{
  "abstract": "Cell-level analysis of high-resolution spatial transcriptomics depends on accurate segmentation and transcript assignment, yet current workflows often trade transcript capture for boundary purity and can require substantial image-analysis expertise. We developed MCseg, a downloadable no-code platform whose fixed segmentation engine was derived by an AI-agent-guided search in which an AI agent iteratively proposed and evaluated combinations of image-processing and segmentation operations against Xenium-derived cell boundaries. In a lung adenocarcinoma development set, fixed-parameter MCseg increased mean panoptic quality from 0.432 to 0.472 relative to an Optuna-tuned two-diameter Cellpose baseline, while a reference-guided calibration analysis reached 0.554. In an independent expert-annotated colorectal cancer region, MCseg showed higher lineage recall and micro-F1 than the StarDist-based ENACT workflow among cells covered by both methods. Across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression relative to Space Ranger at similar UMI density. The fixed workflow also transferred to fresh-frozen breast cancer without tissue-specific architecture search, illustrating an agent-guided route to reproducible, locally deployable cell-level spatial transcriptomic analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 106, Taiwan"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "C.-R. Chan"
    },
    {
      "name": "N.-W. Chang"
    },
    {
      "name": "C.-Y. Wang"
    },
    {
      "name": "H.-Y. Tan"
    },
    {
      "affs": [
        0
      ],
      "name": "S.-J. Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8561bhvzzmv2c.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.20.752837",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752837v1.full",
  "id": "8561bhvzzmv2c",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752837v1",
  "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": "W7214371405",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752837v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8561bhvzzmv2c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:34Z",
      "native_id": "10.64898/2026.09.20.752837",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752837v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics",
  "title_text": "MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:25Z",
  "url": "https://research.draconitic.com/paper/8561bhvzzmv2c",
  "venue": "bioRxiv",
  "versions": []
}