Reducing animal use in thrombosis research with an ex vivo injury model

Cardiovascular disease is a major cause of death in the UK. Thrombosis is the major acute event that triggers a heart attack or stroke. Development of new anti-thrombotic drugs is therefore vital, and the starting point for this process is a better understanding of the molecular regulation of thrombosis. Thrombosis is a complex, multicellular process. The in vivo carotid artery injury model in anaesthetised mice is extensively used to investigate thrombus formation. However, the ability to record only one thrombus in each mouse, and the high variance of the technique, mean that many animals are required to provide sufficient statistical power. Reduction in the number of mice used in these experiments to the minimum possible is an ethical imperative.

In this project an ex vivo carotid artery injury model will be developed that retains the benefits of the in vivo model but requires fewer mice. Carotid arties sections excised from humanely-killed mice will be cannulated to allow luminal perfusion. Mouse blood containing fluorescently-conjugated platelet-labelling antibodies will be perfused through the vessel and thrombus formation monitored by epifluorescence following vessel injury. This means that both carotid arteries can be used, reducing the require number of animals by at least 50%.

To validate the utility of the model, the effects of anti-thrombotic agents will be compared to published reports of the in vivo carotid injury model. A further development of the technique will be to use human blood in excised mouse carotid artery sections. The ability of human and mouse platelets to form thrombi, and the sensitivity to anti-thrombotics, will be compared. Humanizing the ex vivo carotid artery injury model will make the model more relevant to human disease and will be more useful for testing novel therapeutics.

This Studentship was co-awarded with the British Heart Foundation (BHF).

• Blog: NC3Rs/BHF Studentships – advancing cardiovascular science with 3Rs methods