Single-cell analysis of platelet heterogeneity in COVID-19 and sepsis using foundation models 2811

Abstract Description Single-cell RNA sequencing analysis reveals distinct platelet subpopulations that may serve as critical biomarkers in COVID-19 and sepsis patient outcomes. Using an integrated approach combining machine learning, Universal Cell Embeddings (UCE), and single-cell transcriptomics, we identified three key platelet subpopulations: coagulation-active, hypoxic, and quiescent. The platelet-to-T-cell ratio emerged as a significant predictor of survival outcomes (AUC 0.754). Our analysis demonstrated that platelet heterogeneity and their interactions with other immune cells, particularly T-cells and endothelial cells, play crucial roles in disease progression. The study utilized data from multiple COVID-19, sepsis, and SLE patient cohorts, employing foundation models for robust cell type annotation and trajectory analysis. These findings suggest that early platelet precursor populations could serve as potential therapeutic targets, while distinct platelet signatures may help predict disease trajectories and inform treatment strategies. Funding Sources This research received no external funding. Topic Categories Computational and Systems Immunology (COMP)