This study was supported by the Adelson Medical Research Foundation (AMRF), the Melanoma Research Alliance (MRA), and U54CA224068. Figure 1, Figure 4, and Figure 5 were created with BioRender.com.

This study complied with all institutional guidelines regarding human tissue sampling. Informed consent was received from patients, and identifiable sample data was anonymized. Samples are collected in the operation room, placed in a solution of RPMI, saline, or PBS, and confirmed cancerous via the pathology department before use in research. All steps, except when indicated, should be carried out at 4 &#176;C or on ice. Work inside a biosafety hood when processing human tissue. See the Table of Materials for details on all materials, reagents, and instruments used in this protocol.
1. Obtaining the sample
<ol>
	<li>Obtain a solid tumor sample in a tube of media and place it on ice.
	<ol>
		<li>Instruct the operating room staff to transport the sample in a tube containing enough RPMI, saline, or PBS to completely submerge the tissue to avoid sample dehydration. 
		NOTE: Ensuring the sample is not desiccated is important, as this will decrease viability.</li>
	</ol>
	</li>
	<li>Cool down all centrifuges to 4 &#730;C and bring the thermal mixer to 37 &#730;C.</li>
	<li>Take out prepared human or mouse tumor dissociation enzymes (Table of Materials), thaw on the 37 &#730;C thermal mixer, and move to ice once thawed.</li>
	<li>Retrieve a 20 mL aliquot of RPMI.</li>
	<li>Record relevant sample information (e.g., Patient ID, de-identifier, sample type).</li>
	<li>Transfer the sample to a 60 mm tissue culture dish (placed on ice) by inverting the tube several times and pouring the contents into the dish to ensure all sample material is transferred. If the sample was not delivered in media upon arrival, add fresh RPMI media to the dish.
	<ol>
		<li>If the sample is fragmented, spin the tube containing the sample at 450 &#215; g, 4 &#730;C for 5 min, discard the supernatant, resuspend the sample in 420 &#181;L of RPMI, and move to section 2.</li>
	</ol>
	</li>
</ol>
2. Mechanical and enzymatic digestion
<ol>
	<li>Pipette 420 &#181;L of media from the 60 mm sample dish to a 1.5 mL microcentrifuge tube. Attempt to include any sample fragments suspended in the media.</li>
	<li>Transfer the sample tissue from the 60 mm dish to the 1.5 mL tube containing media using tweezers. Cut up the sample with clean scissors inside the tube so the pieces are maximum 2-3 mm in diameter.</li>
	<li>Add digestion enzymes, prepared according to manufacturer specifications (Table of Materials), to the sample in the following volumes: 42 &#181;L of Enzyme H (nonspecific proteases) for human samples, Enzyme D (nonspecific proteases) for murine samples, 21 &#181;L of enzyme R (nonspecific proteases), and 5 &#181;L of enzyme A (nuclease).</li>
	<li>Add another 420 &#181;L of media from the dish or up to the 1 mL mark on the tube. Do not exceed 420 &#181;L of media.</li>
	<li>Vortex the tube for 5 s and place it in a thermal mixer positioned vertically on its side (Figure 1B). Incubate at 37 &#730;C, 450 rpm, for 15 min. 
	NOTE: During incubation, take out all 10x Genomics materials needed for GEM generation, as these require 30 min to equilibrate to room temperature</li>
</ol>
3. Sample filtering
<ol>
	<li>Place a 50 &#181;m cell filter on top of a new 15 mL conical tube and prime the filter by passing 1 mL of fresh RPMI media through it into the tube.</li>
	<li>After digestion, transfer the sample to the 15 mL tube using a 1,000 &#181;L low-retention wide pipette tip. Wash the 1.5 mL tube with 1 mL of fresh media and transfer the media to the filter to ensure all sample material is passed through the filter.</li>
	<li>Obtain a 1 mL plastic syringe and take out just the plunger (discard the remainder of the syringe). Using the back of the syringe plunger, grind and push the sample on the filter in a twisting motion.</li>
	<li>Wash the filter with 5 mL of media and any remaining media from the dish that contained the sample (section 1) to collect any cells that might have detached from the tissue.</li>
	<li>Place a 30 &#181;m cell filter on top of a new 15 mL conical tube, prime the filter with 1 mL of media, and transfer the contents of the tube containing the dissociated sample through the filter. 
	NOTE: This step will remove large debris that might clog or cause a wetting failure when running the sample using the 10x Genomics microfluidic chips.</li>
</ol>
4. Red blood cell lysis
<ol>
	<li>Centrifuge the sample at 450 &#215; g, 4 &#730;C for 5 min. Aspirate the media, being careful not to disturb the pellet.</li>
	<li>Resuspend in ACK Lysis buffer to remove red blood cells (RBCs) and record the volume used. The volume will depend on pellet size and the extent of RBCs in the pellet. 
	NOTE: Resuspended ACK-sample solution should be colorless when enough ACK Lysis buffer has been added, as this will clear the red blood cells from the sample.</li>
	<li>Centrifuge the sample tube at 450 &#215; g, 4 &#730;C for 5 min, and aspirate the supernatant.</li>
	<li>Repeat steps 4.2-4.3 as needed until the sample pellet has no visible red color after centrifugation (Figure 2). Record any extra volumes of ACK Lysis buffer used in this step.</li>
	<li>Resuspend the sample pellet in fresh media in preparation for counting.</li>
</ol>
5. Cell counting
<ol>
	<li>Place 10 &#181;L of trypan blue in a 1.5 mL tube. Gently mix the dissociated cells, take 10 &#181;L of sample, and mix (1:1) with the trypan blue. Load 10 &#181;L on each side of a hemocytometer and count the cells.</li>
	<li>Record cell concentration, total live cell count, viability percentage, and final media volume after count.</li>
	<li>Ensure cell count is between 700 and 1,200 cells/&#181;L (7 &#215; 105- 1.2 &#215; 106/mL) for an optimal 10x Genomics run.
	<ol>
		<li>If the sample is too concentrated, dilute it with media and recount it.</li>
		<li>If the sample is too diluted, centrifuge the sample at 450 &#215; g, 4 &#730;C for 5 min, and resuspend in a lesser volume of media.</li>
		<li>If cell viability is below 80%, perform a live-dead procedure to remove dead cells.</li>
		<li>If there are under one million cells, see section 6.</li>
		<li>If there are over one million cells and viability&#160;&#62; 10%, see section 6.</li>
		<li>If there are over one million cells and viability &#60; 10%, see section 7.</li>
		<li>If cell viability is above 80%, keep the processed cells on ice and proceed immediately to GEM generation according to the manufacturer&#39;s instructions.</li>
	</ol>
	</li>
</ol>
6. Dead cell removal - Kit 1
NOTE: Work inside a biosafety hood when using the dead cell removal reagents, as these can easily get contaminated.
<ol>
	<li>Centrifuge the sample at 450 &#215; g, 4 &#730;C for 5 min.</li>
	<li>Prepare isolation media in a 15 mL conical tube and keep it at room temperature: 9.8 mL of PBS, 10 &#181;L of CaCl2, and 200 &#181;L of heat-inactivated FCS (added in a Biosafety hood).</li>
	<li>After centrifugation, aspirate the media from the pellet and resuspend in 1 mL of the isolation media.</li>
	<li>Centrifuge the tube again at 450 &#215; g, 4 &#730;C for 5 min. Aspirate the supernatant and resuspend the pellet in 100 &#181;L of isolation media.</li>
	<li>Transfer 100 &#181;L of sample in isolation media into one well of a 0.2 mL PCR strip-tube.</li>
	<li>In a biosafety hood, add 10 &#181;L of Annexin V Cocktail and 10 &#181;L of Biotin Selection Cocktail to the well containing 100 &#181;L of the sample in isolation media. Pipette mix the solution and allow it to sit at room temperature for 4 min.</li>
	<li>After 4 min, vortex the dextran-coated magnetic particles for 30 s. Add 20 &#181;L of the beads and 60 &#181;L of isolation media to the sample (a total volume of 200 &#181;L). If viability is lower than 40%, scale the amount of beads up to 40 &#181;L while adjusting the volume of isolation media (up to 40 &#181;L), keeping a total volume of 200 &#181;L. Pipette mix and allow the solution to sit at room temperature for 3 min.</li>
	<li>Place the strip tube onto a 10x Genomics Magnetic Separator (on the high setting) for 5 min at room temperature.</li>
	<li>Transfer the liquid from the strip tube without disturbing the beads into a new 1.5 mL lo-bind microcentrifuge tube. Do not remove the strip-tube from the magnet.</li>
	<li>Centrifuge the 1.5 mL tube containing the sample at 450 &#215; g, 4 &#730;C for 5 min. Aspirate the supernatant and resuspend the pellet in an appropriate volume of RPMI media based on the pellet size and count cells, as described in section 5. Repeat the dead cell removal procedure if viability remains below 80% (Figure 3 and Figure 4). 
	NOTE: Do not resuspend cells in isolation media for downstream GEM generation, as the calcium chloride will interfere with the encapsulation process and the reverse transcription (RT) step.</li>
	<li>Keep processed cells on ice if cell viability is above 80% and proceed immediately to GEM generation according to the manufacturer&#39;s instructions.</li>
</ol>
7. Dead cell removal - Kit 2
NOTE: Work inside a biosafety hood when using the kit reagents.
<ol>
	<li>Place the microbeads and the 20x Binding Buffer stock solution inside a biosafety hood.</li>
	<li>Centrifuge the sample suspension at 450 &#215; g, 4 &#730;C for 5 min.</li>
	<li>Prepare 1x buffer solution under sterile conditions using 1 mL of the 20x Binding Buffer stock solution pipetted into 19 mL of DNAse-, RNAse-free distilled water.</li>
	<li>Once the centrifugation is completed, aspirate the supernatant, add the microbeads, gently mix the sample, and incubate at room temperature for 15 min.
	<ol>
		<li>When the cell count is 107 or less, add 100 &#181;L of the microbeads.</li>
		<li>For counts higher than 107 total cells, adjust the microbeads for a final concentration of 100 &#181;L per 107 live cells.</li>
	</ol>
	</li>
	<li>During incubation, obtain one MACS positive selection column, MACS multistand, and the corresponding magnetic separator. Ensure the separator is leveled on the multistand and no other metal or magnetic object is near the stand, as the magnetic force is powerful and could affect dead cell removal efficiency. Place the column snugly into a separator holder with wings facing out.</li>
	<li>After sample incubation, if the total resuspension volume is less than 500 &#181;L, add 1x Binding Buffer until the total volume is 500 &#181;L.</li>
	<li>Place a 15 mL conical tube below the column and prime the column with 3 mL of 1x Binding Buffer.</li>
	<li>Replace the conical tube with a new 15 mL conical for cell sample collection and add the sample to the column, allowing it to flow completely through the column.</li>
	<li>Wash the column 4 x 3 mL of 1x Binding Buffer, only adding one wash at a time after the previous wash entirely flowed through the column.</li>
	<li>After the fourth wash, centrifuge the conical tube containing the isolated cells for 5 min at 450 &#215; g, 4 &#730;C. Discard the column and the remaining 1x Binding Buffer.</li>
	<li>Resuspend the sample solution in the appropriate amount of RPMI media based on pellet size and count the isolated cells using the method described in section 5 (Figure 3 and Figure 5).
	<ol>
		<li>If viability remains below 80%, and the total number of cells is 106 or less, repeat section 6.</li>
		<li>If cell viability is above 80%, keep processed cells on ice and proceed immediately to GEM generation according to the manufacturer&#39;s instructions.</li>
	</ol>
	</li>
</ol>

Tumor Dissociation Protocol for Single-Cell RNA Sequencing Analysis

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H., Bang, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+technologies+and+bioinformatics+pipelines.">Single-cell RNA sequencing technologies and bioinformatics pipelines.</a> Exp Mol Med. 50 (8), 1-14 (2018).</li><li>Erfanian, N., 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=Deep+learning+applications+in+single-cell+genomics+and+transcriptomics+data+analysis.">Deep learning applications in single-cell genomics and transcriptomics data analysis.</a> Biomed Pharmacother. 165, 115077 (2023).</li><li>Jovic, 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=Single-cell+RNA+sequencing+technologies+and+applications:+A+brief+overview.">Single-cell RNA sequencing technologies and applications: A brief overview.</a> Clin Transl Med. 12 (3), e694 (2022).</li><li>Burja, B., 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=An+optimized+tissue+dissociation+protocol+for+single-cell+RNA+sequencing+analysis+of+fresh+and+cultured+human+skin+biopsies.">An optimized tissue dissociation protocol for single-cell RNA sequencing analysis of fresh and cultured human skin biopsies.</a> Front Cell Dev Biol. 10, 872688 (2022).</li><li>Denisenko, E., 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=Systematic+assessment+of+tissue+dissociation+and+storage+biases+in+single-cell+and+single-nucleus+RNA-seq+workflows.">Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.</a> Genome Biol. 21 (1), 130 (2020).</li><li>Sade-Feldman, M., 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=Defining+T+cell+states+associated+with+response+to+checkpoint+immunotherapy+in+melanoma.">Defining T cell states associated with response to checkpoint immunotherapy in melanoma.</a> Cell. 176 (1-2), 404 (2019).</li><li>Pelka, K., 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=Spatially+organized+multicellular+immune+hubs+in+human+colorectal+cancer.">Spatially organized multicellular immune hubs in human colorectal cancer.</a> Cell. 184 (18), 4734-4752 (2021).</li><li>Bill, 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=CXCL9:SPP1+macrophage+polarity+identifies+a+network+of+cellular+programs+that+control+human+cancers.">CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers.</a> Science. 381 (6657), 515-524 (2023).</li><li>Al'Khafaji, A. M., 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=High-throughput+RNA+isoform+sequencing+using+programmed+cDNA+concatenation.">High-throughput RNA isoform sequencing using programmed cDNA concatenation.</a> Nat Biotechnol. 42 (4), 582-586 (2024).</li><li>Xing, Y., 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=The+effect+of+cell+isolation+methods+on+the+human+transcriptome+profiling+and+microbial+transcripts+of+peripheral+blood.">The effect of cell isolation methods on the human transcriptome profiling and microbial transcripts of peripheral blood.</a> Mol Biol Rep. 48 (4), 3059-3068 (2021).</li><li>Ma, F., Hernandez, G. E., Romay, M., Iruela-Arispe, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+to+study+vascular+diversity+and+function.">Single-cell RNA sequencing to study vascular diversity and function.</a> Curr Opin Hematol. 28 (3), 221-229 (2021).</li><li>Fan, X. -. 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=Impact+of+cold+ischemic+time+and+freeze-thaw+cycles+on+RNA,+DNA+and+protein+quality+in+colorectal+cancer+tissues+biobanking.">Impact of cold ischemic time and freeze-thaw cycles on RNA, DNA and protein quality in colorectal cancer tissues biobanking.</a> J Cancer. 10 (20), 4978-4988 (2019).</li><li>LoCoco, P. M., 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=Reliable+approaches+to+extract+high-integrity+RNA+from+skin+and+other+pertinent+tissues+used+in+pain+research.">Reliable approaches to extract high-integrity RNA from skin and other pertinent tissues used in pain research.</a> Pain Rep. 5 (2), e818 (2020).</li><li>Montanari, M., 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=Automated-mechanical+procedure+compared+to+gentle+enzymatic+tissue+dissociation+in+cell+function+studies.">Automated-mechanical procedure compared to gentle enzymatic tissue dissociation in cell function studies.</a> Biomolecules. 12 (5), 701 (2022).</li><li>Yosefov-Levi, O., Tornovsky, S., Parnas, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pancreatic+tissue+dissection+to+isolate+viable+single+cells.">Pancreatic tissue dissection to isolate viable single cells.</a> J Vis Exp. 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M.S-F. received funding from Bristol Myers-Squibb, Istari Oncology, and was a consultant for Galvazine Therapeutics.

This protocol describes human or murine tumor samples dissociation into a single-cell suspension for scRNA sequencing using the 10x Genomics microfluidic system. In processing tissues that often come from rare cancers or are part of ongoing clinical trials or long experiments for murine tumors, the sample must be optimally and carefully preserved between all steps. All steps should be carried out rapidly on ice or at 4 &#730;C to keep the native cellular RNA profiles and prevent degradation, as working at room temperatur...

Although many advancements in cancer research have led to the development and FDA approval of agents targeting inhibitory receptors expressed on immune and tumor cells, known as checkpoint blockade inhibitors, therapy resistance and identifying mechanisms that drive patient response remain challenging1. The complex challenge of characterizing tumor heterogeneity on its molecular diversity and the intricate interplay between tumor cells and the immune microenvironment necessitates new approaches to dissect this complex ecosystem at the single-cell resolution. Understanding the molecular intricacies within tumors and their microenvironments is pivotal for advancing therapeutic strategies and deciphering the complex biology underlying cancer progression2. Single-cell RNA sequencing (scRNA-seq) has become increasingly popular because it provides high-resolution analysis of individual cells within complex tumor samples3,4.
Tumor heterogeneity, encompassing genetic, epigenetic, and phenotypic diversities, poses a challenge in uncovering the intricacies of cancer biology5. The conventional methods of bulk RNA sequencing tend to obscure the unique expression profiles and phenotypes of heterogeneous cell populations present within tumors, as these methods average signals across entire cell populations4. In contrast, scRNA-seq allows the dissection of individual cell types, revealing diverse gene expression, repression patterns, and cellular states otherwise overlooked4,6. The premise of scRNA-seq lies in its capacity to decode individual cells&#39; genetic and molecular signatures, holding immense promise in discovering new cancer therapeutics7.
By deciphering the gene expression profiles of tumor cells and the surrounding stromal and immune cells, researchers can identify novel therapeutic targets and develop precision genetic medicine strategies tailored to individual patients6. Furthermore, dissecting the immune repertoire within the tumor microenvironment provides crucial insights into the interplay between tumor cells and immune effectors, paving the way for immunotherapeutic interventions3. Despite advances in using scRNAseq on clinical samples, several challenges during the processing steps can harm the cell&#39;s RNA integrity, viability, and quality of the data generated. Moreover, processing tissue with low cell viability can increase ambient RNA levels in the droplets generated during the encapsulation of individual cells, resulting in cross-contamination and incorrect annotation of cell types, while long dissociation protocols performed at 37 &#176;C can lead to the upregulation of a stress response gene signature in multiple cell types8,9,10.
The stepwise procedure (Figure 1A) outlined in this protocol commences with rapid mechanical and enzymatic dissociation of tumors, followed by filtration and the removal of dead cells, depending on the viability of each tumor sample. At present, this procedure has been verified in melanoma11, colorectal12, head and neck squamous cell carcinoma13, pancreatic and lung (unpublished data) tumor samples. These techniques ensure the generation of high-quality viable cell suspensions crucial for downstream analyses14. Notably, removing red blood cells, devoid of synthesized RNA, becomes imperative to purify the sample15. Subsequent evaluation of cell counts and the elimination of dead cells serve as a prerequisite for successful 10x Genomics Gel bead-in Emulsion (GEM) generation, barcoding, and increase the recovery of transcripts and sequenced cells without jeopardizing the exclusion of sensitive populations (e.g., neutrophils and epithelial cells)16. These steps are fundamental in unlocking the potential of single-cell resolution analysis within tumor samples9,10, uncovering novel gene expression profiles and immune signatures necessary for driving innovative therapeutic interventions, and identifying new tumor vulnerabilities.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1x PBS</td>
			<td>Corning</td>
			<td>21-040-CV</td>
			<td></td>
		</tr>
		<tr>
			<td>10 mL serological pipette</td>
			<td>Corning</td>
			<td>357551</td>
			<td></td>
		</tr>
		<tr>
			<td>1000 &#956;L low-retention pipette tips</td>
			<td>Rainin</td>
			<td>30389213</td>
			<td></td>
		</tr>
		<tr>
			<td>1000 &#956;L low-retention wide pipette tips</td>
			<td>Rainin</td>
			<td>30389218</td>
			<td></td>
		</tr>
		<tr>
			<td>1000 &#956;L pipette tips</td>
			<td>Rainin</td>
			<td>30389212</td>
			<td></td>
		</tr>
		<tr>
			<td>10x Magnetic Separator</td>
			<td>10x Genomics</td>
			<td>120250</td>
			<td></td>
		</tr>
		<tr>
			<td>15 mL conical tubes</td>
			<td>Corning</td>
			<td>430052</td>
			<td></td>
		</tr>
		<tr>
			<td>2 mL aspirating pipette</td>
			<td>Corning</td>
			<td>357558</td>
			<td></td>
		</tr>
		<tr>
			<td>20 &#956;L low-retention pipette tips</td>
			<td>Rainin</td>
			<td>30389226</td>
			<td></td>
		</tr>
		<tr>
			<td>20 &#956;L pipette tips</td>
			<td>Rainin</td>
			<td>30389225</td>
			<td></td>
		</tr>
		<tr>
			<td>200 &#956;L low-retention pipette tips</td>
			<td>Rainin</td>
			<td>30389240</td>
			<td></td>
		</tr>
		<tr>
			<td>200 &#956;L pipette tips</td>
			<td>Rainin</td>
			<td>30389239</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL Polypropylene Conical Tube</td>
			<td>Falcon</td>
			<td>352098</td>
			<td></td>
		</tr>
		<tr>
			<td>60 x 15 mm Tissue Culture Dish</td>
			<td>Falcon</td>
			<td>353004</td>
			<td></td>
		</tr>
		<tr>
			<td>ACK Lysing Buffer</td>
			<td>Gibco</td>
			<td>A10492-1</td>
			<td></td>
		</tr>
		<tr>
			<td>BD 1 mL syringe</td>
			<td>Becton, Dickinson and Company</td>
			<td>3090659</td>
			<td></td>
		</tr>
		<tr>
			<td>Bright-Line Hemocytometer</td>
			<td>Hausser Scientific</td>
			<td>551660</td>
			<td></td>
		</tr>
		<tr>
			<td>Calcium Chloride Solution</td>
			<td>Sigma Aldrich</td>
			<td>2115-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Ranger&#160;</td>
			<td>10x Genomics</td>
			<td>N/A</td>
			<td>https://www.10xgenomics.com/support/software/cell-ranger/latest</td>
		</tr>
		<tr>
			<td>Cell counting slides for TC20 cell counter</td>
			<td>Bio-Rad</td>
			<td>1450015</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTrics 30&#956;m sterile disposable filters</td>
			<td>Sysmex</td>
			<td>04-004-2326</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTrics 50&#956;m sterile disposable filters</td>
			<td>Sysmex</td>
			<td>04-004-2327</td>
			<td></td>
		</tr>
		<tr>
			<td>Dead Cell removal Kit&#160;</td>
			<td>Miltenyi Biotec</td>
			<td>130-090-101</td>
			<td>Store at -20 &#176;C; kit 2</td>
		</tr>
		<tr>
			<td>Dissecting Forceps, Fine Tip</td>
			<td>VWR</td>
			<td>82027-386</td>
			<td></td>
		</tr>
		<tr>
			<td>DNA LoBind Microcentrifuge tubes, 1.5 mL</td>
			<td>Eppendorf</td>
			<td>22431021</td>
			<td></td>
		</tr>
		<tr>
			<td>EasySepTM Dead Cell Removal 
			(Annexin V) Kit</td>
			<td>STEMCELL Technologies</td>
			<td>17899</td>
			<td>Store at 4 &#176;C; Kit 1</td>
		</tr>
		<tr>
			<td>Eppendorf centrifuge 5425R</td>
			<td>Eppendorf</td>
			<td>5406000640</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf centrifuge 5910R</td>
			<td>Eppendorf</td>
			<td>2231000657</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf Easypet 3 Electronic Pipette controller</td>
			<td>Eppendorf</td>
			<td>EP4430000018</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf Thermomixer F1.5 Model 5384</td>
			<td>Eppendorf</td>
			<td>EP5384000012</td>
			<td></td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>Gibco</td>
			<td>26140-079</td>
			<td>Store at 4 &#176;C, use in a Biological hood</td>
		</tr>
		<tr>
			<td>German Stainless Scissors</td>
			<td>Fine Science Tools</td>
			<td>14568-12</td>
			<td></td>
		</tr>
		<tr>
			<td>Leica Dmi1 Microscope</td>
			<td>Leica Microsystems</td>
			<td>454793</td>
			<td></td>
		</tr>
		<tr>
			<td>LS Column</td>
			<td>Miltenyi Biotec</td>
			<td>130-042-401</td>
			<td></td>
		</tr>
		<tr>
			<td>MACS Multistand</td>
			<td>Miltenyi Biotec</td>
			<td>130-042-303</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet-Lite LTS Pipette L-1000XLS+</td>
			<td>Rainin</td>
			<td>17014382</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet-Lite LTS Pipette L-200XLS+</td>
			<td>Rainin</td>
			<td>17014391</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet-Lite LTS Pipette L-20XLS+</td>
			<td>Rainin</td>
			<td>17014392</td>
			<td></td>
		</tr>
		<tr>
			<td>Quadro MACS Magnet</td>
			<td>Miltenyi Biotec</td>
			<td>130-091-051</td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI Medium 1640 (1x)</td>
			<td>Gibco</td>
			<td>21870-076</td>
			<td>Store at 4 &#176;C</td>
		</tr>
		<tr>
			<td>TempAssure 0.2 mL PCR 8-Tube strips</td>
			<td>USA Scientific</td>
			<td>1402-4700</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan blue stain 0.4%</td>
			<td>Thermo Fisher Scientific</td>
			<td>T10282</td>
			<td></td>
		</tr>
		<tr>
			<td>Tumor Dissociation Kit, Human</td>
			<td>Miltenyi Biotec</td>
			<td>130-095-929</td>
			<td>Store at -20 &#176;C, prepare enzymes according to kit instructions: 
			Reconstitute lyophilized Enzyme H vial in 3 mL of RPMI 1640 
			Reconstitute lyophilized Enzyme R vial in 2.7 mL of RPMI 1640 
			Reconstitute lyophilized Enzyme A vial in 1 mL of Buffer A supplied with the kit.</td>
		</tr>
		<tr>
			<td>Tumor Dissociation Kit, Mouse</td>
			<td>Miltenyi Biotec</td>
			<td>130-096-730</td>
			<td>Store at -20 &#176;C, prepare enzymes according to kit instructions: 
			Reconstitute lyophilized Enzyme D vial in 3 mL of RPMI 1640 
			Reconstitute lyophilized Enzyme R vial in 2.7 mL of RPMI 1640 
			Reconstitute lyophilized Enzyme A vial in 1 mL of Buffer A supplied with the kit.</td>
		</tr>
		<tr>
			<td>UltraPure Distilled Water</td>
			<td>Invitrogen</td>
			<td>10977-015</td>
			<td>Store at 4 &#176;C</td>
		</tr>
	</tbody>
</table>

dissociation of human and mouse tumor tissue samples for single-cell rna sequencing

Human tumor samples hold a plethora of information about their microenvironment and immune repertoire. Effective dissociation of human tissue samples into viable cell suspensions is a required input for the single-cell RNA sequencing (scRNAseq) pipeline. Unlike bulk RNA sequencing approaches, scRNAseq enables us to infer the transcriptional heterogeneity in tumor specimens at the single-cell level. Incorporating this approach in recent years has led to many discoveries, such as identifying immune and tumor cellular states and programs associated with clinical responses to immunotherapies and other types of treatments. Moreover, single-cell technologies applied to dissociated tissues can be used to identify accessible chromatin regions T and B cell receptor repertoire, and the expression of proteins, using DNA barcoded antibodies (CITEseq).
The viability and quality of the dissociated sample are critical variables when using these technologies, as these can dramatically affect the cross-contamination of single cells with ambient RNA, the quality of the data, and interpretation. Moreover, long dissociation protocols can lead to the elimination of sensitive cell populations and the upregulation of a stress response gene signature. To overcome these limitations, we devised a rapid universal dissociation protocol, which has been validated on multiple types of human and murine tumors. The process begins with mechanical and enzymatic dissociation, followed by filtration, red blood lysis, and live dead enrichment, suitable for samples with a low input of cells (e.g., needle core biopsies). This protocol ensures a clean and viable single-cell suspension paramount to the successful generation of Gel Bead-In Emulsions (GEMs), barcoding, and sequencing.

A melanoma biopsy suspended in RPMI was obtained and immediately placed on ice. The sample was transferred into a 1.5 mL microcentrifuge tube containing 420 &#181;L of RPMI and digestion enzymes, minced into small pieces using scissors, and subjected to subsequent enzymatic digestion for 15 min in a 37 &#730;C vertically positioned thermal mixer (Figure 1). Following digestion, the sample was filtered through a 50 &#181;m sterile disposable filter, and the filter was washed with fresh RPMI t...

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

This protocol outlines the procedure for rapidly dissociating human and mouse tumor samples for single-cell RNA sequencing.

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Human tumor samples hold a plethora of information about their microenvironment and immune repertoire. Effective dissociation of human tissue samples into ...

dissociation-human-mouse-tumor-tissue-samples-for-single-cell-rna

Universidad de Cartagena UDEC

Research

JoVE Journal

Cancer Research

2.4K Views.  Massachusetts General Hospital. This protocol outlines the procedure for rapidly dissociating human and mouse tumor samples for single-cell RNA sequencing.

Video: Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

Tumor Engraftment in a Xenograft Mouse Model of Human Mantle Cell Lymphoma

B lymphocytes are key players in immune cell circulation and they mainly home to and reside in lymphoid organs. While normal B cells only proliferate when stimulated by T lymphocytes, oncogenic B cells survive and expand autonomously in undefined organ niches. Mantle cell lymphoma (MCL) is one such B cell disorder, where the median survival rate of patients is 4 - 5 years. This calls for the need of effective mechanisms by which the homing and engraftment of these cells are blocked in order to increase the survival and longevity of patients. Therefore, the effort to develop a xenograft mouse model to study the efficacy of MCL therapeutics by blocking the homing mechanism in vivo is of utmost importance. Development of animal recipients for human cell xenotransplantation to test early stage drugs have long been pursued, as relevant preclinical mouse models are crucial to screen new therapeutic agents. This animal model is developed to avoid human graft rejection and to establish a model for human diseases, and it may be an extremely useful tool to study disease progression of different lymphoma types and to perform preclinical testing of candidate drugs for hematologic malignancies, like MCL. We established a xenograft mouse model that will serve as an excellent resource to study and develop novel therapeutic approaches for MCL.

Mantle cell lymphoma (MCL) is a difficult to treat B cell disorder and it is equally difficult to establish a xenograft mouse model of primary MCL to study and develop therapeutics. Here, we describe the successful establishment of MCL xenografts in mice to help understand its underlying biology.

B lymphocytes are key players in immune cell circulation and they mainly home to and reside in lymphoid organs. While normal B cells only proliferate when ...

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Rare target cells can be isolated from a high background of non-target cells using antibodies specific for surface proteins of target cells. A recently developed method uses a medical wire functionalized with anti-epithelial cell adhesion molecule (EpCAM) antibodies for in vivo isolation of circulating tumor cells (CTCs)1. A patient-matched cohort in non-metastatic prostate cancer showed that the in vivo isolation technique resulted in a higher percentage of patients positive for CTCs as well as higher CTC counts as compared to the current gold standard in CTC enumeration. As cells cannot be recovered from current medical devices, a new functionalized wire (referred to as Device) was manufactured allowing capture and subsequent detachment of cells by enzymatic treatment. Cells are allowed to attach to the Device, visualized on a microscope and detached using enzymatic treatment. Recovered cells are cytocentrifuged onto membrane-coated slides and harvested individually by means of laser microdissection or micromanipulation. Single-cell samples are then subjected to single-cell whole genome amplification allowing multiple downstream analysis including screening and target-specific approaches. The procedure of isolation and recovery yields high quality DNA from single cells and does not impair subsequent whole genome amplification (WGA). A single cell&#39;s amplified DNA can be forwarded to screening and/or targeted analysis such as array comparative genome hybridization (array-CGH) or sequencing. The device allows ex vivo isolation from artificial rare cell samples (i.e. 500 target cells spiked into 5 mL of peripheral blood). Whereas detachment rates of cells are acceptable (50 - 90%), the recovery rate of detached cells onto slides spans a wide range dependent on the cell line used (&#60;10 - &#62;50%) and needs some further attention. This device is not cleared for the use in patients.

This protocol is to recover and prepare rare target cells from a mixture with non-target background cells for molecular genetic characterization at the single-cell level. DNA quality is equal to non-treated single cells and allows for single-cell application (both screening based and targeted analysis).

Rare target cells can be isolated from a high background of non-target cells using antibodies specific for surface proteins of target cells. A recently ...

Perfusion and Inflation of the Mouse Lung for Tumor Histology

The ability to evaluate lung histology is critical for the fields of lung cancer research and cancer metastasis. It is equally important to perform necropsies rapidly and efficiently from studies without sacrificing the quality of the tissues procured. The goal of this protocol is to present a method to rapidly perfuse, inflate, and fix mouse lungs for downstream histological analysis. This method does not standardize lung inflation; thus, it does not require any special procedures or equipment and instead simply instills fixative directly through the trachea following perfusion through the heart. This allows for sufficient estimation of tumor size, histology, and scoring. This also allows for the collection of frozen tissue prior to lung tissue fixation. This method is limited in that it does not allow for later morphometric quantification of the lung; however, it is more than sufficient for lung tumor analysis from genetically engineered mouse models (GEMMs), syngeneic models, as well as xenograft tumor and metastasis studies.

The purpose of this method is to present a simple and efficient method for the perfusion, inflation, and fixation of mouse lungs for the examination of lung tumor pathology and evaluation of metastases to the lung.

The ability to evaluate lung histology is critical for the fields of lung cancer research and cancer metastasis. It is equally important to perform ...

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Monoclonal antibodies are high affinity multifunctional drugs that work by variable independent mechanisms to eliminate cancer cells. Over the last few decades, the field of antibody-drug conjugates, bispecific antibodies, chimeric antigen receptors (CAR) and cancer immunotherapy has emerged as the most promising area of basic and therapeutic investigations. With numerous successful human trials targeting immune checkpoint receptors and CAR-T cells in leukemia and melanoma at a breakthrough pace, it is highly exciting times for oncologic therapeutics derived from variations of antibody engineering. Regrettably, a significantly large numbers of antibody and CAR based therapeutics have also proven disappointing in human trials of solid cancers because of the limited availability of immune effector cells in the tumor bed. Importantly, nonspecific distribution of therapeutic antibodies in tissues other than tumors also contribute to the lack of clinical efficacy, associated toxicity and clinical failure. As faithful translation of preclinical studies into human clinical trails are highly relied on mice tumor xenograft efficacy and safety studies, here we highlight a method to test the tumor and general tissue distribution of therapeutic antibodies. This is achieved by labeling the protein-A purified antibody with near Infrared fluorescent dye followed by live imaging of tumor bearing mice.

Here we present a protocol to study the in vivo localization of antibodies in mice tumor xenograft models.

Monoclonal antibodies are high affinity multifunctional drugs that work by variable independent mechanisms to eliminate cancer cells. Over the last few ...

Measuring Real-time Drug Response in Organotypic Tumor Tissue Slices

Tumor tissues are composed of cancerous cells, infiltrated immune cells, endothelial cells, fibroblasts, and extracellular matrix. This complex milieu constitutes the tumor microenvironment (TME) and can modulate response to therapy in vivo or drug response ex vivo. Conventional cancer drug discovery screens are carried out on cells cultured in a monolayer, a system critically lacking the influence of TME. Thus, experimental systems that integrate sensitive and high-throughput assays with physiological TME will strengthen the preclinical drug discovery process. Here, we introduce ex vivo tumor tissue slice culture as a platform for medium-high-throughput drug screening. Organotypic tissue slice culture constitutes precisely-cut, thin tumor sections that are maintained with the support of a porous membrane in a liquid-air interface. In this protocol, we describe the preparation and maintenance of tissue slices prepared from mouse tumors and tumors from patient-derived xenograft (PDX) models. To assess changes in tissue viability in response to drug treatment, we leveraged a biocompatible luminescence-based viability assay that enables real-time, rapid, and sensitive measurement of viable cells in the tissue. Using this platform, we evaluated dose-dependent responses of tissue slices to the multi-kinase inhibitor, staurosporine, and cytotoxic agent, doxorubicin. Further, we demonstrate the application of tissue slices for ex vivo pharmacology by screening 17 clinical and preclinical drugs on tissue slices prepared from a single PDX tumor. Our physiologically-relevant, highly-sensitive, and robust ex vivo screening platform will greatly strengthen preclinical oncology drug discovery and treatment decision making.&#160;

We introduce a protocol for measuring real-time drug response in organotypic tumor tissue slices. The experimental strategy outlined here provides a platform to carry out medium-high throughput drug screens on tissue slices derived from clinical or mouse tumors in ex vivo conditions.

Tumor tissues are composed of cancerous cells, infiltrated immune cells, endothelial cells, fibroblasts, and extracellular matrix. This complex milieu ...

Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq

Adult diffuse gliomas exhibit inter- and intra-tumor heterogeneity. Until recently, the majority of large-scale molecular profiling efforts have focused on bulk approaches that led to the molecular classification of brain tumors. Over the last five years, single cell sequencing approaches have highlighted several important features of gliomas. The majority of these studies have utilized fresh brain tumor specimens to isolate single cells using flow cytometry or antibody-based separation methods. Moving forward, the use of fresh-frozen tissue samples from biobanks will provide greater flexibility to single cell applications. Furthermore, as the single-cell field advances, the next challenge will be to generate multi-omics data from either a single cell or the same sample preparation to better unravel tumor complexity. Therefore, simple and flexible protocols that allow data generation for various methods such as single-nucleus RNA sequencing (snRNA-seq) and single nucleus Assay for Transposase-Accessible Chromatin with high-throughput sequencing (snATAC-seq) will be important for the field.
Recent advances in the single cell field coupled with accessible microfluidic instruments such as the 10x genomics platform have facilitated single cell applications in research laboratories. To study brain tumor heterogeneity, we developed an enhanced protocol for the isolation of single nuclei from fresh frozen gliomas. This protocol merges existing single cell protocols and combines a homogenization step followed by filtration and buffer mediated gradient centrifugation. The resulting samples are pure single nuclei suspensions that can be used to generate single nucleus gene expression and chromatin accessibility data from the same nuclei preparation.

Intra-tumoral heterogeneity is an inherent feature of tumors, including gliomas. We developed a simple and efficient protocol that utilizes a combination of buffers and gradient centrifugation to isolate single nuclei from fresh frozen glioma tissues for single nucleus RNA and ATAC sequencing studies.

Adult diffuse gliomas exhibit inter- and intra-tumor heterogeneity. Until recently, the majority of large-scale molecular profiling efforts have focused ...

Isolation and Functional Assessment of Human Breast Cancer Stem Cells from Cell and Tissue Samples

Breast cancer stem cells (BCSCs) are cancer cells with inherited or acquired stem cell-like characteristics. Despite their low frequency, they are major contributors to breast cancer initiation, relapse, metastasis and therapy resistance. It is imperative to understand the biology of breast cancer stem cells in order to identify novel therapeutic targets to treat breast cancer. Breast cancer stem cells are isolated and characterized based on expression of unique cell surface markers such as CD44, CD24 and enzymatic activity of aldehyde dehydrogenase (ALDH). These ALDHhighCD44+CD24- cells constitute the BCSC population and can be isolated by fluorescence-activated cell sorting (FACS) for downstream functional studies. Depending on the scientific question, different in vitro and in vivo methods can be used to assess the functional characteristics of BCSCs. Here, we provide a detailed experimental protocol for isolation of human BCSCs from both heterogenous populations of breast cancer cells as well as primary tumor tissue obtained from breast cancer patients. In addition, we highlight downstream in vitro and in vivo functional assays including colony forming assays, mammosphere assays, 3D culture models and tumor xenograft assays that can be used to assess BCSC function.

This experimental protocol describes the isolation of BCSCs from breast cancer cell and tissue samples as well as the in vitro and in vivo assays that can be used to assess BCSC phenotype and function.

Breast cancer stem cells (BCSCs) are cancer cells with inherited or acquired stem cell-like characteristics. Despite their low frequency, they are major ...

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&#38;E) annotations are used to target small tumor areas using a blade that is 250 &#181;m2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 &#181;m in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Digital annotation with automated tissue dissection provides an innovative approach to enriching tumor in low tumor content cases and is adaptable to both paraffin and frozen tissue types. The described workflow improves accuracy, reproducibility and throughput and could be applied to both research and clinical settings.

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly ...

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education.
Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2.
Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.

A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration ...

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 ...