Researchers have developed an innovative method for the analysis of milk-derived extracellular vesicles (EVs), utilizing optical tweezers and an artificial intelligence algorithm. This technique allows for the individual characterization of EVs without the need for fluorescent labeling, overcoming a significant limitation in the study of these biological nanoparticles. The approach combines the precise manipulation of EVs using optical forces with the analysis of their intrinsic optical properties, opening new avenues for biomedical research and diagnostics.

The study of EVs is crucial due to their role in intercellular communication and their potential as biomarkers for various diseases. However, their small size and heterogeneity have made individualized analysis challenging. Traditional methods often require the use of fluorescent markers, which can alter EV properties or introduce artifacts. The new methodology addresses this challenge by enabling the identification and characterization of EVs based solely on their inherent optical properties, such as refractive index, which is directly related to their composition and internal concentration.

The system employs optical tweezers to trap and manipulate individual EVs, while an artificial intelligence algorithm analyzes light scattering patterns to infer their properties. This label-free analysis not only simplifies the experimental process but also preserves the integrity of the EVs, which is fundamental for understanding their biological function. The ability to analyze EVs individually and non-invasively could accelerate the discovery of new biomarkers and improve the understanding of disease mechanisms, as well as optimize the production of EV-based therapies.