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bioRxiv · 10.1101/2023.12.20.572680

Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics

Qiu, X.·Nair, M. G.·Jaroszewski, L.·Godzik, A.
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Abstract

IntroductionThe transcriptional heterogeneity of activated platelets, play a significant role in contributing to negative outcomes in sepsis, COVID-19, and autoimmune diseases such as systemic lupus erythematosus (SLE). Despite this, our understanding of these heterogeneous platelet responses remains limited. In this study, we aim to investigate the diverse transcriptional profiles of activated platelets in these diseases, with the goal of deciphering this platelet heterogeneity for new therapeutic strategies to target abnormal and pathogenic platelet subtypes. Materials and methodsWe obtained the single cell transcriptional profiles of blood platelets from patients with COVID-19, sepsis, and SLE. Utilizing machine learning algorithms, Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB), we discerned the distinct transcriptomic signatures indicative of fatal versus survival clinical outcomes. Our methodological framework incorporated source data annotations and platelet markers and used SingleR and Seurat for detailed profiling. Additionally, we implemented Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization, aiding in the detection of various platelet subtypes and their correlation with disease status and patient outcomes. ResultsOur study identified distinct platelet subpopulations that are associated with disease severity. We demonstrated that alterations in platelet transcription patterns can exacerbate endotheliopathy, potentially heightening the risk of coagulation in fatal patients. Moreover, these changes can also influence lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. ConclusionsEnhanced transcriptional heterogeneity in activated platelets is linked to adverse outcomes in conditions such as sepsis, COVID-19, and autoimmune diseases. The discovery of these unique platelet subpopulations paves the way for innovative therapeutic strategies targeting platelet activation, which could potentially improve patient outcomes. Summary sentenceSingle-Cell RNA Sequencing Analysis of Platelets from COVID-19, Sepsis, and SLE Reveals Disease Signatures and Treatment Options to Prevent Patient Mortality. Graphical AbstractsO_LIThe platelet to T cell ratio proportion in PBMC was identified as the most potent predictor for distinguishing survivors from fatal patients, underscores the potential of this ratio as a prognostic biomarker. C_LIO_LIThe discovery of different platelet subgroups, especially active coagulation, hypoxic, and quiescent clusters, in fatal COVID-19 patients, indicates potential targeted treatment strategies. C_LIO_LIIn patients with severe and fatal conditions, we observed three key phenomena: the aggregation of platelets with monocytes, the amplification of endothelial dysfunction by platelets, and a decrease in lymphocyte activation and differentiation due to platelets. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/572680v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@141ca55org.highwire.dtl.DTLVardef@4ad1aborg.highwire.dtl.DTLVardef@b400forg.highwire.dtl.DTLVardef@9ad44e_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Qiu, X., Nair, M. G., Jaroszewski, L., Godzik, A.. 2023-12-21. Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics. https://doi.org/10.1101/2023.12.20.572680

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