{
  "abstract": "High-resolution spatial transcriptomics (ST) preserves gene expression within tissue architecture but remains limited to individual two-dimensional (2D) sections, restricting analysis of continuous three-dimensional (3D) structures and cross-section consistency. We present THiRST, a computational framework for reconstructing multi-resolution 3D spatial transcriptomic representations from serial sections. THiRST combines coarse-to-fine morphology-guided registration, spatial matching of ST units, cross-section association of segmented structures, and integration of Visium V2 and high-definition ST. In serial kidney sections, patch-level refinement increased the same-cluster proportions of matched units at adjacent offsets from 63.8% and 65.6% to 70.7% and 70.0%, respectively, while improving expression concordance. The reconstructed correspondences enabled 3D glomerular and object-level expression analysis, with 54.1% of differential genes detected only in 3D. Cross-platform integration further aligned V2 and high-definition measurements in a shared expression space. THiRST demonstrates the feasibility of object-level 3D ST analysis from serial 2D sections and underscores the importance of accurate local registration.",
  "abstract_shown": true,
  "affiliations": [
    "Vanderbilt University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "J. Zhu"
    },
    {
      "name": "Y. Wang"
    },
    {
      "name": "Z. Lu"
    },
    {
      "name": "C. Qu"
    },
    {
      "name": "Y. Zhu"
    },
    {
      "name": "Y. Yang"
    },
    {
      "name": "S. Zhao"
    },
    {
      "name": "H. Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Huo"
    },
    {
      "name": "R. Deng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ac68xp5etx7ng.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.29.762926",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1.full",
  "id": "ac68xp5etx7ng",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ac68xp5etx7ng.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:27:11Z",
      "native_id": "10.64898/2026.10.29.762926",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "THiRST: Computer Vision Empowered Three-dimensional High-Resolution Spatial Transcriptomics Analytics",
  "title_text": "THiRST: Computer Vision Empowered Three-dimensional High-Resolution Spatial Transcriptomics Analytics",
  "type": "preprint",
  "updated_at": "2026-11-10T00:27:11Z",
  "url": "https://research.draconitic.com/paper/ac68xp5etx7ng",
  "venue": "bioRxiv",
  "versions": []
}