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Platelets are small blood cells involved in hemostasis, wound healing, and immune response. After adhesion and spreading, platelets can migrate at sites of injury inducing an early immune response to inflammation or infection. Platelet migration requires fibrinogen-integrin binding and fibrinogen depletion from the substrate inducing a self-generated ligand gradient guiding the direction of migration. This type of cellular motion is referred to as haptotactic migration. The underlying mechanisms of haptotactic platelet migration have just recently been discovered, but the connection to platelet mechanics has remained unknown yet. Using scanning ion conductance microscopy (SICM), we investigated the three-dimensional morphology and mechanics of platelets during haptotactic migration for the first time. Migrating platelets showed a polarized, anisotropic shape oriented in the direction of migration. This polarization goes hand in hand with a characteristic subcellular stiffness distribution showing a region of increased stiffness at the leading edge. Moreover, the mechanical properties of the leading edge revealed a highly dynamic stiffening and softening process with rapid changes of the elastic modulus by a factor of up to 5× per minute. Inhibition of actin polymerization stopped the dynamic stiffening and softening process and halted the migration. By combining SICM with confocal fluorescence microscopy, we found that the increased stiffness and mechanical dynamics at the leading edge coincided with an increased volumetric F-actin density. Our data provide a connection between platelet mechanics and the cytoskeletal contribution to the migration process of platelets.

Platelets are functionally versatile blood cells involved in thrombosis, hemostasis, atherosclerosis, and immune response. Platelet interaction with the immediate microenvironment in blood, vasculature, and tissues alters platelet morphology. The quantification of platelet morphodynamics by geometrical parameters (morphometry) can provide important insights into how platelets sense and respond to stimulatory cues in their vicinity. However, the extraction of platelet shapes from phase contrast microscopy images by conventional image processing is difficult. Here, we used a convolutional neural network (CNN) to develop a deep-learning-based approach for the unbiased extraction of information on platelet morphodynamics by phase contrast microscopy. We then investigated the effect of normal and oxidized low-density lipoproteins (LDL, oxLDL) on platelet morphodynamics, spreading, and haptotactic migration. Exposure of platelets to oxLDL led to a decreased spreading area and rate on fibrinogen, accompanied by increased formation of filopodia and impaired formation of lamellipodia. Haptotactic platelet migration was affected by both LDL and oxLDL in terms of decreased migration velocity and reduced directional persistence. Our results demonstrate the use of deep learning in investigating platelet morphodynamics and reveal differential effects of LDL and oxLDL on platelet morphology and platelet-matrix interaction.