A novel metacyte metafer classifier for platelet morphology using long COVID as a model.

Platelets play a critical role in coagulation and are implicated in pathological clotting. This study analyzes platelet activation and morphology using the cellular fraction remaining after whole blood centrifugation. We briefly examine whether the addition of platelet inhibitors, such as Indomethacin and Prostaglandin E1 (PGE-1), affects platelet activation status during sample preparation. Additionally, we introduce an automated analysis method using the MetaCyte Metafer classifier to enhance objectivity and precision in evaluating platelet fragility and activation. Our disease model includes platelets from participants with Long COVID compared to healthy controls. The automated analysis provided consistent, unbiased data, and detailed metrics such as area, count, and roundness, indicating a significant improvement in platelet analysis. We found that platelet inhibitors did not influence platelet activation during sample processing, suggesting that platelets in Long COVID patients are substantially activated in circulation. Automated analysis using the MetaCyte Metafer classifier offers a reliable approach to identifying subtle morphological changes in platelets, including activation size ranges, which can provide valuable insights into the progression of platelet pathology in Long COVID and other thrombo-inflammatory conditions. Future work should focus on refining algorithms and incorporating artificial intelligence (AI) to enhance automated data interpretation and clinical relevance.