Quantification of protein expression in cells and cellular subcompartments on immunohistochemical sections using a computer supported image analysis system
Martin Braun1,2, Robert Kirsten1,2, Niels J. Rupp3, Holger Moch3, Falko Fend4, Nicolas Wernert1, Glen Kristiansen1 and Sven Perner1,2
1Institute of Pathology, 2Department of Prostate Cancer Research, University Hospital of Bonn, Bonn, Germany, 3Institute of Surgical Pathology, University Hospital Zurich, Zurich, Switzerland and 4Institute of Pathology, Comprehensive Cancer Center, University Hospital of Tuebingen, Tuebingen, Germany.
Offprint requests to: Dr. Sven Perner, Institute of Pathology and Department of Prostate Cancer Research, University Hospital of Bonn, Sigmund-Freud-Straße 25, D-53127 Bonn, Germany. e-mail: email@example.com
Summary. 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. Histol Histopathol 28, 605-610 (2013)
Key words: Digital image analysis, Automated image analysis protein quantification, Immunhistochemistry