Research

Study cell migration in brachytherapy

Glioblastoma is the most common form of brain cancer in adults and remains one of the deadliest of human cancers. Brachytherapy is a form of surgically administered radiotherapy involving the implant of radioactive seeds into target regions with tumor infiltration. This localized approach affords the delivery of higher radiation doses than can be safely achieved through external beam radiation therapy (EBRT). Further development of brachytherapy as a glioblastoma therapeutic platform requires a fundamental understanding of its biologic effects.

Study glioblastoma patient cell migration and its association with patient survival

In a current project, we identified a significant correlation between the migration motility of primary GBM patient cells and disease free survival (DFS) using 2D migration assays on 40 GBM patient samples. We also identified a gene panel that can predict the patient cell migration and patient DFS and overall survival (OS).

In a previous study, we applied migration assays to glioblastoma patient-derived xenograft (PDX) and patient-derived (PD) cells.  We explored the ability of the biophysical modeling to serve as a physics-based framework for glioblastoma subtypes and PD(X) systems. We used the biophysical parameters representing myosin II motors, adhesion protein “clutches,” and F-actin polymerization to predict cell migration generally, and then mechanically parameterized glioblastoma cells obtained from a cohort of 11 glioblastoma patients across all three subtypes and two different culture procedures. 

Hou J, McMahon M, Jubenville T, Sarkaria JN, Chen CC, Odde DJ. Cell migration simulator-based biomarkers for glioblastoma. Neuro-Oncology Advances. 2024 Nov 2;6(1):vdae184.

Develop biophysical model to study cell migration within various microenvironment

In this project, we collaborated with Dr. Ivaska and Dr. Isomursu, and we discovered the directed migration of U-251MG glioma cells towards less stiff regions, a novel phenomena termed “negative durotaxis.” We developed a biophysical model to simulate cell migration on the substrate with stiffness gradients, and the motor–clutch-based model predicted cell migration occurs towards areas of ‘optimal stiffness’, where cells can generate maximal traction. We further validated the biophysical model predictions and discovered key molecular mechanisms that drive “negative durotaxis.”

Isomursu A, Park KY, Hou J, Cheng B, Mathieu M, Shamsan GA, Fuller B, Kasim J, Mahmoodi MM, Lu TJ, Genin GM, Xu F, Lin M, Distefano MD, Ivaska J, Odde DJ. Directed cell migration towards softer environments. Nature Materials. 2022 Sep;21(9):1081-1090.
Dr. Adebowale and Dr. Chaudhuri discovered that cancer cells migrate minimally on substrates with an elastic modulus of 2 kPa that are elastic or exhibit viscoelastic with slow stress relaxation, but migrate robustly on 2-kPa substrates that exhibit viscoelastic with fast stress relaxation. We developed a biophysical model to simulate cell migration on viscoelastic substrates, and the motor–clutch model predicted the observed impact of substrate stress relaxation on cell migration and filopodia dynamics.

Adebowale, K., Gong, Z., Hou, J. C., Wisdom, K. M., Garbett, D., Lee, H. P., Nam, S., Meyer, T., Odde, D. J., Shenoy, V. B., & Chaudhuri, O. (2021). Enhanced substrate stress relaxation promotes filopodia-mediated cell migration. Nature materials, 20(9), 1290–1299.
Dr. Lee and Dr. Levchenko discovered that physically confined cancer cell migration exhibited a periodic motion, which is mediated by cyclic changes in the activity of a small GTPase RhoA and dependent on the oscillatory changes in the activity and abundance of the RhoA guanine nucleotide exchange factor, GEF-H1. We developed a 1D biophysical model and the motor-clutch-based model predicted the periodic 1D cell migration and protrusion dynamics.
We discovered that a cone-shaped force distribution can describe the observed displacements of hydrogels deformed by adherent U251 glioma cells. Also, we developed a biophysical model with spatially distributed clutches in the motor-clutch system, and the model predicted the experimentally observed adhesion radius increasing linearly with the cell protrusion force.

Develop multiphase finite element model to study cell and tissue mechanism and physiology under deformation

With the recent implementation of multiphasic materials in the open-source finite element (FE) software FEBio, three-dimensional (3D) models of cells embedded within the tissue may now be analyzed, accounting for porous solid matrix deformation, transport of interstitial fluid and solutes, membrane potential, and reactions. The cell membrane is a critical component in cell models, which selectively regulates the transport of fluid and solutes in the presence of large concentration and electric potential gradients, while also facilitating the transport of various proteins. The cell membrane is much thinner than the cell; therefore, in an FE environment, shell elements formulated as two-dimensional (2D) surfaces in 3D space would be preferred for modeling the cell membrane, for the convenience of mesh generation from image-based data, especially for convoluted membranes. This study presents a novel formulation of multiphasic shell elements and its implementation in FEBio. This formulation was verified against classical models of cell physiology and validated against reported experimental measurements in chondrocytes. This implementation of passive transport of fluid and solutes across multiphasic membranes makes it possible to model the biomechanics of isolated cells or cells embedded in their extracellular matrix (ECM), accounting for solvent and solute transport.

Hou, J. C., Maas, S. A., Weiss, J. A., & Ateshian, G. A. (2018). Finite Element Formulation of Multiphasic Shell Elements for Cell Mechanics Analyses in FEBio. Journal of biomechanical engineering, 140(12), 121009. 

Hou, C., & Ateshian, G. A. (2016). A Gauss-Kronrod-Trapezoidal integration scheme for modeling biological tissues with continuous fiber distributions. Computer methods in biomechanics and biomedical engineering, 19(8), 883–893.