A biomathematical approaches models to identify human platelet activation signature in response to various agonists
Authors: Cognasse Fabrice , Nguyen Kim Anh , Heestermans Marco , Arthaud Charles-Antoine , Eyraud Marie-Ange , Prier Amelie , de Bernard Simon , Nourikyan Julien , Duchez Anne-Claire , Avril Stephane , Garraud Olivier , Hamzeh-Cognasse Hind 
Published: February 2025

Background

Platelets 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.

Methods

Using 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.

Results

Distinct 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.

Conclusion

This 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 Summary

Platelets 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 body’s 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 what’s 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