Image j intensity ㅇ뮤 측정 방법

Bio Protoc. 2019 Dec 20; 9(24): e3465.

Abstract

Semi-quantitative IHC is a powerful method for investigating protein expression and localization within tissues. The semi-quantitative immunohistochemistry (IHC) involves using software such as free software ImageJ Fiji to conduct deconvolution and downstream analysis. Currently, there is lack of an integrated protocol that includes a detailed procedure of how to measure or compare protein expression. Publications that use semi-quantification methods to quantify protein expression often don’t provide enough details in their methods section, which makes it difficult for the reader to reproduce their data. The current protocol for the first time provides a detailed, step-by-step instruction of conducting semi-quantitative analysis of IHC images using ImageJ Fiji software so that researchers would be able to follow this single protocol to conduct their research. The protocol uses semi-quantitative IHC of organic anion transporting polypeptide (OATP1B1) as an example, and gives detailed steps on how to deconvolute IHC images stained with hematoxylin and 3,3’-diaminobenzidine (DAB) and how to quantify their expression using ImageJ Fiji. The protocol includes clear steps for a reader so that this method can be applied to many different proteins. We anticipate this method will provide a practical guidance to the reader and make semi-quantification of proteins an easier task to include in publications.

Keywords: Immunohistochemistry, Semi-quantification, OATP1B1, ImageJ Fiji, DAB staining, Hematoxylin

Background

Semi-quantitative immunohistochemistry (IHC) is a powerful method for investigating protein expression and localization within tissues (Cregger 2006; Taylor and Levenson 2006; Braun et al., 2013 ; Bauman et al., 2016 ; Pike et al., 2017 ; Crowe et al., 2019 ). The semi-quantitative IHC involves using software such as free software ImageJ Fiji (version 1.2; WS Rasband, National Institute of Health, Bethesda, MD) to conduct deconvolution and downstream analysis. There are several methods and publications (Fedchenko and Reifenrath 2014; Jensen 2017) regarding using semi-quantitative IHC to compare or measure protein expression ( Kokolakis et al., 2008 ; Sysel et al., 2013 ; Li et al., 2014 ; Bauman et al., 2016 ; Lu et al., 2018 ). Many of these methods require either advanced coding/mathematical experience, use of expensive imaging software or specific hardware requirements that may not be feasible for every scientist ( Matkowskyj et al., 2000 ; Shu et al., 2016 ; Jensen et al., 2017 ; Guirado et al., 2018 ). Protocols using ImageJ for image quantification can be found online, but these protocols are generally directed towards the creation of specific plugins for ImageJ, are not from a validated publication or lack the necessary step-by-step protocol ( Guirado et al., 2018 ; Varghese et al., 2014 ; Chen et al., 2017 ). However, there is no integrated protocol that includes a detailed but simple procedure of conducting such steps including using the ImageJ software. There are many open online forums where users post their questions and technical issues for using ImageJ Fiji for image quantification purposes. This is due to publications often not providing enough details in their methods section, which makes it difficult for the reader to reproduce their data. Also, it is time-consuming for researchers who are not initially familiar with ImageJ software to figure out how to use this software. The current protocol for the first time provides a detailed, step-by-step instruction of conducting semi-quantitative analysis of IHC images using ImageJ software so that researchers with minimal experience with ImageJ would be able to follow this single protocol to conduct their research. We anticipate this method will provide practical guidance to the reader and can be used for many different proteins other than OATP1B1 shown in this protocol.

Equipment

Computer Specifications:

1. A 64-bit operating system that has Windows 7 or greater, Mac OS X 10.11 or greater or Linux with kernel supporting GLIBC 2.14 and GLIBCXX 3.4.15 (typically kernels 2.6.39)

3. Need up-to-date NVIDIA drivers (minimum version of 369)

4. Any computer with Java-based operating system and Excel available

Software

1. Free ImageJ Fiji software (Johannes Schindelin, Albert Cardona, Mark Longair, Benjamin Schmid, and others, https://imagej.net/Fiji/Downloads ), version 1.2 (no specific plugin was used)

Procedure

1. Staining of tissue using immunohistochemistry procedure

   1. Reference to procedure used for the staining of cells for the IHC protocol can be found in a previously published manuscript ( Crowe et al., 2019 ).

   2. Brief protocol for immunohistochemistry staining:

      1. Cut FFPE tissue blocks into 4 µm sections and mount on positively charged slides.

      2. Deparaffinize and rehydrate tissue.

      3. Retrieve antigen in buffer of choice (citrate buffer at pH 6 used for this protocol).

      4. Block antigen with normal goat serum and incubate with primary antibodies (i.e., anti-OATP1B1 for this protocol).

      5. Incubate with horseradish peroxidase (HRP)-conjugated secondary antibody and visualized by 3,3’-diaminobenzidine (DAB) to detect the protein of interest (OATP1B1 in the current protocol).

      6. Stain nuclei with hematoxylin.

      7. Hematoxylin and eosin (H&E) staining is not necessary for this protocol but is useful when looking at the pathology of the tissue.

2. Image exporting and saving

   Export and save the raw immunohistochemistry (IHC) immunohistochemistry images to a .tiff or .jpg file format. Tiff format for images is preferred in order to prevent the loss of raw data.

3. Use of Deconvolution of the IHC image using ImageJ Fiji software.

   1. Download and open ImageJ Fiji software.

   2. Click the “File” option and click “Open”. The IHC image will open up on the computer screen.

   3. Click on the IHC image to make the image active.

   4. Click the “Image” option and select “Color” > “Color Deconvolution.”

   5. A new pop-up Color Deconvolution window will show up. For IHC images stained with 3,3’-diaminobenzidine (DAB) and hematoxylin (H), select the “H DAB” vector option. Leave “Show Matrices” and “Hide Legend” unchecked and click “Okay.” (Figure 1).

      

      Color Deconvolution Window.

      The Color Deconvolution Window will be used to separate the staining of the IHC image. The H DAB vector separates the IHC image into DAB staining (brown staining) for the protein of interest and Hematoxylin (H) staining for the nucleus.

   6. After selecting the H DAB option, three different images will pop up on the computer screen. Color 1 window represents only the Hematoxylin staining (blue/purple) and Color 2 window represents only the DAB staining (brown). (Figure 2).

      

      Deconvolution of IHC image.

      Separation of the IHC image into hematoxylin staining for the nulcei (Color 1) and DAB staining for OATP1B1 protein expression (Color 2) in hepatocytes in human liver tissue. Color 3 panel is for another staining if applicable. For only DAB and hematoxylin staining, Color 3 panel can be exited out. In DAB and hematoxylin staining as in current studies, a warning “X” is shown so that users understand that the amount of antibody staining (i.e., DAB staining) in this case cannot be mathematically quantified as it is not stoichiometric.

   7. Exit out of the third Color 3 window, as this will not be needed for the image analysis.

4. Threshold DAB stained IHC image

   1. Click on the DAB Color 2 image to activate it. DAB staining represents your primary antibody of interest. In this case, liver tissue was incubated with an OATP1B1 antibody and the OATP1B1 expression is detected by DAB.

   2. Go to “Image” and select “Adjust” and “Threshold”. After selecting the threshold, the brown image is now converted to a black and white image.

      Note: A shortcut to threshold the image is by pressing “Ctrl” + “Shift” + “T”

   3. A new threshold window will pop up. The top bar indicates your minimum threshold value and the bottom bar indicates your maximum threshold value. (Figure 3).

      

      IHC image pre-threshold.

      IHC image converted to black and white pixels prior to thresholding the image (A). The threshold pop up window pre-threshold indicates the baseline threshold values (B).

   4. Leave the minimum threshold value set at zero.

   5. Adjust the maximum threshold value so that the background signal is removed, without removing the true DAB signal (Figure 4A). This is an arbitrary value, since it is set by the user. The maximum threshold value should be tested for at least five images to get an average maximum threshold value. Once the maximum threshold value is chosen, this will be set for all future IHC images.

      

      IHC image post-threshold.

      The background for the DAB staining in the IHC image was removed by adjusting the maximum threshold value (A). The minimum and maximum threshold values were set and applied (B).

   6. Once the maximum threshold image is set, click “Apply” on the threshold window. After clicking apply, the minimum and maximum threshold values will be 255 (Figure 4B).

5. Quantify the DAB signal in the IHC image

   1. Go to “Analyze” and select “Set Measurements”.

   2. A “Set Measurement” pop up window will open. Select the “Area”, “Mean grey value”, and “Display Label” boxes and leave all other boxes unchecked. “Area” will give the size of the IHC image. “Mean grey value” represents the quantified signal and “Display Label” gives the information on the image name being quantified (Figure 5A).

      

      Measurement of DAB staining.

      The Set Measurements window to choose the options to output for each IHC image is shown. The suggested options for output are highlighted with red boxes (A). The Results output window is shown in (B). The name of the image (Label), area of the image (Area) and mean grey value intensity (Mean) are given in the output.

   3. Select “Okay” in the Set Measurement window. These options only need to be set once for the first image and will be remembered for all other future images measured.

   4. Go to “Analyze” and select “Measure”.

      Note: A shortcut for measuring the signal is “CTL + M”.

   5. A “Results” window will pop up giving the name of the image (Label), size of the image (Area) and the average pixel intensity of the IHC image (Mean) (Figure 5B).

   6. Copy the results to an excel file for later analysis.

   7. Exit out of the Color 2 DAB stained image.

6. Measure the size of the nucleus

   1. Click on the Hematoxylin Color 1 image to activate it.

   2. Select the “Straight line” tool on the ImageJ Fiji panel.

   3. Measure the distance of a nucleus by drawing a line across the nucleus with the Straight line tool (Figure 6A).

      

      Measurement of nuclei size.

      The raw IHC image stained with DAB for OATP1B1 staining and hematoxylin for nuclei staining is used to measure the size of the nuclei. Using the straight-line tool, a line is drawn over the nucleus (A, white arrow pointing to yellow line). The distance of the line is measured by going to Analyze > Measure in the ImageJ Fiji toolbox panel. The Length (highlighted in red box) represents the diameter of the nucleus (B).

   4. Go to “Analyze” and select “Measure”.

   5. A Results window will pop up with the diameter of the nucleus (“Length”) (Figure 6B).

   6. Measure ~10 different nuclei using Steps F1-F5 in a representative IHC image and take the average of these Lengths to determine the average size of the nuclei. This serves as the average size of the nuclei for all IHC images from hereon. Ths step only needs to be done for one image and will be used for all the rest of your images.

7. Threshold Hematoxylin stained IHC image

   1. Click on the Hematoxylin Color 1 image to activate it.

   2. Go to “Image” and select “Adjust” and “Threshold”. After selecting threshold, the blue/purple image is now converted to a black and white image.

   3. Set the minimum threshold value to zero.

   4. Adjust the maximum threshold value so that the background signal is removed, without removing the true hematoxylin/nucleus signal. This is an arbitrary value, since the user sets it. The maximum threshold value should be tested for at least five images to get an average maximum threshold value. Once the maximum threshold value is chosen, this will be set for all future IHC images.

   5. Once the minimum and maximum threshold values for the image are set, click “Apply” on the threshold window. After clicking apply, the minimum and maximum threshold values will be 255.

8. Quantify the hematoxylin/nucleus signal in the IHC image

   1. Select the Color 1 Hematoxylin image to activate it again.

   2. Go to “Process” and select “Binary” > “Watershed”. This action will split the nuclei that are joined together into multiple nuclei.

   3. Go to “Analyze” and select “Analyze Particles”.

   4. An “Analyze Particles” window will pop up giving multiple options (Figure 7A).

      

      Quantification of nuclei in IHC image.

      To measure the number of nuclei in each IHC image, the Analyze Particle window pops up after selecting Analyze > Analyze Particles (A). The size of the particle measured is set to the average diameter of nuclei to infinity. The summarize and exclude on the edge’s options are selected prior to clicking okay. The summarize option leads to the summarized output of the count, total area, and the average size of the nuclei particles in the IHC image (B). Exclude on the eges indicates that no nuclei particles will be included in the analysis.

      1. Size (pixel^2): Set the size of the nuclei to the average nuclei size measured in Step D6 to infinity (i.e., 6-Infinity).

      2. Circularity: Leave it set at 0.00-1.00.

      3. Show: Leave it set to “Nothing”.

      4. Select “Summarize” and “Exclude on Edges” in the window. Summarize will give a summary of the particle’s measurements. Exclude on the edges means that nuclei on the outer edge of the image will not be included in the measurement.

   5. Select “Okay” on the Analyze Particles window and a Summary window with the output will pop up (Figure 7B). The output data includes:

      1. Count indicates the number of nuclei in the IHC image.

      2. Total area indicates the total area of the image.

      3. Average area indicates the average size of the nuclei in the IHC image.

   6. Copy the results in the Summary window to the same excel file for later analysis.

   7. Exit out of the Color 1 Hematoxylin/Nuclei stained image.

9. Semi-quantification analysis of the IHC image

   1. Open the excel file containing the DAB and hematoxylin results from ImageJ Fiji software.

   2. For each image, divide the Mean grey intensity value from Step D5 by the number of nuclei measured in Step H5. This value represents the DAB staining intensity normalized by nucleus.

   3. Take the average of all DAB staining intensities normalized by nuclei number for all IHC images for each sample/treatment, etc. to give an average value and standard deviation value.

Data analysis

Expression of OATP1B1 and OATP1B3 were compared in genotyped human liver tissue stained with OATP1B1 or OATP1B3 DAB staining and hematoxylin. Using 79 genotyped human liver IHC samples, there was no significant difference between the genotypes for OATP1B1 (c.521 TC) polymorphism using a Student’s t-test ( Crowe et al., 2019 ). Similar analysis can be used for comparison of other protein’s expression in other genotypes or versus other drug treatments.

This detailed, step-by-step protocol will allow a user to be able to:

• This protocol can be used for many different proteins and is not specific to OATP1B1 or OATP1B3 in the liver.

• Compare protein expression among treatment groups, different genotypes, etc.

• Measure protein expression through image deconvolution.

• Measure the intensity of nuclei for each image so that the total protein expression value can be normalized by the nuclei intensity value. This allows for a method of controlling your data for images with more or less cells in them.

Acknowledgments

This research was supported by NIH R01 GM094268 [W. Y]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Alexandra Crowe is an American Foundation of Pharmaceutical Education pre-doctoral Fellow. We thank Drs. Wei Zheng, Kar-Ming Fung, Feng Yin and Erin Rubin for providing the FFPE tissues for this research.

The initial publication where this method is published is: Crowe, A., Zheng, W., Miller, J., Pahwa, S., Alam, K., Fung, K., Rubin, E., Yin, F., Ding, K, Yue, W., Characterization of plasma membrane localization and phosphorylation status of organic anion transporting polypeptide (OATP) 1B1 c.521 T>C polymorphism, Pharm Res, 2019, 36:101.

Competing interests

No competing financial interests for this study.

Ethics

79 Formalin-fixed, paraffin-embedded (FFPE) archived human liver (42 from surgical resection and 37 from liver biopsy) and normal kidney tissue blocks were obtained from OUHSC Stephenson Cancer Center Biospecimen Acquisition Core and Bank from the Department of Pathology at the University of Oklahoma Health Sciences Center. Use of human tissues was approved by the Institutional Review Board at the University of Oklahoma Health Sciences Center.

Citation

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