# Asymmetry of acto-myosin cortices as active fluids shape cells in organoids

Tristan Guyomar, Linjie Lu, Tetsuya Hiraiwa, Daniel Riveline

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Tristan Guyomar, Linjie Lu, Tetsuya Hiraiwa, Daniel Riveline. Asymmetry of acto-myosin cortices as active fluids shape cells in organoids. 2024. hal-04774268

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Asymmetry of acto-myosin cortices as active fluids shape cells in organoids

Tristan Guyomar,¹, ², ³, ⁴ Linjie Lu,¹, ², ³, ⁴ Tetsuya Hiraiwa,⁵, ⁶, ⁷, \* and Daniel Riveline¹, ², ³, ⁴, †

¹Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France

²Université de Strasbourg, Illkirch, France

³Centre National de la Recherche Scientifique, UMR7104, Illkirch, France

⁴Institut National de la Santé et de la Recherche Médicale, U964, Illkirch, France

⁵Universal Biology Institute, Graduate School of Science, The University of Tokyo, Tokyo, Japan

⁶Mechanobiology Institute, Singapore, National University of Singapore, Singapore

⁷Institute of Physics, Academia Sinica, Taipei, Taiwan

(Dated: August 14, 2024)

Cell cortex is a thin sheet of actin cytoskeleton spanning cell boundaries with rich out-of-equilibrium dynamics. A theoretical description of the cortex as an active fluid enables to capture cell shapes dynamics in 3D tissues. However models integrated with calibration of parameters and quantitative experiments are lacking so far. Here we report that cells in organoids and in cysts have conserved apico-basal-lateral asymmetric compositions in actin and in myosin, and we quantify their densities and mechanical properties. This allows to calibrate a new model coupling active fluids with a phase field which reproduces the main features of cell shapes. To test our approach, we successfully predict changes in cell shapes by modulating actin polymerisation in experiments and in simulations. Our study shows how active fluid theory integrated with experiments can determine cell shapes in 3D tissues.

Cells and tissues are shaped by the acto-myosin cytoskeleton [1]. This ~200 nm thin layer represents a gel spanning each cell border. Its dynamics offers rich out-of-equilibrium spatio-temporal patterns during local protrusion [2–5], motility [6–9], or division [10, 11]. Its self-organisation properties have shown their relevance in a variety of conditions *in vitro* [12, 13] and *in vivo* [14–16]. This level of achievement results from a progress in the experimental physics of actin and acto-myosin *in vitro* associated with the development of active fluid physics [17]. Actin gels were shown to promote bead motion through polymerisation *in vitro* [18–20] and acto-myosin gels were reported to undergo contraction [21]. These phenomena were compared to the theory of active gel revisiting the Navier-Stokes equation by adding a new term taking into account the stress generation internally driven by the acto-myosin cortex [17, 21–24]. Progress over the past decades has enabled theoretical predictions and their relevance mainly in single cell often in 2D [10, 25, 26].

However, at this stage, tests of this cytoskeleton/active gel approach are needed in more physiological situations. Cells grow in 3D in *in vivo* environments. Also cell cortex requires specific measurements to evaluate its physical properties in order to associate theoretical parameters with actual fluid characteristics in experiments. These limitations to extrapolate results obtained so far call for new approaches combining control of 3D tissue growth with quantitative experiments allowing to calibrate active fluid based simulations.

In this context, epithelial cysts represent interesting systems. They are composed of cell monolayers surrounding a fluid filled cavity called the *lumen* [27]. Cysts are also reported as the basic structure at the origin of organs [27], ranging from the formation of embryos [28] to the generation of pancreatic ducts for example [29]. With

<figure><img src="image_2.png" /><figcaption>FIG. 1. Correlation between acto-myosin cortical density and cell shape in MDCK cysts. (a) Snapshots of MDCK cysts (2D cut and zoom in) fixed and stained for E-cadherin (green), F-actin (cyan), myosin (magenta). Scale bars, 5 µm. (b) Typical mean cell description used to quantify the relevant geometrical quantities to describe the cell cortex and the cell shape. (c) Schematic description of the cell shape diagram. Oblique lines correspond to different apical to basal length ratios α as described in the main text. Moving along the line changes the aspect ratio of cells w.r.t. the lateral length. (d) F-actin apical to basal intensity ratio correlation with cell shape. $P_{\text{cell}}$, cell perimeter. For all measurements, $N = 3$ experiments, $n = 791$ cells quantified in 5-day-old MDCK cysts.</figcaption></figure>

their *in vitro* controls and their relevance for organogenesis with properties shared with organoids (see End Matter), cysts appear as ideal systems to address this interplay between the physics of acto-myosin cortex and its theoretical description with active fluid formalism.

In this Letter, we investigated quantitatively the morphological and mechano-chemical features of cells in a 3D
53 epithelial cyst. We focus on Madin-Darby Canine Kidney (MDCK) epithelial cysts, which allowed us to perform 54 experimental characterizations by quantifying the com- 55 position and structures of the gel density and measure 56 its mechanical characteristics (see Materials and Meth- 57 ods in End Matter). We designed in parallel a theory 58 for the active fluid and performed numerical simulations 59 for the cortex coupled to a phase field. We predict how 60 the cortex dynamics determine cell shapes in epithelial 61 cysts. We tested these predictions successfully by chang- 62 ing specifically actin dynamics and obtained predicted 63 shapes. 64

65 We first characterised the cell shape with these cysts 66 by acquiring typical images of actin and myosin signals 67 in live and in fixed samples (Fig. 1(a), SM [36] and Fig. 68 S3(a)). Cysts were invariant by rotation and we could 69 derive a typical cell shape with its actin and its myosin 70 distributions at the cortex in the middle cross-section of 71 each cyst (Fig. 1(a,b), Fig. S3 and Video 1). Strik- 72 ingly, actin and myosin were asymmetrically distributed 73 around each cell. Actin was thick and enriched in the 74 apical side whereas the myosin layer was thinner and 75 mainly localised at the basal sides (Fig. 1(a) and Fig. 76 S3(e)). The lateral sides were essentially even in actin 77 distributions. We measured that the F-actin intensity 78 was about 5 times higher in the apical cortex than in the 79 basal one and about 7 times higher in the apical cortex 80 than in the lateral one (SM [36], Fig. S3(c), and Ta- 81 ble I). We found that the myosin intensity was about 3 82 times higher in the basal cortex than in the apical one 83 and about 4 times higher in the basal cortex than in the 84 lateral one (Fig. S3(d)). Such anisotropies were con- 85 served in time for MDCK spheroids (Fig. S3(a)) and in 86 several organoids, i.e., in mES epiblast organoids and in 87 pancreas spheres derived from mouse pancreatic progen- 88 itor cells [37] (Fig. S1(a) and SM [36]).

89 Next we quantified and tested potential correlations 90 between cell shapes and the actin and myosin concentra- 91 tion at the cortical layers. We represented the cell shape 92 in a diagram that can capture most of its morphological 93 attributes. We define L ap , L bas , L lat as respectively the 94 apical, basal and lateral lengths and the total cell perime- 95 ter P that we measured on fixed samples (Fig. 1(b) and 96 SM [36]). By construction, P = L ap + L bas + 2 L lat . By 97 defining the apical length as a fraction of the basal length: 98 L ap = αL bas , one can represent L bas P = 1 (1 + α ) (1 − 2 L lat P ) 99 (Fig. 1(c)). We used this cell shape representation to 100 look for correlations between cell shape and intensity ra- 101 tios of actin and myosin, curvatures and lumen occu- 102 pancy (defined as the ratio of lumen to cyst volumes) 103 (Fig. 1(d), SM [36] and Fig. S1(b) and S4). We noted a 104 strong correlation between F-actin apical to basal ratio 105 and trapezoidal shaped cells (Fig. 1(d)), consistent with 106 the hypothesis that the cortical gel is involved.

107 Localisation alone was not yet informative about the 108 mechano-chemical parameters of these active gels. To

109 interrogate their characteristics, we used the Fluores- 110 cence Recovery After Photobleaching (FRAP) and laser 111 ablation of apical, basal and lateral sides with MDCK 112 cysts cultured from MDCK cell lines stably expressing 113 fluorescent Green Fluorescent Protein (GFP) fused pro- 114 teins, actin-GFP or myosin regulatory light chain-GFP 115 (MRLC-GFP), see SM [36]). Results are given in Fig. 2, 116 Fig. S5 and in Videos 2-5. FRAP experiments revealed 117 that the mobile fraction of the apical cortex was lower 118 than the basal and lateral ones. The characteristic time 119 of diffusion (orders of seconds, similar to reported values 120 [38, 39]) of G-actin monomers was small compared with 121 the characteristic time associated to transport and as- 122 sembly of F-actin filaments and network (orders of tens 123 of seconds) (Fig. 2(c,d)). Interestingly, the time of trans- 124 port was different by a factor of 4 between apical and 125 basal sides, which substantiates again the asymmetry be- 126 tween sides. Finally, we determined that both sides were 127 contractile based on laser ablation experiments and ini- 128 tial recoil velocity, v ini recoil , measurements (Fig. 2(e,f)). 129 These experiments allowed to extract mechano-chemical 130 parameters summarized in Table I.

<table><caption>TABLE I. Comparison of the apical-basal-lateral measure- ments with intensity ratios, FRAP and laser ablation experi- ments. Ratios of ρ corresponds to ratios of intensities. Mea- surements are shown as: mean ± standard error of the mean.</caption>
  <thead>
    <tr>
      <th>Quantities</th>
      <th>Apical</th>
      <th>Basal</th>
      <th>Lateral</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>ρ<sup>F-actin</sup><sub>apical</sub> / ρ<sup>F-actin</sup><sub>i</sub></td>
      <td>1</td>
      <td>5.1 ± 0.1</td>
      <td>7.1 ± 0.1</td>
    </tr>
    <tr>
      <td>ρ<sup>MRLC</sup><sub>basal</sub> / ρ<sup>MRLC</sup><sub>i</sub></td>
      <td>3.2 ± 0.1</td>
      <td>1</td>
      <td>4.3 ± 0.1</td>
    </tr>
    <tr>
      <td>τ<sub>recovery</sub> (s)</td>
      <td>122 ± 9.2</td>
      <td>29.4 ± 4.1</td>
      <td>27.3 ± 3.3</td>
    </tr>
    <tr>
      <td>v<sup>ini</sup><sub>recoil</sub> (μm.s<sup>-1</sup>)</td>
      <td>5.6 ± 0.2</td>
      <td>8.4 ± 0.3</td>
      <td>8.2 ± 0.6</td>
    </tr>
  </tbody>
</table>

131 Next, we theoretically tested our hypothesis that the 132 asymmetry in cortex distribution is a key factor to deter- 133 mine cell shape in a cyst, through numerical simulations 134 based on multicellular phase-field (PF) model [37, 40, 41] 135 coupled to active gel equations [42, 43] (see End Matter).

136 The geometry of cells is described by PF variables 137 φ ( r , t ), defined for each cell, and cortical gel is confined 138 in the interface of cellular PF by the coupling free energy:

$$
F_{\text{coupling}}[\phi, \rho] = - \int_{\Omega} f_{\text{coup}} \rho |\nabla \phi(\mathbf{r})| \delta\left(\phi(\mathbf{r}) - \frac{1}{2}\right) d\mathbf{r}(1)
$$

139 with f coup controls the strength of coupling and Ω the 140 simulation domain area. This free energy term adds a 141 new term to the cellular-PF dynamics (see Eq. (S28) of 142 SM [36]). This coupling term induces a global contrac- 143 tility which counteracts the volume control and adhesion 144 energies already set in the cell shape dynamics. ρ ( r , t ) 145 obeys the conservation equation with the source of cor- 146 tical gel at the cellular-PF interface:

$$
\partial_t \rho + \nabla \cdot (\rho \mathbf{v}) = -A_{\text{deg}}\rho + A_{\text{gen}}|\nabla\phi|\delta(\phi - 1/2), \quad (2)
$$

147 where the first and second terms on the right-hand side 148 are the degeneration and source at the cell interface, re-
<figure><img src="image_3.png" /><figcaption>FIG. 2. Quantification of mechano-chemical properties of the acto-myosin gels at different locations of the cell cortex in day 5 MDCK cysts. (a) Snapshots of FRAP experiments performed on the apical cortex of MDCK cysts labelled for actin-GFP (grey) (Video 2). Measurements of (b) mobile fraction, (c) $\tau_{delivery}$ and (d) $\tau_{diffusion}$ at apical, basal and lateral cortices. $N = 3$ experiments, $n = 60$ cells (apical), $n = 30$ cells (basal and lateral). (e) Snapshots of laser ablation of the cortex performed on the apical, basal and lateral cortex of MDCK cysts labelled for actin-GFP (grey) - apical and lateral) or MRLC-GFP (grey - basal) (Videos 3-5). (f) Initial recoil velocity for apical, basal and lateral sides measured from the initial distance created after laser ablation. $N = 4$ experiments, $n = 43$ cells (apical), $n = 45$ cells (basal), $N = 3$ experiments, $n = 8$ cells (lateral). Statistical tests: \*: $p < 0.05$, \*: \*: $p < 0.01$, \*: \*: \*: $p < 0.001$, n.s: $p > 0.05$. Scale bars: $5 \mu m$.</figcaption></figure>

149 spectively. $A_{\text{deg}}$ and $A_{\text{gen}}$ represent the turnover rate and production speed, respectively. $v_{\alpha}(r, t)$ is the gel velocity field and given by the force balance: $\partial_{\beta}\sigma_{\alpha\beta} = \gamma v_{\alpha}$, where $\sigma_{\alpha\beta}$ is the total stress tensor, $\gamma$ is a friction coefficient and $\alpha, \beta = x, y$. We write the stress tensor as $\sigma_{\alpha\beta} = \sigma_{\text{viscous}} + \sigma_{\text{active}} = 2\eta v_{\alpha\beta} - \Pi_{\alpha\beta}(\rho)$ with the velocity gradient tensor $v_{\alpha\beta} = (\partial_{\alpha}v_{\beta} + \partial_{\beta}v_{\alpha})/2$ and choose to follow Ref. [42] to express $\Pi$ in terms of the gel density $\rho$ with $\pi_{\alpha\beta}(\rho) = -a_{\alpha\beta}\rho^3 + b_{\alpha\beta}\rho^4$. For $a > 0$, the active gel is contractile whereas, for $a < 0$, the active gel is extensile [44].

160 We performed the numerical simulations based on the equations presented above (SM [36]). Taking advantage of the phase field formulation of the model, we defined apical, lateral and basal cortices and imposed different parameters to each of them (SM [36] for the complete set of parameters values). We modeled a 2D cut of a cyst composed of 6 cells, and we depicted the main results in Fig. 3 and in Video 6. We first tested how a change in uniform surface tension would impact the cell and cyst shapes by adding no additional active stresses but setting various uniform turnover dynamics in the cell

171 cortices (Fig. 3(a) and SM [36]). As the turnover rate increases, so does the active gel density $\rho$ and the cells became rounder. Using similar diagrams as introduced in Fig. 1(c), we quantified these shape changes (Fig. 3(b)) and we observed that for a given set of parameters (lumen pressure, cell-cell adhesion, ECM elasticity, total cell number) the morphology of cells evolved mainly on a straight line in the cell shape diagram as $A_{\text{gen}}$ varies.

179 Next, we implemented asymmetric turnover rates between apical, lateral and basal cortices (Fig. S8 and SM [36]). Interestingly, asymmetries in the turnover rate distributions yielded a variety of cyst morphologies (Fig. S8(a)), richer than the symmetric case (Fig. 3(a)). This was further illustrated in the cell shape diagram (Supp. Fig. S8(b)) where asymmetric turnover rates led to departure from the previously highlighted trajectories (Fig. 3(a)). We then changed the physical origin of stress generation, by testing how the asymmetry of the nature of active stresses would impact cell shapes in cysts. We kept symmetric turnover rates and we tuned the extensile/contractile nature of apical and basal cortices (Fig. 3(c)). Strikingly, whereas the active gel density remained constant, the asymmetric nature of stresses alone led to large changes in shapes. For example, in the case of apical contractile and basal extensile (Fig. 3(c,d), green point), the extensibility of the basal active gel yielded less active stress and more tangentially elongated cells. Finally, we could reproduce MDCK cell phenotypes (Fig. 3(e) and Fig. 1(a) top) when setting asymmetric turnover rates and asymmetric active stresses (SM [36]) following the measurements presented in Fig. 1, Fig. 2 and recapitulated in Table I.

203 To test predictions associated to changes in the dynamics of the gel parameters (SM [36]), we altered specifically the polymerisation of actin. Upon incubation with the depolymerising agent latrunculin A (LatA) (M&M), we recorded changes in cell shape and the associated modifications in the actin cortical composition (Fig. 4(a), Fig. S6 and Video 7). We also performed the opposite experiment by incubating cysts with the actin polymerisation enhancer C8BPA [35] (Fig. 4(d) and Video 9). Quantifications are shown in Fig. 4(a-d) and in SM [36]. Strikingly, we could obtain shapes predicted by the associated changes in actin polymerisation in the simulation (variation of $A_{\text{gen}}$ in Fig. 4 (e,f) and Video 10).

216 We have shown that asymmetry of the cortex is sufficient to describe cell shapes in cysts and in organoids. We used the physics of active fluids and quantification of the cortex to predict experimental and simulated cell shapes in cysts. This framework could serve as a generic approach to design 3D tissues with molecular strategies and mesoscopic consequences on cell and cyst shapes.

223 We report that actin is primarily present in the apical side whereas acto-myosin is assembled at the basal side. One could have thought that actin gel would be extensile and acto-myosin gel contractile, like in former
<figure><img src="image_4.png" /><figcaption>FIG. 3. Asymmetry in active fluid cortices generates relevant cell shapes in numerical simulations. (a) Snapshots of simulated 2D cuts of cysts with symmetric apical, basal and lateral turnover rates at steady state for different turnover rate magnitudes, active gel density $\rho$ is represented (Video 6). (b) Cell shape evolution when $A_{gen}$ is modified globally. (c) Snapshots of simulated 2D cuts of cysts with symmetric turnover rates but asymmetric additional extensile/contractile active stresses. Cells (red), lumen (blue) and an elastic ECM (yellow) are represented. All simulations started from the same initial condition and were stopped after 300000 steps. (d) Cell shape changes associated with asymmetry in active stresses. Same symbols in (c) and (d). (e) In the case of asymmetric turnover rates, parameters were chosen to match the experimental data presented in Table I.</figcaption></figure>

227 studies [18, 45, 46]. This asymmetry in composition does not translate into extensile and contractile gels respectively. Rather they are both contractile and this suggests that another mechanical asymmetry is at play. This also illustrates that the molecular knowledge of the gel composition is not sufficient to explain the mechanics and shape of the cortex.

234 Our description integrates generic cellular properties and should be broadly applicable in other organoids and in 3D tissues. It will be interesting to interrogate the cortical properties in each case to identify conserved and differences between systems.

## END MATTER

240 **Cysts in organoids** - Cysts also emerge in organoids which are three-dimensional organ-models formed in vitro from stem cells when cultured in the relevant environments [30]. Interestingly, differentiated epithelial cells can also mimic cell shape dynamics observed in organoids. Indeed, they often share common cortical proteins as well as adhesion complexes across systems. These shared points allow to probe and establish phenomena with reliable extrapolation to organs found in vivo. Altogether the integrated comparisons and characterisation of epithelial cysts and organoids with active gels simulations appear as a powerful route to implement and test active fluid descriptions for cell shapes determination in 3D tissues. Quantitative measurement of each cell shape and mechano-chemical characterization of cells in a cyst are lacking so far.

256 **Materials and Methods** - Briefly, mouse embryonic stem cells (mES) epiblasts and pancreas spheres were prepared according to protocols of Refs. [31] and [32], respectively. MDCK II cell lines - wild type and expressing fluorescent markers - were cultured using Minimum Essential Media (MEM) supplemented with 5% Fe-

tal Bovine Serum (FBS). We obtained MDCK cysts lying within the same focal plane using a protocol adapted from Refs. [33, 34]. Coverslips were activated by $O_2$ plasma and coated with a 100% Matrigel solution (Corning BV: 356231). Single cells were deposited at a density of 15,000 cells/cm$^2$ covered next by another layer of 10 $\mu l$ 100% Matrigel. To investigate the role of actin polymerisation, we used 200 nM of inhibitor latrunculin A (Sigma Aldrich #L5163) and 100 $\mu M$ of C8N6 BPA polyamine (C8BPA) activator of actin polymerisation [35]. Images were acquired with a Leica DMi8 confocal microscope equipped with a Yokogawa CSU W1 spinning disk and with an OrcaFlash 4.0 camera for the Fluorescence Recovery After Photobleaching (FRAP). Laser ablations of the acto-myosin cortex were performed using the FRAP module from a Leica SP-5 inverted microscope (see SM [36] for more details in immunofluorescence and in analysis).

280 **Phase Field (PF) modeling** - We simplified cysts into three components: cells, lumen and ECM. Their geometries are described by using PF variables $\phi_i(r, t)$ ($i = 1, ..., m$; one for each cell), $\ell(r, t)$ and $e(r, t)$, respectively. The regions where each PF variable takes 1 and 0 represent the inside and outside of the corresponding phase, respectively. We defined for each cell a cortical gel confined in the vicinity of the cellular interface, set by $\phi_i(r) = 1/2$, which is modeled as an active gel of density field $\rho_i(r, t)$ obeying the 2D extension of the 1D model developed in Refs. [42, 43]. Dynamics of the field variables is given in SM [36]. Each PF variable obeys mainly the standard multicellular PF model [37, 40, 41], under the assumptions: (i) all cells have identical optimum cell size, cell-cell adhesion, cell-ECM adhesion and excluded volume; (ii) dynamics of lumen volume are driven by osmotic pressure differences; and (iii) the ECM is described as an elastic material.

298 **Detailed comparisons for changes in actin dynamics** - To test predictions associated to changes in
<figure><img src="image_5.png" /><figcaption>FIG. 4. Specific modifications of actin polymerisation lead to shapes predicted by the active fluid numerical simulations. (a) Inhibition of actin polymerisation by incubating MDCK cysts with LatA. Snapshots of an individual cell before and after treatment (Video 7). (b) Cell shape diagram showing the effect of LatA on cells: cells tend to become flatter and elongate longitudinally. Black arrow represents the motion of the center of mass of the experimental cloud points before and after LatA treatment. N = 3 experiments - n = 97 cells. (c) Enhancement of actin polymerisation by incubating MDCK cysts with C8BPA. Snapshots of an individual cell before and after treatment (Video 9). (d) Cell shape diagram showing the effect of C8BPA on cells: cells tend to become more radially elongated. Black arrow represents the motion of the center of mass of the experimental cloud points before and after C8BPA treatment. N = 3 experiments - n = 117 cells. Insets of (b) and (d): distribution of $\theta_{\text{traj}}$, the angle of each trajectory vector in the cell shape diagram corresponding to Lat A or C8BPA treatment respectively. (e) Snapshots of simulated cysts with asymmetric turnover rates but with various baseline polymerisation rates $A_{\text{gen}}$ (Video 10). (f) Cell shape diagram showing the effect of enhancing or diminishing active gel polymerisation in simulated cells. Insets of (b) and (d) show that distributions go in opposite directions consistently with insets of (f). Scale bars: 10 $\mu$m.</figcaption></figure>

300 the dynamics of the gel parameters (SM [36]), we al-
301 tered specifically the polymerisation of actin. Upon in-
302 cubation with the depolymerising agent latrunculin A
303 (LatA) (M&M), we recorded changes in cell shape and
304 the associated modifications in the actin cortical com-
305 position. After treatment, cells became flatter, apical

306 cortices bent away from the lumen and the apical actin
307 gel depolymerised (Fig. 4(a), Fig. S6 and Video 7). Af-
308 ter washout, actin cortices were rebuilt and cell shape
309 came back to their configuration prior drug treatment
310 in about 10 hours (Fig. S7 and Video 8). We also per-
311 formed the opposite experiment by incubating cysts with
312 the actin polymerisation enhancer C8BPA [35]. Actin
313 gels polymerised following the drug incubation and cells
314 became more cuboidal with a tendency of the apical cor-
315 tex to adopt a positive curvature w.r.t. the cell center
316 of mass (Fig. 4(d) and Video 9). Quantifications are
317 shown in Fig. 4(a-d) and in SM [36]. Strikingly, we
318 could obtain shapes predicted by the associated changes
319 in actin polymerisation in the simulation (variation of
320 $A_{\text{gen}}$ in Fig. 4 (e,f) and Video 10). This successful com-
321 parison shows that our cortex model can capture the cell
322 shapes and changes its shapes with specific changes in
323 mechano-chemical parameters.

Mesoscopic approach and cytoskeleton organi-
sation - This mesoscopic approach goes also with struc-
tural simplifications which may contribute to setting the
contractile nature of these cortices. For example, the api-
cal side is composed of villi with folded structures and
anchors to the membrane [1]. Orientations of actin fila-
ments are reported to be specific at this level [1]. Our
results show that our mesoscopic approach is still success-
ful to explain cell shapes beyond molecular compositions
and architecture.

ACKNOWLEDGEMENTS

We acknowledge Kana Fuji and Sakurako Tanida for
335 supporting us to implement numerical simulations. We
336 thank the Riveline group for help and discussions and Er-
337 wan Grandgirard and the Imaging Platform of IGBMC.
338 We also thank Anne Grapin-Botton, Masaki Sano and Alf
339 Honigmann and their respective teams for advices and
340 discussions. We thank Makiko Nonomura, Jacques Prost
341 and Karsten Kruse for fruitful discussions. L.L. and T.G.
342 are supported by HFSP and by the University of Stras-
343 bourg, and by la Fondation pour la Recherche Médicale
344 (T.G.). T.H. is supported by Seed Fund of Mechanobi-
345 ology Institute. D.R. acknowledges the Interdisciplinary
346 Thematic Institute IMCBio, part of the ITI 2021-2028
347 program of the University of Strasbourg, CNRS and
348 Inserm, which was supported by IdEx Unistra (ANR-
349 10-IDEX-0002), and by SFRI-STRAT'US project (ANR
350 20-SFRI-0012) and EUR IMCBio (ANR-17-EURE-0023)
351 under the framework of the French Investments for
352 the Future Program. D.R. acknowledges the Research
353 Grant from Human Frontier Science Program (Ref.-No:
354 RGP0050/2018).
* thiraiwa@gate.sinica.edu.tw

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† riveline@unistra.fr

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