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Peer-reviewedCC BY-NC-ND 4.0Scientific Data

A unified spatial transcriptome profiling of ten mouse organs

  • Xinyu Ren1,2
  • Tongxuan Lv1
  • Nanxi Liu3,4
  • Can Shi1
  • Jinghong Fan1
  • Ning Zhao2
  • Qiang Kang1
  • Dan Wang5
5 affiliations
  1. 1BGI Research, Shenzhen, 518083 China
  2. 2College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040 Heilongjiang China
  3. 3Department of Life Sciences, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087 China
  4. 4Hong Kong Baptist University, Kowloon Tong, Hong Kong China
  5. 5Guangdong Provincial/Zhuhai Key Laboratory of IRADS, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087 China
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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.

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Cite

BibTeXRIS
BibTeX
@article{ren2026unified,
  title = {{A unified spatial transcriptome profiling of ten mouse organs}},
  author = {Xinyu Ren and Tongxuan Lv and Nanxi Liu and Can Shi and Jinghong Fan and Ning Zhao and Qiang Kang and Dan Wang},
  journal = {Scientific Data},
  year = {2026},
  month = jun,
  doi = {10.1038/s41597-026-07752-9},
  url = {https://doi.org/10.1038/s41597-026-07752-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A unified spatial transcriptome profiling of ten mouse organs
AU  - Xinyu Ren
AU  - Tongxuan Lv
AU  - Nanxi Liu
AU  - Can Shi
AU  - Jinghong Fan
AU  - Ning Zhao
AU  - Qiang Kang
AU  - Dan Wang
PY  - 2026
DA  - 2026/06/27
JO  - Scientific Data
DO  - 10.1038/s41597-026-07752-9
UR  - https://doi.org/10.1038/s41597-026-07752-9
AB  - 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.
SN  - 2052-4463
ER  -