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Updated: Aug 11, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Asa A Brockman1, Rohit Khurana1, Todd Bartkowiak1,2

  • 1Department of Cell & Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

Cytometry. Part B, Clinical Cytometry
|February 7, 2023
PubMed
Summary

Cyclic immunohistochemistry (cycIHC) generates large datasets. A new open-source pipeline, CASSATT, simplifies processing, registration, and analysis of cycIHC data, enabling deeper insights into cell interactions and tissue organization.

Keywords:
image analysisimagingmicroscopymultiparameter analysismultiplexed analyses

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Immunohistochemistry

Background:

  • Cyclic immunohistochemistry (cycIHC) enables high-dimensional tissue analysis using brightfield microscopy.
  • Current cycIHC data processing is complex, requiring specialized expertise or proprietary software.
  • Analyzing large cycIHC datasets for spatial cell relationships is challenging.

Purpose of the Study:

  • To present a unified, user-friendly pipeline for processing, aligning, and analyzing cycIHC data.
  • To enable researchers to easily extract single-cell data and explore spatial interactions.
  • To facilitate the comparison of cell population neighborhoods across tissue samples.

Main Methods:

  • Development of the Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT) pipeline.
  • Implementation of image registration across multiple cycIHC staining rounds.
  • Nuclei segmentation and marker expression measurement for individual cells.
  • Analysis of spatial relationships, cell-cell interaction frequencies, and neighborhood identification.

Main Results:

  • CASSATT successfully registers, segments, and quantifies marker expression in cycIHC data.
  • The pipeline reveals significant spatial interactions between cell populations.
  • Identification of distinct cellular neighborhoods based on cell type composition.
  • CASSATT provides quantitative measures of cell population interactions and spatial organization.

Conclusions:

  • CASSATT offers a streamlined, open-source solution for cycIHC data analysis.
  • The pipeline enhances the utilization of cycIHC for understanding tissue architecture and cell communication.
  • CASSATT empowers researchers to explore complex spatial biology without requiring expert data science skills.