About this item:

26 Views | 18 Downloads

Author Notes:

Sara Fridovich-Keil, jfridov@emory.edu

We are grateful to all members of the Fridovich-Keil lab for constant support, to members of the Division of Animal Resources at Emory University for expert care of our rats, and to colleagues at the Cancer Tissue and Pathology shared resource of the Winship Cancer Institute of Emory University for scanning our slides.


Research Funding:

JLFK R01DK107900 National Institutes of Health https://www.nih.gov JLFK Project 00097374 Emory University Research Committee https://www.urc.emory.edu Cancer Tissue and Pathology shared resource of the Winship Cancer Institute of Emory University P30CA138292 National Institutes of Health/National Cancer Institute https://www.nih.gov The funders did not have any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


  • 3,3'-Diaminobenzidine
  • Staining and Labeling

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


Journal Title:



Volume 17, Number 7 July


, Pages e0271593-e0271593

Type of Work:

Article | Final Publisher PDF


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).

Copyright information:

© 2022 Patel et al

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/rdf).
Export to EndNote