{
  "abstract": "Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs—including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus—encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 µm × 25 µm) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.",
  "abstract_shown": true,
  "affiliations": [
    "BGI Research, Shenzhen, 518083 China",
    "College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040 Heilongjiang China",
    "Department of Life Sciences, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087 China",
    "Hong Kong Baptist University, Kowloon Tong, Hong Kong China",
    "Guangdong Provincial/Zhuhai Key Laboratory of IRADS, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087 China"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Xinyu Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "Tongxuan Lv"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Nanxi Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Can Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinghong Fan"
    },
    {
      "affs": [
        1
      ],
      "name": "Ning Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiang Kang"
    },
    {
      "affs": [
        4
      ],
      "name": "Dan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3nay0dqnj3ef7.bib",
  "cited_by": 1,
  "doi": "10.1038/s41597-026-07752-9",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736849/",
  "id": "3nay0dqnj3ef7",
  "issns": [
    "2052-4463"
  ],
  "landing_url": "https://doi.org/10.1038/s41597-026-07752-9",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Data Descriptor"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7166575940",
  "pdf_url": "https://www.nature.com/articles/s41597-026-07752-9_reference.pdf",
  "pmcid": "PMC13736849",
  "pmid": "42374103",
  "published": "2026-06-27",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/3nay0dqnj3ef7.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:13Z",
      "native_id": "PMC13736849",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41597-026-07752-9"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A unified spatial transcriptome profiling of ten mouse organs",
  "title_text": "A unified spatial transcriptome profiling of ten mouse organs",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:13Z",
  "url": "https://research.draconitic.com/paper/3nay0dqnj3ef7",
  "venue": "Scientific Data",
  "versions": []
}