Author response:
The following is the authors’ response to the original reviews
Public Reviews:
Reviewer #1 (Public review):
Summary:
In this work, the authors study the migration of isolated cells and of cells in ensembles. They quantify several aspects of the corresponding migration patterns and investigate how these quantities depend on molecules that are known to play an important role in migration. Furthermore, they study the effect of external cues on these migration processes.
Strengths:
The authors provide a clean and uniform setting for comparing the migration of isolated cells and of cells in an ensemble in control and mutant conditions, and in the presence and absence of external cues. This allows for a meaningful comparison between different conditions. In this way, the authors obtain useful data that link the migration of isolated cells to that of cells in collectives.
Weaknesses:
A major weakness of the manuscript is that the authors do not properly introduce the quantities and concepts they are working with. In this way, it is hardly accessible for a reader who does not have a thorough background in cell migration and anomalous transport. In addition, the manuscript uses some notions that are not standard, for example, vinculin or FA stability, which are not properly introduced. Most strikingly, "collective directional memory" is not defined.
We thank the reviewer for pointing this out. We have now added the definition of collective directional memory in the introduction:
“... cells that migrate collectively acquire long-term directional memory, which specifically requires α-catenin in AJs and results from coordination of velocities between neighboring cells. For this reason, we define it as a collective directional memory.” and in the results section:
“... the transition in directional memory observed at confluence are the results of bona fide AJ-mediated intercellular adhesion, but independently of the cadherin subtype. Consequently, we define as a collective directional memory this long-term directional memory that arises from neighbor interactions.”
The definition of anomalous transport is given in the results section too:
“we found that the average Mean Square Displacement as function of lag time ∆t (MSD, see Materials and Methods) was better described by a persistent random walk (Fürth et al. 1920, Uhlenbeck et al. 1930) than anomalous diffusion: the MSD transitioned from ballistic (MSD ∼ ∆t<sup>2</sup>,∆t → 0) to pure diffusion (MSD ∼ ∆t,∆t → ∞) with a persistence time P of the order of 10min, rather than scaling as ∆t<sup>𝛼</sup> with α ̸\= 1” and in the Materials and Methods:
"MSD(∆t) curves were fit with the Fürth equation MSD = 4_D_(∆t−P(1_−exp(−∆_t/P))) where D is the coefficient of diffusion and P the persistence time, as a model of persistent random walk, or with MSD ∼ ∆t<sup>𝛼</sup> (straight line of slope α in ln−ln scale), as a model of anomalous diffusion, with anomalous exponent α > 1 for super-diffusion."
The definition of vinculin and FA stability, which we now specify as “molecular stability” is given in the Materials and Methods section:
“We use the immobile fraction of vinculin and its mutants as a measure of vinculin stability and, by extension of the molecular stability of adhesions, because the stability of other adhesion proteins is regulated by the stability of vinculin (Carisey et al. 2013, Liu et al. 2016, Yonemura et al. 2010).”
The authors infer relationships between different quantities, but they remain qualitative, even though the authors use a language that suggests otherwise. For example, "The combination of Focal Adhesion stability and force transmission from the cytoskeleton predicts the migration speed of single cells" (p 2). I am not sure what is meant by prediction, but this heading suggests that knowledge of FA stability and force transmission yields the migration speed. Reading this line, I expect that if I give you values for FA stability and force transmission, you would give me a value for the migration speed. Such a quantitative mapping is not provided. In fact, it cannot be provided, because - as mentioned before - these quantities are not properly defined, so I would not know how to measure them. I do not even know their units.
We thank the reviewer for suggesting a more quantitative analysis. Initially, we used Boolean logic to build our model. Based on the effects of mutants, vinculin can be either tensed or not, and either stable or not, which yields four combinations that associate with more or less fast migration. We represent the model as a Venn diagram, which we now specify in the results section and figure legends. To complement this simplified approach, we have now added quantitative analyses by fitting changes in single cell speed, in the difference between single and collective cell migration speed, and in the ratio between collective cell migration speed and wound healing rate to the changes in tension, stability, or the gradients of tensions in FAs and AJs based on the quantitative measurements of speed (migration assays), tension (FRET index of vinculin sensors) and stability (immobile fraction of FRAP experiments) re-scaled between their extreme values across vinculin constructs, as explained in the Materials and Methods. This allows us to quantify the contributions of changes in tension, stability, and the gradients of tension to changes in single cell migration speed, in the difference between single and collective cell migration speed, and in the ratio between collective cell migration speed and wound healing rate. As shown in the added Fig. S2I, the change in single cell speed is explained by a linear combination of changes in tension and stability with contributions of 2_/3 and 1/_3, respectively. In Fig. S4J, the difference between single and collective cell migration speed changes with the stability exclusively, and its increased sensitivity to low stability is well captured by a power law. In fig. S5C, the ratio between collective cell migration speed and wound healing rate is set by the difference in mechanosensitivity between FAs and AJs in which 1/3 of that of AJs counts. Overall, these results confirm our initial models.
Furthermore, the authors do interpret some of their results without explaining or justifying the basis for their interpretation. For example, they use the FRET index of vinculin - another notion that is not properly introduced - to make statements about mechanical stress.
We are sorry that we had overlooked this important explanation. We have now added explanations in the main text, and in the Materials and Methods. Briefly, the FRET sensor module inserted in vinculin and its mutants is made of two fluorescent proteins separated by an elastic polypeptidic linker of known elasticity, such that an increase in tension on the protein will stretch the sensor and will decrease the amount of FRET between the two flanking fluorescent proteins. As a result, the lower the FRET, the higher the tension. We cite the original paper that introduced this sensor to allow the reader to get more information (Grashoff et al. 2010). Figures now also include the direction along which the tension increases when FRET changes.
It also seems that the figures could be improved. Some of the sketches are, in my opinion, not helpful. Examples are Figure 3A (how could a cell move while the hexagonal arrangement of the cells is maintained?) or Figures 2F, 4F, and 6F (what do the colored ellipses indicate?). In Figures 1B, 1D, 2A, 2E, 3B, 3D-F, 4A, 4F, 5B-D, it is not clear which lines merely connect data points and which lines are fits to the data.
We thank the reviewer for suggesting these improvements. We have now changed figure 3A for a more realistic drawing of the epithelium. We have specified in figure legends what colored ellipses are (as explained above, they are the Venn diagram categories), and whenever a line was a fit or a curve connecting data points.
Reviewer #2 (Public review):
Summary:
The manuscript by Canever et al was assessed by three Referees at another journal, who brought up a range of critical points. I will not repeat a summary of the work; this can be found in the first-round reviews.
Strengths:
In their revised manuscript, the authors include substantial changes and additional reasoning. Along with their rebuttal letter, I think they make a very convincing case. While the claims are well supported by the analysis, I do not see that the findings need to be universal to be relevant. It might be rather surprising to me if there existed such a universality, in fact. I think that the findings are solid and interesting in their own right and are worthy of publication, especially with the amended discussion in this revision.
Weaknesses:
However, while the more bio-oriented parts are not fully accessible to me, I do have a few points from the data analysis point of view that need amendment. (1) The used mathematical models need to be specified more precisely. First, the authors confuse Levy flights and walks. These are distinct processes in the sense that a Levy flight does not have a finite variance and thus no finite speed. The proper model here would be Levy walks. As in a big body of the literature, both notions are used interchangeably here, while they are distinct processes. Then the authors speak about a "superdiffusive model", for which I do not find a proper definition. There exists an entire range of superdiffusive models, each with a different physical background, so this needs more clarity. The authors may consult one of the standard reviews for more details, e.g., Soft Matter 8, 9043 (2012) or Phys Chem Chem Phys 16, 24128 (2014). Overall, a few equations (maybe in the Supplement) would help to be more specific.
We thank the reviewer for these suggestions. We have changed the main text as follows to address these points:
- "To distinguish between the various walk models that could produce such a super-diffusive behavior (Lévy walk, fractional Brownian motion, or their combinations."
- "we found that the average Mean Square Displacement as function of lag time ∆t (MSD, see Materials and Methods) was better described by a persistent random walk (Fürth et al. 1920, Uhlenbeck et al. 1930) than anomalous diffusion: the MSD transitioned from ballistic (MSD ∼ ∆t<sup>2</sup>,∆t → 0) to pure diffusion (MSD ∼ ∆t,∆t → ∞) with a persistence time P of the order of 10min, rather than scaling as ∆t<sup>𝛼</sup> with α ̸\= 1" and the Materials and Methods as follows:
- "MSD(∆t) curves were fit with the Fürth equation MSD = 4D(∆t−P(1−exp−∆t/P)) where D is the coefficient of diffusion and P the persistence time, as a model of persistent random walk, or with MSD ∼ ∆t<sup>𝛼</sup> (straight line of slope α in ln−ln scale), as a model of anomalous diffusion, with anomalous exponent α > 1 for super-diffusion."
We thank the reviewer for the additional references, we cite the more recent Meroz et al., 2015 for reference on anomalous diffusion.
(2) For fractional Brownian motion, the authors should check the displacement correlation function; it should show slowly decaying, positive correlations. More details on the practical analysis of FBM can be found, e.g., in Phys Chem Chem Phys 27, 14350 (2025). These correlations should decay as a function of the bin time, e.g., as discussed for the opposite case of subdiffusion in Phys Rev E 88, 010101(R) (2013) [cf Fig 3b]. In general, FBM was determined to be a highly relevant process for a number of systems, including amoeba cells at shorter times, see the detailed analysis in Phys Rev Res 4, 033055 (2022). In this paper, there are also different ways to characterise the motion in terms of scaling. Exponents are detailed.
We thank the reviewer for these suggestions and references. We have added explanations in the main text and in the Materials and Methods about the metric we use to characterize fractional Brownian motion together with the suggested reference Phys Chem Chem Phys 27, 14350 (2025).
In summary, we define the angular memory as a modified angular correlation in which we consider the angular deviation θ of each elementary step of duration 10min from its immediate previous step of duration τ equal to the lag time ∆t. Specifically, the angular memory <sub>i,t</sub>(∆t) of a cell i at time t is defined as the cosine of the angle θ<sub>i,t</sub> between consecutive step vectors 
between times t−∆t and t, and 
between times t and t+10min, such that the step duration τ of the first vector and the lag time ∆t before the next are equal. M<sub>i,t</sub>(∆t) = cosθ<sub>i,t</sub> (∆t) = cos 
. Figures display ⟨M<sub>i,t</sub>(∆t)⟩. This metric measures the duration of the past that the cell considers when deciding on its direction. It decays to 0 when ∆t → ∞ for persistent random walkers and remains constant for fractional Brownian motion, similarly to the auto-covariance function of fractional Brownian motion C(∆t/τ) = ((∆t/τ +1)<sup>α</sup> +|∆t/τ −1|<sup>α</sup> −2(∆t/τ) <sup>α</sup>)/2, which is a constant C = 2<sup>α-1</sup> −_1 for all ∆_t \= τ, as in our definition of angular memory (while it decays to 0 when ∆t/τ → ∞, as pointed out by the reviewer) (Metzler et al. 2025). Ballistic motion yields 1 whatever ∆t and uncorrelated random walks, whether anomalous or not, yield 0. For this reason, and also because M<sub>i,t</sub>(∆t) was found more robust to noise than C(∆t/τ), we considered this metric a more compelling discriminant between fractional Brownian motion and other uncorrelated super-diffusive walks.
(Of Note, we have increased the analysed data set in the revised Fig. 1D, 3D,F for the sake of representativeness. The conclusions are unchanged).
(3) Some relevant approaches discussed in literature that should be discussed in the context of this work: eLife 9, e52224 (2020); Rep Prog Phys 86, 126601 (2023); Chaos 35, 023145 (2025). In the context of non-Gaussianity for active particles: Phys Rev E 104, 064615 (2021); New J Phys 25, 013010 (2023).
We thank the reviewer for these interesting references. We have added them in the discussion as follows: "More recently, efforts have focused on developing analysis tools and models to account for heterogeneous migration properties along individual trajectories and among populations (Lemaitre et al. 2023, Khadem et al. 2021, Woszczek et al. 2025, Waigh et al. 2025, Han et al. 2020)." We have also made changes in the main text to specify when and how our data could indicate the presence or the absence of such heterogeneity in the migration of single cells and at confluence.
(4) In the abstract, I am having some issues with the formulation in the sentence: "This directional memory emerges from fractional Brownian motion". It sounds as if FBM were a fully clarified phenomenon. I would prefer some statement along the lines that the data are consistent with such a mathematical modelling approach.
We thank the reviewer for this suggestion. We have changed the formulation to "This behavior is consistent with a fractional Brownian motion that emerges from velocity coordination between neighboring cells with intact intercellular adhesion." After fixing these points, I think the manuscript will clearly warrant being shared.
Reviewer #3 (Public review):
This manuscript focuses on the presence/origin of directional memory during epithelial cell migration. It starts by analyzing single cells and then moves to more complex systems (confluent layers and scratch assays). The paper first demonstrates that the migration in all of these systems is well-described by persistent random walks, which likely emerge from fractional Brownian motion. This is an important demonstration, as it implies orientation memory in the systems. Then the paper proceeds to attempt to discern the origin of this memory and claims to establish key roles for adherens junctions and vinculin dimerization. While for the most part the manuscript is well-written, there are some significant overinterpretations in experimental results. The largest issue is demonstrating the role of vinculin dimerization, which is not a well-studied phenomenon inside living cells, as all data is reliant on a single point mutation (Y1065E). Additionally, the authors seem to be over-interpreting several of the assays; the statistical analysis does not seem to encompass all comparisons made, and the molecular model proposed does not clearly explain the observed results. The discussion could also be strengthened by considering other aspects of vinculin behavior (e.g., vinculin catch bonding) as well as discussing some other recent similar papers.
(1) Likely the most significant issue with the manuscript is the interpretation of the vinculin Y1065E variant and the assumption that the only defect the mutations cause is a lack of dimerization. Vinculin dimerization is mediated by a conformational change in the vinculin tail domain induced by F-actin binding (Thompson, FEBS Letters, 2013). Dimerization of the vinculin tail domain has been clearly demonstrated in in vitro systems involving purified proteins, as the authors point out in the manuscript. However, the dimerization of full-length vinculin has not been well characterised in living cells. There are several reasons to suspect dimerization is potentially not prevalent in cells. For instance, in the presence of other actin-binding proteins, there may not be sufficient binding sites available on neighboring actin filaments to facilitate dimerization. Additionally, pY1065 vinculin and vinculin Y1065E have been associated with increased vinculin activation (Huang, JBC, 2014), so other effects seem possible. While the Y1065E variant clearly has an effect on the tension sensor readout and vinculin dynamics, further experimental evidence is needed to show that these effects are due to a lack of dimerization in living cells. To justify the definitive claims made in the manuscript, the authors likely need to develop, or employ, an assay for detecting vinculin dimerization in living cells. The authors could choose between intermolecular FRET, proximity labeling assays (i.e., antibodies with DNA for signal amplification), bimolecular fluorescent complementation (i.e., split GFP) based approaches, or some other approach. It should be noted that working with full-length vinculin, not just Vt, and designing an assay that can incorporate vinculin Y1065 variants (Y1065E and potentially Y1065A/F) would strengthen results. Also, the authors should be aware that the observation of strong dimerization may invalidate the use of FRET-based tension sensors in this system or at least necessitate intermolecular FRET control experiments.
We thank the reviewer for raising this important point. We agree that we lack direct evidence for the dimerization of vinculin in cells. Therefore, we have replaced “dimerization” with “the ability to dimerize” wherever it is relevant in the revised version of our manuscript, to emphasize that the ability to dimerize does not imply dimerization. The effects of the YE mutant may then be attributed to a lack of proper dimerization, or to any other, yet to be discovered defect dependent on the mutation but independent of the loss of the ability to dimerize. We have added a sentence in the discussion to account for this possibility:
"Whether vinculin dimerization, or another unknown independent function disabled by the YE mutation or phosphorylation of Y1065, is specifically involved in the adhesion and migration phenotypes described above may be addressed in future studies."
We thank the reviewer for suggesting experimental strategies to assess vinculin dimerization in cells. In another work, we have evidenced proximity by intermolecular FRET in FAs between proteins with no known interacting domains (doi: 10.64898/2026.04.22.720102). This raises the possibility that FRET, as well as split-GFP and PLA, the detection ranges of which are equal or larger than FRET, will not be able to discriminate between bona-fide direct interactions and very close proximity in environments such as adhesion complexes where protein densities are very high. As pointed out by the reviewer, this is the major reason why we preferred not to carry the experiments in the present paper in a null background, such that the endogenous vinculin can prevent intermolecular FRET between neighboring sensors, as we explain in the Materials and Methods.
Despite this methodological challenge, indirect evidence is largely in favor of dimerization in cells rather than against it, as explained below.
- While there are indeed many actin binding proteins in cells, the proposed scenario by which they would prevent vinculin dimerization by competing with vinculin for actin binding seems very unlikely to us. Indeed, according to this scenario, vinculin would also fail to be under tension, since it needs to bind to actin for that. We show here, as many others did before, that vinculin is under tension, implying that it is able to bind to actin. Therefore, vinculin-bound actin may subsequently facilitate vinculin dimerization.
- Additionally, vinculin also dimerizes in the absence of actin in solution, albeit at higher concentrations than in the presence of actin, equivalent to at least 0.1 molecule per 100nm² in any 2D section of the volume (doi: 10.1074/jbc.M111.244293, 10.1021/bi801764a). The density of vinculin within adhesions is promoted by the multiple neighboring vinculin-binding sites on its partners (such as talin, in which the neighboring binding sites are occupied nearly all at once upon talin stretch, doi:10.1021/acs.nanolett.6b00650, 10.1039/c5ib00307e), and is likely to reach a molecule per 100nm² in order to accommodate the traction forces that cells exert on their substrates (doi: 10.1021/acs.nanolett.6b04364). This exceeds the threshold concentration for vinculin dimerization in the absence of actin by at least an order of magnitude, and is therefore likely to offer another route for vinculin dimerization in addition to actin binding.
- Furthermore, Huang et al have shown that Y1065 phosphorylation is required for the activation of vinculin, and the YE mutant mimics this behavior, but they have also shown that the activation level is quite similar to that of WT vinculin (compared to the loss of activation of the YA mutant). Additionally, phosphorylation or the YE mutation does not abolish the head-tail interaction of vinculin, as shown by Küpper et al. (doi: 10.1016/j.bbrc.2010.07.110), who also show that YE is less stable in FAs, in agreement with our results. Altogether, these results support a model by which pY1065 is required for vinculin activation but facilitates its inactivation and release from FAs, which is easily explained if dimerization, which antagonizes head-tail interaction, is impaired.
In conclusion, in the absence of identified mechanisms to oppose vinculin dimerization in cells, and to better explain the observed phenotypes at the cellular level, vinculin dimerization in cells remains the most parsimonious scenario. We have added the following sentence to account for this:
"Nevertheless, vinculin dimerization, with its ability to antagonize its head-tail interaction and to promote actin bundling, remains the most parsimonious mechanism that can explain the stabilization of vinculin in FAs and the stiffening of cells described here and elsewhere (Tolbert et al. 2014, Küpper et al. 2010)."
(2) The authors have seemed to assume that FRAP and adhesion stability are interchangeable. To this reviewer’s knowledge, this is not the standard in the field. FRAP informs about molecular dynamics. Stability assays, which probe the spatial position of an entire focal adhesion over time (Zaidel-Bar, JCS, 2007, although other approaches are equally suitable), are typically used for assessing adhesion stability. If the authors wish to make strong claims about the stability of the adhesions, non-FRAP-based assays should be employed. Alternatively, the authors could interpret the FRAP data simply in terms of vinculin dynamics.
We agree with the reviewer. We did not wish to make claims about the positional stability of adhesions as a whole, therefore, we now specify “molecular stability” wherever needed in the revised version of the manuscript, to reflect that we refer to the stability of the molecular components of adhesions. We use the immobile fraction of vinculin and its mutants as a measure of vinculin stability and, by extension of the molecular stability of adhesions, because the stability of other adhesion proteins is dependent on the stability of vinculin, as supported by the various references we cite in the text (Carisey et al. 2013, Liu et al. 2016, Yonemura et al. 2010).
(3) A major conclusion in the manuscript is that in response to overexpression of a specific vinculin construct, focal adhesions behave the same in single cells, confluent cells, and collectively migrating cells for all the mutants but Y1065E. However, outside of the FRET measurements, there is not much evidence to support this claim. The authors should perform a greater comparison of the focal adhesions between the systems used in the manuscript (single cell, confluent cells, collectively migrating cells). Key measurements would include focal adhesion number per cell, focal adhesion size, focal adhesion orientation, vinculin dynamics (e.g., FRAP), focal adhesion stability, and some indicators of focal adhesion composition. For the last aspect, focusing on focal adhesion components that also have roles in adherens junctions, such as VASP, seems appropriate. Without such characterization, it is an overinterpretation to assume that focal adhesions are the same in each system and, therefore, effects are due to vinculin behavior in the adherens junctions.
We thank the reviewer for raising this point. We do not assume or need to assume that FAs are the same in single cells, at confluence or during directed migration. The simplest model that explains our results, however, does imply that the response of FAs to a given construct is the the same whether cells are single, at confluence or involved in directed migration. We explain below our approach:
- First, we show that the migration speeds of WT, FL, TL, T12 but not YE cells at confluence are predictable from their migration speed as single cells (Fig. S4C). Roughly, v<sub>c</sub> ≈ v<sub>s</sub> _−_10(_µ_m/h) for all but YE.
- From here, there are two possible scenarios:
(A) Each construct has the same effect on cell speed in single cells and at confluence through FAs, and AJs provide an additional downregulation of ≈ 10µm/h for all the constructs except YE. This implies that the mutants (except YE) have no effect on cell speed through AJs, since confluence induces the same decrease of ≈ 10µm/h in mutant cells as in FL or WT cells, in which vinculin is not mutated.
(B) One or several mutants have different effects through FAs on cell speed between single cells and at confluence, and there is an additional effect of AJs on migration that depends on the mutant and that compensates for the difference in the effect of the mutant through FAs between single and confluent cells, such as to preserve the observed v<sub>c</sub> ≈ v<sub>s</sub> −10(µm/h). For instance, compared to WT, T12 could increase the speed of 15µm/h in single cells and of 15−xµm/h at confluence through FAs, but decrease it of 10+xµm/h through AJs to compensate. It is already straightforward that this scenario, which adds one unknown parameter per mutant, is the least parsimonious. It would require as many additional pieces of evidence to be considered equally likely as the first scenario.
- If we review the effects of the mutants (except YE), there is no additional evidence in support of the second scenario: overall, mutants have similar effects on tension in FAs of single and confluent cells, as well as in AJs (Fig. 2D, 4D,E, S4G). They also have similar effects on stability in FAs and in AJs (Fig. 2E, 4F).
- As a matter of fact, we do not exclude the possibility that the mutants have different effects in single and confluent cells on other FA properties. In light of the above, however, the principle of parsimony requires that evidence for 1) such differences and 2) their impact on cell speed be brought first before we can consider the second scenario equally plausible.
In summary, there is no need nor other reason for x to be substantially different from 0 for any mutant, and YE is the only mutant for which there is a need for a specific contribution through AJs distinct from the other mutants to explain its difference in speed between single cells and confluent cells. We have now added a quantitative analysis of how this difference in speed changes across mutants (Fig. S4F and Materials and Methods). Results show that a power law provides an excellent description: the speed difference only depends on the molecular stability and not on the tension in AJs, and it is very weakly sensitive to the changes in stability between FL, TL and T12 while very sensitive to the low stability reached by YE. We detail this analysis below in response to Question 6.
(4) What is shown in Figure 3G is not clear. How are P/Po and alpha shown on different areas of the same plot?
We are sorry that our plot was not clear enough. For any given pair of simulation coordinates P<sub>0</sub>/τ and n=πρd<sup>2</sup>, the simulation yields either a persistent random walk or a super-diffusion behavior, based on the Akaike Information Criterion. Simulations show that the vast majority of the points that yield a persistent random walk (in blue) are grouped on the low density side of the plot, and the vast majority of the points that yield a super-diffusion behavior (in orange) are grouped on the high density side of the plot, typical of a system with two distinct phases within the space defined by the simulation parameters. Within each phase, each migration behavior changes as a function of the simulation parameters, these changes are quantified through either P/Po or alpha according to the most likely migration behavior of the phase. Of note, there are 3 regions of slight overlap: very low persistence (bottom line), very high persistence (top line) and the boundary between the two sides of the plot, because the finite number of simulation repeats (5) for each coordinate and their intrinsic stochasticity do not allow us to discriminate the two behaviors when their difference becomes smaller in these regions. Nevertheless, it is possible to determine the most probable behavior for each point and its close neighbors at the cost of some resolution, which results in a boundary that is slightly scaled rather than smooth if one looks closely. We consider that this is sufficient resolution for the purpose of this study. Our procedure is explained in detail in the Materials and Methods section. We have added a reference to this section in the figure legend for clarity.
(5) It seems that an insufficient statistical test was used in many experiments. There are comparisons being made between systems (cell migration speed, FRET index...) that are not directly compared in a statistical test. Statistical tests are limited to differences from control (over-expression of full-length vinculin), and consistent increases or decreases (not quantitative values) are taken as evidence of similarity across systems. It seems that a more rigorous and standard approach would be to use an ANOVA/MANOVA with a suitable post-hoc test to perform all of these.
We thank the reviewer for these suggestions.
- For comparisons within a system (between migration speeds only, or between FRET index only...), we selected a priori the most sensible comparisons for our analysis: between the control FL and mutants (Fig. 2B-E, 4B-F), or between front and back cells for each constructs (Fig. 6C,E). To account for multiple comparisons of FRET to FL, we have now re-analysed our data with non-parametric Kruskal-Wallis and unpaired, two-tailed Dunn’s tests, and applied corrections to the p-values with the Benjamini-Krieger-Yekutieli (BKY) two-stage step-up FDR procedure after a check for positive dependency between comparisons by bootstrapping. To account for multiple comparisons of immobile fractions in FRAP experiments, we applied the same BKY corrections to p-values obtained from fit comparisons. To account for multiple comparisons of FRET between front and back cells, we applied the same BKY corrections to p-values obtained from Mann-Whitney tests since none of the comparisons shared any condition. Overall, the corrections resulted in marginal changes in p-values that did not alter our conclusions.
- For comparisons of effects between systems (migration speed vs FRET index...), MANOVA and related approaches are not suited for comparisons between system-level means with different statistical units between systems. To account for effect sizes, we instead performed Pearson correlations or linear regressions, assuming a linear relationship between effect sizes from different systems (Fig. 6A, S2D-G, S4C,H). This allowed us to identify whether mutants have quantitatively similar effects in different systems and which of the mutants do not. Specifically, this is how we identified the YE mutant as different.
- Finally, to quantitatively support our Boolean models of speed dependence on tension, stability and tension gradients (Fig. 2F, 4G, 6F), which binarized the output of each system for the sake of simplicity (fast/slow, tensed/relaxed, stable/unstable, mechanosensitive/insensitive), we have now performed additional regression analyzes between re-scaled data (as detailed in the Materials and Methods). This allows us to quantify the contributions of changes in tension, stability, and the gradients of tension to changes in single cell migration speed, in the difference between single and collective cell migration speed, and in the ratio between collective cell migration speed and wound healing rate. As shown in the added Fig. S2I, the change in single cell speed is explained by a linear combination of changes in tension and stability with contributions of 2_/3 and 1/_3, respectively. In Fig. S4F, the difference between single and collective cell migration speed changes with the stability exclusively, and its increased sensitivity to low stability is well captured by a power law. In fig. S5C, the ratio between collective cell migration speed and wound healing rate is set by the difference in mechanosensitivity between FAs and AJs in which 1/3 of that of AJs counts. Overall, these results confirm our initial binary models.
(6) It is unclear how a lack of vinculin dimerization at adherences junctions perturbs epithelial migration, but the complete lack of vinculin tail, which can also not dimerize, does not. In other words, how can TL "have no other role in cell migration at confluence than those at FAs as in single cells." Notably, the authors do not include the tailless variation in the schematic model figures. These results should be included and explained.
We are sorry that we lacked clarity. Here was our reasoning:
Both the YE and TL mutants are indeed impaired for dimerization but they differ in two ways:
- YE binds actin and is under tension while TL does not.
- YE is not impaired for head-tail interaction and not stable while TL is.
To determine which of these properties affect cell migration, we first compared YE to the other constructs. Just like YE, FL and T12 also bind actin and are under tension, but the migration speeds of FL and T12 cells at confluence can be predicted from their migration speed as single cells, unlike YE. Therefore, tension is unlikely to explain why YE has an effect on migration speed at confluence not predictable from single cell speed. The irrelevance of tension for the scaling of single to collective cell migration speed is also consistent with the fact that TL lacks tension but still obeys the same relationship as FL and T12 between single and collective cell migration speed. Furthermore, FL and T12 also share with TL their stability in adhesions, unlike YE. The instability of YE is then likely to explain its peculiar effect on migration behavior, because this is the only feature that differentiates YE from all the other constructs, TL included. The Boolean models in Fig. 4G and elsewhere are meant to show within a Venn diagram the main molecular conformations and associated interactions of intact vinculin, and their individual and combined consequences at the adhesion and migration level. We have now specified this is the figure legends of the revised manuscript. There is no 1-to-1 mapping of each mutant to one particular conformation of the intact vinculin. Rather, the effects of the mutations on each conformation are specifically indicated in Fig. S2A.
In support of this model, we have added in the revised manuscript a corresponding quantitative analysis, as mentioned in response to Question 3 and 5. Briefly, we first looked for the optimal linear combination of change in tension and stability that could explain the change in the difference between single and confluent migration speed across vinculin constructs. This step excluded tension as a significant contributor to the speed difference. We then looked for a relationship that was an as simple as a weighted combination of two terms (i.e. with only 1 adjustable parameter) that related the changes in speed difference to those in stability only, and found the power law dependence on stability. These results, which confirm our initial analysis, are shown in Fig. S4F. Notably, they show that 1) none of the constructs have an effect on the speed difference between single and confluent cells through tension, and 2) the changes in stability across TL, FL and T12 have a minimal impact on this difference, only that between YE and the others has an impact, as highlighted by the small exponent of the power law. Overall, this is consistent with a model whereby the migration speed of cells at confluence is largely predictable from their migration speed as individuals for WT, FL, TL and T12 (Fig. S4C), with an effect of the mutants TL and T12 similar to that of the unmutated FL. The most parsimonious explanation is that the effects of those constructs on cell speed at confluence are explained by the same mechanisms as their effects on cell speed in single cells, that is, through their FAs, and that the effects of TL and T12 through AJs are not much different from that of FL, meaning that the mutations have no other role in cell migration speed at confluence than those at FAs as in single cells. We have slightly modified this sentence as above in the text to specify that the lack of effect of these mutations on migration is on migration speed specifically, not that they have no effect whatsoever.
Recommendations for the authors:
Reviewer #1 (Recommendations for the authors):
In general, I would suggest that you properly define all notions that you use. Notably, you should explicitly introduce basic concepts like the Furth model, Lévy flight, fractional Brownian motion, the shape index, and others. It gives you the possibility to introduce your notation. For the readers, it would make your manuscript more accessible as they would not need to look up these notions elsewhere. The following points and questions are not exhaustive, but might serve as a guide to improve your write-up.
We thank the reviewer for this suggestion, we have modified the text in the main manuscript to explain the notions in plain English and have added references to the Material and Methods for the more formal definitions, mathematical formulations and additional explanations whenever appropriate. All the modifications are highlighted in the revised manuscript. We hope this revised version will make our manuscript clearer.
You should give the parameter values for all fits that you provide. Table S1 should give them, if I understand correctly, but in the version I have, this table only provides p-values.
We have now added the fit parameters in Table S1 in the revised version. They are also displayed in Fig. S2D and discussed in the associated text.
Why is vinculin or FA stability related to the immobile fraction in FRAP experiments? I would suggest speaking about vinculin or FA lifetimes as measured by the characteristic recovery time obtained from FRAP. How do you interpret the immobile fraction? Do you think that there is an FA ’core’ that persists until the FA is dissolved? In that case, the immobile fraction would report on the relative core size of the FA.
We indeed consider that this is the amount of protein that remains the longer that is the most relevant to assess the molecular stability of adhesions and its impact on migration, because an adhesion must disassemble entirely to allow cell displacement away from that site.
In Figure 1B, the fits of the exponents are not very convincing, and they extend over as they cover hardly one order of magnitude.
We are sorry that figure was misleading. The extreme slopes of 2 and 1 were not meant to indicate the actual time windows where ballistic and pure diffusion would actually be observed but just the values toward which experimental data would tend at short and long lag times, respectively, for a persistent random walk. We hope the new version of the figure in the revised manuscript will clarify this. Additionally, while the range indeed appears short, it is largely sufficient to discriminate between persistent random walk and pure or anomalous diffusion based on the Akaike Information criterion, which is the quantitative and objective metric we exclusively use throughout this work to discriminate between migration behaviors. The scores range from 0 (pure or anomalous diffusion) to 1 (persistent random walk) and table S1 shows that Fig. 1B (among others) is unambiguously a persistent random walk, even within this limited fitting range.
Figure 2B, D, what do the stars indicate?
Stars indicate p-values. We now explain this in the figure legends in the revised version of the manuscript.
Please explain how the FRET index is related to stress?
We are sorry that we had overlooked this important explanation. We have now added explanations in the main text and in the Material and Methods. Figures now also include the direction along which the tension increases when FRET changes.
"This shows that impaired head-tail interaction, regardless of tension, is sufficient for vinculin stability." Sorry, but I do not understand what you mean.
We are sorry that we lacked clarity. We have modified this sentence as follows in the revised manuscript: “This shows that impaired head-tail interaction, whether associated with impaired actin-binding (TL) or increased tension (T12), is sufficient for vinculin stability with FAs.”
I would avoid the term angular memory. You might use a correlation time, that is, fit the curve to an exponential and take the corresponding characteristic time as a measure for persistence.
We thank the reviewer for this suggestion. The reason why we introduced this metric was not to extract a persistence time, which we already obtained from the MSD, but to provide compelling evidence that the super-diffusive behavior at confluence arises from a fractional Brownian motion, rather than from another type of super-diffusive walk. We now provide in the revised manuscript a more detailed explanation of this metric, why we prefer it to the more conventional velocity auto-correlation function for our purpose, along with its formal mathematical definition, as explained below. The angular memory is a modified angular correlation in which we consider the angular deviation θ of each elementary step of duration 10min from its immediate previous step of duration τ equal to the lag time ∆t. Specifically, the angular memory M<sub>i,t</sub>(∆t) of a cell i at time t is the cosine of the angle θ<sub>i,t</sub> between consecutive step vectors between times t−∆t and t and between times t and t+10min, such that the step duration τ of the first vector and lag time ∆t before the next are equal. M<sub>i,t</sub>(∆t) = cosθ<sub>i,t</sub>(∆t) = cos. This metric measures the duration of the past that the cell considers when deciding on its direction. It decays to 0 when ∆t → ∞ for persistent random walkers and remains constant for fractional Brownian motion, similarly to the auto-covariance function of fractional Brownian motion C(∆t/τ) = ((∆t/τ +1)<sup>𝛼</sup> +|∆t/τ −1|<sup>𝛼</sup> −2(∆t/τ)<sup>𝛼</sup>)/2, which is a constant C \= 2<sup>𝛼-1</sup>−1 for all ∆t \= τ, as in our definition of angular memory (while also decaying to 0 when ∆t/τ → ∞) (Metzler et al. 2025). Ballistic motion yields 1 whatever ∆t and uncorrelated random walks, whether anomalous or not, yield 0. For this reason, and because M<sub>i,t</sub>(∆t) was found more robust to noise than C(∆t/τ), we considered this metric a more compelling discriminant between fractional Brownian motion and other uncorrelated super-diffusive walks. Consistently, we found that the angular memory of single cells decayed to 0 while that of confluent cells did not and remained nearly constant within our experimental time frame.
(Of Note, we have increased the analysed data set in the revised Fig. 1D, 3D,F for the sake of representativeness. The conclusions are unchanged).
"While cells adopt a superdiffusive behavior at confluence, they do so in any possible direction." Sorry, but I do not understand what you mean.
We are sorry we were not clear. We changed the sentence as follows : “While cells adopt a super-diffusive behavior at confluence, they do so in the absence of any external guidance cue, and as a consequence without any preferred direction.”
Reviewer #3 (Recommendations for the authors):
(1) The authors have chosen to use an overexpression system. That means that wild-type vinculin is competing with the mutant vinculin in many cases. Also, the authors have chosen not to study a phospho-deficient form of vinculin or manipulate vinculin dimerization in any other way. Given that these are not the most rigorous experiment set-ups and designs possible, the authors may wish to lessen the strength of some of their conclusions.
This is correct. We have added in the discussion the following sentence to account for the possible contribution of consequences of the YE mutation other than dimerization impairment, and for the presence of the endogenous vinculin:
“Whether vinculin dimerization, or another unknown independent function disabled by the YE mutation or phosphorylation of Y1065, is specifically involved in the adhesion and migration phenotypes described above may be addressed in future studies. Likewise, whether the effects are stronger in the absence of endogenous vinculin may be addressed in a null background.” Additionally, we have modified the text throughout to change "dimerization" into "the ability to dimerize".
(2) Dominant effects have been shown for T12 and VinTL; however, to this reviewer’s knowledge, dominant effects have not been shown for vinculin Y1065E.
This is correct, we now specify it in the revised version.
(3) This statement: "Overall, this is consistent with the idea that vinculin acts primarily as a migration moderator, and this supports that insertion of the sensor module weakens to some extent the auto-inhibition of vinculin, thereby contributing to the dominance of all exogenous constructs over endogenous vinculin, in addition to the effects of their mutations" lacks experimental support. Multiple studies have shown that the effects of the insertion of the tension-sensitive module are small on vinculin, including demonstrations that FRAP recovery is unaltered. To make this statement, the authors should perform comparisons of vinculin and vinculin mutants with and without a tension sensor. If differences are observed, it seems that experiments in the manuscript should be repeated with vinculin mutants not harboring a tension sensor. If the presence of the sensor affects mutant behavior, then it seems unlikely that the claims of the paper are correct. For instance, it would suggest that vinculin dimerization is not the sole driver of the velocity coupling, instead it could be a combination of partial activation and lack of dimerization. Alternatively, the authors could remove or edit this sentence.
We agree that the second part of our statement was incorrect and have removed it. We cannot say that the insertion of the sensor module weakens the auto-inhibition and we agree that there is no difference in turnover compared to a vinculin construct without the sensor.
(4) Vinculin dimerization is drawn incorrectly in many of the schematic figures summarizing results. Vinculin dimerization is dependent on F-actin binding (see work of Sharon Campbell and others). Vinculin is unlikely to form a "dimer" in solution, as shown in these schematics. Also, vinculin dimers are thought to form along the length of two bundled actin filaments, not at the end, as drawn in the schematics.
We thank the reviewer for these suggestions. Sharon Campbell and others have indeed shown that actin binding promotes the dimerization of vinculin tails in solution. They and others have shown, however, that vinculin tails also form dimers in the absence of actin, albeit at higher concentrations, which are equivalent to at least 0.1 molecule per 100nm<sup>2</sup> in any 2D section of the volume (DOI: 10.1074/jbc.M111.244293, 10.1021/bi801764a). The density of vinculin within adhesions is promoted by the multiple neighboring vinculin-binding sites on its partners (such as talin, in which the neighboring binding sites are occupied nearly all at once upon talin stretch, doi:10.1021/acs.nanolett.6b00650, 10.1039/c5ib00307e), and is likely to reach a molecule per 100nm<sup>2</sup> in order to accommodate the traction forces that cells exert on their substrates (doi: 10.1021/acs.nanolett.6b04364). This exceeds the threshold concentration for vinculin dimerization in the absence of actin by at least an order of magnitude, and is therefore likely to offer another route for vinculin dimerization in addition to actin binding. We conclude that vinculin density in adhesions is very likely to allow dimerization in the absence of actin, and the binding to actin additionally promotes dimerization. Due to the omnipresence of actin in adhesions, the dimer unbound to actin certainly accounts for a small fraction of possible vinculin conformations, but this is likely to be very context-dependent and current knowledge is too limited to be more quantitative. We have therefore decided to keep our model simple with all these possible conformations, without indications of their possible proportions.
We agree that the drawing of the interaction with actin does not properly depict the side-binding of actin and we have modified it in the revised version. Nevertheless, vinculin and actin are known to bind by both their sides and ends (DOI: 10.1074/jbc.M110.102830).
(5) Technically, the authors have not shown that conservation of angular momentum is "infinite". It just appears longer than the experimentally accessible terms. The use of term short-lived and long-lived, instead of finite and infinite, might be more appropriate.
We agree, we have changed “virtually infinite” to “long-term” everywhere in the text to avoid confusion.
(6) The conclusions of the manuscript seem to treat vinculin loading and vinculin stability as independently regulated. However, vinculin has been shown to act as a catch, both in vitro (see work of Alex Dunn) and in living cells (see work of Sharon Campbell), suggesting strong coupling of these quantities. A discussion of the points would benefit the manuscript.
We agree that vinculin loading and stability in adhesions depend on each other and thank the reviewer for suggesting these additional references. We formulate our model as follows: “Mechanistically, our results support that the head-tail interaction of vinculin challenges both force transmission and stability of vinculin in FAs, as has been observed in other cell types (Carisey et al. 2013, Humphries et al. 2007, Cohen et al. 2006, Dumbauld et al. 2013, Liu et al. 2016, Omachi et al. 2017)...” This shared dependence and factors that independently affect force transmission or stability, but indirectly feed back to each other, are not mutually exclusive, as we state next: “... and is countered by the actin-vinculin tail interaction, which provides force transmission but is unnecessary for stability, and the ability of the tail to dimerize, which associates with higher stability and is dispensable for force transmission (Fig. 2D,E).”
Such feedback implies that the interaction between actin and the tail of vinculin can promote stability, even though it is not necessary for this (as shown with TL), and that the ability of the tail of vinculin to dimerize can promote force transmission, even though it is not necessary for this (as shown with YE). We have added the following sentence in the revised version to clarify this point:
“As a result of this feedback, stability and force transmission can be further promoted by the interaction of the vinculin tail with actin and its ability to dimerize, respectively, indirectly through their antagonism with the head-tail interaction of vinculin.” Consequently, this opens the possibility that a catch-bond between vinculin and actin also promotes vinculin stability in adhesions, which is indeed shown by the Campbell lab (doi:10.1038/s41467-023-43779-x), thanks to a prolonged antagonism with the head-tail interaction. Interestingly, their results show that the ability to form a catch bond does not appear to oppose the head-tail interaction more than actin binding does (their Fig. S10). This suggests that the actin-vinculin catch bond stabilizes vinculin in adhesions through a mechanism that has yet to be identified. In conclusion, vinculin loading and stability in adhesions are interdependent through the head-tail interaction and through another unknown mechanism that involves the catch bond between vinculin tail and actin. We now include a sentence that refers to this catch bond in the revised manuscript.
(7) Recently, two papers have come out assessing the role of AJs and mechanical coupling during epithelial migration: Noordstra, Dev Cell, 2023, and Shoyer, PNAS, 2023. Comparing the results of this manuscript to those of these publications would strengthen the discussion.
We thank the reviewer for suggesting these references.
Noordstra et al provides a very interesting investigation of the mechanisms whereby the mechanosensitive alpha-catenin down-regulates lamellipodial activity in epithelial sheets that form intercellular contacts during wound closure. This could offer a potential mechanism that would explain why we observe that cells at confluence migrate at a lower speed than single cells. While a minimal lamellipodial activity is indeed required for cell migration, we have previously shown, however, that the down-regulation of lamellipodial activity by the formation of AJs does not decrease cell migration speed (doi:10.1073/pnas.1002662107), but the formation of desmosomes does (doi:10.1242/jcs.146316). We have included these considerations in the discussion of the revised manuscript.
The main finding Shoyer et al is that the phosphomimetic mutant S1033D of vinculin relaxes vinculin tension and reverts it to a close conformation in both AJs and FAs. The mutant affects directed collective cell migration in a manner that preserves the relationship between lateral velocity deviation amplitude and coordination length but consequences on vinculin stability in adhesions, or on single and undirected collective migration are unknown. Thus, it would be worth assessing them to determine whether this mutant affects cell migration through FAs, AJs or both, and how it fits in our model. We have now included this reference in the discussion of our revised manuscript.
[Editors' note: The authors included an author response to reviews from another journal]
Reviewer #1 (Comments to the Authors):
In this manuscript the authors describe that cells in collective movements adopt a superdiffusive behavior to out pace individual cells. This behavior is regulated by cell-cell junctional stability and force transmission. The authors state that speed is regulated by vinculin through mechanosensitivity.
While is makes intuitive sense that cells may move more efficiently collectively as it reduces their exploratory space and therefore increases their efficiency of movement,
We agree that this is an intuitive explanation. However, previous literature had shown that confluent cells may or may not migrate depending on conditions that do not solely depend on the space available per cell, but also involve the intrinsic activity of the cell, its cortical tension, and its adhesion with its neighbors, with sometimes counterintuitive effects (doi: 10.1016/J.CEB.2021.07.011). This was the reason that motivated us to investigate how these various ingredients affected space exploration efficiency on different time scales.
Our results indeed refute the intuition that cells move more efficiently when their exploratory space is reduced by showing that the outcome depends on the time scale considered (Fig. S3B). Specifically, on short time scales (less than 3 hours), the area explored by individual MDCK cells is larger than that explored by MDCK cells at confluence. On a longer time scale (greater than 3 hours), however, the area explored by confluent MDCK cells is larger. This switch is a direct consequence of the change in migratory behavior from persistent random walk to superdiffusion, Moreover, its position in time depends on the cell line: extrapolation of our results on RPE-1 cells suggests that it should theoretically occur after approximately 300hrs, if this time scale was experimentally accessible (Fig. S3F).
…the role of junctions specifically is less clear.
We are sorry that we were not able to clearly convey the roles of junctions. We have substantially rewritten our text to address this and all the changes are highlighted in orange. As summarized in Fig. 6F, junctions have three roles. The first role is on persistence, through velocity coordination between neighbors, the second is on speed, through the stability of junctions, and the third role is on directionality, through the sensitivity of the monolayer to the wound edge.
The first role is evidenced thanks to the comparison of the MSD between single cell and confluent migration assays and the use of the alpha-catenin KD cell line. Alpha-catenin depletion is known to be the most potent disruptor of adherens junctions (DOI:10.1091/mbc.e06-05-0471, , DOI:10.1126/science.aaf7119, (DOI:10.1073/pnas.1002662107, DOI:10.1073/pnas.1119313109), and we show that it significantly alters the superdiffusive behavior that emerges in the confluent migration assay (Fig. 3E,F, 5C). Therefore, junction integrity is critical for the control of cell persistence.
Moreover, alpha-catenin depletion induces a loss of velocity coordination between neighbors (Fig. S3E), which we show through numerical simulations to induce superdiffusion (Fig. 3G). By contrast, E-cadherin KO and vinculin mutants have no effect on the superdiffusion of confluent cells (Fig. 3E, 4A). Therefore, the critical molecular ingredient is the link provided by alpha-catenin to the cytoskeleton that provides junction integrity.
The second role of junctions is evidenced thanks to the comparison of cell speeds between single and confluent migration assays with the vinculin mutants (Fig. S4A). Results show that cell speed is reduced of about 10µm/h by confluence, regardless of the mutant except for YE, whose only difference with other mutants is its lower stability (Fig. 4F). This supports that junction stability, and not the other effects of mutants, controls cell speed (we provide a detailed demonstration in the response to the following question). As expected, junction integrity is required as well, as seen from the higher cell speed of the alpha-catenin KD cell line compared to WT (first MSD point in Fig. 3B, E).
The third role of junctions is evidenced thanks to the comparison between confluent and directed migration assays (Fig. 6A). Results show that the wound healing rate is proportional to cell speed at confluence, regardless of the mutant except for YE, which displays no tension gradient in junctions from front to back cells (Fig. 6C). This supports that such gradient is required for cells to identify on which side is the wound edge. As expected, junction integrity is required as well, as seen from the loss of directional bias of the alpha-catenin KD cell line (Fig. 5F).
The authors chose vinculin as the basis by which to manipulate tensions at cell-cell junctions, but this comes with considerable drawbacks. Namely, since vinculin appears at both cell-cell and cell-matrix junctions, its role and the role of its mutations is not clear here. The authors state that the collective migration speed is related to junctional stability, but because vinculin is also at FA, how can this be concluded?
We apologize for the lack of clarity. We hope that the highlighted changes in the revised manuscript will improve this point. As exemplified above, comparing cell migration between isolated cells and confluent cells is essential to enable us to distinguish between the contributions of AJs and FAs. Indeed, since isolated cells lack AJs, the impact of vinculin mutants on single cell migration can only be explained by their effects on FAs. This is how we first determine the effects of vinculin mutants on migration that depend on FAs. Because confluent cells also have FAs, we expect that the effects of vinculin mutants on the migration of isolated cells will still be present in confluent cells, to which will be added the effects of these mutants on AJs and their consequences on migration, if any.
Therefore, when compared to WT cells, if a given mutant decreases or increases migration speed in individual cells, and does so in confluent cells in the same proportion, then its effects at confluence can be entirely explained by its effects in individual cells, and there are no additional effects of that mutant from AJs. This is indeed what we observe for all mutants except the YE mutant (Fig. S4C), leading us to conclude that none of the vinculin mutants, except the YE mutant, have an effect on migration at confluence that results from AJs. In contrast, the YE mutant has effects on migration at confluence that cannot be explained by its effect on individual cell migration. Therefore, the effects of YE at confluence depend on AJs, whether they result from alterations in AJs, FAs, or both. To distinguish between these scenarios, we proceed by elimination, comparing the effects of YE to those of other mutants on force transmission and adhesion stability, and how these two factors associate with migration speed, as explained below. In FAs, YE alters force transmission differently in individual cells and at confluence, but we already know from Fig. 2 that force transmission in FAs cannot alone explain the speed of migration. This result rules out an indirect effect of AJs on cell migration at confluence through FAs. Furthermore, in AJs, YE affects stability and force transmission, but TL has the same effect on force transmission as YE and we already know that none of the effects of TL on migration depend on AJs (Fig. 3, S4C). This result rules out an effect of force transmission in AJs on migration speed at confluence. We conclude that stability at the AJ level, which is the remaining property specifically impaired by YE, is what regulates migration speed at confluence.
The manuscript's logic and flow are not clear in some places, making the story hard to follow. As one example, the FRAP data, which the authors suggest is used to investigate vinculin's combined role does not help in this capacity as the interpretation and its connection to the bigger story are not clear.
We are sorry again for the lack of clarity. We used FRAP data to evaluate the effects of vinculin mutants on adhesion stability. Indeed, mutants have different effects on adhesion stability (Fig. 2E, 4F). In addition, they also have different effects on force transmission (Fig. 2D, 4D,E). The partial overlap in functional alterations caused by the mutants allows us to test the involvement of the overlapping function (here stability) in the overall migration outcome. For example, if two mutants have a similar effect on adhesion stability but different effects on migration speed (such as TL and T12), we can then rule out that speed results from adhesion stability. Similarly, if two mutants have different effects on stability but a similar effect on speed (such as TL and YE), we can also rule out that speed results from stability. We applied the same reasoning to force transmission to conclude that neither adhesion stability nor force transmission alone is sufficient for cells to migrate rapidly. However, the combination of the two enables rapid migration.
As another example, the information derived from the use of the mutants is not clear in the context of the message in the manuscript since they affect cell-cell and cell-matrix junctions and in some places show results that are counter intuitive and not well-explained, to which the authors admit they are surprising but then do not explain their meaning.
As such, it is very hard to follow the logic with regard to the information resulting from the mutant experiments.
We provide above a detailed break-down of our strategy to analyze the results. We regret that our manuscript did not adequately convey our conclusions and we hope that the new version of the manuscript improves this point.
Proliferation has been shown to play a role in wound healing. Does proliferation change in the various conditions?
This is an important point. The average speed of cells at confluence is approximately 20 µm/h (Fig. 4B), which means that each cell moves approximately its own size in one hour. During this time, assuming a 16-hour cell cycle, 6% of the cells would have divided, each of them likely pushing one of its neighbors a distance equivalent to the size of a cell. Therefore, cell proliferation accounts for at most a few percent of the total cell movement. For this reason, we can assume that growth does not account for a large part of the movement we observe. This is consistent with previous work showing that proliferation does not contribute significantly to wound healing (DOI: 10.1073/pnas.0705062104, DOI: 10.1083/jcb.201207148).
Minor comments:
The authors should provide a better description of the mutants: what does a tailless mutant not bind, or bind differently? More context is needed to help interpret the results. While the mutants have all been published on before, it would be helpful to have more information here so that the manuscript is easier to follow.
We are sorry that the information we provided was insufficient. We have now detailed the mutations to help the reader understand how interactions are altered.
Figure 1A is not necessary. Figure 1 overall is fairly predictable as there have been many papers using the persistent random walk as the best model to single cell migration (dating back to the early 1990's). The authors define a new term, angular memory, which they show decreases with increasing delta t as one would predict.
We acknowledge that persistent random walks have already been observed for individual cells, as in references 3-4 cited in the introduction. Nevertheless, we believe that Figure 1 is important because not all cells migrate as persistent random walkers when isolated. Some migrate in a more exotic manner, resulting in superdiffusive behavior, as in references 5-8 cited in the introduction. Since we observe superdiffusive behavior at confluence (Figure 2), it was therefore necessary to show whether or not single cells were superdiffusive too. We also use this figure to introduce angular memory, a measure that, to our knowledge, has never been used before. According to intuition, it decreases to 0 for persistent random walkers, just as another resembling measure, velocity autocorrelation, would do. However, the angular memory of fractional Brownian walkers does not vanish with increasing delta t (Fig. 3D), while velocity correlation would, just as that of persistent random walkers. This difference makes angular memory much more appropriate for distinguishing between the two migration behaviors, and prompted us to introduce it in the first figure as a reference.
In the wound healing assay, which cells were measured? Leading edge or interior, and does it matter?
Figure 5A shows that cells behave differently depending on their distance from the wound. This is because the traces shown correspond to the first few hours of the movie, during which the cells at the front begin to move first. Figure S5A shows the speed of the cells over time after the wound and indicates that the cells reach a stable speed after approximately 3 to 4 hours. Figure S5B shows the speed of the cells as a function of distance from the wound at steady state. These results show that the speed of the cells no longer depends on the distance from the wound at this stage. As indicated in the “Materials and Methods” section, we only considered time points beyond this stage for subsequent analyses of population-averaged MSD and velocity presented in Figure 5, so the location of cells at the front or rear was irrelevant.
Reviewer #2 (Comments to the Authors):
To migrate cells must spatially explore their environments, a process that is guided by intrinsic signals (adhesive and mechanical properties, etc) and extrinsic (gradient cues) signals. This exploration can occur on the single or multicellular level. In this study, the authors examine the effect of cell-cell interactions, guidance cues, and cell mechanics in the exploratory capacity of MDCK cells. The authors show that cell-cell adhesion provides a "infinite directional memory for migration" and cell speed is dependent upon the focal adhesion stability, cell mechanics, and the mobility of adherens junctions-these processes are modulated by vinculin.
My three major concerns with the manuscript are as follows:
(1) While there is potential new information about the role cell-cell junctions and guidance cues play in cell migration, there is not enough NEW insight presented. Rather the role of vinculin in these processes is expected given what is already known about its ability to control focal adhesion stability, mechanics, and adherens junctions.
We agree that our cell migration results make sense based on the effects of vinculin mutants on the stability and force transmission of adhesions. Nevertheless, we argue that this was not the only possible scenario. Indeed, we find that none of the effects of vinculin mutants on AJs (except YE) have an impact on cell migration (Fig. S4C). One might have expected that the increased stability provided by the TL and T12 mutants would reduce the speed of collective cell migration, just as the YE mutant increased cell speed due to its altered stability. This is not what we found, and this reveals a nonlinear relationship between AJ stability and migration speed that could be investigated more thoroughly in future studies. Another example is that the effects of the mutants on force transmission in AJs do not impact migration speed at confluence but do impact directed collective migration (Fig. 6). One might have expected that vinculin-mediated force transmission in AJs would impact collective migration, whether directed or not.
More importantly, we show that the role of intercellular adhesion in cell migration is more complex than expected. Indeed, it depends on the timescale considered: intercellular adhesion is detrimental to short-term spatial exploration and beneficial in the long term (Fig. S3B). Such a timescale-dependent behavior is impossible to predict from previously known effects of the mutants or other molecular considerations. Furthermore, we show that this behavior can be fully explained by the coordination of velocities between neighbors, which depends on intact connections between AJs and the cytoskeleton via alpha-catenin, but is independent of vinculin mutants that connect AJs to the cytoskeleton in parallel with alpha-catenin. One might have expected these connections to also have an impact on velocity coordination, and thus on spatial exploration, but we show that this is not the case (Fig. 3). Finally, we show that directed collective migration has a negligible impact on cell exploration at our experimental timescale (Fig. 5), whereas we initially expected the wound to make migration more ballistic. This reveals that such a directional signal affects spatial exploration at much longer timescales than expected.
Overall, our results quantify the outcome of competing effects and provide timescales at which one effect outweighs the other in influencing cell migration. We believe this is an original approach that provides substantial new insights into collective cell migration.
(2) The phenotypes of the cells expressing the mutant vinculins varying greatly. These phenotypes are not addressed despite the fact that they could potentially complicate the analyses. For example, there are dramatic differences between focal adhesion numbers and sizes in the cells expressing the different vinculin mutants; cell spreading is also dramatically altered. Likewise, the T12 mutant vinculin has previously been reported to have increased adhesive strength, increased traction forces, and cell spreading. How does this knowledge change the interpretation?
We agree that vinculin mutants may have effects on the size and number of FAs, cell spreading, and traction forces that we do not examine here. These consequences can be explained by the effects of these mutants on force transmission in FAs and on their stability, which we report in our work. They do not affect our interpretations. Here, we provide a predictive model of migration speed based on the combination of two consequences of vinculin function, namely stability and force transmission. An interesting avenue for future research would be to assess whether these combinations can be reduced to a single higherlevel effect of vinculin on the cellular phenotype that would be sufficient to predict migration speed. This work remains to be done, as neither FA size and number, cell spreading, adhesion force, nor traction forces alone are sufficient to predict migration speed.
Along the same lines, it has previously been established that tagged version of vinculin do not efficiently integrate into adherens junctions. Published work from the Nelson laboratory suggests that GFP-vinculins do not localize to cell-cell junctions and work from other laboratories suggests localization occurs only when the endogenous vinculin is silenced.
We are aware that some GFP-vinculin constructs may not localize as well as the endogenous protein at AJs. This is due to the localization of the GFP tag on the head of vinculin and depends on the length of the linker between GFP and the head of vinculin. The longer the linker, the easier the interaction with AJ partners. Unlike these constructs, the vinculinTSMod sensors we use in our work do not carry a GFP on the head and do not suffer from the same limitations.
Furthermore, vinculin recruitment to AJs depends on force, with little or no recruitment when tension on the AJs is relaxed (DOI: 10.1038/ncb2055). Vinculin recruitment has in fact already been used as an indicator of AJ tension in Drosophila (DOI: 10.1038/s41467-01807448-8). Consequently, the amount of vinculin visible at the AJs varies depending on the tension exerted on the AJs, which our results confirm: vinculin is more difficult to detect at the AJs in cells located at the front of a wound than in those located at the back (Fig. 6B), which is consistent with the difference in vinculin tension between front and back cells (Fig. 6C) and to the E-cadherin tension gradient between front and back cells (DOI: 10.1083/jcb.201706013). Overall, these results show that vinculin is not always easy to detect at AJs, but this is due to the properties of vinculin, which the constructs we use reproduce better than previous constructs (see also below).
The images in figure S2 and the prebleach images in figure S4 do not show convincing localization of the mutant vinculins to cell-cell adhesions. This is of special concern given that YE mutant protein hardly has any discernable localization to cell-cell junctions; additionally, none of the mutant proteins were tested for their ability to co-localize with adherens junction components. This raises the question if the parameters being examined and the conclusions drawn from them are affected by a difference in localization.
We agree that the recruitment of vinculin at intercellular contacts may be difficult to see.
Besides force-dependent effects mentioned above, other factors are involved. The images shown in Figures S2 and S4 are from live cells in which cytoplasmic vinculin is still present, and its level proportional to the mobility of vinculin. Indeed, the TL and T12 mutants show a more marked contrast between intercellular contacts and the cytoplasm, which is consistent with their greater stability at AJs (Fig. 4F). Conversely, YE shows lower contrast, which is consistent with the lower stability of this construct at AJs (Fig. 4F). The FL construct lies between the two. As a result, the cytoplasmic content can variably mask vinculin recruitment at the AJs depending on the mutant.
We have now performed additional quantifications of mutant recruitment at intercellular contacts as a function of distance from the basal surface of the cells and relative to their recruitment in FAs, in live cells. Results are shown in the new Fig. S4F. We find that all the constructs are recruited to intercellular contacts with a density that is at most half of that in FAs and that varies along the height. FL shows the highest density, localized more apically, consistent with the localization of an AJ-bound actin belt. The mutants appear to be more homogenously distributed along the height of the lateral surface, which may be explained by their impaired autoinhibition (TL, T12), or mechanosensitivity (YE). This variability also contributes to the difficulty in seeing vinculin recruitment in all cells in a single z-slice.
To confirm the proper recruitment of vinculin constructs to AJs we have performed immunofluorescence against alpha-catenin and phalloidin on each of the stable cell lines. Results are shown in the new Fig. S4D and E. In these experiments, cell permeabilization allows for the release of some of the cytoplasmic pool of vinculin, which highlights the recruitment of all vinculin constructs to intercellular contacts. There, all vinculin constructs colocalize with alpha-catenin and F-actin, as expected. Additionally, images displayed are maximum intensity projections to mitigate recruitment variability along the height.
Overall, our results clearly support the localization of vinculin at intercellular contacts, and the differences between the constructs are consistent with the effects of their mutations.
(3) There is a lack of new mechanistic insight. Conclusions are made about a role of vinculin dimerization. This conclusion appears to be based upon the usage of the mutant version of vinculin Y1065. Did the authors directly measure the ability of this mutant protein to dimerize? Is actin binding also affected.
The binding properties of the Y1065E mutant, including its dimerization and binding to actin, have already been characterized by other researchers (ref. 40 in our manuscript, as well as DOI:10.1111/j.1432-1033. 1997.01136.x or DOI: 10.1016/j.febslet.2013.02.042). We assumed that these properties are now well established and can be used to explain higher-level phenotypes that we show for the first time, to our knowledge.
Reviewer #3 (Comments to the Authors (Required)):
Canever et al. tracked two epithelial cell lines on collagen coated glass and showed that isolated cells (non confluent) move as persistent random walkers, whereas confluent monolayers migrate super diffusive, with long range directional memory. By systematically perturbing adhesion machinery they found that focal adhesion mutations mainly tune the speed of single cell tracks, but cannot create long range memory, while force bearing adherens junctions are essential for the super diffusive regime-genetically perturbing them collapses collective memory. These interesting results identify junctional tension as important to switch epithelial cells/sheets between individual and collective search modes - an important quantitative insight that is of clear relevance to cell biologists.
- The presented data is nicely quantitative and convincing, but I have subtle concerns about the generality of the findings. While the authors show that the differential behavior, they describe is not cell-line specific (MDCK, RPE), there are no experiments evaluating the generality of their conclusions across different matrix conditions. How are the measured migration parameters affected by matrix stiffness? Cell migration on collagen coated glass coverslips is a relatively narrow and artificial condition. How is the collective directional memory expected to behave on softer substrates? The generality of the conclusions could be strengthened by repeating measurements using hydrogels of varying stiff-ness. Further, it should be discussed to which tissues in the body the selected matrix conditions and migration modes plausibly apply.
We agree that the generality of our results and the relevance of glass-rigid substrates is an important point. In vivo, epithelial cells rest on a basement membrane with a typical stiffness of approximately 10 MPa, as demonstrated by experimental evaluations on various tissue explants, including renal glomeruli and Bruch's membrane, which are relevant to MDCK and RPE-1 cells (DOI: 10.1111/j.1742-4658.2007.05823.x, DOI: 10.1172/JCI106898, DOI:10.1038/eye.1987.35), we have added these references in the manuscript to support our experimental strategy. In vitro, the most significant effects of substrate stiffness on FAs and cell migration generally occur at much lower stiffnesses, between 0.2 and 100 kPa, and cell phenotypes generally plateau at levels comparable to those observed on glass, even below 100 kPa (DOI: 10.1242/jcs.133645, DOI: 10.1038/ncb3268, DOI:10.1039/c5ib00307e, DOI: 10.1039/c9sm01893j). Furthermore, substrate stiffness has much more moderate effects on confluent cells than on isolated cells. For example, it has been previously demonstrated that confluent layers of MCF10A epithelium showed no change in velocity coordination in the range of 3 to 65 kPa (DOI: 10.1083/jcb.201207148). Therefore, collagen-coated glass appears to be a reasonable model for the basement membrane. Overall, we believe that we have conducted our experiments under physiological conditions, and that our results apply to a wide range of substrate stiffnesses.
- It would be nice to see how long it takes confluent cell layers to close rectangular wounds of defined size when cells migrate as individual (adherens junctions perturbation) versus collective (wt) (on substrates of different stiffness). Presumably, there should be faster wound closure under the collective regime, at least for simple shaped wounds.
This is an interesting question, which our results indirectly address. In our study, we measured the wound healing speed of the WT MDCK cell line as well as lines expressing mutant vinculin constructs (Fig. 6A). These results show that this speed ranges from 5 to 15 µm/h depending on the construct expressed (and for reasons that we explain in the manuscript). These values make it easy to estimate the time required to close a wound based on its width. For example, it would take 5 hours to close a 100 µm wide wound for the WT cell line, which has a rate of 10 µm/h (on both sides of the wound).
Wound closure for cells with disrupted adhesive junctions has already been documented (DOI: 10.1083/jcb.200910041). The results show that wound closure is indeed slower than with WT cells. Although this previous study does not reveal the underlying causes, our work now shows that there are two factors: weaker directional memory due to impaired intercellular coordination and, in the longer term, an additional lack of sensitivity to the guidance signal provided by the wound.
- Akin to substrate stiffness variation, I am missing experiments that test the effect of cytoskeletal tension on these migration modes. Experiments with Rho kinase or myosin inhibitors could meaningfully broaden the scope of this study.
Rho kinase or myosin inhibitors applied to cells during the time required to assess migration patterns (a movie recorded overnight is necessary to obtain a statistically reliable calculation of MSD over 3 to 4 hours) are likely to affect many other cellular processes in addition to the cytoskeletal tension directly involved in migration. We believe that the accumulation of these effects will make interpretation of the results very difficult. For example, it has been shown that inhibition of ROCK by Y27 promotes healing of corneal endothelial lesions by affecting proliferation through cyclin D and p27 (DOI: 10.1167/iovs.13-12225), or by improving respiration, which would provide the energy necessary for migration (DOI: 10.1096/fj.202101442RR). Consistently, another study on HaCaT epidermal cells confirms that myosin phosphatase accelerates wound healing through proliferation (DOI: 10.1016/j.bbadis.2018.07.013). In contrast, in HUVEC cells, ROCK inhibition significantly impaired the proliferation and migration of vascular endothelial cells in vitro in a dose-dependent manner (DOI: 10.1097/ICO.0000000000000493).
Furthermore, previous studies have highlighted that differential contractility at the subcellular level is important for collective migration (DOI: 10.1038/ncb2133, DOI: 10.1083/jcb.201706013), which is not possible to examine with global activation or inhibition of contractility. This prompts the development of more refined and specific measurement and disruption strategies to assess the respective impact of cytoskeletal tension on cell-cell and cell-matrix adhesion mechanisms. Our work, which uses biosensors to assess how this tension differentially affects cell-cell and cell-matrix adhesions, is a step in this direction. The localized spatio-temporal activation or inhibition of myosin subtypes or Rho GTPase regulators specific to these adhesion structures will likely answer these questions in the future, but we believe that the development and application of these approaches will require a substantial amount of work that goes beyond the scope of our study.
