Name: Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma (Video) | JoVE Journal
Uploaded: 2022-09-13T12:50:00.000+00:00
Duration: 9 min 17 s
Description: Learn Digital Spatial Profiling for characterizing protein expression in glioma tumor microenvironments. This method enables high-throughput spatial quantification to identify proteins linked to invasion and growth.

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The authors acknowledge the support of the Laboratory for Clinical Genomics and Advanced Technology in the Department of Pathology and Laboratory Medicine of the Dartmouth Hitchcock Health System. The authors also acknowledge the Pathology Shared Resource at the Dartmouth Cancer Center with NCI Cancer Center Support Grant 5P30 CA023108-37.

The protocol outlined below follows the guidelines of the Dartmouth-Hitchcock Human Research Ethics Committee. Informed consent was obtained from the patients whose tissue samples were included in this study. See the Table of Materials section for details related to all materials, reagents, equipment, and software used in this protocol.
1. Slide preparation17
<ol>
	<li>Retrieve or prepare formalin-fixed, paraffin-embedded tissue from human adult-type diffusely infiltrating gliomas. 
	NOTE: In this experiment, paraffin-embedded blocks of biopsies from three patients with glioblastoma were used.</li>
	<li>Create a tissue microarray (TMA) block. Take several 2 mm cores from each biopsy and put them in a single TMA block (Figure 1, top). Cut sections from the TMA block at 4 &#181;m and mount on to glass slides. Place each slide inside the slide holder gasket. Incubate the slide at 60 &#176;C for 30 min.</li>
</ol>
2. Semi-automated IHC system preparation and software configuration (for loading and running of slides)17
<ol>
	<li>Set up the reagents. Click on the Reagent Setup button. Click on Add in the Setup tab.
	<ol>
		<li>Register a wash buffer by typing a name for it in the Name field. Select Ancillary in the Type field. Click on Save.</li>
		<li>Register a blocking solution by repeating the same steps as above (modified as applicable, e.g., in the Name field), but selecting Primary Antibody in the Type field. Select the desired protocols in the drop-down boxes for Default Staining Protocol and Default HIER Protocol. Select a Default Enzyme Protocol option if desired (this box was left blank in the current protocol). Click on Save.</li>
	</ol>
	</li>
	<li>Register the Detection System, consisting of a barcoded Reagent Container Tray. Scan the barcode.
	<ol>
		<li>Begin entering reagent system details in the Add Research Reagent System window. Type a Name for the detection system.</li>
		<li>Type an Expiration Date. Highlight the first row of the Reagents chart and Scan the Barcode of a new 30 mL Reagent Container, at which time the barcode will populate row 1.</li>
		<li>Place the container in position 1 of the Detection System. Select the name of the wash buffer in the drop-down box of the Reagent column and click on Add. Repeat these steps to add any additional reagents in subsequent rows, including the blocking solution.</li>
	</ol>
	</li>
	<li>Set up the Protein Protocol for IHC. Click on Protocol Setup. Highlight the row corresponding to the desired protocol and click on Copy. Enter the Name and fill out any other relevant fields in the Edit Protocol Properties window.
	<ol>
		<li>Select the box for Show Wash Steps. Confirm that Inc (min) and DispenseType fields are correct for each reagent (10 min for the Blocking Solution, 0 min for the Wash Buffer, and 150 &#181;L for each DispenseType). Click on Save.</li>
	</ol>
	</li>
	<li>Prepare the study. Fill the container in position 1 with a Wash Buffer. Fill 150 &#181;L per slide and 5 mL of dead volume; leave the lid open. Repeat these steps for the container in position 2, which should be filled with a Blocking Solution. This container should have 150 &#181;L per slide and 350 &#181;L of dead volume.
	<ol>
		<li>Load the Reagent Container Tray onto the machine. Allow the system to perform container recognition and volume confirmation measures. Click on Slide Setup | Add Study. Enter the Study ID and Study Name and select 150 &#181;L under Dispense Volume | the desired protocol in the Preparation Protocol dropdown (Bake and Dewax is suggested). Highlight the study and click on Add Slide.</li>
		<li>Select Test Tissue under Tissue Type | 150 &#181;L under Dispense Volume | Single and Routine from the Staining Mode dropdown boxes. Select a Process (IHC for the current study) | the Blocking Solution Name in the dropdown box of the Marker field | IHC DSP Protocol in the Staining field of the Protocols tab | *Bake and Dewax for Preparation | *HIER 20 min with ER1 for HIER. Leave the Enzyme field blank.</li>
		<li>Repeat this process for every slide. Click on Close once finished, and then click on Print Labels. Check All Slide Labels Not Yet Printed for Current Study and click on Print. Affix labels to the tops of the slides.</li>
	</ol>
	</li>
	<li>Load and run slides. Load the slides onto the slide tray, ensuring the sample and label face upward. Place the cover tiles over the slides, ensuring the slides are oriented with tabs at the bottom. Load the slide trays onto the instrument.
	<ol>
		<li>Press the LED button to lower the tray and allow the instrument to begin the scanning and recognition of slides. Click on the Start button to begin the experiment.</li>
	</ol>
	</li>
	<li>Finish the experiment. Press the LED button when it blinks green, indicating run completion. Remove the tray from the instrument, and carefully lift the cover tiles from each slide. Place the slides in 1x phosphate-buffered saline (PBS), remove excess buffer, and outline each tissue section with a hydrophobic pen to create a hydrophobic barrier.</li>
</ol>
3. Antibody incubation and nuclei staining17
<ol>
	<li>Select a panel of antibodies to localize the antigens of interest (see Table 1 for antibodies used in this experiment). 
	NOTE: Each antibody has already been conjugated to a DNA oligonucleotide with a UV-photocleavable segment (PC-oligo) that uniquely identifies it (indexing oligos, Figure 2).</li>
	<li>Make a working antibody-PC-oligo solution by adding, for each slide, 8 &#181;L of each antibody (diluted 1:40) in the panel. Use the blocking reagent as the diluent to reach the final volume of 200 &#181;L for each slide. Incubate overnight at 4 &#176;C (Figure 3, Step 1). 
	NOTE: Morphology markers, biological dyes, or fluorescently labeled antibodies can also be added to the solution at this step.</li>
	<li>The next day, place the slide in a Coplin jar and wash 3 x 10 min in 1x TBS-T. Postfix in 4% paraformaldehyde for 30 min at room temperature, followed by 2 x 5 min in 1x TBS-T.</li>
	<li>Add SYTO13 nuclear stain (diluted 1:10 in 1x TBS) for 15 min at room temperature. Wash 2x with 1x TBS-T, and then store the slide in 1x TBS-T.</li>
</ol>
4. Fluorescence visualization, ROI identification, and UV photocleavage on the DSP instrument17
<ol>
	<li>After hovering the mouse over Data Collection in the Control Center, select New/Continue Run.</li>
	<li>Place the slide in the slide holder, with the label toward the user. Lower the slide tray clamp, ensuring that the tissue is visible in the elongated window. Add 6 mL of Buffer S.</li>
	<li>Follow the prompts in the Control Center. Zoom between different axes with the X- and Y-sliders to delineate a region for scanning. Select Scan. Allow the scan to proceed until the entire defined target area has been imaged.</li>
	<li>Generate a 20x image. Define the ROIs either automatically or manually (Figure 3, Step 2). To follow this protocol, select three equally sized, circular ROIs (diameter of 250 &#181;m) for each tissue core (Figure 4, bottom). 
	NOTE: ROIs are customizable in size and shape. Several ROIs can be selected within each section. In this experiment, circular ROIs are defined manually.</li>
	<li>Approve the ROIs by clicking on the Exit Scan Workspace button. Wait for UV light to cleave the oligos from the antibodies.</li>
	<li>When the Cleaning Instrument process has been completed, select New Data Collection to continue with the current slides or plate. Otherwise, select Remove Slides and Microplate.</li>
	<li>Open the Finalize Plate window by double-clicking on the plate icon area at the lower right of the Control Center. If the Hybridization (Hyb) Code Pack lot number (#) is known, enter the number and click on Update (optional during this step). Click on Finalize.</li>
	<li>Detach the slide holder and collection plate by following the prompts. Store the slides in TBS-T. If the slides will not be used for a long time, cover them with an aqueous medium and coverslip.</li>
</ol>
5. Protein readout17
<ol>
	<li>Use a permeable seal and dry the aspirates at 65 &#176;C in a thermal cycler with the top open. Add 7 &#181;L of diethyl pyrocarbonate-treated water and mix. Incubate at RT for 10 min, and then spin down quickly.</li>
	<li>Choose the appropriate Probe R and Probe U equations from Table 2 to guide the creation of the probe/buffer mix. Based on the number of Hyb Codes necessary for hybridization in the present experiment, apply equation (1) as below. Mix and spin down quickly. 
	# Hyb Codes Probe R Working Pool Probe U Working Pool Hybridization Buffer n = _______ (n &#215; 8 &#181;L) = _____ &#181;L + (n &#215; 8 &#181;L) = ______ &#181;L + (n &#215; 80 &#181;L) = _____ &#181;L (1)</li>
	<li>Add 84 &#181;L of Probe/Buffer Mix to each Hyb Code Pack to be used. Flick to mix and spin down. In a fresh 96-well Hybridization plate, add 8 &#181;L of each Hyb Code Master Mix into all the 12 wells of the indicated row.</li>
	<li>Transfer 7 &#181;L from the DSP collection plate to the corresponding well in the Hybridization plate. Mix gently. Heat-seal and perform a quick spin. Then, incubate overnight at 67 &#176;C. Cool the plate on ice and perform a quick spin.</li>
	<li>Pool the hyb products from each well into a strip tube, gently pipeting each well 5x to mix. Cap the strip tube and spin down. Freeze any remaining unpooled hyb products at -80 &#176;C. Load the strip tubes into the analysis system.</li>
	<li>Save the CDF file onto a USB drive, and then transfer the data from the DSP instrument to the Digital Analyzer. Complete the setup by performing the following steps.
	<ol>
		<li>Load consumables and pooled samples onto the prep station. On screen, press Start Processing. Select High Sensitivity, followed by Next. Press Select All for the number of wells with samples | Finish | Next on email notification | Start.</li>
		<li>Once prep station is done, seal the cartridge and transfer to the digital analyzer. Press Start Counting, select Stage Position, press Load Existing CDF file and select previously uploaded file. Press Done, select stage position again, press Done | Start to run the program.</li>
	</ol>
	</li>
	<li>Save the zipped file of the reporter count conversion files from the analysis system to a USB drive, and then insert the drive into the DSP machine. In the DSP Control Center, hover over Data Collection, and then click on Upload Counts. Select the relevant zipped file.</li>
</ol>
6. Data analysis17
<ol>
	<li>Click on Records in the DSP Control Center. To view scans in the queue, select Add Selected Scans to Queue | My Analysis Queue. Select New Study from Queue after hovering over the Data Analysis option in the Control Center.</li>
	<li>Choose QC from the Task Bar Options. Continue with the default values or adjust if desired. Select Run QC and review the Results Grid. Click on Run QC to proceed and create a new dataset, normalizing the values to the positive hybridization controls.</li>
	<li>Import the new tags into an XLSX file. Select Manage Annotations in the Scans pane, and then download a template, insert the tags, and import the file.</li>
	<li>OPTIONAL: After running QC, adjust the data further using other toolbar options. Use tools in the Visualizations pane to plot the transformed data.</li>
	<li>Navigate the gray dropdown box of parameters to compose each plot in the Visualizations pane. Select a particular region within a plot to visualize the corresponding highlighted segments in the Scans pane and right-click to generate tags or groups. Within Dataset Summary, review plots from Normalization, Scaling, and other options for analysis and display.</li>
	<li>Save a visualization by selecting the icon to Save; review visualizations already saved under Summary. Select the Export (.svg) button to export a visualization in .svg format. Export data on which a visualization is based by clicking on the Export (.xlsx) button. Export all the data contained within a specific dataset by hovering the cursor over the dataset name in the second pane and clicking on the export icon.</li>
</ol>

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The authors have no conflicts of interest to disclose.

Given the diversity of proteins that could potentially influence the aggressiveness of gliomas and the notion that several of these proteins remain undiscovered, a high-throughput protein quantification method is an ideal technologic approach. Additionally, given that spatial data in oncologic samples often correlates with differential expression18, incorporating spatial profiling into the protein quantification approach allows for more effective analysis.
The high-thro...

Diffusely infiltrating gliomas are the most common type of malignant brain tumor in adults and are invariably lethal. The propensity for glioma cells to migrate extensively in the brain is a major therapeutic challenge. The mechanism by which they spread involves directed migration and unchecked invasion. Invasive glioma cells have been shown to exhibit tropism and migration along white matter tracts1, with recent research implicating demyelination of these tracts as an active, protumorigenic feature2. Invasion is mediated by an epithelial-to-mesenchymal transition, in which glioma cells acquire mesenchymal properties by reducing the expression of genes encoding extracellular matrix proteins and cell adhesion molecules, amplifying migration and facilitating propagation through the tumor microenvironment3,4,5.
At the molecular level, disruption of several proteins that confer cellular stability and interface with immunogenic components has been demonstrated6. Infiltrative gliomas are known to undergo suppression of proteins with anti-apoptotic (e.g., PTEN) properties7. They also overexpress proteins that promote evasion of the host immune response (e.g., PD1/PDL1)8. The dysregulation of these complex pathways enhances tumorigenicity and increases malignant potential.
Within samples of invasive glioma, the aim was to evaluate the differential expression of proteins key to cell growth, survival, and proliferation, and to microenvironment structural integrity between invasive and non-invasive components. Additionally, we sought to study the differential regulation of proteins with an active immunogenic role, offering insight to the mechanism by which compromised host immune defenses may enhance the proliferative and invasive potential of gliomas. This is especially relevant given the recent breadth of research demonstrating how immune markers and drivers of dysregulation in malignancy can serve as targets of immunotherapy. Identifying viable therapeutic targets among the many proteins involved in immunosurveillance and reactivity requires a highly sensitive and comprehensive approach.
Given the wide array of candidate proteins that can be studied, we sought a method akin to immunohistochemistry but with enhanced data processing efficiency. Within the field of cancer biology, DSP has emerged as a powerful technology with important advantages over alternative tools for proteomic analysis and quantification. The hallmark of DSP is its high-throughput multiplexing capability, allowing for simultaneous study of several different proteins within a sample, marking an important distinction from standard but lower-plex technologies such as immunohistochemistry (IHC)9,10. The multiplex feature of DSP does not compromise its fidelity as a quantitative and analytical tool, as demonstrated by studies comparing DSP to IHC. When used for proteomic quantification of non-small cell lung cancer specimens, for example, DSP has been shown to have similar results to IHC11. Additionally, DSP offers customizable regional specification, in which users can manually define regions within which to perform proteomic analysis. This presents an advantage over whole-section multiplex methods10,12. In a single round of processing, DSP thus offers multiple layers of analysis by surveying several protein targets across multiple regions of interest.
DSP has applications in several different pathological settings. DSP is especially advantageous in oncologic analysis, as spatial variation can correlate with cellular transformation and differential protein expression. For example, DSP has been used to compare the proteomic profile of breast cancer to the adjacent tumor microenvironment. This carries important implications for understanding the natural history of this tumor and its progression, as well as potential response to treatment13. Additional contexts illustrating the versatility of DSP include spatial quantification of protein diversity in prostate cancer14, association of immune cell marker expression with disease progression in head and neck squamous cell carcinoma15, and demonstration of an epithelial-mesenchymal gradient of protein expression distinguishing metastatic from primary clear cell ovarian cancer16. By implementing DSP, we characterize the spatial topography of proteins that could impact tumorigenesis and invasion of gliomas.

<table><tbody><tr><td>BOND Research Detection System</td><td>Leica Biosystems, Wetzlar, Germany</td><td>DS9455</td><td>Open detection system containing open containers in a reagent tray</td></tr><tr><td>BOND Wash</td><td>Leica Biosystems, Wetzlar, Germany</td><td>AR950</td><td>10X concentrated buffer solution for washing fixed tissue</td></tr><tr><td>Buffer W</td><td>NanoString, Seattle, WA</td><td>contact company</td><td>Blocking reagent</td></tr><tr><td>Cy3 conjugation kit</td><td>Abcam, Cambridge, UK</td><td>AB188287</td><td>Cy3 fluorescent antibody conjugation kit</td></tr><tr><td>GeoMx Digital Spatial Profiler (DSP)</td><td>NanoString, Seattle, WA</td><td>contact company</td><td>System for imaging and characterizing protein and RNA targets</td></tr><tr><td>GeoMx DSP Instrument BufferKit</td><td>NanoString, Seattle, WA</td><td>100471</td><td>Buffer kit for GeoMX DSP (including buffers for sample processing and preparation)</td></tr><tr><td>GeoMx Hyb Code Pack_Protein</td><td>NanoString, Seattle, WA</td><td>121300401</td><td>Controls for running GeoMX DSP experiemtns</td></tr><tr><td>GeoMx Immune Cell Panel (Imm Cell Pro_Hs)</td><td>NanoString, Seattle, WA</td><td>121300101</td><td>Protein module with targets for human immune cells and immuno-oncologic targets</td></tr><tr><td>GeoMx Pan-Tumor Panel (Pan-Tumor_Hs)</td><td>NanoString, Seattle, WA</td><td>121300105</td><td>Protein module with targets for multiple human tumor types and for markers of epithelial-mesenchymal transition</td></tr><tr><td>GeoMx Protein Slide Prep FFPE</td><td>NanoString, Seattle, WA</td><td>121300308</td><td>Sample preparation reagents for GeoMX DSP protein analysis</td></tr><tr><td>LEICA Bond RX</td><td>Leica Biosystems, Wetzlar, Germany</td><td>contact company</td><td>Fully automated IHC stainer</td></tr><tr><td>Master Kit--12 reactions</td><td>NanoString, Seattle, WA</td><td>100052</td><td>Materials and reagents for use with the nCounter Analysis system</td></tr><tr><td>nCounter Analysis System</td><td>NanoString, Seattle, WA</td><td>contact company</td><td>Automated system for multiplex target expression quantification (to be used with GeoMx DSP)</td></tr><tr><td>TMA Master II</td><td>3DHistech Ltd., Budapest, Hungary</td><td></td><td>To create the tissue microarray block</td></tr></tbody></table>

digital spatial profiling for characterization of the microenvironment in adult-type diffusely infiltrating glioma

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses.
DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Figure 4 shows the representative results from a DSP experiment performed on samples of glioblastoma. A heat map is presented, illustrating one of the methods by which to capture data visually using the DSP software. Rows represent protein targets, and each column corresponds to a region of interest. A color range of blue to red denotes low to high expression, respectively. Variability of color within a row reflects regional protein heterogeneity and suggests a possible spatial association w...

Proteomic dysregulation plays an important role in the spread of diffusely infiltrating gliomas, but several relevant proteins remain unidentified. Digital spatial processing (DSP) offers an efficient, high-throughput approach for characterizing the differential expression of candidate proteins that may contribute to the invasion and migration of infiltrative gliomas.

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically ...

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Cancer Research

This protocol was developed to quantitatively identify tumor microenvironment components in glioblastoma patient resections using chromogenic immunohistochemistry and ImageJ.

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Flexible Organic Electronic Devices for Pulsed Electric Field Therapy of Glioblastoma

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Learn Digital Spatial Profiling for characterizing protein expression in glioma tumor microenvironments. This method enables high-throughput spatial quantification to identify proteins linked to invasion and growth.

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

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