Modeling, signaling and cytoskeleton dynamics: integrated modeling-experimental frameworks in cell migration

A mechanistic protrusive-based model for 3D cell migration

Cell migration is essential for a variety of biological processes, such as embryogenesis, wound healing, and the immune response. After more than a century of research-mainly on flat surfaces-, there are still many unknowns about cell motility. In particular, regarding how cells migrate within 3D matrices, which more accurately replicate in vivo conditions. We present a novel in silico model of 3D mesenchymal cell migration regulated by the chemical and mechanical profile of the surrounding environment. This in silico model considers cell's adhesive and nuclear phenotypes, the effects of the steric hindrance of the matrix, and cells ability to degradate the ECM. These factors are crucial when investigating the increasing difficulty that migrating cells find to squeeze their nuclei through dense matrices, which may act as physical barriers. Our results agree with previous in vitro observations where fibroblasts cultured in collagen-based hydrogels did not durotax toward regions with higher collagen concentrations. Instead, they exhibited an adurotactic behavior, following a more random trajectory. Overall, cell's migratory response in 3D domains depends on its phenotype, and the properties of the surrounding environment, that is, 3D cell motion is strongly dependent on the context.

Density-Dependent Migration Characteristics of Cancer Cells Driven by Pseudopod Interaction

The ability of cancer cells to invade neighboring tissue from primary tumors is an important determinant of metastatic behavior. Quantification of cell migration characteristics such as migration speed and persistence helps to understand the requirements for such invasiveness. One factor that may influence invasion is how local tumor cell density shapes cell migration characteristics, which we here investigate with a combined experimental and computational modeling approach. First, we generated and analyzed time-lapse imaging data on two aggressive Triple-Negative Breast Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration assays at various cell densities. HCC38 cells exhibited a counter-intuitive increase in speed and persistence with increasing density, whereas Hs578T did not exhibit such an increase. Moreover, HCC38 cells exhibited strong cluster formation with active pseudopod-driven migration, especially at low densities, whereas Hs578T cells maintained a dispersed positioning. In order to obtain a mechanistic understanding of the density-dependent cell migration characteristics and cluster formation, we developed realistic spatial simulations using a Cellular Potts Model (CPM) with an explicit description of pseudopod dynamics. Model analysis demonstrated that pseudopods exerting a pulling force on the cell and interacting via increased adhesion at pseudopod tips could explain the experimentally observed increase in speed and persistence with increasing density in HCC38 cells. Thus, the density-dependent migratory behavior could be an emergent property of single-cell characteristics without the need for additional mechanisms. This implies that pseudopod dynamics and interaction may play a role in the aggressive nature of cancers through mediating dispersal.

Integrated in silico and 3D in vitro model of macrophage migration in response to physical and chemical factors in the tumor microenvironment

Macrophages are abundant in the tumor microenvironment (TME), serving as accomplices to cancer cells for their invasion. Studies have explored the biochemical mechanisms that drive pro-tumor macrophage functions; however the role of TME interstitial flow (IF) is often disregarded. Therefore, we developed a three-dimensional microfluidic-based model with tumor cells and macrophages to study how IF affects macrophage migration and its potential contribution to cancer invasion. The presence of either tumor cells or IF individually increased macrophage migration directedness and speed. Interestingly, there was no additive effect on macrophage migration directedness and speed under the simultaneous presence of tumor cells and IF. Further, we present an in silico model that couples chemokine-mediated signaling with mechanosensing networks to explain our in vitro observations. In our model design, we propose IL-8, CCL2, and β-integrin as key pathways that commonly regulate various Rho GTPases. In agreement, in vitro macrophage migration remained elevated when exposed to a saturating concentration of recombinant IL-8 or CCL2 or to the co-addition of a sub-saturating concentration of both cytokines. Moreover, antibody blockade against IL-8 and/or CCL2 inhibited migration that could be restored by IF, indicating cytokine-independent mechanisms of migration induction. Importantly, we demonstrate the utility of an integrated in silico and 3D in vitro approach to aid the design of tumor-associated macrophage-based immunotherapeutic strategies.

Bridging from single to collective cell migration: A review of models and links to experiments

Mathematical and computational models can assist in gaining an understanding of cell behavior at many levels of organization. Here, we review models in the literature that focus on eukaryotic cell motility at 3 size scales: intracellular signaling that regulates cell shape and movement, single cell motility, and collective cell behavior from a few cells to tissues. We survey recent literature to summarize distinct computational methods (phase-field, polygonal, Cellular Potts, and spherical cells). We discuss models that bridge between levels of organization, and describe levels of detail, both biochemical and geometric, included in the models. We also highlight links between models and experiments. We find that models that span the 3 levels are still in the minority.

Computational models of migration modes improve our understanding of metastasis

Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell–extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy.

Hyaluronic acid induces ROCK-dependent amoeboid migration in glioblastoma cells

Glioblastoma (GBM) is the most aggressive and deadly adult brain tumor, primarily because of its high infiltrative capacity and development of resistance to therapy. Although GBM cells are typically believed to migrate via mesenchymal (e.g., fibroblast-like) migration modes, amoeboid (e.g., leucocyte-like) migration modes have been identified and may constitute a salvage pathway. However, the mesenchymal to amoeboid transition (MAT) process in GB is not well characterized, most likely because most culture models induce MAT via pharmacological or genetic inhibition conditions that are far from physiological. In this study, we examined the ability of hyaluronic acid (HA) content in three-dimensional collagen (Col) hydrogels to induce MAT in U87 GBM cells. HA and Col are naturally-occurring components of the brain extracellular matrix (ECM). In pure Col gels, U87 cells displayed primarily mesenchymal behaviors, including elongated cell morphology, clustered actin and integrin expression, and crawling migration behaviors. Whereas an increasing population of cells displaying amoeboid behaviors, including rounded morphology, cortical actin expression, low/no integrin expression, and squeezing or gliding motility, were observed with increasing HA content (0.1-0.2 wt% in Col). Consistent with amoeboid migration, these behaviors were abrogated by ROCK inhibition with the non-specific small molecule inhibitor Y27632. Toward identification of histological MAT classification criteria, we also examined the correlation between cell and nuclear aspect ratio (AR) in Col and Col-HA gels, finding that nuclear AR has a small variance and is not correlated to cell AR in HA-rich gels. These results suggest that HA may regulate GBM cell motility in a ROCK-dependent manner.

A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma

Purpose

Risk stratification in patients with multiple myeloma (MM) remains a challenge. As clinicopathological characteristics have been demonstrated insufficient for exactly defining MM risk, and molecular biomarkers have become the focuses of interests. Prognostic predictions based on gene expression profiles (GEPs) have been the most accurate to this day. The purpose of our study was to construct a risk score based on stemness genes to evaluate the prognosis in MM.

Materials and Methods

Bioinformatics studies by ingenuity pathway analyses in side population (SP) and non-SP (MP) cells of MM patients were performed. Firstly, co-expression network was built to confirm hub genes associated with the top five Kyoto Encyclopedia of Genes and Genomes pathways. Functional analyses of hub genes were used to confirm the biologic functions. Next, these selective genes were utilized for construction of prognostic model, and this model was validated in independent testing sets. Finally, five stemness genes (ROCK1, GSK3B, BRAF, MAPK1 and MAPK14) were used to build a MM side population 5 (MMSP5) gene model, which was demonstrated to be forcefully prognostic compared to usual clinical prognostic parameters by multivariate cox analysis. MM patients in MMSP5 low-risk group were significantly related to better prognosis than those in high-risk group in independent testing sets.

Conclusion

Our study provided proof-of-concept that MMSP5 model can be adopted to evaluate recurrence risk and clinical outcome for MM. The MMSP5 model evaluated in different databases clearly indicated novel risk stratification for personalized anti-MM treatments.

Irreversible disruption of the cytoskeleton as induced by non-cytotoxic exposure to titanium dioxide nanoparticles in lung epithelial cells

Exposure to TiO₂ NPs induces several cellular alterations after NPs uptake including disruption of cytoskeleton that is crucial for lung physiology but is not considered as a footprint of cell damage. We aimed to investigate cytoskeleton disturbances and the impact on cell migration induced by an acute TiO₂ NPs exposure (24 h) and the recovery capability after 6 days of NPs-free treatment, which allowed investigating if cytoskeleton damage was reversible. Exposure to TiO₂ NPs (10 μg/cm²) for 24 h induced a decrease 20.2% and 25.1% in tubulin and actin polymerization. Exposure to TiO₂ NPs (10 μg/cm²) for 24 h followed by 6 days of NPs-free had a decrease of 26.6% and 21.3% in tubulin and actin polymerization, respectively. The sustained exposure for 7 days to 1 μg/cm² and 10 μg/cm² induced a decrease of 22.4% and 30.7% of tubulin polymerization respectively, and 28.7% and 46.2% in actin polymerization. In addition, 24 h followed 6 days of NPs-free exposure of TiO₂ NPs (1 μg/cm² and 10 μg/cm²) decreased cell migration 40.7% and 59.2%, respectively. Cells exposed (10 μg/cm²) for 7 days had a decrease of 65.5% in cell migration. Ki67, protein surfactant B (SFTPB) and matrix metalloprotease 2 (MMP2) were analyzed as genes related to lung epithelial function. The results showed a 20% of Ki67 upregulation in cells exposed for 24 h to 10 μg/cm² TiO₂ NPs while a downregulation of 20% and 25.8% in cells exposed to 1 μg/cm² and 10 μg/cm² for 24 h followed by 6 days of NPs-free exposure. Exposure to 1 μg/cm² and 10 μg/cm² for 24 h and 7 days upregulates SFTPB expression in 53% and 59% respectively, MMP2 expression remain unchanged. In conclusion, exposure of TiO₂ NPs affected cytoskeleton of lung epithelial cells irreversibly but this damage was not cumulative.

From energy to cellular forces in the Cellular Potts Model: An algorithmic approach

Single and collective cell dynamics, cell shape changes, and cell migration can be conveniently represented by the Cellular Potts Model, a computational platform based on minimization of a Hamiltonian. Using the fact that a force field is easily derived from a scalar energy (F = −∇H), we develop a simple algorithm to associate effective forces with cell shapes in the CPM. We predict the traction forces exerted by single cells of various shapes and sizes on a 2D substrate. While CPM forces are specified directly from the Hamiltonian on the cell perimeter, we approximate the force field inside the cell domain using interpolation, and refine the results with smoothing. Predicted forces compare favorably with experimentally measured cellular traction forces. We show that a CPM model with internal signaling (such as Rho-GTPase-related contractility) can be associated with retraction-protrusion forces that accompany cell shape changes and migration. We adapt the computations to multicellular systems, showing, for example, the forces that a pair of swirling cells exert on one another, demonstrating that our algorithm works equally well for interacting cells. Finally, we show forces exerted by cells on one another in classic cell-sorting experiments.

Cells exert forces on their surroundings and on one another. In simulations of cell shape using the Cellular Potts Model (CPM), the dynamics of deforming cell shapes is traditionally represented by an energy-minimization method. We use this CPM energy, the Hamiltonian, to derive and visualize the corresponding forces exerted by the cells. We use the fact that force is the negative gradient of energy to assign forces to the CPM cell edges, and then extend the results to approximate interior forces by interpolation. We show that this method works for single as well as multiple interacting model cells, both static and motile. Finally, we show favorable comparison between predicted forces and real forces measured experimentally.

Articular cartilage regeneration: The role of endogenous mesenchymal stem/progenitor cell recruitment and migration

BACKGROUND

Trauma- or osteoarthritis-related cartilage damage resulted in functional decline of joints and heavy burden of public health. Recently, the reparative role of mesenchymal stem/progenitor cells (MSCs) in articular cartilage (AC) reconstruction is drawing more and more attention.

OBJECTIVE

To provide a review on (1) the locations and categories of joint-resident MSCs, (2) the regulation of chondrogenic capacities of MSCs, (3) the migratory approaches of MSCs to diseased AC and regulatory mechanisms.

METHODS

PubMed and Web of Science were searched for English-language articles related to MSC recruitment and migration for AC repair until June 2019. The presence of various MSCs in or around joints, the potential approaches to diseased AC` and the regenerative capacities of MSCs were reviewed.

RESULTS

Various intra- and peri-articular MSCs, with inherent migratory potentials, are present in multiple stem cell niches in or around joints. The recruitment and migration of joint-resident MSCs play crucial roles in endogenous AC repair. Multiple recruiting signals, such as chemokines, growth factors, etc., emerge during the development of AC diseases and participate in the regulation of MSC mobilization. Motivated MSCs could migrate into cartilage lesions and then exert multiple reparative potentials, including extracellular matrix (ECM) reconstruction and microenvironment modulation.

CONCLUSION

In general, AC repair based on endogenous MSC recruitment and migration is a feasible strategy, and a promising research field. Furthermore, endogenous AC repair mediated by native MSCs would provide new opportunities to efficient preventative or therapeutic options for AC diseases.

Are the Effects of Independent Biophysical Factors Linearly Additive? A 3D Tumor Migration Model

Interstitial fluid flow plays a critical role in tumor cell invasion, yet this role has not been explored extensively in combination with other microenvironmental factors. Here, we establish a novel computational model of three-dimensional breast cancer cell migration to unveil the effect of interstitial fluid flow in the dependence of various extracellular matrix (ECM) physical properties. Our model integrates several principal factors: fluid dynamics, autologous chemotaxis, collagen fiber network structure, ECM stiffness, and cell-fiber and cell-flow interaction. First, independently with an aligned collagen fiber network and interstitial fluid flow, this model is validated by successfully reproducing the cell migration patterns. In the model, the interstitial fluid flow leads to directional symmetry breaking of chemotactic migration and synergizes with the ECM orientation to regulate cell migration. This synergy is universal in both the mesenchymal and the amoeboid migration modes, despite the fact that the cell-ECM interaction are different. Consequently, we construct a cell displacement function depending on these factors. Our cell migration model enables three-dimensional cancer migration prediction, mechanism exploration, and inhibition treatment design in a complex tumor microenvironment.

The knocking down of the oncoprotein Golgi phosphoprotein 3 in T98G cells of glioblastoma multiforme disrupts cell migration by affecting focal adhesion dynamics in a focal adhesion kinase-dependent manner

Golgi phosphoprotein 3 (GOLPH3) is a conserved protein of the Golgi apparatus that in humans has been implicated in tumorigenesis. However, the precise function of GOLPH3 in malignant transformation is still unknown. Nevertheless, clinicopathological data shows that in more than a dozen kinds of cancer, including gliomas, GOLPH3 could be found overexpressed, which correlates with poor prognosis. Experimental data shows that overexpression of GOLPH3 leads to transformation of primary cells and to tumor growth enhancement. Conversely, the knocking down of GOLPH3 in GOLPH3-overexpressing tumor cells reduces tumorigenic features, such as cell proliferation and cell migration and invasion. The cumulative evidence indicate that GOLPH3 is an oncoprotein that promotes tumorigenicity by a mechanism that impact at different levels in different types of cells, including the sorting of Golgi glycosyltransferases, signaling pathways, and the actin cytoskeleton. How GOLPH3 connects mechanistically these processes has not been determined yet. Further studies are important to have a more complete understanding of the role of GOLPH3 as oncoprotein. Given the genetic diversity in cancer, a still outstanding aspect is how in this inherent heterogeneity GOLPH3 could possibly exert its oncogenic function. We have aimed to evaluate the contribution of GOLPH3 overexpression in the malignant phenotype of different types of tumor cells. Here, we analyzed the effect on cell migration that resulted from stable, RNAi-mediated knocking down of GOLPH3 in T98G cells of glioblastoma multiforme, a human glioma cell line with unique features. We found that the reduction of GOLPH3 levels produced dramatic changes in cell morphology, involving rearrangements of the actin cytoskeleton and reduction in the number and dynamics of focal adhesions. These effects correlated with decreased cell migration and invasion due to affected persistence and directionality of cell motility. Moreover, the knocking down of GOLPH3 also caused a reduction in autoactivation of focal adhesion kinase (FAK), a cytoplasmic tyrosine kinase that regulates focal adhesions. Our data support a model in which GOLPH3 in T98G cells promotes cell migration by stimulating the activity of FAK.

Integration of in vitro and in silico Models Using Bayesian Optimization With an Application to Stochastic Modeling of Mesenchymal 3D Cell Migration

Cellular migration plays a crucial role in many aspects of life and development. In this paper, we propose a computational model of 3D migration that is solved by means of the tau-leaping algorithm and whose parameters have been calibrated using Bayesian optimization. Our main focus is two-fold: to optimize the numerical performance of the mechano-chemical model as well as to automate the calibration process of in silico models using Bayesian optimization. The presented mechano-chemical model allows us to simulate the stochastic behavior of our chemically reacting system in combination with mechanical constraints due to the surrounding collagen-based matrix. This numerical model has been used to simulate fibroblast migration. Moreover, we have performed in vitro analysis of migrating fibroblasts embedded in 3D collagen-based fibrous matrices (2 mg/ml). These in vitro experiments have been performed with the main objective of calibrating our model. Nine model parameters have been calibrated testing 300 different parametrizations using a completely automatic approach. Two competing evaluation metrics based on the Bhattacharyya coefficient have been defined in order to fit the model parameters. These metrics evaluate how accurately the in silico model is replicating in vitro measurements regarding the two main variables quantified in the experimental data (number of protrusions and the length of the longest protrusion). The selection of an optimal parametrization is based on the balance between the defined evaluation metrics. Results show how the calibrated model is able to predict the main features observed in the in vitro experiments.

Spatial Moment Description of Birth-Death-Movement Processes Incorporating the Effects of Crowding and Obstacles

Birth-death-movement processes, modulated by interactions between individuals, are fundamental to many cell biology processes. A key feature of the movement of cells within in vivo environments is the interactions between motile cells and stationary obstacles. Here we propose a multi-species model of individual-level motility, proliferation and death. This model is a spatial birth-death-movement stochastic process, a class of individual-based model (IBM) that is amenable to mathematical analysis. We present the IBM in a general multi-species framework and then focus on the case of a population of motile, proliferative agents in an environment populated by stationary, non-proliferative obstacles. To analyse the IBM, we derive a system of spatial moment equations governing the evolution of the density of agents and the density of pairs of agents. This approach avoids making the usual mean-field assumption so that our models can be used to study the formation of spatial structure, such as clustering and aggregation, and to understand how spatial structure influences population-level outcomes. Overall the spatial moment model provides a reasonably accurate prediction of the system dynamics, including important effects such as how varying the properties of the obstacles leads to different spatial patterns in the population of agents.

Computational modeling of single-cell mechanics and cytoskeletal mechanobiology

Cellular cytoskeletal mechanics plays a major role in many aspects of human health from organ development to wound healing, tissue homeostasis and cancer metastasis. We summarize the state-of-the-art techniques for mathematically modeling cellular stiffness and mechanics and the cytoskeletal components and factors that regulate them. We highlight key experiments that have assisted model parameterization and compare the advantages of different models that have been used to recapitulate these experiments. An overview of feed-forward mechanisms from signaling to cytoskeleton remodeling is provided, followed by a discussion of the rapidly growing niche of encapsulating feedback mechanisms from cytoskeletal and cell mechanics to signaling. We discuss broad areas of advancement that could accelerate research and understanding of cellular mechanobiology. A precise understanding of the molecular mechanisms that affect cell and tissue mechanics and function will underpin innovations in medical device technologies of the future. WIREs Syst Biol Med 2018, 10:e1407. doi: 10.1002/wsbm.1407 This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

Integrins in T Cell Physiology

From the thymus to the peripheral lymph nodes, integrin-mediated interactions with neighbor cells and the extracellular matrix tune T cell behavior by organizing cytoskeletal remodeling and modulating receptor signaling. LFA-1 (αLβ2 integrin) and VLA-4 (α4β1 integrin) play a key role throughout the T cell lifecycle from thymocyte differentiation to lymphocyte extravasation and finally play a fundamental role in organizing immune synapse, providing an essential costimulatory signal for the T cell receptor. Apart from tuning T cell signaling, integrins also contribute to homing to specific target organs as exemplified by the importance of α4β7 in maintaining the gut immune system. However, apart from those well-characterized examples, the physiological significance of the other integrin dimers expressed by T cells is far less understood. Thus, integrin-mediated cell-to-cell and cell-to-matrix interactions during the T cell lifespan still represent an open field of research.