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Updated: May 7, 2025

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

Nejla Ozirmak Lermi, Max Molina Ayala, Sharia Hernandez

    Biorxiv : the Preprint Server for Biology
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    PubMed
    Summary
    This summary is machine-generated.

    This study compares commercial spatial transcriptomics (ST) platforms for tumor microenvironment analysis. It reveals platform differences and factors like tissue age influencing data quality for accurate spatial profiling.

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    Area of Science:

    • Oncology
    • Molecular Biology
    • Bioinformatics

    Background:

    • Imaging-based spatial transcriptomics (ST) is crucial for understanding tumor biology and microenvironments.
    • Systematic evaluations of commercially available ST platforms are lacking.

    Purpose of the Study:

    • To rigorously compare the performance of single-cell ST platforms (CosMx, MERFISH, Xenium).
    • To evaluate ST platforms against bulk RNA sequencing, multiplex immunofluorescence, and other spatial profiling methods.
    • To provide insights into optimizing spatial profiling workflows.

    Main Methods:

    • Utilized serial sections of formalin-fixed, paraffin-embedded lung adenocarcinoma and mesothelioma tumor samples.
    • Compared CosMx, MERFISH, and Xenium (uni/multi-modal) platforms.
    • Validated against bulk RNA sequencing, multiplex immunofluorescence, GeoMx DSP, and H&E staining.
    • Performed objective automated and manual pixel-resolution cell segmentation and phenotyping.

    Main Results:

    • Detailed intricate differences between the evaluated ST platforms.
    • Identified tissue age and probe design as critical factors affecting data quality.
    • Demonstrated the importance of both automated and manual phenotyping for pathological relevance.

    Conclusions:

    • The study provides a systematic comparison of leading ST platforms.
    • Highlights key parameters influencing spatial transcriptomics data quality and reliability.
    • Offers guidance for selecting appropriate workflows for spatial profiling and molecular discovery in cancer research.