Document Type

Article

Publication Date

5-12-2026

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This article is the author's final published version in Blood Advances, Volume 10, Issue 9, May 2026, Pages 3143-3153.

The published version is available at https://doi.org/10.1182/bloodadvances.2025017110. Copyright © 2026 American Society of Hematology.

Abstract

Intravital imaging studies have provided insights into the spatial and temporal variations of platelet activation and thrombin generation that occur during hemostasis; however, these studies are generally limited to small vessels due to the practical limitations of imaging in thicker tissues. Recent advances in cleared tissue fluorescence imaging as well as volume electron microscopy (vEM) coupled with machine learning-based image segmentation provide an opportunity for analysis of the 3-dimensional structure of complex tissues. We utilized these technologies to examine hemostatic plugs from murine jugular veins and carotid arteries to investigate the spatial distribution of platelet activation and biochemical responses in these disparate physiologic contexts. Both venous and arterial hemostatic plugs had a heterogeneous structure with regions of sparsely and densely packed platelets. Despite similar injury sizes, arterial hemostatic plugs were at least an order of magnitude larger than venous plugs. The difference in plug size was primarily due to a 19-fold increase in the population of densely packed platelets in the extravascular compartment. Venous plugs displayed significant platelet aggregation extending into the vessel lumen and developed distinctive fibrin and red blood cell-filled cavities. Complementary fluorescence microscopy revealed that platelet activation was spatially heterogeneous in both contexts, with α-granule secretion and phosphatidylserine exposure confined to specific microenvironments, highlighting tightly regulated thrombin activity. Overall, our findings reveal both conserved and distinct mechanisms of hemostatic thrombus formation in different physiologic contexts. They also demonstrate the power of vEM coupled with machine learning-based image segmentation for the quantitative analysis of large imaging data sets from complex tissues.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Language

English

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