{
  "abstract": "Subcellular Spatial Transcriptomics (SST) offers unprecedented opportunities to decipher tissue architecture at subcellular resolution. Consequently, accurate cell segmentation is a fundamental prerequisite for analyzing SST data, as precise transcript assignment is essential for downstream single-cell interpretation. Existing methods often overlook the spatial organization of transcripts, rely on simplified geometric priors, or struggle to coordinate transcript-derived features with morphology-derived spatial guidance. This limitation restricts their adaptability to irregular cell shapes, often leading to transcript misassignment. To address this, we propose SpaCut, a unified deep learning framework designed to effectively integrate transcriptomic feature learning with dynamic morphological guidance. SpaCut comprises a Gene-Wise Attention (GWA) module and a Nuclei-Guided Attentive Fusion (NGAF) module. GWA encodes the high-dimensional transcript map and aggregates features across multiple depths. NGAF uses the encoded transcript features to modulate distance-based nuclear softmasks and refine nucleus-anchored cell boundaries through progressive training. Extensive evaluations across seven datasets from diverse platforms (including Xenium, CosMx, MERSCOPE, and Stereo-seq) demonstrate that SpaCut significantly outperforms state-of-the-art methods in both segmentation accuracy and consistency with single-cell reference data. The source code is available at https://github.com/wenwenmin/SpaCut.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China.",
    "School of Health and Nursing, Yunnan Open University, Kunming, Yunnan, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yongheng Li",
      "orcid": "0009-0005-4473-6276"
    },
    {
      "affs": [
        1
      ],
      "name": "Fangfang Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenwen Min",
      "orcid": "0000-0002-2558-2911"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f95pbkd647d60.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.78423",
  "fulltext_url": "https://europepmc.org/articles/PMC13720276",
  "id": "f95pbkd647d60",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958707",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7224843558",
  "pdf_url": "https://europepmc.org/articles/PMC13720276?pdf=render",
  "pmcid": "PMC13720276",
  "pmid": "42958707",
  "published": "2026-10-29",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/f95pbkd647d60.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:36Z",
      "native_id": "MED:42958707",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958707"
    },
    {
      "first_seen": "2026-11-03T12:21:11Z",
      "native_id": "PMC13720276",
      "source": "pmc",
      "url": "https://doi.org/10.1002/advs.78423"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "SpaCut: Transcript-Aware Morphology Fusion for Robust Cell Segmentation in Subcellular Spatial Transcriptomics.",
  "title_text": "SpaCut: Transcript-Aware Morphology Fusion for Robust Cell Segmentation in Subcellular Spatial Transcriptomics.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:11Z",
  "url": "https://research.draconitic.com/paper/f95pbkd647d60",
  "venue": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
  "versions": []
}