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Updated: May 18, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Cross-Platform Comparative Spatial Transcriptomics in High-Grade Serous Carcinoma.

Omar Youssef1, Lars Selander2, Shuyu Zheng3

  • 1Department of Pathology, University of Helsinki, Helsinki, Finland; Clinical and Chemical Pathology Department, National Cancer Institute, Cairo University, Cairo, Egypt; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|May 16, 2026
PubMed
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This summary is machine-generated.

Spatial transcriptomics platforms GeoMx and Visium show high concordance in profiling high-grade serous carcinoma (HGSC). This reproducibility across platforms enhances the identification of molecular features linked to patient outcomes.

Area of Science:

  • Oncology
  • Genomics
  • Biotechnology

Background:

  • High-grade serous carcinoma (HGSC) is a highly heterogeneous cancer.
  • Understanding HGSC heterogeneity is crucial for identifying therapeutic targets and predicting patient outcomes.
  • Spatial transcriptomics offers a powerful approach to study tumor heterogeneity at a molecular level.

Purpose of the Study:

  • To directly compare the reproducibility of transcriptomic profiles between two spatial transcriptomics platforms: GeoMx Digital Spatial Profiler (GeoMx) and Visium Spatial Gene Expression (Visium).
  • To assess the consistency of biologically relevant molecular features identified using AI-defined tumor regions across these platforms.
  • To evaluate the translational utility of spatial transcriptomics in HGSC research.

Main Methods:

Keywords:
GeoMxRNA in situ hybridizationVisiumhigh-grade serous carcinomaovarian cancerspatial transcriptomics

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  • Applied GeoMx Digital Spatial Profiler to formalin-fixed, paraffin-embedded (FFPE) HGSC samples (n=16) from a cohort previously analyzed with Visium.
  • Analyzed both high-confidence (HC) and lower-confidence background tumor (BG) regions identified by AI.
  • Compared transcriptional profiles and pathway enrichment between the two platforms.

    • Main Results:

    • Identified 8,011 differentially expressed genes (DEGs) shared between GeoMx and Visium, demonstrating strong concordance (r=0.72).
    • Observed similar pathway enrichment patterns, including TNF-alpha and p53 signaling pathways.
    • Found distinct expression patterns for outcome-related genes in HC and BG regions, correlating with progression-free interval (PFI).
    • Validated GeoMx-identified markers using RNA in situ hybridization (RNA-ISH).

    Conclusions:

    • Key transcriptomic features of HGSC are consistently captured by both GeoMx and Visium spatial transcriptomics technologies.
    • The reproducibility across platforms supports the translational utility of spatial transcriptomics in oncology.
    • This consistency enhances the ability to identify biologically significant molecular features relevant to HGSC patient outcomes.