Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue

Three-dimensional single-molecule localization microscopy enables nanometer-resolution mapping of biomolecular organization in native cellular environments, but extending this capability to deep tissue requires in situ point spread function (PSF) modeling to account for specimen-induced optical distortions. Existing in situ approaches rely on a deterministic mapping between each single-molecule detection and the underlying PSF model. In deep tissue, however, specimen-induced aberrations, optical scattering and elevated background degrade the information content of single-molecule observations, posing a fundamental barrier to reliable PSF estimation. Here we introduce Bayesian in situ PSF modeling (BIM), a computational framework that explicitly models uncertainty in emitter axial position and propagates it through PSF reconstruction. By using an uncertainty-aware posterior fusion strategy, BIM retrieves accurate specimen-matched PSF models directly from raw blinking data under native imaging conditions. We demonstrate BIM by resolving hollow mitochondrial membranes, amyloid-β fibrils, dendrites and spines with high fidelity. When combined with adaptive optics, BIM improves high-fidelity 3D reconstruction in deep tissue at depths exceeding one hundred micrometers.