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Related Experiment Video

Updated: Jul 14, 2025

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

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

Published on: April 7, 2023

An end-to-end workflow for multiplexed image processing and analysis.

Jonas Windhager^1,2,3,4, Vito Riccardo Tomaso Zanotelli^1,2,5, Daniel Schulz^1,2

¹Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

Nature Protocols

|October 10, 2023

Summary

This summary is machine-generated.

This study introduces an integrated workflow for analyzing multiplexed tissue imaging data, enabling detailed spatial profiling of cells. The user-friendly protocol streamlines complex computational tasks for researchers with basic bioinformatics training.

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Last Updated: Jul 14, 2025

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

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Area of Science:

Computational Biology
Bioinformatics
Image Analysis

Background:

Multiplexed imaging allows simultaneous spatial profiling of numerous biological molecules in tissues at single-cell resolution.
Extracting biological insights from this data requires complex computational steps like segmentation, feature extraction, and spatial analysis.

Purpose of the Study:

To present an end-to-end, user-friendly, and customizable workflow for processing and analyzing multiplexed tissue imaging data.
To integrate existing computational tools for streamlined image analysis and spatially resolved single-cell analysis.

Main Methods:

Utilized napari-imc for raw data inspection and cytomapper for R-based visualization.
Employed the steinbock toolkit for preprocessing, segmentation (supervised and deep learning), and feature extraction.
Leveraged R/Bioconductor packages and imcRtools for downstream analysis, including dimensionality reduction, phenotyping, and cell-cell interaction testing.

Main Results:

Demonstrated a comprehensive workflow applicable to imaging mass cytometry and adaptable to other multiplexed imaging technologies.
Showcased two distinct cell segmentation approaches within the workflow.
Enabled spatially resolved single-cell analysis, including community and neighborhood detection.

Conclusions:

The presented workflow simplifies complex computational tasks in multiplexed tissue imaging analysis.
The protocol is accessible to researchers with basic bioinformatics training and can be completed efficiently.
Facilitates deeper biological understanding through integrated spatial single-cell analysis.