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Related Experiment Videos

Protocol for integrating immunohistochemistry and H&E annotations with Xenium data at single-cell resolution.

Jun Chen1, William Hartnett1, Vijay S Basava1

  • 1Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

STAR Protocols
|September 27, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a protocol to integrate external data, like immunohistochemistry, into spatial transcriptomic datasets. This method enhances targeted analysis of single-cell resolution data for biological insights.

Area of Science:

  • Single-cell biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics generates high-resolution data but benefits from integrating diverse external information.
  • Customized external data integration is crucial for targeted investigations of spatial transcriptomic datasets.

Purpose of the Study:

  • To present a protocol for embedding external data into spatial transcriptomic datasets.
  • To enable single-cell resolution analysis by integrating field-of-view annotations and immunohistochemistry data.
  • To facilitate targeted analysis of Xenium datasets using open-source tools.

Main Methods:

  • Image registration for aligning external data with spatial transcriptomic data.
  • Embedding irregular field-of-view annotations and immunohistochemistry data.
Keywords:
BioinformaticsCancerMicroscopySingle Cell

Related Experiment Videos

  • Integration into a Scanpy-compatible anndata object.
  • Utilizing open-source bioinformatics tools.

    • Main Results:

    • Successful integration of immunohistochemistry data into a Xenium dataset.
    • Demonstration of spatially aligned information within an anndata object.
    • Application to a head and neck squamous cell carcinoma sample.

    Conclusions:

    • The developed protocol enables versatile integration of external information into spatial transcriptomic data.
    • Facilitates targeted, single-cell resolution analysis of complex biological samples.
    • Enhances the utility of spatial transcriptomic datasets for research.