{
  "abstract": "Spatial transcriptomics (ST) enhances single-cell RNA sequencing by revealing transcript distribution, offering critical insights into heterogeneous diseases such as breast cancer. However, the high cost and lengthy processes of generating high-quality ST data limit clinical application. Recent deep learning methods predict ST from histology images, but often fail to capture both morphological features and spatial context. We introduce FOCST, a foundation model-driven framework for ST imputation that leverages spatial guided contrastive learning. FOCST begins with UNI, a large histopathology foundation model, to extract visual features from tissue images. These are integrated with expression data in a unified embedding space via contrastive learning, enabling cross-modal prediction and imputation. To further enhance spatial awareness, a graph neural network incorporates positional information, improving regional detection and interpretability.Benchmarking demonstrates FOCST’s superior performance over state-of-the-art methods and alternative vision encoders (paired Wilcoxon signed-rank tests, FDR-adjusted p < 0.05, N = 6 images). Predicted profiles enable clinically relevant downstream analyses, including patient stratification by treatment response (ROC AUC (Receiver Operating Characteristic – Area Under the Curve) = 0.79). Our results highlight the promise of combining foundation models and spatially guided learning to efficiently generate ST insights, advancing cancer research and precision medicine. Author summary: Spatial transcriptomics offers powerful insights into tissue heterogeneity but remains expensive and impractical for large-scale clinical use. We developed FOCST, a foundation model-based approach that predicts transcriptomic profiles from standard histology images using spatially guided contrastive learning. By leveraging UNI, a large histopathology model, and a graph neural network to encode spatial context, FOCST produces highly accurate and interpretable ST predictions. Compared to existing methods, it achieves superior accuracy and supports downstream analyses such as patient treatment response prediction. FOCST demonstrates the potential of foundation models to bring cost-effective, scalable spatial biology into cancer research and clinical translation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biochemistry, Western University, London, Canada",
    "Department of Computer Sciences, Western University, London, Canada",
    "Division of Biostatistics, University of Toronto, Toronto, Canada"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zi Huai Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Ziyang Xu"
    },
    {
      "affs": [
        2
      ],
      "name": "Pingzhao Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3p6vxycrcwg56.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014762",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014762",
  "id": "3p6vxycrcwg56",
  "issns": [
    "1553-7358"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Gene prediction"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Gene prediction"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Molecular biology/Molecular biology techniques/Marker genes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Pulmonary imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Breast tumors/Breast cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Pulmonary imaging"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Pulmonary imaging"
    ],
    [
      "plos",
      "/Research and analysis methods/Molecular biology techniques/Marker genes"
    ]
  ],
  "openalex": "W7206778865",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014762&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/3p6vxycrcwg56.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pcbi.1014762",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014762"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
    },
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning",
  "title_text": "Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/3p6vxycrcwg56",
  "venue": "PLOS Computational Biology",
  "versions": []
}