstacle = 48 junctions from 3 independent experiments, two-tailed unpaired t test, P< 0.0001.) (I) Diagram of a positive feedback loop between apical EGFR phosphorylation and cell junction deformation.

Reciprocally, we impeded the physical deformation of contacts during migration by supplementing the medium with 50 μg/mL of dextran (5, 50, and 270 kDa) to increase the medium viscosity. We carefully ensured that the addition of dextran induced minimal osmotic shock (SI Appendix, Fig. S2A). In media with higher viscosity (270 kDa dextran), the migration speed of individual isolated cells decreased by 28% (SI Appendix, Fig. S2B). Within cohesive patches consisting of 20 to 50 cells, both collective migration and contact deformation ceased within 10 min of adding 50 μg/mL dextran (Fig. 1C and SI Appendix, Fig. S2C and Movie S2). The individual cell velocity within the patch decreased from 13.7 ± 0.13 μm/h (N = 1,735 cells) in the control group to 8.2 ± 0.06 μm/h (N = 1,925) for 270 kDa dextran. Immunostaining revealed a significant reduction in the phosphorylation of EGFR at the apical junction (SI Appendix, Fig. S2D). Western blots confirmed a 75% reduction in the phosphorylation of the Src-dependent site Y845 pEGFR (18), while the other phosphorylation sites (Y1068 and Y1173) remained inactive under serum-free conditions (Fig. 1D). Our data strongly suggest that the physical arrest of cell junction deformation directly or indirectly leads to the dephosphorylation of apical pEGFR at its Src-dependent site.

We subsequently investigated whether enhancing pEGFR would favor the dynamics of contact deformation. To do so, we transfected MDCK cells with EGFR coupled to the Retention Using Selective Hooks (RUSH) system (Materials and Methods). The RUSH-EGFR construct was sequestered on the endoplasmic reticulum (ER) membrane until released by biotin addition in the culture medium (19). We established a high-confluence, nonpolarized (95%) MDCK monolayer, resulting in a mosaic expression of RUSH-EGFR, with small patches of positive cells amid nonexpressing control MDCK cells (Fig. 1E). Prior to the addition of biotin, the cells displayed limited junctional localization and weak recruitment of apical EGFR (Movie S3). Upon the addition of biotin, the RUSH-positive cells showed substantial recruitment of EGFR at the cell contacts, leading to a fivefold increase (from 2.6 ± 0.5 to 14.3 ± 1.2 μm/h) in their junction elongation velocity, quantified using Cellpose neural network segmentation of the cell contours (Materials and Methods and Fig. 1 E and F). In RUSH-positive cells, EGFR localized exclusively to cell-cell contacts within minutes (SI Appendix, Fig. S3A). To validate these findings, we repeated the experiment in the presence of Erlotinib (1 μM). Despite the relocalization of EGFR to the junctions (SI Appendix, Fig. S3B and Movie S3), the junction elongation velocity remained as low as the control, with very limited cellular rearrangements. These results strongly suggest that the burst increase in pEGFR at cell-cell contacts favors the dynamics of contact deformation.

Conversely, we induced the physical elongation of cell junctions by embedding obstacles (nonadhesive disks with a diameter of 200 μm; see Materials and Methods) into high confluence monolayers (Fig. 1G). Only the limited number of cell layers that spontaneously elongated and encircled the obstacles displayed deforming junctions (Fig. 1G and Movie S4) and elevated levels of apical pEGFR (Fig. 1G), in sharp contrast to the immobile bulk cells (Fig. 1H). A treatment with Erlotinib inhibited the elongation and circumrotation of the cells around the obstacle (Movie S4). Taken together, our findings support the hypothesis of a positive feedback loop (Fig. 1I) between apical EGFR phosphorylation and junction elongation. We subsequently delved into the molecular mechanisms underlying this phenomenon.

E-Cadherin-Dependent Phosphorylation of EGFR Fine-Tunes Actin Dynamics at AJs.

The absence of the soluble ligand EGF and the specific phosphorylation of Y845 suggested an E-cadherin (Ecad)-dependent activation of EGFR (20–22). Confluent patches of WT-MDCK cells displayed a twofold decrease in apical pEGFR compared to subconfluent patches (27.6 ± 11.1 A.U. vs. 51.6 ± 19.0 A.U.) (Fig. 2A). In contrast, Ecad-KO tissues showed consistently low levels of apical pEGFR in both confluent and subconfluent cases (4.0 ± 2.3 A.U. vs. 6.1 ± 3.2 A.U.), while still forming cohesive patches (likely due to K-cadherins, quantified in (SI Appendix, Fig. S4C) with a proper junctional actin structure. In Ecad-KO MDCK cells with rescued expression of Ecad (Ecad-Res), the pEGFR levels in confluent and subconfluent tissues returned to their control values (22.2 ± 4.0 A.U. vs. 30.2 ± 7.9 A.U.). Furthermore, when EGFR was inhibited by adding Erlotinib (1 μM), the apical pEGFR for both confluent and subconfluent patches in WT-MDCK, Ecad-KO MDCK, and Ecad-Res MDCK all dropped to low levels (SI Appendix, Fig. S4 A and B).

Fig. 2. E-cadherin-dependent phosphorylation of EGFR fine-tunes actin dynamics with minimal impact on cortical tension. (A) Immunostaining of apical pEGFR (Y845) and actin in wild-type (WT) and E-cadherin knock-out (Ecad-KO) MDCKs on confluent (C, Left) and subconfluent (SubC, Right) regions. (Scale bar: 20 μm.) Quantification of apical pEGFR in WT, Ecad-KO, and Ecad-KO-rescued (Ecad-Res) tissues. (WT: nC = 122 junctions, nSubC = 106 junctions from 4 independent experiments, P< 0.0001; Ecad-KO: nC = 67 junctions, nSubC = 61 junctions from 3 independent experiments, P= 0.9893; Ecad-Res: nC = 47 junctions, nSubC = 34 cell junctions from 3 independent experiments, P= 0.0239 (ordinary one-way ANOVA Tukey’s test). (B) Schematic (Left) and time-lapse imaging (Middle) of SH2-Grb2 (tdEOS) and E-cadherin (GFP) localization during cell spreading on E-cadherin-coated patterns. Quantification of the recruitment speed of E-cadherin and SH2-Grb2 (Right). nEcad = 40 cell adhesions and nSH2-Grb2 = 132 cell adhesions from 3 independent experiments, two-tailed unpaired t test, P= 0.49. (C) Pulldown assays on Rho family GTPases (Rac1, Cdc42, and RhoA) and quantification of GTP-bound GTPases post cell arrest by dextran. nRac1 = 4 WB, P= 0.029, nCdc42 = 3 WB, P= 0.4, and nRhoA = 3 WB, P> 1, two-tailed unpaired t test. (D–F) Quantification of apical WAVE2 (D), Arp3 (E), and pMLC (F) under control and EGFR-inhibited conditions. (WAVE2: nCtrl, C = 68 cell junctions, nCtrl, SubC = 59 cell junctions from 2 independent experiments, nErlotinib, C = 50 cell junctions, nErlotinib, SubC = 48 cell junctions from 2 independent experiments; Arp3: nCtrl, C = 60 cell junctions, nCtrl, SubC = 43 cell junctions from 2 independent experiments, nErlotinib, C = 74 cell junctions, nErlotinib, SubC = 52 cell junctions from 2 independent experiments; pMLC: nCtrl, C = 49 cell junctions, nCtrl, SubC = 27 cell junctions from 2 independent experiments, nErlotinib, C = 62 cell junctions, nErlotinib, SubC = 39 cell junctions from 2 independent experiments. Ordinary one-way ANOVA Tukey’s test. (G) Proposed model of E-cadherin-dependent phosphorylation of EGFR reducing junction viscosity through the regulation of Rac1, WAVE2, Arp2/3, fine-tuning actin dynamics, with minimal impact on cortical tension. (H and I) Experimental schematics and characteristic images of fluorescence recovery after photobleaching (FRAP) (H) and laser ablation (I) experiments on intercellular junctions between GFP-Actin-MDCK cells. (Scale bar: 3 μm.) Quantification of fluorescence recovery time (H) and recoil velocities (I) under control and pEGFR-inhibited conditions. FRAP: nCtrl = 36 cell junctions, nErlotinib = 15 cell junctions from 3 independent experiments, two-tailed unpaired t test, P< 0.0001; Laser ablation: nCtrl = 10 cell junctions, nErlotinib = 7 cell junctions from 2 independent experiments, two-tailed unpaired t test, P= 0.8983).

We further validated the direct phosphorylation of EGFR at adherens junctions. We used Total Internal Reflection Fluorescence (TIRF) microscopy to image the live recruitment of cytosolic SH2-Grb2 to the membrane as a proxy for EGFR phosphorylation (23). MDCK cells stably expressing tdEOS-labeled SH2-Grb2 were left to spread on E-Cad coated circular patterns (25 μm diameter) (Materials and Methods and Fig. 2B). SH2-Grb2 dynamically accumulated in elongated structures that dynamically followed the progression of cell edges with a time delay Δt. A parallel experiment using E-cad-GFP MDCK cells revealed a similar accumulation of E-cad in structures with similar time delay Δt (1.3 ± 0.2 min for E-cad-GFP, 1.5 ± 0.2 min for SH2-Grb2) (Fig. 2B). Our findings imply that the engagement of E-cad during junction elongation results in transient phosphorylation of EGFR. Consequently, the arrest of cell junction elongation leads to the dephosphorylation of pEGFR, whereas its physical induction promotes EGFR phosphorylation.

We then scrutinized the alteration of recruitment of actin regulators in the same conditions as above. EGFR phosphorylation is a major regulator of Erk, a kinase extensively implicated in collective cell migration mechanisms (24). Monitoring Erk activity using Fluorescence Resonance Energy Transfer (FRET) did not reveal any changes following the addition of 50 μg/mL dextran to migrating cells (SI Appendix, Fig. S5A). This suggests that Erk signaling is not downstream of EGFR in our experimental context. To further dissect the molecular events, we performed pulldown assays to gauge the activity of Rho family GTPases, which are key regulators of actin dynamics (25). The introduction of dextran to migrating cells resulted in a 28.9% reduction in Rac1 activity, with no discernible effects on Cdc42 and RhoA (Fig. 2C). However, the broad nature of pulldown assays made it challenging to distinguish whether Rac1 activity was junctional or lamellipodial. Notably, Wave2, a downstream target of Rac1, was present at the apical side of the junction (26) (SI Appendix, Fig. S5B). Surprisingly, the recruitment of Wave2 and Arp2/3, two factors promoting branched actin nucleation, was higher in confluent monolayers (58.0 ± 24.0 A.U. and 91.1 ± 18.8 A.U., respectively) than in subconfluent patches (43.9 ± 22.9 A.U. and 63.9 ± 11.9 A.U., respectively), a difference that is substantially reduced upon treatment with Erlotinib (1 μM) (Fig. 2 D and E and SI Appendix, Fig. S5 B and C). The amount of phosphorylated Myosin light chain (pMLC) remained unaffected by pEGFR both in confluent and subconfluent culture conditions (Fig. 2F and SI Appendix, Fig. S5D). Our data advocate for a model wherein the trans-binding of new E-cadherin in elongating junctions, increasing the junctional level of pEGFR, subsequently activating Rac1, decreasing levels of Wave2 and Arp2/3 with a constant myosin level, thereby establishing a balance between branched and linear junctional actin. Fig. 2G illustrates this feedback homeostatic loop (see SI Appendix, Text S1 for more details).

Next, we evaluated the impact of EGFR phosphorylation on the turnover rate of junction actin using Fluorescence Recovery After Photobleaching (FRAP). We compared junctions in confluent monolayers, cells surrounding obstacles, and subconfluent patches with or without EGFR inhibition (Fig. 2H and SI Appendix, Fig. S6 A–C). In all cases, we observed a deceleration in actin turnover when pEGFR levels were lower. Additionally, we probed junctional tension by assessing fast actin recoil following laser ablation (Fig. 2I). We did not detect any substantial difference, proving that pEGFR does not regulate tension in this specific context. Finally, we probed the viscoelastic properties of junctions using Atomic Force Microscopy (AFM), revealing an increase in the loss modulus upon EGFR inhibition, with no changes in the elastic modulus (SI Appendix, Fig. S6 D–F). AFM measurements are performed at a much higher frequency than the cell migration experiments. Hence, quantitative measurement of the viscosity effects cannot be compared. However, the trend of an increase of viscosity upon EGFR inhibition is conserved.

Cell Deformability Influences Collective Migration.

We previously reported that E-cad-dependent phosphorylation of EGFR in suspended cell doublets increases the velocity of de novo junction formation (21) and the toughness of their adhesion (27). In all cases, the microscopic dynamics of the actin cortex is associated with a change in cell deformability, with minimal impact on cortical tension. This implies that the homeostasis of junction viscosity is regulated by the Ecad-dependent EGFR phosphorylation loop, effectively “self-lubricating” junction elongation.

By analogy with the transition from laminar to turbulent flows of fluids at various Reynolds numbers, we monitored how the inhibition of EGFR alters patterns of collective MDCK migration along fibronectin strips (width, 400 μm; length, 3,000 μm) (Materials and Methods). Fig. 3A and Movie S5 show the leading region (0 to 500 μm from the front) of migrating cells in ctrl and Erlotinib conditions over 12 h. Although migration fronts collectively progressed at similar velocities in both conditions, inhibition of pEGFR abolished the vortices observed in the ctrl case, leading to more laminar flows with enhanced cellular elongation in the direction of migration. We subsequently quantified these qualitative observations. The high level of apical pEGFR in Ctrl was significantly reduced in the inhibitory case (Fig. 3 B and F). In the bulk regions (3 mm away from the front), the level of apical pEGFR remained constant in both conditions. We computed the cellular flow lines in the monolayer (Materials and Methods) to establish maps of cell velocities and flow vorticity (Fig. 3 C and D). Additionally, we used CellPose to segment individual cells (Materials and Methods) and to quantify the individual level of strain on each cell. pEGFR inhibition resulted in around a threefold increase in cellular strain (0.06 ± 0.04 in Ctrl, 0.14 ± 0.06 in Erlotinib; N = 1,060 cells) (Fig. 3E). While the collective velocity of the migration fronts was not affected by pEGFR inhibition (15.0 ± 4.7 μm/h in Ctrl, 14.8 ± 2.9 μm/h in Erlotinib; N = 16 strips), it substantially reduced the individual cell velocity within the monolayer (from 18.7 ± 2.0 μm/h to 12.6 ± 2.0 μm/h; N = 182 cells) (Fig. 3G). Likewise, the vorticity of the collective flow (Fig. 3H) was reduced by twofold (0.70 ± 0.10 h-1 to 0.33 ± 0.05 h-1; N = 182 cells), and the correlation length of the cell velocity increased by twofolds (SI Appendix, Fig. S10).

Fig. 3. Phosphorylated EGFR (pEGFR) modulates cell deformability and influences collective migration. (A) Phase-contrast images of MDCK monolayers migrating on 400 μm fibronectin-coated line patterns under control and pEGFR-inhibited conditions (Erlotinib at 1 μM). Migration direction is depicted. (Scale bar: 100 μm.) Five independent experiments yielded consistent results. (B) Immunostaining of apical pEGFR(Y845) highlights its localization at cell junctions in bulk and leading regions under control and leading regions under pEGFR-inhibited conditions. (Scale bar: 20 μm.) Four independent experiments corroborate these findings. (C and D) Representative velocity (C) and vorticity (D) profiles with flow line maps, illustrate MDCK monolayer migration under control and pEGFR-inhibited conditions. (Scale bar: 100 μm.) Three independent experiments yielded consistent results. (E) Quantification of cellular strain states in the monolayer under control and pEGFR-inhibited conditions. nCtrl = 1,060 cells and nErlotinib = 1,060 cells from 3 independent experiments, two-tailed unpaired t test, P< 0.0001. (F) Quantification of apical localization of pEGFR (Y845) at the bulk and leading front region of the monolayer. nleading = 39 cell junctions and nBulk = 42 cell junctions from 3 independent experiments, two-tailed unpaired t test, P< 0.0001. (G) Quantification of cell velocity (Left) and migration front velocity (Right) under control and pEGFR-inhibited conditions. nctrl, cell = 181 cells, nErlotinib, cell = 181 cells from 3 independent experiments, P< 0.0001; nctrl, migration front = 16 strips, nErlotinib, migration front = 12 strips from 3 independent experiments, P= 0.45, two-tailed unpaired t test. (H) Quantification of spatial correlation in the velocity field under control and pEGFR-inhibited conditions. nctrl = 181 cells, nErlotinib = 181 cells from 3 independent experiments, two-tailed unpaired t test, P< 0.0001. (I) Schematic representation of the analysis pipeline to measure the average cell shape relaxation time in a migrating monolayer. Average cell strain profiles along the migration axis are depicted, with bold lines indicating mean values and narrow lines representing SDs. (J and K) Correlative plots between measured and advection-based predicted cellular strain for the best fit of the viscoelastic time (tvisc) under control and pEGFR-inhibited conditions. Two independent experiments yielded consistent results. (L) Measured viscoelastic time (tvisc) under control and pEGFR-inhibited conditions. nctrl = 3 strips, nErlotinib = 3 strips from 2 independent experiments, two-tailed unpaired t test, P< 0.06.

To further substantiate the hypothesis regarding a change in intercellular viscosity, representing the strain-rate-dependent resistance of junction deformation, we inferred the average shape relaxation time tvisc of cells within the monolayer. This parameter proved to be a reliable indicator of cellular viscoelasticity in migrating monolayers (28). Fig. 3 I and J illustrates the analysis procedure. Initially, we segmented phase-contrast images of the monolayer at a specific time point. Subsequently, we computed the average initial cell strain on a coarse-grained grid (Materials and Methods) and used an optic flow method to estimate the flow lines (Materials and Methods). The evolution of local cellular strain was then evaluated using the approach depicted in Fig. 3I and elaborated on in SI Appendix. The only free parameter in this equation is the intrinsic cell shape relaxation time tvisc, which does not depend on the shear level experienced by the cells. By utilizing the strain map at t = 0 and solving the equation along the flow lines, we inferred the final strain map at a later time point (10 h). We varied tvisc to maximize the correlation between the observed and measured strain maps. The best fits lead to tvisc = 75 ± 15 min (R2 = 0.4 ± 0.04; N = 3) for the control group and tvisc = 210 ± 65 min (R2 = 0.34 ± 0.2; N = 3) for the pEGFR inhibition group (Fig. 3 K and L). In the control conditions, cells exhibited shape relaxation when advected in swirling vortices, while under pEGFR inhibitory conditions, they elongated more in directed laminar, plug-like flows (Movie S6). As pEGFR inhibition did not affect the single-cell migration (SI Appendix, Fig. S1 A and C), the approximately threefold increase in cell shape relaxation times supports the hypothesis that the level of pEGFR controls viscous dissipation in cell-cell junction elongation. Note that tvisc is more than a hundred times longer than the actin turnover rate (10 to 30 s), which corresponds to local molecular dynamics. This value aligns well with the local rheological properties of cells (29). However, it does not directly correspond to the tissue-scale relaxation process that defines tissue viscosity. Nevertheless, it has been reported that the tissue relaxation process scales with the cell relaxation time for suspended cell monolayers (30). In the context of migration on a substrate, we implemented a vertex model simulation to bridge the scales between cellular rheology and collective migration modes.

Dynamical Vertex Model.

We designed a vertex model with viscosity (SI Appendix, Text S2) to demonstrate that a modulation of intercellular viscosity can account quantitatively for our observations. In our model, the force balance at each tricellular junction is expressed as follows: Fi(elastic)⏟elasticity+Fi(active)⏟activity+Fi(friction)⏟friction+Fi(viscous)⏟viscosity=0. The first 3 terms are standard for vertex models (31, 32) and correspond to: Fi(elastic) : the elastic forces accounting for the mechanical regulation of the cell shape (33–36), that are assumed to derive from a mechanical energy E : E=∑J12KA(AJ−A0)2︸​area elasticity+∑J12KP(PJ−P0)2︸​perimeter elasticity , where the two terms account for the cell area and the cell perimeter elasticities, respectively. In detail, KA and KP are the area stiffness and perimeter stiffness of cells, respectively; AJ and PJ are the area and the perimeter of the J-th cell, respectively; A0 and P0 are the preferred area and preferred perimeter, respectively. Fi(active) corresponding to the active fluctuations of the cortical tension, assumed to be a Gaussian white noise of amplitude Lambda (SI Appendix, Text S2), and Fi(friction)=−ξvi corresponding to the friction of the cells on the underlying substrates. It is applied at the vertices and independent of the contact area with the substrate.

We complemented this description by adding a viscous dissipation term accounting for cortical deformation and cytoplasmic flows, which reads:Fi(viscous)=∑jη(i,j)t(i,j)·(vj−vi)t(i,j), where t(i,j) is a unit vector oriented between the vertex i and j, which is either another vertex, in which case η(i,j)=η(s) is the viscous modulus dissipation along the cell surface (cortex), or the cell barycenter, in which case η(i,j)=η(b) is a viscous modulus, representing dissipation within the cell bulk (cytoplasm) (details in SI Appendix, Text S2).

First, we conducted simulations for the RUSH-EGFR experiment (Fig. 1 E and F), considering a down-step in intercellular viscosity on selected clusters of cells (N = 4 cells) dispersed in a cell monolayer at equilibrium (Fig. 4A and SI Appendix, Text S2). We used a set of parameters summarized in SI Appendix, Table S1, which were optimized to reproduce the experimental results quantitatively while remaining within the typical range used to describe MDCK monolayers. In the simulations, a 10-fold decrease [from η = 1.4 nN.min.μm-1 (30 a.u) to η = 0.14 nN.min.μm-1 (3 a.u)] in intercellular viscosity leads to a fourfold increase (from 2.6 ± 0.1 to 10.6 ± 3.7 μm/h; N = 100) in the junction elongation velocity (Fig. 4 B and C and Movie S7), aligning closely with the experimental values (from 2.6 ± 0.5 to 14.3 ± 1.2 μm/h) (Fig. 1F).

Fig. 4. Modeling the impact of viscosity changes on epithelial migration. (A) Simulated cellular arrangement in a vertex model with tension fluctuation, illustrating four low viscosity cells (dark blue, η = 3) within a larger population of normal viscosity cells (orange, η = 30). This models RUSH-EGFR activated cells within a large tissue of inactivated cells. (B) Temporal variation of two representative cell profiles for low viscosity (RUSH-EGFR) cells (Top) and control cells (Bottom). (C) Quantifications of the cell-cell junction remodeling velocity for control cells and low viscosity (RUSH-EGFR) cells (η = 30 or η = 3, respectively). Conf = Confluent. nCtrl = 101 cell junctions and nRUSH-EGFR = 101 cell junctions from simulations, two-tailed unpaired t test, P< 0.0001. (D) Simulated cellular arrangement in a vertex model with η = 6 (light blue) and η = 30 (orange), modeling the control and pEGFR-inhibited (Erlotinib) tissues, in the presence of cellular activity and an imposed uniform front migration speed. (E and F) Velocity (E) and, vorticity fields (F), both with flow lines (black) for the model of control and pEGFR-inhibited conditions. (G–I) Distribution in the simulated averaged cellular strain (G), velocity (displayed together with the imposed front migration speed) (H), and vorticity (I), under control and pEGFR-inhibited conditions (see Materials and Methods for averaging procedure). nCtrl, strain = 101 cells and nErlotinib, strain = 101 cells from simulations, two-tailed unpaired t test, P< 0.0001. nCtrl, velocity = 100 cells and nErlotinib, velocity = 100 cells from simulations, two-tailed unpaired t test, P< 0.0001. nCtrl, vorticity = 100 cells and nErlotinib, vorticity = 100 cells from simulations, two-tailed unpaired t test, P< 0.0001. (J) Correlative plots between the measured and advection-based predicted cellular strain for the best fit of the viscoelastic time (tvisc) under control (η = 6) and pEGFR-inhibited (η = 30) conditions. (K) Viscoelastic time (tvisc) for control (η = 6) and pEGFR-inhibited (η = 30). nCtrl = 3 strips and nErlotinib = 3 strips from simulations, two-tailed unpaired t test, P= 0.0011. (L) Table summarizing the experimental and simulation results.

Second, we conducted simulations of collective cell migration along the strips (details in SI Appendix, Text S2). We maintained the same set of parameters while introducing additional conditions: 1) we posited that the inhibition of EGFR by Erlotinib leads to intracellular viscosity equivalent to its downregulation in dense monolayers, given their comparable recruitment levels. We hence set it to η = 1.4 nN.min.μm-1 (30 a.u) and 2) we imposed the velocity of the migration front to align with experimental values of 15 μm/h (Fig. 3G). Subsequently, we tuned the intercellular viscosity of the Ctrl case to match the experimental results, finding that η = 0.28 nN.min.μm-1 (6 a.u) yielded the best quantitative predictions. Movie S7 shows a typical simulation output (Fig. 4D–F). Both experimental and simulated data underwent analysis using the same scheme. Notably, a sizeable increase in cell strain (Fig. 4G), a decrease in cell velocity (Fig. 4H), a reduction in vorticity (Fig. 4I), an expansion in spatial correlation length (SI Appendix, Fig. S10), as well as an elevation in cell shape relaxation times tvisc (Fig. 4 J and K) were observed between η= 0.28 nN.min.μm-1 (6 a.u) au (Ctrl) and η = 1.4 nN.min.μm-1 (30 a.u) (Erlotinib). The identical velocities at the front are imposed, not predicted. These quantitative findings closely matched the experimental observations (Fig. 4L).

In summary, we propose that the E-cadherin-dependent phosphorylation of EGFR fine-tunes the structure of junctional actin, thereby affecting actin dynamics. On a larger scale, it influences junctional viscosity, governing the collective modes of cell migration (Fig. 2G). This insight demonstrates that E-cadherin-dependent EGFR activity could regulate the dynamics of collective cell behavior and sheds light on the role of cellular viscous dissipation in collective cell migration, an important aspect that has been understudied.

Discussion

Over the past decade, many experimental approaches have been developed to measure mechanical tension at the cellular level (37). Its molecular origin traces back to the contractile activity of myosin microfilaments pulling on actin fibers. Active matter theory has effectively described how local contractile dipoles integrate to produce tissue-scale contractions (38). However, the notion of viscous dissipation is somehow less intuitive to apprehend, as it depends on the type of deformation (scale, amplitude, rate). A comprehensive understanding of how molecular-scale events collectively contribute to the emergence of effective viscosity on a larger scale is still lacking. Nevertheless, in this work, we endeavor to connect molecular pathways with collective flow changes.

Central to our hypothesis is the idea that dynamical changes in actin structure likely interfere with the amount of mechanical energy required to deform the junctional cortex during cell migration. Drawing from our previous studies, we propose that changes in actin turnover dynamic (measured by FRAP) serve as an informative proxy that reflects the dissipative behavior of the cortical actin (13, 21, 27). Turnover rate has been associated to the degree of activation of actin polymerizing factors and cross-linkers (39), which in turn alter the structure of the actin cortex.

EGFR phosphorylation has been demonstrated to directly or indirectly modulate actin properties based on its activation by E-cadherins (20, 40). Here, we present an integrated perspective on the role of “self-lubrication” of junctions by EGFR. It suggests a mechanism involving the phosphorylation of EGFR upon new engagement of E-cadherins at developing cell-cell contact, thereby maintaining the “deformability” of the junction and hence self-promoting easier junction rearrangement. Dysfunction in any of these mechanisms leads to junctional “rigidification” on short timescales, potentially influencing the density at which monolayer dynamical arrest occurs during morphogenesis. In this respect, the local recovery of junction elongation dynamics within a dense monolayer, particularly observed in cells expressing RUSH-EGFR, is notably striking. According to our model, local changes in junctional viscosity are sufficient to fluidize local cellular movements within a fixed cell density context. This underscores the importance of not overlooking the regulatory mechanisms through which cells dynamically modulate their viscous properties as crucial factors in regulating the fluid-to-solid transition within collective cell dynamics.

We consider the mechanism we propose here as a consistent and modelizable multiscale explanation of the role of Ecad-dependent phosphorylation of EGFR in collective cell migration. The exact details of how processes at one scale influence others will be further investigated in future work. The exact molecular pathway linking the phosphorylation of EGFR to Rac and Wave2, and ultimately to cortical structure, remains to be fully elucidated through careful analysis of molecular pathways. Molecular models [similar to those previously presented (39)] elucidating the relationship between changes in actin turnover (related to changes in polymerization rate) and modification of the rheological properties of the junctional actin network should also be derived to precisely understand this connection. It is conceivable that rapid actin dynamics could lead to low mechanical dissipation at the junctional scale and rapid elongation of junctions. Actin structure, regulated by Arp2/3 and formin, has previously been implicated in modulating cell cortical properties (41). Last, we propose here a dynamical vertex model with intercellular viscosity as a free parameter. Further refinement of the model can be implemented where the local viscosity of the junction depends on its strain rate under the control of EGFR activation. Such a model could predict second-order effects in cell motion within the monolayer, which could be experimentally validated.

Materials and Methods

Cell Culture and Reagents.

MDCK strain II cells were cultured at 37°C with 5% CO2 in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, Invitrogen). The medium was supplemented with 10% fetal bovine serum (FBS, Invitrogen), and 100 units/mL of penicillin and 100μg/mL of streptomycin (Pen-strep, Invitrogen). To investigate collective migration behaviors, we utilized stable cell lines expressing fluorescent markers or knock-out variants of MDCK cells. The following cell lines were employed: wild-type MDCK (MDCK-WT), stably transfected GFP-actin MDCK (MDCK-actin-GFP), stably transfected GFP-E-cadherin MDCK (MDCK-E-cad-GFP) (kindly provided by W. J. Nelson), histone-1-stable GFP MDCK (MDCK-H1-GFP), E-cad KO MDCK (MDCK-Ecad KO) (kindly provided by B. Ladoux, Institut Jacques Monod), and E-cad Rescue MDCK (MDCK-Ecad Res) (kindly provided by P. Kanchanawong, Mechanobiology Institute). For serum starvation experiments, cells were subjected to serum starvation by incubating them in a growth medium. This medium consisted of high-glucose DMEM lacking FBS, supplemented with 100 units/mL of penicillin and 100 μg/mL of streptomycin. To inhibit EGFR activity, Erlotinib hydrochloride (1 μM, Sigma-Aldrich) was employed.

Plasmids and Transfection.

The Str−KDELSBP−EGFP−EGFR plasmid was generously provided by Dr. David Marc Virshup’s laboratory at Duke National University of Singapore. The SH2−GRB2−tdEOS plasmid was kindly gifted by Dr. Jay T Groves’ Lab. MDCK-WT cells, with 80% confluence, were transfected with 3 μg of DNA using the Neon electroporation system (Invitrogen), following the manufacturer’s instructions. For Erk activity measurement, EKAREV-NLS expressing MDCK cells were a kind gift from Dr. Tsuyoshi Hirashima’s Laboratory at Mechanobiology Institute, NUS.

Stamp Preparation for Collective Cell Migration on Line-Patterned Strips.

Master molds featuring the desired pattern were crafted using SU8-3050 resist on silicon wafers through standard lithography techniques. The pattern employed in this study encompasses a sizable rectangular “reservoir” (approximately 5,000 × 700 μm), interconnected with 10 rectangular strips (around 3,000 × 400 μm each) (42). Subsequently, Polydimethylsiloxane (PDMS) stamps were derived from these wafers and utilized for microcontact printing. The PDMS stamps were incubated with Fibronectin (50 μg/ml, Merck) for a duration of 45 min, after which they were transferred onto a 35 mm uncoated imaging dish (Ibidi) via microcontact printing. Prior to Fibronectin stamping, the dish had been precoated with a layer of PDMS and exposed to ultraviolet light for activation. The PDMS stamps were then air-dried within a laminar hood for 10 min and delicately pressed against the dish’s bottom for 1 min. Following microcontact printing, the PDMS stamps were carefully lifted without causing any agitation. The dish bearing the Fibronectin-stamped pattern underwent additional passivation by treating it with a 2% Pluronic F127 solution (Sigma) for 1 h, aimed at preventing cells from attaching and proliferating in the unstamped areas. Subsequent to passivation, the dishes underwent thorough rinsing with phosphate-buffered saline (PBS) on three times. A PDMS block was strategically positioned atop the microcontact-printed pattern, effectively confining cells within the “reservoir” region. MDCK-H1-GFP, MDCK-Ecad KO, or MDCK-Ecad Res cells were pretreated with Mitomycin C at a concentration of 10 μg/ml (Roche) for a duration of 1 h to inhibit cell proliferation. These MDCK cells were trypsinized and strategically seeded along the periphery of the PDMS block to cover the “reservoir” area. Once the cells reached confluence on the PDMS block’s sides, the block was gently released, enabling cells to migrate along the strips. Migrating cells were subsequently subjected to specific inhibitors as indicated. The process of live imaging was executed using widefield microscopy (Olympus IX81) with a 10× objective. Throughout imaging, the dishes were maintained within a humidified environment at 37°C with 5% CO2. Both phase-contrast and fluorescent images were acquired at intervals of 4 min over a duration ranging from 12 to 24 h.

Migration around Obstacles.

The design employed for obstacle migration involves a substantial rectangular “reservoir” (approximately 5,000 × 700 μm), which is linked to 10 rectangular strips (around 3,000 × 400 μm each). Each strip features a central circle with a diameter of 200 μm. The PDMS stamps crafted from these wafers encompass 200 μm diameter circles within each strip. The subsequent preparation steps remain consistent with those outlined in the preceding section. Following contact printing and passivation, the 200 μm diameter circles exhibit nonadhesive properties, serving as obstacles during cell migration.

Single-Cell without Confinement, Single-Cell Confined to Lines, Cell Trains, and Cell Patches Migration.

For the migration of single cell lines, wafers with a pattern of 20 μm lines were utilized. PDMS stamps were crafted from these wafers and subsequently employed for microcontact printing. The preparation steps mirrored those outlined in the preceding section. Following passivation, the dishes were primed for cell seeding. MDCK-H1-GFP or MDCK-WT cells were trypsinized and their counts were determined prior to seeding. Approximately 4 to 5 × 104 MDCK cells were introduced into the imaging dish and allowed to incubate for 2 h at 37°C within a 5% CO2 incubator, facilitating full cellular spreading. The migrating cells were then subjected to the indicated inhibitors. For the migration of cell trains, the employed pattern comprises a large rectangular “reservoir” (approximately 5,000 × 700 μm), which is linked to 20 rectangular strips (approximately 3,000 × 20 μm). The preparation steps mirror those outlined in the preceding section for the creation of line-patterned strips. The migrating cells were subjected to treatment using the specified inhibitors. In the case of single-cell and cell patch migration, Fibronectin-coated dishes were utilized for direct cell seeding. A quantity of 4 to 5 × 104 MDCK cells (for single cells) and 3 to 4 × 105 MDCK cells (for cell patches) were seeded into the imaging dish and incubated at 37°C within a 5% CO2 incubator for 2 h to allow for complete cell spreading. The migrating cells were subsequently treated with the indicated inhibitors. Live imaging was conducted using widefield microscopy (Olympus IX81) with either a 10× or 20× objective. The dishes were positioned within a humidified chamber at 37°C with 5% CO2 during the imaging process. Phase-contrast and fluorescent images were captured at 10-min intervals, spanning a duration ranging from 12 to 24 h. Migration speeds of individual cells (N > 30) were tracked using either the TrackMate plugin for Image J on phase-contrast images or Imaris for fluorescent nucleus images. The speed of junction deformation at cell-cell junctions was quantified by measuring the lengths of these junctions at each time point.

Dextran Experiments.

MDCK-H1-GFP cells (for live imaging) or MDCK-WT cells (for western blotting) were seeded at a low confluence and subjected to overnight serum starvation. Subsequently, 50 μg/mL of dextran (00269, 00891, 00894, and Sigma-Aldrich) with the specified molecular weight was introduced to the cells before the execution of either western blotting or live imaging. The latter was performed using a widefield microscopy setup (Olympus IX81) equipped with a 20× objective. Cell velocity and confinement ratio were assessed using the Trackmate plugin within Fiji.

EGFR Release Experiment.

MDCK-WT cells were transfected with the RUSH plasmid Str−KDELSBP−EGFP−EGFR. Following transfection, the cells were plated onto an Ibidi imaging dish precoated with Fibronectin and placed in a complete medium at 37°C with 5% CO2. Once the cells reached confluency, overnight serum starvation was conducted. Subsequently, EGFR-GFP was liberated from the ER through the addition of 40 mM biotin. Live imaging was carried out utilizing a spinning-disc confocal microscope (Yokogawa CSU-W1) attached to a Nikon Eclipse Ti-E inverted microscope body, equipped with a 60× NA1.3 water lens. Fluorescent images were captured both prior to and subsequent to the biotin introduction, at 5-min intervals, spanning a duration of 6 h. The speed of deformation at cell-cell junctions was quantified by measuring the lengths of these junctions at each time point, both 30 min before and 30 min after the release of EGFR. The measurements were then averaged over this 30-min period.

Western Blotting.

Cells were incubated on ice with Radioimmunoprecipitation assay lysis buffer (Sigma), supplemented with protease and phosphatase inhibitor cocktails (Sigma). Subsequently, lysates underwent SDS-PAGE and were transferred onto nitrocellulose membranes. The membranes were then blocked using 5% bovin serum albumin (BSA) and 0.05% Tween 20 in Tris-buffered saline (TBS), followed by incubation with the specified primary antibodies. Detection of immune complexes was achieved using appropriate HRP-conjugated secondary antibodies (Cell Signaling Technologies) and an enhanced chemiluminescence reagent (Clarity ECL, BioRad). Protein band intensities were quantified using ImageJ Software. The primary antibodies employed were pEGFRY845 (44784G, Thermo Fisher Scientific), pEGFRY1068 (2234, Cell Signaling Technologies), pEGFRY1173 (4407, Cell Signaling Technologies), EGFR (2232, Cell Signaling Technologies), RhoA (sc418, Santa Cruz), Cdc42 (ab187643, Abcam), Rac1 (610651, BD Biosciences), and β-actin (MA515739, Thermo Fisher Scientific). To assess Rho family GTPases activity, pulldown assay using GST-PBD and GST-RBD were performed on cell lysates as described previously (43).

Immunofluorescence.

After a collective cell migration period of 12 to 24 h, MDCK cells were fixed using prewarmed 4% paraformaldehyde in PBS at 37°C for 15 min. Subsequently, they were permeabilized with 0.2% Triton X-100 in TBS for 30 min at room temperature. Samples were then blocked using 1% BSA in TBS for 1 h. The cells were incubated overnight at 4°C with primary antibodies: rabbit anti-phospho-EGFR (Y845) polyclonal antibody (44-784G, Thermo Fisher Scientific, diluted 1:200); Purified Mouse anti-E-Cadherin monoclonal antibody (Clone 36) (610181, BD Transduction Laboratories); WAVE2 antibody (H-110) (sc-33548, Santa Cruz); Arp3 antibody (A5979, Sigma-Aldrich); Phospho-Myosin Light Chain 2 (Ser19) antibody (3,671, Cell Signaling Technologies, diluted 1:50) according to the manufacturer’s instructions. Following three washes with PBS at 10-min intervals, cells were incubated with secondary antibodies (anti-mouse Alexa555-conjugated secondary antibody and anti-Rabbit Alexa647-conjugated secondary antibody, both from Thermo Fisher Scientific) along with Alexa405-coupled phalloidin (Invitrogen) in darkness at room temperature for 1 h. Subsequently, cells were rinsed with PBS and prepared for imaging acquisition. Confocal images were captured in 3D stacks using a spinning-disc confocal microscope (Yokogawa CSU-W1) mounted on a Nikon Eclipse Ti-E inverted microscope body, equipped with a 100× NA1.5 or 60× NA1.3 lens.

Real-Time Quantitative PCR (qPCR).

The entire RNA was isolated from a single well of a 6-well plate using the RNeasy Plus Micro Kit (QIAGEN), following the guidelines provided by the manufacturer. A quantity of 450 ng of total RNA was employed to generate the cDNA utilizing the cDNA synthesis kit (SensiFAST). For qPCR analysis, the FastStart Universal SYBR Green Master (ROX) mix was employed on a CFX96 Touch Real-Time PCR detection system (Bio-Rad). Glycéraldéhyde-3-phosphate déshydrogénase was employed as the internal reference gene.

Live Cell Spreading on E-Cadherin-Coated Surface.

Disks with a diameter of 25 μm were photopatterned onto glass coverslips, following the previously described method (44). These patterns were then coated overnight at 4°C with a recombinant E-cadherin Fc Tag protein (10204, Sino Biological) at a concentration of 20 μg/mL, and subsequently gently washed with PBS. The patterned coverslips were mounted within imaging chambers. MDCK-WT cells were transfected with the SH2-GRB2-TdEos plasmid, and after selection with 500 μg/mL Geneticin (10131035, Thermo Fisher Scientific), a stable cell line was established following sorting using an SH800S cell sorter (Sony). MDCK-SH2-GRB2-TdEos or MDCK-Ecad-GFP cells were serum-starved overnight, then seeded onto Ecad-Fc patterns, and allowed to spread for a period of 2 h before initiating TIRF time-lapse imaging. This imaging was carried out using a Motorized TIRF Module (Nikon) integrated with a Nikon Eclipse Ti-E inverted microscope body.

FRAP.

Bleaching was performed on the actin of apical cell-cell junctions for FRAP measurements. The cortical actin recovery time (thalf) was calculated by fitting the following exponential function to the recovery curves: I(t)−I(0)=(I∞−I(0))(1−exp(−ln2thalft)).

Laser Ablation.

An initial image was acquired to determine the precise laser spots. In the case of MDCK-actin-GFP cells, the laser spots were positioned at the apical cell-cell junctions. The preacquisition process involved capturing five images at 1-s intervals. During the acquisition phase, a laser power of 60% was utilized, with a duration of approximately 1 to 2 s, targeting the predetermined regions. Subsequently, the postacquisition stage encompassed the capture of 100 images at 1-s intervals. To quantify the recoil velocity, the positions of two nodes within the defined junctions were manually tracked using ImageJ software. Following the laser ablation, the temporal evolution of the distance between these two nodes was fitted using a single exponential function. While the double exponential function is commonly employed in other studies, it proved unsuitable for our analysis. In our investigation, the recoil velocity exhibited a gradual nature, and fitting it with a double exponential function yielded unrealistically high speeds. Consequently, we opted to employ a single exponential function for a more appropriate representation.

AFM.

AFM experiments were conducted using a Nanowizard IV BioAFM system manufactured by JPK Instruments, Germany. Indentations were performed on randomly chosen cells at the junctional regions. This was achieved using a cantilever (with a nominal k value of 0.03 N/m, provided by Novascan Technologies, Inc.) with an attached polystyrene bead (4.5 μm diameter) at its tip. The applied force was set at 3 nN and a loading rate of 5 μm/s was used. For each experimental condition, measurements were taken from over 30 cells across three independent trials and subsequently averaged. Young’s modulus values were used to quantitatively describe the cellular stiffness. These values were calculated using the JPK Data Processing Software (JPK Instruments), which incorporates Hertz’s contact model tailored to spherical indenters (with a diameter of 4.5 μm and a Poisson’s ratio of 0.5). Energy dissipation, representing the heat-based loss of mechanical energy during each indentation cycle by the AFM tip, was determined by assessing the enclosed area between the approach and retraction curves (hysteresis). This phenomenon is largely attributed to frictional and viscous damping within the cell structure at this low speed (45).

Erk Activity Measurement.

30,000 EKAREV-NLS expressing cells were seeded into a well of culture-inserts 2 wells (81,176, ibidi) and allowed to spread for 8 h. Simultaneously, the insert was then removed and cells were serum starved overnight. Time-lapse FRET images were obtained using a Nikon AX point scanning confocal microscope mounted on a Ti-2 Nikon inverted body. To represent the FRET efficiency, FRET/CFP ratio images were generated after the background was subtracted from the original images in the CFP and FRET channel using a matlab code kindly provided by Tsuyoshi Hirashima’s Laboratory at Mechanobiology Institute, NUS. To quantify FRET Ratio for each cells at each timepoints, the Fiji Trackmate plugin was applied to the CFP channel to track each cell position overtime.

Segmentation.

We used Cellpose (46) for the cell segmentation. We performed an erosion with a 3 × 3 square kernel to each mask to limit the occurrence of gaps between cells; we disregard objects with areas lower than 20 pixels. We define the cell inertia tensor, with a constant linear weight density along the segmented cell boundaries, i.e. with xx component Ixx=∫∫(x−x¯)2dxdy, where (x¯,y¯) is the position of the cell barycenter. We call average shape tensor field the spatially and temporally averaged inertia matrix over all cells within 30 × 30 pixel-large boxes (corresponding to approx. 10 cells within each), regularly spaced on a spatial grid. The strain field is defined as ϵ=log(λ1/λ2)2, where λ1 (resp. λ2) is the maximum (resp. minimum) eigenvalue of the average shape tensor. For the cell tracking, we used bTrack (47).

Simulation of EGFR Release Experiments.

In our EGFR release experiments, cells under investigation are located in the bulk of the tissue, far away from the boundary. In this case, we observe fluctuations in cell edge length but no obvious cell motions; see Fig. 1E. To mimic such fluctuations in cell length, we here consider active fluctuations of intercellular tension, Λij(act), at each cell-cell interface ij. These fluctuations contribute to an active force at each vertex, Fi(active)=∑j∈neighborΛij(act)tij, where the summation is over all vertices that connect to the vertex i. We assume such tension fluctuations satisfy an Ornstein–Uhlenbeck stochastic dynamic with time correlation (48), dΛij(act)dt=−Λij(act)τσ+ζij(t), where τσ is the relaxation time of the active tension and ζij(t) are independent Gaussian white noises, satisfying ⟨ζij(t)⟩=0 and ⟨ζij(t)ζkl(t′)⟩=Δσ2δikδjlδ(t−t′) with Δσ being the fluctuation intensity.

We simulated a cell sheet consisting of N = 100 cells in a square box of size L=NA0, using periodic boundary conditions; see SI Appendix, Fig. S7A. We initialize our simulations from a random Voronoi cell pattern and let the system relax toward a dynamic steady state where the cell elongation parameter and cell motion velocity approach a steady plateau (35). To model the effect of the light activation of EGFR and the possibility of a subsequent viscosity modulation, we then randomly selected a small group of four cells in contact and decreased the bulk viscosity ηJ(b) of those four cells, from the default value (with ηJ(b)(t<0)=η(CTL)) to a lower value (with ηJ(b)(t>0)=η(EGFR+)); see SI Appendix, Fig. S7B. Further, the viscosity ηij(s) along the cell-cell interface between the vertices i and j is assumed to be the average viscosity of the two contacting cells (indexed by J and K) as ηij(s)=ηJ(b)+ηK(b)2. We compare the junction remodeling velocity, l˙, before and after the drop in viscosity. We find that the ratio of the junction remodeling velocity l˙after / l˙before increases with the ratio of the cell viscosity decrease, η(CTL)/η(EGFR+) (SI Appendix, Fig. S8). In particular, the data of η(CTL)/η(EGFR+)=10 agree with our experiments (SI Appendix, Fig. S8). We provide the default parameter values for such simulations in SI Appendix, Table S1.

Simulation of Collective Cell Migration Experiments.

To simulate the collective cell migration experiments, we now turn to collective cells initially confined in a rectangular geometry of size Lx×Ly with Lx=18A0 and Ly=100A0, which contains around Ncell≈1,800 cells; see SI Appendix, Fig. S9. As in the RUSH-EGFR model simulation, we first initialize the simulations using a Voronoi tessellation. We then let the system relax, keeping the bottom boundary fixed and simulating the cell sheet in a confined rectangular geometry to reach a steady state. We next relax the bottom boundary and run the simulations to reach a dynamic steady state. At the left, top, and right borders, cells are allowed to slip along but adhere to the boundaries; while at the bottom boundary, we imposed vertices to move at a constant speed v→y (bottom boundary vertices) =−Vfront, v→x (bottom boundary vertices) =0. The value of Vfront is fixed at a comparable value to the one measured in experiments. Specifically, we set Vfront=15μm/h in simulations. With the sole migration at the edge (described above), we were not able to recapitulate the formation of vortices similar to the one observed in experiments.

To recapitulate the formation of vortices similar to the one observed in experiments, we turned to a Vicsek-like model of cell motility (36). Within such model, we associate each cell with an active force FJ(act)=T0(cosθJ,sinθJ) of magnitude T0 and direction θJ; such model mimics the cell motility induced by cell protrusions (36), with the polar direction θJ of each cell J evolving according to the equation:dθJdt=1nJ∑K∈neighbor{μLAsin[θK(vel)−θJ]+μCILsin(αJ,K−θJ)}+ζJ(t),

where μLA and μCIL represent the strengths of local alignment interaction and contact inhibition of locomotion, respectively; θK(vel)=arg(vK) refers to the argument angle of the velocity vK of cell K; αJ,K=arg(rJ−rK) denotes the argument angle of the vector pointing from cell K to cell J; ζJ(t) is a white-noise process with zero mean and variance 2Dr. For the cells at the free boundary, we constrain their polar active force direction θJ to orient normally to the free boundary and toward the free space.

To mimic cell flows from the top boundary (bulk region of MDCK cell sheet), we allow cell divisions on a top region which is within a distance d<5 cell length to the top boundary; see SI Appendix, Fig. S9. We perform cell divisions once cells within such a region exceed an area threshold Adiv=1.5A0=486μm2. We provide the default parameter values for such simulations in SI Appendix, Table S2. We are interested in and examine the collective cell dynamics in a region near the moving front (within a distance of ∼30 cell length to the moving front). Note that to reduce the artificial effect of the top boundary condition on collective cell migration dynamics in a region near the moving front, we have set a sufficiently large scale of the simulated cell monolayer in the vertical direction, i.e., Ly∼100 cell length.

Data Display and Statistics.

Prism (GraphPad Software) and Matlab (Math Works) were used for data analysis and graph plotting. Graphs were mounted using Adobe Illustrator. ANOVA test and paired or unpaired Student’s t test were carried out to analyze the significant difference levels.

Supplementary Material

Appendix 01 (PDF)

Movie S1. Cell-cell reorganization in representative patches of MDCK-H1-GFP cells under control and pEGFR-inhibited (Erlotinib at 1μM) conditions recorded at 12 frames/hour. Scale Bar: 100μm.

Movie S2. Cell patches migration of MDCK-H1-GFP cells with or without dextran addition recorded at 12 frames/hour. Scale Bar: 100μm.

Movie S3. MDCK-WT monolayers with mosaic expression of RUSH-EGFR-GFP. Junction elongation before and after addition of biotin and subsequent release of EGFR from the endoplasmic reticulum under control and pEGFR-inhibited (Erlotinib at 1μM) conditions recorded at 12 frames/hour. Scale Bar: 20μm.

Movie S4. Cells encircling obstacles and in bulk regions under control and pEGFR-inhibited (Erlotinib at 1μM) conditions recorded at 15 frames/hour. Scale Bar: 50μm.

Movie S5. Cells migrating on 400 μm width line strips under control and pEGFR-inhibited (Erlotinib at 1μM) conditions recorded at 15 frames/hour. Scale Bar: 100μm.

Movie S6. The vorticity of the collective flow for the migrating cells under control and pEGFR-inhibited (Erlotinib at 1μM) conditions recorded at 15 frames/hour.

Movie S7. Vertex-model simulations under control (η = 6) and pEGFR-inhibited (Erlotinib at 1μM) (η = 30) conditions. Other parameters see Table S2.

Movie S8.

V.V and M.S acknowledge support from Ministry of Education grant MOE2016-T3-1-002, NRF grant NRFI2018-07, and Seed funding from Mechanobiology Institute. J.-F.R. is hosted at the Laboratoire Adhésion Inflammation. The project leading to this publication has received funding from France 2030, the French Government program managed by the French National Research Agency (ANR-16-CONV-0001), and from Excellence Initiative of Aix-Marseille University—A*MIDEX. J.-F.R. is also funded by ANR-20-CE30-0023.

Author contributions

C.F., F.D., M.S., J.-F.R., S.T., and V.V. designed research; C.F., F.D., H.R., and V.V. performed research; C.F., F.D., S.-Z.L., M.K., A.A., H.T.O., N.M.H.B., S.W.P., T.H., J.-F.R., S.T., and V.V. contributed new reagents/analytic tools; C.F., F.D., S.-Z.L., M.K., S.T., and V.V. analyzed data; and C.F., F.D., S.-Z.L., J.-F.R., and V.V. wrote the paper.

Competing interests

The authors declare no competing interest.

Data, Materials, and Software Availability

All study data are included in the article and/or supporting information.

Supporting Information

This article is a PNAS Direct Submission.
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