This project was funded by a grant from the Canadian Institutes of Health Research and Alberta Innovates - Health Solutions/Alberta Cancer Foundation. The authors wish to acknowledge the Regeneration Unit in Neurobiology core facility for the use of their equipment, the work of Paula Gedraitis in building the foundation upon which slide scanning on the WHCAS was made possible, and the creator of the products mentioned in this article, Molecular Devices.

Tumor specimens were obtained as per the protocol approved by the local institutional review board and ethics committee and conducted in accordance with national regulations. The WHCAS and its associated software used in this article are listed in the Table of Materials.
1. Importing the Journals
<ol>
	<li>Open the associated software.</li>
	<li>Download the journal suite indicated in the Table of Materials.</li>
	<li>Import the journal suite to a suitable directory by clicking on the main menu heading Journal, selecting Import Journal Suite&#8230;, and clicking Import.</li>
</ol>
2. Creating Settings for Preview Scan Acquisition
<ol>
	<li>In the Plate Acquisition Setup dialog box, go to the Objective and Camera tab. Select 4X as the magnification, 1 as the camera binning, and 2 as the gain.</li>
	<li>Input the settings in Figure 1A under the tab Plate &#8211; Slidescanning. 
	NOTE: These settings have been created to allow the user to view and navigate up to 3 slides. Each slide has been divided into 3 adjacent wells for the easy navigation and &#39;live&#39; visualization of the tissue slices or cells.</li>
	<li>In the Sites to Visit tab, fill the wells uniformly with the sites.</li>
	<li>Click on Edit Plate Bottom Settings&#8230; and enter the values displayed in Figure 1B.</li>
	<li>Under the Acquisition Loop tab, enter the desired wavelength (any nuclear stain in this example) for the preview scan. For the best contrast and image-based focusing, use the brightest fluorescent signal (e.g., often a nuclear stain). 
	NOTE: Staining tissues with a bright stain such as a nuclear stain is recommended since this provides a high contrast compared to the background. Not only will this aid in the sample location, but when imaging more than one fluorophore in the following high magnification acquisition, the brightest color will be used to base the imaging offset of the other colors on.</li>
	<li>Enable Shading Correction in Plate Acquisition mode to stitch images together.</li>
	<li>Save these settings as Setting A.</li>
</ol>
3. Creating Settings for Slide Acquisition at High Magnification
<ol>
	<li>In the Plate Acquisition Setup dialog box, go to the Objective and Camera tab. Select 40X as the magnification, 1 as the camera binning (for the highest digital resolution), and 2 as the gain (to increase the signal and brightness of images). 
	NOTE: A lower objective magnification setting can be used, but the authors found 40X to be the optimal setting for the analysis of human microglia and macrophages in brain tumor tissue using the Multi-Wavelength Cell Scoring module.</li>
	<li>Ensure all settings for the Plate &#8211; Slidescanning and Edit Plate Bottom Settings&#8230; tabs are the same as that for Setting A (see step 2).</li>
	<li>If the images obtained are not crisp, change the Well depth, Plate height, and Plate Bottom Settings to adjust the focus. If there is still an issue with the focus, choose Laser with Image Recovery under the Autofocus options section.</li>
	<li>Under the Acquisition Loop tab, enter the number of wavelengths present in the sample. Check the Enable laser-based focusing and Enable image-based focusing (for acquisition or laser recovery) options.</li>
	<li>Under the Autofocus tab, select Focus on plate and well bottom.</li>
	<li>In the Journals tab, select the options as indicated in Figure 2, ensuring Prevent asynchronous hardware moves is also selected.</li>
	<li>Save these settings as Setting B.</li>
</ol>
4. Placing the Slide in the Widefield High-content Analysis System
<ol>
	<li>Press F4 to get the Main Menu. Select Slide Scanning. Click Open Door &#8211; Eject Slide and insert slide(s) and the slide adapter (that can hold up to three slides) into the WHCAS (Figure 3A and 3B). Orient the slide with the coverslip facing down and the label on the side of the notch in the slide adapter (Figure 3A). 
	NOTE: The slides used in this experiment were standard 25 x 75 x 1 mm slides.</li>
	<li>Click on Close Door &#8211; Load Slide.</li>
</ol>
5. Acquiring a Preview Scan
<ol>
	<li>Under the Screening heading, select Plate Acquisition and Control&#8230; and Plate Acquisition Setup&#8230; and load Setting A.</li>
	<li>Under the Wells to Visit tab, move to the well containing the sample, using the right-click button on the mouse. Under the Sites to Visit tab, move to different sites with the Live image capture until the cells are located. Manually focus on the sample or use the Autofocus. Use Snap to capture the image. 
	NOTE: The sample is easier to locate with more sites, and by fitting the sites to the well (see step 2.3).</li>
	<li>On the main menu, select Preview Slides.</li>
	<li>In the dialog box that automatically pops up, select the option reflecting how many slides are going to be scanned. Scan one slide at a time. Press OK. Select 4X as the magnification. Press OK. Select Coverslip down (0.17 mm) as the slide orientation (use a correction collar if a different coverslip thickness is used). Press OK. Click Continue. 
	NOTE: If the user wishes to acquire preview scans for 2 or more slides, each preview scan has to be opened individually prior to the acquisition of the corresponding high magnification image.</li>
	<li>Under Load Scan Slide Setting, ensure the Acquisition settings and Calibrations boxes are checked. Press Cancel. When the Scan Slide Settings box reappears, select Continue.</li>
	<li>When the Scan Slide dialog box appears, adjust the settings as suggested in Figure 4A-4C. Select Close when finished. 
	NOTE: Increasing the camera binning and decreasing the camera exposure time will result in faster scanning, albeit at a lower digital resolution and dynamic range, respectively, hence decreasing the image quality. Shading correction will ensure a uniform intensity across a single field of view. Turning on Hardware and Image Autofocus will ensure the scan is in focus but will slow down the scanning. In the interest of saving time, it is recommended to have Shading correction and Hardware and Image Autofocus off while acquiring the low magnification scan, but to have these options turned on for step 6 during the high magnification imaging. The resolution of the low magnification scan will not affect the resolution of the high magnification scan.</li>
	<li>Select a directory for the slide scanning data. Click OK. Enter a name for the file. Click OK.</li>
	<li>The Draw Region dialog box will automatically appear. Use the Rectangular Region tool (Figure 5A) to select the entire preview scan region as shown in Figure 5B. Click Continue.</li>
	<li>The Setup Complete dialog box will appear. Select Continue. 
	NOTE: The preview slide scan will now commence.</li>
	<li>When the preview is completed, the Scanning Complete dialog box will appear. Select Continue. Do not close the preview scan window (in this example the DAPI Scan window; see Figure 6). 
	NOTE: The setup and low magnification scan acquisition process will take approximately 20 min. The subsequent high magnification image acquisition time will depend on the number of sites chosen as well as the exposure times for each channel.</li>
</ol>
6. High Magnification Scanning
<ol>
	<li>Load Setting B.</li>
	<li>Determine which wells are to be imaged. Under the Wells to Visit tab, move to the well containing the sample, using the right-click button on the mouse. 
	NOTE: The technique is demonstrated here by imaging Well 1.</li>
	<li>Under the Sites to Visit tab, move to different sites with the Live feature until the cells or tissue is located. Manually focus on the sample or use the Autofocus. Use Snap to capture the image. 
	NOTE: To aid in locating the sample at a high magnification, use the Plate Acquisition and Control dialog box to adjust the Step size anywhere from 5 to 100 &#181;m to find the sample if Autofocus cannot.</li>
	<li>Under the W1 DAPI tab, click Auto Expose. Ensure the Plate Acquisition Snap - DAPI image is crisp.</li>
	<li>Under the main menu, click on Journal to reveal the pull-down. Select Run Journal&#8230;.</li>
	<li>Double-click on the Slide Region Acquisition Setup journal to launch it. When the Setup Slide Region Acquisition dialog box appears, select Continue.</li>
	<li>When instructed, properly select the DAPI Scan to select the low-magnification slide image. Press OK. Similarly, highlight the Plate Acquisition Snap - DAPI when asked for the plate acquisition snap. Click on OK.</li>
	<li>When the Create or Load Regions box appears, use the Rectangular Region tool to select (a) region(s) of interest on the DAPI Scan (Figure 6). Press Continue. 
	NOTE: The maximum area that can be selected at 40X is 45 columns by 35 rows. If multiple regions of interest are selected, the regions will be resized to the box with the greatest area. The boxes can be repositioned after the resizing.</li>
	<li>Select No under the Load Regions dialog box. 
	NOTE: Selecting Yes loads previously saved regions for batches of slides with the same region of interest locations.</li>
	<li>Using the Locator tool, highlight the largest region of interest and press Continue under the Select Region dialog box. When the Confirm regions box appears, select Continue. Decide whether the region needs to be saved when the Save Regions dialog box appears.</li>
	<li>The next dialog box, Site Spacing, allows the user to choose Tiled, which results in an image with no overlap, or 10% overlap. Choose an option and click OK. The authors chose Tiled.</li>
	<li>Three dialog boxes will appear now. The Setup Complete box can be dismissed by pressing Continue. Keep the Acquisition Site Setup for 40X box (Figure 7A) open for now since it makes clear how many columns and rows need to be entered into the Plate Acquisition Setup box (Figure 7B). Keep the WellPositions box (Figure 7C) that appears in the bottom left-hand corner open for the duration of the imaging.</li>
	<li>In the Plate Acquisition Setup box, under Wells to Visit, the same number of wells as regions of interest previously selected must be highlighted (Figure 7D). 
	NOTE: The authors developed this protocol for a maximum of nine regions of interest per slide. If there are more, simply increase the number of columns or rows in the Plate - SlideScanning tab to reflect the number of regions of interest. These wells do not spatially represent the location of the regions of interest in any way, but the appropriate number must be highlighted (left-click).</li>
	<li>In the Plate Acquisition Setup box, go to the Sites to Visit tab. Enter the suggested columns and rows (Figure 7B). Remember to click on Tile sites.</li>
	<li>To eliminate the guesswork of finding the sites containing the sample, press Acquire Plate under the Summary tab to position the stage at the pre-selected region of interest. Once the camera pans over a site containing cells, press Cancel at the bottom right-hand corner to stop the image acquisition. Ensure the stage is now located over the sample.</li>
	<li>Optimize the wavelength settings to ensure that a focused and a high dynamic range image is obtained. When satisfied, click on the Summary tab and Acquire Plate.</li>
</ol>
7. Image Analysis
<ol>
	<li>Select the desired custom module. 
	NOTE: The authors chose the Multi-Wavelength Cell Scoring module to demonstrate an automated segmentation of the brain tumor slides.</li>
	<li>Run the analysis on all sites. If the analysis parameters are the same for many slides, the analysis can be included in the Plate Acquisition Setup box under the Post Acquisition tab.</li>
	<li>Once the analysis is finished, exclude any sites of poor image quality so as not to bias the results. Since the goal of this analysis is to quantify microglia and macrophages in the tumor parenchyma, select the region of interest within the tumor sample&#39;s edges. Furthermore, exclude the sites that are out of focus and/or contain tissue folds or bubbles and tears in the tissue. For a demonstration, see the Representative Results. Alternatively, use the Laser focus score generated for each site depending on the amplitude of the laser reflection as a threshold below which sites are to be excluded (the threshold is customizable and depends on parameters such as the exposure time and the type of media used). 
	NOTE: The high magnification images make it possible to specifically exclude structures, such as large blood vessels and areas of necrosis, if necessary. It is also possible to include only areas of interest, such as those that are hypercellular. This way, the specificity and accuracy of the analyses can be increased.</li>
</ol>

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The authors have no financial interests in the products described in this manuscript and have nothing else to disclose.

A common problem still impeding the efficiency of biological science research is the development of protocols for the unbiased, accurate, and precise quantification of fluorescently-labeled tissues and their structures within. Significant amounts of time and effort are directed towards finding ways to analyze tissue slides once they have been imaged. Many existing methods provide algorithms for users to recreate within programs12,13,14. These methods are acceptable, but the significance of this report is that it enables the user to easily and quickly establish a holistic image acquisition and analysis protocol if the user has access to a WHCAS. Assays that differentiate cell types and quantify multiple structures and processes, cell cycle analysis, and nuclear translocation, for example, are already available in the associated software.
The setup involves a few critical steps. Firstly, the spatial parameters of the slide are defined as if it were a plate. Secondly, a low magnification overview scan is created from which regions of interest are selected for high magnification imaging. Lastly, sites that affect the accuracy and precision of subsequent analyses are excluded. The biggest limitation of this technique is that its applicability depends on if the user has access to a WHCAS. However, with the increasing need for high-content analysis systems, many institutions are providing these to their researchers to remain competitive15. Troubleshooting is needed most commonly when tissue sections do not have the same thickness. If multiple regions of interest are selected, some will be in focus while others will not. Ideally, during sectioning, the user would take care to create homogeneous samples. However, if the samples are inconsistent, the focus on the nuclear stain wavelength (or the user&#39;s brightest fluorophore), which is used to focus, can be readjusted for each out-of-focus region of interest and even for each field of view. As the other wavelengths are simply offset from the nuclear stain wavelength, only this wavelength needs readjustment.
In this report, we detail how to scan and analyze slides using a WHCAS and associated software. The Multi-Wavelength Cell Scoring module allows the user to automatically count any nuclear or cytoplasmic markers that are fluorescently labeled. After adjusting the focus settings and defining the cellular characteristics such as width and area to customize the module to the tissue imaged, there is no further need for user intervention to obtain the imaged slides and quantitative data. Up to three slides can be imaged at a time and multiple regions of interest can be defined. This protocol lets WHCAS users who need to analyze slides take advantage of customizable, multipurpose, automated workflows that require little to no optimization and can be applied in any projects in the future that involve the histological analysis of tissue.

The accurate and precise quantitation of fluorescently-labeled tissues on slides is a highly sought-after technique in many scientific fields. However, researchers often manually count specimens or spend considerable amounts of time developing esoteric automated techniques to achieve this. Herein, we provide a protocol for the automated slide scanning and quantitation of cells using a WHCAS and its associated software, with innate immune cells in frozen human brain tumor sections as an example. The associated software offers a wide range of built-in customizable modules from neurite outgrowth counting to differentiation of cell types1,2,3,4,5,6. The goal of this method is to provide researchers with a start-to-finish, easily reproducible protocol to acquire images of and quantitate fluorescently-labeled entities in any slide-mounted tissues.
In this protocol, the WHCAS is mainly used for imaging plates for the subsequent analysis on the associated software even though a slide adapter and the basics to slide scanning7 were available. It was prohibitive to image slides because the careful spatial calibration of the acquisition area, the selection of appropriate journals, the creation of tailored loadouts, and a liaison with product representatives were required. In the wider body of literature, in lieu of purchasing a dedicated slide-imaging and analysis apparatus8, a previous technological report with access to this software circumvented the image acquisition of slides on the WHCAS altogether9. Performing image acquisition or image analysis on different platforms necessitates extra work to ensure each is compatible with the other.
The ability to use the WHCAS and its software for image capture would avoid the unnecessary complications of searching for or developing a workflow alien to these tools. In this article, the steps required to create a low magnification overview scan and the corresponding high magnification images by treating the slide as a plate, and the subsequent analyses using the Multi-Wavelength Cell Scoring segmentation module allow for the repurposing of the WHCAS. This readily usable protocol provides an advantage over alternative techniques because there is no need to develop algorithms or multi-step counting protocols10,11 once the images are acquired on the WHCAS. This protocol mitigates the time required to optimize a quantitation technique, is more precise12 and efficient than manual counting and maximizes the use of the WHCAS. This protocol can be widely and easily used since it enables the imaging and analysis of any fluorescently-labeled tissues on slides.

<table><tbody><tr><td>ImageXpress MicroXLS</td><td>Molecular Devices</td><td>NA</td><td>Apparatus for image acquisition</td></tr><tr><td>MetaXpress 5.1</td><td>Molecular Devices</td><td>NA</td><td>Associated software for ImageXpress MicroXL (runs on a PC with the Windows operating system).</td></tr><tr><td>Slide adapter</td><td>Molecular Devices</td><td>NA</td><td>Metal slide holder that fits into ImageXpress MicroXL</td></tr><tr><td>Slide_Region_Acquisition_revA.jzp</td><td>Molecular Devices</td><td>NA</td><td>The journal can be obtained from metamorph.moleculardevices.com/forum/showthread.php?tid=218&amp;amp;highlight=slide or from contacting a Molecular Devices representative</td></tr><tr><td>Slide_Region_Acquisition&lt;br /&gt;_Setup.JNL</td><td>Molecular Devices</td><td>NA</td><td>Select this journal in Step 6.6.</td></tr></tbody></table>

automated slide scanning and segmentation in fluorescently-labeled tissues using a widefield high-content analysis system

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

The images can be viewed within the WHCAS software. Figure 8 shows the high magnification thumbnails of all sites within the defined region of interest. Review each site to identify the ones that should be excluded from the analysis (examples are shown at low and high magnification in Figure 8 and Figure 9, respectively). For example, Site 149 is out of focus (Figure 9A), Site 219 has bubbles (Figure 9B), and Site 54 contains a fold (Figure 9C) and should be excluded. It is typical to exclude 10 - 15% of all sites imaged. In the example provided, 15.3% of the sites were excluded (25/300 had bubbles, 17/300 were out of focus, 3/300 were folded, and 1/300 was on the edge). Figure 10A is a representative image chosen for analysis (its corresponding low magnification thumbnail is shown in Figure 8). Here, the corresponding overlays generated by the Multi-Wavelength Cell Scoring module demonstrate the results of the automated segmentation performed on Site 59, customized to the authors&#39; specifications (nuclei minimum width = 2.5 &#181;m, maximum width = 7.5 &#181;m, and intensity above local background = 35 graylevels; CD11b-positive cells&#39; minimum width = 4 &#181;m, maximum width = 18 &#181;m, minimum stained area = 15 &#181;m2, and intensity above local background = 310 graylevels; and CD45-positive cells&#39; minimum width = 4 &#181;m, maximum width = 18 &#181;m, minimum stained area = 15 &#181;m2, and intensity above local background = 50 graylevels). Following the segmentation, the quantitative data of the proportion of cells staining positive for each marker (6.2 &#177; 5.1% and 3.8 &#177; 2.1% of CD11b+ and CD45+ cells, respectively), both markers (3.5 &#177; 2.1% CD11b+CD45+ cells), no markers (86.6 &#177; 9.0% CD11b-CD45- cells; Figure 10B), mean stained area (40.0 &#177; 9.2 &#181;m and 36.7 &#177; 7.6 &#181;m of CD11b+ and CD45+ cells, respectively; Figure 10C), and mean fluorescence intensity (408.9 &#177; 40.3 relative fluorescence units and 373.9 &#177; 38.1 relative fluorescence units of CD11b+ and CD45+ cells, respectively; Figure 10D) can be obtained.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57440/57440fig1.jpg" /> 
Figure 1: Settings for slide acquisition at low magnification. A. This image displays the plate settings. B. Here, the plate bottom settings are displayed. <a href="/files/ftp_upload/57440/57440fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57440/57440fig2.jpg" /> 
Figure 2: Settings for slide acquisition at high magnification. This figure displays the selection of the appropriate journals. <a href="/files/ftp_upload/57440/57440fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57440/57440fig3.jpg" /> 
Figure 3: Demonstration of how to load slides using the slide adapter. A. The slide is placed coverslip down with the label on the side of the slide adapter notch. B. The slide adapter is loaded into the widefield high-content analysis system. <a href="/files/ftp_upload/57440/57440fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57440/57440fig4.jpg" /> 
Figure 4: Preview slide settings. A. This figure displays the parameters for the acquisition. B. Here, the values for the Hoescht/DAPI wavelength settings are displayed. C. This image shows the journal settings. <a href="/files/ftp_upload/57440/57440fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57440/57440fig5.jpg" /> 
Figure 5: Drawing a slide region. A. The rectangular region tool (other drawing tools cannot be used) is used to select the preview scan area and create regions of interest on the DAPI scan. B. The teal outline in this figure shows how the entire area of the slide (including the label) should be selected. <a href="/files/ftp_upload/57440/57440fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57440/57440fig6.jpg" /> 
Figure 6: Creating regions of interest on the preview scan. The preview or DAPI scan represents the entire slide area. In this example, there are three serial brain tissue sections (the solid white rectangular outlines). The area above these sections represents the circular labels on the slide. Different section arrangements will not limit the subsequent selection of regions of interest. The region of interest in this particular example is shown with a dashed white rectangular outline. The scalebar = 1 mm. <a href="/files/ftp_upload/57440/57440fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57440/57440fig7.jpg" /> 
Figure 7: Essential windows for high magnification image acquisition. A. The Acquisition Site Setup window will show how many columns and rows are within the region(s) of interest. B. Ensure the correct number of columns and rows indicated in Figure 7A are entered into the Plate Acquisition Setup box and the sites are tiled. C. It is necessary to keep the WellPositions window open for the duration of the image acquisition even though it is blank. D. The appropriate number of regions of interest must be highlighted. <a href="/files/ftp_upload/57440/57440fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/57440/57440fig8.jpg" /> 
Figure 8: Representative image of the region of interest. Each site can be displayed in a composite thumbnail of the region of interest. The representative sites that have been excluded (marked with red boxes) and an example of an included site (marked with a green box) is shown at a higher magnification. It is recommended to review each site to ensure it is of sufficient quality for the analysis. The scalebar = 1 mm. <a href="/files/ftp_upload/57440/57440fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/57440/57440fig9.jpg" /> 
Figure 9: Examples of sites that should be excluded from analysis. A. The human brain tumor sections have been stained with CD11b (green) and CD45 (red), markers of microglia and macrophages. This image is out of focus and has been excluded from the analysis. B. Bubbles are present in this image which has been excluded from the final analysis. C.This site was excluded from the analysis because of the fold in the tissue. The scale bars are 20 &#181;m. <a href="/files/ftp_upload/57440/57440fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/57440/57440fig10.jpg" /> 
Figure 10: Representative images and analysis. A. Each image is displayed next to the overlays representing the results of the automated segmentation. In the overlay, nuclei are displayed in white and positively stained cells are shown in green for CD11b and red for CD45. After excluding the sites of inadequate quality from the analysis and combining the results of all the remaining sites, B. the average proportions of the total number of cells in each site that stained positive for each marker and co-labeled for both markers, C. the average stained area, and D. the average cell intensity are graphically represented. RFU = relative fluorescence units. The data are expressed as mean &#177; standard deviation. The scale bars are 20 &#181;m. <a href="/files/ftp_upload/57440/57440fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System at JoVE.com

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

automated-slide-scanning-segmentation-fluorescently-labeled-tissues

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

8.1K Views.  University of Calgary. Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Video: Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it is now well established that the immune infiltrate in human tumors can correlate with prognosis and response to therapy. The analysis of the immune infiltrate in the tumor microenvironment has become a major challenge for the classification of patients and the response to treatment.
The co-expression of inhibitory receptors such as Program Cell Death Protein 1 (PD1; also known as CD279), Cytotoxic T Lymphocyte Associated Protein 4 (CTLA-4), T-Cell Immunoglobulin and Mucin Containing Protein-3 (Tim-3; also known as CD366), and Lymphocyte Activation Gene 3 (Lag-3; also known as CD223), is a hallmark of T cell exhaustion. We developed a multiparametric in situ immunofluorescence staining to identify and quantify at the cellular level the co-expression of these inhibitory receptors. On a retrospective series of frozen tissue of renal cell carcinomas (RCC), using a fluorescence multispectral imaging technology coupled with an image analysis software, it was found that co-expression of PD-1 and Tim-3 on tumor infiltrating CD8+ T cells is correlated with a poor prognosis in RCC. To our knowledge, this represents the first study demonstrating that this automated multiplex in situ technology may have some clinical relevance.

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Studying tumor microenvironment may identify prognostic or predictive biomarkers of clinical response to immunotherapy. Presented here, is an innovative method based on in situ fluorescence multispectral imaging to analyze and count automatically various subpopulations of CD8+ T cells. This reproducible and reliable technique is suitable for large cohort analyses.

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it ...

Cancer Research

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor1,2, and some of these sub-clones give rise to metastatic (and therefore lethal) disease. 
Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension3, or on macrodissection4. A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue5, providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ.
We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system6. Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index7.
To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.

Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of ...

Medicine

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that participate in regenerating tissue, tumor formation, or vasculogenesis, we devised a multi-colored cellular transplant model of tumor development in which cell populations originate from different fluorescently colored reporter gene mice and are transplanted, engrafted or injected in and around a developing tumor. These colored cells are then recruited and incorporated into the tumor stroma. In order to quantitatively assess bone marrow derived tumor stromal cells, we transplanted GFP expressing transgenic whole bone marrow into lethally irradiated RFP expressing mice as approved by IACUC. 0ovarian tumors that were orthotopically injected into the transplanted mice were excised 6-8 weeks post engraftment and analyzed for bone marrow marker of origin (GFP) as well as antibody markers to detect tumor associated stroma using multispectral imaging techniques. We then adapted a methodology we call MIMicc- Multispectral Interrogation of Multiplexed cellular compositions, using multispectral unmixing of fluoroprobes to quantitatively assess which labeled cell came from which starting populations (based on original reporter gene labels), and as our ability to unmix 4, 5, 6 or more spectra per slide increases, we&#39;ve added additional immunohistochemistry associated with cell lineages or differentiation to increase precision. Utilizing software to detect co-localized multiplexed-fluorescent signals, tumor stromal populations can be traced, enumerated and characterized based on marker staining.1

Complex tissue masses, from organs to tumors, are composed of various cellular elements. We elucidated the contribution of cellular phenotypes within a tissue utilizing multi-labeled fluorescent transgenic mice in combination with multiparameter immunofluorescent staining followed by spectral unmixing to decipher cell origin as well as cell characteristics based on protein expression.

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that ...

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the tumor immune landscape. Although standard immunohistochemistry is suitable for studying tissue architecture, it is limited to the analysis of a small number of markers. Conversely, techniques such as flow cytometry can evaluate multiple markers simultaneously, although information about tissue morphology is lost. In recent years, multiplexed strategies that integrate phenotypic and spatial analysis have emerged as comprehensive approaches to the characterization of the tumor immune landscape. Herein, we discuss an innovative technology combining metal-labeled antibodies and secondary ion mass spectrometry focusing on the technical steps in assay development and optimization, tissue preparation, and image acquisition and processing. Before staining, a metal-labeled antibody panel must be developed and optimized. This hi-plex image system supports up to 40 metal-tagged antibodies in a single tissue section. Of note, the risk of signal interference increases with the number of markers included in the panel. After panel design, particular attention should be given to the metal isotope assignment to the antibody to minimize this interference. Preliminary panel testing is performed using a small subset of antibodies and subsequent testing of the entire panel in control tissues. Formalin-fixed, paraffin-embedded tissue sections are obtained and mounted on gold-coated slides and further stained. The staining takes 2 days and closely resembles standard immunohistochemical staining. Once samples are stained, they are placed in the image acquisition instrument. Fields of view are selected, and images are acquired, uploaded, and stored. The final stage is image preparation for the filtering and removal of interference using the system&#39;s image processing software. A disadvantage of this platform is the lack of analytical software. However, the images generated are supported by different computational pathology software.

In the era of cancer immunotherapy, interest in elucidating tumor microenvironment dynamics has increased strikingly. This protocol details a mass spectrometry imaging technique with respect to its staining and imaging steps, which allow for highly multiplexed spatial analysis.

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the ...

In Situ Immunohistochemical Detection of Immune Cell Markers in Cancer Tissues

Multiplex Cyclic Fluorescent Immunohistochemistry

The tumor microenvironment involves interactions between host cells, tumor cells, immune cells, stromal cells, and vasculature. Characterizing and spatially organizing immune cell subsets and target proteins are crucial for prognostic and therapeutic purposes. This has led to the development of multiplexed immunohistochemistry staining methods. Multiplex fluorescence immunohistochemistry allows the simultaneous detection of multiple markers, facilitating a comprehensive understanding of cell function and intercellular interactions. In this paper, we describe a workflow for the multiplex cyclic fluorescent immunohistochemistry assay and its application in the quantification analysis of lymphocyte subsets. The multiplex cyclic fluorescent immunohistochemistry staining follows similar steps and reagents as standard immunohistochemistry, involving antigen retrieval, cyclic antibody incubation, and staining on a formalin-fixed paraffin-embedded (FFPE) tissue slide. During the antigen-antibody reaction, a mixture of antibodies from different species is prepared. Conditions, such as antigen retrieval time and antibody concentration, are optimized and validated to increase the signal-to-noise ratio. This technique is reproducible and serves as a valuable tool for immunotherapy research and clinical applications.

Author Spotlight: Efficient Detection of Immune Cell-Infiltration in Cancer Tissues Using Fluorescent Immunohistochemistry

Multiplex cyclic immunohistochemistry allows in situ detection of multiple markers simultaneously using repeated antigen-antibody incubation, image scanning, and image alignment and integration. Here, we present the operating protocol for identifying immune cell substrates with this technology in lung cancer and paired brain metastasis samples.

The tumor microenvironment involves interactions between host cells, tumor cells, immune cells, stromal cells, and vasculature. Characterizing and ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Biology

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

Multispectral fluorescence imaging on formalin-fixed paraffin-embedded (FFPE) tissues enables the detection of multiple markers in a single tissue sample that can provide information about antigen coexpression and spatial distribution of the markers. However, a lack of suitable antibodies for formalin-fixed tissues may restrict the nature of markers that can be detected. In addition, the staining method is time-consuming. Here we describe a rapid method to perform multispectral fluorescence imaging on frozen tissues. The method includes the fluorophore combinations used, detailed steps for the staining of mouse and human frozen tissues, and the scanning, acquisition, and analysis procedures. For staining analysis, a commercially available semiautomated multispectral fluorescence imaging system is used. Through this method, up to six different markers were stained and detected in a single frozen tissue section. The machine learning analysis software can phenotype cells that can be used for quantitative analysis. The method described here for frozen tissues is useful for the detection of markers that cannot be detected in FFPE tissues or for which antibodies are not available for FFPE tissues.

We describe a rapid staining method to perform multispectral imaging on frozen tissues.

Multispectral fluorescence imaging on formalin-fixed paraffin-embedded (FFPE) tissues enables the detection of multiple markers in a single tissue sample ...

Immunology and Infection

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.

In this manuscript, we present a high-throughput, semi-automated cryohistology platform to produce aligned composite images of multiple response measures from several rounds of fluorescent imaging on frozen sections of mineralized tissues.

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...