Spatiotemporal coupling of caveolae mechanosensing and RhoA-GEFs regulates cell polarity and directional migration

Domain: physics
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Migrating cells dynamically adapt their morphogenetic programs in response to microenvironmental changes, requiring coordinated spatiotemporal integration of mechanical and biochemical signals. The plasma membrane, through membrane tension and actin dynamics modulation, is essential for cell motility. Caveolae, small plasma membrane invaginations, act as mechanosensors to buffer tension changes under mechanical stress. Recent evidence suggests a role for caveolae in cell migration. Here, we demonstrate that breast cancer cells exhibit a front-rear asymmetry in caveolae and caveolin-1 scaffolds, which is regulated by membrane tension and is crucial for persistent migration and cell directionality. RhoA-driven cell contraction relies on the spatiotemporally coordinated assembly of caveolae and recruitment of RhoA-GEFs at the cell rear. These results are supported by a physical model establishing a feedback loop between local membrane tension and contractility, through caveolae formation and disruption. Our findings underscore the importance of caveolae mechanosensing in regulating RhoA activation and guiding cell migration.

Introduction
Cell migration enables effective navigation through complex environments and is crucial in various physiological processes, such as gastrulation during development, immune response, tissue homeostasis, and wound healing1,2. In pathological conditions, such as cancer, cell migration, and invasion play an essential function during the metastatic process, contributing to tumor progression. Recent studies indicate that the biochemical and mechanical microenvironment of solid tumors play an important role in the control of their progression3. The cell surface, comprising the plasma membrane and the dynamic underlying actin cortex, is a major mechanobiological actor4,5. Migrating cells are polarized and transiently under go cyclic phases of protrusions at the front and contractions at the rear. The plasma membrane is designed to respond to changes in mechanical properties by enabling transformations in shape, geometry, and specialized functions such as the regulation of cell polarity and motility5,6. However, the mechanistic link between mechanosensing and polarization dynamics of migrating cells remains poorly understood.

Results
Caveolae front-rear asymmetry drives directional reorientation of migrating cells
Asymmetric allocalization of caveolae, with fewer at the cell front and more at the cell rear, has been observed in polarized migrating cells21–24. To investigate the role of caveolae mechanosensing during cellular symmetry breaking and directional orientation, we first analyzed single cell migration in various experimental setups. Triple-negative breast cancer Hs578t cells, genome-edited to express Cav1-RFP (Cav1-RFP Hs578t), displayed an asymmetrical distribution of Cav1. The Cav1 signal was significantly lower in the front (F) compared to the rear (R). Caveolae front-rear asymmetry was consistently observed during cell migration, with arrows indicating direction of migration (Fig. 1a and Supplementary Fig. 1a). In contrast, the membrane-anchored CAAX motif used as a negative control for polarization remained uniformly distributed along the plasma membrane during migration (Supplementary Fig. 1d). We stained cells for cavin1 to visualize bona fide caveolae and observed a similar front-rear asymmetry, with higher accumulation of Cav1 and cavin1 at the rear of migrating cells (Supplementary Fig. 1b and Supplementary Movie 1). Knockdown of cavin1 (siCavin1; cells retaining only non-caveolar Cav1) disrupted Cav1 front-rear asymmetry (Fig. 1a and Supplementary Fig. 1c) and led to a 53% reduction in migration speed and a 23% decrease in directionality (Fig. 1b, c). Knockdown of Cav1 (siCav1; cells lacking both caveolae and non-caveolar Cav1) resulted in 60% decrease in migration speed and a 48% reduction in directionality (Fig. 1b, c). These results confirm that caveolae front-rear asymmetry is essential for efficient and directed cell migration.

We further examined the relationship between caveolae asymmetry and cell directionality. We monitored changes in caveolae distribution in cells forced to migrate on 1D line micropatterns (Fig. 1d and Supplementary Movie 2), randomly migrating on coverslips in 2D (Fig. 1e), and within 3D collagen gels (Fig. 1f and Supplementary Movie 3). When caveolae asymmetry was lost, as evidenced by Cav1 redistribution (Fig. 1d, f), cell migration stopped. Symmetry breaking was re-established by caveolae, which by forming a new rear allowed cell migration to resume in the opposite direction (Fig. 1d–f; red arrows). We observed a similar process in Cav1-RFP MDA-MB-231 cells that were forced to migrate and change their direction in Z-shaped micropatterns (Fig. 1h and Supplementary Movie 4). When cells changed direction, the loss of caveolae front-rear asymmetry was the first observable event, resulting in cessation of migration. The front-rear asymmetry was subsequently reestablished before the change of directionality occurred. These results confirm that caveolae front-rear asymmetry is essential for directional reorientation. This conclusion is quantitatively supported by kymographs of migrating cells (bottom panel Fig. 1d, f and Supplementary Fig. 1e, f), where white arrows indicate the initial direction of migration and red arrows indicate the reoriented direction. Additionally, we observed that some cells, following the initial establishment of caveolae front-rear asymmetry, assembled two distinct bipolar pools of caveolae at opposing sides over time (Fig. 1g). When these caveolae pools became symmetrically distributed, the cells failed to migrate (kymograph Fig. 1g and Supplementary Fig. 1g), further underscoring the necessity of caveolae front-rear asymmetry for efficient and persistent cell migration.

Membrane tension regulates caveolae front-rear asymmetry and RhoA activation in migrating cells
Caveolae act as mechanosensors and serve as membrane reservoirs that flatten in response to elevated membrane tension, thereby protecting the plasma membrane from rupture18,25. In addition, caveolae function as mechanotransducers, as their mechanical disassembly is associated with the regulation of signaling events and gene transcription19,20. To investigate the role of caveolae mechanosensing in cell migration, we assessed membrane tension in Hs578t cells using fluorescence lifetime imaging microscopy (FLIM) with the Flipper-TR probe26. This probe intercalates into membranes and reports on membrane tension by measuring lipid packing and order—higher fluorescence lifetimes correspond to increased membrane tension. Under iso-osmotic conditions, Flipper-TR lifetime measurements revealed a front-rear gradient of membrane tension in migrating Hs578t cells, with lower tension at the cell rear and higher tension at the front (Fig. 2a–d), consistent with previous reports22,27–29. To perturb this gradient, we applied an acute hypo-osmotic shock (30 mOsm) to Cav1-RFP Hs578t cells, inducing a global increase in membrane tension. Under these conditions, Flipper-TR lifetimes indicated a uniform membrane tension across the cell, effectively abolishing the front-rear gradient (Fig. 2e–g). This was accompanied by a selective decrease in Cav1 intensity at the rear, while the front remained unaffected (Fig. 3a, b and Supplementary Movie 5).

To locally modulate membrane tension, we used an optogenetic approach to activate PI3 kinase (PI3K) at the cell rear in Cav1-RFP Hs578t cells expressing opto-PI3K (Fig. 2h, I; ref. 28.). Opto-PI3K activation of at the cell rear triggered Rac1-mediated filopodia formation (cyan line) and an increase in cell area within the opto-ROI (gray line), consistent with localized membrane tension increase. This was followed by a decrease in Cav1 intensity (orange line), indicative of caveolae disassembly. The loss of Cav1 asymmetry coincided with an immediate arrest of cell migration. Upon return to isotonic conditions (300 mOsm), membrane tension relaxed, Cav1 levels at the rear were restored, front-rear polarity re-established, and cell migration resumed (Fig. 3a, b and Supplementary Fig. 2b). Conversely, a global decrease in membrane tension induced by hyper-osmotic shock (850 mOsm, Hyper) increased intensity of Cav1 at both front and rear (Fig. 3c, d). However, this enhanced caveolae asymmetry did not accelerate migration, suggesting this process becomes limited at lower membrane tension and/or higher caveolae density (Supplementary Fig. 2c). Finally, using photoconvertible Cav1-Dendra2 expressed in WT-Hs578t cells, we observed that caveolae reassembled at the initial rear during the recovery phase (white arrowheads, bottom panel), ruling out caveolae endocytosis or trafficking as the mechanism for re-establishing caveolae front-rear asymmetry (Supplementary Fig. 2a). Together, these results demonstrate that caveolae front-rear asymmetry is regulated by membrane tension and plays a crucial role in directional cell migration.

Cell migration depends on a highly dynamic and coordinated biochemical cycle involving the Rho GTPase subfamily. Specifically, Rac1 and Cdc42 are localized at the leading edge of migrating cells, where they drive processes such as lamellipodia and filopodia formation. In contrast, RhoA regulates cytoskeletal contraction and focal adhesion turnover at the rear of the cell30,31. Caveolae and caveolin-1 have been implicated in RhoA-mediated contractility, as well as in cell migration and invasion15,16,32,33. We investigated the potential correlation between caveolae front-rear asymmetry and the levels and localization of active RhoA (RhoA-GTP) in migrating cells. Cav1-KO Hs578t cells exhibited an 80% reduction in overall levels of GTP-bound RhoA, while cavin1-KO Hs578t cells showed a 35% decrease, suggesting that the presence of caveolae is essential for the global activation of RhoA (Supplementary Fig. 3a). Immunofluorescence analysis further confirmed a significant decrease in RhoA-GTP levels in Cav1-KO and cavin1-KO Hs578t cells (Supplementary Fig. 3c). We monitored the dynamics of active RhoA in migrating WT and KO Hs578t cells by transiently expressing HaloTag-3xrGBD (RhoA-bio), a biosensor for active RhoA34,35 (Supplementary Fig. 3b). White arrows indicate localized sites of active RhoA associated with cell contraction (Supplementary Fig. 3d and Supplementary Movie 6). Notably, higher levels were localized at the rear of WT-Hs578t cell, establishing a front-rear polarity for active RhoA. In contrast, active RhoA polarization was absent in cavin1-KO and Cav1-KO cells but was restored upon expression of wild-type Cav1 in Cav1-KO cells (Fig. 3e, f). These findings confirm that bona fide caveolae and their front-rear polarity are essential for maintaining the polarity of active RhoA in migrating cells.

We further investigated whether mechanical stress affects the polarity of active RhoA. In WT-Hs578t cells, a global increase in membrane tension induced by hypo-osmotic shock led to a 16% reduction in active RhoA levels at the rear, with no significant change at the front (first panel, Fig. 3g, h). In Cav1-KO cells, the levels of active RhoA were insensitive to mechanical stress at either the front or rear (middle panel, Fig. 3g, h). Remarkably, in Cav1-KO cells re-expressing wild-type Cav1, the sensitivity of rear active RhoA to global membrane tension increase was restored at the rear (12% decrease at the rear, with no effect at the front), similar to WT cells (last panel, Fig. 3g, h). Analysis of Cav1 intensity under each condition, revealed a strict correlation between active RhoA and Cav1 levels at both the rear and the front of the cell (Fig. 3i). These results suggest that caveolae mechanosensing plays a crucial role in regulating active RhoA polarity in migrating cells.

Active RhoA preferentially interacts with Cav1 scaffolds
Co-immunoprecipitation, proximity ligation assay (PLA), and colocalization analyses revealed that Cav1 and active RhoA interact similarly in WT-Hs578t and cavin1-KO cells (Fig. 4a–d, f and Supplementary Fig. 4d). Co-immunoprecipitation experiments revealed an increased interaction between Cav1 and active RhoA under hypo-osmotic shock (Fig. 4e). Additionally, PLA experiments confirmed this effect with an increased proximity between active RhoA and Cav1 (Fig. 4g). These findings strongly suggest that active RhoA is more likely to interact with Cav1 when caveolae have been disassembled into scaffolds. Indeed, a higher level of colocalization between active RhoA and Cav1 was observed in cavin1-KO cells that present only non-caveolar Cav1, albeit at a lower expression level (Fig. 4a and Supplementary Fig. 4d).

The interaction of Cav1 with downstream effectors is often mediated by a conserved 20-amino acid sequence known as the scaffolding domain (CSD)12. To determine whether the Cav1-CSD is necessary for the interaction with active RhoA, we transiently expressed comparable levels of wt-Cav1RFP (wtCav1) and F92A/V94A CSD Cav1RFP (F92A/V94A CSD Cav1), a mutated form with an inactive CSD, in Cav1-KO Hs578t cells (Supplementary Fig. 4a). Co-immunoprecipitation, PLA, and colocalization analyses demonstrated that the interaction between active RhoA and both wt Cav1 and F92A/V94A CSD Cav1 remained unchanged (Supplementary Fig. 4b–d). This indicates that active RhoA does not interact with Cav1 through the CSD, in line with previous studies36.

Non-caveolar Cav1 assemblies, organized into scaffolds of defined sizes, have been characterized using stochastic optical reconstruction microscopy (STORM) and single-molecule localization microscopy (SMLM) network analysis11. The smallest class, termed S1A scaffolds, correspond to the minimal 11-Cav1 protomer-8S complex, as resolved by cryo-electron microscopy9. S1B scaffolds represent dimers of S1A, while S2 scaffolds correspond to larger hemispherical structures formed by the association of several S1A scaffolds11. To examine the spatial contributions of caveolae and Cav1 scaffolds during cell migration, we applied the same computational network modeling of STORM Cav1 localizations in Hs578t cells. This analysis revealed that budded caveolae were predominantly localized at the cell rear, while Cav1 scaffolds, primarily from the S1A population, were concentrated at the cell front (Fig. 5a, b). Upon hypo-osmotic shock, this front-rear asymmetry was markedly perturbed: the caveolae pool at the rear decreased by 24%, while S1A scaffold at the front increased by 23%, relative to cells at rest (Supplementary Fig. 5a, b). These changes were reversed upon restoration of isotonic conditions, indicating a tension-dependent dynamic redistribution of Cav1 assemblies (Supplementary Fig. 5c, d). Importantly, the reduction in caveolae at the rear under elevated tension (−24%) correlated with a decrease in active RhoA in the same region (−16%) (Fig. 3g, h), suggesting a functional link between caveolae disassembly and RhoA signaling. In contrast, cavin1-KO Hs578t cells, which lack budded caveolae, exhibited only S1A and S1B scaffolds, with both structures symmetrically distributed along the cell axis (Fig. 5c, d), further supporting the role of caveolae asymmetry in regulating directional migration.

Altogether, these data suggest that active RhoA is more likely to interact with Cav1 scaffolds than with bona fide caveolae. We further establish this point by applying hypo-osmotic shock to WT-Hs578t, as this condition results in a significant conversion of caveolae into Cav1 scaffolds at the rear of the cell, while it has no significant effect on the amount of Cav1 scaffolds present at the front (Supplementary Fig. 5a, b). Cav1 and active RhoA showed a significantly higher level of colocalization at the rear, with an 87% increase, and a non-significant increase at the front (Supplementary Fig. 6a). Colocalization levels returned to initial levels when membrane tension homeostasis was restored (Supplementary Fig. 6a). Taken together, these data further support the role of caveolae mechanosensing in regulating RhoA activity.

Caveolae, but not Cav1 scaffolds, are required for efficient actomyosin contractility
During migration, the formation of stable lamellipodia at the cell’s leading edge front protrusions involves cycles of actin polymerization and depolymerization37. In contrast, RhoA-mediated actomyosin contraction generates the tension necessary to detach the cell rear, facilitating forward movement38. To better understand the spatiotemporal regulation of actomyosin contractility by RhoA, we performed a morphodynamic analysis of individual migrating cells during edge contraction to correlate caveolae and Cav1 scaffolds functions with cell contractility. We performed time-lapse imaging of WT-Hs578t, cavin1-KO, Cav1-KO, and Cav1-KO+wtCav1 cells expressing the active RhoA biosensor (Fig. 5e). Morphodynamic maps of the cell contour allowed us to visualize contraction activity and active RhoA over time39,40. These maps were constructed by measuring cell contour displacement between two consecutive time points, with displacement color-coded: blue indicating contraction and red indicating protrusion (Fig. 5f, Methods). Contraction events associated with active RhoA (white circles and blue spots) were analyzed across all conditions (Fig. 5f). The data show that caveolae significantly accelerate RhoA-mediated actomyosin contractility with 2.4 events per hour, compared to Cav1 scaffolds in cavin1-KO cells, which exhibit 0.9 events per hour (Fig. 5g). Furthermore, re-expression of wild-type Cav1 in Cav1-KO cells restored the contraction rate from 0.4 events per hour to 1.9 events per hour, confirming that budded caveolae are essential for efficient RhoA-mediated actomyosin contractility. Our data demonstrate that while active RhoA preferentially interacts with Cav1 scaffolds (Fig. 4), only budded caveolae significantly enhance actomyosin contractility (Fig. 5e–g).

Spatiotemporal regulation of ARHGEF25 and ARHGEF11 by caveolae
Guanine nucleotide exchange factors (GEF) act as upstream activators of RhoA GTPase31. To identify potential mediators of caveolae-regulated actomyosin contractility, we investigated the interaction between RhoA-specific GEFs and caveolae at contracting membrane sites in migrating cells. We selectively recruited RhoA-GEFs locally through optogenetics. Among the ~ 14 RhoA-specific GEFs involved in migration that do not affect other Rho family members such as Rac1 and cdc4241, we selected ARHGEF25 and ARHGEF11 for their established roles in RhoA activation and promotion of cell contraction35,42.

Optogenetic stimulation of ARHGEF25 and ARHGEF11 was applied at the front of migrating cells, enabling visualization and quantitative analysis of Cav1 and GEF temporal dynamics at the newly formed rear of the cell. This setup allowed us to track the cellular changes preceding directional re-orientation. We measured ARHGEF25 intensity within the opto-ROI prior to photoactivation including only cells with comparable expression levels (Supplementary Fig. 7e, f). Upon blue-light photoactivation, all conditions—WT-Hs578t, cavin1-KO, cavin1-KO + cavin1, Cav1-KO, and Cav1-KO+wtCav1—displayed the characteristic contractile behavior associated with RhoA-induced actomyosin activity. Notably, WT-Hs578t cells exhibited a Cav1 intensity peak that coincided with ARHGEF25 recruitment within the opto-ROI (Fig. 6a and Supplementary Movie 7). In contrast, cells transfected with the control plasmid containing only the light-sensitive component (Lck-mTurquoise2-iLID) showed no activation of RhoA and exhibited reduced membrane movement throughout the experiment (Supplementary Fig. 7a, b), ruling out the effect of blue light alone. Additional random-ROIs, positioned away from the opto-ROI in all cells, confirmed the specificity of the optogenetic tool (Supplementary Fig. 7c, d).

Within the opto-ROI, the contracting membrane decreased its area by approximately 20% within ~ 6 minutes (blue line, Fig. 6a). Cavin1-KO cells, lacking a Cav1 signal peak linked with ARHGEF25, required over 50 minutes to contract (Fig. 6b and Supplementary Movie 8). However, cavin1-KO cells re-expressing cavin1 exhibited a restored Cav1 peak concurrent with ARHGEF25 recruitment and achieved 20% decrease in membrane area in ~ 8 minutes (Fig. 6c). In contrast, Cav1-KO cells contracted at a significantly slow rate, taking over 70 minutes, despite effective ARHGEF25 recruitment via opto-activation (Fig. 6d). Re-expression of wt Cav1 in Cav1-KO cells restored the Cav1 intensity peak coupled with ARFGEF25 (Fig. 6e). Cav1 intensity changes within the opto-ROI and membrane area contraction dynamics were compared across conditions Supplementary Fig. 7g, h).

To further investigate the spatiotemporal interaction between ARHGEF25 and Cav1, we performed measurements on randomly migrating cells. We generated morphodynamic maps of cell contours and analyzed individual contraction events to quantify ARHGEF25 and Cav1 intensities over time. Consistent with the optogenetic experiments, we found that in WT-Hs578t cells, ARHGEF25 and Cav1 were spatiotemporally coupled, with synchronous recruitment to contracting membrane regions. In contrast, cavin1-KO Hs578t cells showed no increase of Cav1 recruitment (Fig. 6i), despite ARHGEF25 membrane levels being similar to those in WT cells (Fig. 6f, h). Notably, ARHGEF25 displayed a polarized distribution in WT cells, with higher levels at the cell rear, whereas cavin1-KO cells displayed no front-rear polarity (Fig. 6g). We performed a parallel experiment using an optogenetic approach to recruit another RhoA-specific GEF, ARHGEF11, in WT, cavin1-KO, and Cav1-KO Hs578t cells (Supplementary Fig. 8a, b). In WT cells, Cav1 intensity increased prior to contraction, whereas no such increase was observed in cavin1-KO cells. Moreover, contraction kinetics were impaired: contraction times were two- and three-fold slower in cavin1-KO and Cav1-KO cells, respectively, compared to WT cells (Supplementary Fig. 8c, d). As a negative control, we optogenetically activated Tiam1, a Rac1-specific GEF, in WT-Hs578t cells. Photoactivation triggered local recruitment of Tiam1 and a corresponding increase in cell area within the opto-ROI, consistent with Rac1-mediated protrusion. However, Cav1 intensity remained unchanged during this response, indicating that Cav1 is not spatiotemporally coupled with Tiam1 (Supplementary Fig. 8e–f). Together, these results demonstrate that caveolae are selectively and dynamically coupled to specific RhoA-GEFs, such as ARHGEF25 and ARHGEF11, enabling rapid responses to cues for directional change and promoting efficient contraction. In contrast, Cav1 scaffolds alone are not coupled to RhoA-GEFs, resulting in delayed or impaired contractile responses and directional migration.

Caveolae assembly is a prerequisite for RhoA activation at contraction sites
Finally, we examined the temporal coupling between active RhoA and Cav1 at the membrane contraction edge of migrating cells. Contraction events were identified from morphodynamic maps as previously described. Our temporal intensity correlation analysis revealed that Cav1 intensity displayed a positive peak approximately 2 minutes before the appearance of active RhoA at the contracting sites in WT-Hs578t cells (Fig. 7a, b and Supplementary Movie 9). This Cav1 peak was absent at the contracting sites of cavin1-KO cells, which showed a slower contraction rate, as evidenced by the contracting area over time (Fig. 7c, d). Notably, Cav1-KO cells re-expressing wt Cav1 exhibited a positive Cav1 intensity peak roughly 2 minutes before active RhoA appeared at the contraction sites (Fig. 7e, f). The contractile membrane area dynamics over time were comparable between WT and Cav1-KO + wtCav1 but were significantly delayed in cavin1-KO cells (Fig. 7b–d, f). Overall, these results confirm that caveolae play a critical role in sensing and executing migratory signals by accelerating RhoA activation through the recruitment of RhoA-GEFs at contracting sites. In contrast, Cav1 scaffolds are unable to recruit RhoA-GEFs, which significantly delays the contraction process.

A physical model explains cell persistence through feedback between local membrane tension and contractility
To understand how the local regulation of contractility by membrane tension influences the persistence of cell movement, we developed a minimal physical model of cell motility (Fig. 8a). The model, described in detail in the Supplementary Information, is one-dimensional (1D) and designed to represent a cell moving along a linear track (Fig. 1d). It consists of three beads corresponding to the cell body and its two leading edges. These beads are connected by elastic springs that mimic local membrane tension (σm,i), while the cell edges are subjected to active contractile forces (σi), with i=1, 2 representing the left and right edges, respectively. The motion of each bead follows a local force balance between the active force, membrane tension, and a friction force with the substrate proportional to the bead velocity. The cell stochastically switches between a quiescent state (σ1=σ2=0) and polarized states that promote motion in either direction (Left: σ1=−1, σ2=1; Right: σ1=1, σ2=−1). Transitions to the quiescent state occur at a constant rate kd, whereas transitions to polarized states depend on the local membrane tension via a feedback mechanism mediated by caveolae (Fig. 8b). In cells lacking this feedback such as Cav1-KO cells, transitions to contractile states occur independently and symmetrically at both cell edges at a baseline rate ku0. In contrast, when feedback is present, the probability of edge contraction depends on local membrane tension, such that: ku,i = ku,0 − δku σm,i, where δku quantifies the strength of the feedback. This parameter is reduced in cavin-1-KO cells compared to WT cells and is set to zero in Cav1-KO cells. If the calculated ku,i becomes negative due to high tension, it is capped at zero in simulations. Local membrane tension is directly related to the extension of the corresponding cell edge, and its relaxation occurs at a rate kγ, determined by the ratio of the spring constant to the edge friction. We also allow for differential friction between the cell edges (with friction coefficient γg) and the cell body (γ), defining the ratio μ=γg/γ. As shown in Supplementary Information (SI 1), the tension difference between cell edges, which biases cell movement, relaxes at an effective rate k0γ = kγ (1+2μ).

We performed stochastic simulations of this model using parameters chosen to reproduce the contractile activity observed in WT, Cavin-1-KO and Cav1-KO cells (Fig. 5g). We then compute cell directionality as the net displacement (end-to-end distance) divided by the total path length over 4 hours, as in (Fig. 1c). Representative trajectories for cells with (WT) and without (Cav1-KO) feedback are shown in (Fig. 8c). The key indicator of the effect of feedback is the correlation between contractile activity and directionality. Directionality is defined as the ratio of root-mean-square (Euclidean) displacement to the total distance traveled over a given observation period tobs. In a purely random walk—where steps in either direction occur at a constant rate ku,0—this ratio is <x2>=1/sqrt(ku,0tobs), and thus, directionality decreases with increasing contractile events (~ku,0tobs). This behavior is reproduced in simulations without feedback (δku=0). Strikingly, introducing feedback reverses this relationship: directionality increases with contractile activity (Fig. 8d). This provides direct evidence that feedback between local membrane tension and contractility—mediated by caveolae—enhances short-term directional persistence. Additional analytical and stochastic simulation results are show in Supplementary Fig. 9.

The model also offers insight into the mechanism by which this feedback improves directionality. When a contractile event is initiated at one end of the cell—either stochastically or via optogenetic stimulation (Fig. 6)—a protrusion forms at the opposite end, driving cell movement. This leads to a local drop in membrane tension at the rear and an increase at the front (see also Supplementary Fig. 9): in the model (Fig. 8a), the rear spring compresses while the front spring extends. The cell returns to a quiescent state after a characteristic time of order 1/kd, yet the tension gradient persists for a time of order 1/k0γ governed by substrate friction. This residual tension gradient biases subsequent contractile events toward the same direction, thereby reinforcing directional persistence.

Methods
Cell culture
Human triple negative breast cancer Hs578t cells, genome-tagged with Cav1-RFP including WT-Hs578t, Cav1-KO Hs578t and cavin1-KO Hs578t variants were cultured in Dulbecco’s modified Eagle’s medium + GlutaMAX (DMEM) (Gibco #10566016), supplemented with 10% fetal calf serum, 5 mM pyruvate, 1% penicillin-streptomycin. Cells were maintained at 37°C under 5% CO2. MDA-MB-231, genome tagged with Cav1-RFP, were grown in DMEM/F-12 (Gibco #11320033) supplemented with 10% fetal calf serum and maintained under identical conditions. Coverslips and FluoroDish were pre-coated with collagen before cell seeding.

Plasmids and antibodies
Plasmids. Cav1-RFP, cavin-1-eGFP, Cav1-Dendra2, and Cav1-HaloTag plasmids have been published18,19. WT-Cav1-myc-mRFP and Cav1-F92A-V94A-myc-mRFP (Cav1-CSD mutant) were a gift from Robert Nabi (University of British Columbia, Canada). HaloTag-3xrGBD (Addgene #176108) (RhoA biosensor), ARHGEF25 plasmid for optogenetics, pLV-SspB-HaloTag-p63DH (Addgene #176129), Lck-mTurquoise2-iLID (Addgene #176125), Tiam-1 GEF and pLV-SspB-HaloTag-DHPH-TIAM1 plasmids for optogenetics (Addgene #176114) were gifts from Dr. Dorus Gadella (University of Amsterdam, Netherlands). ARHGEF11 plasmid for optogenetics pll7-IRFP-SSPB-P2A-Venus-iLID-CAAX (control), pll7-ARGEF11-iRFP-sSspb-P2A-mVenus-iLID-CAAX and ARHGEF11 (DHPH)-CRY2-mCherry were gifts from Dr. Mathieu Coppey (Institut Curie, France). Opto-PI3K, pHR ISH2-EGFP-SspBMicro and iLID-BFP-CAAX plasmids for optogenetics were gifts from Dr. Orion D. Weiner, University of California, San Francisco.

Primary antibodies. Rabbit anti-Cav1 (Cell signaling 3238S; 1:2000 for WB, 1:500 IF), rabbit anti-cavin1 (abcam, ab48824; 1:1000 for WB), rabbit anti-cavin1 (Proteintech 18892-1-AP; 1:1000 for WB), mouse anti-RhoA-GTP (New East Biosciences, 26904, 1:500 for WB and IF), rabbit anti-total RhoA (Invitrogen, PA599537, 1:1000 for WB), rabbit anti-HaloTag (Recombinant Antibody Platform, Institut Curie, A-P-R#57; 1:1000 for WB), mouse anti-RhoA antibody from RhoA-Pulldown kit (Cytoskeleton, #ARH05, 1:500 for WB), mouse anti-α-tubulin (clone B512; Sigma, cat. no. T5168; 1:5000 for WB), mouse anti-actin B (Sigma A5316; 1:1000 for WB). For STORM acquisition rabbit anti-Cav1 and mouse anti-RhoA-GTP antibodies were used at 1:500 dilution for 48 hours at 4°C.

Secondary antibodies. Mouse-Alexa488 (Invitrogen, A21202), rabbit-Cy3 (Jackson ImmunoResearch, 111166045), were used at 1:200 for IF; mouse-HRP (Jackson ImmunoResearch, 715-035-151) and rabbit-HRP (Jackson ImmunoResearch, 711035152) were used at 1:5000 for WB; goat anti-rabbit (12005870 and 12004162) or anti-mouse (12005867 and 12004159) IgG StarBright Blue 520 or 700 (Bio-Rad) were used at 1:5000 for WB; For STORM acquisition, goat anti-mouse CF568 (Sigma SAB4600312, 1:200 dilution), donkey anti-rabbit Alexa647 (Jackson ImmunoResearch, 711-606-152, 1:200 dilution) for 24 hours at 4°C.

siRNA and plasmid transfection
siRNA knock-down (KD) of caveolin-1 and cavin1 was performed using HiPerFect (Qiagen #301704) following the manufacturer’s protocol, with cells cultured for 48 hours post-transfection. All siRNA sequences were used at the final concentration of 20 nM. Control siRNA (SI03650325, 5′-AAUUCUCCGAACGUGUCACGU-3′), siPTRF/cavin1 (Hs_PTRF_6, SI04178496) were purchased from Qiagen. For siCav1, four oligomers were used (Eurogentec; 5′-CUAAACACCUCAACGAUGA-3′, 5′-GCAUCAACUUGCAGAAAGA-3′, 5′-GCAAAUACGUAGACUCGGA-3′, and 5′-GCAGUUGUACCAUGCAUUA-3′). Knockdown efficiency was validated by western blotting using specific antibodies, with tubulin as a loading control. Plasmids were transiently transfected using Lipofectamine LTX-Plus Reagent (Invitrogen #15338100) according to the manufacturer’s instructions. For one Fluorodish (35 mm diameter), 2 µg DNA was transfected using 2 µl LTX and PLUS reagent in a 1:1 ratio, and cells were cultured overnight prior to imaging. Cells expressing Halo-tag plasmids were stained with Janelia Fluor HaloTag Ligand (#GA1120), at a final concentration of 100 nM for 1 hour at 37°C. Cells were washed three times with 1X PBS, fresh DMEM supplemented with 10% FBS was added, and cells imaged.

Collagen networks and cell migration assay
For 3D collagen gel migration assays, 50 µl droplets of collagen I (Corning #354236) at a final concentration of 2.2 mg/ml were seeded with 20,000 genome-edited Cav1-RFP Hs578t (WT or KD) cells and polymerized on glass-bottom dishes at room temperature (RT) for 30 minutes. The polymerized gels were immersed in pre-warmed complete medium and incubated overnight. Cells were imaged the next day using a spinning disk confocal microscope at 20-minute intervals over a 4-hour period. The collagen droplets were prepared with a 10:1 ratio of unlabeled to Alexa488-labeled collagen. A Z-stack of 9 µm thickness, with 1 µm intervals, was acquired every 2 or 20 minutes, depending on the experiment. Manual tracking of cell migration was performed using the ImageJ Fiji plugins Manual Tracking. Migration parameters were quantified using “Chemotaxis and Migration Tool” (Ibidi), available as a plugin for ImageJ/Fiji (https://ibidi.com/chemotaxis-analysis/171-chemotaxis-and-migration-tool.html; At least 15 cells per condition, across three independent experiments, were analyzed. For preparing thin collagen fiber layers, 35 mm glass-bottom FluoroDishes (World Precision Instruments #FD3510) were coated with 50 μg/ml collagen I (Corning #354236) in 0.01% acetic acid for 1 hour at 37°C. The dishes were then washed twice with PBS and immersed in complete DMEM medium prior to cell seeding. Imaging was performed at acquisition intervals of 1-2 minutes over a duration of 2-6 hours, depending on the experimental conditions.

Direction changes and Cav1 intensity correlation analysis
Cell segmentation in time-lapse movies was performed using SAM2 (Segment Anything Model v2)61; with the pre-trained “Hiera large” model, run via a Jupyter Notebook. For most movies, a single prompt point place at the center of the cell in the first frame was sufficient to achieve accurate segmentation. When necessary, up to four prompt points were added (always in the first frame) to improve segmentation quality. The resulting masks were exported and converted into Fiji ROIs. A custom Fiji macro62 was developed to measure Cav1 intensity within two fixed square ROIs positioned on the left and right sides of the cell. These ROIs were drawn in the first frame and propagated across all subsequent time points. Manual adjustment of the ROIs was performed as needed if significant cell displacement occurred during imaging. The directional movement of the cell along the X-axis was then compared to the relative Cav1 intensity between the left and right ROIs.

Flipper-TR measurement
Cells were incubated with the fluorescent membrane tension probe Flipper-TR (Spirochrome, cat. no. #CY-SC020) at a final concentration of 500 nM for 15 minutes at 37°C in a 5% CO2 atmosphere prior to imaging. Fluorescence lifetime imaging microscopy (FLIM) was performed on a Leica SP8 confocal microscope equipped with a 63x oil-immersion objective (NA 1.4). Flipper-TR was excited using a white light laser set at 488 nm, with power setting ranging from 10% to 25%, and a repetition rate of 40 MHz (pulse picker mode). Fluorescence emission was collected using a HyD SMD detector with a bandwidth set from 550 nm to 650 nm. Images were acquired at a scan speed of 100-200 Hz, and each frame was averaged over 10 repetitions to increase photon yield. For hypotonic shock experiments, cells were first imaged under isotonic conditions. The imaging medium was then diluted with water to 80% (v/v) final concentration to induce hypotonic stress, and imaging was continued for up to 6 minutes following the medium change. Fluorescence decay curves were obtained by pooling photon counts from manually defined ROIs at the front and rear of individual cells. Decay curves were fit using a biexponential model, and fluorescence lifetimes were extracted from the tail region of each curve. Following the methodology described in ref. 26, the longer lifetime component was used as a proxy for Flipper-TR lifetime, while the shorter component was excluded from further analysis.

Osmotic shocks
Isotonic conditions were maintained in DMEM supplemented with 10% FBS (300 mOsm) for the specified duration. A 90% hypo-osmotic shock was induced by diluting complete DMEM with deionized water (1:9 dilution, resulting in ~30 mOsm) for 5–10 minutes. Recovery conditions involved reintroducing DMEM supplemented with 10 % FBS (300 mOsm). Hyper-osmotic shock was induced by adding 600 mM sorbitol to DMEM supplemented with 10% FBS, achieving a final osmolarity of ~850 mOsm.

Spinning-disc confocal microscopy
Time-lapse images were acquired using an inverted Nikon Eclipse Ti-E microscope equipped with a Yokogawa CSU-X1 spinning disk confocal unit, integrated into Metamorph software (Gataca Systems). The system featured an sCMOS camera with 95% quantum efficiency and a pixel size of 11 µm. Appropriate emission filters were used for each fluorophore: DAPI (440 nm), GFP (ET525/50), mCherry (605/64) and Cy5 (ET708/75). Imaging was performed on a heated stage (37°C) with controlled 5% CO2, using a 60X CFI Plan Apo oil objective.

Cell lysates preparation and western blotting
To validate knockdown (KD) efficiency and knockout (KO) cell lines, cells were lysed in a sample buffer containing 2% SDS, 10% glycerol, 4 mM DTT, and Tris (pH 6.8) with phenol Red. Lysates were loaded onto 4–20% Mini-PROTEAN TGX gels (BioRad) and separated by SDS-PAGE. Proteins were transferred to nitrocellulose membranes using the Trans-Blot SD Semi-Dry Transfer Cell (Bio-Rad #1703940). Membranes were blocked with 5% BSA for 1 hour at RT. Primary antibodies were diluted in 0.5% BSA, and the membranes were incubated overnight at 4°C. The following day, membranes were washed three times with 1X TBS-Tween and incubated with secondary antibodies, either HRP-conjugated or fluorescent StarBright (BioRad). Chemiluminescence signals were detected using SuperSignal West Dura Extended Duration or SuperSignal West Femto substrate (Thermo Scientific Life Technologies). Imaging was performed on a ChemiDoc MP Imaging System (BioRad #12003154), and protein band quantification was conducted using ImageJ.

Indirect immunofluorescence assays
Cells were fixed with 4% paraformaldehyde for 15 minutes at RT, washed with PBS, and permeabilized with 0.1% saponin in PBS for 15 minutes. Blocking was performed with 5% BSA for 30 minutes at RT. Primary antibodies, Rabbit anti-Cav1 (BD Transduction Laboratories, cat. no. 610059; 1:200) and mouse anti-RhoA-GTP (New East Biosciences, cat. no. 26904, 1:200), were diluted in 0.5% BSA, and cells were incubated for 1 hour at RT. Appropriate secondary antibodies, along with Phalloidin-Alexa647 (1:500, Invitrogen), were added for 1 hour at RT. Cells were mounted with Fluoromount-G containing DAPI (Invitrogen, cat. no. 00-4958-02) and imaged using a spinning disc confocal microscope.

Co-immunoprecipitation (Co-IP) assays
Caveolin-1 antibody Co-IP assay. 1×106 cells (WT or KO) were seeded in a 100 mm dish and cultured overnight. The next day, cells were washed with cold PBS and lysed by scraping in 600 µl lysis buffer (25 mM Tris pH 7.4, 150 mM NaCl, 10 mM EDTA, 5% glycerol, 1% NP40, and 1X protease inhibitor cocktail (PIC, Roche)). Lysates were centrifuged at 10,600 g for 15 minutes to remove cell debris. A 100 µl aliquot of the lysate was mixed with 4 mM DTT and 50 µl sample buffer and labeled as the input sample. The remaining 500 µl lysate was pre-cleared with 25 µl protein A/G magnetic beads (Thermo Scientific, cat. no. 88802) for 1 hour at 4°C with rotation. After pre-clearing, the lysate was incubated overnight at 4°C with 1 µg/ml rabbit anti-Cav1 antibody (BD Transduction Laboratories, cat. no. 610059) with rotation. The lysate was further incubated with 25 µl protein A/G magnetic beads (Thermo Scientific) for 1 hour at 4°C. Immunoprecipitated beads were washed three times with lysis buffer (without PIC) and eluted in 60 µl elution buffer according to the manufacturer’s instructions. Input (20 µl) and immunoprecipitated (IP) samples (20 µl) were analyzed by SDS-PAGE gel followed by immunoblotting.

RhoA-GTP pulldown assays. The RhoA pulldown assay was performed using the RhoA Activation Assay Biochem Kit (Cytoskeleton, cat. no. BK036) according to the manufacturer’s instructions. Briefly, 2×106 cells were harvested in RhoA lysis buffer. Lysates were cleared by centrifugation at 10,600 g for 2 minutes at 4°C. Supernatants were incubated with 50 µg Rhotekin-RBD protein beads per sample for 1 hour at 4°C with rotation. Pulldown beads were washed, eluted, and analyzed by SDS-PAGE and immunoblotting. The ratio of active RhoA (RhoA-GTP) to total RhoA was quantified and compared between cell lines.

Proximity ligation assays (PLA)
The in situ interaction between endogenous Cav1 and RhoA-GTP was analysed using the NaveniFlex Cell MR kit (Ozyme, #NC.MR.100) according to the manufacturer’s instructions. Cells were fixed with 4% paraformaldehyde (PFA) for 10 minutes at RT, quenched with 50 mM NH4Cl for 10 minutes, and permeabilized with 0.1% Triton X-100 for 10 minutes at RT. Non-specific binding was blocked using the NaveniFlex Blocking buffer for 1 hour at 37°C. Cells were then incubated with the indicated primary antibodies for 1 hour at 370°C. Subsequent steps, including incubation with DNA probes, ligation, amplification, and detection, were carried out using the NaveniFlex Cell MR kit. A single-antibody condition was used as a negative control for the PLA reaction. After washing, cells were mounted in Fluoromount with DAPI to stain nuclei. PLA signals, appearing as fluorescent dots, were imaged using a Nikon AX R confocal microscope equipped with a 40X oil immersion objective. Z-sections were acquired and processed for deconvolution. Cells boundaries were delineated to generate masks, and mean fluorescence intensities were quantified using ImageJ. Data are presented as the mean PLA intensity per cell surface area ± the median with a 95% confidence interval, based on three independent experiments.

dSTORM Imaging
Stochastic optical reconstruction microscopy (STORM) imaging was performed using an inverted microscope (Nikon TiE2) equipped with SAFe360 technology (Abbelight), a high-numerical aperture (NA) oil-immersion objective (100X, NA 1.5), and an sCMOS camera (Hamamatsu Fusion BT C15440-20UP). Total internal reflection fluorescence (TIRF) mode was used during the experiment. Cells were grown on 170 μm coverslips (Menzel-Gläser 18 mm, diameter #1.5) and mounted on glass slides with a 15 mm central hole filled with imaging buffer (Tris 50 mM, NaCl 10 mM, 10% glucose, 100 mM MEA (Sigma 30070), 70 U/mL glucose oxidase (Sigma G2133-10KU), 10 mg/mL catalase (Sigma C40-100MG) and sealed with Picodent Twinsil63. Samples were illuminated with a 637 nm laser (500 mW) and a 405 nm laser (100 mW) for fluorophore reactivation. Cells grown on line micropatterns were selected using widefield microscopy and imaged in dSTORM mode for 35,000 frames with 637 nm laser (21 ms per frame) over a field of view of 20×20 µm). Elongated cells were selected to minimize fluorophore bleaching and to ensure that both extremities were imaged simultaneously. Imaging both the front and rear of each cell allowed for statistical paired comparison between these regions. A total of 28 cells across four experimental conditions, were analyzed, distributed over three independent replicates: 8 cells in isotonic conditions, 6 cells in hypotonic conditions, 7 cells in recovery conditions for WT-Hs578t, and 7 cells for cavin1-KO Hs578t. This resulted in 52 distinct images, representing the front and rear regions of individual cells. Drift correction was performed using carboxylated nanodiamonds (ADÀMAS nano, 140 nm, Lower Brightness, -COOH NDNV 140 nm Md 10 ml) added to the sample (10 μL/mL). Post-processing was performed with AbbelightTM NEO software. Nanodiamonds were isolated in the images using the crop tool in NEO software, and local drift corrections were applied. The resulting drift correction was then propagated to the entire image.

dSTORM analysis, cluster segmentation, S1A, S1B, S2 and caveolae classification
Cav1 proteins were labeled using a rabbit anti-Cav1 antibody (Cell signaling, 3238S), followed by a donkey anti-rabbit Alexa647 secondary antibody (Jackson ImmunoResearch, 711-606-152). The localizations of Cav1 were processed, and the resulting clusters were classified using the 3D SMLM (single-molecule localization microscopy) network analysis pipeline11. For analysis, all Z-coordinates were assumed to be in the same plan (Z=0). The SuperResNet platform was employed with the following parameters: Merging threshold: 12 nm (to correct for multiple blinking of single fluorophores); Proximity threshold: 80 nm (for network construction); Alpha: 1 (for noise filtering); Bandwidth: 120 nm (to segment localizations into blobs/clusters using the mean shift algorithm). A total of 28 features/descriptors, including shape, topology, network properties, size, and hollowness, were extracted for each segmented blob/cluster. The clusters or blobs feature from all conditions were grouped into four categories using the x-means clustering algorithm. Biological identities for these groups i.e., caveolae and S2, S1A, and S1B scaffolds, were assigned by comparing the group centers with previously characterized Cav1 groupings11. Assignments were based on the best match (highest similarity) to these prior classifications.

Cellpose and front-rear intensity measurements
Accurate cell segmentation was performed using the Cellpose algorithm64, a robust deep learning tool for cell detection. To improve segmentation precision, we trained a custom model based on the pre-trained cyto2 model, using manually annotated data from approximately 30 cells. Following segmentation, a Fiji (ImageJ) macro62 was used to compute the mean fluorescence intensity along the periphery of each cell, within a 2 µm-wide band inside the cell boundary. The macro separately measured the intensity in the front and rear regions of interest (ROIs), with each ROI defined as a 50-pixel area. ROIs were automatically determined by the macro, where the rear was defined as the leftmost part of the cell and the front as the rightmost part. For cells oriented differently or in cases where the automatic ROI placement was suboptimal, the macro allowed users to manually adjust the ROI positions. This flexibility ensured accurate intensity measurements for all analyzed cells.

Morphodynamic analysis
To perform cell segmentation during morphodynamic analysis, we utilized the Cellpose framework64, retrained using data from approximately 30 manually annotated cells based on the pre-trained cyto2 model. Accurate segmentation was essential, as this analysis relies on precise delineation of cell contours across all frames. Morphodynamic maps were generated following the methodology described in ref. 39 and adapted from ref. 40 to detect and quantify cell contractions and protrusions. The morphodynamic mapping algorithm, implemented in MATLAB, characterizes edge dynamics by measuring the displacement of cell contours between consecutive frames. Contours in each frame were represented by 100 equidistant markers, which were aligned across frames by applying a circular shift to minimize quadratic distances. Displacements between corresponding markers (representing consecutive segments on the same contour) were computed and visualized as color-coded maps. The X-axis of these maps represents time points, while the Y-axis represents the contour segment displacement. Color-coding was used to distinguish contractions (negative displacement, blue) from protrusions (positive displacement, red), using the Physics lookup table for visualization. These maps were exported as text files and subsequently analyzed using a Fiji macro62. The macro displayed the map, allowing users to identify contraction events (blue regions), by clicking on them using the Point tool. Selected points provided coordinates (X, Y), corresponding to the frame (X) and the specific contour segment (Y). The identified event was visualized as an 80 × 80-pixel square, which could be manually reviewed and adjusted for spatial and temporal accuracy. Intensity measurements within the defined square and across the entire cell were extracted for 10 frames preceding and 15 frames following each event, encompassing all imaging channels.

Optogenetic activation assays and analysis
Optogenetic activation experiments were performed on cells seeded in 35 mm glass-bottom FluoroDishes. Cells were transiently transfected with the specified marker and, when required, labeled with Halo-Tag fluorescent dye prior to imaging. Imaging was performed using an inverted spinning disk CSU-X1 (Yokogawa) microscope equipped with a FRAP/photoactivation module, integrated into Metamorph software (Gataca Systems), and paired with an sCMOS camera (95% quantum efficiency). Optogenetic activation was achieved using the iLas2 laser system, with targeted illumination controlled via Metamorph software. A targeted 80×80-pixel region was positioned at the leading edge of the cell using a 60X objective. Activation was performed using 4.5% power from a 491-nm laser, pulsed every 30 seconds within the defined opto-ROI. Pre-activation images were acquired for 2 minutes for each cell to establish baseline intensity values for subsequent quantification. For data analysis, opto-ROI information from Metamorph was imported into Fiji using the Metamorph .nd & ROI files importer plugin developed by F. Cordelières (available at https://imagejdocu.list.lu/plugin/inputoutput/nd_stacks_builder/start). A custom macro was used to calculate the mean fluorescence intensity within the opto-ROI and a random-ROI, which was symmetrically positioned relative to the centroid of the cell. Mean intensity values were measured at all time points for all fluorescent channels. The intensity measurements for ARHGEF25, Caveolin-1, and contraction-related ROI areas were plotted as functions of time and compared across experimental conditions.

Statistics and reproducibility
The number of cells analyzed and the number of independent experiments are detailed in the corresponding figure legends. For each experimental condition, at least three independent experiments were performed, with measurements obtained from distinct cells in each experiment. Statistical parameters and significance levels are presented within the figures and their legends. Data were considered statistically significant when p<0.05. Statistical tests employed include Wilcoxon’s rank-sum test, Mann-Whitney test, one-way ANOVA, and paired t-test, as appropriate, and were performed using GraphPad Prism 8.0 software.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Vibha Singh investigates the role and mechanism of caveolae mechanosensing in polarity establishment and maintenance during cell migration. The authors showed that that breast cancer cells exhibit a front-rear asymmetry in caveolae and caveolin-1 scaffolds, which is required for cell persistence. They also identified specific RhoA-GEFs that are under the regulation of caveolae and caveolin-1 scaffolds, which links membrane tension change and cell contractility. In general, this manuscript is well written and the evidence is clear and abundance. However, novelties are not significant but rather addition to the already known pieces of knowledge. I thus would not suggest publish it in its current form, but require major revisions and discussions that clarify the new findings from this work.\n1)The authors stated that asymmetrical localization of caveolae, with fewer at the cell front and more at the cell rear, has been observed in polarized migrating cells. They then showed that Triple-negative breast cancer Hs578t cells, genome-edited to express Cav1-RFP (Cav1-RFP Hs578t), displayed an asymmetrical distribution of Cav1. The Cav1 signal was significantly lower in the front (F) compared to the rear (R). Is the Cav1 localization their new finding? Moreover, here lacks important negative control experiment where a uniformly distributed plasma-memebrane tagged fluorescent protein is shown.\n2)The authors stated that knockdown of Cav1 (siCav1; cells devoid of both caveolae and non-caveolar Cav1) resulted in the loss of Cav1 front-rear asymmetry (Fig. 1a). How the authors determine Cav1 asymmetry under its knockdown where the protein itself should be largely reduced? Another concern on Fig.1a is that in the lower two rows, cells seem to barely move, how do the authors assess the directionality and speed?\n3)In the Z-shaped micropatterns which is beautifully designed why switch to a different cell type? The same cells should be demonstrated.\n4)The authors stated When cells changed direction, the loss of caveolae front-rear asymmetry was the first observable event, resulting in cessation of migration. Again, their lacks a negative control, possibly a membrane bound protein showing no such phenotype.\n5)In Fig. 1g, the authors observed that some cells, following the initial establishment of caveolae front-rear asymmetry, assembled two distinct bi-polar pools of caveolae at opposing sides over time (Fig. 1g), and when these caveolae pools became symmetrically distributed, the cells failed to migrate (kymograph Fig. 1g). It is intriguing and would benefit the novelty aspect of the manuscript to go further and investigate at least some of the up stream signaling that control the symmetrical or asymmetrical regulation. It is just randomly appearing?\n6)In Extended Fig. 2a, the authors have ruled out caveolae endocytosis or trafficking as factors in re-establishing caveolae front-rear asymmetry. However, they have not further provided the real mechanism for the re-establishement of polarity. Is membrane tension switch happening and changing the front-rear polarity of caveolae?\n7)By hypo-osmotic shoch, the authors observed a 16% reduction in active RhoA levels at the rear. Compared to other changes, this 16% reduction seems pretty mild. Can the authors elaborate on this?\n8)The authors claimed that co-immunoprecipitation, proximity ligation assay (PLA), and colocalization analyses revealed that Cav1 and active RhoA interact similarly in WT-Hs578t and cavin1-KO cells (Fig. 3a-d, Extended Data Fig. 3d). I cannot found the proximity ligation assay (PLA) data.\n9)Throughout the manuscript, a membrane tension sensor is lacking.\n10)The Cav1 and active RhoA data in extended Fig. 5a is rather co-nonoccurence, short in establishing any causal relationship.\n11)The authors employed an optogenetic approach to selectively recruit RhoA GEFs locally. Is this tool developed by the authors in this manuscript? Otherwise, relevant reference needs to be added here.\n12)The authirs found that in WT-Hs578t cells, ARHGEF25 and Cav1 were spatiotemporally coupled. Here again, they need to provide at least one negative control, a non-relevant GEF not spatiotemporally coupled.\n13)How does the “short time persistence” described in their model related to the author’s experimental observations?\n14)In the authors description of their graphical summary, they claimed that while Cav1 scaffolds at the cell front prevent RhoA-GEF downstream interactions, which enables protrusions (Fig. 7e). Is Cav1 required and played a crucial inhibitory role or just the loss of caveolin that reveal this phenomenon? Evidence is needed.\nMany of the previous findings and literatures have been references and discussed in the discussion. However, it is not obvious where the novelty of this manuscript lies. This remains my major concern that needs to be clarified before addressing my other specific questions.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "In this manuscript, Singh and colleagues demonstrate that the plasma membrane tension sensing properties of caveolae regulate cell polarity and directional migration through their effects on RhoA. First, the authors show that caveolae accumulate at the rear end of cells, and that interfering with caveolae impairs migration. Then, they show that manipulating membrane tension with osmotic shocks impairs both the caveolin localization, and migration. This is related to RhoA, which is more active at the rear of cells, where caveolae localize. Biochemical experiments are then used to show that RhoA interacts with caveolin, and does so preferentially when it is not within caveolae structures. Interestingly, it is caveolae structures, rather than caveolin, that enable the cell actomyosin contractions at the rear that are necessary for migration. Elegant photoactivation experiments then show that this is mediated by Rho GEFs. Finally, the authors show that an increase in caveolin recruitment precedes RhoA activation when cells contract. Overall, the data support a model in which higher membrane tension at the cell migrating front disassembles caveolae, whereas caveolae remain formed at the rear. Rho GEFs interact with caveolae, triggering actomyosin contraction and promoting migration. This framework is nicely supported by a computational model. Overall, this is an elegant and thorough study, which reveals a new mechanism regulating cell migration and coupling it to membrane tension. I therefore recommend publication, after addressing a few important points noted below.\n1- My main concern is that membrane tension is central to the proposed mechanism, but it is not measured, and it is only manipulated through osmotic shocks, which can have many side effects. The authors should either provide an alternative method to manipulate membrane tension (for instance, via cell confinement) or some measurement of membrane tension, for instance through the flipper-TR probe.\n2- Model: the proposed model is very interesting, but its only experimental validation seems to be fig. 7d. First, the authors should clearly specify the values of the parameters used, and exactly which parameters change and how in the “with feedback” and “without feedback” curves. Second, the authors should more broadly compare model to experiments. For instance, predicted trajectories (as in fig. 7c) could be compared to experiments, in terms of movement but also of predicted membrane tension on both sides (which could be compared to caveolin intensity for instance, as they are predicted to follow each other). I understand that the model is simplified and that we can’t expect a one-to-one comparison with experiments, but still the conceptual agreement between model and experimental trends could be explored much more broadly.\n3- The observations on caveolin/cavin redistributions during migration in fig. 1d-h are not quantified nor analyzed statistically. This should be corrected.\n4- Figure 2 also refers to the arrest of cell migration after the loss of cav1 asymetrical distribution. This is not shown or quantified, please correct.\n5- The authors should discuss their findings in light of a recent related preprint that studies the relationship between migration, Rho, Rac, and membrane tension (https://www.biorxiv.org/content/10.1101/2024.10.01.616161v1).", "reviewer_4": "This manuscript investigates the effect of plasma membrane tension changes on caveolae and its impact on cell migration in a breast cancer cell model. Caveolae proteins, as well as RhoA and RhoA-GEFs are visualized using confocal and STORM microscopy techniques in migrating cells, and in cells undergoing osmotic shock to model changes in PM tension. It was shown that caveolin-1 is asymmetrically distributed and present most abundantly at the rear of migrating cells. Caveolin-1 is then redistributed throughout the cell when migration stopped, and subsequently, when the direction of cell movement changes again Caveolin-1 re-distributes to the rear. This redistribution of caveolin-1 is not seen in cells lacking caveolae (cavin1 KO cells). The authors also show that RhoA colocalizes at the cell rear with caveolae and that caveolin-1 is spatiotemporally coupled with ARHGEF25 and 11. This manuscript improves our understanding of caveolae dynamics, and how their (dis)assembly is regulated in migrating cells. The authors present novel data indicating the coupling between caveolae, RhoA and GEFs and their importance in cell mechanosensing and migration. However, some aspects are lacking in the current manuscript, which should be considered by the authors.\nComment 1: Migration measurement\n1) Fig 1: How was directionality of cell migration measured? (fig 1c); Cavin1 KD or complete removal of caveolae results in the similar reduction in migration velocity, however only loss of caveolae results in more dramatic reduction in directionally. Why? Can this be explained by the fact that in cav1 KD actin was used as reference for cell borders in comparison to cav1 as reference in cavin1 KD.\n2) Page 5: ‘caveolae asymmetry was lost, as evidenced by Cav1 redistribution (Fig. 1d,f), cell migration stopped.’ I don’t see/understand this here, fig 1d shows migration from time point 0 until 50’ min with expected Cav1 asymmetry. Why there is Cav1 redistribution at 120’?\nWas this forced or did that happen without any external factor/trigger? If so, why? Where do I see that migration stops after 120’ in fig 1d? In 1f the time course is very different (36 min and 44 min), why? How long does the redistribution take? In line with this, when the cells are forced (?) to migrate again in the opposite direction, how long does it take until the Cav1 asymmetry is re-established (as seen in fig 1d).\nIn addition, the image quantification is missing in fig 1d-h, currently only one example cell is depicted. Where in the cell was the line graph analysis / kymograph placed for fig 1d and fig 1 f?\n3) Page 5: “When cells changed direction, the loss of caveolae front-rear asymmetry was the first observable event, resulting in cessation of migration. The front-rear asymmetry was subsequently reestablished before the change of directionality occurred.”\nWhat kind of time scale does this process of changing direction have? Is it comparable to migration of cells in tissues or not (and why)?\n4) Page 4-5, figure 1: It is not totally clear if all of the experiments were carried out on the same substrate (e.g. it is mentioned only one time in the legend from figure 1 that the experiment is done on a 3D collagen gel). Were all experiments done in a collagen gel or were different substrates used? And how does the substrate stiffness affect the plasma membrane tension in the experiments?\n5) Figure 1 (f) (g)\nI don’t see the difference between the unipolar and bipolar cells. To me it looks in both images like: caveolin1 at the cell rear, even distribution of cav1 at both F and R, after change in direction and cav1 at the new cell rear. Can the authors comment on this?\nIn the figure legend it says: “Representative snapshots from time-lapse movies of Cav1-RFP Hs578t cells migrating in 3D collagen gel.” But the collagen gel is not mentioned in the text. Please explain in the text why the collagen gel is used and if this is also used in the other experiments depicted in the following figures.\nComment 2: Cav1 asymmetry and its relation to cell migration\n1) Page 5, fig 2a: ‘The loss of Cav1’s asymmetrical distribution was associated with an immediate arrest of cell migration.‘\nThe example cell in fig 2a after hypoosmotic shock still shows cav1 asymmetry. In addition, when comparing the FI’s depicted in fig 2b, Cav1 FI also seems to be different in front and rear (after hypoosmotic shock). Did the authors statistically compare FI in front and rear after hypo osmotic shock?\n2) Page 5: ‘initial Cav1 levels were restored, the front-rear polarity re-established, and cell migration resumed (Fig. 2a,b)’\nWhere do I see migration here? How is this quantified?\n3) Page 5: ‘However, a more pronounced caveolae asymmetry did not result in faster migration, ‘ Where do I see this? How was this measured exactly?\n4) Page 6: ‘These results confirm that caveolae front-rear asymmetry is regulated by membrane mechanics and plays a crucial role in cell migration.’ I don’t think the current data shows this entirely, as migration cannot evaluated in the depicted images. The authors should include analysis and quantification of migration velocity to claim this.\n5) Page 5: “… induced by acute hypo-osmotic shock (30 mOsm, Hypo), led to a decrease in Cav1 intensity at the rear, but not at the front…”\nWhat kind of tension do cells undergo when migrating in vivo? Is the osmotic shock here somehow representative for this process or is the tension created by the osmotic shock much larger? It may be interesting to see this experiment for different osmolarities to see what the minimum osmolarity is that is needed for caveolae decrease at the cell rear.\nComment 3: Cav1 and RhoA localization\n1) Page 7: ‘revealed a strict correlation between active RhoA and Cav1 levels at both the rear and the front of the cell (Fig. 2i)’ The colocalization of Cav1 and RhoA depicted in extended fig 3d and 5 does not seem specific. RhoA is found all over the cell (including cytoplasm and PM). In particular, in ext fig 3d for wt and cavin1 KO it doesn’t seem to colocalize at all. How was Pearson coefficient measured – in ROIs covering the complete cell or only specifically at the rear membrane? Raw pearson data should be included here not only the normalization to wt (ext fig 3d). In line with this, the PLA result for Cav1 - RhoA depicted in ext fig 3d seems to be located all over the cell, and not localized to the rear membrane. Can the authors comment on this?\n2) Page 7, fig 3: the authors showed in fig 2 that under hypoosmotic shock Cav1 and RhoA FI intensity is reduced in the same cell areas (rear), however in fig 3 PLA and IP show the exact opposite results – an increased colocalization. Why is that? The depicted PLA images are difficult to evaluate, where are the cell edges in the example images? Where is front / rear? Scale bar is missing in overview images.\nRelated: Extended fig 5: In this figure an increased Pearson correlation was determined at the rear after hypoosmotic shock, but in fig 2a Cav1 localization is reduced at the rear in hypoosmotic shock treated cells. How can this discrepancy be explained?\n3) Page 7:’ These findings strongly suggest that active RhoA is more likely to interact with Cav1 when caveolae have been disassembled. Indeed, a higher level of colocalization between active RhoA and Cav1 was observed in cavin1-KO cells that present only non-caveolar Cav1,\nalbeit at a lower expression level (Extended Data Fig. 3d, Fig. 3a).’ Is the localization mainly at the PM? How was this evaluated? IP or PLA assay does not measure colocalization at the PM but rather in total cell lysates or within the cell. Can the authors comment on this, also in context to the comment above.\n4) Page 7, extended fig 3d: It appears that the cav1 KO and cavin-KO cells are not well stretched (in the actin stain). Is this because of the loss of cav1 or cavin1 or are these just two example cells?\n5) Page 7: “To determine whether the Cav1-CSD is necessary for the interaction with active RhoA, we transiently expressed…”\nIt is shown in this experiment that active RhoA does not interact with the CSD. In the discussion the authors may want to add a part where the authors elaborate on this a bit further. Which Cav1 domain do the authors think RhoA is interacting with based on literature (or experiments.)?\n6) Page 8: ‘…caveolae significantly accelerate RhoA-mediated actomyosin contractility with 2.4 events per hour, compared to…”\nis 2.4 events fast or slow? And is this in any way comparable to physiological conditions?\nFurthermore, fig. 4e-g shows a functional link between RhoA and contraction but no direct interaction with caveolin1. Maybe a co-staining with cav1 can help here.\nComment 4: STORM imaging & segmentation\n1) Page 8, figure 4: What is the size of the segmented S1A scaffolds? Does this fit to the Cav1 11-mer size determined by cryoEM? Based on the description in the text (page 8) ‘smallest class, S1A scaffolds correspond to the minimal 11-Cav1 protomer-8S complex, as characterized by cryo-electron microscopy9. S1B scaffolds represent dimers of S1A,’\nIn fig 4a and c segmented S1A or B do not differ so much in size, sometimes it appears that S1B is much larger the 2xS1A\n(‘dimer’), on the other hand, there are also examples in which S1A seems to be larger than S1B. I agree with the authors that at the rear of the depicted cell more caveolae and S2 areas are segmented, but I don’t see how S1B and S1A were distinguished. The authors should verify that they are able to detect single cav1 11-mers (S1A), and that a single Cav1 11mer can be distinguished by a ‘dimer’ of cav1 – 11oligomer (S1B). Here, quantitative image analysis of the STORM intensities for Cav1 spots can be compared to the actual size determined by cryoEM, and then the measured Cav1 STORM spots can be associated to the segmentation classes. What is the minimal resolution that the used STORM microscope can achieve in these samples? Was this measured beforehand by beads or sth similar?\n2) Page 8: ‘and 23% increase in S1A scaffolds at front was observed under hypo-osmotic shock relative to cells at rest (Extended Data Fig. 4a,b)‘ How does this result fit to the data shown in fig 2a and 2i where at the front of cells Cav1 was either not changed after hypoosmotic shock or even slightly decreased?\nMinor comments:\n1) Was the hypoosmotic shock evaluated in any kind? (E.g. cell swelling or membrane tension)\nFigure 2a-c: why is the hypo osmotic shock only 5 minutes but the hyper osmotic shock 30 mins?\n2) Page 6:’ Cell migration depends on a highly dynamic and coordinated biochemical cycle involving the Rho GTPase subfamily. Specifically, Rac1 and Cdc42 are localized at the leading edge of migrating cells, where they drive processes such as lamellipodia and filopodia formation’ – references are missing here.\n3) Page 6: Why are RhoA FI levels so different in Cav1 KO cells in fig 2e compared to fig 2g (ISO)? Scale bars are missing here.\n4) Fig 2e-h: How was the ROI for front (F) or rear (R) determined in respect to the cell area? Some ROIs for Front or Rear (depicted in fig 2) exclude large parts of the front or rear membrane (e.g. fig 2g cav1ko+cav1 or fig 2e cav1KO) How where the individual ROIs determined?\n5) Fig 4c: STORM and cluster segmentation panels should have the same size e.g. 5 um or 2 um, otherwise it is very difficult to evaluate the segmentation.\n6) Page 8:’ Altogether, these data suggest that active RhoA is more likely to interact with Cav1 scaffolds than with bona fide caveolae. ‘ Did the authors analyze co-localization of cav1 and RhoA in STORM images?\n7) Materials and methods: ‘dSTORM analysis, cluster segmentation, S1A, S1B, S2 and caveolae classification’\n“For analysis, all Z-coordinates were assumed to be in the same plan (Z=0). The SuperResNet platform was employed with the following parameters:…”\nHere is maybe a typo? ‘Plan’ should be ‘plane’?\n8) Material and methods: ‘Cell culture’: which concentration of collagen was used to coat the coverslips?\n9) Materials and methods: ‘Co-immunoprecipitation (Co-IP assays)\nIt is not clear how the cells are harvested: via trypsin+centrifugation/scraping/washing?\n“A 100 µl aliquot of the lysate was mixed with 4 mM DTT and 50 µl sample buffer and labeled as the input sample.” Of what does this sample buffer consist?\n10) Extended data fig. 4\nin the figure legend there is a citation: “Clusters segmentation images display results from SuperResNET Cav1 classification into four distinct classes: caveolae, and Cav1 scaffolds (S2, S1B and S1A), using parameters described in1. “\nI think the wrong number is used for the citation? Possibly it has to be nr 11 from the references list?", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers:\nWe would like to thank the reviewers for their positive comments and their constructive input. In response\nto their remarks, we have conducted a significant number of additional experiments and have introduced\ntextual changes to the manuscript. The new experiments and changes to the manuscript are summarized\nbelow. The reviewers’ comments appear in red; our point-to-point responses are in black. All the figure\nnumbers are those of the revised manuscript, except Figures R that correspond to the figures presented\n1-13\nto the reviewers only in this rebuttal. The new text in the manuscript is written in blue.\nReviewer #1:\nThis manuscript by Vibha Singh investigates the role and mechanism of caveolae mechanosensing in\npolarity establishment and maintenance during cell migration. The authors showed that that breast cancer\ncells exhibit a front-rear asymmetry in caveolae and caveolin-1 scaffolds, which is required for cell\npersistence. They also identified specific RhoA-GEFs that are under the regulation of caveolae and caveolin-\n1 scaffolds, which links membrane tension change and cell contractility. In general, this manuscript is well\nwritten, and the evidence is clear and abundance. However, novelties are not significant but rather\naddition to the already known pieces of knowledge. I thus would not suggest publish it in its current form\nbut require major revisions and discussions that clarify the new findings from this work.\nWe are grateful to the reviewer for his/her positive comments on the interest and quality of our study. In\nthe revised version, we have endeavored to strengthen the significance of our data with new controls and\nexperiments.\nPoint 1. The authors stated that asymmetrical localization of caveolae, with fewer at the cell front and\nmore at the cell rear, has been observed in polarized migrating cells. They then showed that Triple-negative\nbreast cancer Hs578t cells, genome-edited to express Cav1-RFP (Cav1-RFP Hs578t), displayed an\nasymmetrical distribution of Cav1. The Cav1 signal was significantly lower in the front (F) compared to the\nrear (R). Is the Cav1 localization their new finding? Moreover, here lacks important negative control\nexperiment where a uniformly distributed plasma-membrane tagged fluorescent protein is shown.\nAs stated several times in the manuscript (Refs. 21-24), including our previous work (Ref. 22), the\npolarization of Cav1 has been reported before; however, its functional significance has remained unclear.\nIn the present study, we demonstrate for the first time that this polarization corresponds to distinct\ncaveolin structures localized at the leading and trailing edges of migrating cells—namely, Cav1 scaffolds\nand caveolae, respectively. These structures exert opposing effects on RhoA activity via specific RhoA GEF,\ncontributing to spatial regulation of signaling during migration. While the first description of Cav1\nscaffolds was provided by the Nabi and Hamarneh labs in 2018 (Ref. 11), no functional role had previously\nbeen attributed to these non-caveolar Cav1 assemblies.\nIn response to the reviewer’s comment, we have now included a membrane marker (CAAX-GFP) in\nmigrating Hs578t cells displaying Cav1 polarity. This marker is widely used as a membrane marker and\nwas already employed as a control in our optogenetic experiments, although the data were not previously\nshown due to space constraints. We now provide a representative image and quantification from 42 cells\nacross three independent experiments, showing that CAAX is not polarized despite the front-rear polarity\nof Cav1 during migration. This new data is included in revised Extended Data Fig. 1d.\n6\n5\n3\nR\n1\n0\n0 10 20 30 40 50 60\nPosition (µm)\nWT-Hs578t\nPoint 2. The authors stated that knockdown of Cav1 (siCav1; cells devoid of both caveolae and non-\ncaveolar Cav1) resulted in the loss of Cav1 front-rear asymmetry (Fig. 1a). How the authors determine\nCav1 asymmetry under its knockdown where the protein itself should be largely reduced?\nWe thank the reviewer for pointing out this inconsistency. We apologize for the confusion. The sentence\nin the original manuscript has now been corrected in the revised version (Page 4): “Knockdown of cavin1\n(siCavin1; cells only have non-caveolar Cav1) and Cav1 (siCav1; cells devoid of both caveolae and non-\ncaveolar Cav1) (Extended Data Fig. 1c), resulted in the loss of Cav1 front-rear asymmetry (Fig. 1a).”\nAnother concern on Fig.1a is that in the lower two rows, cells seem to barely move, how do the authors\nassess the directionality and speed?\nWe agree with the reviewer’s observation. siCavin1 and siCav1 cells indeed display reduced migration\nspeed and directionality. To quantify migration parameters, we employed the widely accepted\n“Chemotaxis and Migration Tool” (Ibidi), available as a plugin for ImageJ/Fiji\n(https://ibidi.com/chemotaxis-analysis/171-chemotaxis-and-migration-tool.html; cited over 1100\npublications). Using this tool, we tracked individual cell trajectories from time-lapse imaging data and\nextracted migration metrics including speed, directionality, Euclidean distance, and accumulated path\nlength. These values were calculated using the plugin’s built-in algorithms, enabling robust comparison\nacross control and knockdown conditions.\nPoint 3. In the Z-shaped micropatterns which is beautifully designed why switch to a different cell type?\nThe same cells should be demonstrated.\nWe thank the reviewer for the positive feedback on the Z-shaped\nmicropattern design. We did attempt to perform these experiments\nusing Hs578t cells. However, due to their relatively large size, these\ncells extended across multiple arms of the Z-shape pattern, often\nspanning more than half of the structure. This led to inconsistent\nconfinement and compromised the interpretability of subcellular\nlocalization data. For this reason, we opted to use the smaller and\nmore spatially confined MDA-MB-231 cells that better matched the\ndimensions of the micropattern, ensuring more accurate and\nreproducible analyses.\n2\nytisnetnI\n.oulF\n.mron\nCav1 CAAX\nRear Front\nExtended Data Fig. 1d. Line-scan profiles of Cav1 (red) and CAAX (green, membrane marker) fluorescence intensity along the\nfront-rear axis of migrating cells. The direction of migration is indicated by the white arrow. Data are mean ± SEM., n = 42 cells\nfrom three independent experiments. F, front; R, rear. Fluo., fluorescence.\nR Cav1/ Collagen F\nGenome edited Cav1-RFP-tagged MDA-MB-231 cells are triple\nFigure R1. Caveolae front-rear\nnegative breast cancer cells that express Cav1 and cavin1 at levels\nasymmetry in MDA-MB-231 migrating\ncomparable to those in Hs578t cells. MDA-MB-231 cells have been\ncells. Representative time-lapse\npreviously characterized in our lab for caveolae mechanosensing\nimages of migrating Cav1-RFP MDA-\n(Shen., et al. Sci Rep. 2020). As shown in Figure R1, we first validated\nMB-231 cells (A) on 1D-line\nfront-rear polarization of Cav1 in migrating MDA-MB-231 cells on micropattern, (B) embedded in 3D\n1D-line micropatterns (A) and 3D collagen (B), prior to using Z- collagen gels. White arrows indicate the\nshaped micropatterns. This also allowed us to confirm our direction of cell migration, F, front; R,\nobservations in a second cell type.\nrear.\nPoint 4. The authors stated When cells changed direction, the loss of caveolae front-rear asymmetry was\nthe first observable event, resulting in cessation of migration. Again, their lacks a negative control, possibly\na membrane bound protein showing no such phenotype.\nWe thank the reviewer for raising this important point. As noted, the identification of a suitable\nmembrane-bound protein that remains stably polarized when a cell changes direction is inherently\nchallenging. By definition, proteins that exhibit front-rear polarity during migration are expected to\nrepolarize when cells change direction. To address this question, we consulted with experts in the field of\ncell migration and extensively reviewed the literature; however, we were unable to identify any known\nmembrane-bound proteins that maintain stable polarization across directional changes.\nThat said, we fully agree with the importance of including a negative control to support our interpretation.\nAs now mentioned in our revised manuscript (see Point 1 above), we have incorporated a CAAX-tagged\nmembrane marker, which labels the plasma membrane uniformly and does not exhibit front-rear\npolarization. This marker serves as suitable negative control, allowing us to demonstrate that the dynamic\nchanges we observe in Cav1 distribution are specific and not a general feature of membrane-associated\nproteins.\nFinally, we would like to emphasize that our interpretation is based on tracking individual cells before,\nduring, and after directional changes, which clearly illustrates that the loss of Cav1 front-rear asymmetry\nconsistently precedes migration arrest. We believe this reinforces the conclusion that Cav1 polarization is\nfunctionally linked to directional persistence.\nPoint 5. In Fig. 1g, the authors observed that some cells, following the initial establishment of caveolae\nfront-rear asymmetry, assembled two distinct bi-polar pools of caveolae at opposing sides over time (Fig.\n1g), and when these caveolae pools became symmetrically distributed, the cells failed to migrate\n(kymograph Fig. 1g). It is intriguing and would benefit the novelty aspect of the manuscript to go further\nand investigate at least some of the up stream signaling that control the symmetrical or asymmetrical\nregulation. It is just randomly appearing?\nWe appreciate the reviewer’s interest in the mechanisms underlying the switch between asymmetrical\nand symmetrical caveolae distribution during migration. We fully agree that identifying the upstream\nsignaling pathways regulating this symmetry would significantly deepen the mechanistic understanding\nof polarity and migration. However, we respectfully note that elucidating these upstream regulators\nremains a major open question in the field and is beyond the scope of the current study.\nAs the reviewer points out, cell asymmetry is indeed a well-established prerequisite for establishing front-\nrear polarity and directional migration. However, the cause and dynamic regulation of this asymmetry,\nparticularly in the context of spontaneous migrating cells, remains incompletely understood.\nIn our study, we have focused on characterizing the sequence of events in randomly migrating cells\nwithout imposed directional cues. Specifically:\n3\n1. We show that the loss of caveolae front-rear asymmetry precedes changes in direction and migration\narrest (Fig. 1d-h), indicating that caveolae asymmetry is tightly linked to directional persistence.\n2. We identify GEF recruitment as the earliest detectable signaling event associated with the re-\ninitiation of migration, which in turn triggers downstream RhoA activation and actomyosin\ncontractility downstream. (Figs. 5g, 6a, 6i, and Extended Data 8c,d).\n3. Importantly, we demonstrate for the first time that Cav1 scaffolds and caveolae are spatiotemporally\ncoupled with GEF recruitment at the membrane, highlighting a potential mechanosensitive interface\nthat governs polarity establishment.\nAdditionally, we now provide new data using the Flipper-TR membrane tension probe (new Fig.2, see\nPoint 6 below), which suggests that changes in membrane tension may also contribute to the regulation\nof caveolae and Cav1 scaffolds asymmetry. These observations open exciting avenues for future work\naimed at understanding how mechanical and biochemical cues integrate to regulate polarity dynamics.\nPoint 6. In Extended Fig. 2a, the authors have ruled out caveolae endocytosis or trafficking as factors in\nre-establishing caveolae front-rear asymmetry. However, they have not further provided the real\nmechanism for the re-establishment of polarity. Is membrane tension switch happening and changing the\nfront-rear polarity of caveolae?\nWe thank the reviewer for this insightful comment. As noted, our photoactivation experiments of Cav1\nconvincingly demonstrate that caveolae reassemble at the original rear location, effectively ruling out\nendocytosis or intracellular trafficking as the mechanism driving the re-establishment of front-rear\ncaveolae asymmetry.\nTo address the reviewer’s suggestion regarding membrane tension (see Point 9 below), we performed\nadditional experiments using the membrane tension probe Flipper-TR TR (Colom et al. Nat Chem. 2018)\nAs shown in figure 2, under iso-osmotic conditions (Iso), Flipper-TR lifetime measurements reveal a lower\ntension at the cell rear and a higher tension at the front (panel c), indicating the presence of a membrane\ntension gradient in migrating cells, consistent with previous reports (Hetmanski et al. Dev Cell 2019., Shi\net al., Cell 2018; De Belly et al., Cell 2023; García-Arcos et al., bioRxiv 2024). This gradient aligns with the\nobserved front-rear polarity of caveolae, characterized by a predominance of budded caveolae at the rear\nand Cav1 scaffolds at the front, as revealed by super-resolution microscopy (Fig. 5 a-b).\nUpon hypo-osmotic shock (Hypo), the membrane tension gradient is abolished (panel f, g), consistent with\na uniform equilibrium of membrane tension across the cell. Concomitantly, caveolae front-rear polarity\ndisappears, which we attribute to the flattening of caveolae and release of Cav1 scaffolds, as confirmed\nby super-resolution imaging (Extended Data Fig. 5 a-b). Notably, upon return to iso-osmotic conditions\n(Iso), the rear of the cell regains fewer Cav1 scaffolds and more caveolae, consistent with the pre-shock\nstate (Extended Data Fig. 5 c-d). Together, these new data support a model in which local shifts in\nmembrane tension regulate the dynamic re-establishment of caveolae front-rear polarity. These findings\nhave been incorporated into the revised manuscript and are discussed in detail.\n4\n(a) (b) (c) Fig. 2\n(d)\n(e) (f) (g)\n10 µm\n(h) (i)\nPoint 7. By hypo-osmotic shoch, the authors observed a 16% reduction in active RhoA levels at the rear.\nCompared to other changes, this 16% reduction seems pretty mild. Can the authors elaborate on this?\nAs initially reported, hypo-osmotic shock induces an approximately 24% decrease in the pool of budded\ncaveolae at the cell rear compared to cells at rest (Extended Data Fig. 5a-b). Since budded caveolae are\nrequired for the recruitment and activation of RhoA-specific GEFs, which in turn regulate RhoA activity,\nthe observed 24% decrease in budded caveolae correlates well with the 16% reduction in RhoA activity\ndetected at the same location (Fig 3g,h). We have now mentioned this correlation in the revised\nmanuscript (page 8).\n5\nmµ\n02\n2000 7\nFront 1500 6\n1000 5\n500 4\n0 0 5 10 15 20 25\n2000\n500\n0 785 0\nstnuoc\nnotohP\nFront\nTwo-exponential Tail fit Flipper-TR lifetime = 5.546 ns\nAdj-R2 = 0.966\nTime (ns)\nstnuoc\nnotohP\nFlipper-TR intensity counts\nFront Rear\nRear\nTwo-exponential Tail fit Flipper-TR lifetime = 5.421 ns\nAdj-R2 = 0.950\n0 5 10 15 20 25\nTime (ns)\n)sn(\nemitefil\nRT-reppilF\n)sn(\necnereffid\nemitefiL\n✱✱✱✱\n1.5\n1.0\n0.5\n0\n- 0.5 Δ (Front - Rear)\nFlipper-TR intensity counts\nIso Hypo\nRear Rear\n0 234 Iso Hypo Iso Hypo Iso Hypo\n)sn(\necnereffid\nemitefiL\nFront Rear Δ (Front - Rear)\n6.5 2.0\n6.0 1.0\n5.5 0\n5.0 - 1.0\n4.5 - 2.0\n)sn(\nemitefil\nRT-reppilF\nPre-activation optoactivation Area Actin Cav1\nActin 20 µm 0’ 2’ 8’ 1.4\n1.2\n→ → →\n1.0\nCav1 0’ 2’ 8’\n0.8\n0.7 0.5\nWT-Hs578t + opto-Pi3K -2 opto 246810121416\naera\ndna\nytisnetni\n.mroN\n✱✱\nFigure 2. Migrating cell displays intrinsic membrane tension gradient. (a) Representative Flipper-TR intensity images of a\nWT-Hs578t cell under isotonic (Iso) condition. Dashed white boxes indicate regions of interest (ROIs) at the cell front and\nrear. The color bar represents Flipper-TR intensity counts (photons per pixel). Scale bar, 20 µm. (b) Fluorescence decay\ncurves from the front (green) and rear (red) ROIs of a single cell. Experimental data (dots) are fitted with a two-exponential\ntail function (blue line) to derive Flipper-TR lifetime (ns) and adjusted R² (Adj-R²). Insets show the locations of the front and\nrear ROIs. (c) Flipper-TR lifetimes at the cell front (green circles) and rear (red circles) of individual cells. Grey lines\nconnect paired measurements from the same cell. Data are mean ± SEM of n = 44 cells from three independent\nexperiments. Statistical analysis was performed using Mann-Whitney test; ****P < 0.0001, **P < 0.001. (d) Per-cell\nTime (min)\ndifferences in Flipper-TR lifetime (Δ = Front - Rear) shown as black circles. (e) Representative Flipper-TR intensity images\nof a WT-Hs578t cell under Iso (left) and Hypo (right) conditions. ROIs at the cell front and rear are indicated by dashed\nwhite boxes. Color bar as in panel a. Scale bar, 10 µm. (f) Flipper-TR lifetimes at the front and rear under Iso and Hypo\nconditions. (g) Per-cell differences in Flipper-TR lifetime (Δ = Front - Rear) under Iso and Hypo conditions. Data are mean\n± SEM., n = 19 cells from three independent experiments. Statistical analysis was performed using Mann-Whitney test;\n****P < 0.0001. Mann-Whitney test; P < 0.0001. Grey lines link paired data from the same cell; black circles indicate\nindividual differences.\nFig. 4\nMoreover, existing literature supports the functional relevance of relatively small changes in the levels of active GTPases. For example, work from the Theriot lab demonstrates that a change of merely 3-4 % in Cdc42-GTP at the cell front is sufficient to drive protrusion and directional migration (Hadjitheodorou et\nal., Nat. Commun. 2021). In this context, a 16% reduction in active RhoA is biologically significant and\nconsistent with regulatory thresholds observed for other GTPases. Finally, we show that the restoration o fu C f a r v m1 t - K h O e e m cr W a v T i s b n1 u -r K p a O n p e o r t t e in n g s i t o h n e f f o u l n lo c w ti i o n n g a t l h c e o u h p y cl a p W i vi n n o T 1 g -- K o O cb a W s vi e n m T 1 t - K w o O t e ic e n sh m o e ck m i b s r s a u n f e fi c t i e e n n s t i o to n , r c e a c v o e v o e l r a R e h o o r A ga a n c iz ti a v t i i t o y n a , t a t n h d e R r h e o ar A ,\nsignaling.\nPoint 8. The authors claimed that co-immunoprecipitation, proximity ligation assay (PLA), and colocalization analyses revealed that Cav1 and active RhoA interact similarly in WT-Hs578t and cavin1-KO cells (Fig. 3a-d, Extended Data Fig. 3d). I cannot found the proximity ligation assay (PLA) data.\nWe thank the reviewer for noticing this omission. We have now included the PLA data showing the\ninteraction between Cav1 and RhoA-GTP in both WT-Hs578t and cavin1-KO cells.\nThis new panel is presented as Fig. 4f in the revised manuscript and supports our claim that Cav1 and\nactive RhoA interact similarly in both cell types.\nPoint 9. Throughout the manuscript, a membrane tension sensor is lacking.\nAs described above (Point 6), we performed additional experiments using the membrane tension probe\nFlipper-TR. As shown in figure 2, under iso-osmotic conditions, Flipper-TR lifetime measurements reveal\na lower tension at the cell rear and a higher tension at the front (panel c), indicating the presence of a\nmembrane tension gradient in migrating cells, consistent with previous reports (Hetmanski et al. Dev Cell\n2019., Shi et al., Cell 2018; De Belly et al., Cell 2023; García-Arcos et al., bioRxiv 2024). This gradient aligns\nwith the observed front-rear polarity of caveolae, characterized by a predominance of budded caveolae\nat the rear and Cav1 scaffolds at the front, as revealed by super-resolution microscopy (Fig. 4 a-b).\nUpon hypo-osmotic shock, the membrane tension gradient is abolished (panel f), consistent with a\nuniform equilibrium of membrane tension across the cell. Concomitantly, caveolae front-rear polarity\ndisappears, which we attribute to the flattening of caveolae and release of Cav1 scaffolds, as confirmed\nby super-resolution imaging (Extended Fig. 5 a-b). Notably, upon return to iso-osmotic conditions, the rear\nof the cell regains fewer Cav1 scaffolds and more caveolae, consistent with the pre-shock state (Extended\nFig. 5 c-d).\nTogether, these new data support a model in which local shifts in membrane tension regulate the dynamic\nre-establishment of caveolae front-rear polarity. These findings have been incorporated into the revised\nmanuscript and are discussed in detail.\nPoint 10. The Cav1 and active RhoA data in extended Fig. 5a is rather co-nonoccurence, short in\nestablishing any causal relationship.\n6\n1vaC ot evitaler\nPTG-AohR\nca W vi T n1- K O\nPTG-AohR Bead C s av1- W K O T cavin1- K O 2.0 ns ns 25 1.5 25\n1.0\n25\n25 0.5 25 0.0 O O 55 Cav1- K cavin1- K\nCav1- K\nW\nO\nT\ncavin1- K O\n25 25 25 25\n55\nkDa\nBead\nIs\ns oHypo Rec\n55 tubulin\nWT-Hs578t\nns ✱✱✱ 1.2 1.1\n1.0 0.9\n0.8\n0.7\n0.0\nIsoHypoRec\n1vaC\not evitaler\nPTG-AohR\nosI\not .mron\n1vaC:PI\ntupnI\nevitaler PTG-AohR ).u.a( AohR\not\nnilubut ot evitaler\nAohR\n)TW ot .mron(\n(b)\n1vaC\not evitaler PTG-AohR\nIso\nAb.\nctrl. Iso Hypo Rec\nt875sH-W\nDAPI PLA: Cav1 and RhoA-GTP\nDAPI PLA: Cav1 and RhoA-GTP\n.lrtc\n.bA\nosI\nopyH\nosI\nceR\nkDa\nWT\nkDa\nAohR ot evitaler\nPTG-AohR\nnilubut ot evitaler\nAohR\n)TW ot\n.mron(\nllec\nrep\nALP\nytisnetni\n.oufl\n)osI\not evitaler(\nRhoA-GTP Cav1\nRhoA-GTP\nCav1 RhoA tubulin Hs578t\n(d)\nRhoA-GTP Cav1 RhoA Cav1\ntubulin Hs578t\n1vaC:PI\ntupnI\nPTG-AohR:PI\ntupnI\n(a) Input IP Input Input IP 1.5 ✱ ✱ 1.5 ✱ 15 ns ns 1.0 1.0 10\n0.5 0.5 5\n0.0 0.0 0\n(c)\nInput IP\n1.0 ✱✱✱ ✱ 1.5 ns ns 1.5 ns 0.8 0.6 1.0 1.0 0.4 0.5 0.5 0.2\n0.0 0.0 0.0 Cav1- K O\nc\nW\nav\nT in1- K O Cav1- K O\nc\nW\nav\nT in1- K O\nc\nW\nav\nT in1- K O\n(e)\nns\n✱✱✱✱ ✱✱✱✱✱✱✱✱\nWT\n20 µm\n(g)\n✱✱ ✱✱✱✱✱✱✱✱ 250\n200\n150\n100\n50\n20 µm 0\nt875sH\n(f)\n150\n100\n50\nc\n-\na\nK\nv\nO in1- K O\n.lrtc\n.bA\nTW\nOK-1vaC\nllec\nrep\nALP\nytisnetni\n.oufl\n)TW\not\nevitaler(\nWT\nAb. ctrl.\nTW\nOK-1nivac\nFigure 4 (f). Proximity ligation assay (PLA) to detect\nendogenous Cav1-RhoA-GTP interactions in WT, Cav1-\nKO, and cavin1-KO Hs578t cells. Quantifications show\nmedian ± 95% CI; n = 326 cells from 3 independent\nexperiments. One-way ANOVA with Kruskal-Wallis\ntest; ****P < 0.0001; **P < 0.001; ns = not significant. Scale bar, 20 µm.\nWe thank the reviewer for this insightful comment. We agree that the images presented in the original\nExtended Data Fig. 5a may not have clearly conveyed the extent of colocalization between Cav1 and active\nRhoA. In the revised Extended Data Fig. 6a (formerly Extended Data Fig. 5a), we now provide an improved\nseries of images showing colocalization between Cav1 and active RhoA under iso-osmotic (Iso), hypo-\nosmotic (Hypo), and return to iso-osmolarity (Rec) conditions. These images specifically focus on the\nregion within 2 µm of the cell periphery.\nTo support our conclusion, we performed quantitative analysis on 42 cells from 3 independent\nexperiments. Cell segmentation was performed using Cellpose, a robust deep-learning-based cell\nsegmentation algorithm (see Methods). In addition, regions of interest (ROIs) corresponding to the front\nand rear of migrating cells (identified based on the direction of migration in time-lapse sequences) were\nanalyzed for colocalization using Pearson’s coefficient, calculated via the ImageJ Fiji plugin. The data\nshown represent cumulative analysis from three independent experiments and reveal a significant change\nin colocalization at the cell rear under hypo-osmotic stress, which is reversed upon return to iso-\nosmolarity.\nFurther supporting this relationship, we show that caveolae at the cell rear disassemble into Cav1 scaffolds\nunder high membrane tension (Extended Data Fig. 5a), and that this correlates with a decrease in RhoA\nactivity in the same region (Fig 3e-g). Thus, the increase in Cav1 scaffolds and their colocalization with\nactive RhoA (Extended Data Fig. 6a) is associated with reduced RhoA activity specifically at the cell rear\nunder high tension.\nWhile we agree that Extended Data Fig 6a alone may not establish causality, the combination of this\ncolocalization analysis with functional data from Fig 3 e-g and the structural dynamics shown in Extended\nData Fig. 5a strongly support a model in which RhoA activity is regulated by local caveolae-mediated\nmechanosensing.\nPoint 11. The authors employed an optogenetic approach to selectively recruit RhoA GEFs locally. Is this\ntool developed by the authors in this manuscript? Otherwise, relevant reference needs to be added here.\nThe optogenetics tools used in our study were not developed in our laboratory. We have cited the relevant\nreference in the main text (page 10), where we describe the selection of ARHGEF25 and ARHGEF11 based\non their specificity for RhoA (Ref. 36) and their known role in promoting cell contraction (Refs. 35 and 42).\nAdditionally, detailed information about the origin of the optogenetic plasmids, including the source\nlaboratories and Addgene references, is provided in the Material and Method section (Plasmids and\nAntibodies, page18).\nPoint 12. The authors found that in WT-Hs578t cells, ARHGEF25 and Cav1 were spatiotemporally coupled.\nHere again, they need to provide at least one negative control, a non-relevant GEF not spatiotemporally\ncoupled.\nIn our optogenetic experiments, we included multiple controls to ensure the specificity of our findings.\nIn Extended Data Fig.7a and 7C, we used the standard optogenetic control expressing only the light-sensitive\niLID module (not shown), as well as a construct lacking the GEF component. In both cases, no downstream\nRhoA activation or Cav1 recruitment was observed following photoactivation. Additionally, we used an\ninternal control in each cell by analyzing a random ROI, outside the photoactivated region, confirming that\nobserved effects were spatially confined to the opto-ROI.\nns\nopto control opto ARHGEF11 80\nWT WT cavin1-KO Cav1-KO ns\nn 60\no\nX A A\nita\nv itc 40\nCa\n-e 20\nrP\navi\nn\n1-\nC\nav\n1-\nc\nHs578t\nIn response to the reviewer’s request, we\nnow include a new negative control using\na non-relevant GEF. Specifically, we\nperformed optogenetic activation of\nTiam1, a well-characterized Rac-1 specific\nGEF, in WT-Hs578t cells (Extended Data\nFig. 8e-f). Upon photoactivation, we\nobserved significant local recruitment of\nTiam1 and an increase in cell area within\nthe Opto-ROI, consistent with Rac1-\nmediated protrusion. Importantly, we did\nnot observe any significant change in Cav1\nintensity within the OptoROI during the\nsame time window, indicating that Cav1 is\nnot spatiotemporally coupled with Tiam1.\nThis new dataset confirms the specificity of interaction between Cav1 and ARHGEF25 and demonstrate\nthat caveolae are not universally recruited with all GEFs. Thus, Tiam1 serves as a non-relevant GEF control\nthat is spatiotemporally coupled with Cav1.\nReviewer #1 (Point 13). How does the “short time persistence” described in their model related to the\nauthor’s experimental observations?\nThe model aims to explain the differences in motility between WT cells and those depleted of Cav1 or\ncavin-1. We focus on two key experimental observations: (i) the higher migration rate and directionality\nof WT cells (Fig. 1c-d), and (ii) the increased contractile activity of WT cells (Fig. 5g). Directionality is\ndefined as the ratio between the Euclidean distance travelled (i.e., the end-to-end displacement of the\ntrajectory) and the total path length (which includes all back-and-forth motion). This ratio equals one for\nfully persistent, straight-line motion and approaches zero over time for a random walk.\nIn the model, directionality arises from a transient spatial asymmetry in membrane tension that follows a\nprotrusive step. This asymmetry biases future steps and persist over a short time scale—before\nmembrane tension equilibrates—governed by the cell—substrate friction parameter 𝑘 . Thus, “short time\n!\npersistence” refers to this transient directional bias that decays as membrane tension relaxes. controlled\nby the cell's friction with the substrate and quantified by the parameter 𝑘 ) after the cell makes a step.\n!\nOur model reproduces both key experimental features and unifies them into a relationship between\ndirectionality and migration rate (Fig. 8d). The strongest experimental evidence for feedback-induced\npersistence is the positive correlation between contractile activity (number of contractions per hour) and\ndirectionality across the three conditions (WT, siCavin-1 and siCav1). This is in contrast to an unbiased\nrandom walk, which would yield a negative correlation. The experimental data plotted in the theoretical\ndiagram show good agreement with model predictions.\nPoint 14. In the authors description of their graphical summary, they claimed that while Cav1\nscaffolds at the cell front prevent RhoA-GEF downstream interactions, which enables\nprotrusions (Fig. 7e). Is Cav1 required and played a crucial inhibitory role or just the loss of caveolin that reveal this\nphenomenon? Evidence is needed.\n8\n).u.a(\nXAAC\nytisnetni\n.oulF\n✱✱✱✱\n100 ✱✱✱ ns\n80\n60\n40\n20\navi\nn\n1- K O\nC\nav\n1- K O\nc\n)nim(\nemit\nnoitcartnoC\n(a) (b)\n0 4' 8' 15' 57'\n0 4' 8' 15' 18'\n0 4' 8' 15' 43'\n0 4' 8' 15' 57'\nTW\nTW\nOK-1nivac\nOK-1vaC\n(c) (d)\n→\n→\n→\n11FEGHRA\notpo\nopto-Tiam1 GEF\nlortnoc\notpo\n1vaC\ndlefi\nthgirB\n20 µm\nPre-activation optoactivation\n10 µm opto-ROI\nopto-ROI\nopto-ROI\n(e) (f)\nTiam1-GEF\nPre-activation optoactivation Cav1\nArea\nTiam1-GEF\nCav1 opto-ROI\naera\ndna\nytisnetni\n.mroN\n0' 2' 10'\n0' 2' 10' -2 opto 246810\nt875sH-TW\nTime (min)\nmµ\n01\n1.4\n1.3\n1.2\n1.1\n1.0\n0.9 0.5\nExtended Data Fig. 8. (e) Time-lapse images of WT-Hs578t cells\nexpressing Lck-mTurquoise2-iLID, SspB-HaloTag-Tiam1-DH (Tiam1-\nGEF), and Cav1-RFP. Insets show zoomed opto-activated ROI. Time\naxis from -2 min (pre-activation) to 10 min (post-activation). (f)\nQuantification of normalized fluorescence intensity and cell area\nwithin the opto-activated ROI: Tiam1-GEF (orange), Cav1 (grey), and\ncell area (blue). Data are mean ± SEM., n = 21 cells from 3\nindependent experiments.\nMany of the previous findings and literatures have been references and discussed in the discussion.\nHowever, it is not obvious where the novelty of this manuscript lies. This remains my major concern that\nneeds to be clarified before addressing my other specific questions.\nWe thank the reviewer for this critical point. We apologize for not making the novelty and mechanistic\ninsights of our study sufficiently clear in the original submission.\nFirst, to directly address the question: our findings demonstrate that Cav1 is functionally required to\nspatially restrict RhoA activation during migration through its role as a mechanosensor that dynamically\ntransitions between caveolae and non-caveolar scaffolds. This is not merely a phenotype of Cav1 loss but\na regulated, context-specific function of Cav1 structures. In particular:\n1) At the cell front, we show that Cav1 scaffolds inhibit RhoA activation, preventing unwanted\ncontractility and allowing for protrusive activity.\n2) At the cell rear, intact caveolae serve a mechanosensitive role by locally modulating RhoA activity\nin response to tension changes.\n3) This functional compartmentalization is lost in Cav1 KO cells, leading to mislocalized RhoA\nactivation, loss of directional migration, and failure to respond appropriately to membrane\ntension cues.\nWe also provide causal evidence by using optogenetic tools to recruit specific RhoA GEFs (ARHGEF11/25)\nand a negative control GEF (Tiam1), and we demonstrate that the inhibitory interaction between Cav1\nscaffolds and RhoA is specific and spatially regulated. This further supports the conclusion that\nCav1 scaffolds actively inhibit GEF signaling at the cell front, rather than this being a passive\nconsequence of Cav1 absence.\nRegarding novelty, we would like to clarify that:\n1. Cav1 polarization has been observed (Refs. 21–24, including our own work, Ref. 22), but its\nfunctional role has remained unknown.\n2. This is the first study to demonstrate that polarized Cav1 exists in distinct structural states\n(scaffolds vs. caveolae) at front vs. rear, which exert opposing effects on RhoA signaling during\nmigration.\n3. We identify for the first time that ARHGEF25 and ARHGEF11 are spatially regulated by Cav1, with\ndistinct interactions at the leading and trailing edges.\n4. While Cav1 scaffolds were described morphologically by the Nabi and Hamarneh groups in 2018\n(Ref. 11), no mechanical or functional role had previously been attributed to these structures.\n5. We now show that the caveolae-Cav1 scaffold balance is sensitive to local membrane tension\n(supported by Flipper-TR probe data, new Figure 2), and that their dynamics are essential for\ntuning localized signaling during migration.\nTogether, these findings reveal a novel mechanism by which mechanosensitive Cav1 assemblies regulate\ncell migration via spatial control of RhoA signaling—a process previously uncharacterized. Notably,\nwhereas earlier studies have primarily focused on the role of caveolae at the rear of migrating cells, our\nstudy uncovers a distinct function for Cav1 scaffolds at the front. We hope this clarifies the novelty and\nsignificance of our work and are grateful to the reviewer for the opportunity to strengthen our manuscript.\nReviewer 2 (Co-reviewed with reviewer 3)\nand investigated the role of caveolae mechanosensing and dynamics in regulating cancer cell migration.\nThey found that the accumulation of caveolae at the cell rear locally activated RhoA, thereby helping to\nguide directional cell migration. They also performed mathematical modeling to explain how persistent\nmotion emerges from the spatiotemporal control of contractility by membrane tension. Although extensive\nexperiments were conducted to support the proposed model, the study lacks conceptual novelty in this\narea, as the regulation of caveolae formation by membrane tension and the activation of RhoA by caveolae\nare already well-reported. Therefore, it may not be suitable for publication in Nature Communications.\nWe are grateful to the reviewer for his/her positive comments on the interest and quality of our study. In\nthe revised version, we have endeavored to strengthen the significance of our data with new controls and\nexperiments.\nWe apologize for not making the novelty and mechanistic insights of our study sufficiently clear in the\noriginal submission.\nOur findings demonstrate that Cav1 is functionally required to spatially restrict RhoA activation during\nmigration through its role as a mechanosensor that dynamically transitions between caveolae and non-\ncaveolar scaffolds. This is not merely a phenotype of Cav1 loss but a regulated, context-specific function\nof Cav1 structures. In particular:\n1. At the cell front, we show that Cav1 scaffolds inhibit RhoA activation, preventing unwanted\ncontractility and allowing for protrusive activity.\n2. At the cell rear, intact caveolae serve a mechanosensitive role by locally modulating RhoA activity in\nresponse to tension changes.\n3. This functional compartmentalization is lost in Cav1 KO cells, leading to mislocalized RhoA activation,\nloss of directional migration, and failure to respond appropriately to membrane tension cues. We\nalso provide causal evidence by using optogenetic tools to recruit specific RhoA GEFs (ARHGEF11/25)\nand a negative control GEF (Tiam1), and we demonstrate that the inhibitory interaction between\nCav1 scaffolds and RhoA is specific and spatially regulated. This further supports the conclusion that\nCav1 scaffolds actively inhibit GEF signaling at the cell front, rather than this being a passive\nconsequence of Cav1 absence.\nRegarding novelty, we would like to clarify that:\n1. Cav1 polarization has been observed (Refs. 21–24, including our own work, Ref. 22), but its\nfunctional role has remained unknown.\n2. This is the first study to demonstrate that polarized Cav1 exists in distinct structural states (scaffolds\nvs. caveolae) at front vs. rear, which exert opposing effects on RhoA signaling during migration.\n3. We identify for the first time that ARHGEF25 and ARHGEF11 are spatially regulated by Cav1, with\ndistinct interactions at the leading and trailing edges.\n4. While Cav1 scaffolds were described morphologically by the Nabi and Hamarneh groups in 2018 (Ref.\n11), no mechanical or functional role had previously been attributed to these structures.\n5. We now show that the caveolae-Cav1 scaffold balance is sensitive to local membrane tension\n(supported by Flipper-TR probe data, new Figure 2), and that their dynamics are essential for\ntuning localized signaling during migration.\ncell migration via spatial control of RhoA signaling—a process previously uncharacterized. Notably,\nwhereas earlier studies have primarily focused on the role of caveolae at the rear of migrating cells, our\nstudy uncovers a distinct function for Cav1 scaffolds at the front. We hope this clarifies the novelty and\nsignificance of our work and are grateful to the reviewer for the opportunity to strengthen our manuscript.\nPoint 1. There are two sentences that lack data or references to support their conclusions. When cells\nchanged direction, the loss of caveolae front-rear asymmetry was the first observable event, resulting in\ncessation of migration. The loss of Cav1's asymmetrical distribution was associated with an immediate\narrest of cell migration.\nWe thank the reviewer for this comment and apologize for not clearly indicating the data that support\nthese conclusions.\n1. Regarding the sentence: “When cells changed direction, the loss of caveolae front-rear asymmetry\nwas the first observable event, resulting in cessation of migration”—\nThis statement is supported by both qualitative and quantitative data. Specifically, in Fig.1d-h and the\nassociated kymographs, we show dynamic changes in Cav1 localization and corresponding shifts in cell\ndirectionality. These observations are further illustrated in Supplementary Video 2-4, which depict\nindividual cell trajectories and Cav1 redistribution during directional changes. Kymograph analyses\nprovide quantitative measurements of Cav1 intensity and its spatial relationship with cell movement—\nan approach that is widely used to infer correlation between proteins localization and motility, as shown\nin previous studies (Hadjitheodorou et al., Nat. Commun. 2021; Driscoll et al., PLoS Comput. Biol., 2012;\nDe Seze et al., eLife, 2023). Additionally, we now include statistical quantification of these changes in\nExtended Data Fig. 1e-g to substantiate our claim.\n2. Regarding the sentence: “The loss of Cav1's asymmetrical distribution was associated with an\nimmediate arrest of cell migration.”\nWe provide supporting evidence in Fig. 3a and Supplementary Video 5, which demonstrate that hypo-\nosmotic shock disrupts Cav1 asymmetry and coincides with an immediate halt in cell migration (see rear\nROI under Iso and Hypo conditions). This arrest is reversible upon restoration of membrane tension and\nCav1 polarity. In the revised manuscript, we now include additional time-stamped snapshots and\nquantitative analyses in Extended Data Fig. 2b comparing cell migration under Iso, hypo, and Rec\nconditions to reinforce this conclusion.\nPoint 2. Although the authors attempted to label the migration direction in Figs. 2a, 2c, 2e, and 2g, a\nmarker to clearly identify the cell front and rear should be included.\nWe thank the reviewer for this valuable suggestion. We agree that clear identification of the cell front and\nrear would be ideal for interpreting migration direction. However, a reliable, universal, and widely\naccepted marker for front-rear polarization in mesenchymal cells remains lacking. Spontaneous symmetry\nbreaking and front-rear polarization are complex and not yet fully understood processes (Ladoux et al.,\nTrends Cell Biol. 2016). As such, most studies on cell migration rely on bright-field or fluorescent time-\nlapse imaging to infer migration direction and delineate the front and rear based on movement dynamics\n(e.g., Hadjitheodorou et al., Nat. Commun. 2021; Hetmanski et al., Dev. Cell 2019).\ncareful analysis of individual cell trajectories from time-lapse sequences (see Supplementary Video 5).\nFor each cell, the direction of migration was first determined, after which the corresponding front and rear\nregions were assigned accordingly. This methodology was applied consistently across the entire dataset\nto avoid any potential bias.\nTo further support our interpretation of\nmembrane polarity, we have now included\nthe membrane marker (CAAX-GFP) as a\nnegative control for membrane polarization\nin migrating Hs578t cells displaying Cav1\npolarity (revised Extended Data Fig. 1d).\nThis marker is widely used as a membrane\nmarker and was already employed as\na control in our optogenetic experiments,\nalthough the data were not previously shown\ndue to space constraints. Extended Data Fig. 1d. Line-scan profiles of Cav1 (red) and CAAX\nWe now provide a representative image and (green, membrane marker) fluorescence intensity along the front-\nquantification from 42 cells across three rear axis of migrating cells. Direction of migration indicated by the\nindependent experiments, showing that white arrow. Data are mean ± SEM., n = 42 cells from 3 independent\nexperiments. F, front; R, rear. Fluo., fluorescence.\nCAAX is not polarized despite the front-rear\npolarity of Cav1 during migration.\nPoint 3. In Fig. 2, the cell front and rear respond differently to osmotic stress. Should an osmotic gradient\nbe set up to clearly distinguish the difference?\nWe thank the reviewer for this thoughtful suggestion. Osmotic\ngradients have indeed been widely used in our laboratory,\nparticularly in our original report describing caveolae disassembly\nin response to mechanical stress (Sinha et al., Cell 2011). Based\non this prior work, we have found that a 90% hypo-osmotic shock\napplied for 5 minutes provides the most robust and reproducible\nconditions to study caveolae disassembly across a range of cell\ntypes. This setting is widely used for similar studies in many\nlaboratories.\nIn the present study, we initially compared 50% and 90% %\nhypo-osmotic shock during 3D collagen migration assays. As\nshown in Figure R2 on the right, the 50% shock produced only\npartial caveolae disassembly, whereas the 90% shock more\neffectively disrupted caveolae, particularly at the cell rear. This\nstronger perturbation enhances our ability to distinguish Cav1\ndistribution at the front versus rear of migrating cells.\nIt is important to note that migrating cells Figure R2. Effect of 50% and 90% hypo-osmotic shock in migrating\nnaturally exhibit a front-rear gradient in cells. Representative time-lapse images of Cav1-RFP Hs578t cells\nmigrating in 3D collagen gels under isotonic (Iso), at 0 and 5 min\nmembrane, with higher tension at front and\nafter a 50% or 90% hypo- osmotic shock (Hypo), and at 0 and 5 min\nlower tension at the rear (Shi et al., Cell 2018;\nafter return to isotonic conditions (Rec). White arrows indicate\nDe Belly et al., Cell 2023; García-Arcos et al.,\ndirection of cell migration, F, front; R, rear.\n12\nbioRxiv 2024). We have now directly documented this in Hs578t cells using the Flipper-TR membrane\ntension probe (Fig 2a-g).\nA global hypo-osmotic shock increases membrane tension throughout the cell, but because of the pre-\nexisting gradient, the effects of the shock manifest differently at the front and rear. Furthermore, our\nsuper-resolution imaging reveals a predominance of budded caveolae at the rear and Cav1 scaffolds at\nthe front (Fig. 5 a-b). As expected, the budded caveolae at the rear are more sensitive by osmotic stress\nthan the flatter Cav1 scaffolds at the front (Extended Data Fig. 5a-b). These findings support our\nconclusion that the differential response is intrinsic to the front-rear polarity of caveolae architecture\nand membrane mechanics. For these reasons, we do not believe that imposing an external osmotic gradient\nwould further clarify the observed differences in Cav1 localization.\nPoint 4. In Fig. 3c, there are multiple GTP-RhoA bands but not in Fig. 3a and Fig. 3e, why?\nWe thank the reviewer for noting this point. The differences in band patterns across Fig. 3 (now Fig. 4a,\n4c and 4e) are due to the use of different experimental approaches and antibodies:\n• Figures 4a and 4e show results from co-immunoprecipitation (Co-IP) experiment using caveolin-1\nantibody for pull-down, followed by detection with an anti-RhoA-GTP antibody (New East\nBiosciences, Cat. #26904).\n• Fig. 4c, on the other hand, shows results from a RhoA-GTP pull down assay using Rhotekin-RBD beads\n(Cytoskeleton, Cat. #BK036), followed by western blotting with a different anti-RhoA antibody\nprovided in the kit (Cytoskeleton, Cat. #ARH05).\nThus, the variation in RhoA-GTP band appearance is due to:\n1. The experimental method used—Cav1 Co-IP (Figs 4a and 4e) vs. Rhotekin-RBD pulldown (Fig. 4c),\nand\n2. The use of different antibody clones and manufacturers for RhoA detection in each case.\nThese technical differences likely account for the presence of multiple bands in Fig. 4c, which are observed\nin the Co-IP blots. Importantly, these technical differences do not affect the interpretations or conclusions\nof the experiments.\nPoint 5. In Fig. 3f, the quantification of PLA intensity is not acceptable, as the intensity depends on the\namplification level. However, the number of PLA dots reflects the interaction strength.\nWe fully agree with the reviewer that PLA signal quantification is commonly performed by counting\nindividual fluorescent spots, and this is the approach we have used in previous studies (Zanin et al, Nat.\nCell Biol. 2023). In this case, however, we encountered significant challenges with spot counting due to\nthe presence of both small, distinct puncta and larger fluorescent structures. These larger signals likely\nresult from the merging of closely spaced PLA puncta, compounded by the limited spatial resolution of\nconventional confocal microscopy. As a result, spot-based quantification severely underestimated the\nactual signal, producing values inconsistent with the corresponding images.\nTo overcome this limitation, we opted for fluorescence intensity quantification, which provided results\nthat more accurately reflected the observed signal patterns. Notably, intensity-based PLA quantification\nis also an established method in other contexts, including flow cytometry applications.\nTo validate our approach, we contacted the manufacturer of our PLA kit (Navinci), who confirmed that\nintensity-based analysis is appropriate under conditions where signals cluster or overlap. Their guidance\nis as follows:\n“For quantification in cells and tissues: We have found that counting signals works well when they are\nscattered, as they are very easy to distinguish from each other. However, in the case of grouped signals,\nit is not possible to quantify the exact number of signals in each group… We therefore recommend\n13\nquantifying the intensity per signal to take account of both area and intensity, so that the grouped\nsignals have more weight than the individual signals.\nMeasuring only signal intensity and not counting the signal can work if you have very uniform staining\nbetween cells and images, and very little difference between background autofluorescence. “\nGiven this feedback, and to ensure consistency, we used the same quantification approach for all PLA\nexperiments in this study.\nPoint 6. The patterns of RhoA-GTP in Figs. 4a (cell front and rear) and 4e (cytoplasmic) are different. Is\nthere any explanation for this?\nThe apparent differences in RhoA-GTP localization in Fig. 4 (now Figs. 5a and 5e) stem from the distinct\nmethods used to detect active RhoA in each panel:\n• Figure 5a shows fixed-cell immunofluorescence using a mouse-monoclonal anti-RhoA-GTP antibody\n(New East Biosciences, Cat. #26904), which captures a static snapshot of RhoA-GTP distribution at\nthe cell front and rear.\n• Fig. 5e, on the other hand, presents live-cell imaging using a RhoA-biosensor (HaloTag-3xrGBD;\nAddgene #176108, Mahlandt et al. J. Cell Sci. 2021, eLife 2023), which reports dynamic changes in\nRhoA activity during migration and contractile events.\nWe respectfully disagree with the reviewer’s characterization of the RhoA biosensor signal in Fig. 5e as\n“cytoplasmic”. The haloTag-3xrGBD biosensor transiently localizes to the plasma membrane upon RhoA\nactivation and returns to the cytoplasm upon inactivation. As shown in the original biosensor\ncharacterization Figure R3 (Mahlandt et al., J. Cell Sci. 2021, Fig.7B below), this localization is short-lived\nand tightly coupled to specific cellular events such as contraction. Thus, the observed pattern in Fig. 5e\nreflects the dynamic and transient nature of RhoA-GTP accumulation at the plasma membrane during cell\nmigration, rather than a steady-state membrane enrichment captured in fixed samples.\nPUBLISHED DATA FIGURE REDACTED\nFigure R3. Mahlandt et al. J. Cell Sci. 2021). Fig. 7 (B) Spinning disk images of a BOEC transiently expressing\ndimericTomato-2xrGBD sensor randomly migrating after cell division. Arrows indicate contracting cell edge with\nincreased sensor intensity. Scale bar: 25 µm.]\nIn our study, we first validated the RhoA biosensor in WT-Hs578t cells undergoing random migration,\nidentifying transient RhoA activity bursts during contraction events (see Extended Data Fig. 3d, below).\nOnly after this validation did we use the biosensor to generate morphodynamical maps and contractility\nprofiles in Fig. 5e-f.\n14\n✱✱✱✱ ✱✱✱✱\n2.5\n✱✱✱✱\n2.0\n1.5\n1.0\n0.5\n0.0\nol T O O\nT\nA\nb. c o ntr W C a v 1- K\nc a\nvi n 1- K\nW\nExtended Data Fig. 3d. Representative RhoA-bio localization patterns in WT-Hs578t cells during migration. White\narrow indicate spatial and temporal RhoA activity. Scale bar, 20 µm. F, front; R, rear.\nTherefore, the difference in RhoA-GTP localization between the two figures is consistent with the distinct\ntemporal and methodological nature of the assays: fixed versus live imaging, and antibody-based\ndetection versus a dynamic activity reporter.\nPoint 7. The authors used the Z-shaped micropattern to capture changes in migration direction. Why not\nuse the well-developed PA-Rac1 optogenetic system (Yi I. Wu, 2009) to manipulate the migration direction\nand record the caveolae formation?\nWe thank the reviewer for the suggestion and the reference to the PA-Rac1 optogenetic system developed\nby Yi I. Wu et al. (2009), which is indeed a powerful tool for inducing membrane ruffling and protrusion\nthrough localized Rac1 activation. However, the rationale behind our experimental design was to analyze\nthe dynamics of caveolae and RhoA signaling during spontaneous or geometrically constrained changes\nin migration direction—without perturbing upstream regulators such as Rac1, which are known to cross-\nregulate RhoA activity.\nImportantly, manipulation of Rac1 using PA-Rac1 could confound the interpretation of RhoA-dependent\nprocesses by disrupting the intrinsic Rho/Rac balance. Our goal was to study caveolae remodeling and\nRhoA activation in response to natural or geometry-forced directional changes. To this end, we used Z-\nshaped micropatterns, which allow cells to undergo directional reorientation in a controlled, reproducible\nmanner without the need for pharmacological or optogenetic stimulation.\nMoreover, we did employ optogenetics in our study— but specifically to locally manipulate RhoA rather\nthan Rac1. For this, we used a well-established light-inducible dimerization system (iLID/SspB) to control\nthe spatial activation of RhoA and analyze Cav1 dynamics in response to localized contractile events\n(Guntas et al., PNAS, 2015; Mahlandt et al., eLife 2021).\nThe Z-shaped micropattern system was chosen to complement other settings used in our study, including\n1D-line micropatterns, 2D and 3D collagen migration assays, and optogenetically induced direction\nchanges (see Fig. 1, Fig. 6 and Extended Data Figs. 7-8). Together, these approaches support our central\nconclusion regarding the spatiotemporal coupling of RhoGEFs and Cav1 during directional migration.\nFinally, we note that Reviewer#1 explicitly appreciated the Z-shaped micropattern design and its\nrelevance to our study. We believe this system provides a non-invasive, geometry-driven method to probe\ndirectional migration and associated signaling events, without introducing potential artifacts from\nupstream signaling interference.\nReviewer 3\nIn this manuscript, Singh and colleagues demonstrate that the plasma membrane tension sensing\nproperties of caveolae regulate cell polarity and directional migration through their effects on RhoA. First,\n15\n).u.a(\nPTG-AohR\nytisnetni\n.oulF\nExtended Data Fig. 3\nkDa\nRhoA-GTP\nRhoA\ntubulin\nHs578t\n(c) WT Ab.control WT Cav1-KO cavin1-KO\nActin 10 µm\nRhoA-GTP\nCav1\nOK-1vaC\nTW\nOK-1nivac\n25\n25\nW\nRhoA-bio 0 12' 40' 64'\n→\nR →\nR\nF\n→\nF\nWT-Hs578t F R\nevitaler\nolaH\n).u.a(\nnilubut\not\nkDa\n3x-rGBD\n55 tubulin\nHs578t O O\nK K\n1- 1-\nv n\nC\na\na\nvi\nc\nTW\nOK-1vaC\nOK-1nivac\nevitaler\nPTG-AohR\n).u.a(nilubut\not\not\nevitaler\nAohR\n).u.a(nilubut\n(a)\n0.8 ✱✱✱ ✱ 1.5 ns ns\n0.6 1.0\n0.4\n0.5\n0.2\n0.0 0.0\nOT O O T O\nHs578t KW K K W K\n1- 1- 1- 1-\nv n v n\nC\na\na\nvi\nC\na\na\nvi\nc c\n(b)\n0.5 ns\nns ns\n0.4\n0.3\n0.2\n0.1\n0.0\n(d)\nHigh\n20 µm\nLow\nthe authors show that caveolae accumulate at the rear end of cells, and that interfering with caveolae\nimpairs migration. Then, they show that manipulating membrane tension with osmotic shocks impairs\nboth the caveolin localization, and migration. This is related to RhoA, which is more active at the rear of\ncells, where caveolae localize. Biochemical experiments are then used to show that RhoA interacts with\ncaveolin and does so preferentially when it is not within caveolae structures. Interestingly, it is caveolae\nstructures, rather than caveolin, that enable the cell actomyosin contractions at the rear that are necessary\nfor migration. Elegant photoactivation experiments then show that this is mediated by Rho GEFs. Finally,\nthe authors show that an increase in caveolin recruitment precedes RhoA activation when cells contract.\nOverall, the data support a model in which higher membrane tension at the cell migrating front\ndisassembles caveolae, whereas caveolae remain formed at the rear. Rho GEFs interact with caveolae,\ntriggering actomyosin contraction and promoting migration. This framework is nicely supported by a\ncomputational model. Overall, this is an elegant and thorough study, which reveals a new mechanism\nregulating cell migration and coupling it to membrane tension. I therefore recommend publication, after\naddressing a few important points noted below.\nWe sincerely thank the reviewer for their thoughtful and generous summary of our work. We greatly\nappreciate the recognition of the conceptual framework and the experimental depth, particularly the\nintegration of mechanobiology, cell polarity, and live cell optogenetics approaches to elucidate the role\nof caveolae in cell migration. In the revised version, we have endeavored to strengthen the significance\nof our data with new controls and experiments.\nPoint 1. My main concern is that membrane tension is central to the proposed mechanism, but it is not\nmeasured, and it is only manipulated through osmotic shocks, which can have many side effects. The\nauthors should either provide an alternative method to manipulate membrane tension (for instance, via\ncell confinement) or some measurement of membrane tension, for instance through the flipper-TR probe.\nWe thank the reviewer for this important point. To directly address the concern regarding membrane\ntension measurement and its role in the proposed mechanism, we performed additional experiments\nusing two complementary approaches:\n1. Flipper-TR probe (Colom et al. Nat Chem. 2018) We used fluorescence lifetime imaging (FLIM)\nFlipper-TR TR in WT Hs578t cells under under iso-osmotic conditions (Iso). As shown in Figure 2, we\nobserved a significant front-rear membrane tension gradient in Flipper-TR lifetimes with a higher\ntension at the front and a lower one at the rear of migrating cells (panel c). This observation is\nconsistent with recent findings (Shi et al., Cell 2018; De Belly et al., Cell 2023; García-Arcos et al.,\nbioRxiv 2024) and aligns with the observed front-rear polarity of caveolae, characterized by a\npredominance of budded caveolae at the rear and Cav1 scaffolds at the front, as revealed by super-\nresolution microscopy (Fig. 5 a-b).\n(a) (b) (c) Fig. 2\nmembrane tension gradient is abolished\n(panel f, g), consistent with consistent\nwith a uniform equilibrium of membrane\ntension across the cell. Concomitantly,\ncaveolae front-rear polarity disappears,\n(d)\nwhich we attribute to caveolae flattening\nand release of Cav1 scaffolds, as\nsupported by super-resolution imaging\ndata (Extended Fig. 5 a-b). Notably, upon\nreturn to iso-osmotic conditions (Iso),\nthe rear membrane regains fewer Cav1\n(e) (f) (g)\nscaffolds and more caveolae, consistent\nwith a recovery toward the pre-shock\n10 µm\nstate (Extended Fig. 5 c-d). These results\nprovide direct evidence that membrane\ntension dynamically regulates caveolae\norganization and polarity.\n(h) (i)\n2. Optogenetic manipulation of membrane tension using Opto-PI3K: To further validate this\nmechanism using an independent approach, we now include new data from WT Hs578t cells\nexpressing Opto-PI3K (Fig. 2h-i). Local recruitment of Opto-Pi3K to the cell rear led to Rac-mediated\nfilopodia formation (Cyan line) and an increase in cell area within the opto-ROI (grey line), consistent\nwith a localized increase in membrane tension (De Belly et al Cell 2023). This was subsequently\nfollowed by a decrease in Cav1 intensity (orange line), indicating caveolae disassembly. These results\ndirectly support our model that membranae tension gradients regulate caveolae dynamics and Cav1\nscaffolds distribution, and thereby influence cell polarity and directionality.\n17\nmµ 02\n2000 7\nFront 1500 6\n1000 5\n500 4\n0 4 0 5 10 15 20 25\n2000\n500\n0 785 0\nstnuoc\nnotohP\nFront\nTwo-exponential Tail fit Flipper-TR lifetime = 5.546 ns Adj-R2 = 0.966\nTime (ns)\nstnuoc\nnotohP\nFlipper-TR intensity counts\nFront Rear\nRear\nTwo-exponential Tail fit Flipper-TR lifetime = 5.421 ns Adj-R2 = 0.950\n0 5 10 15 20 25\nTime (ns)\n)sn(\nemitefil\nRT-reppilF\n)sn(\necnereffid\nemitefiL\n✱✱✱✱\n1.5\n1.0\n0.5\n0\n- 0.5 Δ (Front - Rear)\nFlipper-TR intensity counts\nIso Hypo\n✱✱✱✱\nRear Rear\n0 234 Iso Hypo Iso Hypo Iso Hypo\n)sn(\necnereffid\nemitefiL\nFront Rear Δ (Front - Rear)\n6.5 2.0\n6.0 1.0\n5.5 0\n5.0 - 1.0\n4.5 - 2.0\n)sn(\nemitefil\nRT-reppilF\nPre-activation optoactivation Area Actin Cav1\nActin 20 µm 0’ 2’ 8’ 1.4\n1.2\n→ → →\n1.0 Cav1 0’ 2’ 8’\n0.8\n0.7\n0.5\nWT-Hs578t + opto-Pi3K -2 opto246810121416\naera\ndna\nytisnetni\n.mroN\n✱✱\nFigure 2. Migrating cell displays intrinsic membrane tension gradient. (a) Representative Flipper-TR intensity images of a\nWT-Hs578t cell under isotonic (Iso) condition. Dashed white boxes indicate regions of interest (ROIs) at the cell front and\nrear. The color bar represents Flipper-TR intensity counts (photons per pixel). Scale bar, 20 µm. (b) Fluorescence decay\ncurves from the front (green) and rear (red) ROIs of a single cell. Experimental data (dots) are fitted with a two-exponential\ntail function (blue line) to derive Flipper-TR lifetime (ns) and adjusted R² (Adj-R²). Insets show the locations of the front and\nrear ROIs. (c) Flipper-TR lifetimes at the cell front (green circles) and rear (red circles) of individual cells. Grey lines connect\npaired measurements from the same cell. Data are mean ± SEM of n = 44 cells from three independent experiments.\nStatistical analysis was performed using Mann-Whitney test; ****P < 0.0001, **P < 0.001. (d) Per-cell differences in\nTime (min)\nFlipper-TR lifetime (Δ = Front - Rear) shown as black circles. (e) Representative Flipper-TR intensity images of a WT-Hs578t\ncell under Iso (left) and Hypo (right) conditions. ROIs at the cell front and rear are indicated by dashed white boxes. Color\nbar as in panel a. Scale bar, 10 µm. (f) Flipper-TR lifetimes at the front and rear under Iso and Hypo conditions. (g) Per-cell\ndifferences in Flipper-TR lifetime (Δ = Front - Rear) under Iso and Hypo conditions. Data are mean ± SEM., n = 19 cells from\nthree independent experiments. Statistical analysis was performed using Mann-Whitney test; ****P < 0.0001. Mann-\nWhitney test; P < 0.0001. Grey lines link paired data from the same cell; black circles indicate individual differences.\n(a) (b) (c) Fig. 2\n(d)\n(e) (f) (g)\n10 µm\n(h) (i)\nFigure 2. (h) Representative time-lapse images of WT-Hs578t cells co-expressing opto-PI3K via iSH2, SiR-Actin, and\nCav1-RFP. Insets show optogenetically activated ROIs. Timepoints span from -2 min (pre-activation) to 16 min (post-\nactivation). (i) Quantification of normalized intensity and cell area in the activated ROI over time for actin (orange),\nCav1 (grey), and cell area (blue). Mean ± SEM., n = 17 cells from 3 independent experiments.\nTogether, these new data—based on both quantitative membrane tension measurements (Flipper-TR)\nand localized mechanical perturbation (Opto-PI3K)—strongly support a mechanistic role for membrane\ntension in regulating front-rear caveolae polarity. These findings have been incorporated into the revised\nmanuscript and are discussed in detail.\nPoint 2. Model: the proposed model is very interesting, but its only experimental validation seems to be\nfig. 7d. First, the authors should clearly specify the values of the parameters used, and exactly which\nparameters change and how in the “with feedback” and “without feedback” curves. Second, the authors\nshould more broadly compare model to experiments. For instance, predicted trajectories (as in fig. 7c)\ncould be compared to experiments, in terms of movement but also of predicted membrane tension on both\nsides (which could be compared to caveolin intensity for instance, as they are predicted to follow each\nother). I understand that the model is simplified and that we can’t expect a one-to-one comparison with\nexperiments, but still the conceptual agreement between model and experimental trends could be\nexplored much more broadly.\nWe thank the reviewer for this insightful comment and have taken steps to address both points in the\nrevised manuscript.\nFirst, we have clarified the parameter values used in the model, and these are now explicitly stated in the\ncaption of Fig. 7. In particular, feedback refers to the influence of local membrane tension on the rate of\ncontraction. In the model, this is implemented by the relation:\n𝑘 = 𝑘 −𝛿𝑘 𝜎"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I find in this revision, the authors have addressed all the previous comments in detail. The current manuscript also includes the revisions suggested by other reviewers, which has further strengthened this study. I have no further comments.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have adequately addressed my comments, and in my view the manuscript is now ready for publication. I commend the authors on an excellent work.", "reviewer_4": "I thank the authors for their very detailed response to all comments raised by me and the other reviewers. All my comments were adressed satisfactorily and potential changes were incoporated into the figure or manuscript when needed. I recommend to publish this manuscript with no further changes or revisions. Congrats to the authors!", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter