Deciphering microvascular changes after myocardial infarction through 3D fully automated image analysis

The microvasculature continuously adapts in response to pathophysiological conditions to meet tissue demands. Quantitative assessment of the dynamic changes in the coronary microvasculature is therefore crucial in enhancing our knowledge regarding the impact of cardiovascular diseases in tissue perfusion and in developing efficient angiotherapies. Using confocal microscopy and thick tissue sections, we developed a 3D fully automated pipeline that allows to precisely reconstruct the microvasculature and to extract parameters that quantify all its major features, its relation to smooth muscle actin positive cells and capillary diffusion regions. The novel pipeline was applied in the analysis of the coronary microvasculature from healthy tissue and tissue at various stages after myocardial infarction (MI) in the pig model, whose coronary vasculature closely resembles that of human tissue. We unravelled alterations in the microvasculature, particularly structural changes and angioadaptation in the aftermath of MI. In addition, we evaluated the extracted knowledge's potential for the prediction of pathophysiological conditions in tissue, using different classification schemes. The high accuracy achieved in this respect, demonstrates the ability of our approach not only to quantify and identify pathology-related changes of microvascular beds, but also to predict complex and dynamic microvascular patterns.The authors would like to thank Angel Colmenar for staining tissues for four of the subjects. The authors would also like to express their gratitude to Carlos Galan Arriola for providing us with the tissue from infarcted pigs at 45 days post MI along with the characteristics of the corresponding subjects and MRI-based estimations of blood flow. The research leading to these results has received funding from the People Programme (Marie Curie Action) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant Agreement 608027. This work was also supported by grants from the Spanish Ministry of Economy and Competitiveness SAF2014-52050-R to A.G.A., TEC2015-66978-R to A.S. and SAF2013-49663-EXP to B.I., from the Institute of Health Carlos III and the European Regional Development Fund (ERDF/FEDER) PI13/01979 to B.I., from La Marato TV3 Foundation to A.G.A., and from NIH grant R01HL101200 to A.P. The CNIC is supported by the Ministry of Economy, Industry and Competitiveness (MINECO) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S