Deep learning, 3D ultrastructural analysis reveals quantitative differences in platelet and organelle packing in COVID-19/SARSCoV2 patient-derived platelets

Sagar S. Matharu, Cassidy S. Nordmann, Kurtis R. Ottman, Rahul Akkem, Douglas Palumbo, Denzel R.D. Cruz, Kenneth Campbell, Gail Sievert, Jamie Sturgill, James Z. Porterfield, Smita Joshi, Hammodah R. Alfar, Chi Peng, Irina D. Pokrovskaya, Jeffrey A. Kamykowski, Jeremy P. Wood, Beth Garvy, Maria A. Aronova, Sidney W. Whiteheart, Richard D. LeapmanBrian Storrie

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number2264978
JournalPlatelets
Volume34
Issue number1
DOIs
StatePublished - 2023

Bibliographical note

Funding Information:
The project described was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR001998 to the University of Kentucky. The Leapman laboratory was supported by the NIBIB intramural program. The Whiteheart laboratory was supported by NIH grants HL156652, HL138179, and HL150818. The Storrie laboratory was supported by NIH grant HL155519 to BS and subawards from NIH grants HL146373 and HL150818. Thanks are given for what was truly a team effort. Content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Publisher Copyright:
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Keywords

  • 3D electron microscopy
  • COVID-19
  • alpha-granules
  • deep learning
  • image analysis
  • platelets
  • ultrastructure

ASJC Scopus subject areas

  • Hematology

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