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Related Experiment Video

Updated: Sep 13, 2025

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A New Tool to Decrease Interobserver Variability in Biomarker Annotation in Solid Tumor Tissue for Spatial

Sravya Palavalasa1,2, Emily Baker1, Jack Freeman1

  • 1Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA.

Current Issues in Molecular Biology
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

Manual annotation of DNA damage markers in spatial transcriptomics is variable. A new MATLAB tool enables reproducible spot-wise image analysis, improving gene expression association studies in irradiated glioblastoma.

Keywords:
immunofluorescent image analysisinterobserver variabilityspatial transcriptomics

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics and immunofluorescence have different resolutions, complicating data integration.
  • Accurate identification of DNA damage regions is crucial for understanding cellular responses in irradiated tissues.
  • Manual annotation of immunofluorescence data in spatial transcriptomics exhibits significant interobserver variability.

Purpose of the Study:

  • To develop a reproducible method for integrating spatial transcriptomics and immunofluorescence data.
  • To overcome interobserver variability in annotating DNA damage markers like γH2AX in spatial transcriptomic spots.
  • To enable accurate comparison of gene expression between DNA-damaged and undamaged regions.

Main Methods:

  • Coupling spatial transcriptomics of irradiated glioblastoma with immunofluorescence for γH2AX.
  • Developing a MATLAB tool for spot-wise image analysis and annotation based on intensity thresholds and cell counts.
  • Comparing gene expression profiles in γH2AX-positive and negative regions.

Main Results:

  • Significant interobserver variability (Kappa = 0.345) was observed in manual annotation of γH2AX positivity.
  • The developed MATLAB tool achieved reproducible annotation of spots, even in regions with high manual variability.
  • The tool facilitated consistent identification of genes associated with DNA repair pathways.

Conclusions:

  • A novel MATLAB tool significantly improves the reproducibility of integrating spatial transcriptomics and immunofluorescence data.
  • This tool addresses the challenge of interobserver variability in manual annotation, leading to more reliable downstream analyses.
  • The developed method enhances the study of DNA damage responses and associated gene expression in glioblastoma.