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Updated: Jul 2, 2025
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining
Iván Durán-Díaz1, Auxiliadora Sarmiento1, Irene Fondón1
1Signal Theory and Communications Department, University of Seville, Avda. Descubrimientos S/N, 41092 Seville, Spain.
Insights
A new automated method simplifies scoring of immunohistochemistry images by using principal component analysis (PCA) to analyze protein expression in tumor tissues, improving robustness and reducing parameter dependency.
Area of Science:
- Biomedical imaging analysis
- Computational pathology
- Biomarker quantification
Background:
- Immunohistochemistry (IHC) is vital for protein expression analysis in research and clinics.
- Quantifying IHC image features, especially in complex tumor tissues, is challenging.
- Previous methods like non-negative matrix factorization (NMF) for IHC image analysis showed promise but had parameter and initialization dependencies.
Purpose of the Study:
- To develop a simpler, more robust, automated, and unsupervised method for scoring immunohistochemical images.
- To overcome the limitations of previous methods, including parameter sensitivity and reliance on reference images.
- To accurately quantify protein of interest expression in tumor tissues using bright-field microscopy.
Main Methods:
- Developed a novel automated scoring method for bright-field immunohistochemistry images of tumor tissues.
- Replaced non-negative matrix factorization (NMF) with principal component analysis (PCA) for color separation and dimension reduction.
- Determined color vectors using point density peaks in the PCA-derived subspace.
- Implemented a new scoring stage that does not require reference images, enhancing robustness.
Main Results:
- The new method effectively separates blue (nuclei) and brown (protein of interest) stains in IHC images.
- Principal component analysis (PCA) with dimension reduction successfully determined the color subspace.
- The automated scoring demonstrated promising and consistent results compared to expert manual scoring.
- The method showed reduced dependency on parameters and initialization compared to NMF-based approaches.
Conclusions:
- The proposed automated method offers a simpler and more robust approach to immunohistochemical image scoring.
- The PCA-based technique eliminates the need for NMF and control images, increasing efficiency.
- This automated scoring method holds potential for consistent and reliable quantification of protein expression in clinical and research settings.
Abstract:
Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.
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