» Articles » PMID: 23361561

Quantification of Protein Expression in Cells and Cellular Subcompartments on Immunohistochemical Sections Using a Computer Supported Image Analysis System

Overview
Date 2013 Jan 31
PMID 23361561
Citations 33
Authors
Affiliations
Soon will be listed here.
Abstract

Quantification of protein expression based on immunohistochemistry (IHC) is an important step for translational research and clinical routine. Several manual ('eyeballing') scoring systems are used in order to semi-quantify protein expression based on chromogenic intensities and distribution patterns. However, manual scoring systems are time-consuming and subject to significant intra- and interobserver variability. The aim of our study was to explore, whether new image analysis software proves to be sufficient as an alternative tool to quantify protein expression. For IHC experiments, one nucleus specific marker (i.e., ERG antibody), one cytoplasmic specific marker (i.e., SLC45A3 antibody), and one marker expressed in both compartments (i.e., TMPRSS2 antibody) were chosen. Stainings were applied on TMAs, containing tumor material of 630 prostate cancer patients. A pathologist visually quantified all IHC stainings in a blinded manner, applying a four-step scoring system. For digital quantification, image analysis software (Tissue Studio v.2.1, Definiens AG, Munich, Germany) was applied to obtain a continuous spectrum of average staining intensity. For each of the three antibodies we found a strong correlation of the manual protein expression score and the score of the image analysis software. Spearman's rank correlation coefficient was 0.94, 0.92, and 0.90 for ERG, SLC45A3, and TMPRSS2, respectively (p⟨0.01). Our data suggest that the image analysis software Tissue Studio is a powerful tool for quantification of protein expression in IHC stainings. Further, since the digital analysis is precise and reproducible, computer supported protein quantification might help to overcome intra- and interobserver variability and increase objectivity of IHC based protein assessment.

Citing Articles

The relationship between thiamin, folic acid and cognitive function in a rat model of uremia.

Lu Y, Xu C, Xie K, Zhao B, Wang M, Qian C Ren Fail. 2024; 46(1):2329257.

PMID: 38482596 PMC: 10946272. DOI: 10.1080/0886022X.2024.2329257.


Machine Learning for Digital Scoring of PRMT6 in Immunohistochemical Labeled Lung Cancer.

Mahmoud A, Brister E, David O, Valyi-Nagy K, Sverdlov M, Gann P Cancers (Basel). 2023; 15(18).

PMID: 37760550 PMC: 10527400. DOI: 10.3390/cancers15184582.


Reliability of CD44, CD24, and ALDH1A1 immunohistochemical staining: Pathologist assessment compared to quantitative image analysis.

Yaghjyan L, Heng Y, Baker G, Bret-Mounet V, Murthy D, Mahoney M Front Med (Lausanne). 2023; 9:1040061.

PMID: 36590957 PMC: 9794585. DOI: 10.3389/fmed.2022.1040061.


Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression.

Jagomast T, Idel C, Klapper L, Kuppler P, Proppe L, Beume S Histol Histopathol. 2022; 37(6):527-541.

PMID: 35146728 DOI: 10.14670/HH-18-434.


Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays.

Roberts M, Baker G, Heng Y, Pyle M, Astone K, Rosner B Cancer Epidemiol. 2021; 74:101999.

PMID: 34352659 PMC: 8887925. DOI: 10.1016/j.canep.2021.101999.