Effects of wall shear stress and its gradient on tumor cell adhesion in curved microvessels

Tumor cell adhesion to vessel walls in the microcirculation is one critical step in cancer metastasis. In this paper, the hypothesis that tumor cells prefer to adhere at the microvessels with localized shear stresses and their gradients, such as in the curved microvessels, was examined both experimentally and computationally. Our in vivo experiments were performed on the microvessels (post-capillary venules, 30-50μm diameter) of rat mesentery. A straight or curved microvessel was cannulated and perfused with tumor cells by a glass micropipette at a velocity of ∼1mm/s. At less than 10 min after perfusion, there was a significant difference in cell adhesion to the straight and curved vessel walls. In 60min, the averaged adhesion rate in the curved vessels (n = 14) was ∼1.5-fold of that in the straight vessels (n = 19). In 51 curved segments, 45% of cell adhesion was initiated at the inner side, 25% at outer side, and 30% at both sides of the curved vessels. To investigate the mechanical mechanism by which tumor cells prefer adhering at curved sites, we performed a computational study, in which the fluid dynamics was carried out by the lattice Boltzmann method , and the tumor cell dynamics was governed by the Newton's law of translation and rotation. A modified adhesive dynamics model that included the influence of wall shear stress/gradient on the association/dissociation rates of tumor celladhesion was proposed, in which the positive wall shear stress/gradient jump would enhance tumor cell adhesion while the negativewall shear stress/gradient jumpwould weaken tumor cell adhesion. It was found that the wall shear stress/gradient, over a threshold, had significant contribution to tumor cell adhesion by activating or inactivating cell adhesion molecules. Our results elucidated why the tumor cell adhesion prefers to occur at the positive curvature of curved microvessels with very low Reynolds number (in the order of 10-2) laminar flow.