Biology subjects

Lemmon, C. A.

Publications and source records attributed to Lemmon, C. A..

4 recordsLinked to original sources

bioRxiv2024

Fibrotic Extracellular Matrix Preferentially Induces a Partial Epithelial-Mesenchymal Transition Phenotype in a 3-D Agent Based Model of Fibrosis

One of the main drivers of fibrotic diseases is epithelial-mesenchymal transition (EMT): a transdifferentiation process in which cells undergo a phenotypic change from an epithelial state to a pro-migratory state. The cytokine transforming growth factor-{beta}1 (TGF-{beta}1) has been previously shown to regulate EMT. TGF-{beta}1 binds to fibronectin (FN) fibrils, which are the primary extracellular matrix (ECM) component in renal fibrosis. We have previously demonstrated experimentally that inhibition of FN fibrillogenesis and/or TGF-{beta}1 tethering to FN inhibits EMT. However, these studies have only been conducted on 2-D cell monolayers, and the role of TGF-{beta}1-FN tethering in 3-D cellular environments is not clear. As such, we sought to develop a 3-D computational model of epithelial spheroids that captures both EMT signaling dynamics and TGF-{beta}1-FN tethering dynamics. We have incorporated the bi-stable EMT switch model developed by Tian et al. (2013) into a 3D multicellular model, capturing both temporal and spatial TGF-{beta}1 signaling dynamics. We show that the addition of TGF-{beta}1 affects cell proliferation, EMT progression, and cell migration. We then incorporate TGF-{beta}1-FN fibril tethering by locally reducing the TGF-{beta}1 diffusion coefficient as a function of EMT to simulate the reduced movement of TGF-{beta}1 when tethered to FN fibrils during fibrosis. We show that incorporation of TGF-{beta}1 tethering to FN fibrils promotes a partial EMT state, independent of exogenous TGF-{beta}1 concentration, indicating a mechanism by which fibrotic ECM can promote a partial EMT state. Author summaryEpithelial-mesenchymal transition (EMT) is a key cellular process where epithelial cells transform and become mesenchymal. The EMT states are not binary, but instead exhibit a spectrum of partial states where epithelial cells express a combination of epithelial and mesenchymal markers, along with several markers distinct to the partial state. In diseases such as fibrosis and cancer, growing evidence supports the finding that diseased epithelial cells exist primarily in a partial EMT state. However, the mechanisms and signaling factors that drive this partial EMT state in fibrotic diseases is unclear. We use an agent-based model that looks at EMT progression in a population of cells embedded in an ECM environment with controllable fibrotic properties to provide a more systematic approach at studying spatial and temporal changes in the microenvironment that could drive EMT progression and maintain specific EMT phenotypes.

cell biology↗

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↗

bioRxiv2018

A Novel Method for Quantifying Traction Forces from Hexagonal Micropatterned Features on Deformable Poly-Dimethyl Siloxane Sheets

Many methods exist for quantifying cellular traction forces, including traction force microscopy and microfabricated post arrays. However, these methodologies have limitations, including a requirement to remove cells to determine undeflected particle locations and the inability to quantify forces of cells with low cytoskeletal stiffness, respectively. Here we present a novel method of traction force quantification that eliminates both of these limitations. Through the use of a hexagonal pattern of microcontact-printed protein spots, a novel computational algorithm, and thin surfaces of polydimethyl siloxane (PDMS) blends, we demonstrate a system that quantifies cellular forces on a homogeneous surface that is stable, easily manufactured, and can quantify forces without need for cellular removal.

bioengineering↗