Researchers have identified unusual microscopic structures in the blood of long COVID patients, consisting of microclots intertwined with neutrophil extracellular traps, or NETs. These formations are more frequent, larger, and denser in affected individuals compared to healthy controls. The findings suggest potential mechanisms behind long COVID's persistent symptoms and open doors to new diagnostics and treatments.
A new study reveals a significant structural association between circulating microclots and neutrophil extracellular traps (NETs) in the blood of people with long COVID. Published in the Journal of Medical Virology, the research was conducted by teams led by Prof. Resia Pretorius from Stellenbosch University's Department of Physiological Sciences and Dr. Alain Thierry from the Montpellier Cancer Institute (IRCM) at INSERM in Montpellier.
Microclots, abnormal clumps of blood clotting proteins, were first identified in COVID-19 patients in 2021 by Prof. Pretorius. NETs form when neutrophils release their DNA through NETosis, creating thread-like structures that trap pathogens but can cause harm if overproduced, contributing to inflammation and clotting in conditions like autoimmune diseases, cancer, diabetes, and arthritis.
Using imaging flow cytometry and fluorescence microscopy, the researchers analyzed plasma from long COVID patients and healthy controls. They quantified NETs via proteic markers and circulating DNA. Key observations include elevated biomarkers for both microclots and NETs in patients, with microclots being more abundant and larger. The most striking finding was the pronounced interaction between microclots and NETs in long COVID samples.
"This finding suggests the existence of underlying physiological interactions between microclots and NETs that, when dysregulated, may become pathogenic," explains Dr. Thierry.
Prof. Pretorius adds, "This interaction could render microclots more resistant to fibrinolysis, promoting their persistence in circulation and contributing to chronic microvascular complications."
The study incorporated artificial intelligence tools, including machine learning, to analyze biomarker patterns, accurately distinguishing long COVID patients from healthy individuals and identifying predictive combinations for diagnostics. These insights support potential personalized treatments targeting thrombo-inflammatory responses and advance the understanding of post-viral syndromes.