Simultaneous tracking of many thousands of individual particles in live cells is possible now with the advent of high-density superresolution imaging methods. We present an approach to extract local biophysical properties of cell-particle interaction from such newly acquired large collection of data. Because classical methods do not keep the spatial localization of individual trajectories, it is not possible to access localized biophysical parameters. In contrast, by combining the high-density superresolution imaging data with the present analysis, we determine the local properties of protein dynamics. We specifically focus on AMPA receptor (AMPAR) trafficking and estimate the strength of their molecular interaction at the subdiffraction level in hippocampal dendrites. These interactions correspond to attracting potential wells of large size, showing that the high density of AMPARs is generated by physical interactions with an ensemble of cooperative membrane surface binding sites, rather than molecular crowding or aggregation, which is the case for the membrane viral glycoprotein VSVG. We further show that AMPARs can either be pushed in or out of dendritic spines. Finally, we characterize the recurrent step of influenza trajectories. To conclude, the present analysis allows the identification of the molecular organization responsible for the heterogeneities of random trajectories in cells.stochastic analysis of trajectories | dendritic spines and synapses | single particle tracking | confined diffusion R egulation of cellular physiological processes such as synaptic transmission, signal transduction relies on molecular interactions (binding and unbinding) at specific places and involves trafficking in confined local microdomains. The efficiency of these regulations crucially depends on the underlying molecular spatial organization, the study of which remains a daunting hurdle in cellular biology. Interestingly, superresolution light optical microscopy techniques for in vivo data (1-3) have allowed monitoring a large number of molecular trajectories at the single molecule level and at nanometer resolution, that can potentially reveal unique cellular organizational features. In the recent years, various techniques based on empirical characterization have emerged to track receptors (4), and estimating the mean square displacement (MSD) along isolated trajectories allowed to differentiate between free and confined diffusion (5, 6). In addition, although a large effort was dedicated to developing single molecule tracking algorithms (5, 7, 8), a general method for the analysis of the massive collection of data and for the extraction of quantitative local information is still lacking.In this article, we derive from the classical stochastic description at a molecular level, a method to extract biophysical features from high throughput superresolution data, associated with AMPA receptor (AMPAR) trafficking on neuronal cells. Indeed, neurons are organized in local microdomains characterized by morphological and functiona...