A recent experiment on zebrafish blastoderm morphogenesis showed that the viscosity (η) of a non-confluent embryonic tissue grows sharply until a critical cell packing fraction (ϕS). The increase in η up to ϕS is similar to the behavior observed in several glass forming materials, which suggests that the cell dynamics is sluggish or glass-like. Surprisingly, η is a constant above ϕS. To determine the mechanism of this unusual dependence of η on ϕ, we performed extensive simulations using an agent-based model of a dense non-confluent two-dimensional tissue. We show that polydispersity in the cell size, and the propensity of the cells to deform, results in the saturation of the available free area per cell beyond a critical packing fraction. Saturation in the free space not only explains the viscosity plateau above ϕS but also provides a relationship between equilibrium geometrical packing to the dramatic increase in the relaxation dynamics.
This fundamental study substantially advances our physical understanding of the sharp increase and saturation of the viscosity of non-confluent tissues with increasing cell density. Through the analysis of a simplified model this study provides compelling evidence that polydispersity in cell size and the softness of cells together can lead to this phenomenon. The work will be of general interest to biologists and biophysicists working on development.
There is great interest in characterizing the mechanical and dynamical properties of embryonic tissues because they regulate embryo development [1–5]. Measurements of bulk properties, such as viscosity and elastic modulus, and the dynamics of individual cells through imaging techniques, have been interpreted by adapting concepts developed to describe phase transitions, glass transition, and active matter [6–9]. Several experiments have shown that during embryo morphogenesis, material properties of the tissues change dramatically [10– 14]. Of relevance to our study is a remarkable finding that provided evidence that a phase transition (PT) occurs during zebrafish blastoderm morphogenesis, which was analyzed using rigidity percolation theory [14–17]. The authors also estimated the viscosity (η) of the blastoderm tissue using the micropipette aspiration technique [13, 18]. It was found that change in η is correlated with cell connectivity (⟨C⟩), rising sharply over a narrow range of ⟨C⟩. Surprisingly, a single geometrical quantity, the cell-cell contact topology controls both the rigidity PT as well as changes in η in this non-confluent tissue, thus linking equilibrium and transport properties.
Here, we focus on two pertinent questions that arise from the experiments on zebrafish blastoderm. First, experiments  showed that η increases as a function of the cell packing fraction (ϕ) till ϕ ≤ 0.87. The dependence of η on ϕ follows the well-known Vogel-FulcherTamman (VFT) law , which predicts that η grows monotonically with ϕ. The VFT law, which is commonly used to analyze the viscosity of a class of glass forming materials , is given by where ϕ is a constant. Surprisingly, for packing fractions, ϕ ≥ ϕS ≈ 0.90, η deviates from the VFT law and is independent of ϕ, which cannot be explained using conventional theories for glasses [8, 21]. Second, the experimental data  was interpreted using equilibrium rigidity percolation theory [15–17] for an embryonic tissue in which cells undergo random cell divisions. A priori it is unclear why equilibrium concepts should hold in zebrafish morphogenesis, which one would expect is controlled by non-equilibrium processes such as self-propulsion, growth and cell division.
We show that the two conundrums (saturation of η at high packing fractions and the use of equilibrium statistical mechanics in a growing system to explain phase transition) may be rationalized by (i) assuming that the interactions between the cells are soft, (ii) the cell sizes are highly heterogeneous (polydisperse), which is the case in zebrafish blastoderm. Using an agent-based (particle) simulation model of a two-dimensional non-confluent tissue, we explore the consequences of varying ϕ (see section Materials and Methods for the definition) of interacting self-propelled polydisperse soft cells, on η. The central results of our study are: (i) The calculated effective viscosity (for details see Appendix F), for the polydisperse cell system, shows that for ϕ ≤ ϕS ≈ 0.90 the increase in viscosity follows the VFT law. Just as in experiments, η is essentially independent of ϕ at high (≥ ϕS) packing fractions. The unusual dependence of η at ϕ ≥ ϕS is quantitatively explained using the notion of available free area fraction (ϕfree), which is the net void space that can be explored by the cells when they are jammed. At high densities, a given cell requires free space in order to move. The free area is created by movement of the neighboring cells into the available void space. One would intuitively expect that the ϕfree should decrease with increasing packing fractions, due to cell jamming, which should slow down the overall dynamics. Indeed, we find that ϕfree decreases with increasing packing fraction (ϕ) until ϕS. The simulations show that when ϕ exceeds ϕS, the free area ϕfree saturates because the soft cells (characterized by “soft deformable disks”) can overlap with each other, resulting in the collective dynamics of cells becoming independent of ϕ for ϕ ≥ ϕS. As a consequence η saturates at high ϕ. (iii) Cells whose sizes are comparable to the available free area move almost like a particle in a liquid. The motility of small sized cells facilitates adjacent cells to move through multi-cell rearrangement even in a highly jammed environment. The facilitation mechanism, invoked in glassy systems  allows large cells to move with low mobility. A cascade of such facilitation processes enable all the cells to remain dynamic even above the onset packing fraction of the phase transition. (iv) We find that the relaxation time does not depend on the waiting time for measurements even in the regime where viscosity saturates. In other words, there is no evidence of aging even in the regime where viscosity saturates. Strikingly, the tissue exhibits ergodic  behavior at all densities. The cell-based simulations, which reproduce the salient experimental features, may be described using equilibrium statistical mechanics, thus providing credence to the use of cell contact mechanics to describe both rigidity PT and dynamics in an evolving non-confluent tissue .
A. Experimental results
We first describe the experimental observations, which serve as the basis for carrying out the agent-based simulations. Fig. 1(A) shows the bright-field images of distinct stages during zebrafish morphogenesis. A 2D section of zebrafish blastoderm (Fig. 1 (B)) shows that there is considerable dispersion in cell sizes. The statistical properties of the cell sizes are shown in Fig. 8 (D). Fig. 1 (C) shows that η increases sharply over a narrow ϕ range, and saturates when ϕ exceeds ϕS ≈ 0.90.
To account for the results in Fig. 1 (C), we first simulated a mono-disperse system in which all the cells have identical radius (R = 8.5 µm). Because the system crystallizes (Fig. 8 (A) & (B)), we concluded that the dynamics observed in experiments cannot be explained using this model. A 1:1 binary mixture of cells, with different radii gives glass-like behavior for all ϕ, with the relaxation time τα as well as the effective viscosity (defined in Eq. 1) following the VFT behavior (see Appendix B).
B. Polydispersity and cell-cell interactions
In typical cell tissues, and zebrafish in particular, there is a great dispersion in the cell sizes, which vary in a single tissue by a factor of ∼ 5 − 6  (Fig. 1 (B) and Fig. 8 (D)). In addition, the elastic forces characterizing cell-cell interactions are soft, which implies that the cells can overlap, with rij −(Ri +Rj) < 0 when they are jammed ((Fig. 1 (B) and (Fig. 1 (D)). Thus, both polydispersity as well as soft interactions between the cells must control the relaxation dynamics. To test this proposition, we simulated a highly polydisperse system (PDs) in which the cell sizes vary by a factor of ∼ 8 (Fig. 1 (D) and Fig. 8 (E)).
A simulation snapshot (Fig. 1 (D)) for ϕ = 0.93 shows that different sized cells are well mixed. In other words, the cells do not phase separate. The structure of the tissue can be described using the pair correlation function, , where is the number density, δ is the Dirac delta function, is the position of the ith cell, and the angular bracket ⟨⟩ denotes an average over different ensembles. The g(r) function (Fig. 1 (E)) has a peak around r ∼ 17µm, which is approximately the average diameter of the cells. The absence of peaks in g(r) beyond the second one, suggests there is no long range order. Thus, the polydisperse cell system exhibits liquid-like structure even at the high ϕ.
C. Effective shear viscosity as a function of ϕ
A fit of the experimental data for η using the VFT [24, 25] relation in the range ϕ ≤ 0.87 (Fig. 1 (C)) yields ϕ0 ≈ 0.95 and D ≈ 0.51 . The VFT equation for cells, which is related to the Doolittle equation  for fluidity that is based on free space available for motion in an amorphous system [27, 28], is , where D is the apparent activation energy. In order to compare with experiments, we calculated an effective shear viscosity for the polydisperse system using a Green-Kubo-type relation ,
The stress tensor Pµν(t) in the above equation is,
where µ, ν ∈ (x, y) are the Cartesian components of coordinates, is the force between ith and jth cells and A is the area of the simulation box. Note that should be viewed as a proxy for shear viscosity because it does not contain the kinetic term and the factor is not included in Eqn. (1) because temperature is not a relevant variable in the highly over-damped model for cells considered here.
Plot of as a function of ϕ in Fig. 2 (A), shows qualitatively the same behavior as the estimate of viscosity (using dimensional arguments) made in experiments. Two features about Fig. 1 (C) and Fig. 2 (A) are worth noting. (i) Both simulations and experiments show that up to follows the VFT relation with ϕ0 ∼ 0.94 and D ∼ 0.5. More importantly is independent of ϕ when ϕ > 0.90. (ii) The values of ϕ0 and D obtained by fitting the experimental estimate of η to the VFT equation and simulation results are almost identical. Moreover, the onset of the plateau packing fraction in simulations and experiments occur at the same value (ϕS ∼ 0.90). The overall agreement with experiments is remarkable given that the model was not created to mimic the zebrafish tissue.
To provide additional insights into the dynamics, we calculated the isotropic selfintermediate scattering function, Fs(q, t),
where is the wave vector, and is the position of a cell at time t. The degree of dynamic correlation between two cells can be inferred from the decay of Fs(q, t). The angle bracket ⟨…⟩ is an average over different time origins and different trajectories. We chose , where rmax is the position of the first peak in g(r) between all cells (see Fig. 1 (E)). The relaxation time τα is calculated using .
From Figs. 2 (B) & (C), which show Fs(q, t) as a function of t for various ϕ, it is clear that the dynamics become sluggish as ϕ increases. The relaxation profiles exhibit a two step decay with a plateau in the intermediate time scales. The dynamics continues to slow down dramatically until ϕ ≤ 0.90. Surprisingly, the increase in the duration of the plateau in Fs(q, t) ceases when ϕ exceeds ≈ 0.90 (Fig. 2 (C)), a puzzling finding that is also reflected in the dependence of τα on ϕ in Fig. 2 (D). The relaxation time increases dramatically, following the VFT relation, till ϕ ≈ 0.90, and subsequently reaches a plateau (see the inset in Fig. 2 (D)). If the VFT relation continued to hold for all ϕ, as in glasses or in binary mixture of 2D cells (see Appendix B), then the fit yields ϕ0 ≈ 0.95 and D ≈ 0.50. However, the simulations show that τα is nearly a constant when ϕ exceeds 0.90. We should note that the behavior in Fig. 2 (D) differs from the dependence of τα on ϕ for 2D monodisperse polymer rings, used as a model for soft colloids. Simulations  showed τα increases till a critical ϕS but it decreases substantially beyond ϕS with no saturation.
D. Relaxation dynamics of individual cells
Plot of τα as a function of the radius of cells Ri (Fig. 3 (A)) shows nearly eight orders of magnitude change. The size dependence of τα on ϕ is striking. That τα should increase for large sized cells (see the data beyond the vertical dashed line in Fig. 3 (A)) is not unexpected. However, even when cell sizes increase beyond Ri = 4.25 µm, the dispersion in τα is substantial, especially when ϕ exceeds ϕS. The relaxation times for cells with Ri < 4.25 µm are relatively short even though the system as a whole is jammed. For ϕ ≥ 0.90, τα for small sized cells have a weak dependence on ϕ. Although τα for cells with radius < 4 µm is short, it is clear that for a given ϕ (for example ϕ = 0.93) the variations in τα is substantial. In contrast, τα’s for larger cells (R ≥ 7µm) are substantially large, possibly exceeding the typical cell division time in experiments. In what follows, we interpret these results in terms of available free area ⟨Afree⟩ for cells. The smaller sized cells have the largest (RS is the radius of the small cell).
The effect of jamming on the dramatic increase in τα occurs near Ri ≈ 4.5 µm, which is comparable to the length scale of short range interactions. For ϕ ≤ 0.90, τα increases as the cell size increases. However, at higher packing fractions, even cells of similar sizes show substantial variations in τα, which change by almost 3 − 4 orders of magnitude (see the data around the vertical dashed line for ϕ ≥ 0.915 in Fig. 3 (A)). This is a consequence of large variations in the local density (Fig. 14). Some of the similar-sized cells are trapped in the jammed environment, whereas others are in less crowded regions (see Fig. 14). The spread in τα increases dramatically for ϕ > ϕS (≈ 0.90) and effectively overlap with each other. This is vividly illustrated in the the histogram, P(log(τα)) shown in Fig. 3 (B). For ϕ < ϕs, the peak in P(log(τα)) monotonically shifts to higher log(τα) values. In contrast, when ϕ exceeds ϕS there is overlap in P(log(τα)), which is reflected in the saturation of and τα. There are cells (typically with small sizes) that move faster even in a highly jammed environment (see Fig. 12 (C) and Fig. 13). The motions of the fast-moving cells change the local environment, which effectively facilitates the bigger cells to move in a crowded environment (see Fig. 12 (D) and Fig. 13 and and Movie 1 (ϕ = 0.92 > ϕS) and Movie 2 (ϕ = 0.90 = ϕS)). In contrast, for ϕ = 0.85 < ϕS small and large sized cells move without hindrance because of adequate availability of free area (Movie 3). The videos vividly illustrate the large scale facilitated rearrangements that enable the large sized cells to move.
The dependence of τα on ϕ for ϕ ≤ ϕS (Fig. 2 (D)) implies that the polydisperse cell systems behave as a soft glass in this regime. On theoretical grounds, it was predicted that in glass-forming systems . Remarkably, we found that this prediction is valid in the polydisperse cell system (Fig. 3 (C)). However, above ϕS the predicted relation is not satisfied (see Fig. 3 (D)).
E. Available free area explains viscosity saturation at high ϕ
We explain the saturation in the viscosity by calculating the available free area per cell, as ϕ increases. In a hard disk system, one would expect that the free area would decrease monotonically with ϕ until it is fully jammed at the close packing fraction (∼ 0.84 [31, 32]). Because the cells are modeled as soft deformable disks, they could overlap with each other even when fully jammed. Therefore, the region where cells overlap create free area in the immediate neighborhood. The extent of overlap (hij) is reflected in distribution P(hij). The width in P(hij) increases with ϕ, and the peak shifts to higher values of hij (Fig. 4 (A)). The mean, ⟨hij⟩ increases with ϕ (Fig. 4 (B)). Thus, even if the cells are highly jammed at ϕ ≈ ϕS, free area is available because of an increase in the overlap between cells (see Fig. 5). When ϕ exceeds ϕS, the mobility of small sized cells facilitates the larger cells to move, as is assumed in the free volume theory of polymer glasses [28, 33–35]. As a result of the motion of small cells, a void is temporarily created, which allows other (possibly large) cells to move. In addition to the release of space, the cells can also interpenetrate (Figs. 4 (A) & (B)). If hij increases, as is the case when the extent of compression increases (Figs. 4 (A)), the available space for nearby cells would also increase. This effect is expected to occur with high probability at ϕS and beyond, resulting in high overlap between the cells. These arguments suggest that the combined effect of polydispersity and cell-cell overlap creates, via the self-propulsion of cells, additional free area that drives larger cells to move even under jammed conditions.
In order to quantify the physical picture given above, we calculated an effective area for each cell by first calculating Voronoi cell area A. A plot for Voronoi tessellation is presented in Fig. 5 (A) for ϕ = 0.93, and the histogram of A is shown in Fig. 5 (B). As ϕ increases, the distribution shifts towards lower Voronoi cell size ⟨A⟩. The mean Voronoi cell size ⟨A⟩ as a function of ϕ in Fig. 5 (C) shows ⟨A⟩ decreases as ϕ is increased. As cells interpenetrate, the Voronoi cell size will be smaller than the actual cell size in many instances (Fig. 5 (A)). To demonstrate this quantitatively, we calculated . The value of Afree could be negative if the overlap between neighbouring cells is substantial; Afree is positive only when the Voronoi cell size is greater than the actual cell size. Positive Afree is an estimate of the available free area. The histograms of Afree for all the packing fractions in Fig. 5 (D) show that the distributions saturate beyond ϕ = 0.90. All the distributions have a substantial region in which Afree is negative. The negative value of Afree increases with increasing ϕ, which implies that the amount of interpenetration between cells increases.
Because of the overlap between the cells, the available free area fraction ϕfree, is higher than the expected free area fraction (1.0 − ϕ) for all ϕ. We define an effective free area fraction ϕfree as,
where Np is the number of positive free area in jth snapshots, Nt is the total number of snapshots, Abox is the simulation box area and is the positive free area of ith cell in jth snapshot. The calculated ϕfree, plotted as a function of ϕ in Fig. 5 (E), shows that ϕfree decreases with ϕ until ϕ = 0.90, and then it saturates near a value ϕfree ≈ 0.22 (see the right panel in Fig. 5 (E)). Thus, the saturation in as a function of ϕ is explained by the free area picture, which arises due to combined effect of the size variations and the ability of cells to overlap.
F. Aging does not explain viscosity saturation
Our main result, which we explain by adopting the free volume theory developed in the context of glasses [28, 33–35], is that above a critical packing fraction ϕS ∼ 0.90 the viscosity saturates. Relaxation time, τα, measured using dynamic light scattering, in nearly monodisperse microgel poly(Nisopropylacrylamide) (PNiPAM)  was found to depend only weakly on the volume fraction (3D), if ϕV, exceeds a critical value. It was suggested that the near saturation of τα at high ϕV is due to aging, which is a non-equilibrium effect. If saturation in viscosity and relaxation time in the embryonic tissue at high ϕ is due to aging then τα should increase sharply as the waiting time, τω, is lengthened. We wondered if aging could explain the observed saturation of η in the embryonic tissue above ϕS. If aging causes the plateau in the tissue dynamics, then η or τα should be an increasing function of the waiting time, τω. To test the effect of τω on τα, we calculated the self-intermediate scattering function Fs(q, t+τω) as a function of t by varying τω over three orders of magnitude at ϕ = 0.92 (Fig. 6 (A)). There is literally no change in Fs(q, t + τω) over the entire range of tω. We conclude that, τα, extracted from Fs(q, t+τω) is independent of τω. The variations in τα (Fig. 6 (B)), with respect to τω, is significantly smaller than the errors in the simulation. Thus, the saturation in η or τα when ϕ > ϕS is not a consequence of aging.
There are two implications related to the absence of aging in the dynamics of the nonconfluent embryonic tissues. (i) Although active forces drive the dynamics of the cells, as they presumably do in reality, the cell collectives can be treated as being near equilibrium, justifying the use of Green-Kubo relation to calculate η. (ii) Parenthetically, we note that the absence of significant non-equilibrium effects, even though Zebrafish is a living system, further justifies the use of equilibrium rigidity percolation theory to analyze the experimental data .
Extensive computer simulations of a two-dimensional dense tissue using a particle-based model of soft deformable cells with active self-propulsion have successfully reproduced the dynamical behavior observed in the blastoderm tissue of zebrafish. The dependence of viscosity (η) and relaxation time (τα) (before the saturation) is well fit by the VFT equation. The value of ϕ0 obtained from simulations, ϕ0 ∼ 0.95, is close to ϕ0 ∼ 0.94 extracted by fitting the experimental data to the VFT equation. Thus, the dynamics for ϕ ≤ ϕS resembles the behavior expected for glass forming systems. Remarkably, the dependence of η on ϕ over the entire range (VFT regime followed by a plateau) may be understood using available free area picture with essentially a single parameter, an idea that was proposed nearly 70 years ago. We discovered that polydispersity as well as the ease of deformation of the cells that creates free area under high jamming conditions, is the mechanism that explains viscosity saturation at high cell densities. The mechanism suggested here is an important step that links equilibrium phase transition to dynamics during zebrafish development .
One could legitimately wonder if the extent of polydispersity (PD) used in the soft discs simulations, which seems substantial, is needed to recapitulate the observed dependence of η on ϕ. Furthermore, such large values of PD may not represent biological tissues. Although the choice of PD was made in part by the two-dimensional projection of area reported in experiments , it is expected that PD values have to be less in three dimensions. We performed preliminary simulations in three dimensions with considerably reduced PD, and calculated the dependence of relaxation time (τα) as a function of ϕ. The results show that τα does indeed saturate at high volume fractions.
The proposed model neglects adhesive interactions between cells, which of course is not unimportant. It is crucial to wonder if the proposed mechanism would change if cell-cell adhesion is taken into account. We wanted to create the simplest model to explain the experimental data. We do think that realistic values of adhesion strength does not significantly alter the forces between cells . Thus, we expect a similar mechanism. Furthermore, the physics of the dynamics in glass forming materials does not change in systems with and without attractive forces . For these reasons, in the first pass we neglected adhesion, whose effects have to be investigated in the future.
In the physical considerations leading to Eq. 6, the random activity term (µ) plays an important role. Is it possible to create a passive model by maintaining the system at a finite temperature using stochastic noise with µ = 0, which would show the observed viscosity behavior? First, in such a system of stochastic equations, the coefficient of noise (a diffusion constant) would be related to γi in Eq.6 through fluctuation dissipation theorem (FDT). Thus, only γi can be varied. In contrast, in Eq.6 the two parameters (γi and µ) maybe independently changed, which implies that the two sets of stochastic equations of motion are not equivalent. Second, the passive system describes particles that interact by soft Hertz potential. In analogy with systems in which the particles interact with harmonic potential , we expect that the passive model would form a glass in which the viscosity would follow the VFT law.
We find it surprising that the calculation of viscosity using linear response theory (valid for systems close to equilibrium), and the link to free area quantitatively explain the simulation results and by implication the experimental data for a living and growing tissue. The calculation of free area of the cells is based on the geometrical effects of packing, which in turn is determined by cell to cell contact topology. These considerations, that are firmly established here, explain why equilibrium phase transitions are related to steep increase in viscosity  as the packing fraction changes over a narrow range. The absence of aging suggests that, although a large number of cell divisions occur, they must be essentially independent, thus allowing the cells to reach local equilibrium.
We acknowledge Anne D. Bowen at the Visualization Laboratory (Vislab), Texas Advanced Computing Center, for help with video visualizations. We are grateful to Mauro Mugnai and Nicoletta Petridou for useful discussions. This work was supported by grants from the National Science Foundation (PHY 23-10639 and CHE 23-20256, and the Welch Foundation (F-0019).
Materials and Methods
Two dimensional cell model
Following our earlier studies [38, 40], we simulated a two dimensional (2D) version of a particle-based cell model. We did not explicitly include cell division in the simulations. This is physically reasonable because in the experiments  the time scales over which cell division induced local stresses relax are short compared to cell division time. Thus, local equilibrium is established in between random cell division events. We performed simulations in 2D because experiments reported the dependence of viscosity as a function of area fraction. In our model, cells are modeled as soft deformable disks [38, 41–43] interacting via short ranged forces. The elastic (repulsive) force between two cells with radii Ri and Rj is Hertzian, which is given by,
where . The repulsive force acts along the unit vector , which points from the center of the jth cell to the centre of ith cell. The total force on the ith cell is
where NN(i) is the number of near neighbor cells that is in contact with the ith cell. The jth cell is the nearest neighbor of the ith cell, if hij > 0. The near neighbor condition ensures that the cells interpenetrate each other to some extent, thus mimicking the cell softness. For simplicity, we assume that the elastic moduli (E) and the Poisson ratios (ν) for all the cells are identical. Polydispersity in the cell sizes is important in recovering the plateau in the viscosity as a function of packing fraction. Thus, the distribution of cell areas is assumed to have a distribution that mimics the broad area distribution discovered in experiments.
Self-propulsion and equations of motion
In addition to the repulsive Hertz force, we include an active force arising from selfpropulsion mobility (µ), which is a proxy for the intrinsically generated forces within a cell. For illustration purposes, we take µ to independent of the cells, although this can be relaxed readily. We assume that the dynamics of each cell obeys the phenomenological equation,
where γi is the friction coefficient of ith cell, and 𝒲i(t) is a noise term. The friction coefficient γi is taken to be γ0Ri . By scaling t by the characteristic time scale, in Eqn. (6), one can show that the results should be insensitive to the exact value of µ. The noise term 𝒲i(t) is chosen such that ⟨𝒲i(t)⟩ = 0 and . In our model there is no dynamics with only systematic forces because the temperature is zero. The observed dynamics arises solely due to the self-propulsion (Eqn. (6)).
We place N cells in a square box that is periodically replicated. The size of the box is L so that the packing fraction (in our two dimensional system it is the area fraction) is . We performed extensive simulations by varying ϕ in the range 0.700 ≤ ϕ ≤ 0.950. The results reported in main text are obtained with N = 500. Finite size effects are discussed in Appendix G.
To mimic the variations in the area of cells in a tissue , we use a broad distribution of cell radii (see Appendix A for details). The parameters for the model are given in Table I. In the present study, we do not consider the growth and division of cells. Thus, our simulations describe steady-state dynamics of the tissue. For each ϕ, we performed simulations for at least (5−10)τα before storing the data. For each ϕ, we performed 24 independent simulations. The calculation of viscosity was performed by averaging over 40 independent simulations at each ϕ.
Calculation of viscosity
We calculated the effective viscosity for various values of ϕ by integrating the off-diagonal part of the stress-stress correlation function ⟨Pµν(t)Pµν(0)⟩, using the Green-Kubo relation  (without the pre-factor ),
where µ and ν denote Cartesian components (x and y) of the stress tensor Pµν(t) (see main text for the definition of Pµν(t)). The definition of , which relates the decay of stresses as a function of times in the non-confluent tissue, is akin to the methods used to calculate viscosity in simple fluids. (Eq.7) The time dependence of ⟨Pµν(t)Pµν(0)⟩, normalized by ⟨Pµν(0)2⟩, for different values of ϕ (Fig. 7 (A) & (B)) shows that the stress relaxation is clearly non-exponential, decaying to zero in two steps. After an initial rapid decay followed by a plateau at intermediate times (clearly visible for ϕ ≥ 0.91) the normalized ⟨Pµν(t)Pµν(0)⟩ decays to zero as a stretched exponential. The black dashed lines in Fig. 7 (C)) show that a stretched exponential function, , where τ is the characteristic time in which stress relax and β is the stretching exponent, provides an excellent fit to the long time decay of ⟨Pµν(t)Pµν(0)⟩ (from the plateau region to zero) as a function of t. Therefore, we utilized the fit function, , to replace the noisy long time part of ⟨Pµν(t)Pµν(0)⟩ by a smooth fit data before evaluating the integral in Eqn. (7). The details of the procedure to compute is described below.
We divided ⟨Pµν(t)Pµν(0)⟩ in two parts. (i) The short time part (⟨Pµν(t)Pµν(0)⟩short) – the smooth initial rapid decay until the plateau is reached (for example see the blue circles in Fig. 7(D) for ϕ = 0.93). For the n data points at short times, t1, ⟨Pµν(t1)Pµν(0)⟩short, …, (tn, ⟨Pµν(tn)Pµν(0)⟩short), we constructed a spline S(t) using a set of cubic polynomials:
The polynomials satisfy the following properties. (a) Si(ti) = ⟨Pµν(ti)Pµν(0)⟩short and Si(ti+1) = ⟨Pµν(ti+1)Pµν(0)⟩short for i = 1, .., n − 1 which guarantees that the spline function S(t) interpolates between the data points. (b) for i = 2, .., n − 1 so that S′(t) is continuous in the interval [t1, tn]. (c) for i = 2, .., n − 1 so that S′′(t) is continuous in the interval [t1, tn]. By solving for the unknown parameters, bi, ci and di using the above mentioned properties, we constructed the function S(t). We used S(t) to fit ⟨Pµν(t)Pµν(0)⟩short to get an evenly spaced (δt = 10s) smooth data (solid blue line in Fig. 7 (D)). The fitting was done using the software “Xmgrace”.
(ii) The long time part (⟨Pµν(t)Pµν(0)⟩long) – from the plateau until it decays to zerois shown by the red circles in Fig. 7 (D). The long time part was fit using the analytical function (black dashed line in Fig. 7 (D)). We refer to the fit data (δt = 10s) as . We then combined ⟨Pµν(t)Pµν(0)⟩short and to obtain ⟨Pµν(t)Pµν(0)⟩combined (see inset of Fig. 7 (D)). Finally, we calculated using the equation,
where t1δt is the end point of ⟨Pµν(t)Pµν(0)⟩short and T δt is the end point of ⟨Pµν(iδt)Pµν(0)⟩combined.
Appendix A: Cell polydispersity is needed to account for viscosity saturation
To explain the observed saturation in viscosity (Fig. 1 (C)) when cell area fraction, ϕ, exceeds ϕS (≈ 0.90), we first simulated a monodisperse cell system using the model described in the Method section. The monodisperse cell system (R = 8.5µm) crystallizes (see Fig. 8 (A) and (B)), which excludes it from being a viable model for explaining the experimental findings. We also find that a system consisting of 50:50 binary mixture of cells can not account for the experimental data even though crystallization is avoided (see the next section). These findings forced us to take polydispersity into account.
In order to develop an agent based model that accounts for polydispersity effects, we first extracted the distribution, P(Ai), of the area (Ai) in the Zebrafish cells from the experimental data . Figure 8 (D), shows that P(Ai) is broad, implying that cell sizes are highly heterogeneous. Based on this finding, we sought a model of the non-confluent tissue that approximately mimics polydispersity found in experiments. In other words, (ΔA is the dispersion in P(Ai) and ⟨A⟩ is the mean value) should be similar to the data in Fig. 8 (D). The radii (Ri) of the cells in the simulations are sampled from a Gaussian distribution ∼ exp (−(Ri − ⟨R⟩)2/2ΔR2) with ⟨R⟩ = 8.5µm and ΔR = 4.5µm. The resulting P(Ai) for one of the realizations (see Fig. 8 (C)) is shown in Fig. 8 (E). The value of is ∼ 0.5, which compares favourably with the experimental estimate (∼ 0.4). The unusual dependence of the viscosity (η) as a function of packing fraction (ϕ) cannot be reproduced in the absence of polydispersity.
Appendix B: Relaxation time in the binary mixture of cells does not saturate at high ϕ
We showed that the saturation in the relaxation time above a critical area fraction, ϕS, is related to two factors. One is that there ought to be dispersion in the cell sizes (Fig. 8). The extent of dispersion is likely to less in three rather in two dimensions. The second criterion is that the cells should be soft, allowing them to overlap at high area fraction. In other words, the cell diameters are explicitly non-additive. The Hertz potential captures the squishy nature of the cells.
In order to reveal the importance of polydispersity, we first investigated if a binary mixture of cells (see Fig. 9 (A)) would reproduce the observed dependence of τα on ϕ. We created a 50 : 50 mixture with a cell size ratio ∼ 1.4 (RB = 8.5µm and RS = 6.1µm). All other parameters are the same as in the polydisperse system (see Table I). The radial distribution function, g(r), calculated by considering both the cell types, exhibits intermediate but not long range translational order (Fig. 9 (B)). A peak in g(r) appears at rmax = 14.0µm.
In order to determine the dependence of the relaxation time, τα, on ϕ, we first calculated Fs(q, t) at q = 2π/rmax (Fig. 9 (C)). The decay of Fs(q, t) is similar to what one finds in typical glass forming systems. As ϕ increases, the decay of Fs(q, t) slows down dramatically. When ϕ exceeds 0.93 there is a visible plateau at intermediate times followed by a slow decay. By fitting the long time decay of Fs(q, t) to exp(−(t/τα)β) (β is the stretching exponent), we find that τα(ϕ) as a function of ϕ is well characterized by the VFT relation (Fig. 9 (D)). There is no evidence of saturation in τα(ϕ) at high ϕ. The ϕ dependence of the effective shear viscosity (Fig. 9 (E)), calculated using equation (8), also shows no sign of saturation. The VFT behavior, with ϕ0 ≈ 1 and D ≈ 1.2, shows that the two component cell system behaves as a “fragile” glass . A comment regarding the binary system of cells is in order.
The variables that characterize this system are, λ = RB/RS (RB (RS) is the radius of the big (small) cells), ΦB = NB/(NA + NB) (NB (NA) is the number of big (small) cells) with ΦA = 1 − ΦB, and the packing fraction, AS where AS is the area of the sample. The results in Fig. 9 were obtained using λ = 1.4, ΦB = 0.5. With this choice the value of polydispersity is (λ − 1)/(λ + 1) ≈ 0.17, which is smaller than the experimental value. It is possible that by thoroughly exploring the parameter space, λ and ϕ, one could find regions in which the τα in the two component cell system would saturate beyond ϕS. However, in light of the results in Fig. 8 (D)), we choose to simulate systems that also have high degree of polydispersity (Fig. 8 (E)).
Appendix C: Absence of saturation in the free area in binary mixture of cells
In the main text, we established that the effective viscosity, , which should be a proxy for the true η and the relaxation time (τα) in the polydisperse cell system saturates beyond ϕS. The dynamics saturates beyond ϕS because the available area per cells (quantified as ϕfree) is roughly a constant in this region (see Fig. 5 (E) in main text). In the binary system, however, we do not find any saturation in the τα. Therefore, if ϕfree controls the dynamics, one would expect that ϕfree should decrease monotonically with ϕ for the binary cells system. We calculated the average Vornoi cell size ⟨A⟩ (Fig. 10 (A)) and ϕfree (Fig. 10 (B)) for the binary system. Both ⟨A⟩ and ϕfree decrease with ϕ, which is consistent with the free volume picture proposed in the context of glasses.
Appendix D: Absence of broken ergodicity
We have shown in the earlier section that, the saturation of τα above ϕS is not a consequence of aging. In the context of glasses and supercooled liquids , it has been shown that if ergodicity is broken then a variety of observables would depend on initial conditions. In order to test if ergodicity is broken in our model of non-confluent tissues, we define a measure Ω(t) given by,
where, k and l represent systems with different initial conditions, and with Pµν(s) being the value of stress (see Eq. (2) in the main text) at time s. If the system is ergodic, implying the system has explored the whole phase space on the simulation time scale, the values of ωk(t) and ωl(t) would be independent of k and l. Therefore, Ω(t) should vanish at very long time for ergodic systems. On the other hand, if ergodicity is broken then Ω(t) would be a constant whose value would depend on k and l.
The long time values of Ω(t), normalized by Ω(0) (at t = 0), are 0.01, 0.016 and 0.026 for ϕ = 0.85, 0.90 and 0.92 respectively (Fig. 11 (A), (B) & (C)). Because these values are sufficiently small, we surmise that effectively ergodicity is established. Therefore, our conclusion in the earlier section that the polydisperse cell system is in near equilibrium is justified, and also explains the absence of aging. Furthermore, it was also predicted previously  that in long time the ergodic measure (Ω(t) in our case) should decay as ≈ 1/t. Fig. 11 (D) shows that this is indeed the case at long time Ω(t)/Ω(0) decays approximately as 1/t.
Appendix E: Dynamics of small and large cells are dramatically different
The structural and the dynamical behavior of the small (RS ≤ 4.5µm) and large (RB ≥ 12.0µm) cells are dramatically different in the non-confluent tissue. The pair correlation functions between small cells (gSS(r)) and between large cells (gBB(r)) (Fig. 12 (A) & (B)) for ϕ = 0.905 and ϕ = 0.92 show that, both small and large cells exhibit liquid like disordered structures. However, it is important to note that, gSS(r) has only one prominent peak and a modest second peak. In contrast, gBB(r) has three prominent peaks. Thus, the smaller sized cells exhibit liquid-like behavior whereas the large cells are jammed. This structural feature is reflected in the decay of Fs(q, t) with , where rmax is the position of the first peak in the g(r) (Fig. 12 (C) & (D)). There is a clear difference in the decay of Fs(q, t), spanning nearly four orders of magnitude, between small and large cells (compare Figs. 12 (C) and (D)). At the highly jammed packing fraction (ϕ = 0.92), small sized cells have the characteristics of fluid-like behavior.
A typical snapshot from one of the simulations (ϕ = 0.91) and the trajectories for a few small and big sized cells are displayed in Fig. 13 (A), (B), (C) and (D). The figures reflect the decay in Fs(q, t). Not only are the mobilities heterogeneous it is also clear that the the Mean Square Displacements (MSDs) of the small cells are greater than the large cells.
Appendix F Dynamical changes in local packing fraction cause jammed cells to move
The mobilities of all the cells, even under highly jammed conditions (ϕ ≥ ϕS), is only possible if the local area fraction changes dynamically. This is a collective effect, which is difficult to quantify because it would involve multi-cell correlation function. As explained in the main text, the movement of a jammed cell can only occur if several neighboring cells move to create space. This picture is not that dissimilar to kinetic facilitation in glass-forming materials [47, 48]. However in glass like systems facilitation is due to thermal excitation but in the active system, it is self-propulsion that causes the cells to move.
The creation of free space may be visualized by tracking the positions of the nearest neighbour cells. In Fig. 14, we display the local free area of a black coloured cell at different times. The top panels show the configurations where the black cell is completely jammed by other cells. The cell, coloured in black, can move if the neighboring cells rearrange (caging effect in glass forming systems) in order to increase the available free space. The bottom panels show that upon rearrangement of the cells surrounding the black cell its mobility increases. Such rearrangement occurs continuously, which qualitatively explains the saturation in viscosity in the multicomponent cell system.
Appendix G: Finite system size effects
In the main text, we report results for N = 500. To asses if the unusual dynamics is not an effect of finite system size, we performed additional simulations with N = 200 and N = 750. As shown in Fig. 15 (A) & (B), Fs(q, t) saturates at ϕ ≥ ϕS, which is reflected in the logarithm of τα as a function of ϕ (Fig. 15 (C) & (D)). The saturation value ϕS ∼ 0.90 is independent of the system size. The value of ϕ0 (≈ 0.95) is also nearly independent of system size. Therefore, the observed dynamics, reflected in the plateau in the viscosity at high ϕ, is likely not an effect of finite system size.
Appendix H: Dependence of viscosity on average coordination number and average connectivity
In the Zebrafish blastoderm experiment , the change in viscosity (η) was shown as a function of mean connectivity ⟨C⟩. To test whether our two dimensional tissue simulations also exhibit similar behavior, we calculated viscosity as a function of mean coordination number ⟨Nc⟩ (defined below), which is equivalent to ⟨C⟩. We define the coordination number, Nc, as the number of cells that are in contact with a given cell. Two cells with indices i and j are in contact if hij = Ri + Rj − rij > 0. We calculate Nc for all the cells for each ϕ and calculate the histogram P(Nc). The distributions of P(Nc)s are well fit by a Gaussian distribution function A (Figs. 16 (A), (B) & (C)). The calculated mean, ⟨Nc⟩, from the fit is linearly related to the cell area fraction ϕ (Fig. 16 (D)). We also calculate the average connectivity ⟨C⟩ defined in the following way. Each cell is defined as a node and an edge is defined as the line connecting two nodes. If a snapshot has n nodes and m edges then the connectivity is defined as . We calculate C for all the snapshots for each ϕ and estimated its mean value ⟨C⟩. We find that ⟨C⟩ and ⟨Nc⟩ are of similar values (Fig. 17 (A)), and the dependence of viscosity on ⟨Nc⟩ and ⟨C⟩ is similar to experimental results (Fig. 17 (B) & (C)) .
Appendix I: Connectivity Map
To pictorially observe the percolation transition in the simulations, we plot the connectivity map for various values ϕ in Fig. 18. Note that for smaller ϕ ≤ 0.89 the map shows that cells are loosely connected, suggesting a fluid-like behavior. For ϕ ≥ 0.89 the connectivity between the cells spans the entire system, which was noted and analysed elsewhere thoroughly . Cells at one side of simulation box are connected to cells at the side. The cell connectivity extends throughout the sample. The transition from a non-percolated state to a percolated state occurs over a very narrow range of ϕ, which corresponds to the onset of rigidity percolation transition occurs . It is gratifying that the simple simulations reproduce the experimental observations.
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