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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
Automated selection of DAB-labeled tissue for immunohistochemical quantification
Eric M Brey1, Zahid Lalani, Carol Johnston
1Laboratory of Reparative Biology and Bioengineering, Department of Plastic Surgery, University of Texas M. D. Anderson Cancer Center and University of Texas Center for Biomedical Engineering, Houston 77030, USA.
Insights
This study introduces a new imaging method for quantitative immunohistochemistry (IHC) analysis. The novel technique accurately quantifies diaminobenzidene (DAB) staining, improving diagnostic and prognostic applications.
Area of Science:
- Biomedical Imaging
- Computational Pathology
- Histopathology Analysis
Background:
- Immunohistochemistry (IHC) is increasingly used for quantitative protein analysis in clinical and research settings.
- Standardized and automated IHC analysis is crucial for accurate patient diagnosis and prognosis.
- Existing automated imaging techniques face challenges with background interference and consistency.
Purpose of the Study:
- To develop a novel automated imaging technique for quantitative analysis of diaminobenzidene (DAB)-labeled antigens in IHC.
- To compare the accuracy and consistency of the proposed method against seven existing imaging techniques.
- To assess the impact of misclassification on staining quantification using Bland-Altman analysis.
Main Methods:
- Development of a new imaging technique converting brightfield DAB images to normalized blue images.
- Automated identification of positively stained tissue using the novel technique.
- Statistical comparison with seven prior methods using manual analysis by two observers on 18 DAB-stained images.
Main Results:
- The proposed method demonstrated statistically superior accuracy and consistency compared to seven other techniques.
- Manual analysis by observers served as the ground truth for accuracy assessment.
- Bland-Altman analysis confirmed the robustness of the new method in staining quantification.
Conclusions:
- The novel imaging technique provides a more accurate and consistent automated analysis of DAB-stained IHC images.
- This method has the potential to standardize IHC analysis, improving its utility in clinical diagnosis and prognosis.
- The technique effectively overcomes background interference issues present in other methods.
Abstract:
The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.
