Quantitative Immunohistochemistry by Measuring Cumulative Signal Strength Using Commercially Available Software Photoshop and Matlab

Abstract

SUMMARYCurrently available techniques for performing quantitative immunohistochemistry (Q-IHC) rely upon pixel-counting algorithms and therefore cannot provide information as to the absolute amount of chromogen present. We describe a novel algorithm for true Q-IHC based on calculating the cumulative signal strength, or energy, of the digital file representing any portion of an image. This algorithm involves subtracting the energy of the digital file encoding the control image (i.e., not exposed to antibody) from that of the experimental image (i.e., antibody-treated). In this manner, the absolute amount of antibody-specific chromogen per pixel can be determined for any cellular region or structure. Q uantitative immunohistochemistry (Q-IHC) in the predigital era depended on the observations of multiple investigators using an arbitrary scale to grade the extent and intensity of chromogen present (Shi et al. 1991(Shi et al. ,1993. This approach was inherently limited by observer subjectivity and bias, by inter-and intraobserver variation, and generated data of limited range (i.e., amount is usually quantified on a 0-4 scale). With the advent of digital photomicroscopy, however, these weaknesses could in theory be eliminated and true quantification achieved.Early attempts at Q-IHC involved converting analog images into a digital format and then transforming the 256 separate shades of red, green and blue that are obtained when working in 24-bit RGB color to singlechannel grayscale (Mosedale et al. 1996). The area of interest is defined and the mean gray level of the selected pixels determined. Later attempts at Q-IHC employed color thresholding using commercial software (i.e., Adobe Photoshop) (Fermin and Degraw 1995), followed by counting the total number of pixels of appropriate value (Lehr et al. 1997(Lehr et al. ,1999Ruifrok 1997). For example, in DAB-based immunohistochemistry all the pixels containing "brown" within a prespecified spectral range are identified in Photoshop using the Magic Wand tool. The total numbers of pixels identified are then counted using the histogram function (Lehr et al. 1999). Although these algorithms represent significant improvements over traditional methods for evaluating analog images, these approaches still do not allow true Q-IHC to be performed.Determining the absolute amount of chromogen present necessitates calculating the cumulative signal strength of the image being evaluated. This can be done only by calculating signal energy, E, which is defined in the mathematical (Jain 1989) and not the physical sense. Here we provide an algorithm for true Q-IHC that relies on calculating the energy of images captured in Photoshop using a high-resolution digital camera and then processing the image's unmodified and full digital file using the powerful enabler language Matlab. To demonstrate this algorithm, we studied the gastrin-releasing peptide receptor (GRPR) aberrantly expressed by human colon cancers. Because we have previously shown that human colon cancers variably express this ...

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