Deep learning, 3D ultrastructural analysis reveals quantitative differences in platelet and organelle packing in COVID-19/SARSCoV2 patient-derived platelets
Creators: name: Matharu, Sagar S.
name: Nordmann, Cassidy S.
name: Ottman, Kurtis R.
name: Akkem, Rahul
name: Palumbo, Douglas
name: Cruz, Denzel R. D.
name: Campbell, Kenneth
name: Sievert, Gail
name: Sturgill, Jamie
name: Porterfield, James Z.
name: Joshi, Smita
name: Alfar, Hammodah R.
name: Peng, Chi
name: Pokrovskaya, Irina D.
name: Kamykowski, Jeffrey A.
name: Wood, Jeremy P.
name: Garvy, Beth
name: Aronova, Maria A.
name: Whiteheart, Sidney W.
name: Leapman, Richard D.
name: Storrie, Brian
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Biophysics
subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Mental Health
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences

description: Platelets contribute to COVID-19 clinical manifestations, of which microclotting in the pulmonary vasculature has been a prominent symptom. To investigate the potential diagnostic contributions of overall platelet morphology and their α-granules and mitochondria to the understanding of platelet hyperactivation and micro-clotting, we undertook a 3D ultrastructural approach. Because differences might be small, we used the high-contrast, high-resolution technique of focused ion beam scanning EM (FIB-SEM) and employed deep learning computational methods to evaluate nearly 600 individual platelets and 30 000 included organelles within three healthy controls and three severely ill COVID-19 patients. Statistical analysis reveals that the α-granule/mitochondrion-to-plateletvolume ratio is significantly greater in COVID-19 patient platelets indicating a denser packing of organelles, and a more compact platelet. The COVID-19 patient platelets were significantly smaller –by 35% in volume – with most of the difference in organelle packing density being due to decreased platelet size. There was little to no 3D ultrastructural evidence for differential activation of the platelets from COVID-19 patients. Though limited by sample size, our studies suggest that factors outside of the platelets themselves are likely responsible for COVID-19 complications. Our studies show how deep learning 3D methodology can become the gold standard for 3D ultrastructural studies of platelets. COVID-19 patients exhibit a range of symptoms including microclotting. Clotting is a complex process involving both circulating proteins and platelets, a cell within the blood. Increased clotting is suggestive of an increased level of platelet activation. If this were true, we reasoned that parts of the platelet involved in the release of platelet contents during clotting would have lost their content and appear as expanded, empty “ghosts.” To test this, we drew blood from severely ill COVID-19 patients and compared the platelets within the blood draws to those from healthy volunteers. All procedures were done under careful attention to biosafety and approved by health authorities. We looked within the platelets for empty ghosts by the high magnification technique of electron microscopy. To count the ghosts, we developed new computer software. In the end, we found little difference between the COVID patient platelets and the healthy donor platelets. The results suggest that circulating proteins outside of the platelet are more important to the strong clotting response. The software developed will be used to analyze other disease states.