An Automated Quantitative Method to Analyze Immunohistochemistry and Immunocytochemistry Images

Abstract

Immunohistochemistry (IHC) plays an important role in target protein analysis. However, many researchers analyze IHC images by five/three-tier manual ranking methods based on stained area and density. Such manual scoring might be biased by the antibody amount, counterstaining density, overall brightness, and most importantly, researchers' ranking experience. The potential lack of reliability in manual approach drives us to develop an automatic tool to quantitatively analyze IHC, which can also be used for immunocytochemistry (ICC). We applied a “color deconvolution” method based on an red-green-blue (RGB) color vector matching the color of desired immunochemistry agent, 3,3′-diaminobenzidine (DAB) with haematoxylin in this case, to acquire pseudo-color images. Subsequently, Density, the product of integrating the single pixel staining density by area stained, is used as an index of immunostaining. We observed a strong correlation between the results by our automatic method and the manual scoring from experienced researchers, demonstrating the utility of this method in IHC and ICC. For IHC analysis, five-tier ranking based on density (n = 161) shows a high Spearman's coefficient (rho) of 0.80 (P < 0.0001) with the annotation given by two experienced scientists. However, the rho between experienced and inexperienced researchers' annotation (n = 154) is only 0.66 (P < 0.0001). In immunocytochemistry, the rho between density and experienced researchers' annotation is 0.80 (n = 44, P < 0.0001). In conclusion, our method can rank multiple protein targets in immunohistochemistry and may be also used in immunochemistry.