{
  "abstract": "Histology and spatial transcriptomics provide complementary views of cancer morphology and molecular organization, yet their spatial correspondence has not been learned at scale. We developed SQUALL, a multimodal foundation model pretrained on histMol, comprising 1.93 billion spatially aligned histology-transcriptomics spots/bins from 3,446 sections across 33 tissues and 12 platforms. By learning spatial morphology-molecular correspondences, SQUALL enables virtual spatial transcriptomics, tumor microenvironment characterization, and molecularly informed histology representations for clinical prediction. SQUALL generalized virtual spatial gene expression profiling across cohorts, cancers, and platforms. Its representations resolve tumor microenvironments including progression-associated tumor trajectories in breast cancer and recurrence-associated immune-excluded regions in ovarian tumor, together with underlying transcriptional programs. Applied to whole-slide histology, SQUALL enabled outcome and treatment-response prediction while revealing interpretable features associated with risk. Together, these results establish large-scale spatially aligned histology-transcriptomics pretraining as a general framework for connecting tissue morphology, molecular programs, microenvironmental organization, and clinical phenotypes.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 28,
  "authors": [
    {
      "name": "Z. Zhang"
    },
    {
      "name": "B. Qin"
    },
    {
      "name": "Y. Zhao"
    },
    {
      "name": "Z. Qi"
    },
    {
      "name": "H. Xu"
    },
    {
      "name": "Y. Wang"
    },
    {
      "name": "W. Zheng"
    },
    {
      "name": "J. Dai"
    },
    {
      "name": "A. Chen"
    },
    {
      "name": "N. Wang"
    },
    {
      "name": "L. Nie"
    },
    {
      "name": "P. Zhang"
    },
    {
      "name": "H. Zhang"
    },
    {
      "name": "Y. Zhao"
    },
    {
      "name": "T. Xu"
    },
    {
      "name": "S. Lin"
    },
    {
      "name": "P. Ren"
    },
    {
      "name": "Z. Zhang"
    },
    {
      "name": "L. Xue"
    },
    {
      "name": "X. Xue"
    },
    {
      "name": "Z. Yang"
    },
    {
      "name": "J. Xu"
    },
    {
      "name": "J. Ma"
    },
    {
      "name": "D. Pan"
    },
    {
      "name": "C. Wang"
    },
    {
      "name": "Z. Liu"
    },
    {
      "name": "Y. Meng"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. Zeng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3fkn3tk39zsb4.bib",
  "cited_by": 1,
  "doi": "10.64898/2026.06.01.729028",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.01.729028v2.full",
  "id": "3fkn3tk39zsb4",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.01.729028v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7163528553",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.01.729028v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3fkn3tk39zsb4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "10.64898/2026.06.01.729028",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.01.729028v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A multimodal foundation model to integrate histology with spatial molecular programs",
  "title_text": "A multimodal foundation model to integrate histology with spatial molecular programs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/3fkn3tk39zsb4",
  "venue": "bioRxiv",
  "versions": []
}