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Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.

Jasmine T Plummer1,2,3,4, Felipe Segato Dezem5,6, David P Cook7,8,9

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This study introduces standardized metrics and procedures for spatial transcriptomics technologies. It provides a dataset and software tool to evaluate and compare imaging-based spatial omics data across different platforms and sites.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Spatial transcriptomics technologies lack standardized evaluation metrics.
  • In situ hybridization (ISH) platforms vary in chemistry and performance.
  • Cross-site comparability of spatial omics data is a significant challenge.

Purpose of the Study:

  • To develop standardized metrics and operating procedures for evaluating imaging-based spatial transcriptomics.
  • To create a comprehensive dataset and open-source software for data analysis and comparison.
  • To establish best practices for integrating multi-omics data in spatial studies.

Main Methods:

  • Generated the Spatial Touchstone (ST) dataset across six tissue types and multiple global sites.
  • Analyzed data using Xenium and CosMx platforms, assessing key performance metrics.
  • Developed the open-source SpatialQM software for standardized data evaluation and cell annotation.

Main Results:

  • Established standardized operating procedures (STSOPs) for spatial transcriptomics.
  • The ST dataset includes 254 spatial profiles with diverse tissue types and platforms.
  • SpatialQM enables reproducible evaluation of reproducibility, sensitivity, dynamic range, SNR, FDR, and cell annotation congruence.

Conclusions:

  • Standardized metrics and tools are crucial for reliable spatial transcriptomics data.
  • The ST dataset and SpatialQM software facilitate cross-platform and cross-site data comparison.
  • Best practices are defined for integrating multi-omics data into spatial transcriptomics and proteomics.