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Related Experiment Video

Updated: Nov 17, 2025

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Characterizing the Tumor Immune Microenvironment with Tyramide-Based Multiplex Immunofluorescence.

Hidetoshi Mori1, Jennifer Bolen2, Louis Schuetter3

  • 1Center for Immunology and Infectious Diseases, University of California, Davis, CA, USA. hmori@ucdavis.edu.

Journal of Mammary Gland Biology and Neoplasia
|February 16, 2021
PubMed
Summary

Automated multiplex immunofluorescence (mIF) staining using tyramide signal amplification (TSA) enhances reproducibility and throughput for immune profiling the tumor microenvironment. Standardized mIF protocols are crucial for advancing diagnostics and immunotherapy biomarker discovery.

Keywords:
Breast cancerImmune cellsImmunohistochemistryMultiplex

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Area of Science:

  • Oncology
  • Immunology
  • Biotechnology

Background:

  • Multiplex immunofluorescence (mIF) is vital for immune profiling the tumor microenvironment.
  • Understanding cancer-immune interplay and identifying immunotherapy biomarkers are key research areas.
  • Standardization of mIF protocols is needed for clinical translation.

Purpose of the Study:

  • To develop and validate automated mIF protocols for enhanced reproducibility and throughput.
  • To characterize the tumor immune microenvironment using two distinct mIF panels.
  • To facilitate the transition of mIF techniques into clinical diagnostic applications.

Main Methods:

  • Utilized an automated slide stainer with tyramide signal amplification (TSA) for mIF.
  • Developed two mIF panels targeting immune cells (CD3, CD20, CD117, FOXP3, Ki67, CD8, PD-1, PD-L1, CD68) and tumor markers (pancytokeratins).
  • Employed multispectral imaging (Vectra 3.0) and outlined various image analysis methods for cell identification and spatial association.

Main Results:

  • Demonstrated the successful application of automated mIF for high-throughput immune profiling.
  • Established two comprehensive mIF panels for detailed characterization of the tumor immune microenvironment.
  • Provided a framework for image analysis to quantify cellular phenotypes and interactions.

Conclusions:

  • Automated mIF protocols significantly improve reproducibility and efficiency compared to manual methods.
  • These standardized mIF protocols are valuable tools for immune profiling and biomarker discovery in cancer research.
  • The developed methods support the advancement of mIF for clinical diagnostics and immunotherapy response prediction.