Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue
- 1Beijing Institute of Technology; Beijing Institute of Technology - Zhuhai Campus
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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BibTeX
@misc{cao2026bayesian,
title = {{Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue}},
author = {J. Cao and Y. Li and Y. Qiao and T. Chen and Y. Zhang and H.-C. Gao and J. Zhang and L. Qiu and J. Liu and F. Xu},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.761841},
url = {https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue
AU - J. Cao
AU - Y. Li
AU - Y. Qiao
AU - T. Chen
AU - Y. Zhang
AU - H.-C. Gao
AU - J. Zhang
AU - L. Qiu
AU - J. Liu
AU - F. Xu
PY - 2026
DA - 2026/10/27
PB - bioRxiv
DO - 10.64898/2026.10.27.761841
UR - https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1
AB - 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.
ER -