{
  "abstract": "Spatial transcriptomic super-resolution recovers tissue organization obscured by coarse sampling, but fitting a model to each section greatly increases computational cost and reduces practicality. We introduce ASTRA, a cross-platform pretrained model that reconstructs expression from histology and measured RNA by zero-shot inference. Training with arbitrarily shaped count-bearing regions, termed parents, enables one frozen model to accommodate dense bins and separated spots. Across six held-out Visium HD sections, ASTRA achieved higher correlations under dense-bin and pseudo-Visium sampling, lower dense-bin errors and substantially shorter processing times than existing methods. It improved colorectal tissue segmentation and tumor-boundary recovery. In real colorectal Visium, reconstruction spatially resolved a stromal transcriptional state supported by measured RNA. The same checkpoint supported regional and nucleus-associated analyses across 43 invasive lobular carcinoma sections and within-spot interpretation across 92 triple-negative breast cancer sections. ASTRA establishes a reusable framework for practical, cohort-scale spatial transcriptomic super-resolution across platforms.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "J. Ye"
    },
    {
      "affs": [
        0
      ],
      "name": "Q. Cui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6c0x4qhhw9t9z.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.30.755663",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755663v1.full",
  "id": "6c0x4qhhw9t9z",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755663v1",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7220648342",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755663v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6c0x4qhhw9t9z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:19Z",
      "native_id": "10.64898/2026.09.30.755663",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755663v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "ASTRA enables zero-shot super-resolution of spatial transcriptomics across capture geometries",
  "title_text": "ASTRA enables zero-shot super-resolution of spatial transcriptomics across capture geometries",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:19Z",
  "url": "https://research.draconitic.com/paper/6c0x4qhhw9t9z",
  "venue": "bioRxiv",
  "versions": []
}