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Updated: Aug 8, 2026
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical
Yves-Rémi Van Eycke1,2, Justine Allard1, Isabelle Salmon1,3
1DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium.
Insights
This study introduces a new method to assess and correct variations in immunohistochemistry (IHC) staining using digital pathology. The validated normalization transforms ensure reliable protein expression analysis from tissue samples.
Area of Science:
- Pathology
- Digital Pathology
- Computational Biology
Background:
- Immunohistochemistry (IHC) is crucial for protein expression analysis in pathology.
- IHC staining is susceptible to inter-batch variations, affecting result reliability.
- Digital pathology and image normalization offer potential solutions to standardize IHC.
Purpose of the Study:
- To develop a methodology for objectively evaluating the necessity of image normalization in IHC.
- To identify optimal image normalization techniques for IHC data.
- To correct non-relevant staining variations and enable quantitative feature extraction.
Main Methods:
- Utilized tissue microarray (TMA) materials for standardized sample preparation.
- Employed statistical analyses to quantify color and intensity variations.
- Adapted blind color deconvolution methods for IHC image normalization.
- Tested normalization techniques across diverse IHC experiments, tissue types, and protein targets.
Main Results:
- Established and validated inter-batch normalization transforms for IHC.
- Demonstrated correction of non-relevant IHC staining variations.
- Successfully processed normalized image series for quantitative feature extraction.
- Confirmed the methodology's effectiveness across various experimental conditions.
Conclusions:
- The proposed methodology objectively assesses and corrects IHC staining variations.
- Validated normalization transforms improve the reliability of protein expression quantification.
- Digital pathology tools enhance the consistency and accuracy of IHC analysis.
Abstract:
Immunohistochemistry (IHC) is a widely used technique in pathology to evidence protein expression in tissue samples. However, this staining technique is known for presenting inter-batch variations. Whole slide imaging in digital pathology offers a possibility to overcome this problem by means of image normalisation techniques. In the present paper we propose a methodology to objectively evaluate the need of image normalisation and to identify the best way to perform it. This methodology uses tissue microarray (TMA) materials and statistical analyses to evidence the possible variations occurring at colour and intensity levels as well as to evaluate the efficiency of image normalisation methods in correcting them. We applied our methodology to test different methods of image normalisation based on blind colour deconvolution that we adapted for IHC staining. These tests were carried out for different IHC experiments on different tissue types and targeting different proteins with different subcellular localisations. Our methodology enabled us to establish and to validate inter-batch normalization transforms which correct the non-relevant IHC staining variations. The normalised image series were then processed to extract coherent quantitative features characterising the IHC staining patterns.
