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Related Experiment Video

Updated: Jun 27, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3.

Andrea Behanova¹, Christophe Avenel¹, Axel Andersson¹

¹Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden.

Biological Imaging

|March 15, 2024

Summary

This summary is machine-generated.

New plugins enhance large-scale multiplex tissue analysis by improving cell staining quality, classification accuracy, and cell interaction review. These tools aid in quality control and build trust in automated tissue analysis methods.

Keywords:

Cell classification quality control spatial omics visualization

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Last Updated: Jun 27, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Published on: April 8, 2016

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Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Published on: April 7, 2023

Area of Science:

Biomedical imaging
Computational pathology
Cellular biology

Background:

Large-scale multiplex tissue analysis is crucial for understanding biological processes like development and tumor formation.
Current analysis involves cell delineation, classification, and spatial relationship analysis, facing several challenges.
Automated approaches require visual verification to ensure accuracy and build user trust.

Purpose of the Study:

To introduce novel plugins for visualization and quality control in large-scale multiplex tissue analysis.
To address challenges in cell delineation, classification, and spatial interaction analysis.
To enhance the reliability and user trust in automated tissue analysis workflows.

Main Methods:

Development of three specialized plugins for microscopy image analysis.
Plugin 1: Focuses on assessing and controlling the quality of cell staining.
Plugin 2: Enables interactive evaluation and comparison of cell classification outcomes.
Plugin 3: Facilitates the review of interactions between different cell types.

Main Results:

The plugins provide essential tools for quality control throughout the multiplex tissue analysis workflow.
Improved visualization aids in identifying bottlenecks and optimizing analysis parameters.
The tools support visual verification, crucial for validating automated analysis results.

Conclusions:

The presented plugins significantly improve the quality control and visualization capabilities for large-scale multiplex tissue analysis.
These tools are vital for optimizing analysis steps, ensuring data accuracy, and fostering confidence in automated methods.
Implementation of these plugins can lead to more precise and reliable insights from complex tissue imaging data.