{"paper_id":"0873e2b4-ee3b-4bde-923c-1202be4d720b","body_text":"Page 1 of 27 \nHeterotypic intercellular adhesion tunes efficiency of cell-on-1 \ncell migration 2 \n 3 \nChandrashekar Kuyyamudi1,2*#, Suhrid Ghosh1,2#, & Cassandra G. Extavour1,2,3* 4 \n 5 \n1. Department of Organismic & Organismic Biology, Harvard University. 6 \nCambridge, MA, USA 7 \n2. Howard Hughes Medical Institute. Chevy Chase, MD, USA 8 \n3. Department of Molecular & Cellular Biology, Harvard University. Cambridge, 9 \nMA, USA 10 \n 11 \n* Correspondence:  CK ckuyyamudiashwinikumar@fas.harvard.edu  12 \n    CGE extavour@oeb.harvard.edu  13 \n# These authors contributed equally.  14 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 2 of 27 \nAbstract 15 \nCell migration across epithelial barriers occurs in diverse developmental, 16 \nimmunological, and pathological contexts. Here we investigate the contribution of 17 \nheterotypic adhesion between migrating cells and epithelial “substrate” cells to 18 \ntransepithelial migration. Using an in silico model inspired by the migration of 19 \nprimordial germ cells across the midgut epithelium in the Drosophila embryo, we show 20 \nthat heterotypic adhesion modulates migration efficiency in a non-monotonic manner, 21 \nrevealing the existence of an optimal adhesion regime. Consistent with this prediction, 22 \nin vivo overexpression of E-cadherin in germ cells accelerated their exit from the midgut 23 \nrelative to controls. Beyond providing experimentally testable predictions, our model 24 \nintegrates and explains previous observations on the role of heterotypic adhesion in 25 \ncell-on-cell migration, offering a framework for understanding transepithelial migration 26 \nacross biological contexts. 27 \nKeywords: 28 \nGerm cell migration, intercellular adhesion, E-cadherin, Cellular Potts model, 29 \nCompuCell3D, in vivo live imaging  30 \n31 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 3 of 27 \nIntroduction 32 \nCells frequently carry out their functions at locations distinct from their site of origin, 33 \nmaking the individual cellular motility that is required for cell migration a critical 34 \naspect of their behavior (1, 2). To achieve motility, cells require persistent and directed 35 \nmembrane activity, which is frequently guided by external chemical cues (3–6). This 36 \ndirected membrane activity is enabled by the dynamic nature of cell membranes, which 37 \nexhibit continuous turnover of cytoskeletal components (7–9) 38 \nCell migration, division, differentiation and death enable the development of 39 \nmulticellular organisms from a single cell (10). Cells in various developmental contexts 40 \nmigrate individually as well as collectively (1, 2, 11). Embryonic development unfolds 41 \nas a tightly regulated cascade of events, where each step must occur within a specific 42 \ntime window to prevent disruption of subsequent stages (12–15). These temporal 43 \nconstraints also apply to cell migration, making not only the path taken but also the 44 \ntiming of arrival critical to proper development (1, 2, 16, 17). 45 \nIn the developing embryo of the fruit fly Drosophila melanogaster, the primordial 46 \ngerm cells (germ cells) are the first cells to form (18). The germ cells form at the 47 \nposterior tip of the embryo roughly 2.5 hours after egg laying (18, 19). Around 15 48 \nminutes after their formation, the embryo undergoes gastrulation, and the cluster of 49 \ngerm cells enters the primordial midgut upon invagination of the surrounding tissue 50 \nduring gastrulation (20). Here, the germ cell cluster eventually disassembles, and the 51 \ngerm cells exit the gut by individually passing through the monolayer of epithelial cells 52 \nthat form the midgut (20, 21). The proteins Wunen and Wunen2, which are expressed in 53 \nthe ventral midgut among other tissues, serve as repellents driving the germ cells to exit 54 \nthe midgut dorsally (22–25). Once outside the midgut, the germ cells continue their 55 \nmigration to join the mesodermal cells that form the somatic gonad primordium (26).  56 \nThe Drosophila adhesion protein E-cadherin is encoded by the shotgun (shg) gene, 57 \nand is maternally deposited in the early embryo (27, 28). Germ cells require this 58 \nmaternally deposited E-cadherin both to form a cohesive cluster and to tether to the 59 \ninvaginating midgut primordium during gastrulation (29–31). Regulation of germ cell 60 \nE-cadherin during internalization into the midgut is orchestrated by the Trapped in 61 \nendoderm (Tre1) protein, a G protein-coupled receptor (30, 31). Tre1 enables the germ 62 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 4 of 27 \ncell cluster to form a radial configuration of polarized cells that detach from the cluster 63 \nprior to transepithelial migration (30, 31). In embryos laid by females expressing a loss 64 \nof function shotgun allele (𝑠ℎ𝑔!\"#$\"), individual germ cells separate from the cluster 65 \nprematurely (30). Furthermore, in 38% of 𝑠ℎ𝑔!\"#$\" mutant embryos, germ cells exhibit a 66 \ndelay in crossing the midgut epithelium (30). The posterior midgut, which is the barrier 67 \nthat the germ cells must breach, is made up of epithelial cells (32, 33). Remodeling of the 68 \nmidgut epithelium endows the epithelial barrier with increased permeability, allowing 69 \nthe germ cells to migrate through it (20, 34). High resolution imaging of the germ cells 70 \ncrossing the midgut epithelium has shown that germ cells maintain contacts with the 71 \nepithelial cells with no evident intercellular space around them (20). In addition, FGF 72 \nsignaling regulates the distribution of zygotic E-cadherin in the posterior midgut to 73 \nmaintain its epithelial integrity (35). FGF mutants display mislocalized E-cadherin and 74 \ncollapse of the midgut lumen, trapping the germ cells within (35).  75 \nGerm cells expressing defective E-cadherin (𝑠ℎ𝑔!\"#$\") show delayed 76 \ntransepithelial migration (30), reminiscent of the delay in border cell migration that 77 \noccurs when nurse cells either lack or overexpress E-cadherin in the D. melanogaster egg 78 \nchamber (36). Together, these observations suggest the existence of a non-monotonic 79 \nrelationship between migration efficiency and adhesion between migrating and 80 \nsubstrate cells. We set out to understand the role of heterotypic adhesion between the 81 \nmigrating cells and “substrate” cells in transepithelial migration, taking as a case study 82 \nthe role of intercellular adhesion mediated by E-cadherin in enabling the germ cells to 83 \ntransmigrate. Leveraging genetic tools and two-photon microscopy, we live imaged the 84 \nprocess of transepithelial migration in vivo. We combined the insights gained from live 85 \nimaging with previously published observations to formulate an in silico model of the 86 \nprocess. This model, formulated in the Cellular Potts Model framework (37–39), 87 \nprovides a novel mechanistic understanding of this transepithelial migration process. 88 \nOur model reveals the existence of optimal heterotypic adhesion between germ cells 89 \nand the epithelial cells, which maximizes the efficiency of midgut exit. In addition, our 90 \nin silico model yields testable, potentially generalizable predictions about the role of E-91 \ncadherin and intercellular adhesion that may be applicable to other systems of 92 \nheterotypic cell adhesion and migration.   93 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 5 of 27 \nResults 94 \nI. Germ cells exit the midgut individually and maintain E -cadherin 95 \nexpression throughout exit 96 \nTo gain insight into the process of germ cell transepithelial migration, we live imaged 97 \nthe process in vivo [Fig. 1b-b”]. Before migration, the germ cells are contained by the 98 \nepithelial barrier of the midgut [Fig. 1b], and located at a depth of more than 25𝜇𝑚 from 99 \nthe cortex within the embryo (40). To visualize the germ cells, we used the previously 100 \ngenerated fly line nos-LifeAct-tdTomato (41) which specifically labels germ cell 101 \nmembrane and cortex through posteriorly localized maternally deposited protein. The 102 \ncells in the gut epithelium also show some residual labelling since the gut arises from 103 \nthe invaginating posterior blastoderm. The residual labelling generates contrast and 104 \nhelps us estimate the position of the gut during transepithelial migration.  105 \nWe tracked germ cells throughout transepithelial migration, characterizing their 106 \nprogress at a five minute time resolution [SI Videos 1 - 3], and quantified the distances 107 \nof germ cells from the center to the edge of the midgut lumen as a function of time [Fig. 108 \n1 c]. Figure 1d-d’ shows the distance covered relative to their initial position (i.e. t = 0) 109 \nas a function of time for two different embryos. We found that individual germ cells 110 \ntook 30 minutes on average to go through the midgut epithelial barrier [Fig. 1d-d’; SI 111 \nVideos 1 - 3]. Our live imaging of the transepithelial migration process did not reveal 112 \nany cell divisions in the midgut epithelial cells that could have generated spaces in the 113 \nepithelial barrier that would allow for easier exit of germ cells, in contrast to the 114 \nmechanism that regulates macrophage infiltration in D. melanogaster (42).  115 \nWe also observed E-cadherin localization on the surface of germ cells during the 116 \ntransepithelial migration by immunostaining for GFP in endogenously GFP-tagged E-117 \ncadherin embryos [Fig. 1e-e’] , a result consistent with previously published 118 \nexperiments (30) that detected E-cadherin by immunostaining. We quantified the 119 \nintensity of E-cadherin signal at apical and lateral junctions between midgut epithelial 120 \ncells, as well as at the epithelial-germ cell junctions [Fig 1e’’]. Our quantification 121 \nrevealed that the E-cadherin concentration at the germ cell-epithelial cell interface is not 122 \nsignificantly different from that observed between epithelial cells on the lateral side 123 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 6 of 27 \n[Fig. 1e’, p = 0.31]. The presence of E-cadherin in germ cells during transepithelial 124 \nmigration, where germ cells physically contact epithelial cells (20), along with the 125 \ndelayed midgut exit previously observed in shg loss of function mutants (30), suggests 126 \nthat the expression of E-cadherin in germ cells is functional rather than incidental. 127 \n 128 \nII. Time to exit the midgut has a non-monotonic dependence on germ cell 129 \nE-cadherin level  130 \nAlthough inspired by germ cell transepithelial migration, our central question is 131 \nbroader: what role does heterotypic E-cadherin adhesion play in cell migration? To 132 \ngeneralize our question beyond germ cell transepithelial migration, we turned to in 133 \nsilico modeling, retaining only essential features of the phenomenon. We developed a 134 \ntwo-dimensional model of transepithelial migration, where a single motile cell breaches 135 \na ring of epithelial cells under the influence of an external, radial chemoattractant 136 \ngradient [Fig. 2a]. Our two-dimensional model, formulated as a Cellular Potts Model 137 \n(37, 38), enabled us to consider the cells as finite sized objects capable of deformations in 138 \nshape. We primarily explored the potential role of chemoattractant strength (λ%) and 139 \nthe concentration of E-cadherin in the germ cells (𝐶&) in the time taken for the germ cells 140 \nto exit the epithelial barrier (τ'). We ran our simulations for a maximum of 10,000 141 \nMonte Carlo steps, and saved the position of the germ cell as a function of time for each 142 \nindependent iteration for a given choice of system parameters. Figure 2a shows a 143 \ntypical snapshot from our CompuCell3D simulation, and Figure 2b-b’’ shows the 144 \nmodeled germ cell at three different stages of the transepithelial migration. In the CPM 145 \nframework, the temperature parameter (𝑇) controls boundary pixel activity, where 146 \nhigher temperatures increase the likelihood of energy-raising pixel flips. To investigate 147 \nhow membrane fluctuations at interepithelial junctions affect migration, we modulated 148 \nthe temperature governing pixel acceptance rates at these boundaries. This allowed us 149 \nto study how epithelial remodeling influences transepithelial migration.  150 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 7 of 27 \nOur simulations [Fig. 2c-c’] revealed that elevated epithelial temperature 151 \nincreased tissue permeability to germ cells, resulting in faster exit times (𝜏') and higher 152 \nescape probabilities (𝑝'). This is consistent with prior experimental findings where 153 \nfailure in epithelial remodeling of the midgut adversely affected germ cell 154 \ntransepithelial migration (20, 34, 43). 155 \nThe strength of the modeled chemoattractant 𝜆% beyond which germ cells 156 \nsuccessfully migrated through the barrier was positively correlated with epithelial cell 157 \nE-cadherin concentration 𝐶( [Fig. 3a–a’’]. We interpret this to mean that higher 𝐶( 158 \nvalues, indicating stronger adhesion between the epithelial cells, offered greater 159 \nresistance to the trans-migrating cell. Regardless of the value of 𝐶(, we observed that 160 \nthe time taken to exit the barrier decreased with increased germ cell E-cadherin 161 \nconcentration only up to a point, beyond which there was a sharp increase in the time 162 \ntaken to breach the epithelial barrier [Fig. 3a–a’’]. This non-monotonic dependence of τ' 163 \non 𝐶& suggested that there is an optimal range of heterotypic adhesion between germ 164 \ncell and the epithelial cell. Within this range, adhesion aids the transepithelial 165 \nmigration, but beyond the range, it hinders germ cell movement. The minima of the 166 \ncurves for different values of λ% in Figure 3a–a’’ coincide, indicating that the optimal 167 \nadhesion value for transepithelial migration is independent of the chemoattractant 168 \nstrength. Interestingly, the coefficient of variation in the time to exit τ' as a function of 169 \n𝐶& has a trend that is inverse of the one exhibited by τ'\t[Fig. 3b]. Specifically, the 170 \ncoefficient of variation is highest when the mean is the lowest, which is not due to 171 \nstandard deviation being independent of 𝐶&. Instead, the standard deviation increases 172 \nwith 𝐶& while the mean decreases with 𝐶& [SI Fig. 1]. 173 \nTo better understand the potential role of heterotypic adhesion in determining 174 \nthe speed of exit, we focused on the trajectories of the migrating cells at different values 175 \nof 𝐶&. We ran our simulations for a maximum duration of 10,000 Monte Carlo steps (44), 176 \nand the cells which had not exited by then were considered to have failed to exit. In 177 \nFigure 3c, the trajectories from ~1000 independent iterations are shown for 𝐶& \t = \t4.89 , 178 \n𝐶( \t = \t10.0 and λ% = 7000. While there are numerous instances of the migrating cell 179 \nfailing at early stages of the transepithelial migration, we see many instances of germ 180 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 8 of 27 \ncells stuck to the outer edge of the epithelial barrier and failing to complete midgut exit. 181 \nWe considered all the successful trajectories for 𝐶( = 10 and λ% = 7000 and normalized 182 \nthe time for each trajectory, such that\tτ = \t0 and τ = \t1\tmark the start and end of the 183 \ntransepithelial migration respectively. The time-normalized mean trajectories are shown 184 \nin Figure 3d, colored according to their values of 𝐶& . We observed that germ cells with 185 \nlower E-cadherin concentrations spent much of their time getting to the middle of the 186 \nepithelial barrier, while the second half of the journey, culminating in complete exit 187 \nfrom the midgut, was quite rapid. The fraction of total time spent in getting to the 188 \nmiddle dropped with increasing germ cell–epithelial cell adhesion, with speed through 189 \nthe barrier becoming constant for high values of 𝐶& (~7.0). To highlight the difference in 190 \nthe fraction of total travel time spent at different halves of the epithelial barrier, we 191 \nshow the fraction of time spent to cover the second half of the epithelial barrier (referred 192 \nto as τ)) in Figure 3d’. τ) showed a nonlinear increase with 𝐶& approaching 0.5 at lager 193 \nvalues of 𝐶&. The probability of success in exiting the epithelial barrier had an inverse 194 \nnon-linear relation to time to exit τ' regardless of the 𝐶( , 𝐶& and λ%, as evident from the 195 \nscatter plot in Figure 3e.  196 \n 197 \nIII. Overexpression of E-cadherin in germ cells leads to faster exit through 198 \nthe midgut 199 \nThe results of our model suggested that increasing E-cadherin levels in germ cells 200 \nwould enable them to complete the most time-consuming part of their journey, namely 201 \ntraversing from the middle of the midgut lumen through the luminal epithelial surface, 202 \nmore rapidly, thus resulting in a faster net transepithelial migration time. To test this 203 \nprediction in vivo, we employed the UAS-GAL4 system (45). Maternally deposited E-204 \ncadherin serves as the primary source of E-cadherin for all early embryonic cells, 205 \nincluding both germ cells and somatic cells (28, 30, 46). Notably, even during 206 \ntransepithelial migration (~3 hours after egg laying), germ cell E-cadherin remains 207 \npredominantly of maternal origin, making RNAi-mediated knockdown in germ cells 208 \nimpractical without destroying embryonic integrity (28, 30, 46). 209 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 9 of 27 \nGiven these constraints, to assess the effects of E-cadherin overexpression on 210 \ntransepithelial migration, we analyzed germ cell progression in embryos of four 211 \ndifferent maternal genotypes, collected within 30 minutes from synchronously 212 \novipositing mothers and aged for five hours, as follows: (I) WT: Outcrossed wild-type 213 \ncontrol, expressing endogenous levels of E-cadherin in all cells [nos-Gal4/+ ]; (II) Pan-214 \nEcad-OE-A: with ubiquitous E-cadherin overexpression in all cells [nos>Ecad]; (III) Pan-215 \nEcad-OE-B: with ubiquitous GFP-tagged E-cadherin overexpression in all cells 216 \n[nos>Ecad-GFP]; and (IV) GC-Ecad-OE: with germ cell-targeted overexpression of 217 \nmClover2-tagged E-cadherin [nos> Ecad-mClover2-nosTCE-pgc 3′UTR]. The position of 218 \ngerm cells relative to the midgut lumen boundary in representative embryos from each 219 \ngroup is shown in Figure 4a–a’’’. Quantitative analysis revealed that germ cells in all 220 \noverexpression lines (II–IV) were located farther from the lumen center compared to 221 \ncontrols (I) [Fig. 4b]. Furthermore, both the mean germ cell distance from the lumen 222 \ncenter [Fig. 4c] and the fraction of germ cells that had exited the midgut [𝑓'*+,  in Fig. 4d] 223 \nwere higher in overexpression groups, with the most pronounced effect in the germ 224 \ncell-targeted overexpression line (IV). 225 \nThese results suggest that higher levels of E-cadherin in germ cells accelerates 226 \ntransepithelial migration, consistent with our theoretical model. We favor the 227 \ninterpretation that the germ cell E-cadherin expression corresponds to the gray region 228 \nin Figure 4e. Under this interpretation, loss of function shg mutants like 𝑠ℎ𝑔!\"#$\" (30) 229 \nwould drive the embryo toward the red region shown in Figure 4e, associated with 230 \nlonger τ', while shg overexpression would drive it towards the green region in Figure 231 \n4e associated with faster germ cell exit.  232 \nOur model predicted that elevated epithelial E-cadherin would hinder migration 233 \nby increasing resistance to germ cell exit. Although our experiments did not test this 234 \nprediction directly, germ cells in our experiments overexpressing E-cadherin in both 235 \nsomatic and germ cells showed reduced germ cell exit times. This apparent discrepancy 236 \ncan be explained by the hypothesis that wild-type E-cadherin levels in somatic 237 \nepithelial cells are already near saturation. Under this interpretation, finite adhesion 238 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 10 of 27 \njunction availability and membrane transport limitations would prevent additional E-239 \ncadherin from forming new junctions at high concentrations (47–50). Thus, in embryos 240 \nwith E-cadherin overexpression in both germ cells and somatic cells, like the ones we 241 \nexamined herein (Fig. 4, groups II and III), we hypothesize that E-cadherin levels higher 242 \nthan wild type levels would have a stronger effect on germ cells than on epithelial cells, 243 \neffectively mimicking germ cell-specific overexpression. 244 \n 245 \nDiscussion  246 \nOur work sheds light on the potential role of heterotypic E-cadherin adhesion during 247 \nthe transepithelial migration of germ cells through the midgut. The predictions of our in 248 \nsilico model could in principle apply to any heterotypic adhesive migration process, as 249 \nwe incorporated only a few features specific to germ cell transepithelial migration. Our 250 \nresults suggest a non-monotonic dependence of migration efficiency on the heterotypic 251 \nadhesion between the migrating cell and the substrate cells [Fig. 4d]. We posit that 252 \ntraction is essential for cell migration, and that transmembrane proteins like integrins 253 \nfacilitate traction by anchoring cells to the extracellular matrix, thereby enabling 254 \nforward movement (51). Additionally, cadherin-like molecules mediate homotypic 255 \nadhesion, allowing cells of the same type to form cohesive tissues such as epithelia (46, 256 \n52). These molecules also participate in heterotypic adhesion when migrating cells 257 \ninteract with others expressing cadherins (53–55). A notable example is the 258 \ntransendothelial migration of melanoma cells, where heterotypic adhesion is clearly 259 \nobserved (53). More broadly, diapedesis - the process by which cells breach endothelial 260 \nbarriers - relies on heterotypic interactions between transmembrane adhesion proteins 261 \n(54, 55). The prevalence of heterotypic adhesion during transepithelial migration 262 \nsuggests that our work may have broader implications beyond the specific context of 263 \nDrosophila primordial germ cell migration. 264 \nMany conserved features of cell migration are applicable well beyond a specific 265 \nbiological context. In a migrating cell, actin filaments polymerize at the leading edge 266 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 11 of 27 \nand undergo retrograde flow, functioning much like the rotation of wheels in a car (56–267 \n58). However, the forward motion of a cell depends on its physical coupling to the 268 \nsubstrate, just as the tires of a car grip the road to provide traction (56, 57, 59). This 269 \nmechanism is described by the molecular clutch model of cell motility (60). Adhesion 270 \nmolecules such as integrins and cadherins allow cells to bind to surfaces or to other 271 \ncells, engaging the molecular clutch (60, 61). Integrin-mediated traction is typically used 272 \nwhen cells migrate over an extracellular matrix, whereas cadherins facilitate cell-on-cell 273 \nmigration, as seen in the case of border cell migration (36, 61), and in the transepithelial 274 \nmigration case that we consider here. 275 \nOur in silico model predicts that an intermediate adhesion strength between 276 \nmigrating cells and substrate cells optimizes transmigration. Our finding aligns with 277 \nprevious observations on D. melanogaster border cell migration in the egg chamber (36, 278 \n62). In the case of border cell migration, a cluster of six to eight border cells, enclosing a 279 \npair of non-motile polar cells, migrates through a “substrate” formed by nurse cells (62). 280 \nOf these three cell types, polar cells express the highest levels of E-cadherin, which they 281 \nuse to anchor to motile border cells, which have the second highest E-cadherin levels 282 \n(36). Meanwhile, border cells form transient junctions with nurse cell E-cadherin (which 283 \nis at the lowest levels of the three cell types in the system), pulling on nurse cells as they 284 \nmigrate (36). Notably, E-cadherin knockdown in nurse cells impedes migration more 285 \nseverely than knockdown in border cells, whereas E-cadherin overexpression in nurse 286 \ncells slows migration (36). These observations further support the concept that 287 \nintermediate heterotypic adhesion is optimal for transmigration, specifically E-cadherin 288 \nenabling the engagement of a molecular clutch (61). Migration in the absence of 289 \nsufficient intercellular adhesion resembles climbing a slippery ladder—lacking the 290 \ntraction needed for upward movement. Conversely, excessive adhesion is akin to 291 \nascending a ladder coated in glue, where detachment becomes the limiting factor. Thus, 292 \nan optimal level of \"stickiness\" is crucial for efficient migration.  293 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 12 of 27 \nMethods 294 \nFly stocks and maintenance 295 \nFly lines w[1118]; P{w[+mC]=GAL4::VP16-nanos.UTR}CG6325[MVD1] (abbreviated 296 \nherein as nanos-gal4-VP16; stock #4937), w[1118]; P{w[+mC]=UASp-shg.GFP}5B (#58445), 297 \ny[1] w[*]; TI{TI}shg[GFP] (#60584), and w[*]; P{w[+mC]=UASp-shg.R}5 (#58494) were 298 \nobtained from the Bloomington Drosophila Stock Center (Indiana, U.S.A.). Fly lines w; 299 \nnos-Lifeact-tdTomato-P2A-tdKatushka2-CAAX (Lifeact-tdTomato landing site VK00027; 300 \nabbreviated herein as nos-LifeAct-tdTomato) and w; UASp-DE-cadherin-mClover2-nosTCE-301 \npgc 3′UTR (landing site attP2) (Lin et al., 2022) were a gift from Ruth Lehmann 302 \n(Whitehead Institute, USA). All flies and crosses were maintained on standard fly 303 \nmedium (0.8% agar, 2.75% yeast, 5.2% cornmeal, 11% dextrose) in an incubator at 25˚C, 304 \n65% RH and 12H:12H light-dark cycle. 305 \n 306 \nFly crosses and embryo staging  307 \nTo generate embryonic over-expression, UASp (over-expression) or Oregon R (wild 308 \ntype control) lines were crossed to nanos-gal4-VP16 and heterozygous F1 virgin females 309 \nwere collected. 40-50 F1 virgin females were then crossed with 8-10 Oregon R males and 310 \ntransferred into an egg collection cage with a 5mm apple juice agar plate as base (63). In 311 \nthe case of nos-LifeAct-tdTomato flies, an approximate 1:1 ratio of homozygous males and 312 \nfemales were transferred into an identical cage setup. Egg laying was allowed to 313 \nproceed in 30-minute windows and embryos were aged for five hours after the 314 \nmidpoint of the egg laying window. After collection, embryos were either fixed 315 \n(embryonic over-expression) or used for live imaging (nos-LifeAct-tdTomato). 316 \n 317 \nLive imaging 318 \nAppropriately staged nos-LifeAct-tdTomato embryos were manually dechorionated with 319 \nforceps on double-sided scotch tape and attached to a standard 90 mm polystyrene Petri 320 \ndish using heptane glue. The dish was then filled with sterile distilled water and 321 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 13 of 27 \nmounted on a Zeiss LSM 980 multi-photon microscope. Using a 20x water immersion 322 \nobjective, the total volume of the embryonic gut was imaged at a two-photon excitation 323 \nwavelength of 1000nm, every 5 or 10 minutes for the indicated period. 324 \n 325 \nFixation and immunostaining 326 \nAppropriately staged embryos were collected on a mesh, rinsed with Milli-Q water, and 327 \ndechorionated using 70%(v/v) commercial bleach until most of the embryos had lost 328 \nthe chorion (confirmed visually). Embryos were fixed in a 1:1 mix of heptane and 4% 329 \nparaformaldehyde in 1X phosphate buffered saline (1X PBS) at room temperature with 330 \nnutation for 20 minutes. After removing the aqueous layer, an equal volume of 100% 331 \nmethanol was added, and embryos were devitellinized by vigorous manual shaking for 332 \n2–3 minutes. Fixed embryos were stored in 100% methanol at –20 °C. 333 \nFor staining, embryos were rehydrated in 1X PBS, then washed, permeabilized 334 \nand blocked twice for 15 minutes in PBTB (1X PBS, 0.2% Triton X-100, 1 mg/mL bovine 335 \nserum albumin (Sigma-Aldrich, A9418). Embryos were then incubated with primary 336 \nantibodies diluted in PBTB overnight at 4 °C. The next day, embryos were washed (4 × 337 \n15 min, PBTB) and incubated overnight at 4 °C with fluorophore-conjugated secondary 338 \nantibodies and DAPI diluted in PBTB containing 4% normal goat serum (Jackson 339 \nImmunoresearch, 005-000-121). On the third day, embryos were washed (4 × 15 min in 340 \n1X PBS) and mounted in Vectashield (Vector laboratories, H1000) for imaging. 341 \nPrimary antibodies used were chicken anti-Vasa (1:800) (Repouliou et al., 2025) 342 \nand mouse anti-GFP conjugated to AlexaFluor 488 (1:1000; (Invitrogen, A-21311). 343 \nSecondary antibody used was goat anti-chicken (1:200) conjugated to AlexaFluor 568 344 \n(Invitrogen, A-11041). DNA was stained with DAPI (Sigma-Aldrich, D9542) at 1:2000 345 \ndilution of a 10 mg/mL stock. 346 \n 347 \nIn silico model of transepithelial migration 348 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 14 of 27 \nA cross-sectional slice of the epithelial midgut was modelled as a two-dimensional ring 349 \nformed by 20 epithelial cells. The epithelial ring is embedded in a radial chemical field 350 \nrepresenting the spatial concentration of the chemoattractant. The magnitude of the 351 \nchemical concentration increases linearly with the radius, and the minimum coincides 352 \nwith the center of the epithelial ring. At t=0, the germ cell begins slightly off-center, 353 \nwith its initial angle randomly chosen between 0 and 360 degrees, and then migrates 354 \noutward toward regions of higher chemoattractant concentration.  355 \nOur two-dimensional model of the epithelial ring and the germ cells was 356 \nformulated in the Cellular Potts Model (CPM) framework (37–39). The cells in a CPM 357 \nare represented as a contiguous collection of pixels. Pairwise interactions between 358 \npixels at the cell boundaries have an associated energy cost, 𝐽. Given two pixels 𝑎 and 𝑏, 359 \nwe use the value of 𝐽-,/ to reflect the adhesion energy of the cells to which the two 360 \ninteracting boundary pixels belong. Our model assumes that adhesion energy increases 361 \nwith the number of adherens junctions formed, and that the number of adherens 362 \njunctions is in turn a function of the E-cadherin concentration. The maximum possible 363 \nnumber of junctions between two cells is thus determined by the surface E-cadherin 364 \nconcentration of the cell that has fewer such molecules. As a result of our assumptions, 365 \nif σ(𝑎), σ(𝑏) are the indices of the cells to which pixels 𝑎 and 𝑏 belong, and 𝐶?σ(𝑎)@\tand 366 \n𝐶?σ(\t𝑏)@ are the E-cadherin concentrations of the two cells, then the interaction energy 367 \nterm is 𝐽-,/ = 𝑚𝑖𝑛?𝐶?σ(𝑎)@, 𝐶?σ(𝑏)@ @. In our work, we have assumed that all epithelial 368 \ncells have E-cadherin concentration equal to 𝐶(, and that all germ cells have E-cadherin 369 \nconcentration equal to 𝐶&. The energy function of the system of pixels is given by the 370 \nfollowing equation: 371 \nE  =   − F 𝑚𝑖𝑛?𝐶?σ(𝑎)@, 𝐶?σ(𝑏)@ @ \n-,/\n−   F λ!\n0\n (𝐴0  −  𝐴1)) −   F λ2\n0\n (𝐿0  −  𝐿1)) 372 \nThe λ! and λ2 terms determine the magnitude of the energy cost required for a cell area 373 \nand perimeter to deviate from the steady state values 𝐴1 and 𝐿1 respectively. The 374 \nsystem is simulated using the metropolis algorithm (44), where the probability of a pixel 375 \nflip is related to the change in the magnitude of the energy of the system as a result of 376 \nthat specific pixel flip. Under appropriate energy conditions, pixels at the boundary 377 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 15 of 27 \nmay flip to the cell state of one of their neighbors that has a different cell state. At each 378 \niteration of the simulation algorithm, one such potential pixel flip is considered, and the 379 \npixel is flipped if the resulting system has lower energy than the current state. If the 380 \npixel flip would not reduce the energy of the system, the pixel flips with probability 381 \n𝑝34+5 = 𝑒𝑥𝑝(−Δ𝐸/𝑇), where Δ𝐸 is the change in energy due to the pixel flip and 𝑇 is the 382 \ntemperature. The temperature term 𝑇 represents the extent of stochasticity (noise) in the 383 \npixel dynamics, with higher 𝑇 increasing the probability of pixel flips that increase the 384 \nenergy of the system. Chemotaxis is implemented within the CPM framework by 385 \nbiasing pixel flips that aid cell movement in the direction of the gradient of 386 \nchemoattractant. In the presence of the attractant whose value at any pixel in 2D space 387 \nis 𝑀-,/, the change in energy due to a pixel flip, where a pixel at (𝑎, 𝑏) is copying the 388 \nstate of a pixel at (𝑎’, 𝑏’) becomes Δ𝐸6 = Δ𝐸 − λ7 ?𝑀-!,/! − 𝑀-,/ @ (39). This additional term 389 \nfavors the pixel flip when the chemical field value at (𝑎’, 𝑏’) is higher than (𝑎, 𝑏). To 390 \nprevent the epithelial ring from collapsing and forming a cell aggregate without a 391 \nlumen, we model the lumen as one large cell with a volume constraint. In addition, we 392 \nmechanically couple the epithelial cells by connecting the centroids of the adjacent cells 393 \nwith springs using the FocalPointPlasticity plugin of CompuCell3D (64). The key 394 \nparameters that we explored in our work are 𝐶&, 𝐶( and λ%. The CompuCell3D 395 \nimplementation of our model can be found at 396 \nhttps://github.com/boyonpointe/GermCellTransepithelialMigration with commit ID 397 \n7ce2534. 398 \n 399 \n  400 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 16 of 27 \nAcknowledgements 401 \nWe thank members of the Extavour Lab for helpful discussions, and Ruth Lehmann for 402 \nsharing fly lines. 403 \n 404 \nData Availability 405 \nScripts for Compucell3D simulations and image analysis are available at 406 \nhttps://github.com/boyonpointe/GermCellTransepithelialMigration with commit ID 407 \n7ce2534. 408 \n 409 \nAuthor Contributions 410 \nCK conceived of the study, designed and performed all computational experiments, 411 \nperformed data analysis and interpretation, and wrote the first draft of the manuscript. 412 \nSG conceived of the study, designed and performed wet lab experiments, and reviewed 413 \nand edited the manuscript. CGE obtained funding for the study, supervised its execution, 414 \nand reviewed and edited the manuscript. 415 \n 416 \nFunding 417 \nThis study was supported by a postdoctoral fellowship to CK through the NSF-Simons 418 \nCenter for Mathematical and Statistical Analysis of Biology at Harvard (award number 419 \nDMS-1764269), the Harvard Quantitative Biology Initiative, and by funds from Harvard 420 \nUniversity and the Howard Hughes Medical Institute (HHMI). CGE is an HHMI 421 \nInvestigator.  422 \n 423 \nConflicts of Interest  424 \nThe authors declare no conflicts of interest.425 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 17 of 27 \n \nFigure 1: Germ cells exit the midgut individually at comparable speeds. (a-a’’) Schematic of D. \nmelanogaster embryonic germ line development. The primordial germ cells are formed at the posterior tip \nat stage 5. Following gastrulation, they are contained in the midgut at stage 7, and eventually exit the \nmidgut through transepithelial migration at stage 10. (b-b’’) Three snapshots from live imaging of the \nTEM process as the germ cells make their way through the midgut. (c) The distribution of the distances of \nthe germ cells from center of the midgut lumen at five different time points are shown. Small numbers \nunder plots indicate sample sizes (number of cells). (d- d’’) Movement of germ cells in time measured \nwith respect to their initial (t = 0) position for two different live imaged embryos. Black circles: positions \nwithin lumen; magenta rhomboids: positions within the midgut epithelium; white squares: positions \noutside the midgut. (e) Higher magnification of the midgut during germ cell transepithelial migration. \nMagenta: E-cadherin; green: germ cell-specific protein Vasa. (e’) Germ cells express E-cadherin during the \ntransepithelial migration (boxes). (e’’) The relative intensity (au: arbitrary units) of the E-cadherin \nfluorescent signal at the epithelial cell – germ cell junction (𝐸𝐺), lateral epithelial – epithelial junction \n(𝐸\"#$) and apical epithelial – epithelial junctions (𝐸#%& ). n = 24 junctions for all categories. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 18 of 27 \n \nFigure 2: Greater epithelial junctional remodeling aids germ cell transmigration. (a) Snapshot of Cellular Potts Model simulation of a germ cell \n(yellow) exiting the two-dimensional epithelial ring (green). Pixels at the boundary of the cells are shown in black. Colored arrows point in the \ndirection of chemoattractant gradient; colors represent the concentration of the chemoattractant. (b-b’’) Snapshots of the germ cell (b) before \nentering the epithelial barrier, (b’) while inside the barrier and (b’’) just before exiting the barrier. Numbers at top right indicate the time in units of \nMonte Carlo steps (mcs). (c) Average time taken (τ') for the germ cell to exit the midgut decreases with increase in interepithelial dynamics (T(). \n(c’) Probability of successfully exiting (𝑝') increases with T(. Trends in c and c’ are independent of the strength of the chemoattractant (λ)). The \nshaded silver line corresponds to 𝑇( = 10, the value at which al simulations described herein are carried out, unless specified otherwise. Simulation \nparameters: λ) = 7000, 𝐶( = 10, 𝐶* = 5, 𝑇 = 10.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 19 of 27 \n \nFigure 3: Time to exit the epithelial barrier has a non-monotonic dependence on germ cell E-cadherin \nconcentration. (a-a’’) The time a simulated germ cell takes to exit the 2D midgut epithelial barrier as a \nfunction of germ cell E-cadherin concentration (𝐶*). (a), (a’) and (a’’) represent increasing epithelial cell E-\ncadherin concentrations (𝐶( = 8, 𝐶( = 10, 𝐶( = 12 respectively), offering increasing resistance to the germ \ncell transepithelial migration. The curves in each of the three panels are colored according to the strength \nof the chemical field (λ)). Regardless of the value of 𝐶(, for strong enough values of\tλ) the time to exit \nshows a minimum that is independent of the λ) value. (b) The coefficient of variation in time to exit as a \nfunction of 𝐶* for the case of 𝐶( = 10. (c) Trajectories of germ cells for a typical parameter set obtained \nfrom 1000 independent stochastic simulations. Silver: successful TEM; brown: germ cell exit failure; black \nlines: mean position of the epithelial cells forming the 2D barrier. (d) Mean distance covered by the germ \ncell within the barrier (𝑙') as a function of time . The time 𝜏 is normalized by the total time of travel, \nwhere 0 represents time of entry into the barrier and 1 represents time of exit. When the germ cell E-\ncadherin concentration is low, the germ cell covers much of the distance in a very short interval of time at \nthe end of the journey. Higher germ cell E-cadherin levels lead to the germ cell migrating at roughly \nuniform speed throughout the journey. (d’) The time taken to cover the second half of the journey 𝜏+ as a \nfunction of germ cell E-cadherin concentration. (e) The relationship between 𝜏',&$ and 𝑝',&$ is shown using \ndata aggregated by simulating the model over various values of λ), 𝐶* and 𝐶(. 𝜏',&$ decreases \nmonotonically with 𝑝',&$, implying that the shorter the time to exit, the higher the chances of exiting.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 20 of 27 \n \nFigure 4: Higher germ cell E-cadherin promotes quicker midgut exit in vivo. (a) (L-R) Representative \nmicrographs showing the extent of transepithelial migration in the four experimental groups. I corresponds to the \ncontrol, II and III represent lines where E-cadherin is overexpressed in all cells and IV corresponds to embryos \nwhere E-cadherin overexpression is targeted to the germ cells. (b) The distribution of distances of germ cells from \nthe center of the lumen for the four experimental groups. Grey and black symbols represent cells inside and \noutside the gut respectively. The distances of the germ cells from the lumen are largest in the condition with \nincreased germ cell-targeted E-cadherin overexpression (IV). Small numbers under plots indicate sample sizes \n(number of germ cells). (c) Mean distance of the germ cells from the center of the lumen in each embryo for the \nfour experimental groups. Small numbers under plots indicate sample sizes (number of embryos). (d) Fractions of \ngerm cells per embryo that have exited the midgut by 5.25 hours after egg laying in the four experimental groups. \nSmall numbers under plots indicate sample sizes (number of embryos). (e) We hypothesize that the WT embryos \n(group I) have 𝐶* in the pink shaded region, and that germ cell-targeted E-cadherin expression pushes the system \ntowards the green shaded region, leading to shortening of 𝜏'.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 21 of 27 \n \n \nSI Figure 1: The mean and the variance of 𝛕𝒆 exhibit converse trends. The figure shows the mean τ' (top) and \nstandard deviation µ.! (bottom) as a function of germ cell E-cadherin concentration 𝐶* for the case when 𝐶( = 10. \nThe peak in the standard deviation as a function of 𝐶* coincides with the minima in the case of time to exit. The \ndifferently colored curves correspond to different strengths of the chemoattractant pull λ) . \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 22 of 27 \nSI Video Legends \n \nSI Video 1: Transepithelial migration of germ cells. Dorsal cross-sectional view of the gut \nduring TEM in a nos-LifeAct-tdTomato embryo. Maximum intensity projection of 15 planes \nspaced at 2 μm. Scale bar is 20 μm. \n \nSI Video 2: Transepithelial migration of germ cells: higher magnification 1. Dorsal cross-\nsectional view of the gut during TEM in a nos-LifeAct-tdTomato embryo. Maximum intensity \nprojection of 20 planes spaced at 2 μm. Scale bar is 20 μm. \n \nSI Video 3: Transepithelial migration of germ cells: higher magnification 2. Lateral cross-\nsectional view of the gut during TEM in a nos-LifeAct-tdTomato embryo. Maximum intensity \nprojection of 20 planes spaced at 2 μm. Scale bar is 20 μm. \n \nSI Video 4: CompuCell3D simulation of a germ cell successfully exiting the midgut. The \nvideo shows an instance of a germ cell successfully exiting the epithelial ring in our in silico \nmodel simulated in CompuCell3D. The parameter values used here are λ% = 4000, 𝐶& =\n5.0, 𝐶( = 10.0. \n \nSI Video 5: CompuCell3D simulation of a germ cell failing to exit the midgut. The video \nshows an instance of a germ cell failing to exit the epithelial ring in our in silico model \nsimulated in CompuCell3D. The parameter values used here are λ% = 4000, 𝐶& = 1.0, 𝐶( =\n10.0. \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 31, 2025. ; https://doi.org/10.1101/2025.08.29.673192doi: bioRxiv preprint \n\nPage 23 of 27 \nReferences \n \n1. A. Aman, T. Piotrowski, Cell migration during morphogenesis. Dev. Biol. 341, 20–33 (2010). \n2. E. Scarpa, R. 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