# Force-mediated recruitment and reprogramming of healthy endothelial cells drive vascular lesion growth

Apeksha Shapeti, Jorge Barrasa-Fano, Abdel Rahman Abdel Fattah, Janne de Jong, José Antonio Sanz-Herrera, Mylène Pezet, Said Assou, Emilie de Vet, Seyed Ali Elahi, Adrian Ranga, et al.

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Apeksha Shapeti, Jorge Barrasa-Fano, Abdel Rahman Abdel Fattah, Janne de Jong, José Antonio Sanz-Herrera, et al.. Force-mediated recruitment and reprogramming of healthy endothelial cells drive vascular lesion growth. Nature Communications, 2024, 15, pp.8660. 10.1038/s41467-024-52866-6. hal-04796315

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# Force-mediated recruitment and reprogramming of healthy endothelial cells drive vascular lesion growth

Received: 20 November 2023

Accepted: 19 September 2024

Published online: 06 October 2024

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Apeksha Shapeti Ⓔ<fnref n="1" />✉, Jorge Barrasa-Fano Ⓔ<fnref n="1" />, Abdel Rahman Abdel Fattah<fnref n="1" />,², Janne de Jong<fnref n="1" />, José Antonio Sanz-Herrera³,⁴, Mylène Pezet⁵, Said Assou⁶, Emilie de Vet Ⓔ<fnref n="1" />, Seyed Ali Elahi Ⓔ<fnref n="1" />,⁷, Adrian Ranga Ⓔ<fnref n="1" />, Eva Faurobert Ⓔ⁵,⁹✉ & Hans Van Oosterwyck Ⓔ<fnref n="1" />,⁸,⁹✉

Force-driven cellular interactions are crucial for cancer cell invasion but remain underexplored in vascular abnormalities. Cerebral cavernous malformations (CCM), a vascular abnormality characterized by leaky vessels, involves CCM mutant cells recruiting wild-type endothelial cells to form and expand mosaic lesions. The mechanisms behind this recruitment remain poorly understood. Here, we use an in-vitro model of angiogenic invasion with traction force microscopy to reveal that hyper-angiogenic Ccm2-silenced endothelial cells enhance angiogenic invasion of neighboring wild-type cells through force and extracellular matrix-guided mechanisms. We demonstrate that mechanically hyperactive CCM2-silenced tips guide wild-type cells by transmitting pulling forces and by creating paths in the matrix, in a ROCKs-dependent manner. This is associated with reinforcement of β1 integrin and actin cytoskeleton in wild-type cells. Further, wild-type cells are reprogrammed into stalk cells and activate matrisome and DNA replication programs, thereby initiating proliferation. Our findings reveal how CCM2 mutants hijack wild-type cell functions to fuel lesion growth, providing insight into the etiology of vascular malformations. By integrating biophysical and molecular techniques, we offer tools for studying cell mechanics in tissue heterogeneity and disease progression.

Angiogenesis is emerging as a complex process regulated by not just biochemical signaling but also mechanical cues, with particular relevance for pathologies such as cancer<fnref n="1" />⁻⁴. Yet, how cellular forces drive aberrant angiogenesis in vascular diseases remains insufficiently explored. Cerebral Cavernous Malformations (CCM) is one such disease of the brain associated with regions of active angiogenesis⁵, and is

caused by mutations in one of three CCM genes (Ccm1/Ccm2/Ccm3)⁶,⁷. CCM lesions are thought to initiate through a cancer-like clonal proliferation of mutant endothelial cells (ECs)⁸,⁹. In advanced stages, symptomatic CCM lesions manifest as hemorrhagic, multicavernous structures comprising both mutant and wild-type (WT) ECs¹⁰⁻¹⁴. Interestingly, various other vascular anomalies such as Sturge Weber

<footnote n="1">¹KU Leuven, Department of Mechanical Engineering, Biomechanics section, Leuven, Belgium. ²CeMM The Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. ³Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Seville, Spain. ⁴Instituto de Biomedicina de Sevilla (IBIS), Seville, Spain. ⁵Univ. Grenoble Alpes, Inserm 1209, CNRS 5309, Institute for Advanced Biosciences, Grenoble, France. ⁶IRMB, University of Montpellier, INSERM, CHU Montpellier, Montpellier, France. ⁷KU Leuven, Department of Movement Sciences, Human Movement Biomechanics Research Group, Leuven, Belgium. ⁸KU Leuven, Prometheus, Division of Skeletal Tissue Engineering, Leuven, Belgium. ⁹These authors jointly supervised this work: Eva Faurobert, Hans Van Oosterwyck. ✉ e-mail: apeksha.shapeti@kuleuven.be; eva.faurobert@univ-grenoble-alpes.fr; hans.vanoosterwyck@kuleuven.be</footnote>
Syndrome (SWS), PI3K-driven vascular and lymphatic malformations and hereditary hemorrhagic telangiectasia (HHT), also present mosaic lesions and show similar overactivation of angiogenic pathways¹⁵⁻²⁰. As CCM lesions expand, mutant cells attract WT ECs into the growing lesions⁸,⁹, making this step in CCM formation a crucial therapeutic target for reducing symptoms. The precise mechanisms underlying this recruitment remain unknown and could be relevant for the growth of other mosaic vascular malformations as well. Observations of dysregulated Rho-associated kinase (ROCK) signaling in CCM mutants, as well as the successful inhibition of symptomatic late stage lesions using Fasudil, a ROCK inhibitor, suggest a role for ROCK-dependent altered cell mechanics in lesion growth²¹⁻²⁵. However, nothing is known about the mechanical coupling between mutant and wild type cells, their surrounding extracellular matrix (ECM), and the consequences of these interactions on the fate of WT ECs during lesion growth.

Expanding upon the cancer-like mechanisms identified for CCM formation²⁶,²⁷, we asked if cooperative migration strategies such as those used by fibroblasts to regulate tumor cell invasion²⁸,²⁹ might also contribute to recruitment and reprogramming of WT ECs by CCM mutants. Since this multifactorial question requires an approach to systematically explore the mechanics of both cell-matrix and cell-cell interactions, we leveraged our three-dimensional (3D) biomimetic model of angiogenesis³⁰ integrated with biophysical computations using 3D traction force microscopy and molecular investigations using single-cell RNA sequencing. Doing so, we discovered that hyperangiogenic *Ccm2*-silenced endothelial cells enhance angiogenic invasion of neighboring wild-type cells through force- and extracellular matrix-guided mechanisms. Mechanically hyperactive *Ccm2*-silenced tips steer wild-type cells by exerting and transmitting pulling forces and leaving degraded paths in the matrix as cues to promote invasion in a ROCKs-dependent manner. Further, this recruitment of WT ECs involves their reprogramming into proliferative stalk cells characterized by upregulated expression of matrisome and DNA replication genes.

## Results

### Loss of *Ccm2* activates ROCK1-dependent filopodia formation in angiogenic tip cells

Human umbilical vein EC (HUVEC) monolayers were allowed to sprout into degradable, 3D polyethylene glycol (PEG) hydrogel based ECMs with sphingosine-1-phosphate (SIP) as a proangiogenic cue (Fig. 1a, Supplementary Movie 1). siRNA silencing of *Ccm2* in EC monolayers previously showed ROCK1-dependent increased angiogenic invasion compared to siCT ECs in this model³⁰. siRNA transfection alone affects the robustness of invasion which can be seen in Supplementary Fig. 1A, B. This however, only further emphasizes the hyper-angiogenic capacities of the siCCM2 cells which also undergo the same transfection protocol. Given that filopodia are known to guide endothelial tip cells towards angiogenic stimuli³¹,³², we wondered if these structures were altered in response to *Ccm2* loss. Upon closer examination, we observed that highly angiogenic *Ccm2*-silenced sprouts, referred to as siCCM2 ECs, (Fig. 1b), exhibited denser localization of F-actin (Fig. 1b, d) and a pronounced branchy tip cell phenotype³² characterized by longer filopodia compared to control sprouts, denoted as siCT (Fig. 1b, c). Despite the reduced invasion of siCT ECs compared to non-transfected ECs (Supplementary Fig. 1A, B), siRNA transfection did not significantly affect filopodia formation in siCT ECs (Supplementary Fig. 1C). Additionally, siCCM2 ECs displayed hypersensitivity to angiogenic signaling, demonstrated by their increased sprouting even at lower SIP concentrations, while siCT ECs rarely sprouted under these conditions (Supplementary Fig. 2A). Notably, siCCM2 ECs also exhibited an enhanced ability to sense the matrix as evidenced by their extension of numerous small filopodia into a non-degradable PEG-based matrix, a behavior not observed in siCT ECs (Supplementary Fig. 2B). ROCKs are direct effectors of RhoA known to regulate the

morphology of the actomyosin cytoskeleton in 2D monolayers³³. In particular, in CCM mutant ECs we previously showed that ROCK1 mediates integrin-anchored actin stress fibers while ROCK2 promotes cortical actin polymerization³⁴,³⁵. In 3D, we saw that ROCK1 but not ROCK2 mediates the increased invasive capacities of *Ccm2*-silenced ECs³⁰. We therefore explored the sprouting behavior of siCCM2 ECs following additional silencing of *ROCK1* (siCCM2+siROCK1) or *ROCK2* (siCCM2+siROCK2). The hyperactivated filopodial matrix-sensing phenotype of CCM2 mutant ECs was rescued by the additional silencing of *ROCK1* but not *ROCK2* (Fig. 1b, d), consistent with the rescue of invasiveness observed only with ROCK1 (Fig. 1b). These findings demonstrate that only ROCK1 is necessary for filopodia formation, reminiscent of its role in the generation of dendritic spines through the segregation of activated Rac1³⁶.

### Loss of *Ccm2* enhances cell-ECM forces and matrix degradation through ROCKS

Collective angiogenic invasion requires complex sprout-ECM interactions and active cellular machinery for applying mechanical force³⁷,³⁸. Given that inhibition of actomyosin contractility with blebbistatin treatment reduces sprouting of siCCM2 ECs³⁰, we hypothesized that the enhanced invasion accompanied by a hyperactivated tip phenotype of *Ccm2* mutant ECs must also translate to higher forces on the matrix. To test this, we incorporated fluorescent beads into soft (0.5kPa, Supplementary Fig. 3A), PEG-based ECMs as markers for measuring cell-induced hydrogel deformations during angiogenic sprouting using 3D traction force microscopy (TFM, Supplementary Fig. 3B). As predicted, siCCM2 sprout tips pulled more strongly on the ECM to generate 45% higher bead displacements and 20% higher subsequent tractions on the ECM compared to siCT sprout tips (Fig. 1e–g). This is further supported by a twofold increase in the strain energy of siCCM2 tips (Fig. 1h). Additional silencing of *ROCK1* reverted the increase in ECM displacements and strain energy, and partially lowered tractions. This was consistent with the decrease in filopodia upon *ROCK1* depletion, as previously observed in dendritic cells where ROCK1 was necessary for Rac1-dependent filopodia at the front (Fig. 1b, c)³⁶. Taken together, this suggests that tractions exerted by siCCM2 ECs were associated with the abundant ROCK1-dependent tip cell filopodia. The highly invasive siCCM2+siROCK2 sprouts with their numerous filopodia did not generate higher forces on the ECM (Fig. e-h), suggesting a lack of myosin II-dependent contractility. Interestingly, ROCK2 was previously reported to generate contractile force at filopodia through the phosphorylation of myosin-light chain and cofilin in dendritic spines³⁶. Consistently, we found that *ROCK2* silencing had a stronger effect on rescuing forces than ROCK1 at the numerous ROCK1-dependent filopodia, as is visible in the lower displacements, tractions and strain energies. However, a complete restoration of the increased forces observed upon *Ccm2* loss seemed contradictory with the sustained increase in invasiveness and F-actin of these siCCM2+siROCK2 sprouts (Fig. 1b, d).

We wondered if matrix degradation was an additional factor contributing to this increase in invasion. To answer this, we combined PEG matrices with fluorescently labeled gelatin to visualize matrix degradation. Loss of *Ccm2* increased ECM degradation in 3D during angiogenic invasion (Fig. 1i, j), consistent with previous observations on 2D matrices³⁰. ECM degradation was rarely noted in filopodial tip regions and was higher at the rear of the cell in the vicinity of the nuclei (Fig. 1i, white arrows), as is also reported during confined tumor migration³⁹. MT1-MMP is a transmembrane metalloprotease which localizes to focal adhesion sites and activates MMP2 and MMP9, both of which are overexpressed in CCM lesions⁴⁰⁻⁴². Further, MT1-MMP-dependent ECM degradation is known to drive cancer cell invasion and angiogenesis⁴³,⁴⁴. Immunostaining for MT1-MMP, also confirmed an increase in MMP expression, especially at the rear of the cell (Fig. 1k, l). siCCM2 sprouts additionally silenced for *ROCK2* also showed higher
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matrix degradation which correlated with their increase in invasion (Fig. 1i, j). Only *ROCK1* but not *ROCK2* silencing restored MT1-MMP expression and ECM degradation. Subsequently, treating siCCM2 ECs with GM6001 (broad spectrum MMP inhibitor, MMPi) also reduced invasion (Supplementary Fig. 3C). Timelapse imaging of the matrix during siCCM2 sprouting showed that degradation did not co-localize with the force exerting tip regions, consistent with previously shown matrix degradation at sprout stalks⁴⁵, thereby confirming it was not the driver for higher bead displacements (Supplementary Movie 2).

Combined, these data attribute the enhanced angiogenic invasion upon *Ccm2* loss to a combination of hyperactivated filopodia of CCM2 mutant tip cells at the front, increased pulling forces by high matrix-sensing, and MMP-driven matrix degradation at the rear aiding migration of the cell body. We noted that an increase in cell-ECM forces upon *Ccm2* loss requires both *ROCK1* and *ROCK2*, while degradation is solely mediated by *ROCK1*. Above all, we confirmed that dysregulated mechanics is crucial to the overactivation of sprouting angiogenesis upon *Ccm2* loss.
**Fig. 1 | Enhanced angiogenic sprouting in Ccm2-silenced endothelial cells relies on ROCKs-dependent force generation and ECM degradation.** a Schematic representation of the PEG hydrogel-based 3D angiogenic invasion assay. (sphingosine-1-Phosphate (SIP), endothelial cells (ECs)) b Maximum intensity projections of F-actin expression after overnight sprouting of ECs transfected with siRNAs. c Filopodial morphological metrics in leading tip regions (dot color indicates 2 independent trials). Number of filopodia (n = 20; siCT-siCCM2 *p* < 0.0001, siCT-siCCM2+siROCK2 *p* = 0.003, siCCM2-siCCM2+siROCK1 *p* = 0.003), maximum filopodial length (siCT *n* = 26, siCCM2 *n* = 29, siCCM2 + ROCK1 *n* = 29, siCCM2 + ROCK2 *n* = 31; siCT-siCCM2 *p* < 0.0001, siCT-siCCM2+siROCK2 *p* = 0.001, siCCM2-siCCM2+siROCK1 *p* = 0.0001, siCCM2 + ROCK1-siCCM2 + ROCK2 *p* = 0.004), number of branchpoints in each tip (n = 20; siCT-siCCM2 *p* = 0.0003, siCT-siCCM2+siROCK2 *p* = 0.0003, siCCM2-siCCM2+siROCK1 *p* = 0.005, siCCM2 + ROCK1-siCCM2 + ROCK2 *p* = 0.005). d Total actin intensity in leading tip regions (2 independent trials; n = 20; siCT-siCCM2 *p* < 0.0001, siCT-siCCM2+siROCK2 *p* = 0.003, siCCM2-siCCM2+siROCK1 *p* = 0.003). e Representative maximally projected images of beads (in red) overlaid with GFP-labeled sprouts (top) and the corresponding 3D renders of displacements (middle) and traction fields (bottom) computed around angiogenic sprout tips of silenced ECs after 18 h of invasion. f-h Quantification of 99th percentile bead displacements (f, siCT-siCCM2 *p* = 0.001, siCCM2-siCCM2+siROCK1 *p* = 0.0004, siCCM2-siCCM2 + ROCK2 *p* < 0.0001) and tractions (g, siCT-siCCM2 *p* = 0.01, siCCM2-siCCM2 + ROCK2 *p* = 0.008), and strain energy (h, siCT-siCCM2 *p* = 0.0006, siCCM2-siCCM2+siROCK1 *p* = 0.001, siCCM2-siCCM2 + ROCK2 *p* < 0.0001) exerted by force exerting sprout tip regions (3 independent trials; siCT *n* = 24, siCCM2 *n* = 43, siCCM2 + ROCK1 *n* = 37, siCCM2 + ROCK2 *n* = 35). i Representative minimum intensity projections of fluorescent gelatin degraded by silenced ECs during 18 h of invasion (left) and the corresponding sprouts labeled with f-actin (green) and nuclei with Hoechst (magenta) (right). White arrows showing higher degradation under the cell body in the rear. j Total area of gelatin degradation normalized to the number of nuclei (3 independent trials; siCT *n* = 28, siCCM2, *n* = 30, siCCM2 + ROCK1, *n* = 29, siCCM2 + ROCK2, *n* = 30; siCT-siCCM2 *p* < 0.0001, siCT-siCCM2 + ROCK2 *p* < 0.0001, siCCM2-siCCM2+siROCK1 *p* = 0.0001) k Representative maximally projected immunofluorescence images of MT1-MMP expression (magenta) and nuclei stained with Hoechst (cyan). l Total MT1-MMP intensity per nuclei (3 independent trials; *n* = 30 for siCT, siCCM2 and siCCM2 + ROCK1, *n* = 20 for siCCM2 + ROCK2; siCT-siCCM2 *p* < 0.0001, siCT-siCCM2 + ROCK2 *p* = 0.0006, siCCM2-siCCM2+siROCK1 *p* = 0.007). All statistical comparisons used Kruskal-Wallis significance test with Dunn's correction for multiple comparisons. Error bars represent SEM, scalebars 50 µm. Source data are provided as a Source Data file.

## Hyper-angiogenic Ccm2-silenced EC tips lead and stimulate wild-type EC invasion

We then turned our attention to the concept of mosaicism in CCM lesions evidenced by the presence of not just mutant, but also wild-type cells likely recruited from the healthy endothelium⁸,⁹. We have previously shown that siCCM2 ECs use a senescence-associated secretory phenotype (SASP) to attract surrounding wild-type (WT) ECs³⁰. Moreover, cancer cells undergoing SASP aid tumor development by promoting tumor angiogenesis and tumor cell invasion¹⁶. Theorizing that CCM progression is driven by CCM2 mutants similarly activating angiogenic invasion in WT ECs, we questioned the underlying mechanism of such recruitment. To first assess the role of putative paracrine factors secreted by siCCM2 ECs on WT ECs, we combined PEG matrices with media conditioned by siCCM2 or siCT ECs for 24 h. We observed no significant paracrine angiogenic effects on invasion (Supplementary Fig. 4A), suggesting that the recruitment of WT ECs observed in our in-vitro model was likely due to physical mechanisms of cooperative invasion.

To further explore angiogenic activation of WT ECs by CCM mutants, we visualized collective sprouting in two situations (Fig. 2a): (i) CT mosaics where LifeAct-RFP tagged WT ECs were mixed with LifeAct-GFP tagged siCT ECs, and (ii) CCM2 mosaics where LifeAct-RFP tagged WT ECs were mixed with LifeAct-GFP tagged siCCM2 ECs in various percentages. Late stage symptomatic lesions are found to contain only 10–20% mutant ECs⁸,¹²⁻¹⁴. In mice, Dejana's group showed that mutant ECs first clonally proliferate, which is then followed by a progressive enrichment of WT ECs to expand the lesions⁸. At P8, lesions contained only 10% WT cells which increased to 60% by P30. By varying percentages of silenced ECs in mosaics (Fig. 2a), we noted an increase in invasion of WT ECs in CCM2 mutant mosaics composed of 50% and 20% siCCM2 ECs, but not at 10% (Fig. 2b). This implied that a minimum critical mass of siCCM2 ECs was necessary in our model to modify angiogenic behavior of neighboring WT ECs.

Our conditions containing 80 and 90% WTs (10%, 20% siCCM2) would be comparable to in vivo later stage lesions. Since we did not see effects on WT ECs in the 10% condition, our findings would suggest that recruiting capacities of mutant cells at this late stage of the lesion might be limited. Hence, we chose 50–50% cultures to model less mature lesions, which we used for all further mosaic studies. To this end, we performed timelapse imaging of these mixed populations during mosaic sprouting. Tracking dynamics of angiogenic sprouting over 18 h showed that both siCCM2 and WT ECs begin invading earlier and faster in CCM2 mutant mosaics than in CT mosaics (Fig. 2c-e,

Supplementary Movies 3, 4). However, siCCM2 ECs initiated invasion earlier than the WT ECs in these CCM2 mutant mosaics and had an invasive advantage as shown by their increased invasion speed (Fig. 2d, e). Further, we saw an enrichment of both siCCM2 and WT ECs with time in the gels, as seen with the matrix area occupied by them in Fig. 2f. Consistent with Fig. 2c, WT ECs were delayed in invading the matrix confirming the invasive advantage of mutant ECs. After this initial delay, WT ECs began to progressively occupy more area of the matrix. Moreover, there was an increase in the relative area occupied by WT versus siCCM2 ECs with time (Fig. 2g), showing that WTs got enriched once the highly invasive mutant tips had migrated forward. Finally, when looking at mosaic sprouts at the end of the experiment after 18 h, we saw a ratio of 1 CCM mutant for every 3 WT ECs (Fig. 2h). This was quantified in one representative experiment and is consistent with in vitro findings where CCM spheroids got enriched with WT ECs from neighboring monolayers⁹.

We identified four invasion modes of WT ECs based on their positions in relation to siCCM2 or siCT ECs at the start of ECM invasion (Fig. 2i). WTs began invading the matrix as: leading tips (“lone tips”), leading tips in contact with a following silenced stalk EC (“mosaic tips”), following stalks in contact with a leading silenced EC (“mosaic stalks”), or tip cell trailing behind a distant silenced EC (“trailing tips”). We observed that 80% of invading WT ECs in CCM2 mutant mosaics begin doing so in “mosaic stalk” positions in direct contact with a leading siCCM2 EC in front at the tip of the angiogenic sprout (Fig. 2c, j, k). In contrast, WT ECs in CT mosaics begin invading the matrix mainly as “lone tips” and never assume “mosaic stalk” positions (Fig. 2c, j), suggesting that CCM2 mutant ECs are capable of inhibiting a tip phenotype in WT ECs. We concluded that siCCM2 cells are capable of outcompeting WT cells to take tip cells position and recruiting them into mosaic invasion.

## ROCKs dependent forces and ROCK1-dependent tunnels guide mosaic intercellular communication

A small percentage of the sprouting WT ECs in CCM2 mutant mosaics (10%, Fig. 2i, j) began invading the matrix without any direct contact based communication as “trailing tips” behind distant leading siCCM2 ECs. Dynamic co-visualization of matrix degradation and migration occasionally showed trailing WT ECs modify their direction of migration to turn towards tunnels created by distant leading mutant ECs (Fig. 2l, Supplementary Movie 2). This combined with the increase in matrix degradation during mosaic sprouting of siCCM2 and WT ECs (Supplementary Fig. 5A, B) indicated that, in a subset of cases, reduced physical impedance and the likely residual, fragmented ECM
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components in empty tunnels could serve as a recruitment cue for contact-free guidance of invading WT ECs.

WT ECs in mutant mosaics regain tip cell behavior when either *ROCK1* or *ROCK2* is depleted in the siCCM2 EC (Fig. 2j, m, Supplementary Movies 5 and 6), suggesting that the invasive advantage of CCM2 mutants over WT ECs might result from their ability to exert higher tractions (Fig. 1g, h). Further, *ROCK1* depletion was more efficient than *ROCK2* in counteracting tip cell inhibition of WT ECs (Fig. 2j). This indicates that ROCK1-dependent matrix degradation contributes

to the invasive advantage of CCM2 mutants (Supplementary Fig. 5A, B). Since these hyper-degradative skills were restored by both blebbistatin and MMPi treatment, aberrant actomyosin contractility again proved necessary for increased degradation during mosaic sprouting in 3D (Supplementary Fig. 5A, B). Blocking myosin contractility with blebbistatin or MT1-MMP-driven degradation with MMPi restored the increase in invasion of both WT and siCCM2 ECs in mosaics confirming that these mechanisms were necessary (Supplementary Fig. 5C, D). To exclude biochemical signaling through Notch to be a factor in the
**Fig. 2** | **Hyper-angiogenic siCCM2 ECs stimulate invasion of wild-type ECs by leading mosaic sprouting in a ROCKs-dependent manner.** a Schematic of mosaic invasion assay combining wild-type (WT, red) with endothelial cells (ECs) siRNA-silenced for control (CT), CCM2, CCM2 + ROCK1 or CCM2 + ROCK2 (green). **b** Invasion distance of WT ECs in CT mosaics (siCT+WT) versus CCM2 mosaics (siCCM2+WT) at varying percentages of silenced ECs (4 independent trials). **c** Maximally projected confocal images from 18h-invasion of 1:1 mixtures of WT ECs (red) and siCT or siCCM2 ECs (green) (Supplementary Movies 3 and 4). siCCM2 ECs lead WTs (white arrows). Images are representative of 12 fields of view (FOVs), 4 independent trials. **d** Invasion distance of WT or silenced ECs in mosaics over time. (Panels d-g present quantifications from one representative example of 3 independent timelapse experiments). **e** Invasion speed (µm/h) of WT or silenced ECs in mosaics. **f** Progressive area of the matrix occupied by each cell type in CCM2 mosaics. **g** Relative matrix area occupied by WT or siCCM2 EC in CCM2 mosaics. **h** Nuclei in CCM2 mosaic sprouts (WT+siCCM2 ECs) after 18 h of invasion. Dot color

indicates 2 independent trials with 16 mosaic sprouts i Schematic of wild-type EC categories defined based on their position relative to silenced-ECs at the start of ECM invasion. j Percent categorical distribution of modes of invasion described (in i) for WT ECs in mosaics. Total invading WT ECs in mosaics: CT = 18, CCM2 = 41, CCM2 + ROCK1 = 27, CCM2 + ROCK2 = 39 (3 independent trials, 3 FOVs/trial) k XZ and YZ section of a CCM2 mutant mosaic sprout. l Confocal images during overnight invasion of a 1:1 mix of WT and siCCM2 ECs. Composite images of a single z-plane of fluorescently labeled gelatin and the two cell types. m Maximally projected confocal images after 16h-invasion of 1:1 mixtures of WT and siCCM2 + ROCK1 or siCCM2 + ROCK2 ECs (scale bar, 100 µm) (Supplementary Movies 5 and 6). Statistical comparisons in panels b (20% p = 0.03, 50% p = 0.006) and e (WT p = 0.005, silenced-ECs p = 0.005) use a paired two-tailed t-test and panel h uses a Wilcoxon paired two-tailed t-test (siCCM2 p = 0.0001). Error bars represent SEM. scalebars are 50 µm unless otherwise stated. Panel a created with BioRender.com. Source data are provided as a Source Data file.

enhanced invasion of WTs, we evaluated DAPT (indirect inhibitor of Notch) treated CCM2 mutant mosaic sprouting and noted no change in invasion of WT ECs (Supplementary Fig. 5C, D). Taken together, we conclude that siCCM2 ECs lead angiogenic invasion by imposing a ROCK-dependent, contact-guided stalk position in migrating WT ECs. This provides evidence for a role of cell mechanics in CCM lesion mosaicism.

## Mechanically hyperactive *Ccm2*-silenced tips steer passive WT ECs through pulling forces and reinforce their actin cytoskeleton

Building upon the coherent increase in force exertion and invasion observed for siCCM2 EC-only sprouts in Fig. 1e, we asked if the increase in invasion of WT ECs in the presence of siCCM2 ECs (Fig. 2c, d) was also accompanied by higher forces on the matrix. To answer this, we used 3D TFM to quantify force-induced matrix displacements generated by both cell populations after overnight mosaic invasion (Fig. 3a, b). Analyzing solely cell-associated matrix displacements (Fig. 3c), we discovered that a significantly lower percentage of invading WT ECs actively exerted forces on the matrix in CCM2 mutant versus CT mosaics (Fig. 3d). This was in line with their stalk cells-like behavior (Fig. 2j), which are shown to exert only low contractile forces on the matrix⁴⁷. Depletion of either one of the *ROCKs* rescued the percentage of force exerting WTs (Fig. 3d), consistent with their rescue as tip cells (Fig. 2j, m). However, the magnitude of matrix displacements generated by the force exerting WT ECs did not vary between the four conditions of cell mosaics and remained similar to control levels (Fig. 3e). Meanwhile, the percentage of force-exerting silenced ECs in mosaic sprouts was drastically higher for the hyper angiogenic tip-like siCCM2 ECs than siCT ECs, and was not modulated by ROCKs (Supplementary Fig. 6A). Their increase in force exertion was however ROCKs-dependent in agreement with our findings during non-mosaic invasion (Fig. 3f, Fig. 1e, h). Taken together, this establishes that *Ccm2* loss drives a ROCKs-dependent non-autonomous inhibition of leader cell behavior in WT ECs, preventing them from transmitting forces to the matrix. This inhibition depends on the increased capacity of siCCM2 tip cells to generate forces. Above all, cell-ECM forces of WT ECs did not increase and could not explain their increased invasiveness in CCM2 mutant mosaics.

Cancer-associated fibroblasts have been shown to guide cancer cell invasion through intercellular force transmission²⁸. We wondered if a similar mechanical communication could explain the collective migration of non-force exerting WT stalks behind highly force exerting siCCM2 tips. When observing invasion dynamics, we noted that hyper-migratory siCCM2 tip cells detached and invaded farther away from a WT stalk leaving it behind (Figs. 2c, 3g, Supplementary Movie 7). Interestingly, the WT stalk seemed to either retract (Fig. 3g, white arrow) or decelerate migration (Fig. 3g, yellow arrow) after detachment of the siCCM2 tip cell (Supplementary Fig. 7). This suggested that

WT ECs were likely pulled forward by the leading siCCM2 tip. Therefore, to evaluate if leading siCCM2 ECs were mechanically interacting with stalk WT ECs at cell-cell contacts, we developed a 3D TFM approach based on first principles, i.e. linear and angular momentum conservation⁴⁸, to quantify the resultant (net) force at this interface (Fig. 3h, Supplementary Note 2). By recovering the forces exerted on the ECM by a detached cell pair of a siCCM2 leader and a WT follower, we found that the average cell-ECM traction exerted by the mutant tip largely exceeded that by the WT stalk (Fig. 3h, Supplementary Table 1 in Supplementary Note 2). Further, quantifying the resultant cell-cell force vector at the interface between these cells showed a substantial, non-zero force (-14 nN) and corresponding average cell-cell traction (-7 pN/µm²), indicating that the two cell types mechanically interact by means of pulling forces. Given that the resultant force from the tip cell on the stalk cell was aligned with the direction of migration of the cell pair (see Fig. 3h), we deduced that these cell-cell pulling forces are important for bringing the WT stalk forward and to ensure collective migration of the tip and stalk cell (see detailed interpretation presented in Supplementary Note 2). Taken together, we concluded that siCCM2 tip ECs are capable of mechanically steering the invasion of passive WT stalks through the combined action of tip cell-ECM pulling forces to direct the sprout tip, and tip cell-stalk cell pulling force transmission to direct the sprout stalk.

These pulling forces experienced by WT ECs were further associated with a reinforcement of the actin cytoskeleton, as evidenced by an increase in β1 integrin-dependent focal adhesions (FA) and actin stress fibers transduced along the follower WT ECs (Fig. 3i, j). Despite the lower percentage of mechanically active WT ECs, β1 integrin-dependent cell-matrix contacts were elevated in WT ECs from CCM2 mutant mosaics. Interestingly, this cytoskeleton reinforcement was only dependent on ROCK1 but not ROCK2, consistent with the specific role of ROCK1 in the formation of β1 integrin-anchored stress fibers at the rear of migrating cells (Supplementary Fig. 6B, C)⁴⁹. We concluded that the mechanically hyperactive siCCM2 tip cells stimulate actin cytoskeleton reinforcement of the passive WT stalk cells to encourage their invasion and speculated on a mechanism involving pulling forces at cell-cell junctions.

## Single-cell RNA sequencing reveals an in vivo-like angiogenic transcriptomic program in sprouting *Ccm2*-silenced ECs

Contextual mechanical forces experienced by cells from mechanosensing interactions with the ECM, or through force transmission from adjacent cells, have a considerable influence on gene expression programs as demonstrated for stem cell fate, tissue morphogenesis and collective cell migration⁵⁰⁻⁵³. We, therefore, asked if the dysregulated mechanics of siCCM2 ECs, and the subsequent exposure of neighboring WT ECs to abnormal mechanical interactions, mediated transcriptomic changes in WT ECs. To investigate this, we performed single cell RNA sequencing (scRNA-seq) on FACS-sorted WT and
<figure><img src="image_20.png" /><figcaption>**Fig. 3** | Mechanically hyperactive Ccm2-silenced tips steer passive WT ECs through pulling forces and reinforce their β1 integrin adhesions and actin cytoskeleton. **a** Schematic of 3D TFM-compatible mosaic invasion assay using 1:1 mixture of wild-type (WT, red) and siRNA silenced ECs (green). **b** Representative 3D renders of displacements (in µm) around mosaic angiogenic sprouts. **c** Criteria for selecting ECs actively exerting pulling forces on the matrix. **d** Percentage of WT ECs actively exerting forces on the matrix based on criteria in (c). 3 independent trials; CT (n = 19), CCM2 (n = 21, p = 0.02), siCCM2 + ROCK1 (n = 16, p = 0.01); siCCM2 + ROCK2 (n = 17). **e, f** Maximum pulling displacements (µm) by force exerting WT ECs (e, 3 independent trials; CT, n = 23; CCM2, n = 11; CCM2 + ROCK1, n = 23; CCM2 + ROCK2, n = 23) or silenced ECs (f, 3 independent trials; CT mosaic, n = 18; CCM2, n = 44, p = 0.04 (*); siCCM2 + ROCK1, n = 24, p = 0.006 (**); siCCM2 + ROCK2, n = 25, p = 0.01 (*)). **g** Maximum intensity projections of confocal images taken at 50 min intervals during overnight invasion of mosaic WT (red) and siCCM2 (green) ECs. Follower WT ECs decelerate invasion (yellow arrow) or retract (white arrow, dotted</figcaption></figure>

line showing migration front) upon losing contact with leading siCCM2 ECs. **h** An approach to compute average forces at a 3D interface of a cell pair (Supplementary Note 2). **(left)** Schematic of a mosaic cell pair depicting the two cell-hydrogel interfaces (green, Ω₁; red, Ω₂), the cell-cell interface (yellow, Ω₃), forces exerted by the cells on the hydrogel (F), and the force balance equation to quantify resultant force on the interface. **(middle)** Force exerted by cells on the ECM for a WT and siCCM2 mosaic cell pair. **(right)** **i** Representative immunofluorescence images (maximum projections) of **(top)** β1 integrin (green) and Hoechst-labeled nuclei (blue), **(middle)** silenced EC (magenta) and **(bottom)** F-actin (gray). Cells not labeled in magenta are WT ECs. **j** β1 integrin and F-actin in WT ECs compared using Mann-Whitney two-tailed t-test with *p* values of 0.0002 (*****) and 0.0006 (***) (dot color indicates 2 independent trials; CT mosaic, *n* = 18; CCM2 mosaic, *n* = 20). Panels **d**–**f** use one-way ANOVA Kruskal-Wallis test with Dunn's correction. Panels **a** and **c** created with BioRender.com. Error bars represent SEM, scalebars 50 µm. Source data are provided as a Source Data file.
siCCM2 ECs 24 h after invasion in either CT or CCM2 mutant mosaic conditions (Fig. 4a). WT cells isolated from CT mosaics were annotated as WT, and those from CCM2 mutant mosaics as WT-CCM2. The dataset was equally distributed between WT (32%), WT-CCM2 (32%) and siCCM2 (36%) conditions (Fig. 4b). However, a clear segregation was observed between siCCM2 clusters and both the WT/WT-CCM2 clusters confirming the evident effect of *Ccm2* silencing on the transcriptomic profile of the mutant ECs (Fig. 4b). Graph-based clustering of the 1434 ECs retained for analysis identified seven clusters based on upregulated genes associated with biological functions related to proliferation, senescence, and angiogenesis (clusters angiogenesis1/2/3) as well as two additional clusters with genes related to vasculature remodeling and RNA splicing (Fig. 4c, Supplementary Data 1). The WT and WT-CCM2 conditions largely comprised proliferative, blood vessel remodeling and angiogenesis regulating cells, whereas loss of *Ccm2* prompted migration and sprouting angiogenesis activity, as well as non-proliferative G1 arrest, consistent with our prior discovery of SASP in CCM<sup>30,54</sup>. To corroborate the biological relevance of the siCCM2 ECs derived from our 3D in-vitro model of angiogenic invasion, we sought to match their dysregulated genes with previously established transcriptomic signatures of CCMs in vivo. siCCM2 ECs indeed showed a clear upregulation of signaling pathways known to play a pivotal role in lesion progression, such as TGF-beta, p53, nuclear beta catenin, inflammation, endothelial to mesenchymal transition, upregulation by RhoA and angiogenesis (Fig. 4d, Supplementary Data 2).

## Recruitment of WT ECs by *Ccm2*-silenced leaders induces reprogramming into a proliferative stalk cell identity

Beyond their phenotypic change observed during mosaic invasion with siCCM2 ECs (Fig. 3h, i), we wondered if WT ECs cells undergo changes in their gene expression program. To address this, we further compared the transcriptomic landscape of WT versus WT-CCM2. Heatmaps of top 50 differentially expressed genes (DEGs) showed a distinct heterogeneity in the transcriptional signatures of the three conditions (Fig. 4e, Supplementary Data 3). While siCCM2s showed the most pronounced differences as expected, WT-CCM2 signatures discernibly deviated from those of WT. Selecting for CD34+ cells<sup>35</sup>, we confirmed a significant increase in tip cells for siCCM2 ECs, alongside a distinct decrease in tip cells for WT-CCM2 relative to WT (Fig. 4f, g). Consistent with their tip cell identities characterized by high force exertion (Fig. 3b), siCCM2 ECs displayed elevated expression of genes associated with ECM degradation, focal adhesions, and cell adhesion (Fig. 4h).

In contrast, WT-CCM2s presented a stalk cell signature associated with tumor endothelial cells but not normal endothelial cells, suggesting that WT-ECs behave as pathological stalk cells in a CCM environment (Fig. 4h)<sup>86,57</sup>. These predominantly stalk WT-CCM2s further presented upregulated genes linked to ECM deposition termed “matrisome” (Fig. 4h, Supplementary Data 4). Others have identified an upregulated matrisome signature underlying the angiogenic switch of normal cells during tumorigenesis, as well as matrix adhesion being an adaptive response driving cancer aggressiveness<sup>38-60</sup>. Further, increased mutant proliferation is typically associated with CCM growth<sup>26</sup>, a concept seemingly at odds with the non-proliferative, senescent nature of siCCM2 ECs in our in-vitro model. We wondered if, in conjunction with activated matrisome-driven signaling, WT-CCM2 may contribute to proliferation-driven CCM growth in line with their stalk identities. When compared with WT and siCCM2 ECs, WT-CCM showed a distinct upregulation of DNA replication and proliferation pathways (Fig. 4i, j). To validate this finding, we first checked for EdU-positive WT nuclei in CT or CCM2 mutant mosaics after 24 h of invasion and observed no differences. We then explored this in 2D by comparing EdU-positive WT nuclei from a 1:1 mosaic with siCCM2 versus siCT ECs cultured for 24 h or 48 h. Notably, we observed a significant increase in EdU positive WT nuclei in CCM2 mutant

compared to CT mosaics after 48 h in 2D (Fig. 4k), but saw no clear differences at the 24 h timepoint (Supplementary Fig. 8A). This suggested that our 24 h sprouting assay may have limitations in exploring this mechanism in the 3D context, despite the upregulation of DNA replication genes observed by single cell RNA sequencing. In summary, this demonstrated that mechanically dysregulated siCCM2 ECs are capable of upregulating stalk-like matrix deposition and proliferation programs in wild-type ECs.

## Discussion

Several recent discoveries have significantly advanced our understanding of CCM lesion pathogenesis through genetic and molecular investigations<sup>7,10,26,61</sup>. Nevertheless, key questions regarding the role of wild-type endothelial cells in fueling CCM lesion growth and their non-autonomous activation by CCM mutants remain unanswered. The most noteworthy conceptual advance of our study is the establishment of a model in which CCM mutants use force- and ECM-mediated physical mechanisms to attract and reprogram wild-type cells from the healthy endothelium, potentially exacerbating lesion growth (Fig. 5). This cooperative invasion strategy involving force transmission and microenvironmental cues bears a noticeable resemblance to mechanisms observed in cancer progression<sup>28,29,62</sup>. The highly contractile, tip-like, leading CCM2 mutant ECs exhibit a competitive angiogenic advantage, allowing them to assert a stalk position upon neighboring wild-type ECs. Notably, *Ccm2* acts as a gatekeeper of ROCK-dependent mechanotransduction, attenuating forces on the ECM through both ROCKs and regulating matrix degradation through ROCK1. Our finding that modulation of ROCK-dependent force exertion also modifies the assignment of tip-stalk phenotypes represents evidence for a cell mechanics-driven mechanism for attracting wild type ECs in CCM and identifies dysregulated cell mechanics as a “third hit” for CCM pathogenesis. One limitation to our study is that our synthetic, nanoporous hydrogel does not perfectly mimic the micro-porous structure of the in-vivo ECM so that our findings might be specific to its biochemical composition. However, in order to make it suitable for cell adhesion, the hydrogel was conjugated with RGD peptide, which is found in both fibronectin and collagen IV, and interacts with β1 integrins that are overactivated in CCMs<sup>49,63</sup>. Further an MMP-cleavable peptide was also included which is degradable by MMP2 and MMP9 – both shown to be overexpressed at CCM lesion boundaries<sup>40,41,64</sup>. We believe this model even though reductionist provides important clues driving invasive behaviors.

Our findings demonstrate that the aggravation of force exertion on the ECM by CCM2 mutants can be felt by follower wild type ECs. While it is plausible that there is always force transmission between tip and stalk cells to sustain collective migration during physiological sprouting, the substantial increase in cell-ECM forces due to CCM2 mutants suggest that any force transmission between a mutant leader and a follower would likely be amplified, given also the force equilibrium boundary conditions. Unfortunately, quantifying and comparing this phenomenon in our model was impossible because wild type ECs rarely formed mosaic sprouts with control ECs, and they never occupied stalk positions behind them. This even suggested a complete lack of physical intercellular communication between populations in CT mosaics. Importantly, the proposed mechanism of hypercontractile mutants pulling forward passive wild type ECs was also associated with the reinforcement of the actin cytoskeleton and β1 focal adhesions in follower WT ECs. While it is also possible that cell-cell adhesions between CCM2 mutants and WT ECs are strengthened to promote this force transmission, the weakening of cell-cell junctions previously observed in *Ccm2*-silenced EC 2D monolayers contradicts this idea<sup>34</sup>. However, the junctional adhesion strength has not yet been explored between mutant and non-mutant ECs. We speculate that a mechanical continuum is established between the cytoskeletal machinery of the mutants and wild-type ECs, although the underlying
<figure><img src="image_21.png" /><figcaption>**Fig. 4** | Single-cell RNA sequencing reveals a siCCM2 EC mediated reprogramming of WT ECs into a stalk cell identity. **a** Schematic representation of the single-cell RNA-sequencing workflow. **b** UMAP and distribution of the combined dataset (WT, WT-CCM2 and siCCM2 ECs). **c** UMAP of the combined dataset annotated with identified clusters of upregulated genes associated with proliferation, senescence, angiogenesis (angiogenesis 1, 2, 3), vasculature remodeling and RNA splicing (Supplementary Data 1). **d** Differential expression of major signaling pathways genes known to play a crucial role in CCMs in-vivo and in-vitro, shown in a dotplot (Supplementary Data 2). **e** Heatmap of top 50 differentially expressed genes for each cell dataset. **f** UMAP of the combined dataset showing CD34 positive tip cells. **g** Quantification of normalized CD34 expression in CD34+ cells in different conditions. Unpaired two-tailed t-test with exact p-values of 0.008 (**) and 0.0008 (****). **h** Dotplot representation of ECM degradation, focal adhesion, cell adhesion, matrisome, angiogenic stalk ECs and tumor-associated stalk ECs signaling pathways compared across the dataset.</figcaption></figure>

**i** Heatmap showing the expression of genes associated with DNA replication and proliferation. **j** Dotpot representation for comparison of DNA replication and proliferation gene expression between cell types. **k** Quantification of the fraction of edU positive wild type ECs in 2D mosaics of WT and silenced (siCT or siCCM2) ECs 48 h after seeding. Mann-Whitney two-tailed test with *p* < 0.0001 (3 independent trials; WT, *n* = 60; WT-CCM2, *n* = 59).

**l** Representative confocal images of fluorescently labeled wild-type ECs (blue), Hoechst-labeled nuclei (pink), EdU-positive nuclei (green) and the merged image. Nuclei in white upon merge represent EdU positive wild-type ECs (see yellow arrows). Panel **a** created with BioRender.com. Source data are provided as a Source Data file.
<figure><img src="image_22.png" /><figcaption>**Fig. 5 | Proposed model for wild-type EC recruitment by siCCM2 mutant ECs.**

Ccm2-silenced endothelial cells enhance angiogenic invasion of neighboring wild-type cells through force-guided and extracellular matrix-guided mechanisms. Mechanically hyperactive Ccm2-silenced tips guide wild-type cells by exerting and transmitting pulling forces and by leaving degraded paths in the matrix as cues to</figcaption></figure>

further promote invasion. This further leads to a reinforcement of β1 integrin-dependent adhesions and actin stress fibers. This is accompanied by a transcriptomic reprogramming of wild-type ECs into stalk cells presenting increased proliferation.

mechanisms remain to be addressed. It is conceivable that localized high forces at mosaic cell-cell junctions induce a force-dependent global activation of signaling resulting in increased active β1 integrins and actin assembly in WT ECs. This is similar to the observed reinforcement of β1 integrin-dependent adhesive structures in response to PECAM mechanical activation involving PI3K and Rho signaling pathways⁶⁵. Besides enabling focal adhesions and invasion, CCM2 mutants also initiate a transcriptomic reprogramming of wild type ECs into a stalk-like identity, characterized by the downregulation of tip cell markers and the upregulation of matrix deposition genes.

Cell proliferation and recruitment both play significant roles in the pathogenesis of CCM lesions. However, their respective contributions and temporal sequence remain unclear and may vary depending on the specific *Ccm* gene involved. In zebrafish embryonic *Ccm1* knockout models and acute conditional *Ccm2* knockout mouse models induced shortly after birth, there is no evidence of EC proliferation⁶⁶,⁶⁷. In chronic *Ccm3* mouse models, an initial wave of proliferation of mutant cells is followed by the recruitment of wild-type ECs, which further promotes the growth of multicavernous lesions⁸,⁹. Notably, in symptomatic human *Ccm1-3* lesions, wild-type ECs are present in significant numbers, supporting the idea that recruitment of wild-type ECs is a common mechanism in familial CCMs. Most interestingly, our study has shown that when exposed to a CCM2 mutant environment, wild-type ECs activate their DNA replication and proliferation programs, leading to increased DNA synthesis. Our study suggests an increase in proliferation of non-mutant endothelial cells in the context of CCMs. In contrast, CCM2 mutants exhibit cell cycle arrest associated with senescence³⁰. Based on these results, we speculate that the proliferative response of wild-type cells might be the driver of multicavernous lesion growth after initial clonal expansion of CCM mutants. This model is supported by evidence that PCNA-positive proliferative ECs in human CCMs⁶⁸ are likely present in zones of positive immunoreactivity for the corresponding CCM protein⁶⁹. Further in vivo investigations will be required to ascertain this important change in paradigm in our understanding of CCM development.

Mechanistically, given the evidence of force transmission between mutant and WT cells, we speculate that the reprogramming of WT cells into a proliferative state may be mechanically driven. How cell-cell forces are then transmitted to the nucleus to activate a reprogramming remains to be explored. Existing studies provide some essential clues; for instance, increased cell-matrix interactions and actin stress fibers in both cardiomyocytes and epithelial monolayers lead to YAP activation, driving increased proliferation⁷⁰,⁷¹. The application of external stretch to polarize the collagen ECM in epithelial networks or pulling forces experienced from neighboring cells in MDCK monolayers, both stimulated cell proliferation through ERK activation, providing evidence of a direct force-based mechanism⁷²⁻⁷⁴.

Our unique perspective on the mechanics of the heterogenous vascular tissue during lesion growth opens up a complementary avenue for further in-vivo exploration. Owing to the shared mechanisms, studies on CCM pathogenesis have proven exceptionally valuable in shedding light on vascular development and other diseases. SWS is one such low flow vascular malformation (VM) which is also associated with increased EC migration through the activation of RhoA and Rac1 signaling, and a low mutation frequency in affected tissues¹⁵,¹⁹. Mutations associated with high flow malformations, such as those in HHT, can also lead to vascular overgrowth, high migratory and invasive behaviors and increased sensitivity to proangiogenic cues from an overactivation of PI3K signaling¹⁶,¹⁷. On the other hand, PI3K-driven VMs in mice require active angiogenesis for the pathogenesis of malformations¹⁸. Lymphatic malformations associated with mutations in PI3KCA result in hypersprouting, but only a fraction of lymphatic ECs carry the mutation. One suggested possibility is that the small population of mutant clones use clonal cooperation to signal to normal ECs in the neighboring regions leading to their potential functional alterations⁷⁵. As such, our findings here could also offer a mechanics-driven model for lesion growth in these other conditions seeded by somatic mutations and presenting mosaic lesions.

Our computational development to quantify cell-cell forces within a 3D multicellular system represents a significant advancement for other fields such as mathematical modeling of physiological
processes⁷⁶. Furthermore, our comprehensive methodology integrating 3D in-vitro systems, biophysical computations and molecular biology techniques holds broader application for exploring the role of mechanics and cellular interactions in the progression of other diseases characterized by aberrant angiogenesis.

## Methods

### Cell culture and siRNA silencing of HUVECs

Pooled Human Umbilical Vein Endothelial Cells (HUVEC) were obtained from Pelobiotech. HUVECs received at P0 were expanded over 2 passages in 100 µg/ml collagen 1 (from rat tail, BD) coated flasks in complete EGM-2 medium (Lonza) supplemented with 100 U/ml penicillin and 100 µg/ml streptomycin at 37 °C in a 5% CO₂ – 3% O₂ humidified chamber. HUVECs at passage 3 were transfected twice at 24 h-intervals with 30 nM siRNA and Lipofectamine RNAi max (Life Technologies, ref. 13778-150) according to the manufacturer's instructions in OptiMEM at 37 °C in a regular 5% CO₂ in a humidified chamber. For double transfections, 30 nM of each siRNA duplexes (Dharmacon smartpool ON-TARGET plus Thermo Scientific) was used: Non-targeting siRNA #1 (ref. D-001810-01), *Ccm2* siRNA (ref. L-014728-01), *Rock1* siRNA (ref. L- ref. L-003536-00) and *Rock2* siRNA (ref. L-004610-00). siRNA transfected HUVECs were transduced with adenoviral LifeAct-GFP2 (Ibidi, Martinsried, Germany) at a multiplication of infection (MOI) of 5 and incubated for 16 h while the wild-type HUVECs used in mosaics were similarly transduced with LifeAct-RFP2.

### PEG hydrogel and 3D invasion assay

A previously reported 3D angiogenesis assay³⁷ was adapted to incorporate a polyethylene glycol based hydrogel (PEG) (Fig. 1a). Degradable PEG hydrogel precursors and buffers were prepared as previously described⁵²,⁷⁷ and supplemented with 1:60 far-red fluorescent beads (FluoSpheres Carboxylate-Modified Microspheres, 200 nm, Invitrogen). Briefly, a PEG hydrogel was prepared by using activated FXIII to enzymatically crosslink an MMP-sensitive peptide modified PEG precursor (8-arm 40 kDa), lysine-RGD peptide at 50 µM (Pepmic), and 0.1–0.5 µM Sphingosine-1-Phosphate (Sigma-Aldrich). Sonicated beads, milliQ water, 10X Buffer, lysine-RGD and SIP were first mixed in that order, followed by 10X FXIII which activated crosslinking. We used a wt/vol hydrogel composition of 1.2% at Young's modulus (E) of 520 Pa for 3D TFM experiments to obtain large enough matrix deformations, and 1.5% at E = 800 Pa for all other experiments. The pre-polymerized PEG solution was pipetted into a pre-cut imaging chamber (Invitrogen Secure-Seal™ S24732) adhered to the glass bottom of a 24 well plate (Cellvis, #P24-0-N), and allowed to crosslink at room temperature for 30 minutes. 40,000 HUVECs were seeded per chamber to obtain a confluent monolayer by incubating the dish vertically for 1 hour at 37 °C, 5% CO₂ leading to cell attachment onto the PEG meniscus. Dishes were then placed horizontally and incubated for 18 hours in complete growth medium. Samples were either imaged live for traction force microscopy experiments or fixed for 1.5 h in 4% paraformaldehyde for immunostaining or invasion measurements.

### Conditioned media experiments

MiliQ water normally used for PEG hydrogel preparation was replaced with 48 h conditioned media from control or *Ccm2* siRNA-transfected HUVECs. Lifeact-RFP labeled wild-type HUVECs were seeded in the invasion assay as described above and fixed after 18 h of invasion and quantified (Supplementary Fig. 4A, B).

### Image acquisition

For overnight timelapse imaging during sprouting, samples were placed at the microscope immediately after cell attachment and media addition. All images were acquired on Leica SP8 confocal microscopes with a 25 × 0.95 NA water-immersion objective (for immunostaining), 20 × 0.4 NA dry objective (for non-mosaic 3D TFM, overnight

timelapse imaging), 10 × 0.4 NA dry objective (for fixed invasion imaging) or on a Nikon confocal microscopy (AIR + MP) combined with an adaptive optics (AO) corrector at the MicroCell facility of IAB, Grenoble (for mosaic 3D TFM and a subset of overnight timelapse imaging).

### Hydrogel mechanical characterization

Homebuilt cylindrical molds treated with Sigmacote were used to prepare circular, disk-shaped hydrogels with a diameter of 7 mm and a height of ~2 mm (Supplementary Fig. 9D). After crosslinking for 30 minutes at room temperature, hydrogels were carefully scooped using a stainless steel micro spoon spatula and transferred to phosphate-buffer saline (PBS)-filled petri dishes and maintained overnight at 4 °C. A micro-laser scanning device (in house developed Micro Laser Scanner in collaboration with Acacia Technology, KU Leuven Core Facility for Biomechanical Experimentation – FIBEr) was used to measure the diameter and thickness of the sample before using an unconfined compression testing protocol⁷⁸. This was crucial for applying a strain-controlled unconfined compression to the sample using a mechanical loading setup (Bose ElectroForce, TA Instrument Company). Our unconfined compression loading protocol comprised of a 20% applied strain at a rate of 1% per second adjusted to the measured height of each individual sample, followed by 15 minutes of relaxation⁷⁸,⁷⁹. The reaction force was recorded during the compression-relaxation experiment. Each PEG hydrogel with different concentration was measured from three independent trials in triplicates.

To obtain the mechanical properties of the hydrogels, an inverse finite element (FE)-based mechanical characterization approach was implemented. To this end, an FE simulation of the unconfined compression tests for each sample was created in Abaqus using a porous-hyperelastic material model as previously reported⁷⁹. Briefly, the gels were simulated using 2D axisymmetric models and the material model consists of a Neo-Hookean solid matrix and a fluid phase. The biphasic behavior was simulated using Darcy's law. Three material constants of the porous-hyperelastic material model include Young's modulus (E) and Poisson's ratio (ν) of the solid matrix and permeability parameter (k). An inverse parameter fitting algorithm was then used to fit the reaction force versus time obtained from the FE model to the experimental measurements by modifying the three material constants. For this purpose, *fminsearch* optimization function in MATLAB was used. The mechanical properties were determined for the three compression-relaxation testing trials of the PEG hydrogels and an average value was calculated for each material constant of each PEG hydrogel.

### Immunofluorescence staining

Sprout-laden PEG hydrogels were fixed for 2 h in 4% methanol-free paraformaldehyde (Thermo Scientific) followed by washing with PBS. Samples were then treated with non-permeabilizing blocking solution constituting 1% BSA, 10% goal serum and 0.05% Tween 20 for 5 h. Primary antibodies suspended in blocking solution were applied to fixed and blocked samples and left overnight at 4 °C. Anti-activated β1 integrin clone 9EG7 (BD Biosciences #553715, 1:100) and recombinant anti-MMP14 antibody EP1264Y (abcam #ab51074, 1:100) were used as primary antibodies. Five PBS+Tween 20 washes were performed over 9 h followed by overnight immunolabeling with secondary antibodies diluted in non-permeabilizing blocking solution. Goat anti-rabbit or anti-rat IgG (H + L) highly cross-adsorbed secondary antibodies conjugated with Alexa Fluor AF 488, AF 546, AF 647 (Invitrogen, 1:500) and phalloidin conjugates with Atto 647 (Sigma-Aldrich, 1:1000) were used. Samples were then extensively washed over 24 h with PBS + Tween 20 before imaging.

### EdU proliferation assay

Twenty-four hours after the second siRNA transfection, transfected HUVECs were combined with CellTracker green CMFDA dye
(Invitrogen) labeled wild-type HUVECs in a 1:1 ratio and seeded at a final density of $7.5 \times 10^4$ in 24-well plates coated with collagen I (from rat tail, BD). After culturing for 48 h in complete medium at 37 °C and 5% CO₂, cells with active DNA synthesis were labeled using the edU Staining Proliferation Kit (iFluor 647, abcam) per manufacturer instructions.

### 3D traction force microscopy image acquisition

Confocal images were acquired at a 512 × 512 resolution using a 20 × 0.4 NA dry objective at 2X zoom, a 56.6 µm pinhole set to 1 AU and bidirectional imaging with phase correction. The excitation wavelengths were 488 nm (green cell channel), 552 nm (red cell channel) and 638 nm (far-red beads channel). The emission wavelengths for the PMT detectors were 498-538 nm, 557-619 nm and 673-704 nm respectively. For mosaic experiments, the images were acquired sequentially to minimize crosstalk between the green and red channels, while for non-mosaic experiments the green cell channel and far-red beads channel were imaged simultaneously. The voxel size was set to $0.568 \times 0.568 \times 1 \mu m^3$ (see example images in Fig. 1e, top row). All TFM experiments were performed on Leica SP8 microscopes equipped with a temperature controlled stage maintained at 37 °C and a humidified 5% CO₂ environment. First, all positions of interest from each conditions in a well plate were identified and marked. The stressed (actively pulling) state was first acquired followed by a treatment with Cytochalasin D (dissolved in DMSO, Sigma Aldrich) at a final concentration of 4 µM for 1 hour. Finally, the relaxed (mechanically inactive) state was acquired in the same location.

### 3D traction force microscopy analysis

3D TFM data was analyzed using the TFMLAB software toolbox⁸⁰. Briefly, bead and sprout images were filtered and enhanced. Sprout images were binarized to obtain a 3D cell mask. Any possible microscope stage drifts that occurred between the stressed and the relaxed states were corrected for using phase correlation. Free form deformation-based image registration was used to measure the 3D bead displacements⁸¹. After displacement measurement, regions of interest around the sprout tips were selected within TFMLAB. The tractions exerted by the cells were calculated in a Finite Element (FE) framework using our previously described physically based inverse method⁸²,⁸³. Briefly, this method searches for a displacement field that is as similar as possible to the measured one while satisfying the force equilibrium condition in the hydrogel domain. The hydrogel domain was modeled as a linear elastic material with E = 520 Pa (see hydrogel mechanical characterization section) and v = 0.3⁸⁴. TFMLAB assumes a negligible stiffness for the cell internal domain (Young's modulus of $10^{-6}$ Pa). More details about the implementation of the method can be found in Supplementary Note 1. We also developed a novel approach to quantify cell-cell forces from TFM data. A detailed description of this development can be found in Supplementary Note 2. In Fig. 3c, d, we defined which sprouts were exerting forces by selecting the ones that generated matrix displacements larger than the noise level. Here, the noise level corresponds to matrix displacements greater than the pixel size, which is -0.5 µm. Only displacements larger than 0.5 µm and clearly localized in the vicinity of the cell were considered to be cell-associated active pulling on the matrix.

### Quantification of filopodia and actin

3D fluorescence image stacks of phalloidin labeled sprouts were maximally projected and filtered with a median filter (function medfilt2 of MATLAB. Filtered images were binarized using a manually adjusted intensity threshold to provide a spout mask. The total actin intensity metric (Fig. 1d) was obtained by summing intensity values of the pixels within this mask and normalizing to the number of nuclei. The number of filopodia in sprout tips (<2 µm thick) were quantified by applying an opening operation on the binary mask using a disk-shaped

structuring element of a 7 pixels radius. The resulting binary image was subtracted from the sprout mask and small objects (<50 pixels) were removed. The count of the connected components in the resultant image normalized for the number of nuclei provided the number of filopodia in Fig. 1c. The maximum filopodia length per sprout was measured manually using ImageJ. To quantify branchiness, the binary sprout masks were skeletonized followed by the removal of spurious structures shorter than 10µm (functions bwskel and bwmorph of MATLAB with options 'MinBranchLength', 'spur' and 'hbreak'). Finally, the number of branch points (points where a skeleton branch ends) were counted in the resultant skeleton (Supplementary Fig. 9A depicts all operations).

### Quantification of matrix degradation

Gelatin labeled with Alexa 647 dye⁸⁵ was combined at 2x final concentration in the pre-polymerization PEG solution to give a uniformly fluorescent hydrogel. Hydrogels were visualized after angiogenic invasion using a 647 laser to identify degraded (black zones) regions. The following image processing workflow was used to quantify the degraded area (Supplementary Fig. 9B). Gelatin-Alexa647 images were filtered with a disk-shaped top-hat filter to remove uneven background illumination, followed by a smoothing Gaussian filter and a contrast stretching operation. A minimum-intensity projection was then applied to obtain the complementary image. The degraded region was binarized using a manually adjusted intensity threshold and the degradation area was computed around the sprout of interest, normalized to the number of nuclei, and displayed in Fig. 1j and Supplementary Fig. 5A, B.

### Quantification of MT1-MMP, and β1 integrin

MT1-MMP and β1-integrin immunofluorescence were quantified based on 3D confocal stacks images. Images were first maximally projected followed by a 5 × 5 wiener filter to reduce background noise and a contrast stretching operation. All images were processed equally such that the quantifications are comparable. The intensity values of the pixels within a cropped region of interest were summed, normalized by the number of nuclei, and displayed in Figs. 1l and 3j.

### Quantification of invasion distance

100 µm stacks of 3D confocal images were maximally projected using ImageJ (Version 1.52a) and the maximum invasion distance was manually measured over ~10 field of views (FOVs) of the monolayer for each replicate at multiple positions within each FOV (35-40 positions/condition). The mean invasion distance for each condition quantified per trial was then normalized to the control of that trial (Fig. 2b). This was done for the cell population of interest by splitting channels based on fluorescence.

### Quantification of dynamic invasion and area by cell tracking

Confocal images stacks (100 µm thick, 1 µm step size) of mosaic sprouting acquired every 45 minutes over 16 hours were used to visualize dynamic imaging. These images were captured at a 3 times lower resolution and magnification than those acquired for visualizing the filopodia morphology in Fig. 1b. Further, they captured a larger z-stack resulting in overlay of multiple sprouts in the maximal intensity projected movies. As a result, these images should be used for studying dynamics but not morphometric analysis. To track cell dynamics for invasion and area quantifications, the images were first maximally projected in ImageJ and converted to.avi files in RGD format. First, an automated algorithm was developed to automatically segment the hydrogel domain (Supplementary Fig. 9C). Specifically, the entire time-lapse of both cell channels was first maximum-intensity projected). The resultant image was then filtered, its complementary obtained and binarized. Only the largest component of the image corresponding to the hydrogel domain was kept. This was followed by obtaining the convex hull of the binary image (MATLAB function bwconvhull) and calculating its distance transform.
Each image of the timelapse was then binarized, and the inner holes were removed (Supplementary Fig. 9C). Spurious binary objects with areas lower than ~30 µm³ or those that did not coincide with the segmented hydrogel domain were removed. Binarized cells in the last time point were tracked by comparing the distances between their centroids and those of the binarized cells of the previous time point. The nearest neighbor was selected as the match, consequently attributing to the pertinent label. This process was repeated iteratively until covering the entire timelapse. Notably, in instances where a cell division event occurred, the existing label was allocated to one of the cells while a new label was assigned to the other cell. The invasion distance of each cell was tracked by storing the maximum value of the distance transform of the hydrogel domain within the cell's binary mask. The results are shown in Fig. 2d and Supplementary Movie 8. For area tracking, the total number of pixels of each cell channel were counted and multiplied by the pixel size and presented in Fig. 2f. Modes of invasion of wild-type ECs shown in Fig. 2i, j were quantified by manually counting the number of WT ECs in each mode at the first moment of invading the hydrogel as visualized in overnight timelapses.

## Quantification of EdU staining

The WT cell, nuclei, and EdU-stained images underwent a series of preprocessing steps programmed in MATLAB to enhance their quality. Initially, denoising was performed by applying a median filter, followed by a Gaussian filter, and a contrast stretching operation using the respective functions medfilt2, imgaussfilt, and imadjust. Subsequently, a binarization process was applied and any holes within these binary images were filled using the imbinarize and imfill functions. To address cases where nuclei were in close proximity or overlapping due to segmentation inaccuracies, we employed the watershed algorithm on the binary nuclei image, which separated closely situated nuclei watershed function. We then counted the EdU nuclei that were overlapping with both the WT cell and nuclei binary masks. We reported the percentage of EdU positive WT cells in Fig. 4k.

## Single-cell RNA sequencing sample preparation

Four independent trials of mosaic invasion samples were prepared for control (WT-RFP + siCT-GFP ECs) and siCCM2 (WT-RFP + siCCM2-GFP ECs) conditions and allowed to sprout for 24 h into degradable PEG 1.5% hydrogels with a high concentration of SIP (400 nM) to allow for significant hydrogel invasion by both cell populations. Hydrogels were then collected using a spatula into a falcon tube and dissociated into single cells using an incubation period of 15 min in trypsin, thereby leaving behind the cells attached in 2D on the dish bottom. The solutions were neutralized with a 1:10 volume of EGM-2 without antibiotics and centrifuged for 5 min at 300 RCF, followed by 10 min at 500 RCF. After decanting the supernatant, the cell pellets were resuspended in 200 µL of EGM-2 and put on ice. Cells were isolated by fluorescence activated cell sorting (FACS) by flow cytometry on a Aria IIu (BD Biosciences). Discrimination of cell populations was based on fluorescence at 530/30 nm from 488 nm excitation and 610/20 nm from 561 nm excitation to identify respectively cells expressing Lifeact-GFP or Lifeact-RFP. Cells were run through a 100 um nozzle under 20 psi at a flow rate of 1 and recovered simultaneously in tubes in a 4-way purity mode. The sorted cells were labeled as WT, WT-CCM2, siCCM2 and frozen in EGM-2 + 40% FCS + 10% DMSO.

After thawing the cells, their viability (>80%) and aggregation were tested before starting the single-cell library preparation. The concentration of freshly dissociated cells was adjusted to 1000 cells/µL in HBSS/0.05% BSA, and then the 10x Chromium Controller and the Chromium Single Cell 3' Reagent kit V3.1 were used to perform the scRNAseq experiments. Library construction was performed according to the manufacturer's instructions using the Chromium Chip B Single Cell kit and Chromium Multiplex Kit (10X Genomics, Pleasanton, CA, USA). Sequencing was performed in paired-end

mode with HighOutput flow cell (read 1: 28 cycles and read 2: 90 cycles) and a NextSeq 550 sequencer (Illumina, San Diego, CA, USA) at the Transcriptome Core Facility of the Institute in Regenerative Medicine and Biotherapy, CHU-Inserm-UM Montpellier, France. CellRanger was used to process scRNAseq data. We first use CellRanger mkfastq to convert and demultiplex the bcl files (from illumina sequencer, raw data) in fastq files for each sample. Then we use CellRanger count to generate the matrix data containing gene counts for each cell per sample. Fastq files were aligned to the human genome reference sequence GRCh38.

## Single-cell sequencing data processing and clustering

After QC steps we have retained a total of 1434: 464, 461 and 509 for the WT, WT-CCM2 and CCM2KD conditions respectively. We implemented the Seurat⁸⁶ 4.3.0 pipeline to perform scRNAseq analysis. The data was normalized and 2000 highly variable genes (features) were identified using the FindVariableFeatures. The data was scaled and described using principal component analysis (PCA) using the RunPCA function. The data was clustered by the top 7 PCs and the FindNeighbors function parameter k.param = 5. The FindCluster resolution was set to 0.15. Data visualization was done using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique using the RunUMAP package with the top 7 PCs and identified 7 clusters in the data. Using the top 50 upregulated genes we annotated the 7 clusters at a high-level as 1) Proliferation, 2) Quiescence, 3-5) Angiogenesis 1/2/3, 6) Vasculature remodeling, 7) RNA splicing. Genesets were created based on previous studies⁸⁶⁻⁹⁶ and geneset scores per data type were reported in Fig. 4d, h and j (Supplementary Data 1 and 2). Normalized expression levels of CD34 in CD34⁺ cells were extracted using the FetchData function. Average gene expressions were obtained by the AverageExpression function.

## Statistical analysis

Our data across all conditions to be compared did not always pass the normality test. In such cases, we employed an unpaired Mann Whitney non-parametric t-test and a Kruskal Wallis non-parametric ANOVA statistical test with corrections where appropriate with a 95% confidence interval (GraphPad Prism 9, Version 9.5.1, GraphPad Software, Inc.). Error bars always represent SEM. Statistical significance was considered for all comparisons with the following p-values < 0.05(*) < 0.01(**) < 0.001(***). When determining significance difference between conditions, the multiple comparisons were performed comparing all conditions. Measurements were taken from several distinct cell sprouts within a sample and the same cell sprout was repeatedly measured only for overnight imaging to study the dynamics. The selection of sprouts was done by taking multiple positions starting from one end of the monolayer to the other at consistent intervals to ensure that we capture the behavior of this entire global invasion. Further, at least 10 positions were captured for every given monolayer (1 entire chamber or sample) to ensure that we appropriately captured the local as well as global behavior of the sprouting without bias. Additional aspects of experimental design are detailed in the Reporting Summary. The source data file provides detailed tables including all raw data and the associated statistical tests and p values. No statistical method was used to predetermine sample size. An experiment was considered a failure and data were excluded from analysis when significant cell death was observed or when no angiogenic invasion was observed in controls. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Data availability

Single-cell RNA sequencing row datasets generated in this study have been deposited in the Gene Expression Omnibus repository under the accession series number GSE246174. Supplementary Information is available for this paper as a separate document. All data generated in this study are provided in the Supplementary files and the Source Data file. Raw microscopy data files used for a subset of non-mosaic and mosaic 3D TFM trials have been published on Zenodo [https://doi.org/10.5281/ZENODO.13773792] and can be processed using the open-source TFMLAB code to generate the results presented here⁹⁷. Additional datasets are available upon request. Source data are provided with this paper.

## Code availability

The TFMLAB code used to analyze 3D traction force microscopy data is open-source and can be accessed from the Gitlab repository [https://gitlab.kuleuven.be/MMatrix/Jorge/tfmlab_public]. Implemented cell-cell force recovery and image analyses routines developed for all quantifications are available on Zenodo [https://doi.org/10.5281/ZENODO.13773083]⁹⁸.

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## Acknowledgements

We thank the members of the Van Oosterwyck and Faurobert laboratories for thoughtful feedback; Transcriptome Core Facility of the Institute in Regenerative Medicine and Biotherapy, CHU-INSERM-UM Montpellier, https://irmb-montpellier.fr/ for the single-cell RNA Sequencing processing, the MicroCell microscopy & flow cytometry facility at IAB, R. Nuyts and A. Grichine for support with microscopy. These studies were supported by Research Foundation Flanders (FWO) PhD fellowship 1S68820N (AS), postdoctoral fellowship 1259223 N (JBF),

projects G087018N (HVO), GOC2422N (HVO, EF), G086622N (AR), GOACA24N (AR); infrastructure grant I009718N (HVO), KU Leuven grants IDN/19/031 (HVO), IDN/20/007 (AR), C14/22/108 (AR), iBOF/21/083 C (HVO); ERANET NEURON JTC 2022 call - project G0L1522N (EF, HVO); Project PID2021-1260510B-C42 funded by MCIN/AEI/10.13039/501100011033 (JAS); Marie Skłodowska-Curie Individual Fellowship (CREATION project: MSCA-IF-2019-893771, SAE) Ambition Internationale, Région AURA (EF, HVO); Allen Distinguished Investigator Award, a Paul G. Allen Frontiers Group advised grant of the Paul G. Allen Family Foundation (AR).

## Author contributions

A.S. designed, conducted, imaged, analyzed, interpreted all experiments, and wrote the manuscript. J.B.F developed all image analysis tools, J.B.F and J.A.S developed the cell-cell force recovery algorithms and interpreted the results. M.P. performed FACS sorting of mosaic cell populations, S.A. generated and A.A., E.F., A.S. analyzed and interpreted the scRNAseq data. A.S. and E.D.V. conducted and analyzed experiments for hydrogel mechanical characterization while S.A.E. implemented the finite element analyses. J.D.J. conducted tissue culture experiments. A.S., J.B.F., A.A., J.D.J., J.A.S, M.P, S.A, E.D.V, S.A, S.A.E, A.R., E.F., and H.V.O. edited the manuscript. E.F. designed and interpreted experiments and wrote the manuscript. H.V.O. interpreted experiments and wrote the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-52866-6.

**Correspondence** and requests for materials should be addressed to Apeksha Shapeti, Eva Faurobert or Hans Van Oosterwyck.

**Peer review information** *Nature Communications* thanks Roger Kamm, Douglas Marchuk, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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