Intracellular Dynamics Of Nanoparticles Probed By An Image-Derived Mean Square Displacement Approach

The interactions among living matter and nanoparticle-based drug delivery systems highly regulate their efficiency and strongly affect their intracellular trafficking, which can be explored by manifold fluorescence spectroscopy techniques. However, none of these experimental tools has been specifically developed to take into account a spatial distribution of directed motions, commonly arising from the active transport of nanoparticles along cytoskeletal networks. To fulfill this gap, we show how a two-dimensional motion driven by Brownian diffusion and flow terms that are uniformly distributed in an angular range can be fully characterized by exploiting general concepts of the spatiotemporal image correlation analysis. The proposed approach can be regarded as an extension of the image-derived mean square displacement method and recovers dynamic and geometric features, which are commonly achieved through single particle analyses. Starting from a time series of the collected images, a spatiotemporal correlation function is computed and studied over the entire domain of the lag-variables. Then, overall information about the investigated dynamics is obtained by decoupling the flow terms, to quantify both the net displacement of the ensemble and the strength of the driving speed. These interdependent contributions are related to the intrinsic anisotropy of the particle flow and the symmetry arising when an angular dispersion affects the directionality of motion. The method has been validated by numeric simulations and in vitro experiments, which demonstrate high stability in the measurement procedure, accurate description of the particle dynamics and low sensitivity to background. Therefore, we argue that it will contribute to advance our understanding about the movement of nanoparticles in cells, their interactions with the biological environment and the subsequent effects on their therapeutic efficiency.