Biology
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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Chapters
Summary April 8th, 2016
Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.
Transcript
The overall goal of this method is to objectively quantitate protein expression and co-localization using multispectral imaging. This method can help answer key questions in the basic research and diagnostic pathology field, such as how proteins change expression and localization after treatment, or in disease progression. The main advantage of this technique is that it removes the operator subjectivity inherent to traditional quantification methods.
To begin the quantification process, open the multispectral imaging software to build a spectral library from previously prepared control slides stained with individual chromogens and the hematoxylin stained slide. Next, open an image cube acquired from a control slide, and select four to five positively stained areas to optically define the chromogen. Repeat the steps with image cubes from other control slides until a complete spectral library representing all chromogens is created, and then save the spectral library.
Begin a new project within the multispectral imaging software by selecting Multispectral or im3 for the image format option, and Brightfield for the sample format. Configure the project by choosing segment tissue, find features, phenotyping, score, and export. Change the image resolution to expedite the analysis time, if desired.
Import the previously created spectral library and select all chromogens to be included in the analysis. Open the image cubes to be included in the training data set by selecting the Open Image Cube option. Select at least 18%of the total number of images to be analyzed to ensure training accuracy.
Next, choose the training set of images. Images that represent all disease states, to increase segmentation accuracy. Include abundant negative staining images in the training set to avoid bias during this step.
White balance the images in the training set by selecting the eye dropper tool, and choosing an area in one image that is white. Select the advance button to move tissue segmentation. Then use the tissue categories panel to choose the tissue types to be analyzed for more accurate protein tissue localization select tissue categories can be used.
Begin creating the algorithm and defining tissue categories by using the pen tool and drawing around groups of cells within training images. When finished with one tissue category repeat the step for other tissue categories. Be sure to choose groups of cells within images that are characteristic of the tissue category type.
Select components to be included in training for the tissue segmenter. Choose an appropriate pattern scale to train the tissue segmenter. Then select the trained tissue segmenter button.
Observe a pop up box displaying accuracy of the proportion of pixels within properly classified training regions. Segment the entire training set of images by clicking segment images. When finished review the training set to find any misclassified tissue with a current training algorithm.
When confident with tissue segmentation algorithm results select the advance button. Ensure that nuclei is already selected to choose cytoplasm and/or membrane. Select the nuclei tab and choose the appropriate settings for nuclear segmentation.
Then choose whether individual or all tissue categories will be included in segmentation. Select the counterstain object based threshold approach for a simplified method to obtain good results. Next, select the object based threshold approach on a reliable nuclear counterstain.
Select cytoplasm shaped parameters by choosing the cytoplasm tab. Next, select outer distance to nucleus. Then select minimum size.
Select the next option component with primary, secondary and select tertiary as secondary options. Move to the membrane tab. Choose first for the membrane specific marker used.
Adjust the full scale optical density or OD to find a minimum threshold or a positive for each marker on cell membranes. Continuing under the membrane tab select the maximum cell size. After selecting all of the options select segment all.
Apply the settings to the images and observe them. Choose advance to move to the score IHC step and choose a tissue category to score. Choose a desired scoring type and choose the cell compartment to be used in the scoring analysis.
Next, select view component data and move the cursor over the training images to find appropriate optical density minimal threshold of staining for positive cells for the components of interest. Export the data for the training set to test the algorithm created through tissue segmentation cell segmentation and scoring values follow the prompts to create a new folder for the export directory. Select images and tables to be created and included in analysis.
Next, perform the analysis by selecting export for all. When the analysis is complete, view the cell segmentation and scoring data for images with both high and low staining to evaluate the accuracy of the settings. Once satisfied with the settings, click on the batch analysis tab to copy the algorithm to the active project.
Then choose a new export directory and select the images and tables to include in the analysis. Under the input files option choose all images to be included in the batch analysis. Select the run option to perform the analysis.
When completed advance to the review merge tab. Select include all and select merge to create data sheets with summary data for analysis. Training was performed on prostate tissues to segment images into epithelial and stromal portions.
Along with a non-tissue compartment. A set of training images were imported into multispectral imaging software representing tissue types and disease states of the entire set of images. Tissue categories were created including stroma, epithelia and non-tissue and categories were defined by manually drawing on top of training images.
An algorithm for tissue segmentation was created and applied to the training set of images. Accurately segmenting the tissues. Using the multiplex immunohistochemistry technique cells positive for nuclear expression of ER-alpha seen as red and AR seen as brown were identified despite overlapping color and metric signals The cell membrane specific expression of CD-147 was quantified by using E-catherine seen as black as a marker protein.
After watching this video you should have a good understanding of how to quantitate protein expression and co-localization inform and fix paraffin embedded tissues.
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