{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing Institute of Technology; Beijing Institute of Technology - Zhuhai Campus"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "J. Cao"
    },
    {
      "name": "Y. Li"
    },
    {
      "name": "Y. Qiao"
    },
    {
      "name": "T. Chen"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "H.-C. Gao"
    },
    {
      "name": "J. Zhang"
    },
    {
      "name": "L. Qiu"
    },
    {
      "name": "J. Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "F. Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2rkgcha8z77p8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.27.761841",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1.full",
  "id": "2rkgcha8z77p8",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1304|Advanced Fluorescence Microscopy Techniques"
    ]
  ],
  "openalex": "W7224123711",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2rkgcha8z77p8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "10.64898/2026.10.27.761841",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.27.761841v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue",
  "title_text": "Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/2rkgcha8z77p8",
  "venue": "bioRxiv",
  "versions": []
}