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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

A framework for multiplex imaging optimization and reproducible analysis.

Jennifer Eng^1,2, Elmar Bucher¹, Zhi Hu¹

¹Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR, 97239, USA.

Communications Biology

|May 11, 2022

Summary

This summary is machine-generated.

Researchers developed mplexable, a Python software for reproducible multiplex imaging analysis. This framework enhances cyclic immunofluorescence (CyCIF) methods and provides shareable tools for image analytics.

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

Published on: April 8, 2016

Area of Science:

Biotechnology
Computational Biology
Histology

Background:

Multiplex imaging is vital for single-cell phenotyping and tissue spatial characterization.
Transparent methods are lacking for comparing multiplex imaging platforms, protocols, and analytical pipelines.
Cyclic immunofluorescence (CyCIF) is a key multiplex imaging technique requiring robust analytical frameworks.

Purpose of the Study:

To develop a transparent and reproducible framework for multiplex imaging analysis.
To optimize signal removal, antibody specificity, background correction, and batch normalization for CyCIF.
To provide shareable tools for multiplexed image analytics.

Main Methods:

Development of a Python software package named mplexable for reproducible image processing.
Utilization of Jupyter notebooks to document and share the optimization process.
Focus on cyclic immunofluorescence (CyCIF) data processing.

Main Results:

mplexable enables reproducible image processing for multiplex imaging.
Optimization strategies for signal removal, antibody specificity, background correction, and batch normalization were established.
The framework facilitates the comparison of different multiplex imaging platforms and protocols.

Conclusions:

The developed mplexable software and framework improve the CyCIF methodology.
This work provides a foundation for easily shared and reproducible multiplexed image analytics.
Enhanced analytical frameworks are crucial for advancing single-cell and spatial tissue characterization.