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Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed,

Nejla Ozirmak Lermi1,2, Max Molina Ayala3,2, Sharia Hernandez Ruiz3

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

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|January 29, 2025
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Summary
This summary is machine-generated.

This study systematically compares three spatial transcriptomics (ST) platforms: CosMx, MERFISH, and Xenium. It reveals critical differences in their performance for tumor microenvironment analysis, guiding future spatial biology research.

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) is crucial for understanding tumor microenvironments.
  • Commercial ST platforms lack systematic performance evaluations.
  • Tumor heterogeneity necessitates advanced spatial profiling techniques.

Purpose of the Study:

  • To rigorously compare the performance of CosMx, MERFISH, and Xenium single-cell ST platforms.
  • To evaluate ST platform data against bulk RNA sequencing, multiplex immunofluorescence, and digital spatial profiling.
  • To provide insights into optimizing spatial profiling workflows for molecular discovery.

Main Methods:

  • Utilized serial sections of formalin-fixed, paraffin-embedded lung adenocarcinoma and mesothelioma.
  • Compared CosMx, MERFISH, and Xenium (uni/multi-modal) platforms.
  • Integrated data with bulk RNA sequencing, multiplex immunofluorescence, GeoMx DSP, and H&E staining for validation.

Main Results:

  • Detailed intricate differences between the evaluated ST platforms.
  • Demonstrated the impact of tissue age and probe design on data quality.
  • Highlighted the importance of both automatic and manual cell segmentation/phenotyping for pathological relevance.

Conclusions:

  • Identified key performance variations among leading single-cell ST platforms.
  • Emphasized the need for careful consideration of technical parameters for reliable spatial profiling.
  • Suggested optimized workflows for accurate molecular discovery in tumor microenvironments.