TECHNICAL NOTE |
Correspondence to: Richard V. Benya, Dept. of Medicine, U. of Illinois at Chicago, 840 South Wood Street (M/C 787), Chicago, IL 60612.
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Summary |
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Currently 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. (J Histochem Cytochem 48:303311, 2000)
Key Words: bombesin, cancer, gastrin-releasing peptide, methods
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Introduction |
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Quantitative 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 (
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 single- channel grayscale (
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 (
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Materials and Methods |
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Reagents
Antibody against the GRPR (
Immunohistochemical Technique
Four separate colon cancers were selected from the the Chicago Veterans Administration Medical Center Gastrointestinal Tissue Bank representing a broad spectrum in terms of tissue differentiation. Differentiation was assessed by standard criteria as previously defined (Anonymous 1992). Immunohistochemistry was then performed using a standard three-stage indirect immunoperoxidase technique on 5-µm tissue sections (
For all specimens, control tissues were processed identically and at the same time, except that primary antibody was not applied. Control tissues were within 5 µm of the tested tissue and treated with secondary antibody, DAB, and counterstained with hematoxylin for precisely the same time as the experimental tissue. Therefore, all differences between the experimental tissue and the control tissue are ultimately due to DAB identification of the relevant protein.
Digital Image Capture
All specimens were evaluated using a Nikon E600 microscope with Axioplan objectives. Images were acquired at x1000 using a Microlumina Ultra Resolution Scanning Digital Camera [3380 x 2700 pixels (Leaf Systems; Fort Washington, PA)]. In all cases, files were saved in tagged-image file format (TIFF), which allows LZW compression without discarding any data, as occurs when the more popular joint photographic experts group (JPEG) format is used. Consequently, the average file size of each original digital image was 2225 MB.
Determination of Image (File) Energy
We assume that all information contained in the experimental image file (i.e., from the primary antibody-exposed slide), minus the information contained in the control image file (i.e., from the slide not exposed to primary antibody but otherwise treated identically), is of importance. In other words, the amount of antibody-generated chromogen is represented by all the information contained in the experimental image file, minus that contained in the control image file. In this manner we make use of all the numerical data encoded within the file describing each pixel.
After acquisition with a digital camera, the file for the experimental image is opened in Photoshop (Adobe; San Jose, CA) using a Macintosh 400 MHz G3 workstation (Apple; Cupertino, CA). The region of interest is defined in Photoshop using the Marquee tool. This tool allows an area of any pixel dimension to be determined by the operator by using the "options window." To do this, the appropriately sized region of interest is identified and stored in a new file. This subset of the experimental image is labeled "EXP" and saved in TIFF format, as follows.
Overview of the algorithm for determining cumulative signal (i.e., image) intensity
At the cursor (>>) type
>>a=double(imread(EXP,tiff));
>>b=double(imread(CONTROL, tiff));
>>ea=norm(a(:,:,1))+norm(a(:,:,2))+norm(a(:,:,3));
>>eb=norm(b(:,:,1))+norm(b(:,:,2))+norm(b(:,:,3));
>>et=abs(ea-eb);
>>en=sqrt(et^2/N)
Note:
The file for the control image is processed similarly. The control slide is acquired from a sequential 5-µm tissue section and is treated identically to the experimental slide, except for not being exposed to antibody. The same area that was studied in the experimental situation is identified. The Marquee icon is used at the same size setting as was used for the experimental image and is placed over the homologous region in the control image. As above, the selected region is cut out and stored in a new file in TIFF format. This subset of the control image will now be referred to as "CONTROL." For ease of subsequent operation, both files "EXP" and "CONTROL" are stored in the Matlab folder.
Both files "EXP" and "CONTROL" are opened in Matlab (Math Works; Worcester, MA) using the read image command "imread." The data contained within each RGB image file is stored as an m x n x 3 matrix. Thus, the "norm" function can be used to determine the singular value of the matrix, which represents the magnitude, or mathematical energy, of the image file (the rationale for this algorithm is provided in the Discussion). Finally, the amount of energy present per pixel is determined by taking the square root of the difference of each file's energy squared divided by the number of pixels evaluated (see above). The purpose of computing the square root of the energy of the image file squared, divided by the number of pixels, is to determine the normalized energy of the image file per pixel. This transformation is necessary because the computation of the energy is determined by the square root of the summation of the squares of the image intensity in each pixel. The normalized energy per pixel therefore requires the determination of the square root of the average of the squares of the image intensity in each pixel.
Statistical Analysis
All values reported herein are unit-less. Values are therefore reported as energy units per pixel (eu/pix). Statistical analysis was performed using StatView (Abacus Concepts; Berkeley, CA), with differences between tissue regions evaluated by ANOVA. In all instances, data are expressed as the means ± SEM.
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Results |
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We have previously shown that whereas epithelial cells lining the human colon do not normally express GRPR (
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We first evaluated how large a window was necessary for accurate determination of chromogen per pixel. Specifically, we compared the information obtained using different-sized windows (10 x 10 pixels, 100 x 100 pixels, and 200 x 200 pixels; actual sizes are shown in Figure 2). In all instances, the energy contained within three separate regions was evaluated for each of the three window sizes, thereby enabling us to determine the coefficient of variation between observations. When a tumor containing a large homogeneous amount of cytoplasmic chromogen was evaluated (i.e., Figure 1A), the coefficient of variation between separate regions evaluated at even the relatively small window size of 10 x 10 pixels was low (Table 1). In contrast, tumors containing less entoplasm were more dependent on window size. For example, moderately poorly differentiated colon cancer (Figure 1C) on average expressed 55 eu/pix for both the 10 x 10 pixel region and the 100 x 100 pixel region. However, the coefficient of variation was ~sixfold higher for regions evaluated using the smaller window (Table 1). Although the coefficient of variation becomes smaller when windows between 10,000 and 40,000 pixels in size are used to evaluate homogeneous regions, larger window sizes are not better for evaluating non-homogeneous regions (Table 1). For example, the small amount of area devoid of nuclei in poorly differentiated cancers (i.e., Figure 1C and Figure 1D) makes it impossible to place a 200 x 200 pixel window that fits entirely over cytoplasm (Figure 2, bottom panel). Indeed, the inclusion of noncytoplasmic regions in such a large region is what makes the coefficient of variation larger than that observed when the energy contained in a 100 x 100 pixel region is determined. The importance of performing Q-IHC on areas that are homogeneous within the window being evaluated is underscored by the results obtained using the 200 x 200 pixel window on poorly differentiated cancer. Whereas the smaller window sizes indicate that there is ~5 eu/pix GRPR in the cytoplasm of poorly differentiated cancers, the necessary inclusion of nuclei dramatically increases this value to >30 eu/pix in the 200 x 200 pixel window (Table 1). Therefore, these data demonstrate that only homogeneous regions within an image can be evaluated. However, these data also show that windows as small as 10 x 10 pixels still can provide accurate information, the precision of which is increased by sampling multiple different regions within a specimen. However, all subsequent experiments were performed using 100 x 100 pixel windows, given the larger size and associated low coefficient of variation independent of the degree of tissue differentiation.
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It is well appreciated that increasing exposure to DAB increases the amount of nonspecific staining of tissues. Therefore, we next evaluated the effect of varying the time of DAB exposure. Sequential sections were evaluated after exposure to DAB for 15 sec to 10 min (Figure 3). In all instances, control tissues were processed simultaneously and treated identically to the experimental tissue, except that they were not exposed to primary antibody. For short DAB exposure times (i.e., <2 min), the calculated energy increased logarithmically but then plateaued thereafter (Figure 3). Therefore, for DAB exposure times of more than 2 min, the amount of cytoplasmic chromogen remained constant as a function of tumor differentiation. This result is best explained as due to the fact that once the antigenic sites of interest have reacted with DAB, all subsequent nonspecific increases in "brown" due to DAB equally affect the control and experimental tissues. This result also supports our exposure of tissues to DAB for 2 min, and indicates that longer times of exposure do not significantly alter experimental results.
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We next determined if our results could be influenced by the time tissues were exposed to counterstain. We treated consecutive experimental and control sections of well, moderately, and poorly differentiated tumors with DAB for 2 min and then exposed them to Gill's hematoxylin for various lengths of time. Because the control tissue was exposed to counterstain for the same amount of time as the experimental tissue, no significant differences in chromogen quantity were detected (Table 2). Therefore, chromogen quantity, as determined by our algorithm, is independent of the time of exposure to counterstain.
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Finally, we studied whether our algorithm suffered from day-to-day variability. We determined chromogen quantity in sequential sections of well, moderately, and poorly differentiated colon cancers on 4 consecutive days. The average amount of chromogen for all 4 days was 267.8 ± 4.7 eu/pixel for well-differentiated cancers, 171.8 ± 8.4 eu/pixel for moderately differentiated cancers, and 4.1 ± 1.9 eu/pixel for poorly differentiated cancers. Coefficients of variation ranged from 0.1 to 0.3 for all observations, indicating negligible variation in results across time (Table 2). These data therefore indicate that this algorithm provides consistent results independently of DAB exposure time (for times 2 min), duration of exposure to counterstain, and without significant day-to-day variability.
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Discussion |
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Currently available algorithms for Q-IHC are best suited to counting pixel number (
These limitations can be appreciated by briefly reviewing how color images are represented digitally. Each pixel, x1,2, contained within an image is represented by separate grayscale values for each of the three primary colors. Thus, any image can be graphically depicted in red, green, or blue such that i(x) is the intensity of color "i" represented by the number of pixels with gray level xi (Figure 4). In essence, this is the representation provided in Photoshop's histogram function, and shows that any specific color is but a linear combination of the three primary colors. For the DAB-generated "brown" in our immunohistochemistry, therefore, any pixel x1,2 can be represented by:
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Equation (1)
for the unique predetermined set of coefficients i, i=1,2,3, corresponding to the color "brown." As an aside, it should be observed that these coefficients are used to characterize the perceived "brown" component of the image as viewed by the user from the digital file. The image generated by the digital file, however, displays the three primary colors simultaneously and depicts the entire visual spectrum (i.e., the visual system enables us to perceive all possible colors simultaneously).
For quantitative purposes, one could attempt to isolate color using equation (1). However, such an approach has three significant limitations. First, the coefficients need to be determined. These coefficients vary for each different wavelength studied and must be determined experimentally for each tissue section evaluated. Second, the range of wavelengths involved in generating the color "brown" generally is not known to the viewer and needs to be determined, especially to allow determination of the
coefficients. Third, this approach would be useful only for studies using systems generating chromogen of identical "brown" color. In other words, such an algorithm would not be transferable to techniques using other color detection systems.
These limitations can be circumvented by assessing all the changes in the experimental image corresponding to the additive contributions of chromogen to the control image. This is manifested by additive changes in the entire color spectrum, or to the red, green, and blue representations encoded within the image's digital file. When DAB is used these changes generally appear in the "brown" spectrum, but our technique applies equally to any color-related change. Specifically, we calculate the cumulative strength, or energy, of the control image and subtract this value from that of the experimental image. For an image represented as a continuous variable as shown in Figure 4, energy is:
Equation (2)
where x1 and x2 identify the specific position of the data encoded within the matrix representing the image data file (
In 24-bit color, red, green, and blue are represented as discrete variables, with each channel limited to integrating brightness values between 0 and 255. Thus, equation (2) becomes:
Equation (3)
for an image matrix file of dimensions N1 by N2 pixels (and where n1 and n2 still identify the specific position any particular value contained within the image file matrix). To calculate the chromogen-specific energy, we subtract the energy of the region contained in the control image from the homologous region of the experimental image such that
Equation (4)
In this report we show that Q-IHC performed using this algorithm reveals a ~50-fold difference in GRPR expression between well-differentiated and poorly differentiated colon cancers. This range is well beyond that which otherwise could be accomplished by individual investigators studying analog images using a 04 grading scale. The precision, as shown by the small coefficients of variation as well as the numerical range of the data obtained using this algorithm, allows chromogen amounts to be subjected to meaningful statistical analysis. In this study we evaluated four or five separate tumors of varying histology per experiment. Overall, we found that the amount of GRPR-induced chromogen was significantly higher in well-differentiated cancers (266.8 ± 5.7) as compared to moderately (171.8 ± 7.8) or poorly differentiated tumors (4.1 ± 1.1) (p<0.01, ANOVA). Therefore, Q-IHC permits significant differences between various immunohistochemical images to be elucidated with a hitherto unachievable degree of precision that is independent of the time of exposure to DAB (Figure 3) or counterstain (Table 2), and is devoid of significant day-to-day variability (Table 2).
Q-IHC as performed in our study also reduces potential observer bias when specific cell regions are being evaluated. Nonhomogeneous tissue specimens (i.e., Figure 2C) are difficult to evaluate because the actual amount of cytoplasmic chromogen present is small relative to distracting features such as vacuoles and nuclei. By determining the energy resident in a number of tissue regions containing only cytoplasm, the precise amount of chromogen specific to that region can be calculated. Although we did not specifically evaluate this process in this study, chromogen present in the nucleus distinct from that observed in the cytoplasm also can be quantified by using the appropriately sized window. Finally, the algorithm described is not restricted to evaluation of DAB-based immunohistochemistry. Because it is the image file itself that is subject to analysis, the algorithm described herein can be used to quantify chromogen regardless of the type of secondary antibody detection system.
It might appear that the use of an RGB camera is unnecessary and that a simple monochrome camera could suffice. Although a monochrome camera might be adequate in some cases, it would not perform well in all circumstances. The major limitation to the use of a monochrome camera is the fact that, unlike RGB cameras, the color space is represented equivalently by either the RGB color space or the IUV color space. The RGB color space is used to denote the decomposition of all images into the three primary colors: red, green, and blue, whereas the IUV color space provides a decomposition of all images into the illuminance (I) and two chrominances (U and V). In contrast, the monochrome camera captures only the illuminance component of the image and the information for chrominance (U and V) is completely ignored. Although our algorithm could be used to determine the amount of chromogen present in the image specimen based on the energy differences present in the illuminance, this approach would lead to a high error rate because the differences contained by the chrominance images would be lost.
Even though our algorithm is best served using a more expensive RGB digital camera, expensive software is not necessary. Photoshop is commonly included in the purchase price of desktop scanners and otherwise can be purchased by students for ~$100. Similarly, Matlab can be obtained by students for the same price, providing a version that can assess matrices of up to 128 x 128 pixels. Larger images can be studied using the professional version of Matlab, which can be obtained for ~$500 (academic pricing). Therefore true Q-IHC, which allows the absolute amount of chromogen present to be determined, is possible for all individuals operating in a digital imaging environment.
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Acknowledgments |
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Supported by a Crohn's and Colitis Foundation of America Student Research Award (to K.A. Matkowskyj) and by NIH grant DK51168 and a VA Merit Review (to R.V. Benya).
Received for publication May 25, 1999; accepted September 29, 1999.
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