Super-resolution and single-molecule microscopy are increasingly applied to complex biological systems. A major challenge of this approach is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false positive puncta that other analysis methods detect, and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art in precision and speed, using image gradients to separate Gaussian-shaped objects from background. We demonstrate RASP's power by showing it can extract spatial correlations between microglia, neurons, and alpha-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments with a sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as supplementary files and links to third-party repositories.Aligning Science Across Parkinson's [ASAP-000509
