Quantitative Analysis of Immunohistochemistry in Melanoma Tumors

Abstract

Identification of positive staining is often qualitative and subjective. This is particularly troublesome in pigmented melanoma lesions, because melanin is difficult to distinguish from the brown stain resulting from immunohistochemistry (IHC) using horse radish peroxidase developed with 3,3'-Diaminobenzidine (HRP-DAB). We sought to identify and quantify positive staining, particularly in melanoma lesions. We visualized G-protein coupled estrogen receptor (GPER) expression developed with HRP-DAB and counterstained with Azure B (stains melanin) in melanoma tissue sections (n = 3). Matched sections (n = 3), along with 22 unmatched sections, were stained only with Azure B as a control. Breast tissue (n = 1) was used as a positive HRP-DAB control. Images of the stained tissues were generated using a Nuance Spectral Imaging Camera. Analysis of the images was performed using the Nuance Spectral Imaging software and SlideBook. Data was analyzed using a Kruskal-Wallis one way analysis of variance (ANOVA). We showed that a pigmented melanoma tissue doubly stained with anti-GPER HRP-DAB and Azure B can be unmixed using spectra derived from a matched, Azure B-only section, and an anti-GPER HRP-DAB control. We unmixed each of the melanoma lesions using each of the Azure B spectra, evaluated the mean intensity of positive staining, and examined the distribution of the mean intensities (P = .73; Kruskal-Wallis). These results suggest that this method does not require a matched Azure B-only stained control tissue for every melanoma lesion, allowing precious tissues to be conserved for other studies. Importantly, this quantification method reduces the subjectivity of protein expression analysis, and provides a valuable tool for accurate evaluation, particularly for pigmented tissues.

Figure 1

IHC using HRP-DAB with Azure as a counterstain can be unmixed and quantified using spectral imaging. The images presented in this figure, with the exception of the GPER-DAB only control, are from participant C. A, Original image: GPER-DAB and Azure in melanoma (participant C). This is a single-image representation of a spectral image cube (containing 16 individual images). B, Images used to create spectral library form left to right: nonpigmented tissue from breast reduction mammoplasty surgery stained with GPER-DAB only. Pigmented melanoma section (participant C) stained with Azure only. C, Spectral library developed from images in (B) with GPER-DAB spectral library denoted by red-brown and the blue denoting the Azure spectral library. D, GPER-DAB component image resulting from unmixing of original image in (A) with the spectral library in (C) representing positive staining for GPER. GPER = G protein-coupled estrogen receptor 1, HRP-DAB = horse radish peroxidase developed with 3,3’-Diaminobenzidine, IHC = immunohistochemistry.

Figure 2

Azure spectra do not vary between melanoma tissue sections. Spectra derived from unpigmented control stained for GPER-DAB (n = 1) and Azure-only matched controls for each melanoma lesion stained with GPER-DAB + Azure (n = 3). Each image is unmixed with the GPER-DAB spectra and an Azure-only spectra. GPER = G protein-coupled estrogen receptor 1-Diaminobenzidine.

Figure 3

Component images unmixed with different spectral libraries. A, Original image: GPER-DAB and Azure in melanoma (participant A). This is a single-image representation of a spectral image cube (containing 16 individual images). B, Component images representing GPER-DAB in melanoma: original image (participant A) unmixed with 3 spectral libraries. GPER = G protein-coupled estrogen receptor 1-Diaminobenzidine.