Three-dimensional multi-scale model of deformable platelets adhesion to vessel wall in blood flow

When a blood vessel ruptures or gets inflamed, the human body responds by rapidly forming a clot to restrict the loss of blood. Platelets aggregation at the injury site of the blood vessel occurring via platelet-platelet adhesion, tethering and rolling on the injured endothelium is a critical initial step in blood clot formation. A novel three-dimensional multiscale model is introduced and used in this paper to simulate receptor-mediated adhesion of deformable platelets at the site of vascular injury under different shear rates of blood flow. The novelty of the model is based on a new approach of coupling submodels at three biological scales crucial for the early clot formation: novel hybrid cell membrane submodel to represent physiological elastic properties of a platelet, stochastic receptor-ligand binding submodel to describe cell adhesion kinetics and Lattice Boltzmann submodel for simulating blood flow. The model implementation on the GPUs cluster significantly improved simulation performance. Predictive model simulations revealed that platelet deformation, interactions between platelets in the vicinity of the vessel wall as well as the number of functional GPIb{\alpha} platelet receptors played significant roles in the platelet adhesion to the injury site. Variation of the number of functional GPIb{\alpha} platelet receptors as well as changes of platelet stiffness can represent effects of specific drugs reducing or enhancing platelet activity. Therefore, predictive simulations can improve the search for new drug targets and help to make treatment of thrombosis patient specific.Comment: 38 pages, 10 figures, (accepted for publication). Philosophical Transactions of the Royal Society A, 201

FigureS3.pdf

<p>Fig. S3. Effect of different treatments on time to peak. Time to peak values were averaged over all cells/ex­periment and all independent experi­ments (n=4-10) for control (shear), Ca²⁺-free+La³⁺, U73122, U73343, FCCP (0.5), FCCP (2), antimycin A, oligomycin, CGP37157 (10), WT and MCU KD. *<i>P</i><0.05 vs. con­trol. ^†<i>P</i> < 0.05 vs. WT (MCU KD was only compared to WT).</p

FigureS1.pdf

<p>Fig. S1. Effects of different treatments on baseline fluorescence (a.u.). Baseline fluorescence was defined as either the fluo­rescence intensity of the 1^st digital frame at the beginning of 2 min static or the average fluorescence intensity of 2 min static. Values were averaged over all cells/experiment and all independent experi­ments (n=4-17) for untreated (vehicle-treated ECs that, at the end of the 2 min, were exposed to shear at either 1, 4 or 10 dynes/cm²), Ca²⁺-free+La³⁺, U73122, U73343, FCCP (0.5), FCCP (2), antimycin A, oligomycin, CGP37157 (10), WT and MCU KD. No statistically significant differences were found (MCU KD was only compared to WT).</p

FigureS3.pdf

<p>Fig. S3. Effect of different treatments on time to peak. Time to peak values were averaged over all cells/ex­periment and all independent experi­ments (n=4-10) for control (shear), Ca²⁺-free+La³⁺, U73122, U73343, FCCP (0.5), FCCP (2), antimycin A, oligomycin, CGP37157 (10), WT and MCU KD. *<i>P</i><0.05 vs. con­trol. ^†<i>P</i> < 0.05 vs. WT (MCU KD was only compared to WT).</p

FigureS2.pdf

<p>Fig. S2. Effect of the addition of different chemicals on normalized fluorescence during the preincubation period. Char­acteris­tic nor­mal­ized fluorescence signals vs. time are shown for ECs on a single field of view during the 20 min preincuba­tion period with either FCCP (2), antimycin A or CGP37157 (10).</p