Digital Quantification of Membrane DAB Immunohistochemical Staining in FFPE Cervical Cancer Tissues Using an Open-Source CellProfiler Pipeline

Background

Immunohistochemistry (IHC) is an important technique in histopathology for detecting the localization of specific proteins in formalin-fixed paraffin-embedded (FFPE) tissue sections. It enables the visualization of protein expression while preserving tissue architecture [1,2]. 3,3'-diaminobenzidine (DAB) is the most commonly used chromogenic substrate since it is stable and compatible with brightfield microscopy; also, it has the ability to produce a permanent brown precipitate at sites of antigen–antibody interaction [3]. Because of these advantages, DAB-based IHC has become an indispensable tool for routine diagnostics and in biomarker research [4]. However, the interpretation of DAB staining is often observer-dependent, relying on manual scoring systems (e.g., Q-score or H-score). This introduces inter-observer variability, thereby limiting reproducibility across studies and laboratories [5,6].

Recent advances in the domains of digital pathology and image analysis have overcome many limitations associated with manual assessment by enabling high-throughput quantification of immunohistochemical signals, thereby making the detection of signals very quick and accurate [7,8]. Open-source software such as CellProfiler provides a user-friendly interface for designing image analysis pipelines. It enables segmentation of cellular compartments and extraction of quantitative features from histological images, making it convenient for researchers to analyze images without requiring programming expertise [9,10]. These open-source tools are very useful as they eliminate the need for expensive proprietary software, also supporting reproducibility and transparent workflows.

In the context of DAB staining, due to the heterogeneity in staining intensity, the addition of counterstains such as hematoxylin, which causes difficulty in the separation of signals and variability in tissue morphology, makes it harder to accurately quantify signals [11–13]. Although color deconvolution methods can separate chromogenic signals [14], implementing these workflows in a standardized and reproducible manner remains a practical barrier for many laboratories. Furthermore, there is a growing need for standardized and accessible pipelines that can be readily adapted to different tissue types and biomarkers. To address these challenges, this protocol describes a simple and rapid pipeline for quantifying DAB-based IHC staining using CellProfiler, with minimal user intervention and no requirements for advanced computational expertise.

In this protocol, FFPE sections of human cervical cancer tissues were used to perform IHC using an antibody that targets ASCT2 (SLC1A5), which is a protein involved in the transport of glutamine, predominantly localized in the cell membrane. These slides were stained and imaged using a brightfield microscope. The primary focus of this protocol is the application of the pipeline created using CellProfiler to process the images taken after performing IHC, followed by data analysis steps.

The current protocol differs from image analysis protocols from previous studies, as these do not have a fixed pipeline to perform image processing and analysis after IHC. Furthermore, traditional imaging software, such as ImageJ and QuPath, could not provide accurate individual cell detection and membrane staining intensity quantification. Moreover, many workflows require manual region-of-interest selection, sequential processing steps, or macro scripting for high-throughput analysis. In contrast, CellProfiler provides a modular, pipeline-based workflow that enables automated batch processing, reproducible object identification, and quantitative extraction of multiple cellular features without requiring programming expertise. This protocol allows for relatively precise and efficient means of intensity quantification and further downstream analysis through the combination of software such as CellProfiler and Microsoft Excel. This allows for simplified image analysis across multiple tissue types and experimental groups. However, it is important to note that DAB chromogenic intensity, as measured here, is best suited to relative comparisons between samples processed, stained, and imaged under standardized conditions. It should not be interpreted as an absolute measure of protein abundance, since chromogenic intensity can be affected by variables such as antibody, incubation time, and section thickness that are not directly related to the target expression level.