A biomathematical approaches models to identify human platelet activation signature in response to various agonists

Cognasse, F.; Nguyen, K. A.; Heestermans, M.; Arthaud, C.-A.; Eyraud, M.-A.; Prier, A.; de Bernard, S.; Nourikyan, J.; Duchez, A. C.; Avril, S.; Garraud, O.; Hamzeh-Cognasse, H.

doi:10.1101/2025.02.06.636809

bioRxiv · 10.1101/2025.02.06.636809

A biomathematical approaches models to identify human platelet activation signature in response to various agonists

Cognasse, F.·Nguyen, K. A.·Heestermans, M.·Arthaud, C.-A.·Eyraud, M.-A.·Prier, A.·de Bernard, S.·Nourikyan, J.·Duchez, A. C.·Avril, S.·Garraud, O.·Hamzeh-Cognasse, H.

View original source ↗Export metadata ↓

Abstract

BackgroundPlatelets are crucial mediators at the crossroads of hemostasis, immunity, and inflammation, adapting their responses to diverse stimuli. Despite their recognized role, the precise pathways and markers associated with platelet activation remain poorly understood. This study aimed to unravel these mechanisms by evaluating platelet responses to various agonists, employing biomathematical models to map activation patterns and identify key biomarkers. MethodsUsing samples from ten healthy donors, platelets were exposed to seven stimulation conditions: unstimulated, PAR-1 agonist TRAP, PAR-4 agonist AYPGKF, ADP, collagen, sCD40L, and fibrinogen. A comprehensive analysis of 47 biological markers--covering membrane activation, soluble mediators, and signaling pathways--was conducted. Statistical and machine learning models, including hierarchical clustering and random forests, were applied to classify and interpret platelet activation signatures. ResultsDistinct activation profiles were observed for each agonist. A streamlined panel of six markers--AKT, CD40 ligand, CD62P (mean fluorescence intensity and percentage), PKC, RANTES, and TSLP--achieved 86.8% accuracy in identifying the activating stimulus. The study highlighted significant variations, influenced by both the stimulus and donor-specific factors. Machine learning approaches further refined classification, achieving a multiclass accuracy of 87.9%. Hierarchical clustering demonstrated clear distinctions, particularly between PAR-1/PAR-4 responses and other agonists. ConclusionThis innovative research redefines platelets as dynamic "biological sensors" capable of decoding complex danger signals. By integrating biomathematical modeling and artificial intelligence, it identifies a precise biomarker panel with transformative potential for diagnostics and therapies in inflammation and immune disorders. This work positions platelets not just as key players in hemostasis but as programmable agents for precision medicine, heralding a new era in adaptive, AI-driven healthcare solutions. Author SummaryPlatelets are often seen as simple players in blood clotting, but they do much more. They sit at the crossroads of hemostasis (stopping bleeding), innate immunity (our bodys first defense), and inflammation. They even influence adaptive immunity and play key roles in maintaining healthy blood vessels and contributing to disease. What makes platelets fascinating is their ability to respond quickly to their environment. They carry various receptors and release substances like growth factors, immune signals, clotting factors, RNA, and tiny vesicles. This helps them react to threats and communicate with other cells. But the big question is: can platelets tailor their response based on specific stimuli? In my research, I set out to answer this. Using mathematical models and analytical techniques, I studied how platelets react to different triggers, especially those linked to immune and clotting responses. My goal was to identify specific molecular "signatures" that define how platelets respond. I found that platelets can distinguish between danger signals and adjust their secretory responses. Normally, this helps manage threats efficiently. However, when this response exceeds whats needed, it can contribute to diseases like cardiovascular disorders, severe infections, autoimmune conditions, and cancer. Understanding these pathways opens new doors for treatment. Since platelet activity can be influenced by drugs, we could shift their role from harmful to beneficial in many diseases. This could revolutionize how we approach conditions driven by inflammation and immune dysregulation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/636809v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@9d9b5forg.highwire.dtl.DTLVardef@1438071org.highwire.dtl.DTLVardef@a59b29org.highwire.dtl.DTLVardef@6dd66b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

cell biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Cognasse, F., Nguyen, K. A., Heestermans, M., Arthaud, C.-A., Eyraud, M.-A., Prier, A., de Bernard, S., Nourikyan, J., Duchez, A. C., Avril, S., Garraud, O., Hamzeh-Cognasse, H.. 2025-02-08. A biomathematical approaches models to identify human platelet activation signature in response to various agonists. https://doi.org/10.1101/2025.02.06.636809

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Not all TOP RNAs are created equal: 3'UTR length and TSS selection predict the translational regulation of LARP1-bound mRNAs in CD4+ T cells

Naive T cells are poised for activation and contain a pool of translationally repressed ribosomal protein (RP) mRNA prepared to induce ribosome biogenesis to support protein synthesis, cell growth and proliferation. RP mRNA are the prototypical members of a class of transcripts initiating at cytosine followed by a CU rich element called terminal oligo pyrimidine (TOP) RNAs. TOP RNAs are regulated by an RNA binding protein LARP1, which promotes transcript stabilisation and translational repression. We investigated LARP1 function in T cell activation by generating cross-linking immunoprecipitation (CLIP) datasets detailing the LARP1-RNA interactions in naive and activated CD4+ T cells and identifying novel TOP RNAs. TOP RNAs identified by this analysis were functionally diverse. RP mRNAs were typified by high stability, and translational repression in naive T cells followed by MTORC1-dependent translation increases following T cell activation. However, other TOP RNAs varied in these aspects of their regulation. Notably, TOP RNAs with longer 3'UTRs had a relaxed dependency on LARP1 for stability and a reduced dependency on MTORC1 for their translation. Transcription start site heterogeneity also impacted TOP RNA regulation by generating a mixture of transcript isoforms with different TOP motif lengths. Longer terminal oligo pyrimidine stretches were associated with a greater dependency on MTORC1 for translation. Differential regulation of TOP RNAs may allow tuneable translational responses to MTORC1 and indicates potential roles for LARP1 beyond translation regulation and stability.

cell biology↗

bioRxiv2026

Sex-specific metabolic regulation by the Drosophila RNA-binding protein Nab2

Conserved RNA binding proteins (RBPs) regulate key steps of gene expression including mRNA processing, export, localization, stability and translation. Human ZC3H14 is a conserved RBP that regulates pre-mRNA processing in neurons and loss of ZC3H14 leads to neurological defects. Studies of Nab2, the Drosophila orthologue of ZC3H14, have identified potential target RNAs involved in metabolism, suggesting Nab2 may influence neurometabolic circuitry. Here, we show a female-specific increase in dilp2 and dilp5 mRNA levels. The dilps encode insulin-like peptides that signal from the brain insulin producing cells (IPCs) to peripheral tissues. Nab2null females have enlarged lipid droplets in the fat body, a tissue analogous to human adipose tissue and liver. Notably, neuronal depletion of Nab2 increases lipid droplet size while neuronal expression of Nab2 in Nab2null female rescues this phenotype supporting a role for Nab2 in a neuronal circuit that regulates dilp levels. Furthermore, depletion of dilp2 or dilp5 from IPCs rescues the enlarged lipid droplet phenotype in Nab2null females indicating that elevated dilp2/dilp5 contributes to enlarged lipid droplets. Together, these data support a female-specific role for Nab2 in brain neurons to support insulin signaling and fat storage, expanding the known functions of RBPs linking neuronal function and metabolic homeostasis.

cell biology↗

bioRxiv2026

Deep generative embeddings of gene expression and splicing reposition the interpretation of single-cell transcriptomic signatures

Single-cell transcriptomic analysis predominantly derives cell identity from gene expression analysis, while alternative splicing is processed separately despite its fundamental role for cell homeostasis. To overcome the limits of separate investigations, we developed a probabilistic deep learning framework, Crecerelle, enabling resolution of the contributions of gene expression and alternative splicing in each cell. Crecerelle learns cell embeddings from gene expressions and alternative splicing isoforms, to decipher their mutually dependent impact on the functional characterisation of cells in a data-driven manner, exemplified for the Tabula Muris dataset. This is enabled through a zero-and-N-inflated Dirichlet-Multinomial for a variational autoencoder that learns cell embeddings solely from splicing profiles, as well as a bi-modal variational autoencoder with a relevance-weighted mixture-of-experts variational posterior to consolidate the modality-specific contribution at single-cell level. Crecerelle reveals cell-type-specific isoform markers as well as subpopulations with unique isoforms and uncovers regulatory and disease-associated pathways not detected by gene expression analyses alone. This scalable and interpretable framework thus allows a more holistic study of transcriptomic regulation and will open a route to modality-relevance-weighted investigations across single-cell multiomics datasets and their influence on cellular homeostasis, tissue development and disease phenotypes.

cell biology↗