Computational imaging analysis of fibrin matrices with the inclusion of erythrocytes from homozygous SS blood reveals agglomerated and amorphous structures.

Abstract

Sickle cell disease is a single point mutation disease that is known to alter the coagulation system, leading to hypercoagulable plasma conditions. These hypercoagulable conditions can lead to complications in the vasculature, caused by fibrin clots that form undesirably. There is a need to understand the morphology and structure of fibrin clots from patients with sickle cell disease, as this could lead to further discovery of treatments and life-saving therapies. In this work, a computational imaging analysis method is presented to evaluate fibrin agglomeration in the presence of erythrocytes (RBCs) homozygous for the sickle cell mutation (SS). Numerical algorithms were used to determine agglomeration of fibrin fibers within a matrix with SS RBCs to test the hypothesis that fibrin matrices with the inclusion of SS RBCs possess a more agglomerated structure than native fibrin matrices with AA RBCs. The numerical results showed that fibrin structures with SS RBCs displayed an overall higher degree of agglomeration as compared to native fibrin structures. The computational algorithm was also used to evaluate fibrin fiber overlap (aggregation) and anisotropy (orientation) in normal fibrin matrices compared to fibrin matrices polymerized around SS RBCs; however, there was no statistical difference. Ultrasound measurements of stiffness revealed rigid RBCs in the case of samples derived from homozygous SS blood, and densely evolving matrices, when compared to normal fibrin with the inclusion of AA RBCs. An agglomeration model is suggested to quantify the fibrin aggregation/clustering near RBCs for both normal fibrin matrices and for the altered structures. The results of this work are important in the sense that the understanding of aggregation and morphology in fibrin clots with incorporation of RBCs from persons living with sickle cell anemia may elucidate the complexities of comorbidities and other disease complications.