Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Despite these advances, accurately extracting quantitative information on molecular arrangements of proteins at the 1–20 nm scale through rigorous image analysis remains a significant challenge. Here, we present SPINNA (Single-Protein Investigation via Nearest-Neighbor Analysis): an analysis framework that compares nearest-neighbor distances from experimental single-protein position data with those obtained from realistic simulations based on a user-defined model of protein oligomerization states. We demonstrate SPINNA in silico, in vitro, and in cells. In particular, we quantitatively assess the oligomerization of the epidermal growth factor receptor (EGFR) upon EGF treatment and investigate the dimerization of CD80 and PD-L1, key surface ligands involved in immune cell signaling. Importantly, we offer an open-source Python implementation and a GUI to facilitate SPINNA’s widespread use in the scientific community.