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Related Experiment Video

Updated: Sep 6, 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

MIAMI: mutual information-based analysis of multiplex imaging data.

Souvik Seal¹, Debashis Ghosh¹

¹Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, CO 80045, USA.

Bioinformatics (Oxford, England)

|June 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel method to analyze protein co-expression in cancer, improving risk assessment for survival and recurrence. The method uses mutual information to quantify marker interactions, offering a more robust approach than traditional techniques.

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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

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Last Updated: Sep 6, 2025

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

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

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Published on: June 2, 2023

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

Area of Science:

Computational Biology
Cancer Research
Bioinformatics

Background:

Assessing cancer patient risks like death or recurrence is crucial.
Conventional methods for analyzing protein marker co-expression in the tumor microenvironment rely on manual thresholds, which are subjective and lack robustness.
This subjectivity is amplified when analyzing multiple markers.

Purpose of the Study:

To develop a novel, robust computational method for quantifying protein marker co-expression in cancer.
To overcome the limitations of manual thresholding in analyzing complex marker interactions.
To provide a more objective and reliable approach for cancer risk assessment.

Main Methods:

A new method is presented that treats marker intensities as dependent random variables, using mutual information (MI) as a metric for co-expression.
An alternative formulation of MI with an efficient estimation technique is developed, addressing challenges in estimating joint density with increasing numbers of markers.
The method was applied to lung cancer and triple-negative breast cancer datasets.

Main Results:

Co-expression of HLA-DR and CK markers in lung cancer was associated with patient survival.
Co-expression of immuno-regulatory proteins (PD1, PD-L1, Lag3, IDO) in triple-negative breast cancer was linked to disease recurrence.
Simulation studies demonstrated the robustness of the proposed method.

Conclusions:

The developed method offers a robust and objective approach to analyzing protein marker co-expression in cancer.
This quantitative co-expression analysis can improve the assessment of cancer patient risks, including survival and recurrence.
The associated R package, MIAMI, is available for broader application in cancer research.