Publications

Cell and tissue movement in development, cancer invasion, and immune response relies on chemical or mechanical guidance cues. In many systems, this behavior is locally guided by self-generated signaling gradients rather than long-range, pre-patterned cues. However, how heterogeneous mixtures of cells interact non-reciprocally and navigate through self-generated gradients remains largely unexplored. Here we introduce a theoretical framework for the self-organized chemotaxis of heterogeneous cell populations. We find that relative chemotactic sensitivity of cell populations controls their long-time coupling and co-migration dynamics, with boundary conditions such as external cell- and attractant reservoirs substantially influencing the migration patterns. We further predict an optimal parameter regime that leads to robust and colocalized migration. We test our theoretical predictions with in vitro experiments demonstrating the co-migration of different immune cell populations, and quantitatively reproduce observed migration patterns under wild-type and perturbed conditions. Interestingly, immune cell co-migration appears close to the predicted optimal regime. Finally, we incorporate mechanical interactions into our framework, revealing a phase diagram that illustrates non-trivial interplays between chemotactic and mechanical non-reciprocity in driving collective migration. Together, our findings suggest that self-generated chemotaxis is a robust strategy for emergent multicellular navigation.

Recent advances in the field of bottom-up synthetic biology have led to the development of synthetic cells that mimic some features of real cells, such as division, protein synthesis, or DNA replication. Larger assemblies of synthetic cells may be used to form prototissues. However, existing prototissues are limited by their relatively small lateral dimensions or their lack of remodeling ability. In this study, we introduce a lipid-based tissue mimetic that can be easily prepared and functionalized, consisting of a millimeter-sized “lipid-foam” with individual micrometer-sized compartments bound by lipid bilayers. We characterize the structural and mechanical properties of the lipid-foam tissue mimetic. We demonstrate self-healing capabilities enabled by the fluidity of the lipid bilayers. Upon inclusion of bacteria in the tissue compartments, we observe that the tissue mimetic exhibits network-wide tension fluctuations driven by membrane tension generation by the swimming bacteria. Active tension fluctuations facilitate the fluidization and reorganization of the lipid-foam tissue mimetic, providing a versatile platform for understanding and mimicking biological tissues.

Branching morphogenesis is a ubiquitous process that gives rise to high exchange surfaces in the vasculature and epithelial organs. Lymphatic capillaries form branched networks, which play a key role in the circulation of tissue fluid and immune cells. Although mouse models and correlative patient data indicate that the lymphatic capillary density directly correlates with functional output, i.e., tissue fluid drainage and trafficking efficiency of dendritic cells, the mechanisms ensuring efficient tissue coverage remain poorly understood. Here, we use the mouse ear pinna lymphatic vessel network as a model system and combine lineage-tracing, genetic perturbations, whole-organ reconstructions and theoretical modeling to show that the dermal lymphatic capillaries tile space in an optimal, space-filling manner. This coverage is achieved by two complementary mechanisms: initial tissue invasion provides a non-optimal global scaffold via self-organized branching morphogenesis, while VEGF-C dependent side-branching from existing capillaries rapidly optimizes local coverage by directionally targeting low-density regions. With these two ingredients, we show that a minimal biophysical model can reproduce quantitatively whole-network reconstructions, across development and perturbations. Our results show that lymphatic capillary networks can exploit local self-organizing mechanisms to achieve tissue-scale optimization.

Immune responses rely on the rapid and coordinated migration of leukocytes. Whereas it is well established that single-cell migration is often guided by gradients of chemokines and other chemoattractants, it remains poorly understood how these gradients are generated, maintained, and modulated. By combining experimental data with theory on leukocyte chemotaxis guided by the G protein–coupled receptor (GPCR) CCR7, we demonstrate that in addition to its role as the sensory receptor that steers migration, CCR7 also acts as a generator and a modulator of chemotactic gradients. Upon exposure to the CCR7 ligand CCL19, dendritic cells (DCs) effectively internalize the receptor and ligand as part of the canonical GPCR desensitization response. We show that CCR7 internalization also acts as an effective sink for the chemoattractant, dynamically shaping the spatiotemporal distribution of the chemokine. This mechanism drives complex collective migration patterns, enabling DCs to create or sharpen chemotactic gradients. We further show that these self-generated gradients can sustain the long-range guidance of DCs, adapt collective migration patterns to the size and geometry of the environment, and provide a guidance cue for other comigrating cells. Such a dual role of CCR7 as a GPCR that both senses and consumes its ligand can thus provide a novel mode of cellular self-organization.

When in equilibrium, thermal forces agitate molecules, which then diffuse, collide and bind to form materials. However, the space of accessible structures in which micron-scale particles can be organized by thermal forces is limited, owing to the slow dynamics and metastable states. Active agents in a passive fluid generate forces and flows, forming a bath with active fluctuations. Two unanswered questions are whether those active agents can drive the assembly of passive components into unconventional states and which material properties they will exhibit. Here we show that passive, sticky beads immersed in a bath of swimming Escherichia coli bacteria aggregate into unconventional clusters and gels that are controlled by the activity of the bath. We observe a slow but persistent rotation of the aggregates that originates in the chirality of the E. coli flagella and directs aggregation into structures that are not accessible thermally. We elucidate the aggregation mechanism with a numerical model of spinning, sticky beads and reproduce quantitatively the experimental results. We show that internal activity controls the phase diagram and the structure of the aggregates. Overall, our results highlight the promising role of active baths in designing the structural and mechanical properties of materials with unconventional phases.

Branching morphogenesis governs the formation of many organs such as lung, kidney, and the neurovascular system. Many studies have explored system-specific molecular and cellular regulatory mechanisms, as well as self-organizing rules underlying branching morphogenesis. However, in addition to local cues, branched tissue growth can also be influenced by global guidance. Here, we develop a theoretical framework for a stochastic self-organized branching process in the presence of external cues. Combining analytical theory with numerical simulations, we predict differential signatures of global vs. local regulatory mechanisms on the branching pattern, such as angle distributions, domain size, and space-filling efficiency. We find that branch alignment follows a generic scaling law determined by the strength of global guidance, while local interactions influence the tissue density but not its overall territory. Finally, using zebrafish innervation as a model system, we test these key features of the model experimentally. Our work thus provides quantitative predictions to disentangle the role of different types of cues in shaping branched structures across scales.

In the living cell, we encounter a large variety of motile processes such as organelle transport and cytoskeleton remodeling. These processes are driven by motor proteins that generate force by transducing chemical free energy into mechanical work. In many cases, the molecular motors work in teams to collectively generate larger forces. Recent optical trapping experiments on small teams of cytoskeletal motors indicated that the collectively generated force increases with the size of the motor team but that this increase depends on the motor type and on whether the motors are studied in vitro or in vivo. Here, we use the theory of stochastic processes to describe the motion of N motors in a stationary optical trap and to compute the N-dependence of the collectively generated forces. We consider six distinct motor types, two kinesins, two dyneins, and two myosins. We show that the force increases always linearly with N but with a prefactor that depends on the performance of the single motor. Surprisingly, this prefactor increases for weaker motors with a lower stall force. This counter-intuitive behavior reflects the increased probability with which stronger motors detach from the filament during strain generation. Our theoretical results are in quantitative agreement with experimental data on small teams of kinesin-1 motors.

Many active cellular processes such as long-distance cargo transport, spindle organization, as well as flagellar and ciliary beating are driven by molecular motors. These motor proteins act collectively and typically work in small teams. One particularly interesting example is two teams of antagonistic motors that pull a common cargo into opposite directions, thereby generating mutual interaction forces. Important issues regarding such multiple motor systems are whether or not motors from the same team share their load equally, and how the collectively generated forces depend on the single motor properties. Here we address these questions by introducing a stochastic model for cargo transport by an arbitrary number of elastically coupled molecular motors. We determine the state space of this motor system and show that this space has a rather complex and nested structure, consisting of multiple activity states and a large number of elastic substates, even for the relatively small system of two identical motors working against one antagonistic motor. We focus on this latter case because it represents the simplest tug-of-war that involves force sharing between motors from the same team. We show that the most likely motor configuration is characterized by equal force sharing between identical motors and that the most likely separation of these motors corresponds to a single motor step. These likelihoods apply to different types of motors and to different elastic force potentials acting between the motors. Furthermore, these features are observed both in the steady state and during the initial build-up of elastic strains. The latter build-up is non-monotonic and exhibits a maximum at intermediate times, a striking consequence of mutual unbinding of the elastically coupled motors. Mutual strain-induced unbinding also reduces the magnitude of the collectively generated forces. Our computational approach is quite general and can be extended to other motor systems such as motor teams working against an optical trap or mixed teams of motors with different single motor properties.

Intracellular transport is performed by molecular motors that pull cargos along cytoskeletal filaments. Many cellular cargos are observed to move bidirectionally, with fast transport in both directions. This behaviour can be understood as a stochastic tug-of-war between two teams of antagonistic motors. The first theoretical model for such a tug-of-war, the Müller–Klumpp–Lipowsky (MKL) model, was based on two simplifying assumptions: (i) both motor teams move with the same velocity in the direction of the stronger team, and (ii) this velocity matching and the associated force balance arise immediately after the rebinding of an unbound motor to the filament. In this study, we extend the MKL model by including an elastic coupling between the antagonistic motors, and by allowing the motors to perform discrete motor steps. Each motor step changes the elastic interaction forces experienced by the motors. In order to elucidate the basic concepts of force balance and force fluctuations, we focus on the simplest case of two antagonistic motors, one kinesin against one dynein. We calculate the probability distribution for the spatial separation of the motors and the dependence of this distribution on the motors' unbinding rate. We also compute the probability distribution for the elastic interaction forces experienced by the motors, which determines the average elastic force〈F〉and the standard deviation of the force fluctuations around this average value. The average force〈F〉is found to decrease monotonically with increasing unbinding rate ε0. The behaviour of the MKL model is recovered in the limit of small ε0. In the opposite limit of large ε0,〈F〉is found to decay to zero as 1/ε0. Finally, we study the limiting case with ε0 = 0 for which we determine both the force statistics and the time needed to attain the steady state. Our theoretical predictions are accessible to experimental studies of in vitro systems consisting of two antagonistic motors attached to a synthetic scaffold or crosslinked via DNA hybridization.