Margination and adhesion dynamics of tumor cells in a real microvascular network

Abstract

In tumor metastasis, the margination and adhesion of tumor cells are two critical and closely related steps, which may determine the destination where the tumor cells extravasate to. We performed a direct three-dimensional simulation on the behaviors of the tumor cells in a real microvascular network, by a hybrid method of the smoothed dissipative particle dynamics and immersed boundary method (SDPD-IBM). The tumor cells are found to adhere at the microvascular bifurcations more frequently, and there is a positive correlation between the adhesion of the tumor cells and the wall-directed force from the surrounding red blood cells (RBCs). The larger the wall-directed force is, the closer the tumor cells are marginated towards the wall, and the higher the probability of adhesion behavior happen is. A relatively low or high hematocrit can help to prevent the adhesion of tumor cells, and similarly, increasing the shear rate of blood flow can serve the same purpose. These results suggest that the tumor cells may be more likely to extravasate at the microvascular bifurcations if the blood flow is slow and the hematocrit is moderate.

Computational analysis of cancer cell adhesion in curved vessels affected by wall shear stress for prediction of metastatic spreading

“…Frontiers in Bioengineering and Biotechnology frontiersin.org adhesion (Figures 3B, E) lower stiffness (Ca = 0.015) leads to firm adhesion (Figures 3C, F). These states closely parallel the findings by Wang et al (2021).…”

“…The adhesive dynamics approach by Hammer and Apte (1992) is renowned for studying cell interactions with ligand-coated surfaces, particularly in tumor cell metastasis and adhesion to endothelial cells, as well as in inflammatory responses and lymphocyte homing. Its widespread adoption by previous researchers further validates its suitability for modeling CTC adhesion (Xiao et al, 2017;Zhang et al, 2018;Lenarda et al, 2019;Dabagh et al, 2020;Cui et al, 2021;Wang et al, 2021;Xiao et al, 2021). This model is characterized by a spring-based mechanism with a probability distribution function that governs the adhesion of receptor-ligand pairs, employing a stochastic Monte Carlo technique and integrating a distancedependent kinetics model as first delineated in the Dembo model (Dembo et al, 1988;Dembo et al, 1988).…”

Mapping the topography of spatial gene expression with interpretable deep learning

“…Another potential clinical implication is that the spatial gradients learned by GASTON could reveal spatial trajectories of metastatic migration, similar to how the spatial gradients learned by GASTON in the olfactory bulb show spatial trajectories of neural migration (Figure 5B). For example, the variation of EMT genes along the spatial gradients near the tumor boundary may reveal the molecular underpinnings of the margination process in which tumor cells migrate towards a vascular wall before metastasis [142, 165].…”

Red blood cell passage through deformable interendothelial slits in the spleen: Insights into splenic filtration and hemodynamics

“…This includes models operating at the protein level [41, 42, 43, 44, 45, 46, 47, 48, 49] and cellular level [ ? 44, 50, 51, 52, 53, 54, 55, 56, 57, 40, 58]. While protein-level RBC models can simulate pathological alterations in RBC membrane structure associated with blood disorders, their application to modeling blood cell suspensions or blood flow is hindered by computational costs.…”