Deep learning analysis of 3D electron microscopy data reveals quantitative differences in platelet and organelle Packing in COVID-19 patient derived platelets

Authors

• KN Shamsian
• S Lin
• SS Rajan
• NJ Caplen
• KC Cheng

Abstract

It has been shown that COVID-19 patients can exhibit a range of symptoms, one of which is microclotting in the pulmonary vasculature, a prominent contributor to respiratory deficits. Clotting is a complex process, which involves both circulating proteins and platelets, anucleated cells in the blood. When clotting occurs, it can be suggestive of an increased level of platelet activation. Thus, we reasoned that parts of the platelet involved in the release of platelet contents during clotting would lose their content and appear as expanded, empty “ghosts”. To test this, we drew blood from three severely ill COVID-19 patients and compared the platelets within the blood draws to those from three healthy volunteers. We used 3D high-resolution focused ion beam scanning electron microscopy (FIB-SEM) and employed deep learning computational methods to evaluate nearly 600 individual platelets and 30,000 organelles such as a-granules and mitochondria from both, the healthy donors and severely ill COVID-19 patients. We found that COVID-19 patient platelets were 35% smaller in volume, with most of the difference in organelle packing density being due to decreased platelet size, rather than differences in organelle count or their volume. There was little to no 3D ultrastructural evidence for differential activation of the COVID-19 patients’ platelets. Though limited by sample size, our studies suggest that factors outside of the platelets themselves are likely responsible for such COVID-19 complications as microclotting. In addition, our studies show how deep learning 3D methodology can become the gold standard for 3D ultrastructural studies.

Scientific Focus Area: Structural Biology

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