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Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine
Xinru Qiu1, Meera G Nair1, Lukasz Jaroszewski1
1Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA 92521, USA.
Insights
Activated platelets show diverse gene expression patterns impacting sepsis, COVID-19, and SLE. Identifying these platelet subtypes offers new therapeutic targets for severe diseases.
Area of Science:
- Hematology
- Immunology
- Genomics
Background:
- Platelet heterogeneity in activated states is poorly understood.
- Activated platelets play a role in sepsis, COVID-19, and systemic lupus erythematosus (SLE).
Purpose of the Study:
- To analyze single-cell transcriptional profiles of activated platelets.
- To identify platelet subpopulations linked to disease severity and patient outcomes.
- To discover biomarkers for targeted platelet therapies.
Main Methods:
- Single-cell RNA sequencing of 47,977 platelets from 413 patient samples.
- Machine learning (Deep Neural Network, eXtreme Gradient Boosting) for outcome prediction.
- Bioinformatic analysis including UMAP, SingleR, and Seurat for subtyping.
Main Results:
- Identified distinct platelet subpopulations correlating with disease severity.
- Found that altered platelet transcription intensifies endotheliopathy and coagulation in fatal cases.
- Observed potential impact of platelet changes on lymphocyte function, suggesting broader immune roles.
Conclusions:
- Platelet transcriptional heterogeneity is a key factor in severe diseases.
- Biomarkers for platelet dysfunction can guide novel therapeutic strategies.
- Targeting platelet activation may improve outcomes in critical illnesses.
Abstract:
This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.
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