Poster K3 - Single-Cell Stiffness Analysis Using a Coarse Grain Molecular Dynamics Simulation Via LAMMPS (Based on Experimental Results Using Ferrohydrodynamic-Microfluidic Platform)

Saturday, October 26, 2024

10:00 AM - 11:00 AM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Jacob M. Foster (he/him/his)

Undergraduate Researcher for Computational Methods in Nanoparticles
University of Georgia
Peachtree Corners, Georgia, United States

Co-Author(s)

YZ

Yuhao Zhang

Graduate Student
University of Georgia, United States

Primary Investigator(s)

Xianqiao Wang

Associate Professor of Mechanical Engineering
University of Georgia, United States

Co-Author(s)

He Li

Assistant Professor for School of Chemical, Materials, and Biomedical Engineering
University of Georgia, United States
Yang Liu, PhD

Assistant Professor for School of Chemical, Materials, and Biomedical Engineering
University of Georgia
Athens, Georgia, United States

Introduction: Single-cell stiffness is an important parameter of cells. Many researchers conclude that cell motility is highly dependent on this parameter [1]. Cross et all [5] has shown that metastatic cancer cells are softer than benign cells, which started the exploration of how the mechanical properties of cells affect motility. In regards to cancer therapies, cell stiffness can explain why some therapies are more effective than others . For example, exposure to chemotherapy increases leukemia cell stiffness and can reduce the cell’s invasiveness and motility [2]. Many studies verify that cell stiffness is predominantly affected by the cytoskeletal arrangement (consisting of semi-flexible polymers) and the cortical actin meshwork [2]. Any cellular process that changes the mechanical properties of these polymers (polymerization, cross-linking protein function, external stimuli that activate signaling pathways which affect these proteins) will change the elastic modulus of the cell (a parameter that denotes cell stiffness) [2]. Various methods have been explored to try and measure the cell stiffness. Unlike the limitations of traditional methods for detecting cell stiffness, such as AFM, a novel ferrohydrodynamic microfluidic platform offers high throughput and the ability to detect proteoforms within the cytoskeleton [3]. This experiment utilizes ferrohydrodynamic microfluidics to precisely analyze cell stiffness and protein expression at the single-cell level (fig. 1). The deformation and dynamics of the cells observed in the experimental results are compared via a Molecular Dynamics Simulation using Large Scale Atomic Molecular/Massively Parallel Simulator (LAMMPS) to confirm the results. This research will explore the simulation results.

Materials and

Methods: The first step before running the simulation is to construct the microfluidic tube in CAD with the experimental dimensions (fig.2a&2b). This rendering is then transferred to a meshing software (ANSYS) to generate a mesh (fig.2c). The nodal coordinates are transferred to Microsoft Excel (fig.2d&2e), where this information is used to construct a data file of the tube’s atom positions. Following the given atom style of “hybrid DPD/full”(fig.2f&2g), setting up the data file via LAMMPS documentation and inputting positional coordinates for the tumor cell, the data file can be completed (fig.2h). The initial set up can be visualized in OVITO (fig.3a&3b), a visualization software. The final step is to create the LAMMPS input script and run the simulation. In the LAMMPS input script (fig.3c), two atom groups were defined: the tube and the tumor cell. The tumor cell’s surface area and volume were controlled with angle and dihedral styles. The pairwise interactions between the tube and tumor were “morse” style and all other interactions were “DPD” style, commonly used in biological simulations. The tumor cell’s velocity was generated by specifying a relevant temperature. A force was then applied to the tumor cell to mimic the magnetic buoyancy force due to the experimentally used ferrofluids [1]. The force on the tube was set to zero to mimic its rigidity compared to the tumor cell. Finally, different variables were adjusted among simulations: force, cell surface area, and cell stiffness to explore how they affect microfluidic particle transport.

Results, Conclusions, and Discussions: This study, through systematic simulation experiments, thoroughly analyzed the effects of various variables (surface area, force, and stiffness) on the motility of tumor cells in a microfluidic environment. The results indicate that these variables significantly impact cell deformation and migration behavior.

Surface area parameters (nondimensional units-LJ style):

-279: the tumor cell experiences some deformation and makes it through the tube (fig.4a)

-879 (fig.4b), 1779 (fig.4c), and 4379 (fig.4d): The cells experience large deformations when entering the channel, which is not observed in experimental results.

-879: The cell gets stuck momentarily at the channel's entrance, observed in experiments.

-4379: The cell remains deformed in the initial part of the simulation suggesting that the simulated cell size is too large.

Force parameters (nondimensional units-LJ style):

-1 (fig.4e): The cell experiences minimal deformation in the channel and approaches the entrance many times before entering. Due to the cell diameter being around 17 micrometers and the channel depth being 10 micrometers, the cell must exhibit low stiffness to enter the channel.

-20 (fig.4f): The cell experiences large amounts of deformation, and cytoplasm-like material is being extruded, suggesting the simulated force is much higher than the experimental magnetic buoyancy force.

Stiffness parameters:

-20 (fig.5a): The cell is significantly distorted when traveling through the channel, showing how cell stiffness keeps the cell unified.

-20, 100 (fig.5b), 200 (fig.5c) and 5000 (fig.5f): the cell makes it through the channel.

-500 (fig.5d) and 1000 (fig.5e): the cell gets stuck in the tube, the larger stiffness parameter (1000) cell gets earlier, indicating how stiffness affects cell migration and motility.

These simulation experiments further elucidated the mechanisms by which different physical parameters affect cell motility. Future research should focus on incorporating spatially dependent magnetic buoyancy forces to make the simulation results more consistent with actual experimental observations. Additionally, these findings are crucial for optimizing microfluidic technology in the biomedical field, particularly in cancer diagnosis and treatment. Once simulation results fully corroborate with experimental data, various cell conditions/parameters can be easily manipulated as opposed to generating a new experimental set-up repeatedly.

Acknowledgements (Optional): This material is based upon work supported by a National Science Foundation Research Experiences for Undergraduates (REU) site program under Grant No. 1950581.

References:

[1] R. Cheng, T. Zhu, L. Mao, “Three-dimensional and analytical modeling of microfluidic particle transport in magnetic fluids ," Microfluid Nanofluid (2014) 16:1143-1154

[2] P. Galie, P. Georges, P. Janmey, “How do cells stiffen?," Biochemical Journal (2022) 479 1825-1842

[3] Y. Zhang, “Single Cell Stiffness Analysis Using Ferrohydrodynamic-microfluidic Platform”, Powerpoint (2024)hydrodynamic-microfluidic Platform

[4] Q. Liu, D. Kuang, B. Zhang, G. Song, “Cell stiffness determined by atomic force microscopy and its correlation with cell motility”, Biochimica et Biophysica Acta 1860 (2016) 1953-1960

[5] S.E. Cross, Y.S. Jin, J. Rao, J.K. Gimzewski, Nanomechanical analysis of cells from cancer patients, Nat. Nanotechnol. 2 (2007) 780–783.