AFM Force Volume for Extracellular Vesicle Detection and Membrane Blebbing Analysis Through Mechanical Signature Mapping

Membrane blebbing is a dynamic cellular phenomenon that plays a key role in numerous biological processes such as migration, apoptosis, cytokinesis, and extracellular vesicle (EV) formation. This phenomenon is traditionally observed via high-resolution imaging techniques on fixed samples, since it is difficult to quantify and monitor on live cells. This aspect limits our understanding of their temporal evolution and mechanical behavior. In this work, we present and validate a novel approach based on Atomic Force Microscopy (AFM) force spectroscopy combined with a custom data analysis pipeline that is capable to detect and map bleb- and EV-like structures directly on live cells. The method leverages the mechanical signatures embedded in force-distance (FD) curves-specifically, the presence of breakthrough points, which indicate the rupture or collapse of soft, vesicular structures. A custom MATLAB algorithm was developed to automatically identify and spatially map FD curves exhibiting EV-like mechanical behavior, based on signal smoothing, inflection point analysis, and user-defined force thresholds. This algorithm was at first validated against isolated EVs in liquid and then applied to live cultures of MDA-MB-231 breast cancer cells, monitored over 3, 7, 10, and 14 days to follow the temporal changes in membrane activity. This method enables the quantitative mapping of vesicle- and bleb-associated curves on the cell surface, highlighting their spatial distribution. A progressive increase in the number of EV-like events was observed up to Day 10, followed by a decline at Day 14-suggesting a dynamic remodeling of the membrane during cell culture maturation or stress. Moreover, they exhibit a preferential localization at the cell periphery. To validate the correspondence between EV-like force signatures and blebs, standard AFM high-resolution images were acquired on the same cells fixed and then analyzed using a Circular Hough Transform-based algorithm to detect and count blebs based on their circular geometry. The time trends obtained from both techniques showed good agreement, confirming the robustness of the force spectroscopy-based detection on live samples. This approach represents a significant advancement in the use of AFM for live-cell analysis. By extracting meaningful biophysical information from FD curves beyond standard elasticity metrics, we demonstrate that AFM spectroscopy can serve as a high-content, non-invasive tool for the detection of nanoscale membrane phenomena.