Optimization of immunofluorescence methods by quantitative image analysis.

Mosedale, David E.; Metcalfe, James C.; Grainger, David J.

doi:10.1177/44.9.8773570

Cited by 47 publications

(51 citation statements)

References 5 publications

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“…Note that the sum of the different color pixels almost equals the pixel count of the entire tissue section, emphasizing the accuracy of the color separation. et al 1993), Quantimed 600 (Kohlberger et al 1996), COSAS (Black and Rosen 1996), MAGICSAN (Mosedale et al 1996), and NIH image (public domain program; Ruifrok 1997). In these previous studies, color separation was performed by automatically thresholding red-, green-, and blue-filtered gray-scale values of the image.…”

Section: Discussionmentioning

confidence: 99%

Complete Chromogen Separation and Analysis in Double Immunohistochemical Stains Using Photoshop-based Image Analysis

Lehr

Loss

Teeling

et al. 1999

J Histochem Cytochem.

203

153

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SUMMARYSimultaneous detection of two different antigens on paraffin-embedded and frozen tissues can be accomplished by double immunohistochemistry. However, many double chromogen systems suffer from signal overlap, precluding definite signal quantification. To separate and quantitatively analyze the different chromogens, we imported images into a Macintosh computer using a CCD camera attached to a diagnostic microscope and used Photoshop software for the recognition, selection, and separation of colors. We show here that Photoshop-based image analysis allows complete separation of chromogens not only on the basis of their RGB spectral characteristics, but also on the basis of information concerning saturation, hue, and luminosity intrinsic to the digitized images. We demonstrate that Photoshop-based image analysis provides superior results compared to color separation using bandpass filters. Quantification of the individual chromogens is then provided by Photoshop using the Histogram command, which supplies information on the luminosity (corresponding to gray levels of black-and-white images) and on the number of pixels as a measure of spatial distribution. (J Histochem Cytochem 47:119-125, 1999)

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Section: Discussionmentioning

confidence: 99%

Complete Chromogen Separation and Analysis in Double Immunohistochemical Stains Using Photoshop-based Image Analysis

Lehr

Loss

Teeling

et al. 1999

J Histochem Cytochem.

203

153

View full text Add to dashboard Cite

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“…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.…”

mentioning

confidence: 99%

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

Matkowskyj

Schonfeld

Benya

2000

J Histochem Cytochem.

134

120

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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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“…A linear relationship between fluorophore concentration and emitted light can be expected (12,13). The response of the camera is also linear (14).…”

Section: Discussionmentioning

confidence: 99%

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

et al. 2002

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Background: Visualization of more than one antigen by multicolor immunostaining is often desirable or even necessary to explore spatial and temporal relationships of functional significance. Previously presented staining protocols have been limited to the visualization of three or four antigens. Methods: Immunofluorescence staining was performed both on slices of formalin-fixed tissue and on cells in culture. Images of the stained material were recorded using digital imaging fluorescence microscopy. The primary and secondary antibodies, as well as the fluorophores, were thereafter removed using a combination of denaturation and elution techniques. After removal of the fluorescence stain, a new immunofluorescence staining was performed, visualizing a new set of antigens. The procedure was repeated up to three times. A method for

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Optimization of immunofluorescence methods by quantitative image analysis.

Cited by 47 publications

References 5 publications

Complete Chromogen Separation and Analysis in Double Immunohistochemical Stains Using Photoshop-based Image Analysis

Complete Chromogen Separation and Analysis in Double Immunohistochemical Stains Using Photoshop-based Image Analysis

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

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei

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