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>

<ol>
	<li>Pedersen, P. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Migratory+patterns+of+lac-z+transfected+human+glioma+cells+in+the+rat+brain.">Migratory patterns of lac-z transfected human glioma cells in the rat brain.</a> International Journal of Cancer. 62 (6), 767-771 (1995).</li><li>Wang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Invasion+of+white+matter+tracts+by+glioma+stem+cells+is+regulated+by+a+NOTCH1-SOX2+positive-feedback+loop.">Invasion of white matter tracts by glioma stem cells is regulated by a NOTCH1-SOX2 positive-feedback loop.</a> Nature Neuroscience. 22 (1), 91-105 (2019).</li><li>Iwadate, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epithelial-mesenchymal+transition+in+glioblastoma+progression.">Epithelial-mesenchymal transition in glioblastoma progression.</a> Oncology Letters. 11 (3), 1615-1620 (2016).</li><li>Tao, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomics+and+prognosis+analysis+of+epithelial-mesenchymal+transition+in+glioma.">Genomics and prognosis analysis of epithelial-mesenchymal transition in glioma.</a> Frontiers in Oncology. 10, 183 (2020).</li><li>Cuddapah, V. A., Robel, S., Watkins, S., Sontheimer, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+neurocentric+perspective+on+glioma+invasion.">A neurocentric perspective on glioma invasion.</a> Nature Reviews Neuroscience. 15 (7), 455-465 (2014).</li><li>Barthel, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioma:+molecular+signature+and+crossroads+with+tumor+microenvironment.">Glioma: molecular signature and crossroads with tumor microenvironment.</a> Cancer and Metastasis Reviews. 1 (1), 53-75 (2021).</li><li>Ziegler, D. S., Kung, A. L., Kieran, M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anti-apoptosis+mechanisms+in+malignant+gliomas.">Anti-apoptosis mechanisms in malignant gliomas.</a> Journal of Clinical Oncology. 26 (3), 493-500 (2008).</li><li>Berghoff, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Programmed+death+ligand+1+expression+and+tumor-infiltrating+lymphocytes+in+glioblastoma.">Programmed death ligand 1 expression and tumor-infiltrating lymphocytes in glioblastoma.</a> Neuro-Oncology. 17 (8), 1064-1075 (2015).</li><li>Merritt, C. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+digital+spatial+profiling+of+proteins+and+RNA+in+fixed+tissue.">Multiplex digital spatial profiling of proteins and RNA in fixed tissue.</a> Nature Biotechnology. 38 (5), 586-599 (2020).</li><li>Van, T. M., Blank, C. U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+user's+perspective+on+GeoMxTM+digital+spatial+profiling.">A user's perspective on GeoMxTM digital spatial profiling.</a> Immuno-Oncology Technology. 1, 11-18 (2019).</li><li>Garcia-Pardo, M., Calles, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROS-1+NSCLC+therapy+resistance+mechanism.">ROS-1 NSCLC therapy resistance mechanism.</a> Precision Cancer Medicine. , (2021).</li><li>Ye, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+spatial+profiling+of+individual+glomeruli+from+patients+with+anti-neutrophil+cytoplasmic+autoantibody-associated+glomerulonephritis.">Digital spatial profiling of individual glomeruli from patients with anti-neutrophil cytoplasmic autoantibody-associated glomerulonephritis.</a> Frontiers in Immunology. 13, 831253 (2022).</li><li>Bergholtz, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+practices+for+spatial+profiling+for+breast+cancer+research+with+the+GeoMx+digital+spatial+profiler.">Best practices for spatial profiling for breast cancer research with the GeoMx digital spatial profiler.</a> Cancers. 13 (17), 4456 (2021).</li><li>Brady, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inter-+and+intra-tumor+heterogeneity+of+metastatic+prostate+cancer+determined+by+digital+spatial+gene+expression+profiling.">Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling.</a> Nature Communications. 12 (1), 1426 (2021).</li><li>Kulasinghe, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+multiplexed+digital+spatial+profiling+of+the+tumor+microenvironment+of+head+and+neck+squamous+cell+carcinoma+patients.">Highly multiplexed digital spatial profiling of the tumor microenvironment of head and neck squamous cell carcinoma patients.</a> Frontiers in Oncology. 10, 607349 (2021).</li><li>Wang, D. Y. -. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Case+study:+Digital+spatial+profiling+of+metastatic+clear+cell+carcinoma+reveals+intra-tumor+heterogeneity+in+epithelial-mesenchymal+gradient.">Case study: Digital spatial profiling of metastatic clear cell carcinoma reveals intra-tumor heterogeneity in epithelial-mesenchymal gradient.</a> bioRxiv. , (2021).</li><li>GeoMx DSP. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+slide+preparation+user+manual.">Automated slide preparation user manual.</a> GeoMx DSP. , (2022).</li><li>Allam, M., Cai, S., Coskun, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+bioimaging+of+single-cell+spatial+profiles+for+precision+cancer+diagnostics+and+therapeutics.">Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics.</a> NPJ Precision Oncology. 4, 11 (2020).</li><li>Wolchok, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Overall+survival+with+combined+nivolumab+and+ipilimumab+in+advanced+melanoma.">Overall survival with combined nivolumab and ipilimumab in advanced melanoma.</a> New England Journal of Medicine. 377 (14), 1345-1356 (2017).</li><li>Blank, C. U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neoadjuvant+versus+adjuvant+ipilimumab+plus+nivolumab+in+macroscopic+stage+III+melanoma.">Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma.</a> Nature Medicine. 24 (11), 1655-1661 (2018).</li><li>Matthews, R. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-enriched+hyaluronan+binding+(BEHAB)/brevican+cleavage+in+a+glioma+cell+line+is+mediated+by+a+disintegrin+and+metalloproteinase+with+thrombospondin+motifs+(ADAMTS)+family+member.">Brain-enriched hyaluronan binding (BEHAB)/brevican cleavage in a glioma cell line is mediated by a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) family member.</a> Journal of Biological Chemistry. 275 (30), 22695-22703 (2000).</li><li>Paganetti, P. A., Caroni, P., Schwab, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioblastoma+infiltration+into+central+nervous+system+tissue+in+vitro:+involvement+of+a+metalloprotease.">Glioblastoma infiltration into central nervous system tissue in vitro: involvement of a metalloprotease.</a> Journal of Cell Biology. 107, 2281-2291 (1988).</li><li>Beli&#235;n, A. T., Paganetti, P. A., Schwab, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Membrane-type+1+matrix+metalloprotease+(MT1-MMP)+enables+invasive+migration+of+glioma+cells+in+central+nervous+system+white+matter.">Membrane-type 1 matrix metalloprotease (MT1-MMP) enables invasive migration of glioma cells in central nervous system white matter.</a> Journal of Cell Biology. 144 (2), 373-384 (1999).</li><li>Coulie, P. G., Vanden Eynde, B. J., vander Bruggen, P., Boon, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumour+antigens+recognized+by+T+lymphocytes:+At+the+core+of+cancer+immunotherapy.">Tumour antigens recognized by T lymphocytes: At the core of cancer immunotherapy.</a> Nature Reviews Cancer. 14 (2), 135-146 (2014).</li><li>Finn, O. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Vaccines+for+cancer+prevention:+a+practical+and+feasible+approach+to+the+cancer+epidemic.">Vaccines for cancer prevention: a practical and feasible approach to the cancer epidemic.</a> Cancer Immunology Research. 2 (8), 708-713 (2014).</li><li>Sharma, P., Allison, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+future+of+immune+checkpoint+therapy.">The future of immune checkpoint therapy.</a> Science. 348 (6230), 56-61 (2015).</li><li>Chen, L., Han, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anti-PD-1/PD-L1+therapy+of+human+cancer:+past,+present,+and+future.">Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future.</a> Journal of Clinical Investigation. 125 (9), 3384-3391 (2015).</li><li>Cai, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioma-Associated+stromal+cells+stimulate+glioma+malignancy+by+regulating+the+tumor+immune+microenvironment.">Glioma-Associated stromal cells stimulate glioma malignancy by regulating the tumor immune microenvironment.</a> Frontiers in Oncology. 11, 672928 (2021).</li><li>Ishii, , et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histological+characterization+of+the+tumorigenic+&amp;#34;peri-necrotic+niche&amp;#34;+harboring+quiescent+stem-like+tumor+cells+in+glioblastoma.">Histological characterization of the tumorigenic &amp;#34;peri-necrotic niche&amp;#34; harboring quiescent stem-like tumor cells in glioblastoma.</a> PLoS One. 11 (1), 0147366 (2016).</li><li>Lewis, C. E., Pollard, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+role+of+macrophages+in+different+tumor+microenvironments.">Distinct role of macrophages in different tumor microenvironments.</a> Cancer Research. 66 (2), 605-612 (2006).</li><li>NanoString. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CosMx+Spatial+Molecular+Imager:+True+Single-Cell+In+Situ+Solution.">CosMx Spatial Molecular Imager: True Single-Cell In Situ Solution.</a> NanoString. , (2022).</li></ol>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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>IDH1-R132H antibody</td>
			<td>Dianova, Hamburg, Germany</td>
			<td>DIA-H09</td>
			<td>Monoclonal antibody against human IDH1 R132H</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

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...

Watch this Scientific Journal Video about Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma at JoVE.com

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

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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Research

JoVE Journal

Cancer Research

2.2K Views.  Dartmouth-Hitchcock Medical Center. 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.

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

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma (DIPG)

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a viable treatment strategy and biopsy is not routinely performed, the availability of patient samples for research is limited. Consequently, efforts to study this disease have been challenged by a paucity of faithful disease models. To address this need, we describe here a protocol for the rapid processing of post-mortem autopsy tissue samples in order to generate durable patient-derived cell culture models that can be used in in vitro assays or in vivo orthotopic xenograft experiments. These models can be used to screen for potential drug targets and to study fundamental pathobiological processes within DIPG. This protocol can further be extended to analyze and isolate tumor and microenvironmental cells using Fluorescence-activated Cell Sorting (FACS), which enables subsequent analysis of gene expression, protein expression, or epigenetic modifications of DNA at the bulk cell or single cell level. Finally, this protocol can also be adapted to generate patient-derived cultures for other central nervous system tumors.

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma DIPG

This protocol describes a method for the rapid processing of post-mortem diffuse intrinsic pontine glioma samples for the establishment of patient-derived cell culture models or direct characterization of tumor and microenvironmental cells.

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a ...

Generation of Orthotopic Pancreatic Tumors and Ex vivo Characterization of Tumor-Infiltrating T Cell Cytotoxicity

In vivo models of pancreatic cancer provide invaluable tools for studying disease dynamics, immune infiltration and new therapeutic strategies. The orthotopic murine model can be performed on large cohorts of immunocompetent mice simultaneously, is relatively inexpensive and preserves the cognate tissue microenvironment. The quantification of T cell infiltration and cytotoxic activity within orthotopic tumors provides a useful indicator of an antitumoral response.
This protocol describes the methodology for surgical generation of orthotopic pancreatic tumors by injection of a low number of syngeneic tumor cells resuspended in 5 &#181;L basement membrane directly into the pancreas. Mice bearing orthotopic tumors take approximately 30 days to reach endpoint, at which point tumors can be harvested and processed for characterization of tumor-infiltrating T cell activity. Rapid enzymatic digestion using collagenase and DNase allows a single-cell suspension to be extracted from tumors. The viability and cell surface markers of immune cells extracted from the tumor are preserved; therefore, it is appropriate for multiple downstream applications, including flow-assisted cell sorting of immune cells for culture or RNA extraction, flow cytometry analysis of immune cell populations. Here, we describe the ex vivo stimulation of T cell populations for intracellular cytokine quantification (IFN&#947; and TNF&#945;) and degranulation activity (CD107a) as a measure of overall cytotoxicity. Whole-tumor digests were stimulated with phorbol myristate acetate and ionomycin for 5 h, in the presence of anti-CD107a antibody in order to upregulate cytokine production and degranulation. The addition of brefeldin A and monensin for the final 4 h was performed to block extracellular transport and maximize cytokine detection. Extra- and intra-cellular staining of cells was then performed for flow cytometry analysis, where the proportion of IFN&#947;+, TNF&#945;+ and CD107a+ CD4+ and CD8+ T cells was quantified.
This method provides a starting base to perform comprehensive analysis of the tumor microenvironment.

Generation of Orthotopic Pancreatic Tumors and Ex vivo Characterization of Tumor-Infiltrating T Cell Cytotoxicity

This protocol describes the surgical generation of orthotopic pancreatic tumors and the rapid digestion of freshly isolated murine pancreatic tumors. Following digestion, viable immune cell populations can be used for further downstream analysis, including ex vivo stimulation of T cells for intracellular cytokine detection by flow cytometry.

In vivo models of pancreatic cancer provide invaluable tools for studying disease dynamics, immune infiltration and new therapeutic strategies. The ...

Analysis of Side Population in Solid Tumor Cell Lines

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great significance for tumor research. Currently, several techniques are used for the identification and purification of CSCs from tumor tissues and tumor cell lines. Separation and analysis of side population (SP) cells are two of the commonly used methods. The methods rely on the ability of CSCs to rapidly expel fluorescent dyes, such as Hoechst 33342. The efflux of the dye is associated with the ATP-binding cassette (ABC) transporters and can be inhibited by ABC transporter inhibitors. Methods for staining cultured tumor cells with Hoechst 33342 and analyzing the proportion of their SP cells by flow cytometry are described. This assay is convenient, fast, and cost-effective. Data generated in this assay can contribute to a better understanding of the effect of genes or other extracellular and intracellular signals on the stemness properties of tumor cells.

A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great ...

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

T cell-mediated immunity plays a crucial role in immune responses against tumors, with cytotoxic T lymphocytes (CTLs) playing the leading role in eradicating cancerous cells. However, the origins and replenishment of tumor antigen-specific CD8+ T cells within the tumor microenvironment (TME) remain obscure. This protocol employs the B16F10-OVA melanoma cell line, which stably expresses the surrogate neoantigen, ovalbumin (OVA), and TCR transgenic OT-I mice, in which over 90% of CD8+ T cells specifically recognize the OVA-derived peptide OVA257-264 (SIINFEKL) bound to the class I major histocompatibility complex (MHC) molecule H2-Kb. These features enable the study of antigen-specific T cell responses during tumorigenesis.
Combining this model with tumor transplantation surgery, tumor tissues from donors were transplanted into tumor-matched syngeneic recipient mice to precisely trace the influx of recipient-derived immune cells into transplanted donor tissues, allowing the analysis of the immune responses of tumor-inherent and periphery-originated antigen-specific CD8+ T cells. A dynamic transition was found to occur between these two populations. Collectively, this experimental design has provided another approach to precisely investigate the immune responses of CD8+ T cells in TME, which will shed new light on tumor immunology.

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

Here, we present a tumor transplantation protocol for the characterization of tumor-inherent and periphery-derived tumor-infiltrated lymphocytes in a mouse tumor model. Specific tracing of the influx of recipient-derived immune cells with flow cytometry reveals the dynamics of the phenotypic and functional changes of these cells during antitumor immune responses.

T cell-mediated immunity plays a crucial role in immune responses against tumors, with cytotoxic T lymphocytes (CTLs) playing the leading role in ...

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Live-3D-Cell Immunocytochemistry Assays of Pediatric Diffuse Midline Glioma

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using three-dimensional (3D) cell-based invasion and migration assays. In this study, we have optimized these 3D assays for live-cell immunocytochemistry. An Antibody Labeling Reagent was used to detect in real-time the expression of the adhesion molecule CD44, on the plasma membrane of migrating and invading cells of a DMG H3K27M primary patient-derived cell line. CD44 is associated with cancer stem cell phenotype and tumor cell migration and invasion and is involved in the direct interactions with the central nervous system (CNS) extracellular matrix. Neurospheres (NS) from the DMG H3K27M cell line were embedded into the basal membrane matrix (BMM) or placed onto a thin coating layer of BMM, in the presence of an anti-CD44 antibody in conjunction with the antibody labeling reagent (ALR). The live-3D-cell immunocytochemistry image analysis was performed on a live-cell analysis instrument to quantitatively measure the overall CD44 expression, specifically on the migrating and invading cells. The method also allows visualizing in real-time the intermittent expression of CD44 on the plasma membrane of migrating and invading cells. Moreover, the assay also provided new insights into the potential role of CD44 in the mesenchymal to amoeboid transition in DMG H3K27M cells.

This study presents a protocol of live-3D-cell immunocytochemistry applied to a pediatric diffuse midline glioma cell line, useful to study in real-time the expression of proteins on the plasma membrane during dynamic processes like 3D cell invasion and migration.

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using ...

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

Multiplex Immunofluorescence and Spatial Image Analysis of the Tumor Microenvironment

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers.
We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types.
This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment  

In this article, a protocol for manual tyramide signal amplification (TSA) multiplex immunofluorescence (mIF) combined with image analysis and spatial analysis is described. This protocol can be used with formalin-fixed paraffin-embedded (FFPE) sections for the staining of two to six antigens per slide depending on the slide scanner available in the laboratory.

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending ...

Mapping the Tumor Immune Landscape: An In-Depth Multiplex IHC Analysis Protocol

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...

Isolation and Characterization of Bone Marrow Microenvironment in Myeloid Neoplasms

Identifying Bone Marrow Microenvironmental Populations in Myelodysplastic Syndrome and Acute Myeloid Leukemia

The bone marrow microenvironment consists of distinct cell populations, such as mesenchymal stromal cells, endothelial cells, osteolineage cells, and fibroblasts, which provide support for hematopoietic stem cells (HSCs). In addition to supporting normal HSCs, the bone marrow microenvironment also plays a role in the development of hematopoietic stem cell disorders, such as myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). MDS-associated mutations in HSCs lead to a block in differentiation and progressive bone marrow failure, especially in the elderly. MDS can often progress to therapy-resistant AML, a disease characterized by a rapid accumulation of immature myeloid blasts. The bone marrow microenvironment is known to be altered in patients with these myeloid neoplasms. Here, a comprehensive protocol to isolate and phenotypically characterize bone marrow microenvironmental cells from murine models of myelodysplastic syndrome and acute myeloid leukemia is described. Isolating and characterizing changes in the bone marrow niche populations can help determine their role in disease initiation and progression and may lead to the development of novel therapeutics targeting cancer-promoting alterations in the bone marrow stromal populations.

Author Spotlight: Analyzing Bone Marrow Microenvironment in Murine Hematological Malignancies 

Here a detailed protocol to isolate and characterize bone marrow microenvironmental populations from murine models of myelodysplastic syndromes and acute myeloid leukemia is presented. This technique identifies changes in the non-hematopoietic bone marrow niche, including the endothelial and mesenchymal stromal cells, with disease progression.

The bone marrow microenvironment consists of distinct cell populations, such as mesenchymal stromal cells, endothelial cells, osteolineage cells, and ...