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Updated: Jul 15, 2025

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CROST: a comprehensive repository of spatial transcriptomics.

Guoliang Wang1,2, Song Wu1,3,2, Zhuang Xiong4

  • 1CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China.

Nucleic Acids Research
|October 4, 2023
PubMed
Summary
This summary is machine-generated.

CROST is a new database for spatial transcriptomics data, offering 182 datasets and tools for analyzing tumor-related genes. This resource enhances understanding of tissue biology and disease mechanisms, particularly in cancer.

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Area of Science:

  • Life and health sciences
  • Genomics
  • Bioinformatics

Background:

  • Spatial omics, driven by spatial transcriptome sequencing, offers novel insights into complex tissues.
  • Existing databases for spatial transcriptomic data are limited, hindering comprehensive analysis.
  • A centralized repository is needed to integrate and analyze diverse spatial transcriptomic datasets.

Purpose of the Study:

  • To establish CROST, a comprehensive and user-friendly database for spatial transcriptomic data.
  • To provide standardized analysis pipelines and integrated multi-omics data for exploring tissue biology and disease.
  • To facilitate the discovery and understanding of tumor-associated spatially variable genes (SVGs) and their roles in cancer.

Main Methods:

  • Curated and integrated 182 spatial transcriptomic datasets from diverse species, organs, and diseases.
  • Developed a standardized data processing pipeline and integrated single-cell RNA sequencing deconvolution.
  • Incorporated multi-omics data (transcriptome, epigenome, genome) for comprehensive analysis of SVGs.

Main Results:

  • CROST houses 182 spatial transcriptomic datasets (1033 sub-datasets) and 48,043 tumor-associated SVGs.
  • Integrated analysis revealed correlations, colocalization, intercellular communication, and biological functions of SVGs.
  • Provided two online tools (gene set enrichment analysis and SpatialAP) for data annotation and analysis.

Conclusions:

  • CROST serves as a valuable resource for spatial transcriptomics, offering unprecedented insights into tissue structure and function.
  • The database and integrated analyses provide a foundation for understanding disease mechanisms, especially in tumor tissues.
  • CROST empowers researchers with tools for browsing, analyzing, visualizing, and downloading spatial transcriptomic data for further discovery.