Effects of clot contraction on clot degradation: A mathematical and experimental approach

Abstract

Thrombosis, resulting in occlusive blood clots, blocks blood flow to downstream organs and causes life-threatening conditions such as heart attacks and strokes. The administration of tissue plasminogen activator (t-PA), which drives the enzymatic degradation (fibrinolysis) of these blood clots, is a treatment for thrombotic conditions, but the use of these therapeutics is often limited due to the time-dependent nature of treatment and their limited success. We have shown that clot contraction, which is altered in prothrombotic conditions, influences the efficacy of fibrinolysis. Clot contraction results in the volume shrinkage of blood clots, with the redistribution and densification of fibrin and platelets on the exterior of the clot and red blood cells in the interior. Understanding how these key structural changes influence fibrinolysis can lead to improved diagnostics and patient care. We used a combination of mathematical modeling and experimental methodologies to characterize the process of exogenous delivery of t-PA (external fibrinolysis). A three-dimensional (3D) stochastic, multiscale model of external fibrinolysis was used to determine how the structural changes that occur during the process of clot contraction influence the mechanism(s) of fibrinolysis. Experiments were performed based on modeling predictions using pooled human plasma and the external delivery of t-PA to initiate lysis. Analysis of fibrinolysis simulations and experiments indicate that fibrin densification makes the most significant contribution to the rate of fibrinolysis compared with the distribution of components and degree of compaction (p < 0.0001). This result suggests the possibility of a certain fibrin density threshold above which t-PA effective diffusion is limited. From a clinical perspective, this information can be used to improve on current therapeutics by optimizing timing and delivery of lysis agents.

Figure 1

Schematic of 3D mathematical model of external fibrinolysis. (A) The model is composed of the fibrin-free region containing unbound t-PA (black asterisks), the fibrin network periphery with unbound and bound t-PA (green stars), and the core containing fibrin and RBCs (red circles). (B) Time series of t-PA diffusion (green stars) into the fibrin network (blue and black) and RBC region (180, 240, 300, 360 s). To see this figure in color, go online.

Figure 2

Clot contraction results in structural changes. (A) Colorized scanning electron microscopy image of a contracted blood clot. (B) Magnified view of a contracted clot and (C) uncontracted clot. Pink arrowhead, periphery edge; blue arrowhead, fibrin; orange arrowhead, pores. Scale bar: 10 μm. To see this figure in color, go online.

Figure 3

Fibrinolysis in contracted versus uncontracted clot simulations. Model contracted (A, left) and uncontracted (A, right) clots are built to mimic the experimental clots. Three independent simulations were conducted for both contracted and uncontracted clots with t-PA added at the clot’s edge, and degradation of the clot was measured by the fraction of fibrin remaining in the clot over the course of time (B). The experimental rate of clot degradation (contracted versus impaired contraction with blebbistatin, a myosin inhibitor (22)), and modeling rate of clot degradation (90RCT versus 0RU) are compared (C); experimental degradation is measured as percentage of fibrin degradation products (% FDP) per hour; modeling degradation was measured in fraction of fibrin degraded per second (ff/s), and was determined by calculating the slope of the linear portion of (B). Time to 50% degradation (D) and 90% degradation (E) were analyzed. Data are represented as mean $\pm$ SEM. Data were analyzed using a one-way ANOVA followed by a Tukey’s multiple comparison test. ^∗∗∗∗p < 0.0001. To see this figure in color, go online.

Figure 4

Influence of clot structure on external fibrinolysis. Additional simulations were developed to understand the role of contraction, redistribution, and densification on the lysis of blood clots. Fraction of fibrin remaining in the clot is plotted as a function of time for simulations described in Table 2 (A). Clot degradation rate (fraction of fibrin degraded per second) was determined as described previously (B). Time to 50% degradation (C), and time to 90% degradation (D) were analyzed. Ns, p > 0.05; ^∗p < 0.05; ^∗∗p < 0.01; ^∗∗∗p < 0.001; ^∗∗∗∗p < 0.0001. Data represented as mean ± SEM. (E) The column mean differences for the degradation rates of the entire clot. Scenarios with only one dissimilar component are highlighted in green, red, and purple for comparison. The comparison highlighted in blue represents the baseline contracted and uncontracted clots. To see this figure in color, go online.

Figure 5

Analysis of fibrinolysis in the periphery and core of the clot. Degradation rates (ff/s) of the different subregions of the clots—periphery (A) and core (B)—are compared for the scenarios in Table 2. Degradation rates of the periphery, core, and entire clot are compared with each other for each scenario in Table 2 (C). Data represented as mean ± SEM. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001, ^∗∗∗∗p < 0.0001. To see this figure in color, go online.

Figure 6

Model verification using experiments to study the role of densification. Confocal microscopy images showing loose (A) and tight (B) fibrin networks. Maximum OD and fiber area fraction were used to verify densification with varying fibrinogen concentrations. (C) Normalized lysis curves of the different fibrinogen concentrations after the delivery of t-PA and initiation of lysis, with black dotted lines showing time at 50% lysis (in seconds), and a representative linear fit for degradation rate analysis (in terms of fraction of clot remaining per second) (D). Time at 50% lysis (E) and degradation rate (F) are used to assess clot lysis. A side-by-side comparison of experiments (0.22 versus 1.94 mg/mL) and modeling (90RCL versus 90RCT) of loose and dense clot degradation rates (F). Data represented as mean ± SEM. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001, ^∗∗∗∗p < 0.0001. For all figures, blue, red, purple, and green represent 0.22, 0.65, 1.34, and 1.94 mg/mL, respectively. To see this figure in color, go online.

Figure 7

Proposed mechanism for limited diffusion of t-PA molecules in tight fibrin networks. More bound t-PA molecules (green stars) get stuck in the tight clot periphery because of high affinity for fibrin (left, contracted clot) and are unable to diffuse through the core. For a Figure360 author presentation of Figure 7, see https://doi.org/10.1016/j.bpj.2022.07.023. Clots with impaired contraction have large enough pores that unbound t-PA molecules (black stars) are less likely to encounter a fiber while diffusing, and hence are less likely to have their diffusion interrupted by a binding event; this results in the t-PA displaying faster effective diffusion than in dense clots. In the above scenarios, the core of the clot is left constant with uniform, circular RBCs and identical fibrin network. To see this figure in color, go online.