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Related Experiment Video

Updated: Jul 1, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

A unified spatial transcriptome profiling of ten mouse organs.

Xinyu Ren1,2, Tongxuan Lv1, Nanxi Liu3,4

  • 1BGI Research, Shenzhen, 518083, China.

Scientific Data
|June 29, 2026
PubMed
Summary

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A new spatial transcriptomics dataset offers high-resolution gene expression data for 10 mouse organs. This resource aids in developing and benchmarking deep learning models for spatial biology research.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics is crucial for deep learning models in biological research.
  • High-quality, paired expression matrices and histological images are essential for training these models.
  • Existing datasets may lack standardization or comprehensive coverage.

Purpose of the Study:

  • To present a unified, high-quality spatial transcriptomic dataset for mouse organs.
  • To provide standardized data for developing and benchmarking spatial transcriptomics methods and deep learning models.
  • To compare different data resolutions for cell annotation.

Main Methods:

  • Generation of a spatial transcriptomic dataset using the Stereo-seq platform across 10 mouse organs.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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Last Updated: Jul 1, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

  • Inclusion of matched ssDNA or H&E staining images for each tissue section.
  • Generation of expression matrices at single-cell (cell-bin) and 50µm bin (bin-50) resolutions.
  • Cell type annotation and validation using marker gene expression and cross-section concordance.

    • Main Results:

    • A comprehensive dataset covering 10 mouse organs (brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, uterus) from 23 tissue sections.
    • Paired histological images and expression matrices at cell-bin and bin-50 resolutions are available.
    • Cell type annotations demonstrated robustness through cross-tissue consistency and marker gene validation.
    • Cell-bin resolution proved advantageous for accurate cell annotation compared to bin-50.

    Conclusions:

    • The presented dataset serves as a standardized resource for advancing spatial transcriptomics.
    • It facilitates the development, benchmarking, and multimodal analysis of deep learning models in spatial biology.
    • The findings highlight the utility of cell-bin resolution for precise cell type identification in spatial transcriptomics.