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Updated: May 9, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Rigor and Reproducibility of Spatial Transcriptomics Performed on Clinically Sourced Human Tissues.

Kelly D Smith1, James W MacDonald2, Xianwu Li1

  • 1Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|May 1, 2025
PubMed
Summary
This summary is machine-generated.

Digital spatial profiling shows high reproducibility for analyzing gene expression in human tissues. Normalization methods impact results, and tradeoffs exist between spatial and single-cell resolution platforms for clinical applications.

Keywords:
data normalizationhuman biopsy tissuehuman resection tissuerigor and reproducibilityspatial transcriptomics

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Area of Science:

  • Biotechnology
  • Genomics
  • Molecular Biology

Background:

  • Spatial transcriptomics offers gene expression localization within tissue structures.
  • Commercial platforms are emerging for disease mechanism research.
  • Clinical applications in trials and diagnostics are of significant interest.

Purpose of the Study:

  • To evaluate the performance of spatial transcriptomics technologies in real-world conditions.
  • To assess technical reproducibility, normalization, and sensitivity of digital spatial profiling.
  • To compare digital spatial profiling with single-molecule imaging for clinical utility.

Main Methods:

  • Vetting technical reproducibility and assay sensitivity of digital spatial profiling.
  • Analyzing data normalization methods for spatial transcriptomics.
  • Comparing digital spatial profiling with single-molecule imaging using human kidney tissues.

Main Results:

  • Digital spatial profiling demonstrated high rigor and reproducibility in human kidney tissues.
  • Normalization strategies significantly influence biological interpretations of spatial transcriptomics data.
  • Multicellular and single-cell resolution methods show concordance but differ in cost, time, and sensitivity.

Conclusions:

  • Spatial transcriptomics methods are reliable for clinical research and diagnostics.
  • Careful consideration of normalization and platform tradeoffs is crucial for clinical integration.
  • This study provides a foundation for incorporating spatial transcriptomics into clinical workflows.