{
  "abstract": "Imaging spatial transcriptomics (iST) has revolutionized our ability to contextualize gene expression within intact tissue architectures at single-cell and subcellular resolution. However, these platforms introduce complex technical biases that span the entire measurement pipeline -- from sample preparation to data acquisition to quantification. Here, we categorize these biases into experimental (e.g., sample preservation and spatial sampling) and computational sources (e.g., z-stack registration and field-of-view stitching). Using public datasets, we demonstrate how different biases can compound and manifest, and propose a unified count modeling framework to formalize these sources of variation. Finally, we call for a more integrated understanding of iST data origins and greater knowledge sharing within and across sectors to support the prospective development of more robust experimental design and reliable downstream analysis.",
  "abstract_shown": true,
  "affiliations": [
    "University of Padova"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "C. Chen"
    },
    {
      "name": "L. E. Borm"
    },
    {
      "name": "A. Pascual-Reguant"
    },
    {
      "name": "I. Ruano"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Risso"
    },
    {
      "name": "H. L. Crowell"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cc2g74cyxqjzx.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.759529",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1.full",
  "id": "cc2g74cyxqjzx",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1",
  "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",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7222526161",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cc2g74cyxqjzx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:07Z",
      "native_id": "10.64898/2026.10.16.759529",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759529v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "On technical biases in imaging spatial transcriptomics",
  "title_text": "On technical biases in imaging spatial transcriptomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:07Z",
  "url": "https://research.draconitic.com/paper/cc2g74cyxqjzx",
  "venue": "bioRxiv",
  "versions": []
}