{
  "abstract": "Subcellular spatial transcriptomics captures where RNA is in tissue, but a single location holds too few molecules of any one gene to estimate composition alone. Every current method fixes in advance where to borrow -- a smoothing scale, a cell outline or a factor model -- and the fixed choice shapes what is visible. Kintsugi removes the fixed choice and lets held-out molecules decide, gene by gene, how much to borrow from spatial neighbours and from other genes at the same location. On a lung section measured by both Xenium and Visium HD, the data-chosen allocation placed an epithelial programme where the Xenium molecules were, ahead of smoothing, cell segmentation and a factor model; the result replicated across tissues and against protein. Across a 45-core pulmonary fibrosis cohort, separating composition from captured amount shows that a fibroblastic focus is not a place with more RNA but a place with different RNA: 2.8-fold higher in activated-fibroblast composition while segmented nuclear density is at most 1.08-fold higher.",
  "abstract_shown": true,
  "affiliations": [
    "Texas A&M University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "C. Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Zhang"
    },
    {
      "name": "J. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2pkv2d7p5wx33.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.30.748061",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748061v1.full",
  "id": "2pkv2d7p5wx33",
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  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748061v1",
  "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": "W7207515739",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748061v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2pkv2d7p5wx33.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:10Z",
      "native_id": "10.64898/2026.08.30.748061",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748061v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Kintsugi maps nucleus-poor RNA compartments in subcellular spatial transcriptomics",
  "title_text": "Kintsugi maps nucleus-poor RNA compartments in subcellular spatial transcriptomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:28Z",
  "url": "https://research.draconitic.com/paper/2pkv2d7p5wx33",
  "venue": "bioRxiv",
  "versions": []
}