Multiphysics and multiscale modeling of microthrombosis in COVID-19

Abstract

Emerging clinical evidence suggests that thrombosis in the microvasculature of patients with Coronavirus disease 2019 (COVID-19) plays an essential role in dictating the disease progression. Because of the infectious nature of SARS-CoV-2, patients' fresh blood samples are limited to access for in vitro experimental investigations. Herein, we employ a novel multiscale and multiphysics computational framework to perform predictive modeling of the pathological thrombus formation in the microvasculature using data from patients with COVID-19. This framework seamlessly integrates the key components in the process of blood clotting, including hemodynamics, transport of coagulation factors and coagulation kinetics, blood cell mechanics and adhesive dynamics, and thus allows us to quantify the contributions of many prothrombotic factors reported in the literature, such as stasis, the derangement in blood coagulation factor levels and activities, inflammatory responses of endothelial cells and leukocytes to the microthrombus formation in COVID-19. Our simulation results show that among the coagulation factors considered, antithrombin and factor V play more prominent roles in promoting thrombosis. Our simulations also suggest that recruitment of WBCs to the endothelial cells exacerbates thrombogenesis and contributes to the blockage of the blood flow. Additionally, we show that the recent identification of flowing blood cell clusters could be a result of detachment of WBCs from thrombogenic sites, which may serve as a nidus for new clot formation. These findings point to potential targets that should be further evaluated, and prioritized in the anti-thrombotic treatment of patients with COVID-19. Altogether, our computational framework provides a powerful tool for quantitative understanding of the mechanism of pathological thrombus formation and offers insights into new therapeutic approaches for treating COVID-19 associated thrombosis.

Fig 1. Schematics of the simulation setup.

Top row: particle-based models for WBCs, RBCs and platelets. Second row: A 3D circular microchannel with a diameter of 15 μm and a length of 100 μm is filled with RBCs, platelets, one WBC and solvent particles which carry 23 factors. Solvent particles and RBCs close to the WBC are omitted in visualization for clarity. One thrombogenic site (highlighted in magenta) with a length of 30 μm is assigned on the vessel wall. Mass fluxes of four factors (listed in Table C in S3 Text) depending on the level of [TF-VIIa] are injected into the channel from the thrombogenic site to initiate and drive the coagulation cascade. Blood flows from left to right.

Fig 2. Global sensitivity analysis of how aberrant levels of coagulation factors in COVID-19 affect the generation of FIIa (A) and FIa (B).

Circles in the figure signify the sensitivity of (A) thrombin and (B) fibrin to a single varying factor and the diameters of the circles correspond to values of the sensitivity for the examined factors. The lines that connect two factors designate the sensitivity of (A) thrombin and (B) fibrin to the two connected factors when they are varied synchronously. The thickness of the lines reflect the values of the sensitivity.

Fig 3. Platelet activation and adhesion driven by the coagulation cascade at the thrombogenic site.

(A) Four sequential snapshots of platelets activation and adhesion at the thrombogenic site (highlighted by magenta). Passive platelets (green) become activated and adhesive after exposing to [FIIa] ≥ 1 nM, which is described by the concentration field in the figure. RBCs close to the thrombogenic site are not plotted for virtualization of the platelet aggregate. Blood flows from left to right. See S1 Movie. (B) Typical examples of the number of adhered platelets at the thrombogenic site vs. time (t), plotted in semi-log axes. The exponential growth rate is computed by fitting the data using dotted lines. Each of the growth rate measurement consists of four stochastic realizations to compute the mean and standard deviation.

Fig 4. Impacts of inflammation levels, hemodynamics and aberrant levels of coagulation factors on the thrombus growth rate (α_g).

(A) Variation of thrombus growth rates with respect to different blood flow velocities at vascular expression of [TF-VIIa]_v = 0.005, 0.01 and 0.2 nM. (B) Variation of thrombus growth rates with respect to different blood flow velocities at aberrant levels of coagulation factors. Vascular expression of [TF-VIIa]_v is maintained at 0.01 nM for all cases. Guided by values listed in Table 1, [AT] is selected to be 45% of the reference value; [FVIII] is selected to be 470% of the reference value; [PC] is selected to be 156% of the reference value; [FV] is selected to be 181% of the reference value; [FX] is selected to be 125% of the reference value.

Fig 5. Adhesion of WBCs to the thrombogenic site precipitates the thrombus formation.

Three sequential snapshots of platelet activation and adhesion at the thrombogenic site (highlighted by magenta) triggered by (A) vascular expression of [TF-VIIa]_v = 0.005 nM and (B) cellular expression of [TF-VIIa]_c = 0.005 nM on the adhered WBC. See S2 and S3 Movies. Passive platelets (green) become adhesive after exposing to [FIIa] ≥ 1 nM. Yellow color highlights the iso-surface of [FIIa] = 1 nM, within which the platelets become adhesive. RBCs close to the thrombogenic site are omitted for virtualization of the platelet aggregate. Blood flows at a velocity of 0.6 mm/s from left to right. (C) Variation of thrombus growth rates with respect to different blood flow velocities with and without presence of a WBC. [TF-VIIa]_v = 0.005 nM and [TF-VIIa]_c = 0.005 nM, 0.01 nM and 0.2 nM are examined, respectively.

Fig 6. Interaction of a flowing WBC with existing thrombus.

(A) Four sequential snapshots illustrate that at an initial blood flow velocity of 0.38 mm/s, a flowing WBC first attaches to an existing thrombus at the thrombogenic site and subsequently detaches from the thrombus, forming a WBC-platelet cluster (highlighted by an orange dotted circle) when WBC-RBC adhesion is not considered. See S4 Movie. (B) Under the same flow condition, the flowing WBC adheres to the surrounding platelets and RBCs during its interaction with the thrombus when WBC-RBC adhesion is considered, forming a WBC-RBC-platelet aggregate (highlighted by a magenta dotted circle). See S5 Movie. Blood flows from left to right. Flow cytometry images with staining of CD61, CD45, DAPI and CD66b show (C) a WBC-platelet cluster and (D) a WBC-platelet-RBC cluster from blood samples of patients with COVID-19 [84].

Fig 7. WBC-thrombus interaction affects the instantaneous mean blood velocity upstream of the thrombogenic site.

Blue curve: when the mean blood velocity in the microvessel is 0.38 mm/s, the flowing WBC first attaches to the thrombus at the thrombogenic site and subsequently detaches from the thrombus. See S4 Movie. The instantaneous mean blood velocity upstream of the thrombogenic site first decreases induced by the short-term WBC-thrombus attachment and then recovers after the WBC detaches. Magenta curve: when the mean blood velocity is reduced to 0.3 mm/s, the WBC attaches to the thrombus at the thrombogenic site, establishing a firm adhesion. See S6 Movie. The instantaneous mean blood velocity upstream of the thrombogenic site decreases to ∼0.13 mm/s when the WBC-RBC adhesion is not considered. Red curve: under the same flow condition as the magenta curve, the instantaneous mean blood velocity upstream of the thrombogenic site further decreases to ∼0.05 mm/s when the WBC-RBC adhesion is considered. See S7 Movie.