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Cancer Research

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

Published: September 13, 2022 doi: 10.3791/63620

Abstract

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.

Introduction

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.

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Protocol

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

  1. 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.
  2. 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 µm and mount on to glass slides. Place each slide inside the slide holder gasket. Incubate the slide at 60 °C for 30 min.

2. Semi-automated IHC system preparation and software configuration (for loading and running of slides)17

  1. Set up the reagents. Click on the Reagent Setup button. Click on Add in the Setup tab.
    1. Register a wash buffer by typing a name for it in the Name field. Select Ancillary in the Type field. Click on Save.
    2. 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.
  2. Register the Detection System, consisting of a barcoded Reagent Container Tray. Scan the barcode.
    1. Begin entering reagent system details in the Add Research Reagent System window. Type a Name for the detection system.
    2. 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.
    3. 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.
  3. 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.
    1. 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 µL for each DispenseType). Click on Save.
  4. Prepare the study. Fill the container in position 1 with a Wash Buffer. Fill 150 µL 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 µL per slide and 350 µL of dead volume.
    1. 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 µL under Dispense Volume | the desired protocol in the Preparation Protocol dropdown (Bake and Dewax is suggested). Highlight the study and click on Add Slide.
    2. Select Test Tissue under Tissue Type | 150 µL 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.
    3. 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.
  5. 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.
    1. 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.
  6. 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.

3. Antibody incubation and nuclei staining17

  1. 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).
  2. Make a working antibody-PC-oligo solution by adding, for each slide, 8 µL of each antibody (diluted 1:40) in the panel. Use the blocking reagent as the diluent to reach the final volume of 200 µL for each slide. Incubate overnight at 4 °C (Figure 3, Step 1).
    NOTE: Morphology markers, biological dyes, or fluorescently labeled antibodies can also be added to the solution at this step.
  3. 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.
  4. 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.

4. Fluorescence visualization, ROI identification, and UV photocleavage on the DSP instrument17

  1. After hovering the mouse over Data Collection in the Control Center, select New/Continue Run.
  2. 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.
  3. 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.
  4. 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 µm) 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.
  5. Approve the ROIs by clicking on the Exit Scan Workspace button. Wait for UV light to cleave the oligos from the antibodies.
  6. 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.
  7. 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.
  8. 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.

5. Protein readout17

  1. Use a permeable seal and dry the aspirates at 65 °C in a thermal cycler with the top open. Add 7 µL of diethyl pyrocarbonate-treated water and mix. Incubate at RT for 10 min, and then spin down quickly.
  2. 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 × 8 µL) = _____ µL + (n × 8 µL) = ______ µL + (n × 80 µL) = _____ µL (1)
  3. Add 84 µL 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 µL of each Hyb Code Master Mix into all the 12 wells of the indicated row.
  4. Transfer 7 µL 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 °C. Cool the plate on ice and perform a quick spin.
  5. 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 °C. Load the strip tubes into the analysis system.
  6. 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.
    1. 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.
    2. 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.
  7. 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.

6. Data analysis17

  1. 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.
  2. 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.
  3. 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.
  4. OPTIONAL: After running QC, adjust the data further using other toolbar options. Use tools in the Visualizations pane to plot the transformed data.
  5. 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.
  6. 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.

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Representative Results

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 with differential protein expression. For example, in the present experiment, S100 and CD56 are universally high because they are neural markers.

Markers with the most variability included B7-H3, Ki-67, CD44, and fibronectin. These markers have important associations with tumor proliferation, migration, and metastasis, respectively. It is thus reasonable that they would exhibit regional variability between samples of malignant core, malignant invasion, and non-malignant tissue. Numerical data representation is also possible; a file containing the expression value of each protein marker within an ROI in table form can be exported for mathematical and statistical analysis. This expression value is produced by the digital counting feature available through DSP, which quantifies the microaspirated PC-oligos within each ROI (Figure 1 and Figure 2).

Figure 1
Figure 1: Defining regions of interest in tissue microarray from glioblastoma biopsies. Top, Hematoxylin and eosin-stained section of tissue microarray containing several 2 mm diameter core biopsies from a total of three glioblastoma patients. Bottom, regions of interest defined on fluorescence image. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Photocleavable oligonucleotides. Antibody or antisense oligonucleotide probes for protein or RNA targets, respectively, are covalently linked to oligonucleotides with photocleavable linkers. Tissue sections are stained with the probes. UV light is then directed at ROIs to release the PC-oligos, which are digitally counted. This figure was modified from9, with permission of Springer Nature (copyright 2020). Abbreviations: PC-oligos = photocleavable oligonucleotides; ROIs = regions of interest. Please click here to view a larger version of this figure.

Figure 3
Figure 3: DSP procedure. 1) Tissue processing: A tissue slide is stained with oligo-conjugated antibody or RNA probes. 2) ROI selection: The tissue slide is imaged, and ROIs are delineated, either manually or automatically based on certain fluorescence patterns. 3) Cleavage of conjugated oligonucleotides: UV light is directed at the ROIs, resulting in cleavage of the photocleavable oligonucleotides. 4) PC-oligo collection: The PC-oligos are aspirated into a microcapillary tube. 5) Plating: Aspirated PC-oligos are deposited into a microtiter plate. 6) Repeat: Steps 3-5 are repeated for each ROI; between each cycle, meticulous washing is performed. 7) Quantification: Spatially resolved pools of PC-oligos can be hybridized to fluorescent barcodes; this allows for digital counting of up to approximately 1 million binding events per ROI. Alternatively, PC-oligos can be quantified via NGS, in which the entire microtiter plate is pooled into a single tube and sequenced. The reads are then translated into digital counts and mapped back to their original ROI, creating a visual map of protein or RNA expression within each tissue section. This figure was modified from9, with permission of Springer Nature (copyright 2020). Abbreviations: DSP = digital spatial processing; PC-oligos = photocleavable oligonucleotides; ROIs = regions of interest; NGS = next-generation sequencing. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Representative heatmap from a DSP experiment. Columns represent individual regions of interest. Rows represent a protein target. Low expression is shown in blue, and high expression is shown in red. Abbreviation: DSP = digital spatial processing. Please click here to view a larger version of this figure.

GeoMx Immune Cell Profiling- human Pan-Tumor Module
PD-1 MART1
CD68 NY-ESO-1
HLA-DR S100B
Ki-67 Bcl-2
Beta-2 Microglobulin EpCAM
CD11c Her2
CD20 PTEN
CD3 ER-alpha
CD4 PR
CD45
CD56
CD8
CTLA4
GZMB
PD-L1
PanCk
SMA
Fibronectin
Rb IgG
Ms IgG1
Ms IgG2a
Histone H3
S6
GAPDH

Table 1: A representative sample of proteins that can be assessed using predesigned antibody panels.

Probe U Working Pool
Module 2 Probe R Other modules DEPC-treated water (microliters) total volume  (microliters) Probe U Master Stock (microliter) DEPC-treated water (microliter) total volume
2 ... 16.5 2 14.5 16.5
4 ... 33 4 29 33
6 ... 49.5 6 43.5 49.5
8 ... 66 8 58 66

Table 2: Hybridization codes and Probe R and Probe U working pools. Abbreviation: hyb = hybridization; DEPC = diethyl pyrocarbonate.

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Discussion

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-throughput approach of DSP also enables it to be used in a shotgun-like approach, which is ideal for discovering potential novel biomarkers of disease and response to therapy. In two studies of melanoma, DSP was used to evaluate the responses to combination therapy with ipilimumab and nivolumab versus nivolumab monotherapy19 and to neoadjuvant combination therapy with ipilimumab and nivolumab versus standard adjuvant treatment20. In both studies, DSP profiling of a variety of protein targets post treatment demonstrated differential relative expression of key immunologic markers, suggesting a role for potential biomarkers of therapeutic response.

DSP also facilitates the determination of the ultimate pathological significance of complex, molecular-level processes. For instance, a distinctive feature of invasive gliomas is their propensity to migrate directly through the extracellular matrix (ECM), and their migratory path is often guided by vascular and white matter tracts1,2. Variability in protein expression of the tissue microenvironment is a hallmark of the epithelial-to-mesenchymal transition that drives invasion. Several studies have examined how gliomas can upregulate matrix metalloproteinases (MMPs), resulting in the breakdown of the surrounding ECM and increasing invasiveness21,22,23. By visualizing and quantifying the cumulative, downstream effect of this differential regulation, DSP provides a widescale, holistic analysis.

A key determinant of the malignant potential of gliomas is their ability to interact with and manipulate host immune defenses. Similar to other solid tumors, gliomas employ a variety of mechanisms to evade host immunosurveillance and disrupt the activation of proteins critical to mounting an immune response. Recent advancements in immuno-oncology have focused on identifying immunogenic proteins that are expressed or exploited by tumors. At the forefront of these developments are the use of T cells to detect immunologically active tumor antigens against which oncolytic viruses can be directed24,25, and the development of checkpoint inhibitors against T cell inhibitory proteins (e.g., PD1/PDL1 and CTLA4) to potentiate host immunity against tumors26,27. Other stromal populations that figure prominently into the glioma microenvironment include macrophages, microvascular endothelial cells, immune cells, and a recently identified subpopulation termed 'glioma-associated stromal cells' (GASCs)28. The interactions of these cells with chemokines, cytokines, and components of the ECM have important implications for tumor proliferation, invasion, and response to treatment, and thus serve as important targets for regional quantification and comparison through technologies such as DSP.

The DSP protocol is user-friendly and allows for multiple layers of customization. As much of the quantification process is automated, the main user-dependent steps are geared toward attaining the highest possible sensitivity and specificity of target detection. Key steps within the protocol thus include determination of a stain for initial fluorescent visualization of the sample, delineation of ROIs, and selection of the antibody panels.

When designing a staining protocol, it is important to first identify a target that is likely to demonstrate quantifiable differences between various regions of the sample, corresponding to a critical characteristic or behavior of the tissue of origin. Next, an agent that will effectively bind the target must be selected. For example, if one hopes to visualize regions of hyperplasia or nuclear atypia, H&E or a nuclear stain may be chosen; alternatively, if aiming to visualize changes in the expression of a gene known to be associated with malignant or premalignant tissues, a fluorophore-conjugated antibody may be chosen. The tumor cells themselves can be specifically labeled in the case of IDH1-R132H mutant tumors. The present protocol could be modified by the addition of fluorescently labeled IDH1-R132H as a morphologic marker (protocol step 3.2). Up to four stains per sample may be used. Once visualization of the stained tissue has occurred, ROIs are designated. Selection of ROIs should reflect where significant differences in protein target expression are expected to occur. Key considerations when selecting ROIs include size (10-600 µm diameter), shape (circle, rectangle, or user-drawn), and segmentation (further demarcating sub-regions within a single ROI, offering an additional potential layer of analysis). These variables should be optimized to most effectively capture spatial variation in target expression that is anticipated based on the selected antibody panel.

Selection of the antibody panel is of critical importance, as differential antibody expression ultimately serves as the study end point. When performing this step, it is important to consider which markers may exhibit expression that correlates either positively or negatively with stages along the spectrum of tumor development, from benign, to preinvasive, to invasive or malignant. It is likewise important to carefully consider properties of both the tissue of origin and the tumor type, as both of these features may influence the expression of certain markers.

Because much of the process is automated, any flaws in the procedure are generally due to hardware or software malfunction, offering limited user troubleshooting capability. Issues that may arise include disruption of the built-in quality control (QC) measure (designed to flag and potentially omit data suggestive of low signal-to-noise ratio, low counts, and inadequate target detection), aberrant software updates, and procedural issues (e.g., premature termination of various steps). For these issues, it is recommended that the user contact the manufacturer for remote support. Alternatively, if the user encounters intrinsic data issues (e.g., low nuclei count overall, low variability in expression between ROIs), they may consider repeating earlier steps in the procedure (e.g., repooling data from the original TMA, redefining ROIs). It is also important to consider variations in tissue density as a potential confounder of antigenic production and thus antibody expression; hence, tissues of similar densities should aim to be selected for analysis.

Control measures are built into the protocol. The program provides both an internal positive and negative control that account for variations in hybridization of the PC-oligos to the fluorescent barcodes. Housekeeping proteins offer another positive control that can additionally serve as a normalization factor on the basis of cellularity.

The ability to define ROIs within which quantification occurs is the foundation of the spatial profiling capability of DSP. This customizable demarcation of ROIs marks a critical and distinctive step within the protocol. The option to create an ROI as small as a single cell allows for regional precision in protein or nucleic acid quantification, and the ability to define multiple ROIs and quantify their contents in tandem through multiplex analysis yields a high-throughput approach.

When ROIs are selected to represent pathologically distinct regions within a glioma sample (e.g., necrotic, perivascular), proteomic profiling and regional comparison may reveal mechanisms of tumor propagation and progression. For example, IHC has been used to regionally quantify hypoxia-inducible factor (HIF-1α) expression in glioblastoma samples and has revealed higher expression near necrotic areas compared to perivascular zones29. This is consistent with several studies indicating a role for hypoxia in the tumorigenesis of glioma.

Regional variation has also been widely studied among immune cell components of the tumor microenvironment. Macrophages/microglia constitute the predominant immune cell population in gliomas and have been comprehensively studied on a regional basis. Subpopulations of tumor-associated macrophages (TAMs) have been shown to play different roles in tumor progression depending on their location within the tumor and microenvironment. Those within the tumor invasive edge break down the basement membrane, promoting spread; those in hypoxic areas exert an angiogenic effect, facilitating growth; those in proximity to tumor vasculature secrete EGF and associated factors to direct stromal tumor cells toward blood vessels, driving metastasis30. These critical, spatially dependent properties demonstrate the value of regional proteomic data in cancer biology and represent an important additional application of DSP to the characterization of immune cell populations within a tumor and its microenvironment.

The technique has a few limitations, including cost and the relatively small number of targets that are available in the core antibody panels. In addition, although subcellular resolution is not possible with DSP, it will be possible with upcoming new technologies31. Despite these limitations, DSP is a powerful technique for certain types of targeted, previously unanswerable questions in glioma cell biology. This innovative technology presents an exceptional opportunity for uncovering new biological perspectives through precise assessment of various protein targets.

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Disclosures

The authors have no conflicts of interest to disclose.

Acknowledgments

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.

Materials

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

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References

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Karbhari, N., Barney, R., Palisoul, S., Hong, J., Lin, C. C., Zanazzi, G. Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma. J. Vis. Exp. (187), e63620, doi:10.3791/63620 (2022).More

Karbhari, N., Barney, R., Palisoul, S., Hong, J., Lin, C. C., Zanazzi, G. Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma. J. Vis. Exp. (187), e63620, doi:10.3791/63620 (2022).

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