Reconstructing stochastic cell population trajectories reveals regulators and heterogeneity of endothelial flow-migration coupling driving vascular remodelling
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Abstract
Emerging concepts of developmental vascular remodelling recently identified that selectively labelled sprouting tip cells and/or venous endothelial cells (ECs) accumulate in developing arteries, suggesting directional migration of specific ECs drives artery formation. However, a general population analysis and detailed quantitative investigation of migratory mechanisms is so far lacking. Here, we developed a universally applicable quantitative approach and a computational model allowing to track and simulate stochastically labelled EC populations irrespective of labelling density and origin. Dynamic mapping of EC distributions in a bespoke coordinate system revealed how ECs move during the most active remodelling phases in the mouse retina. Simulation and parameter sensitivity analysis illustrated that the population shift from veins to arteries cannot be explained by random walk, but best fits to a tuneable dual force-field between shear-force induced directionality against blood flow, and hypoxia mediated VEGFA-gradients. High migration rates require only weak flow-migration coupling, whereas low migration rates require strong coupling to the flow direction. Functional analysis identified Cdc42 as the critical mediator of overall population movement from veins to arteries, yet with surprising heterogeneity suggesting the existence of distinct cell populations. This new quantitative understanding will enable future tailored intervention and tuning of the remodelling process.
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Manuscript number: RC-2023-02131
Corresponding author(s): Holger, Gerhardt
Reviewer #1
Evidence, reproducibility and clarity
Summary: Giese et al. use genetic lineage tracing techniques and novel computational analysis methods to quantify and predict how the spatial distribution of ECs changes over time in the developing mouse retina from P5 to P9. They also develop mathematical models to describe and predict the response of ECs to the hypothesized dual force-field formed by the chemoattractant VEGFA, and the flow-induced shear stress.
Given that the mouse retina cannot be live imaged, these new methods are essential …
Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.
Learn more at Review Commons
Reply to the reviewers
ReviewCommons Reviews Point-by-Point
Manuscript number: RC-2023-02131
Corresponding author(s): Holger, Gerhardt
Reviewer #1
Evidence, reproducibility and clarity
Summary: Giese et al. use genetic lineage tracing techniques and novel computational analysis methods to quantify and predict how the spatial distribution of ECs changes over time in the developing mouse retina from P5 to P9. They also develop mathematical models to describe and predict the response of ECs to the hypothesized dual force-field formed by the chemoattractant VEGFA, and the flow-induced shear stress.
Given that the mouse retina cannot be live imaged, these new methods are essential to infer cell dynamics from static images. With these methods the authors confirm previous findings that arteries are formed by endothelial cells derived from veins, or tip cells, or capillaries. They then combine their genetic system with Cdc42 and Rac1 floxed alleles to understand the function of these genes in EC mobilization. They find that Cdc42 has a very strong role in EC mobilization to arteries, not so much to the sprouting front, whereas the loss of Rac1 has relatively minor effects in vivo. Loss of Rac1 slows the cells but they maintain their directionality towards arteries. The discussion section and integration of these paper findings with previous work in the field is excellent.
Overall, this work provides a much higher level of quantitative analysis of endothelial cell dynamics in the developing vasculature of the mouse retina. It also provides mathematical models that can be useful to explain and predict the impact of other genetic mutations or pharmacological interventions during vascular development.
Reviewer #1, Major comment 1: This work provides one of the finest examples of quantitative biology in the vascular biology field. The conclusion on cell dynamics is however largely based on static images measurements and pre-defined mathematical models given also previous work and the proposed model of a dual-force field. The authors conclude that sprouting front cells mainly migrate away towards VEGF, whereas remodelling plexus cells migrate towards arteries. However, this is based on the entire EC population measurements/displacement and averaging, and does not account for the possibility of a few ECs having a different behaviour from most of their neighbours. This comment is related with the fact that arteries are formed mainly by tip-derived ECs, the cells closest to the VEGF source and further away from the flow/shear stress force. It seems the authors model presented here would not allow this to happen. According to the model presented, it seems that an EC close to the VEGF source, and subjected to low flow (shear stress), would always migrate to the front and never turn back towards arteries. Can a more complex model enable the consideration of the stochastic loss of VEGF signalling (or gain of shear stress sensing) by some ECs at the sprouting front ? And their subsequent formation of arteries ?
Response: We would like to thank the reviewer for these insightful views and for raising these questions. The aim of the computational model is to provide the simplest possible model that can be used to obtain estimates of EC migration patterns. This computational model can be used to introduce other aspects of endothelial cell behaviour, including proliferation and subpopulations with different migration sensitivities. However, the stochastic loss of VEGF signalling or gain of shear stress sensing is modelled by a stochastic term. For example, the coupling strength for control is 0.36, meaning that 0.36 is explained by directed migration along the force field, while the remaining 0.64 of the movement is stochastic. We agree that this could also be modelled differently to disentangle this stochastic term if more data on subpopulations were given. This will certainly be a fruitful direction for future research. As we do not have explicit proliferation data or data on subpopulations, direct inclusion of these extensions is difficult to validate and justify, or must be based on further assumptions or speculation.
We agree with the reviewer that there are likely distinct subpopulations, given also that ECs can compensate for higher sensitivity to shear stress or VEGF-A with higher migration speed (see Figure 2 F). Currently, the shear stress cue is overwritten by the VEGF-A cue in the sprouting front. Furthermore, both cues cancel each other out in the transition region from remodelling plexus to sprouting front, this was also suggested in (Barbacena et al., 2022) though this behaviour can be explained by heterogeneity or ECs being randomly polarised due to conflicting cues, which is not directly resolved by the data.
Nevertheless, random migration is a very inefficient strategy as shown in our theoretical investigation. Therefore, at the system level, it seems to be a much more efficient strategy to have EC subpopulations, since in this case ECs would migrate directly along one of the cues, rather than behaving randomly due to conflicting cues. We will add these considerations to the discussion of the mathematical model.
*Reviewer #1, Major comment 2: Previous work by Laviña et al showed that Cdc42 is required for the migration of ECs to the sprouting front. The authors' data suggest that Cdc42 is not necessary for this process. *
__Response: __We thank the reviewer for highlighting these points. We report a significant phenotype for Cdc42 depleted cells in the sprouting front: There is a significant shift of labelled cells from arterial to venous direction from P5 to P9 and in particular from P8 to P9. Therefore, the region of the sprouting front above the artery lacks Cdc42-depleted ECs, which can be clearly seen in the KDE plots in Figure 3.
A very important difference in our study is the definition of the sprouting front. In our study, the sprouting front is a whole region that extends to the tip of the vein, see Figure 2 and explanation on page 7: “Additionally, each retina was divided into a remodelled region containing mature veins and arteries (in the following called remodelling plexus) and a region lacking mature vessels (called sprouting front)”. However, in Lavina et al., as well as in some other studies, for example Barbacena et al., the sprouting front is the very end of the retinal vasculature. In Barbacena et al. the sprouting front is VEGFA dependent on the 100-200 μm from the very edge of the vasculature, whereas our region of interest extends much further for about 500 μm and we may lose definition for a tip cell related Cdc42 phenotype in our analysis. Our KDE plots extending from 0 (optic nerve) to 2000 μm (end of the vasculature) show a clear sprouting front Cdc42 phenotype, indicating that there is an accumulation of Cdc42 KO ECs at the end of the veins. However, these plots lose definition in the region analysed in Lavina et al. and Barbacena et al. Therefore, we will extend our analysis and explicitly report cell number proportions in the sprouting front that are compatible with (Lavina et al., 2018) and (Barbacena et al., 2022).
We will add a quantification of EC proportions in the sprouting front to make our study more comparable.
*Reviewer #1, Major comment 2 (continued): Could the difference between previous and the authors results be technical and related with the different stage of induction/analysis or the extent of Cdc42 deletion ? Did the authors tried inducing at P1 and collecting at P6/P7 ? *
Response: Lavina et al. used earlier induction time points at P0, P1 and P2. We do not know whether a retina at P5 requires less Cdc42 for sprouting front development. At this time point (P5), there is a distinct vasculature of veins, arteries forming a vascular plexus, and a sprouting front in contrast to P1, which is mainly a small vascular plexus. We will add these important points to the discussion of our manuscript.
In order to understand the role Cdc42 and Rac1 play in sprouting angiogenesis and migration to the sprouting front that is not influenced by different Cre lines and induction regimen, we will co-culture control and Cdc42/Rac1-deficient HUVECs in 3D microfluidic devices and analyse their sprouting over a number of days . With this assay we will be able to quantify the proportion of siCdc42/siRAC1 cells in tip and stalk positions compared to control, as well as do junctional and cytoskeletal analysis via immunofluorescent staining. See preliminary data in __Additional Fig. 1 __.
Reviewer #1, Major comment 2 (continued): The reporters used were also different and they may have different sensitivities to tamoxifen (and hence report Cdc42 deletion differently).
__Response: __This is correct as different Cre reporters have been shown to exhibit different sensitivities to recombine, including some with tamoxifen independent activity. The mTmG reporter we used however has been shown to reliably report tamoxifen induced recombination. Nevertheless, given that the reporter allele and the floxed gene alleles can recombine independent from each other, we cannot exclude the possibility that some GFP expressing cells still express Cdc42 or Rac1, or that some cells that have lost expression of Cdc42 or Rac1 due to recombination remain GFP negative. Statistically, these will however be rare events. The fact that we track all GFP positive cells allows us to draw conclusions on population behaviour, but not necessarily on the validity of any specific cell. The power of our analysis lies in the ability to draw conclusions on many randomly labelled populations across multiple time points without the necessity to validate each individual cell. The fact that the GFP population that carries floxed alleles for Cdc42 or Rac1 behave differently from those that do not provides strong evidence for successful loss of function for most of the cells. Importantly, unlike conventional full KO, this altered population behaviour occurs in the absence of an overt overall tissue phenotype, as we only lose gene function in a subpopulation of endothelial cells. The fact that we observe distinct deficiencies for migration towards the artery but not towards the sprouting front is therefore likely a true reflection of distinct functional importance and not evidence for a technical problem. Orthogonal evidence for such a selective role stems further from our in vitro cell culture experiments.
In the revised manuscript, we will include new mosaic flow-migration microfluidic studies as well as the mosaic vessel-on-chip assays mentioned above to independently verify the selective role for Cdc42 in flow-migration coupling versus sprouting (Additional Fig. 1).
Reviewer #1, Major comment 3: In the last section, some of the junctional/polarity/actin markers and analysis done in vitro could be also done in vivo.
Response: We agree with the reviewer that these experiments could be very informative to investigate junctional, polarity and actin markers. However, we believe that without a specific question these experiments would be rather explorative, do not add significant information to the current message of the study and can therefore not justify further animal experiments. This should in our opinion be the subject of future work.
We will however, quantify junctional markers in a sprouting 3D assay, see response to Reviewer #1, Major comment 2.
*Reviewer #1, Major comment 4: *The extent of Rac1 deletion in the mosaic experiments (done with suboptimal doses of
tamoxifen) could be analysed. This is especially relevant since minor effects for Rac1 were observed in these in vivo experiments.
Response: Lavina et al. used earlier induction time points at P0, P1 and P2. We do not know whether a retina at P5 requires less Cdc42 for sprouting front development. At this time point (P5), there is a distinct vasculature of veins, arteries forming a vascular plexus, and a sprouting front in contrast to P1, which is mainly a small vascular plexus. We will add these important points to the discussion of our manuscript.
In order to understand the role Cdc42 and Rac1 play in sprouting angiogenesis and migration to the sprouting front that is not influenced by different Cre lines and induction regimen, we will co-culture control and Cdc42/Rac1-deficient HUVECs in 3D microfluidic devices and analyse their sprouting over a number of days . With this assay we will be able to quantify the proportion of siCdc42/siRAC1 cells in tip and stalk positions compared to control, as well as do junctional and cytoskeletal analysis via immunofluorescent staining. See preliminary data in __Additional Fig. 1 __.
Reviewer #1, Minor comment 1: Lee et al., 2022 is a review. Better cite the original papers if possible: Some examples: Xu et al., 2014, Pitulescu et al 2017 and Lee et al., 2021.
Response: We would like to thank the reviewer for this suggestion and have added the citations to original publications where appropriate (see page 2 in the “Introduction” section).
Reviewer #1, Minor comment 2: Figure 1A: Stage of induction with tamoxifen is missing. Likely P5.
Response: We added this information to the caption of Figure 1A, Figure 3A and Figure 4A.
Reviewer #1, Minor comment 3: Figure 3 and 4 data would be easier to compare/understand by readers if part of the Wt data in Figure 1 was also plotted here. Or at least a Wt trend/average line on top of the mutant data, for us to see how much Cdc42 or Rac1 deletion changes the behaviour of the mutant cells versus the Wt cells.
Response: We agree with this suggestion and will add the wild type data for comparison in Figure 3 and 4.
Reviewer #1, Minor comment 4: Overall, for all dot plots and heatmaps, would be better to indicate the total number of cells analysed/plotted since the power of the analysis is related with cell number rather than number of retinas.
Response: We would like to thank the reviewer for this comment and agree that indicating the number of cells analysed allows the reader to contextualise the results more easily. We will therefore add estimated numbers of cells analysed to the respective figures. However, it is important to note that we used labelled pixels (GFP and ERG positive) as a proxy for the EC distribution, but did not segment out single cells. We always used the same number of 10,000 randomly selected pixels by bootstrapping to quantify the endothelial cell distributions. The heat map plots summarise the single cell behaviour pooled together for all retina samples. Therefore, each retina contributed equally to the analysis. This way, we could provide statistics on independent biological replicated samples for a thorough analysis.
Significance
General assessment: This paper is very strong on the quantitative analysis and mathematical modelling. Methods used and the model proposed may be of broad relevance for the field of vascular biology. It is however based on certain author-defined parameters and assumptions. EC dynamics in vivo can be much more complex than what can be modelled by equations. For example, heterogenous single cell genetics and signalling inputs can induce changes in cells that override the normal/average behaviour of the cells that are modelled. Despite the high level of quantitative analysis and modelling, the main findings here presented are not entirely novel, given previous work. For example, it was previously known that arteries are formed by vein/tip/capillary cells. It was also known that Cdc42 was required for proper EC migration away from veins (Laviña et al., 2018). However, the better quantitative analysis here presented does provide a higher level of detail and reliability.
The mosaic genetics used to delete Cdc42 is in general clear since few reporter positive cells can make arteries, suggesting efficient deletion of this gene. The data also goes in line with previous work. However for Rac1, given that a much weaker phenotype was observed, is not possible to be sure that all GFP+ ECs had deletion of Rac1. This is especially important in mosaic genetic experiments, using a suboptimal dose of tamoxifen. The extent of Rac1 deletion in GFP+ cells was not analysed. Which leaves the open question if Rac1 is really dispensable for EC migration and arterialization. Embryos lacking Rac1 in endothelial cells die early during development. Therefore ECs fully lacking Rac1 may have stronger defects than the ones shown here. All this data was obtained in the postnatal retina angiogenesis system. Other organ vessels may develop differently. Future work will tell if the models proposed can explain the dynamics of ECs during the growth of other vascular beds.
*Audience: Vascular/Cell biology researchers and bioinformaticians developing tools for image analysis and/or cell migration/dynamics modelling. *
Expertise of Reviewer: Vascular Biology. I do not have sufficient expertise to evaluate the mathematical modelling part of this paper.
Reviewer #2
Evidence, reproducibility and clarity
Summary: Giese et al., develop a computational model to track the migration of tip and venous endothelial cells (ECs) into developing arteries in the mouse retina. They validate prior studies which have identified important roles of shear stress and VEGFA gradient in driving EC migration and argue through their model that these dual forces shift in their relative influence along the vein-artery axis. Through in vivo lineage tracing in combination with Cre-inducible mouse knockout models, Giese et al., explore the role of Cdc42 and Rac1 in flow-migration coupling. They conclude that Cdc42, and not Rac1, is the primary driver of polarized flow-migration, further confirming this result with in vitro flow experiments involving siRNA depletion of Cdc42. Finally, the authors show that Cdc42 also plays a role in EC migration independent of flow, highlighting that its role in migration is universal and directly related to both actin regulation and cell junction dynamics.
Reviewer #2, Major comment 1: In Figure 1 the authors show retinas from a Vegfr3CreERT2 mouse model and make the claim that Vegfr3 is expressed in ECs in developing veins and tip cells, but not in arteries. Ralf Adam's group has previously shown that while Vegfr3 is more abundant in veins and capillaries than in arteries, expression is not exclusive to these EC subtypes (Ehling 2013). They, along with studies from Christer Betsholtz and Kari Alitalo's groups, show that Vegfr3 is observed in postnatal arteries (Tammela 2008, Ehling 2013). Indeed, from the panels in figure 1 (specifically P6 and P7) and supplemental figure 1, it appears that there may also be low levels of Vegfr3 expression in the arterial branches of postnatal Vegfr3CreERT2 mouse retinas. The authors should consider revising their statement that "ECs in arteries, however, do not express Vegfr3" and provide quantification of the number of GFP-labeled cells in arteries, veins and capillaries for each postnatal time point. Additional lineage tracing from later stages when migration into arteries is halted would be a good control for demonstrating that Vegfr3CreERT2 is not expressed in arteries, further support the authors' argument and conclusions.
Response: We would like to thank the reviewer for this comment. The percentage of labelled ECs in arteries of retinas for mice injected at P5 and collection at P6 is close to zero in all conditions (0.38 % +- 0.08 % (SEM) for control, 0.35 % +- 0.05 % (SEM) for Cdc42 depleted and 0.36 % +- 0.07% (SEM) for Rac1 depleted ECs). The same is true for mice injected at P8 and collected at P9 (0.22 % +- 0.06 % (SEM) for control, 0.11 % +- 0.03 % (SEM) for Cdc42 depleted and 0.11 % +- 0.01 % (SEM) for Rac1 depleted ECs).
As suggested by the reviewer, we will therefore revise the statement "ECs in arteries, however, do not express Vegfr3" and instead present the exact numbers in the revised manuscript, as this claim cannot be rejected or supported by our data. We will also add a discussion with references to (Tammela et al. 2008, Ehling et al. 2013) in our revised manuscript.
We would like to point out that this does not change the results of our study, since for us the Cre line is primarily a tool to label ECs of venous and microvascular origin to follow the change in EC distributions over time, and any pan endothelial Cre line would be suitable for our analysis. This was demonstrated for example in (Jin et al. 2022), where ECs with Cdh5Cre-induced expression of iSureCre+ MbTomato were used.
In addition, we will provide percentages as well as total cell numbers for labelled ECs in veins, arteries and capillaries in the revision of our manuscript for all time points and conditions.
Reviewer #2, Major comment 2: In their computational simulations, the authors investigate three models: random walking of ECs (M1), VEGFA gradient driven migration (M2), and integrated VEGFA /shear-stress driven migration (M3). The reader naturally wonders why a model considering only shear-stress driven migration is not presented as a control simulation. The absence of this model reduces the strength of the claims that only the M3 model captures observed EC movement rates, the mean population shift in vein-artery distance, and the arterial proportion of ECs.
__Response: __We agree with the reviewer and will certainly add a simulation of a shear stress only model and introduce it as model M4 in the revised manuscript.
Reviewer #2, Major comment 3: Much of the terminology in this study needs more detailed explanation and carefully usage. It would be more friendly to readers if they were consolidated into a box figure or table. (For example, how is coupling strength related or different from coupling rate?) The reported numbers of these factors are noted separately in text here and there. It would be helpful to put them together to highlight the difference between different models and mutant strains, as this is one of the novel findings for this study.
Response: Thank you for pointing this out. The word 'coupling rate' was incorrectly used in the introduction on page 3. It has been replaced by 'coupling strength', which is used throughout the text. We will add a table with quantitative information and explanations of parameters.
Reviewer #2, Major comment 4: The lack of labelled cells in the P9 retinas of Cdc42iECKO mice is striking (Figure 3), and strong evidence for the importance of Cdc42 in the migration of ECs towards arteries. The authors cite Lavina et al, 2018 when they note that Cdc42 depleted ECs proliferate at normal rates, but independent verification of this observation through EdU quantification would allow the authors to distinguish between the two possibilities outlined on page 15 of the manuscript: local proliferation vs enhanced migration of non-recombined ECs. These experiments and analyses are expected to be quick (depending on the availability of mice) and low cost. An independent EC proliferation analysis would also give the authors insight into the degree to which localized proliferation likely impacts vein-artery migration, a parameter which is currently unaccounted for in their computational model. The authors recognize that the lack of EC proliferation parameters is a limitation of their current model and speculate that this makes their estimates of vein to artery migration slightly too low. Independent EC proliferation analysis would thus be informative and may allow the authors to remove this speculation from their discussion.
__Response: __We do not see any reduction in the labelled Cdc42 population (Supplementary Figure 2) at P9. for all conditions we observe an increase of the labelled population from earlier to later stages (P6, P7, P8, P9). Our analysis is robust with respect to EC number, meaning that changes in EC number does not change the distribution. Our computational model would only slightly be affected by the difference in proliferation between arterial and venous beds, but not by the total number of proliferation events.
It is known from the literature that endothelial activation by shear stress is associated with inhibition of EC proliferation (Dejana et al. 2004; Bogorad et al. 2015). We used immunostaining to label phospho-histone H3 (pHH3) in perfused monolayers after 12 hours of flow exposure to uncover the effects that downregulation of Rho GTPases might have on the proliferation of ECs after exposure to flow. The biomarker pHH3 is a well-established standard for detecting the late G2 phase of mitosis. As shown in Additional Fig. 5, and in agreement with the literature, proliferation of ECs under flow conditions decreased by 27.38% compared to control static conditions. Following Rho GTPase depletion, siCdc42 cells further decreased their proliferation by 22.48% compared to control flow conditions, respectively. No significant difference was observed in siRac1 cells. It has been shown that the absence of Cdc42 increases EC apoptosis both in vivo and in vitro (Barry et al. 2015; Jin et al., 2013), but the role of Cdc42 in endothelial proliferation remains unclear in the literature. The data presented here suggest that Cdc42 has a modest effect on endothelial proliferation under flow in vitro. As shown in Additional Fig. 5, none of the loss-of-function conditions appeared to drastically alter the effect of flow on reducing proliferation in confluent monolayers. This suggests that RhoGTPases may not play a major role in the regulation of proliferation under the influence of flow.
We do not expect any further insight from EdU staining since also with EdU staining we could only quantify ECs entering the S phase in static images and how this translates into proliferation would still introduce further speculation. We therefore addressed this question in our discussion, as a quantification of proliferation would go beyond the scope of this study.
Of note, any requests for additional in vivo experiments would require new animal licence application and therefore a considerable time delay which we would only suggest to accept if substantial additional insight was to be expected. As it stands, all our claims are supported by several independent observations and can where necessary and as detailed in our revision plan, be addressed by orthogonal means.
Reviewer #2, Major comment 5: In the legends of Figure 1B & C, no information about the x or y-axis were mentioned. Even though the authors explained it elsewhere in the text or other figures, it is the first time that these parameters show up in the paper, which would be a proper place to note the details of these KDE plots.
Response: We added this information in the caption of Figure 1B,C, Figure 3B,C, and Figure 4B,C.
Reviewer #2, Major comment 6: In the legends of Figure 2B & C, it mentioned the results of coupling strength and fraction of shear stress, which was not obvious by just looking at the figures provided. It is also not mentioned whether the numbers are calculated by collected or predicted data.
__Response: __We agree with the reviewer that this information should be more prominent in the figure and will add it accordingly. These are predicted data, where we systematically tested different values of key parameters. We will therefore also change the caption of the figure from “Simulation of EC migration in the retinal vasculature” to “Prediction of EC distributions from computational model simulations” to make this clear.
Reviewer #2, Major comment 7: Please direct the reader to the supplemental figure containing the tamoxifen injection strategy when the Vegfr3CreERT2 mouse model is introduced in the text and when Cdc42 and Rac1 EC knockout is discussed in the methods.
__Response: __We also moved Supplementary Figure 1 and 2 to the beginning of the supplement as they are now referenced first in the main text and hope this will erase the confusion.
Reviewer #2, Major comment 8: There appears to be a significant degree of variance in the KDE plots from Figures 1, 3, and 4. It would therefore be helpful if the authors could include plots of the remodeling plexus and sprouting front that show the median for each dataset (similar to Figure 1 D and E), since this is less likely to be skewed by extrema. Additionally, for clarity and ease to read, it would be nice if author can consolidate the results of the mean from WT, Cdc42 and Rac 1 iECKO side by side. The images of the retina are striking, but the quantification is not as well-communicated.
Response: We plotted the median (including 10th and 90th percentile) already in Figure 1 Supplement 1, Figure 3 Supplement 1 and Figure 4 Supplement 1 and found significant
Reviewer #2, Major comment 8 (continued): If the argument is that Cdc42 only disrupts the migration, but not the differentiation, of artery EC, the authors should see a significant difference between WT and Cdc42 iECKO only in P9, but not early stages.
Response: We are not sure that we have fully understood the reviewer's reasoning, but our data confirm the reviewer's prediction of disrupted Cdc42 KO EC migration: The timeline for the percentage of labelled arterial ECs relative to the total labelled EC population is shown in Additional Fig. 2. Here, a significant increase was observed for both control and Rac1 iECKO ECs between time points P8 and P9, but not at earlier stages. For Cdc42 iECKO no significant increase was found at all stages. A direct comparison of all conditions is shown in Additional Fig. 3. Similarly, the proportion of Cdc42 iECKO compared to control is significantly different at P9 for mice injected at P5, but not at earlier stages.
Reviewer #2, Minor comment 1: For KDE plots in Figure 1B and Figure 3B, the authors labeled the x-axis differently (r vs dr). No description about the x-axis is noted in the legends. Please consider keeping the label consistent so the readers can relate and compare them.
Response: Thank you for spotting this mislabelling. We changed the axis annotation to d_r in Figure 1 as it is used in Figure 3,4 and throughout the text. We added furthermore information on the x and y axis in the caption of Figure 1B,C, Figure 3B,C, and Figure 4B,C. See also major comment 5.
Reviewer #2, Minor comment 2: For the in vitro study, the cell line used is not mentioned in the results, even though there is a method section about HUVEC. Authors should note this information in the main text.
Response: We agree with the reviewer and have added the following information in the main text on page 18 in the section “Cdc42, but not Rac1, drives polarised flow-migration in vitro” stating:
“To assess EC migration under coupling to shear forces in vitro, siScrambled (siScr) control, siCdc42 and siRac1 treated human umbilical venous endothelial cell (HUVEC) monolayers were exposed to 20 dynes/cm2 of flow using the Ibidi perfusion system and observed for up to 17 hours using a non-toxic fluorescent DNA dye for nuclear tracking.”
*Reviewer #2, Minor comment 3: Please note the flow direction in Figure 5B. *
__Response: __We have added an additional indication of flow direction for Figure 5B to improve the clarity of the figure. Note that the direction of flow is already indicated in Figure 5A and is the same throughout the figure.
Reviewer #2, Minor comment 4: The naming of supplemental figures requires revisiting. Currently there are two figures labeled with variations of supplementary Figure 1, which makes identifying the correct data challenging.
Response: We regret that the reviewer found the labelling of supplementary figures ambiguous and thank the reviewer for spotting this mislabelling. We corrected the label Figure S1 to Supplementary Figure 1. Furthermore, we also moved Supplementary Figure 1 and 2 to the beginning of the supplement as they are now referenced first in the main text and hope this will erase the confusion (also see Reviewer #2, major comment 7).
Reviewer #2, Minor comment 5: There appears to be a small typo on page 16 in second paragraph in the sentence that currently reads "Nevertheless, only few cells reached the arteries by P9"
Response: We changed this sentence: “Nevertheless, the accumulation of Rac1-deficient ECs in the artery was less pronounced compared to control.” Also we provide actual numbers, see Additional Fig. 2 and Additional Fig. 3 and our response to Reviewer #1, Major comment 4.
Reviewer #2, Minor comment 6: The axis labels for the plots in Figure 5, Figure 2 Supplement 1, and Supplementary Figure 2 are currently very tiny and difficult to read.
Response: We agree with this suggestion and will increase the size of the axis labels.
Reviewer #2, Minor comment 7: Additional information is required to describe the trajectory plots in Figure 2 Supplement 1, Figure 3 Supplement 3 and Figure 4 Supplement 3. I assume that blue trajectories move in the positive x direction while orange trajectories move in the negative x direction, but this is currently not specified in any of the legends.
Response: We added a specification in the figure legend.
Significance
Giese et al advance current understanding of the molecular mechanisms that guide vein to artery migration by convincingly demonstrating (both in-vivo and in-vitro) that Cdc42 is essential for this process. In addition, they present a computational model that captures the dual force field of VEGFA and shear stress gradients to simulate and quantify this migratory process in the mouse retina: a tool which will likely be useful for future mechanistic studies of vascular remodeling and EC migration. As far as I am aware, no standard coordinate system exists in the field for the quantification and modeling of this migratory process, so the introduction of this method alone serves as a useful innovation for the field of vascular biology.
It should be noted by authors and the editors that the mathematical details of the computational model and its statistical accuracies are not evaluated in this review, which instead focuses on the study's findings as they relate to EC biology and vascular development.
This study complements previous work that has identified populations of ECs that appear to be primed for incorporation into arteries, termed "pre-arteries" (Su et al 2018, Phansalkar 2021, Luo 2021). The authors' observation of heterogeneous EC movements during migration supports the intriguing suggestion that actin regulation through Cdc42 may be related to the establishment or phenotype of this pre-artery identity, though further mechanistic work will be required to validate this hypothesis, as noted by Giese et al in their thoughtful discussion.
Reviewer #3
Evidence, reproducibility and clarity
Summary: this study uses VEGFR3cre as a lineage tracking system to track venous endothelial cells within the maturing retina postpartum in order to investigate the cues related to sprouting and remodeling of the vascular network. They use complex computation modeling to predict outcome and come with in vivo/vitro observations.
They identify that venous EC move towards the arterial bed with flow and oxygen tensions as critical parameter. It is an impressive study that in principle confirms earlier studies on the role of EC population in sprouting and vascular remodeling however utilizes an interesting cell population based computational approach. Since these finding are pretty new, confirmation with different technologies and approaches is important.
However for more "traditional" vascular biologist it is very difficult to understand and follow particular if not familiar with the data presentation and mathematical modeling. This is a major shortcoming of this manuscript because I feel that if the authors would put more effort to better communicate their findings to a broader community not only enhance the value of their findings but also disseminate their computational approaches to a broader vascular scientist community.
__Response: __We thank the reviewer for this valuable critique, we will add a table where all parameters are described. Furthermore, we will add an additional subpanel in Figure 2 to explain the computational model.
I do have a couple of model related comments. The authors are using different models without adequate description.
The VEGFR3 is a lymphatic EC tracking model that potential can be used to track venous tip cells in the retina. But this model is not characterized within the retina in the study the author cite. I believe it would be necessary to characterize this model in traditional way before it can be used as described.
__Response: __We appreciate this comment and generally agree with the wish to see Cre lines well characterised. This is particularly relevant in studies where researchers delete a gene of interest using a Cre line and then make claims about the role of the gene in certain tissues with assumed recombination specificity. In our study however, we are not using the Cre line as a lymphatic or venous specific line to make such claims. Instead we use it in combination with the mTmG reporter, to quantify population distributions. Therefore every sample is its own “characterization”.
We could cite literature that support our claim of lack of arterial expression (Ehling et al., 2013, Tammela et al., 2008) of Vegfr3 in postnatal retina, but these studies did not use this Cre-line. For the purpose of our study, and in line with previous comments by referee 2, we feel it is best to moderate the claim, as very rarely single arterial cells can be found to have recombined 24 h after tamoxifen injection, see Additional Fig. 2 and Additional Fig. 3. The revised manuscript therefore has the claim toned down to better reflect this. Nevertheless, the utility of the Cre-line is not dependent on whether or not single arterial cells can be labelled, as the coordinate system and population quantification shows the population shift. This would even be valid using Cre-lines with random endothelial recombination in all vascular segments (Jin et al., 2022).
If deemed necessary, we have reporter expression from various stages of recombination in the postnatal retina, as well as in the developing brain, as well as comparison with arterial Cre-lines such a BMX cre. A rather complete characterization could be provided in supplements. However, we would argue that this is not relevant for the present study.
Distance to artery /veins is one parameter the authors are using in their modeling. I'm not sure I understand how they determine it since ECs can original form any place with the venous network, then again they might not.
__Response: __In the computational model we assume an initial distribution that is derived from the distribution at P6, which is shown in Figure 2A (middle panel). In summary, we do not hypothesise any origin of the ECs but start with the experimentally observed distribution at P6. From this starting distribution and for the following time steps we can then compute the exact distances to the closest vein and artery.
SiRNA experiment do not show knock down efficiency, which is probably also heterogenous. I'm not sure if this affects the modeling. In the last set they use HUVECs which is a very specialized "venous" ECs which I would not use for their modelsystem.
__Response: __The knockdown efficiency is shown in the supplementary data, see Figure 5 Supplementary data 2: qPCR knockdown validation. We do not use the in vitro data for the computational modelling, only the distribution in the in vivo data. Vegfr3 is expressed in endothelial tip cells and ECs in the developing vein, as well as in scattered ECs throughout the primitive vascular plexus. Therefore, despite the general limitations of in vitro systems, HUVECs are very similar to the in vivo situation shown in our study. HUVECs, despite being of venous origin, are a very versatile tool for endothelial studies. They express both venous and arterial genes, including dll4 and many components of the notch signalling cascade. Importantly, they are heterogenous, but adapt to media and flow conditions. The medium we use stimulates a microvascular growth pattern, and exposing HUVECs to flow results in transcriptional and proteomic changes that fit well with microvascular responses. Using fully differentiated arterial endothelial cells would not be useful as we are modelling endothelial responses that set in venous and microvascular regions of the vascular plexus in vivo, and stimulate a response that leads to movement towards arteries. We have therefore purposefully chosen and validated this model system.
Significance
I already commented in the above paragraph on this topics
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Referee #3
Evidence, reproducibility and clarity
Summary:
this study uses VEGFR3cre as a lineage tracking system to track venous endothelial cells within the maturing retina postpartum in order to investigate the cues related to sprouting and remodeling of the vascular network. They use complex computation modeling to predict outcome and come with in vivo/vitro observations.
They identify that venous EC move towards the arterial bed with flow and oxygen tensions as critical parameter. It is an impressive study that in principle confirms earlier studies on the role of EC population in sprouting and vascular remodeling however utilizes an interesting cell population based …
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Referee #3
Evidence, reproducibility and clarity
Summary:
this study uses VEGFR3cre as a lineage tracking system to track venous endothelial cells within the maturing retina postpartum in order to investigate the cues related to sprouting and remodeling of the vascular network. They use complex computation modeling to predict outcome and come with in vivo/vitro observations.
They identify that venous EC move towards the arterial bed with flow and oxygen tensions as critical parameter. It is an impressive study that in principle confirms earlier studies on the role of EC population in sprouting and vascular remodeling however utilizes an interesting cell population based computational approach. Since these finding are pretty new, confirmation with different technologies and approaches is important.
However for more "traditional" vascular biologist it is very difficult to understand and follow particular if not familiar with the data presentation and mathematical modeling. This is a major shortcoming of this manuscript because I feel that if the authors would put more effort to better communicate their findings to a broader community not only enhance the value of their findings but also disseminate their computational approaches to a broader vascular scientist community. I do have a couple of model related comments. The authors are using different models without adequate description. The VEGFR3 is a lymphatic EC tracking model that potential can be used to track venous tip cells in the retina. But this model is not characterized within the retina in the study the author cite. I believe it would be necessary to characterize this model in traditional way before it can be used as described. Distance to artery /veins is one parameter the authors are using in their modeling. I'm not sure I understand how they determine it since ECs can original form any place with the venous network, then again they might not. SiRNA experiment do not show knock down efficiency, which is probably also heterogenous. I'm not sure if this affects the modeling. In the last set they use HUVECs which is a very specialized "venous" ECs which I would not use for their modelsystem
Significance
The VEGFR3 is a lymphatic EC tracking model that potential can be used to track venous tip cells in the retina. But this model is not characterized within the retina in the study the author cite. I believe it would be necessary to characterize this model in traditional way before it can be used as described.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Giese et al., develop a computational model to track the migration of tip and venous endothelial cells (ECs) into developing arteries in the mouse retina. They validate prior studies which have identified important roles of shear stress and VEGFA gradient in driving EC migration and argue through their model that these dual forces shift in their relative influence along the vein-artery axis. Through in vivo lineage tracing in combination with Cre-inducible mouse knockout models, Giese et al., explore the role of Cdc42 and Rac1 in flow-migration coupling. They conclude that Cdc42, and not Rac1, is the primary driver of …
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Referee #2
Evidence, reproducibility and clarity
Summary:
Giese et al., develop a computational model to track the migration of tip and venous endothelial cells (ECs) into developing arteries in the mouse retina. They validate prior studies which have identified important roles of shear stress and VEGFA gradient in driving EC migration and argue through their model that these dual forces shift in their relative influence along the vein-artery axis. Through in vivo lineage tracing in combination with Cre-inducible mouse knockout models, Giese et al., explore the role of Cdc42 and Rac1 in flow-migration coupling. They conclude that Cdc42, and not Rac1, is the primary driver of polarized flow-migration, further confirming this result with in vitro flow experiments involving siRNA depletion of Cdc42. Finally, the authors show that Cdc42 also plays a role in EC migration independent of flow, highlighting that its role in migration is universal and directly related to both actin regulation and cell junction dynamics.
Major Comments:
In Figure 1 the authors show retinas from a Vegfr3CreERT2 mouse model and make the claim that Vegfr3 is expressed in ECs in developing veins and tip cells, but not in arteries. Ralf Adam's group has previously shown that while Vegfr3 is more abundant in veins and capillaries than in arteries, expression is not exclusive to these EC subtypes (Ehling 2013). They, along with studies from Christer Betsholtz and Kari Alitalo's groups, show that Vegfr3 is observed in postnatal arteries (Tammela 2008, Ehling 2013). Indeed, from the panels in figure 1 (specifically P6 and P7) and supplemental figure 1, it appears that there may also be low levels of Vegfr3 expression in the arterial branches of postnatal Vegfr3CreERT2 mouse retinas. The authors should consider revising their statement that "ECs in arteries, however, do not express Vegfr3" and provide quantification of the number of GFP-labeled cells in arteries, veins and capillaries for each postnatal time point. Additional lineage tracing from later stages when migration into arteries is halted would be a good control for demonstrating that Vegfr3CreERT2 is not expressed in arteries, further support the authors' argument and conclusions.
In their computational simulations, the authors investigate three models: random walking of ECs (M1), VEGFA gradient driven migration (M2), and integrated VEGFA /shear-stress driven migration (M3). The reader naturally wonders why a model considering only shear-stress driven migration is not presented as a control simulation. The absence of this model reduces the strength of the claims that only the M3 model captures observed EC movement rates, the mean population shift in vein-artery distance, and the arterial proportion of ECs.
Much of the terminology in this study needs more detailed explanation and carefully usage. It would be more friendly to readers if they were consolidated into a box figure or table. (For example, how is coupling strength related or different from coupling rate?) The reported numbers of these factors are noted separately in text here and there. It would be helpful to put them together to highlight the difference between different models and mutant strains, as this is one of the novel findings for this study.
The lack of labelled cells in the P9 retinas of Cdc42iECKO mice is striking (Figure 3), and strong evidence for the importance of Cdc42 in the migration of ECs towards arteries. The authors cite Lavina et al, 2018 when they note that Cdc42 depleted ECs proliferate at normal rates, but independent verification of this observation through EdU quantification would allow the authors to distinguish between the two possibilities outlined on page 15 of the manuscript: local proliferation vs enhanced migration of non-recombined ECs. These experiments and analyses are expected to be quick (depending on the availability of mice) and low cost. An independent EC proliferation analysis would also give the authors insight into the degree to which localized proliferation likely impacts vein-artery migration, a parameter which is currently unaccounted for in their computational model. The authors recognize that the lack of EC proliferation parameters is a limitation of their current model and speculate that this makes their estimates of vein to artery migration slightly too low. Independent EC proliferation analysis would thus be informative and may allow the authors to remove this speculation from their discussion.
In the legends of Figure 1B & C, no information about the x or y-axis were mentioned. Even though the authors explained it elsewhere in the text or other figures, it is the first time that these parameters show up in the paper, which would be a proper place to note the details of these KDE plots.
In the legends of Figure 2B & C, it mentioned the results of coupling strength and fraction of shear stress, which was not obvious by just looking at the figures provided. It is also not mentioned whether the numbers are calculated by collected or predicted data.
Please direct the reader to the supplemental figure containing the tamoxifen injection strategy when the Vegfr3CreERT2 mouse model is introduced in the text and when Cdc42 and Rac1 EC knockout is discussed in the methods.
There appears to be a significant degree of variance in the KDE plots from Figures 1, 3, and 4. It would therefore be helpful if the authors could include plots of the remodeling plexus and sprouting front that show the median for each dataset (similar to Figure 1 D and E), since this is less likely to be skewed by extrema. Additionally, for clarity and ease to read, it would be nice if author can consolidate the results of the mean from WT, Cdc42 and Rac 1 iECKO side by side. The images of the retina are striking, but the quantification is not as well-communicated. If the argument is that Cdc42 only disrupts the migration, but not the differentiation, of artery EC, the authors should see a significant difference between WT and Cdc42 iECKO only in P9, but not early stages.
Minor Comments:
For KDE plots in Figure 1B and Figure 3B, the authors labeled the x-axis differently (r vs dr). No description about the x-axis is noted in the legends. Please consider keeping the label consistent so the readers can relate and compare them.
For the in vitro study, the cell line used is not mentioned in the results, even though there is a method section about HUVEC. Authors should note this information in the main text.
Please note the flow direction in Figure 5B.
The naming of supplemental figures requires revisiting. Currently there are two figures labeled with variations of supplementary Figure 1, which makes identifying the correct data challenging.
There appears to be a small typo on page 16 in second paragraph in the sentence that currently reads "Nevertheless, only few cells reached the arteries by P9"
The axis labels for the plots in Figure 5, Figure 2 Supplement 1, and Supplementary Figure 2 are currently very tiny and difficult to read.
Additional information is required to describe the trajectory plots in Figure 2 Supplement 1, Figure 3 Supplement 3 and Figure 4 Supplement 3. I assume that blue trajectories move in the positive x direction while orange trajectories move in the negative x direction, but this is currently not specified in any of the legends.
Significance
Giese et al advance current understanding of the molecular mechanisms that guide vein to artery migration by convincingly demonstrating (both in-vivo and in-vitro) that Cdc42 is essential for this process. In addition, they present a computational model that captures the dual force field of VEGFA and shear stress gradients to simulate and quantify this migratory process in the mouse retina: a tool which will likely be useful for future mechanistic studies of vascular remodeling and EC migration. As far as I am aware, no standard coordinate system exists in the field for the quantification and modeling of this migratory process, so the introduction of this method alone serves as a useful innovation for the field of vascular biology.
It should be noted by authors and the editors that the mathematical details of the computational model and its statistical accuracies are not evaluated in this review, which instead focuses on the study's findings as they relate to EC biology and vascular development.
This study complements previous work that has identified populations of ECs that appear to be primed for incorporation into arteries, termed "pre-arteries" (Su et al 2018, Phansalkar 2021, Luo 2021). The authors' observation of heterogeneous EC movements during migration supports the intriguing suggestion that actin regulation through Cdc42 may be related to the establishment or phenotype of this pre-artery identity, though further mechanistic work will be required to validate this hypothesis, as noted by Giese et al in their thoughtful discussion.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Giese et al. use genetic lineage tracing techniques and novel computational analysis methods to quantify and predict how the spatial distribution of ECs changes over time in the developing mouse retina from P5 to P9. They also develop mathematical models to describe and predict the response of ECs to the hypothesized dual force-field formed by the chemoattractant VEGFA, and the flow-induced shear stress. Given that the mouse retina cannot be live imaged, these new methods are essential to infer cell dynamics from static images. With these methods the authors confirm previous findings that arteries are formed by endothelial …
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Referee #1
Evidence, reproducibility and clarity
Summary:
Giese et al. use genetic lineage tracing techniques and novel computational analysis methods to quantify and predict how the spatial distribution of ECs changes over time in the developing mouse retina from P5 to P9. They also develop mathematical models to describe and predict the response of ECs to the hypothesized dual force-field formed by the chemoattractant VEGFA, and the flow-induced shear stress. Given that the mouse retina cannot be live imaged, these new methods are essential to infer cell dynamics from static images. With these methods the authors confirm previous findings that arteries are formed by endothelial cells derived from veins, or tip cells, or capillaries. They then combine their genetic system with Cdc42 and Rac1 floxed alleles to understand the function of these genes in EC mobilization. They find that Cdc42 has a very strong role in EC mobilization to arteries, not so much to the sprouting front, whereas the loss of Rac1 has relatively minor effects in vivo. Loss of Rac1 slows the cells but they maintain their directionality towards arteries. The discussion section and integration of these paper findings with previous work in the field is excellent. Overall, this work provides a much higher level of quantitative analysis of endothelial cell dynamics in the developing vasculature of the mouse retina. It also provides mathematical models that can be useful to explain and predict the impact of other genetic mutations or pharmacological interventions during vascular development.
Major comments:
1 - This work provides one of the finest examples of quantitative biology in the vascular biology field. The conclusion on cell dynamics is however largely based on static images measurements and pre-defined mathematical models given also previous work and the proposed model of a dual-force field. The authors conclude that sprouting front cells mainly migrate away towards VEGF, whereas remodelling plexus cells migrate towards arteries. However, this is based on the entire EC population measurements/displacement and averaging, and does not account for the possibility of a few ECs having a different behaviour from most of their neighbours. This comment is related with the fact that arteries are formed mainly by tip-derived ECs, the cells closest to the VEGF source and further away from the flow/shear stress force. It seems the authors model presented here would not allow this to happen. According to the model presented, it seems that an EC close to the VEGF source, and subjected to low flow (shear stress), would always migrate to the front and never turn back towards arteries. Can a more complex model enable the consideration of the stochastic loss of VEGF signalling (or gain of shear stress sensing) by some ECs at the sprouting front ? And their subsequent formation of arteries ?
2 - Previous work by Laviña et al showed that Cdc42 is required for the migration of ECs to the sprouting front. The authors data suggest that Cdc42 is not necessary for this process. Could the difference between previous and the authors results be technical and related with the different stage of induction/analysis or the extent of Cdc42 deletion ? Did the authors tried inducing at P1 and collecting at P6/P7 ? The reporters used were also different and they may have different sensitivities to tamoxifen (and hence report Cdc42 deletion differently).
3 - In the last section, some of the junctional/polarity/actin markers and analysis done in vitro could be also done in vivo.
4 - The extent of Rac1 deletion in the mosaic experiments (done with suboptimal doses of tamoxifen) could be analysed. This is especially relevant since minor effects for Rac1 were observed in these in vivo experiments.
Minor comments:
1 - Lee et al., 2022 is a review. Better cite the original papers if possible: Some examples: Xu et al., 2014, Pitulescu et al 2017 and Lee et al., 2021.
2 - Figure 1A: Stage of induction with tamoxifen is missing. Likely P5.
3 - Figure 3 and 4 data would be easier to compare/understand by readers if part of the Wt data in Figure 1 was also plotted here. Or at least a Wt trend/average line on top of the mutant data, for us to see how much Cdc42 or Rac1 deletion changes the behaviour of the mutant cells versus the Wt cells.
4 - Overall, for all dot plots and heatmaps, would be better to indicate the total number of cells analysed/plotted since the power of the analysis is related with cell number rather than number of retinas.
Significance
Significance
General assessment: This paper is very strong on the quantitative analysis and mathematical modelling. Methods used and the model proposed may be of broad relevance for the field of vascular biology. It is however based on certain author-defined parameters and assumptions. EC dynamics in vivo can be much more complex than what can be modelled by equations. For example, heterogenous single cell genetics and signalling inputs can induce changes in cells that override the normal/average behaviour of the cells that are modelled. Despite the high level of quantitative analysis and modelling, the main findings here presented are not entirely novel, given previous work. For example, it was previously known that arteries are formed by vein/tip/capillary cells. It was also known that Cdc42 was required for proper EC migration away from veins (Laviña et al., 2018). However, the better quantitative analysis here presented does provide a higher level of detail and reliability. The mosaic genetics used to delete Cdc42 is in general clear since few reporter positive cells can make arteries, suggesting efficient deletion of this gene. The data also goes in line with previous work. However for Rac1, given that a much weaker phenotype was observed, is not possible to be sure that all GFP+ ECs had deletion of Rac1. This is especially important in mosaic genetic experiments, using a suboptimal dose of tamoxifen. The extent of Rac1 deletion in GFP+ cells was not analysed. Which leaves the open question if Rac1 is really dispensable for EC migration and arterialization. Embryos lacking Rac1 in endothelial cells die early during development. Therefore ECs fully lacking Rac1 may have stronger defects than the ones shown here. All this data was obtained in the postnatal retina angiogenesis system. Other organ vessels may develop differently. Future work will tell if the models proposed can explain the dynamics of ECs during the growth of other vascular beds.
Audience: Vascular/Cell biology researchers and bioinformaticians developing tools for image analysis and/or cell migration/dynamics modelling.
Expertise of Reviewer: Vascular Biology. I do not have sufficient expertise to evaluate the mathematical modelling part of this paper.
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