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# Interfacial tension and growth both contribute to mechanical cell competition

Léo Valon, Alexis Matamoro-Vidal, Alexis Villars, Romain Levayer

► To cite this version:

Léo Valon, Alexis Matamoro-Vidal, Alexis Villars, Romain Levayer. Interfacial tension and growth both contribute to mechanical cell competition. Current Biology, 2025, 35 (21), pp.5372-5383.e4. 10.1016/j.cub.2025.09.040. pasteur-05347230

HAL Id: pasteur-05347230

https://pasteur.hal.science/pasteur-05347230v1

Submitted on 4 Nov 2025

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# Current Biology

## Interfacial tension and growth both contribute to mechanical cell competition

### Graphical abstract

<figure><img src="image_3.png" /></figure>

### Authors

Léo Valon, Alexis Matamoro-Vidal, Alexis Villars, Romain Levayer

### Correspondence

romain.levayer@pasteur.fr

### In brief

Valon and Matamoro-Vidal et al. combine quantitative live imaging in the *Drosophila* pupal notum and vertex modeling to show that two modes of compaction with distinct features can contribute to mechanical cell competition and cell elimination near oncogenic clones: compaction driven by growth and compaction driven by high interfacial tension.

### Highlights

* Growth and interfacial tension of oncogenic clones compact neighboring WT cells

* These two factors cause distinct patterns of cell deformation and elimination

* Interfacial tension triggers cell compaction/elimination only at the curved interface

* These modes can co-exist and promote cell death during mechanical cell competition

<figure><img src="image_4.png" /></figure>
Report

Interfacial tension and growth both contribute
to mechanical cell competition

Léo Valon,<fnref n="1" />,<fnref n="3" /> Alexis Matamoro-Vidal,<fnref n="1" />,<fnref n="3" /> Alexis Villars,<fnref n="1" />,<fnref n="2" /> and Romain Levayer<fnref n="1" />,<fnref n="4" />,*

SUMMARY

Tissue plasticity and homeostasis rely on the interplay between cell behavior and mechanical inputs.<fnref n="1" /> Me-
chanical stress also contributes to the evolution of pathologies, notably by accelerating pretumoral cell
expansion through mechanical cell competition (MCC).<fnref n="2" />⁻⁵ MCC is a conserved process in which one cell pop-
ulation is preferentially eliminated when mixed with another cell population due to its higher sensitivity to me-
chanical stress.<fnref n="2" />,<fnref n="3" />,⁵⁻⁸ Theoretical and experimental explorations of MCC have mostly focused on the contri-
bution of growth and pressure to cell elimination and a few genetic contexts, including the activation of Ras
*in vivo*<fnref n="2" />,⁸ and mutation of the polarity gene *scribble* in mammalian cell culture.<fnref n="3" />,<fnref n="4" />,⁹ However, it remains unclear
whether other oncogenes can trigger MCC and whether growth is generally the only central regulator of MCC.
Using the *Drosophila* pupal notum (a single-layer epithelium), quantitative live imaging, and vertex modeling,
we revisited the mechanisms contributing to MCC. Doing so, we outlined the co-existence of two modes of
wild-type (WT) cell compaction near oncogenic cells, namely compaction driven by growth versus local
compaction driven by increased tension at tumoral/WT cell interfaces in zones of high 2D curvature. We high-
lighted distinct signatures in cell deformation and cell elimination distribution that can delineate these two
modes of compaction, and we recapitulated them *in silico* and *in vivo* using genetic backgrounds affecting
growth and/or interfacial tension independently. Altogether, this study reveals the contribution of interfa-
cial-tension-driven compaction to MCC and outlines the co-existence of various modes of compaction dur-
ing MCC.

RESULTS AND DISCUSSION

Sorting and preferential cell compaction and elimination
in curved area near active Ras clones

Previously, we outlined the process of mechanical cell competi-
tion (MCC) in the pupal notum using the conditional activation of
an active form of the oncogene Ras (RasV12) in clones, which
promotes growth and blocks apoptosis and extrusion, and re-
vealed local compaction and elimination of wild-type (WT) cells
several cell rows away from the clones.<fnref n="2" />,⁸ We decided to charac-
terize more extensively the features associated with WT cell
elimination in the proximity of RasV12 clones. We first focused
on relatively flat interfaces between large RasV12 clones and
WT cells. As previously reported, activation of Ras over 12 h is
sufficient to observe clone expansion (Figures 1C–1F and
S1A–S1C; Video S1A), leading to an average area expansion of
roughly 50% over 12 h. This correlated with compaction and
an increased death rate in the neighboring WT cells located 1–
3 cell rows away from the clone border (Figures 1A–1F; Video
S1A; 30%–40% area reduction and up to a 3-fold increased
death rate). By increasing the proportion of tissue coverage by
RasV12 cells using longer heat shock, we could obtain more

diverse geometry of interface between the two populations,
including WT cell patches entirely surrounded by Ras<sup>V12</sup> cells.
Doing so, we noticed a clear modification of the shape of WT
cell patches surrounded by Ras<sup>V12</sup> cells over time, which tended
to become rounder (Figures 1G and 1H; Video S1B; a significant
increase in patch circularity over 10 h). This was indicative of a
cell-sorting phenomenon that can be driven by the modulation
of interfacial tension between mutant and WT cells<sup>10,11</sup>
(Figure 1l). Accordingly, we observed a clear increase in line ten-
sion at the clone interface using force inference<sup>12</sup> (Figures S1D–
S1F) and a clear enrichment of non-muscle myosin II light chain
(Myoll) at the interface between WT and Ras<sup>V12</sup> cells
(Figures S1G–S1l), with no apparent change with clone bound-
ary curvature (Figures S1K and S1L). This is in good agreement
with previously published junction tension estimation obtained
by laser ablation in the pupal notum<sup>11</sup> and the observed accumu-
lation of actomyosin at Ras<sup>V12</sup> clone interfaces in the wing imag-
inal discs.<sup>13,14</sup> At the interface between two fluids, interfacial ten-
sion can generate a difference in pressure called Laplace
pressure, which is proportional to the interfacial tension and
the local 2D curvature of the interface (ΔP = <sup>2γ</sup>/<sub>R</sub>, where R is the
in-plane curvature radius and γ the line tension).<sup>10,13</sup> By analogy,

<footnote n="1">¹Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France</footnote>

<footnote n="2">²Present address: Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056 Basel, Switzerland</footnote>

<footnote n="3">³These authors contributed equally</footnote>

<footnote n="4">⁴Lead contact</footnote>

<footnote>*Correspondence: romain.levayer@pasteur.fr</footnote>

<footnote>https://doi.org/10.1016/j.cub.2025.09.040</footnote>
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<figure><img src="image_7.png" /></figure>

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Distance to clone border
(cell rows)

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

**Figure 1. Sorting and preferential cell compaction and elimination in curved area near active Ras clones**

MCC scenario: all cells express E-cad::GFP (in green) and clones (Gal4 positive, in magenta) conditionally express UAS-Ras<sup>V12</sup>. Movies start about 12–20 h APF and last for 16–20 h.

(A) Clone border is underlined by white lines. Cells that will die during the 20-h movies are highlighted by round white markers. Scale bar, 20 µm.

(B) Quantification of cell death probability in WT cells as a function of distance (in cell row) to the clone boundary. Cell death numbers for each row are divided by the number of cells in each row at time 0 using large and flat clone/WT cell interfaces (N = 626 deaths localized across 4 nota, 4,658 cells tracked). **p < 0.05 from paired t test, error bars are SEM.

(C–F) Quantification of single-cell compaction over time.

(C and D) Representative example of a clone boundary at time 0 (C) and associated apical cell area quantification at 0 and 7.5 h in Ras<sup>V12</sup> (D). Scale bar, 20 µm.

(E) Normalized and averaged apical cell area 20–25 h after beginning of Ras induction—at large and flat clone/WT cell interfaces—as a function of distance (in cells) to clone boundary. Areas are normalized by mean cell area of WT cells at 4 and 5 rows away (N = 393 cells, from 3 snapshots). Error bars are SEM.

(F) Evolution in time of the mean apical area on the first two rows of cells on both sides of the Ras<sup>V12</sup> clone boundary at large and flat clone/WT cell interfaces. N = 90 cells over 4 time points from 3 movies. t test, ****p < 10<sup>-8</sup>. Error bars are SEM.

(G and H) Evolution of WT cell patch shape over time.

(G) Two representative examples of WT large patches surrounded by Ras<sup>V12</sup> cells, clone boundaries are highlighted by a white line. Scale bar, 20 µm.

(legend continued on next page)
the cell population located in convex boundaries with negative in-plane curvature will experience a local pressure or normal stress, similar to beads surrounded by a circular spring. We expected WT cell patches near curved and convex interfaces to experience higher compaction in comparison with cells close to less curved interfaces. Focusing specifically on WT patches (composed of 25–50 cells) with an initial elliptical shape, we indeed observed a more significant compaction of WT cells located in highly curved and convex areas near Ras<sup>V12</sup> clones relative to WT cells near flatter boundaries (Figures 1J–1L; Video S1C). We also observed a 2.5-fold increase in the rate of cell elimination at the curved tips of the WT patches relative to the central/flat areas (Figure 1M; Video S1C). Altogether, this suggested that interfacial tension combined with boundary curvature could also contribute to WT cell compaction and cell elimination near Ras<sup>V12</sup> clones. Note that growth still contributes to cell compaction, as we did observe significant compaction and cell death near flat interfaces where Laplace pressure should be negligible (Figures 1C–1F; Levayer et al.<sup>2</sup>).

## Vertex modeling of MCC

We therefore decided to test theoretically and experimentally how interfacial tension could contribute to cell compaction and elimination relative to compaction driven by differential growth. We used a 2D vertex model<sup>15</sup> (Figure 2A; STAR Methods) to explore how variation in tension at the clone interface, on one hand, and growth, on the other hand, impacts cell shape and cell elimination. We used parameters that recapitulate the distribution of polygons in the notum and are in agreement with previous modeling of cell competition<sup>16</sup> (STAR Methods) and focused initially on simulations in the absence of cell elimination. We first confirmed that higher interfacial tension ($\lambda$ = 10: ratio of tension at the interface over tissue bulk) at the interface between a “mutant” population (outer) and a WT population (inner) is sufficient to induce cell compaction of the WT population and that the degree of compaction is inversely proportional to the initial radius of the clone (confirming the analogy with the Laplace pressure law) (Figures S2A and S2B). We then explored the impact of the interface shape on cell compaction and cell-shape evolution. When we started from a rectangular patch of WT cells surrounded by mutant cells (Figure 2A), increasing interfacial tension was sufficient to induce significant cell area reduction, specifically at the tips where curvature is higher, as well as a progressive increase in patch circularity (Figures 2B, 2C, and S2C; Video S2A), qualitatively in agreement with WT cells surrounded

by Ras<sup>V12</sup> clones (Figures 1G, 1H, and 1J–1L; Videos S1B and S1C). Cell compaction was also more significant in the first row of WT cells (Figure S2D). Interestingly, this also led to distinct cell-shape changes. First, we observed a cell elongation orthogonal to the interface on both side of the patch interface (Figure 2B), as observed in the first layer of cells near Ras clone interfaces (Figure S1J), which can be explained intuitively by the reduction in the size of the junction at the interface, driven by high tension. Second, we observed a stretching of external cells orthogonal to the ellipse tips, and third, a global alignment of WT cell orthogonal to the patch’s long axis (Figures 2D and S2C; Video S2A).

We then explored the impact of differential growth on cell compaction, elongation, and patch shape. To simulate growth, we modulated the resting area of the surrounding mutant cells (to impose a progressive increase in their apical area), as this corresponds to the behavior observed in Ras clones and the fact that cell division in the notum is not associated with cell growth (pure cleavage<sup>17</sup>). We first simulated the impact of cell growth on a rectangular patch of WT cells (like we did for interfacial tension). This led to a rapid and relatively homogeneous compaction of the central rectangular patch (Figures 2G, 2H, and S2C; Video S2B). Moreover, the WT cell patches did not become more circular and rather became more elongated due to compaction orthogonal to the patch’s long axis (Figures 2G, 2H, and S2C; Video S2B), in agreement with the elongation of cells in the direction of this axis (Figure 2I; Video S2B), and in stark contrast with what is observed upon an increase in interfacial tension (clone rounding and cell elongation orthogonal to the long axis of the patch; Figures 2B–2D). Interestingly, in this condition, cell compaction is more homogeneous throughout the WT population (slightly higher compaction in the first cell row; Figure S2F) and does not correlate with local clone border curvature (Figure 2H; Video S2B), contrasting with the deformations observed upon increased interfacial tension.

Next, we introduced cell elimination in the model. Cell eliminations (T2 transitions) were triggered below a threshold area strain (ratio of current area to initial area) with a given probability in order to disperse cell death in time and space (STAR Methods). Doing so, we confirmed that increased interfacial tension drives the progressive elimination of WT cells at the extremities of the patches, showing a higher death probability in curved regions relative to less curved regions—irrespective of the selected area strain death threshold (Figures 2E and 2F; Video S2C)—and a higher death rate in the first row of WT cells

(H) Boxplot of WT cell patch circularity ($\frac{4\pi \text{ area}}{\text{perimeter}^2}$) over time for 15 groups (black crosses) of WT cells from 4 experiments over 10 h. The lines connect values for the same WT patch over time. Paired t test, \*\*\*p < 10<sup>-6</sup>.

(I) Schematics of mechanical effect of increased clone boundary tension on boundary and cell patch shape. The boundaries smooth progressively and the patches become rounder.

(J–M) Differential WT cell behavior as a function of their localization in curved/convex versus flat areas of the clone interface using elongated WT patches surrounded by Ras<sup>V12</sup> cells.

(J) Representative example of an elongated patch of 25–50 WT cells used for the quantification. Scale bar, 20 µm.

(K) WT cell classification in either “curved” (orange) or “flat” regions (yellow).

(L) Averaged ratio of single-cell areas between cells in curved versus flat regions of the same WT patch. Single points are the ratio of mean apical cell areas for a single WT patch. N = 560 cells from 15 elongated patches obtained from 4 nota. t test for the ratio being different from 1, \*\*\*p < 10<sup>-4</sup>. Error bar is SEM.

(M) Ratio of cell death probabilities for WT cell in curved versus non-curved regions. N = 199 deaths, from 12 elongated patches obtained from 4 nota. t test for the ratio being different from 1, \*\*\*p = 0.0035. Error bar is SEM.

See also Figures S1 and S4 and Video S1.
<figure><img src="image_16.png" /><figcaption>A</figcaption></figure>

Case 1: interfacial tension

λ: fold increase of line tension
at the junctions of the boundary

Case 2: differential growth

κ: fold increase of resting area
of clonal cells

A°: cell resting area

Λ: junctional line tension

<caption>B</caption>

Case 1: interfacial tension

<figure><img src="image_17.png" /><figcaption>C</figcaption></figure>

Area change (three 1st cell rows)

<figure><img src="image_18.png" /></figure>

Boundary Circularity

<figure><img src="image_19.png" /></figure>

Compression strength

<caption>D</caption>

Cell orientation θ°
(three 1st rows)

<figure><img src="image_20.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>F</figcaption></figure>

Proportion of deaths

<figure><img src="image_22.png" /><figcaption>G</figcaption></figure>

Case 2: differential growth

<figure><img src="image_23.png" /><figcaption>H</figcaption></figure>

Area change (three 1st cell rows)

<figure><img src="image_24.png" /></figure>

Boundary Circularity

<figure><img src="image_25.png" /></figure>

Compression strength

<caption>I</caption>

Cell orientation θ°
(three 1st rows)

<figure><img src="image_26.png" /><figcaption>J</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>K</figcaption></figure>

Proportion of deaths

<figure><img src="image_28.png" /></figure>

**Figure 2. Vertex modeling of MCC**

(A) Topology used for initial conditions of the simulations. The tissue is made of ~3,600 cells with fixed boundary conditions. In the center of the tissue, a group of ~150 cells is set as WT (blue cells), whereas all other cells in the tissue are set as mutant (red cells). The inset shows the parameters used in the vertex model to simulate two different MCC scenarios. For the “interfacial tension” scenario (case 1) the line tension parameter of the junctions at the boundary (green line)

(legend continued on next page)
(Figure S2E), all in agreement with the experimental observations near Ras<sup>V12</sup> clones (Figure 1M; Video S1C). In contrast, growth-induced compaction led to a more homogeneous distribution of cell death in the patches without preferential enrichment at the curved parts of the patch (Figures 2J and 2K; Video S2C) and a higher elimination rate in the two first rows of cells (Figure S2G; Video S2C). Both conditions could eventually lead to a full elimination and/or fragmentation of the WT patches, depending on the simulation duration, on the growth/tension characteristics, and on the area strain death threshold (Figures S2H and S2I). To complement the simulation of rectangular patches, we then also simulated the context of a flat interface between a WT population and a large overgrowing clone (Figures S2J-S2M''''; Video S2D). This was sufficient to recapitulate the gradient of deformation and cell elimination observed near the flat interfaces of large Ras<sup>V12</sup> clones (Figures 1C-1F and S2J-S2M''''), confirming that growth also contributes to WT cell elimination near Ras clones, especially near flat interfaces.

Alternatively, we simulated growth by inducing cell division in 20% of randomly picked winner cells (Figures S2N and S2O''''; Video S2E), which corresponds to the maximal differences in cell division observed experimentally between winner and loser cells (see below; Figure S3C). Interestingly, this led to much less efficient WT cell compaction and more compaction within the overgrowing population, suggesting that global cell growth is more efficient at triggering WT cell compaction (Figures S2N and S2O''''; Video S2E).

Altogether, the simulations suggested that high interfacial tension and differential growth are both sufficient to compact

and eliminate cells, while exhibiting distinct features. Increased interfacial tension induced higher cell compaction and cell elimination in curved regions, a preferential elongation of cells orthogonal to the long axis of the patch, and a progressive rounding of the cell patch. In contrast, differential growth led to a more isotropic compaction and elimination in patches, which is not influenced by boundary curvature, cell elongation parallel to the long axis of the patch, and the absence of patch rounding. Differential growth can also trigger a gradient of cell deformation and cell death near flat interfaces.

## Activation of Yki in clones drives MCC through differential growth

To validate these predictions, we tried to find experimental conditions that could impact growth and line tension independently (unlike Ras<sup>V12</sup> activation, which modulates both). Activation of Yki (the fly ortholog of YAP in mammals) can promote growth as well as cell survival.<sup>18</sup> Moreover, Yki/YAP activation in clones was previously associated with neighboring cell elimination in *Drosophila* wing discs,<sup>19</sup> the pupal notum,<sup>16</sup> glioma cells,<sup>20</sup> MDCK cells,<sup>21</sup> and human pluripotent stem cells.<sup>22</sup> Conditional expression of a constitutively active form of Yki (Yki<sup>CA</sup>, Yki<sup>S111A,S168A,S250</sup> triple mutants<sup>23</sup>) in large clones led to a rapid expansion in clone surface and compaction of the neighboring cells near flat interfaces (Figures 3A-3D, 3H, 3I, and S3A; Video S3A), as well as high death rates in WT cells located up to 4 cell rows away from the clone (Figures 3E-3G; Video S3A). This is in good agreement with the simulation of growth near a flat interface (Figures S2J-S2M''''; Video S2D).

between WT and mutant cells is increased by a factor $\lambda$ with respect to the line tension $\Lambda$ of all the other junctions in the tissue. For the "differential growth" scenario (case 2), the resting area of the mutant cells is increased by a factor $\kappa$ with respect to $A^0$, the resting area of all other cells in the tissue.

(B-K) Simulation outcomes from two MCC scenarios: interfacial tension (case 1, B-F) and differential growth (case 2, G-K).

(B) Final state for simulations of case 1 obtained with four different values of $\lambda$. Top panels show the topology of the tissue, and bottom panels show single-cell area fold change at the final simulation step, relative to area at the beginning of the simulation. Black line is the boundary between WT and mutant cells.

(C) Top: cell area fold change relative to initial area (averaged over all the cells of the first three rows of cells from the boundary between WT cells and mutant cells) as a function of simulation step, $\lambda$, genotype, and curvature of the boundary (flat versus curved). See top left panel in (B) for a schematic of flat versus curved regions. Bottom left: circularity ($\frac{4x\text{area}}{\text{perimeter}^2}$) of the boundary of the WT cell population, as a function of simulation step and of $\lambda$. Bottom right: cell compression strength ($1 - \frac{\text{area final}}{\text{area initial}}$) for the WT cell population as a function of $\lambda$ and of boundary curvature.

(D) Left: inset from (B) (top right) showing for each cell the direction of its longest axis (blue lines, indicating the direction from cell's centroid to its farthest vertex). Cell orientation is measured by $\theta$, the acute angle enclosed between the y axis of the tissue plane and the direction of the cell's longest axis. Right: $\theta$ averaged over the first three rows of cells from the boundary as a function of simulation step, $\lambda$, and cell's genotype (WT or mutant). Data shown for the flat region only.

(E) Back tracking of dying cells. Cells that will die during the simulation (case 1, $\lambda = 10.0$) are highlighted on the topology at simulation step 0 (green dots).

(F) Proportion of deaths for the first three rows of cells from the boundary, as a function of simulation step, genotype, curvature of the boundary, and death area threshold. Data are shown for case 1, $\lambda = 10.0$. Proportion is given relative to the number of cells of a given genotype and curvature region at simulation step 0.

(G) Final state for simulations of case 2 obtained with four different values of $\kappa$. Top panels show the topology of the tissue, and bottom panels show single-cell area fold change at the final simulation step relative to area at the beginning of the simulation. Black line is the boundary between WT and mutant cells. Note that the color scale of the heatmap is different from the one of (B).

(H) Top: cell area fold change relative to initial area (averaged over all the cells of the first three rows of cells from the boundary between WT cells and mutant cells) as a function of simulation step, $\kappa$, genotype, and curvature of the boundary (flat versus curved). See top left panel in (G) for a schematic of flat versus curved regions. Bottom left: circularity ($\frac{4x\text{area}}{\text{perimeter}^2}$) of the boundary of the WT cell population, as a function of simulation step and of $\kappa$. Bottom right: cell compression strength ($1 - \frac{\text{area final}}{\text{area initial}}$) for the WT cell population as a function of $\kappa$ and of boundary curvature.

(I) Left: inset from (G) (top right) showing for each cell the direction of its longest axis (blue lines, indicating the direction from cell's centroid to its farthest vertex). Cell orientation is measured by $\theta$, the acute angle enclosed between the y axis of the tissue plane and the direction of cell's longest axis. Right: $\theta$ for the first three rows of cells from the boundary as a function of simulation step, $\lambda$, and cell's genotype (WT or mutant). Data shown for the flat region only.

(J) Back tracking of dying cells. Cells that will die during the simulation (case 2, $\kappa = 1.25$) are highlighted on the topology at simulation step 0 (green dots).

(K) Proportion of deaths for the first three rows of cells from the boundary, as a function of simulation step, genotype, curvature of the boundary, and death area threshold. Data are shown for case 2, $\kappa = 1.25$. Proportion is given relative to the number of cells of a given genotype and curvature region at simulation step 0. All quantifications are averages $\pm$ SEM, obtained by running the simulations five times using different seeds in the Monte Carlo random walk.

See also Figure S2 and Video S2.
A

<figure><img src="image_29.png" /></figure>

C

<figure><img src="image_30.png" /></figure>

E

<figure><img src="image_31.png" /></figure>

F

<figure><img src="image_32.png" /></figure>

B

<figure><img src="image_33.png" /></figure>

D

<figure><img src="image_34.png" /></figure>

G

<figure><img src="image_35.png" /></figure>

H

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

I

<figure><img src="image_38.png" /></figure>

J

K

<figure><img src="image_39.png" /></figure>

L

<figure><img src="image_40.png" /></figure>

M

<figure><img src="image_41.png" /></figure>

O

<figure><img src="image_42.png" /></figure>

N

<figure><img src="image_43.png" /></figure>

P

<figure><img src="image_44.png" /></figure>

**Figure 3. Yorkie clonal activation and growth-driven mechanical competition**

Differential growth generated by the overexpression of an active form of Yorkie (Yki<sup>CA</sup>). All cells express E-cad::GFP (green). Magenta: conditional overexpression of UAS-Yki<sup>CA</sup>, begun 8 h before the start of the imaging phase, which lasts 16–20 h.
(A–D) Quantification of single-cell compaction over time.
and with the pattern of deformation and elimination observed
near the flat interfaces of large Ras<sup>V12</sup> clones (Figures 1C-1F).
We then focused on conditions where WT patches are entirely
surrounded by Yki<sup>CA</sup> cells. Contrary to WT cells surrounded by
Ras<sup>V12</sup>, we did not observe a smoothening of the WT/clone
interface nor the rounding of WT patches (Figures 3H
and 3J; Video S3B), suggesting shape-conservative compac-
tion that is not associated with sorting. Accordingly, we
could not find any significant increase in line tension at clone
interfaces using force inference and Myoll distribution (Figures
S3E-S3G). Focusing on elliptical patches of WT cells, we
found that the compaction was associated with a progressive
elongation of WT cells along the long axis of WT patches, in
agreement with predictions of the vertex model of elongated
WT patches with differential growth (Figures 3K and 3L;
Video S3B; compare with Figures 2G and 2I; Video S2B).
More importantly, in contrast to Ras<sup>V12</sup> competition and the
model of high interfacial tension, we could not find any differ-
ence in WT cell compaction and death probability in curved
versus flat interfaces near Yki<sup>CA</sup> clones (Figures 3M-3P;
Video S3C). The more homogeneous distribution of cell death
in WT patches fits the predictions of the model of WT patches
surrounded by growing cells (Figures 2H, 2J, 2K, S2F, and
S2G; Videos S2B and S2C). Altogether, we found that WT
cell compaction and elimination induced by Yki<sup>CA</sup> clones fit
all the features observed in the vertex simulations of growth-
induced compaction (Figures 2G-2K, S2F, S2G, and S2J-
S2M"). Of note, the compaction and elimination of WT cells
is more likely driven by Yki cell area expansion than their slight
increase in proliferation (~20% more compared with WT:
Figure S3C), as simulating a 20% increase in proliferation
was not sufficient to recapitulate the pattern of compaction

of WT cells (Figures S2N and S2O'''; Video S2E), while the
pattern of deformation and death could be well recapitulated
by a general increase in cell resting area (Figures S2J-S2L''';
Video S2D).

Ectopic expression of N-cad drives cell elimination
through an increase in interfacial tension

We then examined conditions affecting interfacial tension independently of growth. Previously, ectopic expression of N-cadherin (N-cad) was shown to stimulate Myoll accumulation and increase tension at the border with cells that do not express N-cad.²⁴ We recapitulated these observations in the pupal notum using conditional expression of N-cad in clones: clone borders progressively smoothen and are associated with an up-regulation of tension and Myoll recruitment at the boundary (Figures 4A-4F and S3H-S3J; Video S4A), leading to progressive rounding of clone/WT patches and the smoothening of their interfaces (Figures 4A-4F; Video S4A). Of note, the levels of Myoll do not vary according to the local boundary curvature, suggesting that the assumption of constant line tension around the clone is also reasonable here (Figures S3M and S3N). We also confirmed that no significant differences in cell proliferation or global growth rate could be observed between the two populations (Figures S3A and S3D), suggesting that N-cad mostly affects interfacial tension without a significant impact on growth. Strikingly, N-cad induction led to rapid compaction of both WT and N-cad cells located near convex borders (Figures 4G-4I; Video S4B), which was also associated with cell elimination in WT or N-cad cells located in the convex zone (Figures 4B and 4C; Video S4 B). In contrast, we could not observe any cell death in N-cad or WT cells located in concave/negatively curved areas (Figure 4B; Video S4B; focusing on cells on the other side of the

(A) Zoom on one boundary between Yki<sup>CA</sup> and WT population 10 h after Yki induction. Scale bar, 20 µm.

(B) Segmentation of two snapshots of a Yki<sup>CA</sup> clone interface separated by 10 h, color code (on the left) shows the apical area.

(C) Quantification of apical cell areas as a function of distance (in cells) to clonal boundary between Yki<sup>CA</sup> clone and WT population 20–25 h after Yki induction—at large and flat clone/WT cell interfaces—normalized by mean cell area of WT cells at 4- and 5-cell distance (N = 440 cells, 3 nota).

(D) Quantification of the evolution of mean apical area of the first two cell rows on both sides of the boundary at large and flat clone/WT cell interfaces, N = 90 cells, 5 time points from 3 nota. Time points are separated by 2.5 h. *t* test, ****p < 10<sup>-16</sup>. Error bars are SEM.

(E) Snapshots of a nota at time t0 (top) and t0 + 16 h (bottom) expressing E-cad::GFP and activating Yki<sup>CA</sup> in magenta clones. Clone borders are highlighted by white lines. Scale bar, 20 µm.

(F) Representation of clone boundaries. Black asterisk: back tracking of the localization of all the cells that will die during the 20 h movie.

(G) Quantification of cell death probability as a function of distance to the border (in number of cells) at large and flat clone/WT cell interfaces. Cell death numbers in each row are divided by the initial number of cells at the same distances (N = 1,074 deaths localized over 4 nota and normalized by 4,463 cells). **p < 0.05 from paired *t* test with cell death probability at row 6 used as reference.

(H–J) Evolution of WT population shapes over time using patches completely surrounded by Yki<sup>CA</sup> cells.

(H) Snapshots of two representative examples, with border of WT population surrounded by Yki<sup>CA</sup> clones (magenta) highlighted by a white line. Images are separated by 2 h. E-cad is shown in green and gray. Scale bar, 20 µm.

(I and J) Boxplot of WT apical cell area (I) (normalized by the first time point) and circularity (J) (4x area / perimeter²) for 22 groups of WT cells surrounded by Yki<sup>CA</sup> cells (single curves) from 6 experiments over about 6 h. The lines connect the values coming from the same group of cells.

(K–P) Quantification of single-cell shape, compaction, and death as a function of curvature in the WT cell groups using elongated patches completely surrounded by Yki<sup>CA</sup> cells.

(K) Schematic of the determination of single-cell orientation (θ) relative to the main axis of the WT group (dashed line). Cells at the tips are excluded from the analysis. Scale bar, 20 µm.

(L) Polar distribution of the orientation of WT cells surrounded by Yki<sup>CA</sup> cells 20–25 h after Yki<sup>CA</sup> induction. N = 342 cells from 16 groups from 6 nota.

(M) Example of an elongated WT patch composed of ~25–50 cells used for the curvature analysis. Representation of WT cell classification in either curved (orange) or flat (yellow) area.

(N) Mean apical area of cells in flat and curved areas. One dot = one WT patch. Paired *t* test: n.s., not significant, *p* = 0.84.

(O and P) Cell death probability of WT cells in flat or curved areas, N = 219 cell deaths, from 9 groups from 4 nota. Error bars are SEM.

(O) Mean of death probabilities. Paired *t* test: n.s., not significant, *p* = 0.91.

(P) Ratio of cell death probabilities. *t* test for testing difference to 1, n.s., not significant, *p* = 0.54.

See also Figures S3 and S4 and Video S3.
<figure><img src="image_45.png" /></figure>

A

E-cad::GFP

uas-N-cad

<figure><img src="image_46.png" /></figure>

E-cad::GFP,tub-gal80ts;
act-gal4<u;UAS-NLS::RFP x UAS-N-cad (II)

B

<figure><img src="image_47.png" /></figure>

C

<figure><img src="image_48.png" /></figure>

Distance to clone border
(cell rows)

D

<figure><img src="image_49.png" /></figure>

E

<figure><img src="image_50.png" /></figure>

F

<figure><img src="image_51.png" /></figure>

G

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

Cell categories : Flat / Curved / First row

H

<figure><img src="image_55.png" /></figure>

I

<figure><img src="image_56.png" /></figure>

J

<figure><img src="image_57.png" /></figure>

K

<figure><img src="image_58.png" /></figure>

L

uas-N-cad

<figure><img src="image_59.png" /></figure>

M

All angles

<figure><img src="image_60.png" /></figure>

First row

<figure><img src="image_61.png" /></figure>

Second row

<figure><img src="image_62.png" /></figure>

**Figure 4.** N-cad overexpression drives sorting and curvature-dependent cell elimination

MCC scenario generated by differential junctional tension generated by N-cadherin overexpression. All cells express E-cad::GFP (in green) and N-cadherin-
overexpressing cells are in magenta. N-cadherin expression started 8 h before the start of the imaging phase that lasts 16–20 h.

(A) Evolution of clone shape boundaries for two snapshots separated by 10 h. Clone boundaries are highlighted by white lines. Scale bar, 20 µm.
"finger" tips). In other words, this genetic condition leads to compaction and elimination of cells surrounded by the other cell type, irrespective of their genotype. Accordingly, focusing on elliptical patches of both WT and N-cad cells (of 25-50 cells) surrounded by the other genotype (Figure 4G), we found that WT or N-cad cells in convex areas relative to flat boundaries were roughly 30% smaller (Figures 4H and 4I) and their probability of death was twice as high (Figures 4J and 4K), in agreement with the predictions of the vertex model with high interfacial tension (Figures 2B, 2C, and 2F). Similarly, the first rows of WT/N-cad cells were elongated orthogonal to the patch/clone borders, as predicted in the model (Figures 2D, 4L, and 4M). Surprisingly, we observed two important features that were not predicted by the vertex model: a relatively higher apical cell area in the first row of WT and N-cad cells near the interface (Figures 4G, 4H, and 4L) and a lower death rate in the first row of WT and N-cad cells (Figure 4C), whereas the model predicted that the first row of cells was the most compacted and the most susceptible to death (Figures S2D and S2E). We therefore tried to find which features might be missing in our initial vertex simulation of increased interfacial tension. A closer look at Myoll distribution as well as force inference not only revealed increased tension at clone boundaries but also an unexpected decrease in Myoll and tension levels in junctions orthogonal to clone boundaries (Figures S3H–S3J). We therefore implemented, in our vertex model, a similar reduction of tension in junctions orthogonal to clone boundaries (corresponding to a reduction in total tension by a factor of 3; Methods S1, Figure G), combined with an increase in clone boundary tension. This was sufficient to recapitulate the relative increase in cell area at the boundary and the reduction of cell elimination in the first rows of cells near convex boundaries (Figures S3O–S3S; Video S4C), suggesting that the pattern of cell death could be fully explained by the

combination of high interfacial tension and tension reduction in orthogonal junctions. Using the same simulating conditions, we could also recapitulate cell death distribution and the evolution of interface shape, starting from the "digit"-like topology observed experimentally (compare Figures 4B and 4D, top, and Video S4A with Figure S3T; Video S4D). Altogether, this provides, to our knowledge, the first clear experimental evidence that increased line tension is sufficient to drive cell compaction and cell elimination in vivo.

As previously characterized in the pupal notum,²,⁸ we confirmed that compaction driven by increased growth and/or line tension triggers caspase activation (using effector caspase sensor GC3AI²⁵; Figures S4A–S4E) and that caspase activation is required for cell extrusion (combining p35 with N-cad in clones blocked cell elimination; Figures S4F and S4G). Ras activation in clones in the larval wing disc was previously associated with JNK pathway activation at the clone interface, which is responsible for WT cell elimination.²⁶ However, we did not observe any obvious sign of JNK activation at the boundaries of RasV12 or YkiCA clones and found no correlation with WT cell compaction and elimination in the pupal notum (Figures S4H and S4I). This suggests either that cell elimination is driven by a different mechanism in the notum compared with the wing disc or that it is based on different mechanisms that operate on the shorter timescales of our experiments (hours versus days in the larval wing disc). We also noticed that the level of sorting and the characteristic cell shape in WT patches surrounded by Ras clones varied depending on their localization in the notum (with more pronounced sorting in posterior regions), suggesting that the relative contributions of growth versus line tension may also depend on tissue localization and underlying patterning in the Ras context (Figure S4J), as observed in the wing disc.²⁷ Previously, the pattern of the

(B) Zoom on a zone with curved boundaries. All cell deaths in this zone have been back tracked and are represented on this time point by a white circle for WT (green) and clone (magenta) cells. Note the localization of death in convex interfaces and the absence of death in the opposite concave regions.

(C) Quantification of cell death probability as a function of distance to the boundary (in number of cells). Negative distance values are for cell rows inside the N-cad clones. Cell death numbers in each row are divided by the initial number of cells in this row (N = 644 deaths localized over 4 nota and normalized by 7,893 cells). Two rounds of *t* tests for death probability of rows (−3, −2) and (+2, +3) have been performed against reference Ref1 and Ref2 rows. **p* < 0.05.

(D) Two representative examples (red squares in A) with border of WT population represented by a white line. Images are separated by 6 h. Scale bar, 20 µm.

(E and F) Analysis of N-cad clone (magenta curves) and WT (black curves) group boundary shape evolution over time while being surrounded by the other population.

(E) Normalized area.

(F) Circularity, ""*p* < 10⁻⁵, paired *t* test (N = 31 groups of cells from 8 nota). Single lines represent the evolution of a clone/group of cells.

(G–K) Quantification of single-cell compaction and probability of cell death as a function of boundary curvature in N-cad (magenta) and WT cells (black).

(G) Two representative examples of the elongated patches surrounded by other cell types considered for the following quantifications. Clone boundaries are represented by white lines. Cells are classified into 3 independent categories: the first cellular row (in blue), a region close to a curved boundary (in orange), and a region close to a flat boundary (in yellow). Scale bars, 20 µm.

(H) Quantification of mean cell area for cells in the 3 different categories after 20–25 h of N-cad induction. One black cross is one WT patch; one magenta cross is one N-cad clone. Paired *t* test, n.s., not significant, *p* = 0.56; ""*p* < 0.0004 (N = 140, 125, and 157 cells, from 12 cell groups from 6 nota).

(I) Quantification of the ratio of mean cell areas for curved over flat category. ""*p* < 10⁻⁵, paired *t* test against "1," one black cross is one WT patch, one magenta cross is one N-cad clone.

(J and K) Quantification of cell death probabilities for cells in the 2 categories flat and curved; one black circle is one WT patch; one magenta circle is one N-cad clone (N = 96 cell death events from 11 groups of cells from 6 nota).

(J) Mean values, **p* = 0.0056, paired *t* test.

(K) Ratio of cell death probabilities. ""*p* = 0.014, paired *t* test against "1."

(L) Example showing cell orientation in a WT cell patch surrounded by N-cad cells relative to the long axis of the patch after 20–25 h of N-cad induction. The green cells (inner row) and the blue cells (first cell layer) are the ones used for the analysis. We excluded the cells in the most curved regions as the angle relative to the boundary orientation is more difficult to define. Scale bar, 20 µm.

(M) Polar distribution of cell orientation of compacted population. N = 166 "first row" (blue) and 150 "second row" (green) cells from 16 elongated groups of WT and N-cad cells surrounded by other cell types from 6 nota.

See also Figures S3 and S4 and Video S4.
myosin Dachs was shown to affect local mechanics and oncogenic cell sorting in the pupal notum.¹¹,²⁸ However, it is unlikely to explain the tissue pattern of Ras sorting because Dachs was only very marginally enriched at RasV12 clone boundaries.¹¹ The regions of high RasV12 clone sorting (posterior and midline) correspond to regions of normally low ERK activity,⁸ which might be compatible with the impact of higher spatial differences in ERK activity on sorting. A recent study outlined the higher compressibility of HRasV12 MDCK cells, which behave like “losers” and are expelled by the neighboring WT cells.²⁹ The situation is different in the pupal notum, where cell extrusion relies on caspase activation, hence making RasV12 cells intrinsically resistant to mechanical stress and cell extrusion and thus behave like “winner” cells.²

Interestingly, a very similar mechanism of elimination was outlined for MDCK cells depleted of the tight-junction protein ZO1,³⁰ which are eliminated through the formation of an actomyosin purse-string and cell compaction, suggesting that mechanical competition driven by interfacial tension is conserved in mammals.

The impact of Ras on interfacial tension has already been described in the pupal notum,¹¹ the wing disc,¹³ and cell culture.¹⁴ Similarly, the modulation of cell specification, including activation of Ras, was shown to promote cell exclusion through interfacial tension and cyst formation in the wing disc.¹³ However, the link between interfacial tension and cell elimination/apoptosis was not formally demonstrated and was instead associated with JNK activation at the clone boundary in the wing disc.²⁶ Here, we demonstrate experimentally and theoretically, for the first time, that interfacial tension can be causal for cell compaction and apoptosis and that different modes of compaction can co-exist during MCC, including compaction and elimination driven by increased interfacial tension and compaction driven by growth. These different modes can co-exist in certain backgrounds (e.g., upon Ras activation) and show distinct features in terms of cell shape and localization of cell elimination. Cell shape and membrane curvature can also influence biochemical signaling.³¹⁻³⁴ Although we cannot totally exclude their contribution to the correlation between curvature and death, they cannot explain the increase in apoptosis rate beyond the first row of cells and fail to explain why, along the same boundary, WT/N-cad cells die on the convex side of the interface while surviving on the concave side. Future work will help to reveal the relative contributions and potential synergies of growth versus interfacial-tension-driven compaction upon activation of other oncogenes and in the vicinity of solid tumors.

## RESOURCE AVAILABILITY

### Lead contact

Further information and requests for resources and reagents should be directed to, and will be fulfilled by, the lead contact, Romain Levayer (romain.levayer@pasteur.fr).

### Materials availability

All the reagents generated in this study will be shared upon request to the lead contact without any restrictions.

### Data and code availability

* All raw data corresponding to each figure panel (including images and local projection) have been deposited in a single Zenodo repository

(https://doi.org/10.5281/zenodo.17091096) and are publicly available as of the date of publication. The DOI is also listed in the key resources table.

* All original code has been deposited in a Zenodo repository (https://doi.org/10.5281/zenodo.17091096) and is publicly available as of the date of publication. The DOI is also listed in the key resources table.

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

We would like to thank all members of the CDEH group for constructive comments on the manuscript. We would like to thank Yohanns Bellaïche and Pierre-François Lenne for sharing fly strains, the Bloomington Drosophila Stock Center and their NIH funding (P40 OD018537), and Flybase for sharing essential information, stocks, and reagents. Work in the R.L. lab is supported by the Institut Pasteur, the Cercle FSER and the CNRS (UMR3738), the ANR PRC CoECECa, the ANR PRC MAPEFLU, the ANR-10-LABX-0073, the ERC starting grant CoSpaDD (Competition for Space in Development and Disease, grant number 758457), and the European Union (ERC, PrApEDoC, 101085444). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. L.V. was supported by a Marie Skłodowska-Curie postdoctoral fellowship (MechDeath, 789573).

## AUTHOR CONTRIBUTIONS

L.V., A.M.-V., and R.L. designed the project. L.V. performed all the experiments and analyses. A.M.-V. performed all the vertex simulations and analyses. A.V. adapted the force inference method. R.L. supervised the project and provided funding. All the authors contributed to writing and editing the article.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* **KEY RESOURCES TABLE**

* **EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS**

* Drosophila melanogaster husbandry

* Drosophila melanogaster strains

* **METHOD DETAILS**

* Live imaging and movie preparation

* **QUANTIFICATION AND STATISTICAL ANALYSIS**

* Measurement of cell shape and compaction

* Determination of cell "curved/flat" categories

* Myosin levels

* Group of cell shapes

* Calculation of cumulated cell division rates

* Calculation of cell death probability

* Force inference

* Vertex modeling of mechanical cell competition

* Statistics

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.cub.2025.09.040.

Received: January 15, 2025

Revised: July 3, 2025

Accepted: September 12, 2025

Published: October 9, 2025
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## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3">Deposited data</td></tr><tr><td>Raw data</td><td>https://doi.org/10.5281/zenodo.17091096</td><td>This manuscript</td></tr><tr><td colspan="3">Experimental models: Organisms/strains</td></tr><tr><td>D. melanogaster: E-cad-3XmKate (KI)</td><td>Pinheiro et al.<sup>35</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: E-cad-GFP (KI)</td><td>Bloomington Drosophila stock center<sup>36</sup></td><td>BDSC_60584</td></tr><tr><td>D. melanogaster: sqh-Sqh-3XmKate2</td><td>Pinheiro et al.<sup>35</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: UAS-his3.3 mIFP-T2A-H01</td><td>Bloomington Drosophila stock center<sup>37</sup></td><td>BDSC_64184</td></tr><tr><td>D. melanogaster: tub-Gal80<sup>ts</sup></td><td>Bloomington Drosophila stock center</td><td>BDSC_7019</td></tr><tr><td>D. melanogaster: tub-Gal80<sup>ts</sup></td><td>Eduardo Moreno</td><td>N/A</td></tr><tr><td>D. melanogaster: UAS-N-cad</td><td>Chan et al.<sup>24</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: UAS-Ras<sup>V12</sup></td><td>Bloomington Drosophila stock center<sup>38</sup></td><td>BDCS_4847</td></tr><tr><td>D. melanogaster: UAS-Yki<sup>S11A S168A S250A V5</sup></td><td>Bloomington Drosophila stock center<sup>23</sup></td><td>BDCS_28817</td></tr><tr><td>D. melanogaster: UAS-GC3Ai</td><td>Schott et al.<sup>25</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: lexAOP-GC3Ai</td><td>Our group<sup>39</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: TRE-GFP</td><td>Chatterjee and Bohmann<sup>40</sup></td><td>N/A</td></tr><tr><td>D. melanogaster: UAS-p35</td><td>Bloomington Drosophila stock center</td><td>BDSC_5073</td></tr><tr><td colspan="3">Software and algorithms</td></tr><tr><td>Code developed in this study</td><td>https://doi.org/10.5281/zenodo.17091096</td><td>This manuscript</td></tr><tr><td>Matlab with Image processing toolbox and Statistics and Machine learning toolbox</td><td>https://fr.mathworks.com/</td><td>RRID: SCR_001622</td></tr><tr><td>Fiji (ImageJ)</td><td>Schindelin et al.<sup>41</sup></td><td>https://fiji.sc/</td></tr><tr><td>Black Zen Software</td><td>Zeiss</td><td>RRID: SCR_01863</td></tr><tr><td>MetaMorph</td><td>https://www.moleculardevices.com/</td><td>N/A</td></tr><tr><td>Local z-projector plugin (Fiji)</td><td>Herbert et al.<sup>42</sup></td><td>https://gitlab.pasteur.fr/iah-public/localzprojector</td></tr><tr><td>Epyseg</td><td>Aigouy et al.<sup>43</sup></td><td>https://github.com/baigouy/EPySeg</td></tr><tr><td>Tissue analyser</td><td>Aigouy et al.<sup>44</sup></td><td>https://github.com/baigouy/tissue_analyzer</td></tr><tr><td>gfortran</td><td>https://fortran-lang.org/learn/os_setup/install_gfortran/</td><td>N/A</td></tr><tr><td>ReadImageJROI plugin</td><td>https://github.com/DylanMuir/ReadImageJROI</td><td>N/A</td></tr><tr><td>R</td><td>https://www.r-project.org/</td><td>N/A</td></tr><tr><td>OpenGL</td><td>https://www opengl.org/</td><td>N/A</td></tr><tr><td>Vertex model (gfortran)</td><td>This manuscript</td><td>https://doi.org/10.5281/zenodo.17091096</td></tr><tr><td>Force inference</td><td>Kong et al.<sup>12</sup></td><td>https://data.mendeley.com/datasets/78ng4tmj75/4</td></tr></tbody></table>

### EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

#### Drosophila melanogaster husbandry

All the experiments were performed with *Drosophila melanogaster* fly lines with regular husbandry techniques. The fly food used contains agar agar (7.6 g/l), saccharose (53 g/l) dry yeast (48 g/l), maize flour (38.4 g/l), propionic acid (3.8 ml/l), Nipagin 10% (23.9 ml/l) all mixed in one liter of distilled water. Flies were raised in plastic vials in a room set at 22°C unless specified in the legends and in Methods S1, Table A (alternatively raised in incubators at 18°C or 29°C, 12/12h light cycles). Females and males were used without distinction for all the experiments. We did not determine the health/immune status of pupae, adults, embryos and larvae. Flies were not involved in previous procedures, and they were all drug naïve.
## Drosophila melanogaster strains

The strains used in this study and their origin are listed in the key resources table. Females and males were used without distinction for all the experiments. We did not determine the health/immune status of pupae, adults, embryos and larvae, they were not involved in previous procedures, and they were all drug and test naive. The exact genotype used for each experiment is listed in the Methods S1, Table A.

## METHOD DETAILS

### Live imaging and movie preparation

After Heat Shock at 37°C, crosses were kept at 18°C for 2 to 4 days. The pupae were collected at the early stage (0-6 hour after pupal formation), and transferred at 29° for 6-8 hours, then glued on double sided tape on a slide and surrounded by two home-made steel spacers (thickness: 0.64 mm, width 20x20mm). The pupal case was opened up to the abdomen using forceps and mounted with a 20x40mm #1.5 coverslip where we buttered halocarbon oil 10S. The coverslip was then attached to spacers with two pieces of tape. Most timelapse movies last at least 16 hours with each position visited every 5 minutes. Pupae were imaged on a confocal spinning disc microscope (Gataca systems) with a 40X oil objective (Nikon plan fluor, N.A. 1.30). The autofocus was performed using fluorescence of cadherin as a reference using a custom made Metamorph journal. Movies were performed in the nota close to the scutellum region containing the midline and the aDC and pDC macrochaetae. For myosin quantifications and force inference measurements, a few static images were acquired with an LSM880 using an oil 40X objective (N.A. 1.3) equipped with a fast Airyscan (Zeiss).

Movies shown are adaptive local Z-projections. Briefly, E-cad planes are used as a reference to locate the plane of interest (plane of the epithelium/plane of cells) on sub windows. This procedure was either applied using a custom Matlab routine⁵ or through the Fiji⁴¹ plugin LocalZprojector.⁴² Fluorescent signals were then obtained by projecting maximum of intensity on a few micrometers around a focal point which was centered on the surface of interest with or without an offset of a few micrometers (the exact parameters can be found in Methods S1, Table B).

## QUANTIFICATION AND STATISTICAL ANALYSIS

### Measurement of cell shape and compaction

To get single shape measurement, fluorescent signal of apical cues (cadherin or myosin) were first projected, then segmented using Epqseg.⁴³ Segmentation were then curated by hand using the Tissue Analyzer plugin from FiJi⁴⁴ and then saved. Cells of interest were selected by hand on FiJi, and list of cells were saved. Cells positions, shapes, and morphometrics were extracted and plotted using Matlab (including ReadImageJROI.m plugin).

### Determination of cell "curved/flat" categories

Curved versus flat interfaces are determined by eye. Cells in the "curved" category are the 4 rows of cells the closest to the tip of the curved interface. Validation of curvatures were performed by placing circular FiJi region of interest and measuring their radius (Figures S3K and S3L). Curved interfaces correspond to curvature of 1.1 µm⁻¹, while flat interfaces correspond to curvature of approx. 0.25 µm⁻¹.

### Myosin levels

From segmented myosin junctions, we also obtain boundaries of single cells and measure myosin levels at junctions with Matlab. MyoII levels along lines were determined using the "plot Profile" FiJi function.

### Group of cell shapes

To get "area" and "circularity" of large group of cell over time, group contour where drawn by hand on FiJi and shape characteristics were extract via Matlab. For the determination of group areas over time, groups of cells were sometimes approximated by drawing triangles in-between sensory organ precursors (SOPs) for WT and clonal populations.

### Calculation of cumulated cell division rates

To determine cell division rates, divisions were pointed by hand over the duration of the movie with their localizations (in clones or WT). Rates were determined by dividing by the total number of cells (at an arbitrary timepoint of the movies). The two movies of a same genotypes were temporally aligned using stereotypical developmental events (SOP divisions).

### Calculation of cell death probability

To determine probability of death, most (if not all) deaths were pointed by hand over the duration of the movie. Positions of deaths were determined at the last time point the cells have a non-zero apical area. Death probabilities at a given distance were then determined by considering the number of death at that distance divided by the number of cell at that given distance at the first time point of the movie. Death probabilities in a zone of interest are determined by considering the number of deaths in a given zone divided by the initial number of cells in that zone.
## Force inference

Junctional tensions and cell pressures were inferred using Bayesian inference as implemented in Kong et al.¹² based on mathematical formalisms of the method developed by Ishihara and Sugimura.⁴⁵ Tissues were segmented using TissueAnalyzer and manually corrected. Then bonds and cells files were extracted from TissueAnalyzer to csv files. MATLAB Codes from Kong et al.¹² were adapted and simplified in order to load these files and track the junctions/cells of interest. Then inference was run for all segmented images.

Cells and junction of interest – at the boundary vs. in the clone vs. in the WT population – were clicked using FiJi and applied on cell pressure maps and cell junction tension maps using Matlab.

## Vertex modeling of mechanical cell competition

To infer signatures of the different processes at play during RasV12 mediated mechanical cell competition, we used our vertex model program⁵,⁴⁶,⁴⁷ which is based on the existing framework for the study of developmental processes in the epithelial tissues of *Drosophila*.⁴⁸,⁴⁹ The model was implemented in gfortran, using openGL and R to visualize the outputs.

In the vertex model, only the apical sides of the cells are considered. Cells are represented as 2D polygons, made of vertices connected by edges. The vertices can move over time as a result of intra- and inter-cellular mechanical forces. The movement of the vertices is implemented by comparing the mechanical energy of a vertex in its current position (x, y) with the energy of a randomly chosen point nearby (x+δd, y+δd) with δd [0,0.005]. When the energy in the new position is smaller, then the movement is accepted as the new vertex location. When the energy is bigger, the movement is accepted with probability P_accept = 0.05 in order to introduce stochastic fluctuations.

The energy (E) of a vertex i is given by

$$E(R_i) = \sum_{\alpha} \frac{K_{\alpha}}{2} (A_{\alpha} - A_{\alpha}^{(0)})^2 + \sum_{(i,j)} \Lambda_{ij} \cdot l_{ij} + \sum_{\alpha} \frac{\Gamma_{\alpha}}{2} L_{\alpha}^2 \quad (\text{Equation 1})$$

where Rᵢ = (xᵢ, yᵢ) is the position of the vertex i. The first and the third summations are over all the cells α in which the vertex i is present, and the second summation is over all the cell edges [i, j] in which the vertex i is present. A_α is the apical area of the cell α and K is the area elasticity modulus, which is assumed to be equal for all the cells in our simulations. A_α⁽⁰⁾ is the resting area of the cell α. The distance and the line tension between the pairs of vertices [i, j] are denoted lᵢⱼ and Λᵢⱼ, respectively. The third term includes the perimeter of the cell α (L_α) and the perimeter contractility coefficient (Γ_α). By choosing √A_α⁽⁰⁾ as a unit of length and (KA_α⁽⁰⁾)² as a unit of energy (as in Farhadifar et al.⁴⁸), dividing both sides of Equation 1 by (KA_α⁽⁰⁾)² results in the following dimensionless equation:

$$\dot{E}(R_i) = \sum_{\alpha} \frac{1}{2} \left( \frac{A_{\alpha}}{A_{\alpha}^{(0)}} - 1 \right)^2 + \sum_{(i,j)} \frac{\tilde{\Lambda}_{ij}}{\sqrt{A_{\alpha}^{(0)}}} + \sum_{\alpha} \frac{\tilde{\Gamma}_{\alpha}}{2} \frac{L_{\alpha}^2}{A_{\alpha}^{(0)}} \quad (\text{Equation 2})$$

Where (A_α/A_α⁽⁰⁾), (lᵢⱼ/√A_α⁽⁰⁾) and (L_α²/A_α⁽⁰⁾) are, respectively, dimensionless area, bond length and perimeter. This model is characterized by dimensionless line tension (Λᵢⱼ = Λᵢⱼ / K(A_α⁽⁰⁾)³/²) and dimensionless perimeter contractility (Γ_α = Γ/KA_α⁽⁰⁾) that were set respectively to 0.06 and 0.02 as in Tsuboi et al.¹⁶ A_α⁽⁰⁾ was initially set to 2.598 (area of an hexagon with side length of 1) for all the cells, and was subsequently modified for some cells depending on the simulated scenario (see below).

Rearrangements of the topology of the vertices (T1 transitions¹⁸) were allowed when two vertices i, j were located less than a minimum distance d_min= 0.2 apart, and a movement of one of the vertices was energetically favorable such that the distance between the vertices decreases.

To generate initial conditions, we started from a set of 1801 hexagonal cells (Methods S1, Figure A), into which we introduced topological noise by allowing each cell to divide once following the Hertwig's rule (i.e., by adding a new junction cleaving the cell orthogonally to its longest axis). Equation 2 was then iteratively resolved by randomly updating vertices positions 300.10⁶ times such that average cell area reached a plateau (Methods S1, Figure C). This resulted in a topology made of 3598 cells (Methods S1, Figure A'), for which both the average polygon distribution and average cell area of each polygon class qualitatively matched the ones of previous works¹⁶,⁴⁸ (Methods S1, Figures D and E).

Next, the positions of the vertices located at the boundary of the tissue were fixed thus imposing a boundary constraint to the tissue. We implemented a large cell population relative to the domain of analysis to reduce as much as possible border effects. Two cell types were defined. In the center of the tissue, a group of ~150 cells was set as WT whereas all the other cells of the tissue were set as mutant (red cells, Methods S1, Figure B). For two simulated scenarios ("Case 2" and "Case 4" described below) a second topology with a larger group of WT cells (Figure S2J) was also used in order to obtain a large flat boundary between WT and mutant cells, thus allowing a better comparison between simulations and experimental conditions in these scenarios.

Four different scenarios of mechanical competition were explored. In the "interfacial tension" scenario (referred as Case 1, Figure 2A), the line tension parameter of the junctions at the boundary between WT and mutant cells was increased by a factor λ with respect to the line tension of all the other junctions of the tissue. Four different values of λ were examined (1.0; 2.0; 5.0; 10.0). To sense the variation in total tension created by such variation in the line tension parameter, we computed the total junctional
tension resulting from line tension and cortical contractility. Total junctional tension for junction between vertices [i, j] belonging to cells [a, b] was computed as $\lambda \cdot \tilde{A}_{ij} + \tilde{\Gamma} \cdot (L_a + L_b)/2$, where $\lambda$ is as described above, $\tilde{A}$ is line tension, $\tilde{\Gamma}$ is perimeter contractility and $L$ is cell perimeter. We found that increasing the line tension parameter of a factor $\lambda = 10.0$ led to a ~ 4-fold total junctional tension increase (Methods S1, Figure F). We also monitored the resulting inferred junctional tension from the simulations using the pipeline of force inference to verify that the variation in junction tension explored in the simulation was in the same range than the one observed experimentally (Methods S1, Figures H-J, for $\lambda = 10.0$, compare with Figures S1F, S1H, and S3H).

For the “differential growth” scenario (referred as Case 2, Figure 2A), the resting area of the mutant cells was increased of a factor $\kappa$ with respect to the resting area $A^{(0)}$ of all the other cells of the tissue, with $\kappa \in [1.0; 1.10; 1.25; 1.50]$.

For the third scenario (Case 3, Figures S3O-S3T, inset), conditions were set as in Case 1 ($\lambda = 10.0$) but with an additional condition for the junctions having one (and only one) of their vertices belonging to the boundary separating the WT and mutant populations. For these junctions, the line tension was multiplied by (-1), corresponding to a reduction of a factor 3 for total tension (Methods S1, Figure G). This allowed to simulate an increase of the adhesion at these junctions, thus reproducing our experimental observations according to which junctional tension is decreased at these junctions (Figure S3J).

In the fourth scenario (Case 4, Figures S2N and S2O''), we explored the effect of cell competition mediated by differential proliferation. This was done by randomly dividing mutant cells such that, by the end of the simulation, 20 % of the mutant cells have divided once whereas WT cells were not allowed to divide. Cell division was implemented by doubling the resting area $A^{(0)}$ of the dividing cell, resulting in an increase of its area. Then, once the area increase has reached a threshold (here set to an increase area of 50 % relative to the area at the time of $A^{(0)}$ doubling), the cell was divided by adding a new edge passing through the center of mass of the cell. After division, the $A^{(0)}$ of the two daughter cells was reset to the value that the mother cell had before entering into the mitotic process. The direction of the new edge was defined following the Hertwig's rule. This is an empirical rule that is observed in many different cell types, including the pupal notum epithelial cells,⁵⁰ according to which the direction of cell division is perpendicular to the longest axis of the cell. The longest axis was defined as the vector between the cell's center of mass and the farthest vertex relative to the center.

Cell extrusion initiation was triggered by cell area change. We set a threshold for cell area change relative to its initial area. Four values were explored for this threshold (0.75; 0.80; 0.85, and 0.90). Once the ratio ($\frac{\text{area current}}{\text{area initial}}$) for a given cell passed below the threshold, the preferred area parameter $A^{(0)}$ of that cell was set to 0 thus making this cell collapse further. The final step of cell extrusion was done through a T2 transition⁴⁸: once the cell has three vertices only, and its area passes below an area threshold (= 0.015), the three vertices were merged into a single one, and the cell was eliminated from the network.

Simulations were run for 50.10⁶ iterations (except for long runs of Figures S2H and S2I, 2000.10⁶ iterations), one iteration consisting in moving a randomly chosen vertex, updating its energy, and deciding to accept the movement or not. Simulation outputs were printed every 1.10⁶ iterations, thus giving outputs for 50 simulation steps.

Outputs retrieved from the simulations were: cell area change relative to initial area; circularity of the boundary between the WT cell population and the mutant population ($\frac{4\pi \cdot \text{area WT pop.}}{\text{perimeter WT pop.}^2}$); cell's compression strength (1 - $\frac{\text{cell area final}}{\text{cell area initial}}$); cell orientation (acute angle enclosed between the y-axis and cell's longest axis); and proportion of cell deaths, defined as the number of cells of a given type that will die during the simulation, relative to the number of cells of that type at the beginning of the simulation. Cell type was defined according to the genotype (WT or mutant) (Methods S1, Figures B and B'), to the distance relative to the boundary (Row 1, row 2, row 3), and to the geometry of the boundary (flat or curved) (Methods S1, Figures B and B'). We defined “curved” and “flat” regions by dividing the WT patch of cells in four regions of equal size along the y-axis. The two central regions were considered as regions having a “flat” boundary, whereas the two peripheral ones were considered as having a “curved” boundary (Methods S1, Figures B and B'). All quantification were based on 5 replicates of the simulations, obtained by changing the seed for the random walk used to update the locations of the vertices.

## Statistics

Data were not analyzed blindly. No specific method was used to predetermine the number of samples. The definition of number of samples and cells considered are given in each figure legend, as well as the meaning of error bars (standard deviation or standard error of the mean (SEM). p-values are calculated through group or paired Student's t-test if the data passed normality test (Shapiro-Wilk test). Statistical tests were performed on Matlab using the ttest and ttest2 functions. For the analysis of probability of cell death as a function of distance to boundary (Figures 1B, 3G, and 4C), the paired Student's t-test is performed by comparing averaged death probability per row versus "reference" rows from the same notum. For the analysis of death probability in "curved" vs. "flat" zone (Figures 1L, 1M, 3M-3P, and 4H-4K), the paired Student's t-test compare death probability in curved versus flat zone within the same WT patch. Box plot are represented using the Matlab boxplot function with the central mark indicating the median, and the bottom and top edges of the box indicating the 25th and 75th percentiles, respectively.
Supplemental Information

Interfacial tension and growth both contribute
to mechanical cell competition

Léo Valon, Alexis Matamoro-Vidal, Alexis Villars, and Romain Levayer
<figure><img src="image_64.png" /><figcaption>A E-cad::GFP</figcaption></figure>

Ras<sup>V12</sup>

SOP

<figure><img src="image_65.png" /></figure>

t0+12h

<figure><img src="image_66.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>D E-cad::GFP</figcaption></figure>

<figure><img src="image_68.png" /></figure>

Ras<sup>V12</sup>

WT

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /><figcaption>G Ras<sup>V12</sup></figcaption></figure>

<figure><img src="image_71.png" /></figure>

Myoll::mKate

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /><figcaption>K UAS-Ras<sup>V12</sup></figcaption></figure>

<figure><img src="image_75.png" /></figure>

Myoll::mKate

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /><figcaption>L Myoll levels</figcaption></figure>

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>
**Figure S1: Ras<sup>V12</sup> cell competition and mechanical properties. Related to Figure 1 and Video S1.**

**A-C:** Quantification of the growth of UAS-Ras<sup>V12</sup> clones and WT population distant from the clones. **A:** Cells express E-cad::GFP (in green) and clones (in magenta) have been expressing UAS-Ras<sup>V12</sup> for 8 hours at the onset of the movie (**A**, Left, t0) and for 12 extra hours (**A**, right). Example regions used for quantification are highlighted by white areas using magenta clones or SOPs as landmarks (triangles). **B:** Cell areas of WT (black) and Ras<sup>V12</sup> (magenta) groups of cells are quantified at time 0, 4, 8 and 12 hours after the beginning of the movie (t0). Data from 9 Ras and 9 WT groups, from 2 nota. Error bars are s.e.m. **C:** Quantification of area increase after 12h (area at time t0+12h over area at time t0). **D-G:** Force inference from cadherin apical junction images. **D:** Example image: in green E-cad::GFP; in magenta, UAS-Ras<sup>V12</sup> clone. **E:** From segmented image, we obtain maps of junctional tensions (See **STAR Methods**). **F:** Quantification of inferred junctional tension for junctions from 3 categories: in the bulk WT population (left), at the clone boundary in between WT and Ras<sup>V12</sup> population (middle), and in the bulk of Ras<sup>V12</sup> population (right). Data from 10 images extracted from 5 nota, with 267, 235 and 217 junctions respectively in each category. P-values are obtained from Student t-test. Error bars are s.e.m. **G-I:** Myoll distribution and force inference. **G:** Example image: in greyscale, Myoll::mKate (MRCL::mKate), in magenta, UAS-Ras<sup>V12</sup> clone. **H,I:** Data from 6 images extracted from 6 nota and segmented (208, 109 and 143 junctions quantified for each category). **H:** value of inferred junctional tensions obtained from force inference. P-values from Student's t-test. Error bars are s.e.m. **I:** Averaged Myoll junctional intensity. Student's t-test, **** = p<10<sup>-4</sup>. Error bars are s.e.m. **J:** Polar distribution of the orientation of WT cells surrounded by Ras<sup>V12</sup> cells 20 to 25 hours after Ras<sup>V12</sup> induction. N=110 cells from 10 groups. **K:** Three representative examples of Myoll levels (in green) at the boundary between Ras<sup>V12</sup> (magenta) and WT populations. **L:** Linescan of Myoll intensity along the boundary between the two populations (along the dashed lines in **K**). Starting, middle and last point of the linescan are represented by orange, yellow and blue circles. Scale bars are 20µm.
A

<figure><img src="image_83.png" /><figcaption>B</figcaption></figure>

Single cell area fold change (three 1st rows)

<figure><img src="image_84.png" /></figure>

C

<figure><img src="image_85.png" /></figure>

Case 2 : differential growth

<figure><img src="image_86.png" /></figure>

D

<figure><img src="image_87.png" /></figure>

E

<figure><img src="image_88.png" /></figure>

F

<figure><img src="image_89.png" /></figure>

G

<figure><img src="image_90.png" /></figure>

H

<figure><img src="image_91.png" /></figure>

I

<figure><img src="image_92.png" /></figure>
J

K

<figure><img src="image_93.png" /></figure>

Simulation step 0

<figure><img src="image_94.png" /></figure>

Simulation step 10

<figure><img src="image_95.png" /></figure>

Simulation step 50

L

Area change

<figure><img src="image_96.png" /></figure>

L'

<figure><img src="image_97.png" /></figure>

L''

<figure><img src="image_98.png" /></figure>

L'''

<figure><img src="image_99.png" /></figure>

M

Proportion of deaths (death threshold 0.80)

<figure><img src="image_100.png" /></figure>

M'

<figure><img src="image_101.png" /></figure>

M''

<figure><img src="image_102.png" /></figure>

M'''

<figure><img src="image_103.png" /></figure>

N

<figure><img src="image_104.png" /></figure>

Simulation step 0

<figure><img src="image_105.png" /></figure>

Simulation step 10

<figure><img src="image_106.png" /></figure>

Simulation step 35

<figure><img src="image_107.png" /></figure>

Simulation step 50

O

Area change

Flat 20% proliferation

<figure><img src="image_108.png" /></figure>

O'

<figure><img src="image_109.png" /></figure>

O''

<figure><img src="image_110.png" /></figure>

O'''

<figure><img src="image_111.png" /></figure>
**Figure S2: Vertex modelling of mechanical cell competition. Related to Figure 2 and Video S2.**

**A:** Illustration of the Laplace's law in an epithelial context. Simulations were run by applying a 10-fold increase of the line tension for the junctions located at the boundary between the bulk population (orange cells) and the centre patch (blue cells). Variation in the curvature of the boundary was obtained by varying the radius of the central patch of cells, from small radius (= higher curvature) to high radius (= lower curvature). Top panels show the topology of the tissue at the final step of the simulation, and bottom panels show single cell area fold change at the final simulation step relative to area at the beginning of the simulation. Left to right: increased values of the centre patch radius (in number of cells). **B:** Quantifications from the simulations shown in A for single cell area fold change relative to initial area. Average +/- s.e.m. of cell area fold change is shown, as a function of cell radius and "genotype". Areas were averaged over the three first rows of cells away from the boundary between the bulk population and the centre patch population. **C:** Close-up views of final time points from simulations shown in **Figures 2 B and 2 G**, emphasising cell main axis orientation (blue lines) for flat (blue insets) and curved (orange insets) regions. **D:** Cell area fold change relative to initial area, as a function of simulation step, genotype, cell row, and curvature of the boundary (flat vs. curved) for a simulation of Case 1 ($\lambda$ = 10.0). Cell row is given in number of rows apart from the boundary between WT and mutant populations (see diagram in the inset). **E:** Proportion of deaths as a function of simulation step, genotype, curvature of the boundary and cell row (given in number of rows apart from the boundary between WT and mutant populations, see inset in panel D). Data are shown for Case 1, $\lambda$ = 10.0, death threshold = 0.80. Proportion is given relative to the number of cells of a given genotype, row and curvature region at simulation step 0. **F:** Cell area fold change relative to initial area, as a function of simulation step, genotype, cell row, and curvature of the boundary (flat vs. curved) for a simulation of Case 2 ($\kappa$ = 1.25). Cell row is given in number of rows apart from the boundary between WT and mutant populations (see diagram in the inset). **G:** Proportion of deaths as a function of simulation step, genotype, curvature of the boundary and cell row (given in number of rows apart from the boundary between WT and mutant populations, see inset in panel F). Data are shown for Case 2, $\kappa$ = 1.25, death threshold = 0.80. Proportion is given relative to the number of cells of a given genotype, row and curvature region at simulation step 0. **H:**
Steels for six time points from a long run (2000 millions iterations) simulation of Case 1 ($\lambda$ = 10.0, death threshold 0.90) showing complete elimination of the WT population. White cells are cells undergoing the process of T2 transition. **I:** steels for six time points from a long run (2000 millions iterations) simulation of Case 2 ($\kappa$ = 1.10, death threshold 0.90) showing fragmentation of the WT population. White cells are cells undergoing the process of T2 transition. For D-G, quantifications are averages +/- s.e.m. obtained by running 5 times the simulations using different seeds in the Monte Carlo random walk. **J:** Topology used for initial conditions of the simulations. The tissue is made of 3598 cells with fixed boundary conditions. In the central area of the tissue, a large group of 1066 cells is set as WT (blue cells) whereas all the other cells of the tissue are set as mutant (red cells). **K:** Steels (retrieved from Video S2 D, simulation steps 0, 10 and 50) from a simulation of "Case 2", where the resting area of the mutant cells is increased by a factor $\kappa$ = 1.25 with respect to the resting area of all the other cells of the tissue. Cells are coloured according to their area change relative to their area at simulation step 0. The black line denotes the boundary between WT and mutant cells.

**L:** Cell area fold change relative to initial area as a function of simulation step, genotype, and cell row (i. e., number of rows apart from the boundary between WT and mutant populations). Only cells located in the flat region of the boundary (inset in A) are considered. Data are shown for Case 2, $\kappa$ = 1.25. **L'-L''**: Snapshots from **L** for simulation steps 5 (C'), 20 (C'') and 50 (C''') showing cell area fold change relative to initial area as a function genotype and cell row. **M:** Cumulative proportion of cell deaths as a function of simulation step, genotype and cell row (given in number of rows apart from the boundary between WT and mutant populations). Key is the same than panel **L**. Only cells located in the flat region of the boundary are considered. Data are shown for Case 2, $\kappa$ = 1.25, death threshold = 0.80. Proportion is given relative to the number of cells of a given genotype and row at simulation step 0. **M'-M''**: Snapshots from **M** for simulation steps 5 (D'), 20 (D'') and 50 (D''') showing cumulative proportions of cell deaths as a function cell row. Data are shown for WT cells only since no deaths in mutant cells were observed. **N:** Steels (retrieved from Video S2 E, simulation steps 0, 10, 35 and 50) from a simulation of "Case 4", where the mutant cells were under a proliferative regime (20 % of proliferative cells) whereas WT were not. Cells are coloured as according to their area change relative to their area at simulation step 0 (same colour scale than in panel **K**). The black line denotes the boundary between WT and mutant cells. **O:** Cell area fold change relative to initial area as a function of
simulation step, genotype, and cell row (i. e., number of rows apart from the boundary between WT and mutant populations). Only cells located in the flat region of the boundary (inset in A) are considered. Data are shown for a simulation of “Case 4”, with 20 % of mutant proliferative cells. O’-O””: Snapshots from O for simulation steps 5 (O’), 20 (O”) and 50 (O””) showing cell area fold change relative to initial area as a function genotype and cell row. All plots are averages +/- s.e.m. obtained by running 5 times the simulations using different seeds in the Monte Carlo random walk.
A

<figure><img src="image_112.png" /></figure>

B

<figure><img src="image_113.png" /></figure>

C

<figure><img src="image_114.png" /></figure>

D

<figure><img src="image_115.png" /></figure>

Relative developmental time (hours)

E

<figure><img src="image_116.png" /></figure>

Yki<sup>CA</sup>

Myoll::mKate

<figure><img src="image_117.png" /></figure>

● Bulk WT

● Boundary

● Bulk Yki<sup>CA</sup>

F

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

H

Myoll::mKate

<figure><img src="image_121.png" /></figure>

UAS-N-cad

Myoll::mKate

<figure><img src="image_122.png" /></figure>

I

<figure><img src="image_123.png" /></figure>

J

<figure><img src="image_124.png" /></figure>

K

E-Cad::GFP

UAS-N-cad

<figure><img src="image_125.png" /></figure>

L

<figure><img src="image_126.png" /></figure>

M

UAS-N-cad

Myoll::mKate

<figure><img src="image_127.png" /></figure>

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

N

Myoll levels

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>

Norm. position
O

$\lambda = 1.0$

<figure><img src="image_136.png" /></figure>

<figure><img src="image_137.png" /></figure>

<figure><img src="image_138.png" /></figure>

<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /></figure>

Case 3 : interfacial tension at the boundary ($\lambda..\Lambda$) + enforced adhesion at junctions in contact with the boundary ($-\Lambda$)

$A^0$: cell resting area

$\Lambda$ : junctional line tension

P

$\lambda = 10.0$

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>

<figure><img src="image_144.png" /></figure>

<figure><img src="image_145.png" /></figure>

<figure><img src="image_146.png" /></figure>

Q

$\lambda = 10.0$, Death threshold = 0.80

<figure><img src="image_147.png" /></figure>

R

Area change by cell row

<figure><img src="image_148.png" /></figure>

<figure><img src="image_149.png" /></figure>

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>

$\lambda = 10.0$, Death threshold = 0.80
+ enforced adhesion at junctions in
contact with the boundary

<figure><img src="image_152.png" /></figure>

S

Proportion of deaths by cell row (Death threshold = 0.80)

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>

<figure><img src="image_156.png" /></figure>

T

<figure><img src="image_157.png" /></figure>

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

<figure><img src="image_161.png" /></figure>
**Figure S3: Growth and mechanical properties of Yki<sup>CA</sup> and N-cad clones and vertex modelling. Related to Figures 3, 4 and Videos S3 and S4.**

**A:** Normalised cell area of group of cells (relative to area at movie onset), for WT and UAS-Ras<sup>V12</sup> cells (left), WT and UAS-Yki<sup>CA</sup> cells (middle), and WT and UAS-N-cad cells (right) after 12 hours of movie (Ras<sup>V12</sup>, 9 groups from 2 nota; Yki<sup>CA</sup> 5 groups from 2 nota; N-Cad 18 groups from 3 nota). **B-D:** Cumulated cell division number (normalised by number of cell in each category at movie onset) over developmental time for Ras<sup>V12</sup> and WT (**B**, 207 divisions over 2 nota), Yki<sup>CA</sup> and WT (**C**, 1016 divisions over 2 nota) and N-cad and WT (**D**, 383 divisions over 2 nota). Vertical grey lines show the time at which cell divisions globally stop. Time 0 is the end of the movies. **E-G:** Force inference near Yki<sup>CA</sup> clones. **E:** Example image, Myoll::mKate in green, UAS-Yki<sup>CA</sup> clone in magenta. Scale bar is 20 µm. The colour dots show three categories of junctions: WT bulk (green), clone boundaries (orange) and Yki<sup>CA</sup> bulk (purple). **F-G:** Quantification of Myoll::mKate junctional intensity level (**F**) and inferred junctional tensions obtained from force inference (see **STAR Methods**) (**G**) for junctions from the 3 categories (8 images extracted from 7 nota, junction numbers: 102, 49 and 73 respectively). Error bars are s.e.m.. Student's t-test, **** = p<10<sup>-4</sup>. **H-J:** Force inference near UAS-N-cad clones. **H:** Example of an image segmented to apply the force inference pipeline. Myoll::mKate, in green, N-cad clone in magenta. The white square shows the inset with the definition of bulk WT, orthogonal WT, boundary, orthogonal N-cad and bulk N-cad junctions. Scale bar is 20 µm. **I-J:** Quantification of Myoll junctional intensity (**I**) and junctional inferred tensions obtained from force inference (**J**) for junctions from the 5 categories, see left scheme (6 images extracted from 5 nota, junction numbers: 316, 101, 172, 97, and 66 respectively). Error bars are s.e.m.. Student's t-test, **** = p<10<sup>-4</sup>. **K,L:** Measurements of curvature for “curved” vs. “flat” zones. **K:** E-Cad::GFP image of WT cells surrounded by UAS-N-cad cells (magenta cells in the inset). Curvature radii are determined by drawing circles (dashed white circles) whose border follows the boundaries of the clone(dashed white line). **L:** Plot of curvatures (1/circle radius) for curved and flat zones (from 12 patches). **M:** Three representative examples of Myoll levels (in green) at the boundary between N-cad (magenta) and WT populations. **N:** Linescan of Myoll levels along the boundary between the two populations (along the dashed line in **M**). Starting, middle and last point of the linescan are represented by orange, yellow and blue circles. Scale bars
are 20 µm. O: Steels from five time points for a control simulation of Case 1 (λ = 1.0) and with the addition of enforced adhesion at the orthogonal junctions in contact with the boundary. Top panels show the topology of the tissue over simulation steps (from initial (0 %, left) to final (100 %, right)), and bottom panels show single cell area fold change relative to area at the beginning of the simulation. Inset: to simulate our assumption of enforced adhesion resulting in the lengthening of junctions observed in the vicinity of the boundary of UAS-N-cad clones, we set a specific line tension condition for the junctions in contact with the boundary (magenta junctions in the inset). These junctions were assigned a negative value of λ, thus making the lengthening of the junction mechanically favourable. Running a control simulation of Case 1 (λ = 1.0) with this additional condition was sufficient to lengthen the junctions in contact of the boundary, thus increasing cell area of cells in the contact with the boundary (bottom panels). Interestingly, this addition was also sufficient to slightly smooth the boundary between WT and mutant cells, as well as to generate slight compaction within the WT and mutant populations away from the cell rows in direct contact with the boundary. P: Steels from five time points of a simulation of mechanical cell competition by interfacial tension (Case 1, λ = 10.0) and with the addition of enforced adhesion at the orthogonal junctions in contact with the boundary. Top panels show the topology of the tissue over simulation steps, and bottom panels show single cell area fold change relative to area at the beginning of the simulation. Q: Back tracking of dying cells. Cells that will die during the simulation are highlighted on the topology at simulation step 0 (green dots). Top panel shows the pattern of dying cells for Case 1, λ = 10.0, and bottom panel shows the pattern for Case 1, λ = 10.0 + enforced adhesion at orthogonal junctions in contact with the boundary (Case 3). Note the quasi absence of dying cells for cells in direct contact with the boundary in Case 3 (bottom) compared to Case 1 (top). R: Cell area fold change relative to initial area, as a function of simulation step, genotype, cell row, curvature of the boundary (curved, left panels vs. flat, right panels) and λ (1.0, top panels vs. 10.0, bottom panels) in simulations with enforced adhesion at orthogonal junctions in contact with the boundary (Case 3). Cell row is given in number of rows apart from the boundary between WT and mutant populations (see diagram in the inset). S: Proportion of deaths as a function of simulation step, genotype, cell row, curvature of the boundary (curved vs. flat) and λ (1.0 vs. 10.0) in simulations with enforced adhesion at orthogonal junctions in contact with the boundary (Case 3) and death threshold set to 0.80. Cell row is given in number of rows apart from the boundary
between WT and mutant populations (see diagram in the inset of O). Proportion is
given relative to the number of cells of a given genotype, row and curvature region at
simulation step 0. T: steels for four time points (retrieved from Video S4 D) from a long
run (2000 millions iterations) simulation of Case 3 (λ = 10.0, enforced adhesion at
orthogonal junctions in contact with the boundary, death threshold 0.80) showing
smoothing of the boundary between the WT cells (blue) and the mutant cells (orange
cells). The localisation of all the cells dying during the early time points (0-2,5 % of
simulation steps) of the simulations is shown on the topology at simulation step 0
(green dots). The topology of the boundary has been designed to mimic the topology
observed in our experimental conditions (compare with Figure 4 A,B,D). For R-T,
quantifications are averages +/- s.e.m. obtained by running 5 times the simulations
using different seeds in the Monte Carlo random walk.
<figure><img src="image_162.png" /><figcaption>A E-cad::mKate</figcaption></figure>

his3::mIFP UAS-Ras<sup>V12</sup>

<figure><img src="image_163.png" /><figcaption>E-cad::mKate</figcaption></figure>

lexAOP-GC3Ai

<figure><img src="image_164.png" /><figcaption>B E-cad::mKate</figcaption></figure>

his3::mIFP UAS-Yki<sup>CA</sup>

<figure><img src="image_165.png" /><figcaption>E-cad::mKate</figcaption></figure>

lexAOP-GC3Ai

<figure><img src="image_166.png" /><figcaption>F E-cad::GFP</figcaption></figure>

<figure><img src="image_167.png" /></figure>

UAS-N-cad + UAS-P35

<figure><img src="image_168.png" /><figcaption>H E-cad::GFP</figcaption></figure>

<figure><img src="image_169.png" /></figure>

TRE-GFP; UAS-Ras<sup>V12</sup>

<figure><img src="image_170.png" /><figcaption>I E-cad::GFP</figcaption></figure>

TRE-GFP; UAS-Yki<sup>CA</sup>

<figure><img src="image_171.png" /><figcaption>TRE-GFP</figcaption></figure>

<figure><img src="image_172.png" /><figcaption>E-cad::GFP</figcaption></figure>

<figure><img src="image_173.png" /><figcaption>J E-cad::GFP</figcaption></figure>

UAS-Ras<sup>V12</sup>

<figure><img src="image_174.png" /></figure>
**Figure S4: Caspase and JNK activity during UAS-Ras<sup>V12</sup>, UAS-Yki<sup>CA</sup> and UAS-N-cad mechanical cell competition and tissue context-dependent sorting. Related to Figures 1, 3 and 4.**

**A:** Representative snapshot (local z-projection) of movie showing E-cad::mKatex3 in green, and Ras<sup>V12</sup> clones in magenta (left) and the caspase sensor lexAOP-GC3Ai in "fire red" (right, ubiquitous expression driven by act-lexA). Some cells with high caspase activity are highlighted with white arrowheads. Scale Bar is 20 µm. **B:** Representative snapshot (local z-projection) of movie showing E-cad::mKatex3 in green, and Yki<sup>CA</sup> clones in magenta (left) and the caspase sensor lexAOP-GC3Ai in "fire red" (right, ubiquitous expression driven by act-lexA). Some cells with high caspase activity are highlighted with white arrowheads. Scale Bar is 20 µm. **C-E:** Representative example of movies showing E-cad::GFP and UAS-GC3Ai in green or "fire red", UAS-N-cad clones in magenta before (C) and after 15 hours (D). Of note, here GC3Ai caspase sensor is only expressed in the clones (contrary to **A** and **B**). **E:** Zoom on one group of N-cad cells, and snapshots of E-cad::GFP and UAS-GC3Ai every 3 hours. Scale Bar is 20 µm. **F:** Representative example of a movie showing E-cad::GFP in green and UAS-N-Cad + UAS-p35 (effector caspase inhibitor) clones in magenta for 3 time points separated by 4 hours. Most extrusions appearing in the yellow square during the whole movie are back tracked and represented on timepoint t0+4h as asterisks (*) and in its zoom (G). White stars are dying WT cells, black stars UAS-N-Cad, UAS-p35 dying cells. Note that there is hardly any extruding cell in the N-cad, UAS-p35 clone. Representative of 2 movies. **H,I:** JNK activity during mechanical competition mediated by Ras<sup>V12</sup> and Yki<sup>CA</sup> is not specifically localised at compacted and/or dying cells. Some WT compacted cells at clone boundaries are highlighted by black arrowheads. **E-cad::GFP** and **TRE-GFP** (transcriptional reporter of JNK activity, some cells with high activity are highlighted by white arrowheads) in green and clones in magenta. **H:** representative example of 4 experiments for which pupae are imaged 20h after induction of Ras<sup>V12</sup> in clones. **I:** Representative example of a movie in the case of Yki<sup>CA</sup> clones. Snapshots of E-cad::GFP and TRE-GFP in "fire red" for 2 timepoints separated by 4 hours. Scale bars are 20mm. **J:** Ras<sup>V12</sup> clone sorting is region specific in the pupal notum. Representative example of E-cad::GFP in green (left) or grayscales (right) and UAS-Ras<sup>V12</sup> clones in magenta. Posterior, left, Anterior, right. Right, highlight of contours of some group of WT cells. Magenta arrowheads: WT patches with clear
sorting behaviour (mostly in the posterior region). Green arrowheads: WT group with
no obvious sorting behaviour (mostly anterior). Scale bars are 20µm.