Biology subjects

Weinberg, S. H.

Publications and source records attributed to Weinberg, S. H..

3 recordsLinked to original sources

bioRxiv2019

A Predictive Model of Intercellular Tension and Cell-Matrix Mechanical Interactions in a Multicellular Geometry

Epithelial cells form continuous sheets of cells that exist in tensional homeostasis. Homeostasis is maintained through cell-to-cell adhesions that distribute tension and balance forces between cells and their underlying matrix. Disruption of tensional homeostasis can lead to Epithelial-Mesenchymal Transition (EMT), which is a transdifferentiation process in which epithelial cells adopt a mesenchymal phenotype, where cell-cell adhesion is lost and individual cell migration is acquired. This process is critical during embryogenesis and wound healing, but is also dysregulated in many disease states. To further understand the role of intercellular tension in spatial patterning of epithelial cell monolayers, we developed a multicellular computational model of cell-cell and cell-substrate forces. This work builds on a hybrid Cellular Potts-finite element model to evaluate cell-matrix mechanical feedback of an adherent multicellular cluster. Thermodynamically-constrained cells migrate by generating traction forces on a finite element substrate to minimize the total energy of the system. Junctional forces at cell-cell contacts balance these traction forces, thereby producing a mechanically stable epithelial monolayer. Simulations were compared to in vitro experiments using fluorescence-based junction force sensors in clusters of cells undergoing EMT. Results indicate that the multicellular CPM model can reproduce many aspects of EMT, including epithelial monolayer formation dynamics, changes in cell geometry, and spatial patterning of cell geometry and cell-cell forces in an epithelial colony.\n\nAuthor summaryEpithelial cells line all organs of the human body and act as a protective barrier by forming a continuous sheet. These cells exert force on both their neighboring cells as well as the underlying extracellular matrix, which is a network of proteins that creates the structure of tissues. Here we develop a model that encompasses both cell-cell forces and cell-matrix forces in an epithelial cell sheet. The model accounts for cell migration and proliferation, and regulates how cell-cell adhesions are formed. We demonstrate how the interplay between cell-cell forces and cell-matrix forces can regulate the formation of the epithelial cell sheet, the organization of cells within the sheet, and the pattern of cell geometries and cell forces within the sheet. We compare computational results with experiments in which epithelial cell sheets are disrupted and cell-cell junction forces are measured, and demonstrate that the model captures many aspects of epithelial cell dynamics observed experimentally.

cell biology↗

bioRxiv2019

Cell fate forecasting: a data assimilation approach to predict epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-{beta} (TGF{beta}) is a major and potent inducer of this cellular transition, which is comprised of transitions from an epithelial state to an intermediate or partial EMT state, then to a mesenchymal state. Using computational models to predict state transitions in a specific experiment is inherently difficult for many reasons, including model parameter uncertainty and the error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGF{beta}-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept \"synthetic\" in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGF{beta} doses associated with the state transitions. We performed synthetic experiments for a wide range of TGF{beta} doses to investigate different cell steady state conditions, and we conducted a parameter study varying several properties of the data-assimilation approach, including the time interval between observations, and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates a feasible proof-of-concept for a data assimilation approach to forecasting the fate of cells undergoing EMT.\n\nAuthor summaryEpithelial-mesenchymal transition (EMT) is a biological process in which an epithelial cell loses core epithelial-like characteristics, such as tight cell-to-cell adhesion, and gains core mesenchymal-like characteristics, such as an increase in cell motility. EMT is a multistep process, in which the cell undergoes transitions from epithelial state to a partial or intermediate state, and then from a partial state to a mesenchymal state. In this study, we apply data assimilation to improve prediction of these state transitions. Data assimilation is an approach well known in the weather forecasting community, in which experimental observations are iteratively combined with predictions from a dynamical model to provide an improved estimation of both observed and unobserved system states. We show that this data assimilation approach can reconstruct cell state measurements and predict state transition dynamics using noisy observations, while minimizing the error produced by the limitations and imperfections of the dynamical model.

cell biology↗

bioRxiv2019

Mechanochemical coupling and junctional forces during collective cell migration

Cell migration, a fundamental physiological process in which cells sense and move through their surrounding physical environment, plays a critical role in development and tissue formation, as well as pathological processes, such as cancer metastasis and wound healing. During cell migration, dynamics are governed by the bidirectional interplay between cell-generated mechanical forces and the activity of Rho GTPases, a family of small GTP-binding proteins that regulate actin cytoskeleton assembly and cellular contractility. These interactions are inherently more complex during the collective migration of mechanically coupled cells, due to the additional regulation of cell-cell junctional forces. In this study, we present a minimal modeling framework to simulate the interactions between mechanochemical signaling in individual cells and interactions with cell-cell junctional forces during collective cell migration. We find that migration of individual cells depends on the feedback between mechanical tension and Rho GTPase activity in a biphasic manner. During collective cell migration, waves of Rho GTPase activity mediate mechanical contraction/extension and thus synchronization throughout the tissue. Further, cell-cell junctional forces exhibit distinct spatial patterns during collective cell migration, with larger forces near the leading edge. Larger junctional force magnitudes are associated with faster collective cell migration and larger tissue size. Simulations of heterogeneous tissue migration exhibit a complex dependence on the properties of both leading and trailing cells. Computational predictions demonstrate that collective cell migration depends on both the emergent dynamics and interactions between cellular-level Rho GTPase activity and contractility, and multicellular-level junctional forces.

biophysics↗