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bioRxiv · 10.64898/2026.10.27.761841

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

Cao, J.·Li, Y.·Qiao, Y.·Chen, T.·Zhang, Y.·Gao, H.-C.·Zhang, J.·Qiu, L.·Liu, J.·Xu, F.
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Abstract

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-{beta} 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.

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Cao, J., Li, Y., Qiao, Y., Chen, T., Zhang, Y., Gao, H.-C., Zhang, J., Qiu, L., Liu, J., Xu, F.. 2026-10-28. Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue. https://doi.org/10.64898/2026.10.27.761841

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