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. 2018 Feb 27;114(4):978-991.
doi: 10.1016/j.bpj.2017.12.034.

Impact of Tissue Factor Localization on Blood Clot Structure and Resistance Under Venous Shear

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Free PMC article

Impact of Tissue Factor Localization on Blood Clot Structure and Resistance Under Venous Shear

Vijay Govindarajan et al. Biophys J. .
Free PMC article

Abstract

The structure and growth of a blood clot depend on the localization of tissue factor (TF), which can trigger clotting during the hemostatic process or promote thrombosis when exposed to blood under pathological conditions. We sought to understand how the growth, structure, and mechanical properties of clots under flow are shaped by the simultaneously varying TF surface density and its exposure area. We used an eight-channel microfluidic device equipped with a 20- or 100-μm-long collagen surface patterned with lipidated TF of surface densities ∼0.1 and ∼2 molecules/μm2. Human whole blood was perfused at venous shear, and clot growth was continually measured. Using our recently developed computational model of clot formation, we performed simulations to gain insights into the clot's structure and its resistance to blood flow. An increase in TF exposure area resulted not only in accelerated bulk platelet, thrombin, and fibrin accumulation, but also in increased height of the platelet mass and increased clot resistance to flow. Moreover, increasing the TF surface density or exposure area enhanced platelet deposition by approximately twofold, and thrombin and fibrin generation by greater than threefold, thereby increasing both clot size and its viscous resistance. Finally, TF effects on blood flow occlusion were more pronounced for the longer thrombogenic surface than for the shorter one. Our results suggest that TF surface density and its exposure area can independently enhance both the clot's occlusivity and its resistance to blood flow. These findings provide, to our knowledge, new insights into how TF affects thrombus growth in time and space under flow.

Figures

Figure 1
Figure 1
2D geometric representation of the microfluidic device. The arrows show the blood flow direction. (A) Photograph of the overall setup of the microfluidic device. Image reproduced with permission (1). Operating under the pressure relief mode requires a set of two channels, one with and one without a thrombogenic surface. Thus, the eight channels were divided into four pairs, and each pair was utilized to study one clotting event. In a given pair, under the pressure relief mode, one of the channels was coated with a thrombogenic (collagen + TF) surface to promote thrombus growth, while blood could flow freely through the other. To abolish platelet adhesion in the second channel and allow free flow of blood, EDTA-treated blood was delivered through its dedicated inlet. CTI-treated blood was delivered in the channels with the thrombogenic surface (1). Thrombus formation is initiated when blood flows over the thrombogenic surfaces in the microfluidic device channels. When CTI-treated blood formed a clot over the thrombogenic surface, it started occluding the channel. As a consequence, blood flow automatically increased in the corresponding EDTA channel to maintain a constant outflow (withdrawn from a syringe at the outlet) (1, 21, 24, 25). Thus, the eight-channel microfluidic device allowed us to induce and monitor four separate clotting events simultaneously. (B) 3D geometry of the microfluidic device section that consists of two separate inlets and a single outlet. The dimensions of the channels are as indicated in the figure. (C) 2D vertical representation of the microfluidic device (following (1)). This 2D geometry was constructed so that it maintained a channel height of 60 μm. The channel without a thrombogenic surface is stacked above the channel with one. This geometry allowed us to perform simulations in the pressure-relief mode in a 2D setting. Also indicated are the flow boundary conditions used in our simulations. Finally, no-slip and no-penetration conditions were imposed on the walls.
Figure 2
Figure 2
Clotting kinetics measured in the microfluidic channels and those predicted by our computational model for the high TF surface density (∼2 TF molecules/μm2). For proper comparisons between model predictions and experimental data, normalization was necessary because of differences in reporting units between experimental data and model predictions. (A) Model-predicted and experimentally measured platelet accumulation. The solid line (no markers) and the dashed line show the normalized model-predicted values of the mean concentrations of bound platelets calculated over the entire flow domain with the 100- and 20-μm thrombogenic surfaces, respectively. The solid lines with square and circular markers show the normalized values of the mean platelet fluorescence intensity, which represent the levels of platelet accumulation measured in the microfluidic channels with the 100- and 20-μm thrombogenic surfaces, respectively. For each donor blood sample, experiments were repeated four times (i.e., four independent clotting events were measured) to obtain the average time course for that sample. Finally, the overall mean of the average time courses obtained for each donor was calculated to represent the average behavior across all donors (N = 5). Shaded regions represent one standard deviation corresponding to the overall mean and characterizing interdonor variability. Clotting events were measured at 60-s time intervals, and the data points (represented by the markers) were connected with solid lines to enhance the visual representation of the displayed trends. The experimental data were normalized by dividing by the maximal average value; the model-generated time courses were normalized by dividing by the maximal simulated value. (B) Thrombin generation simulated by our computational model and those obtained from experimental measurements, as explained above. Time-course normalization was performed similarly to the platelet data. (C) Fibrin accumulation simulated by our computational model and that obtained from experimental measurements. Time-course normalization was performed similarly to the platelet data. The differences in the experimentally measured platelet deposition, thrombin generation, and fibrin accumulation between the 100- and 20-μm surfaces at 400 s were statistically significant (in the respective pairwise comparisons).
Figure 3
Figure 3
Profiles of the average platelet deposition region at 400 s over thrombogenic surfaces with the high TF surface density (∼2 TF molecules/μm2). The profiles (black lines) are shown for the 20-μm (A) and 100-μm (B) thrombogenic surfaces; shaded regions represent one standard deviation. The x axis represents the horizontal coordinate along the microfluidic channel (and thrombogenic surface) length. The profiles shown were obtained by averaging platelet deposition profiles across all donors (N = 5). Before averaging, individual clot profiles were aligned with respect to the middle of the thrombogenic surface (shown by thick horizontal black lines). The alignment was performed under the assumption that the clot’s upstream and downstream sides are roughly symmetrical with respect to the thrombogenic patch location. The upstream and downstream edges of the platelet deposition domains were defined as the first positions where the clot height increased above and decreased below, respectively, 5% of the maximal height.
Figure 4
Figure 4
Clotting kinetics measured in the microfluidic channels and those predicted by our computational model for the 100-μm thrombogenic surface. (Left and right panels) Clotting kinetics detected for the low and high TF density surfaces, respectively. For proper comparisons between model predictions and experimental data, normalization was necessary because of differences in reporting units between experimental data and model predictions. Lines with square markers show normalized mean fluorescence intensities (N = 5), which represent the clotting kinetics of individual donor blood samples. The color-shaded areas correspond to one standard deviation. Lines with circular markers represent the clotting kinetics averaged across all donors. Clotting events were measured at 60-s time intervals, and the data points (represented by the markers) were connected with solid lines to enhance visual presentation. Thick black lines (no markers) show the normalized model-predicted values of the mean concentrations of bound platelets calculated over the entire flow domain. (A) Platelet accumulation. The experimental data were normalized by dividing by the maximal average value; the model-generated time courses were normalized by dividing by the maximal simulated value. The differences in the experimentally measured platelet deposition between the high and low TF surface density cases at 420 s were statistically nonsignificant (in the pairwise comparisons). (B) Thrombin generation. (C) Fibrin accumulation. Time-course normalization for thrombin and fibrin was performed similarly to the platelet data. The differences in the experimentally measured thrombin generation and fibrin accumulation between the high and low TF surface density cases at 420 s were statistically significant (in the respective pairwise comparisons). To see this figure in color, go online.
Figure 5
Figure 5
Model-predicted spatial distribution of the clot at 400 s. (Left and right panels) Model-predicted spatial distributions of clots on low and high TF density surfaces, respectively, where the thrombogenic surface (100 μm) was centered at 300 μm from the origin. The blood flow direction is from left to right. (A) Our computational model captured the spatial distribution of the platelet deposition domain in the microfluidic device. The experimentally measured platelet deposition is indicated by the white line superimposed over the density plot of model-predicted platelet deposition. (The white lines in the left and right panels correspond to the data also shown as the black lines in Figs. 3B and S8B, respectively.) (B) Model-predicted thrombin generation. (C) Model-predicted fibrin accumulation. To see this figure in color, go online.
Figure 6
Figure 6
Model-predicted clot viscous resistance and axial velocity at 400 s. The lower channel of the microfluidic device was occluded, owing to the resistance imparted by platelet and fibrin deposition at the thrombogenic surface. The blood flow direction is from left to right. (AD) (Left panels) Density plots of viscous resistance, whose magnitude depends on the deposited platelets and fibrin. (Right panels) Density plots of the resultant axial velocity, which is reduced owing to an increased viscous resistance leading to blood flow occlusion over the thrombogenic surface. As the channel with the thrombogenic surface becomes occluded, blood flow is diverted to the upper channel, which has no thrombogenic surfaces. To see this figure in color, go online.
Figure 7
Figure 7
Computational fluid dynamics predictions of shear rate and intrathrombus velocity patterns for a representative platelet deposition profile (donor #5) over the 100-μm thrombogenic surface at 420 s. The platelet deposition profiles were assumed to be porous. (A) Shear rate distribution on the clot boundaries for the high and low TF density. (B) The colors represent blood flow velocity inside the porous clot region (i.e., the intrathrombus blood flow velocity). (Top and bottom panels) Intrathrombus blood flow velocity for the high and low TF surface density, respectively. The thrombogenic surface was centered at 300 μm. The blood flow direction is from left to right. Note that the shear rate magnitudes at the upstream and downstream points of the clot are small but nonzero values (due to the small velocity gradients in these regions). To see this figure in color, go online.

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