DAB-quant: An open-source digital system for quantifying immunohistochemical staining with 3,30-diaminobenzidine (DAB)

• Authors: Sneh Patel, Emory University; Sara Fridovich-Keil, University of California at Berkeley; Shuana A Rasmussen, Emory University; Judith Fridovich-Keil, Emory University
• Journal Title: PLoS ONE
• Volume: Volume 17, Number 7 July
• Publisher: (publisher) | 2022-07-01, Pages e0271593-e0271593
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0271593
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: 3,3'-Diaminobenzidine; Staining and Labeling

Abstract

Here, we describe DAB-quant, a novel, open-source program designed to facilitate objective quantitation of immunohistochemical (IHC) signal in large numbers of tissue slides stained with 3,30-diaminobenzidine (DAB). Scanned slides are arranged into separate folders for negative controls and test slides, respectively. Otsu's method is applied to the negative control slides to define a threshold distinguishing tissue from empty space, and all pixels deemed tissue are scored for normalized red minus blue (NRMB) color intensity. Next, a user-defined tolerance for error is applied to the negative control slides to set a NRMB threshold distinguishing stained from unstained tissue and this threshold is applied to calculate the fraction of stained tissue pixels on each test slide. Results are recorded in a spreadsheet and pseudocolor images are presented to document how each pixel was categorized. Slides can be analyzed in full, or sampled using small boxes scattered randomly and automatically across the tissue area. Quantitation of sampling boxes enables faster processing, reveals the degree of heterogeneity of signal, and enables exclusion of problem areas on a slide, if needed. This system should prove useful for a broad range of applications. The code, usage instructions, and sample data are freely and publicly available on GitHub (https://github.com/sarafridov/DAB-quant) and at protocols.io (dx.doi.org/10.17504/ protocols.io.dm6gpb578lzp/v1).