Single-cell spatial multi-omics research on gastrointestinal tumors
IDSTT0000222(Source: STOmics DB)
STOmics technology:BGI Stereomics Stereo-Seq
Organism(s):
Homo sapiens
Data type:Transcriptome or Gene expression, Spatial transcriptomics, , Images
Sample scope:Monoisolate
Summary:Colorectal and gastric cancers are common, high-incidence malignancies of the digestive tract, ranking among the top five cancers worldwide in both incidence and mortality. Based on SURF, a foundation model developed for matched spatial transcriptomics at pixel-level resolution, this study aims to develop artificial intelligence algorithms for colorectal cancer-related downstream applications.By leveraging spatial omics to enhance H&E-based intelligent diagnosis and fully integrating the advantages of big data and AI, the project seeks to create a clinically competitive pathology diagnostic product. This multi-omics pathology data resource links histology, spatial transcriptomics, and cell-level annotations to support reproducible biomedical research.
Contributor(s):Yu GAO, Yutong YAN, Shujun ZHANG, Qian DA, Yuze JIANG et al.
Publication(s):
  • Yu GAO, Yutong YAN, Shujun ZHANG, Qian DA, Yuze JIANG et al. SURF: A Subcellular Unified Representation Foundation Model Linking Histology to Spatial Omics.
Submitter:闫雨童(YutongYAN),BGI Chongqing
Release date:2026-09-30
Updated:2026-09-30
Relations:
GBI Pathology
Statistics:
  • Sample: 26
  • Tissue Section: 26
Datasize:310.59GB
Spatial Gene Expression
ProjectSampleTissue SectionOrganismFiles