Abstract:
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) has received more attention since the characterization of Long COVID (LC), a condition somewhat similar in symptom presentation and, to some extent, pathophysiological mechanisms. A prominent feature of LC pathology is amyloid, fibrinolysis-resistant fibrin(ogen) fragments, termed microclots. Despite prior identification of microclots in ME/CFS, quantitative analysis has remained challenging due to the reliance on representative micrographs and software processing for estimations.
Addressing this gap, the present study uses a cell-free imaging flow cytometry approach, optimized for the quantitative analysis of Thioflavin T-stained microclots, to precisely measure microclot concentration and size distribution across ME/CFS, LC, and healthy cohorts. We refer to our cell-free flow cytometry technique for detecting microclots as ‘flow clotometry’.
We demonstrate significant microclot prevalence in ME/CFS and LC, with LC patients exhibiting the highest concentration (18- and 3-fold greater than the healthy and ME/CFS groups, respectively). This finding underscores a common pathology across both conditions, emphasizing a dysregulated coagulation system. Moreover, relating to microclot size distribution, the ME/CFS group exhibited a significantly higher prevalence across all area ranges when compared to the controls, but demonstrated a significant difference for only a single area range when compared to the LC group.
This suggests a partially overlapping microclot profile in ME/CFS relative to LC, despite the overall higher concentration in the latter. The present study paves the way for prospective clinical application that aims to efficiently detect, measure and treat microclots.
Source: Etheresia Pretorius, Massimo Nunes, Jan pretorius et al. Flow Clotometry: Measuring Amyloid Microclots in ME/CFS, Long COVID, and Healthy Samples with Imaging Flow Cytometry, 24 June 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4507472/v1] https://www.researchsquare.com/article/rs-4507472/v1 (Full text)