A hybrid model of intercellular tension and cell–matrix mechanical interactions in a multicellular geometry

Scott, Lewis E.; Griggs, Lauren; Narayanan, Vani; Conway, Daniel E.; Lemmon, Christopher A.; Weinberg, Seth H.

doi:10.1007/s10237-020-01321-8

Cited by 19 publications

(28 citation statements)

References 45 publications

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“…Finally, a subset of experiments were performed in an additional epithelial cell line, specifically Madin–Darby Canine Kidney (MDCK) cells, treated with 2 ng/mL TGF-β1 alongside a negative control, and imaged under similar conditions as the MCF10A cells (Griggs et al, 2017; Scott et al, 2020). There were 19 and 13 images in the control and 2 ng/mL TGF-β1 treated groups, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…0.1% BSA was applied again for 5 min before labeling cells with the respective secondary antibodies for 30 min at 37°C. Primary and secondary antibody concentrations were based on previous studies from our lab, which were optimized for the maximum signal-to-background ratio (Griggs et al, 2017; Scott et al, 2020). Images were acquired on a Zeiss AxioObserver Z1 fluorescence microscope using ZEN2011 software and using an AxioCam MR R3 camera and a Plan-Apochromat 20× objective with 0.8 numerical aperature.…”

Section: Methodsmentioning

confidence: 99%

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Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial–Mesenchymal Transition

et al. 2021

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial–Mesenchymal Transition

et al. 2021

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“…Additionally, cell-matrix interactions and the resultant mechanical dynamics can be simulated via the CPM to demonstrate tissue formation [59]. We recently developed a multicomponent CPM model that investigates the intercellular forces and mechanical dynamics that occur during EMT [49]. Our recent modeling effort integrated the CPM with a finite element mesh (FEM) and first moment of area (FMA) models (described further below).…”

Section: Computational Modeling Of Emtmentioning

confidence: 99%

“…Finally, the third term, 𝐻 𝑑𝑢𝑟𝑜𝑡𝑎𝑥𝑖𝑠 (Equation S3), is based on the cell's migration tendency along the mechanical strain gradients in the ECM. Additional details about the Hamiltonian terms in Equation (1) can be found in our prior work [49]. Cell proliferation in the CPM is a random event, such that once a cell exceeds a threshold area (defined as 2/3 of the target area), then the cell has the likelihood to divide with a phenotype-dependent probability 𝑝 𝑑𝑖𝑣𝑖𝑑𝑒 (described further below).…”

Section: Cellular Movement Proliferation and Mechanical Signalingmentioning

confidence: 99%

“…Thus, the multicellular FMA model predicts that mechanical equilibrium in each cell is maintained through cell-cell junctional forces, predicted at each cell-cell junction, and total junctional forces are given by 𝑗 𝑘 = ∑ (𝑥,𝑦)∈𝑘 √ 𝑗 2 𝑥 + 𝑗 2 𝑦 , where 𝑗 𝑥 and 𝑗 𝑦 are the 𝑥 and 𝑦 components of junctional forces acting on cell 𝑘. A simplified representation of this interaction is shown by the FMA model component of the schematic in Figure 1 and full details and mathematical calculations are presented in Scott et al [49].…”

Section: Cellular Movement Proliferation and Mechanical Signalingmentioning

confidence: 99%

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Multicellular mechanochemical hybrid cellular Potts model of tissue formation during epithelial‐mesenchymal transition

2021

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Epithelial-mesenchymal transition (EMT) is the transdifferentiation of epithelial cells to a mesenchymal phenotype, in which cells lose epithelial-like cell-cell adhesions and gain mesenchymal-like enhanced contractility and mobility. EMT is crucial for tissue regeneration and is also implicated in pathological conditions, such as cancer metastasis. Prior work has shown that transforming growth factor-β1 (TGF-β1) is a potent inducer of this biological process. In this study, we develop a computational model coupling mechanical and biochemical signaling in a multicellular tissue undergoing EMT. Specifically, we utilize a recently developed formulation that integrates a multicellular cellular Potts model (CPM), a lattice-based stochastic model governing cell movement; a first moment of area model, governing cellular traction and junctional forces; a finite element model, which defines extracellular matrix (ECM) substrate strains; an intracellular signaling TGF-β1-mediated EMT model that governs cellular phenotype; and an extracellular signaling component governing ECM and TGF-β1 signaling. In this study, we modeled the spatial cellular patterns that occur in tissue and the ECM during EMT. Our model predicts that EMT often initially occurs at a tissue boundary due to mechanochemical coupling, which results in transdifferentiation to progress inwards toward the center. Variation in model parameters demonstrated conditions enhancing and suppressing EMT, especially to drive EMT in the absence of TGF-β1 and inhibit EMT in the presence of TGF-β1. Specifically, enhancing the mechanochemical feedback typically promoted EMT, whereas greater assembled ECM degradation suppressed EMT. Simulated scratch test experiments illustrate that ECM composition can impact closure directly through EMT signaling. In conclusion, we integrated mechanical, biochemical, and extracellular signaling networks in a novel hybrid computational model to reproduce tissue formation dynamics of EMT.

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A review of computational modeling, machine learning and image analysis in cancer metastasis dynamics

Hirway

Weinberg

2023

Comp Sys Onco

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Cancer is a life-threatening process that stems from genetic mutations in cells, which leads to the formation of tumors, and is a major cause of deaths in the United States, with secondary metastasis being a major driver of fatality. The development of an optimal metastatic environment is an essential process prior to tumor metastasis. This process, called pre-metastatic niche formation, involves the activation of resident fibroblast-like cells and macrophages. Tumormediated factors introduced to this environment transform resident cells that secrete additional growth factors and remodel the extracellular matrix, which is thought to promote tumor colonization and metastasis in the secondary environment. Furthermore, an important component of metastasis is the biological process of epithelial-mesenchymal transition, which can be exploited by cancer cells to change their phenotype, to migrate and proliferate as necessary. In this review, we discuss recent advances in the investigation of cancer growth and migration. Computational models that focus on biochemical signaling and multicellular dynamics are examined. Machine learning models and image analysis that classify cancer-related data are also explored. Through this review, we highlight advances in the study of important aspects of cancer and metastasis signaling and computational tools to study these dynamics.

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A hybrid model of intercellular tension and cell–matrix mechanical interactions in a multicellular geometry

Cited by 19 publications

References 45 publications

Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial–Mesenchymal Transition

Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial–Mesenchymal Transition

Multicellular mechanochemical hybrid cellular Potts model of tissue formation during epithelial‐mesenchymal transition

A review of computational modeling, machine learning and image analysis in cancer metastasis dynamics

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