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Updated: Jun 29, 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

PENGUIN: A rapid and efficient image preprocessing tool for multiplexed spatial proteomics.

A M Sequeira^1,2, M E Ijsselsteijn², M Rocha¹

¹Department of Informatics, School of Engineering, University of Minho, Braga, Portugal.

Computational and Structural Biotechnology Journal

|November 19, 2024

Summary

This summary is machine-generated.

We developed PENGUIN, a user-friendly tool for denoising multiplex spatial proteomics imaging data. This method enhances downstream analysis like cell segmentation without manual input.

Keywords:

Background subtraction Denoising Immunophenotyping Multiplex imaging Normalization Spatial omics

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Area of Science:

Spatial biology
Proteomics
Bioinformatics

Background:

Multiplex spatial proteomics offers insights into biological systems.
Analyzing imaging data faces challenges in noise reduction and normalization.
Existing solutions for denoising are often complex or require manual intervention.

Purpose of the Study:

To introduce PENGUIN (Percentile Normalization GUI Image deNoising), a novel image preprocessing tool.
To provide a user-friendly, efficient solution for denoising multiplex spatial proteomics data.
To improve the accuracy of downstream analyses like cell segmentation and phenotyping.

Main Methods:

PENGUIN utilizes percentile normalization for signal-to-noise ratio adjustment.
The tool operates without manual annotation or machine learning models.
It is available as a script and a Jupyter notebook for flexible parameter adjustment.

Main Results:

PENGUIN effectively reduces background noise while preserving signal intensity.
The tool demonstrates improved performance in cell segmentation and phenotyping tasks.
Comparative analysis shows PENGUIN outperforms conventional and specialized image processing techniques.

Conclusions:

PENGUIN offers a simple, fast, and effective solution for multiplex spatial proteomics data preprocessing.
The tool enhances the reliability and efficiency of spatial biology research.
Its intuitive interface facilitates broader adoption and application in the field.