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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Spatial transcriptomics technologies have advanced, necessitating systematic benchmarking.
  • High-throughput platforms require evaluation for resolution and accuracy.

Purpose of the Study:

  • To systematically evaluate and benchmark four high-throughput spatial transcriptomics platforms.
  • To generate a multi-omics dataset for computational method development and biological discovery.

Main Methods:

  • Generated serial tissue sections from colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer.
  • Acquired spatial transcriptomics data using Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K.
  • Established ground truth datasets via CODEX protein profiling and single-cell RNA sequencing on adjacent sections.

Main Results:

  • Systematically assessed platform performance in capture sensitivity, specificity, diffusion control, cell segmentation, and annotation.
  • Evaluated spatial clustering and concordance with adjacent CODEX protein data.
  • Created a uniformly processed multi-omics dataset for spatial biology research.

Conclusions:

  • The generated dataset and benchmarking provide critical insights into spatial transcriptomics platform capabilities.
  • This resource facilitates the development of new computational tools and enhances biological discoveries.
  • The SPATCH web server offers user-friendly access to the dataset for visualization and download.