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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

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DenVar: density-based variation analysis of multiplex imaging data.

Souvik Seal¹, Thao Vu¹, Tusharkanti Ghosh¹

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

Bioinformatics Advances

|January 26, 2023

Summary

This summary is machine-generated.

This study introduces a novel threshold-free method for analyzing protein expression in the tumor microenvironment (TME). This approach enhances subject-specific risk assessment by reducing subjectivity in cancer research.

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Published on: July 24, 2010

Area of Science:

Computational biology
Cancer research
Single-cell analysis

Background:

Multiplex imaging is vital for understanding tumor microenvironment (TME) single-cell biology.
Subject-specific risk assessment relies on quantifying protein intensity in cancer.
Conventional methods use dual thresholds, introducing subjectivity.

Purpose of the Study:

To present a threshold-free computational approach for analyzing protein expression in the TME.
To enable more objective and interpretable subject-specific risk assessment in cancer.
To associate protein density variations with clinical outcomes.

Main Methods:

Developed a method to compute subject distances based on protein probability density in the TME.
Utilized distance matrices for subject classification and kernel machine regression.
Eliminated subjectivity bias inherent in traditional thresholding techniques.

Main Results:

Identified significant association between HLA-DR density and overall survival in lung cancer.
Analyzed multi-protein effects on survival and recurrence in triple-negative breast cancer.
Demonstrated method reliability through extensive simulation studies.

Conclusions:

The threshold-free method offers an unbiased and interpretable alternative for TME analysis.
This approach can improve subject risk stratification and clinical outcome prediction.
The developed R package facilitates the application of this novel methodology.