(A-27) Investigating the Influence of Heterogeneity within Circulating Tumor Cell Types on Microvascular Transport

Introduction:: Biophysical properties play a significant role in a cell’s response to fluid forces [1–3], transport [4], and metastatic potential [5–8]. However, characterizing cell lines by the average radius or membrane elasticity overlooks inherent heterogeneity within the cell population. The objective of this study is to determine whether variance in parameters can lead to differences in a cell’s trajectory and the fluid forces it experiences. Using the L1210 cell line as a representative example, we employed a systematic approach involving a computational fluid-structure interaction model to analyze its transport through a microvascular geometry. The in-silico approach allowed us to isolate the effect of small changes in the cell’s radius or elasticity and quantify their subsequent effects on trajectory and fluid force exposure. Our findings demonstrate that even minor changes in the deformation, as small as < ![if !msEquation] >< ![if !vml] >< ![endif] >< ![endif] > in shear elastic modulus, can result in significant differences in fluid dynamic forces. This study highlights the significance of accounting for cell heterogeneity even within a single cell line when investigating how these biophysical properties impact the cell’s interactions with a surrounding microfluidic environment. By precisely controlling cell parameters, our study reveals the impact of its biomechanical properties on cell behavior in complex geometry. In addition, it provides a computational reference for experimental work with cell-fluid interaction.


Materials and Methods:: The simulations use HARVEY [9, 10], a massively paralleled 3D lattice Boltzmann method-based computational fluid dynamics solver. HARVEY utilizes the finite element method to capture the mechanics of cells in circulation and combines it with numerically solved fluid dynamics using immersed boundary method. Cell stiffness is represented by shear stress modulus Gs, which is part of the strain energy function defined by the Skalak constitutive law [11]. By bridging the distribution of force and pressure between the cell and fluid, HARVEY creates a fluid-structure interaction model that precisely captures the dynamics of the cells and the fluid. The result gives the spatial and temporal distribution of the cell motion and the surrounding fluid.

We use an idealized multi-branching topology to represent a microvascular geometry designed to highlight differences in transport. The fluid flows in from the left side tube and the right side. With channel branching, the velocity drops in gradient. Both geometries and fluid velocity patterns are shown in Figure 1A. To capture the heterogeneity of the L1210 cell line, we generate simulations of cells with the size ranging from 5µm to 7µm and vary the shear elastic modulus (Gs) ranging from 5*10^(-5) to 3*10^(-4). In this study, we specifically investigate the impact of cell elasticity by comparing cells with the same size (radius equals 7 µm) and similar shear elastic modulus.


Results, Conclusions, and Discussions:: We compared two cells with the same radii (7 µm) but slightly varying stiffness Gs = 2.25E-4 and 3E-4, among a wide range of cells with different radii and stiffness. While keeping the fluid and the cells' starting position the same, various cell deformations resulted in changes in trajectory (Figure 2A). We tracked the spatial distribution of cell residence time, which resulted in a pattern marking regions where the cell spent significantly more time (Figure 1B, C). To provide a direct comparison of the motion of the cells, we subtracted the residence time by the vertex of each discretized fluid particle. We marked the regions where cells spent significantly different time with different colors (Figure 2B). At the early stage, where cell trajectories overlap, the stiffer cell spent more time around the center of the blood vessel, while the softer cell spent more time around the wall while traveling. Cells branched differently at the bottom of the geometry, marking a drastic path difference. By thresholding high residence time regions differently, regions where the cell spent the most time, were uniquely captured for each cell stiffness. Figure 2C demonstrated a significant difference in the average velocity of the cell's surrounding environment. The stars annotation indicates that the p-value between the two datasets is smaller than 0.001. Figure 2D shows the percentage of time the cell spent in different velocity regions. With increased cell stiffness, the cell spent more time in high and middle-velocity regions (Figure 2D, right), while a softer cell (Figure 2D, left) spent more time in low-velocity regions.

Research has shown that fluid velocity impacts epithelial cell surface protein, which affects the adhesion pattern between vessel walls and cells [12]. Given the complexity of cell-blood interplay, this study demonstrates that a minor difference in cell deformation could lead to a drastic variation in its microfluidic environment. Ignoring the variation caused by cell heterogeneity could mislead experimental results in cell adhesion st, such as cancer metastasis. This study provides a computational method that can guide future experiments in quantifying the effect of cell heterogeneity on transportation behavior.


Acknowledgements (Optional): : Computing support for this work came from the Duke Compute Cluster. The research reported in this publication was supported by the National Institutes of Health under Award Number U01-CA253511. The content does not necessarily represent the official views of the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.


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