We gratefully acknowledge the UND Genomics Core facility for outstanding technical assistance.
This work was funded by the National Institutes of Health [P20GM104360 to M.T., P20 GM104360 to A.D.] and start-up funds provided by the University of North Dakota School of Medicine and Health Sciences, Department of Biomedical Sciences [to M.T.].

1. Preparations before beginning the experiment
<ol>
	<li>Prepare lysis buffer stock. 
	NOTE: The optimal nuclear isolation buffer can be different for each cell type. We recommend testing both the hypotonic buffer used in the original paper8 and a CSK buffer12,13 for each cell type using trypan blue staining.
	<ol>
		<li>To prepare hypotonic buffer (Greenleaf buffer), mix 10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, and 0.1% NP-40.</li>
		<li>To prepare CSK buffer, mix 10 mM PIPES pH 6.8, 100 mM NaCl, 300 mM sucrose, 3 mM MgCl2, and 0.1% Triton X-100.</li>
	</ol>
	</li>
	<li>Ensure that the cell culture conditions are optimal; examine the cells under a light microscope to make sure that there are no elevated levels of apoptosis.</li>
	<li>Turn on the centrifuge to let it reach 4 &#176;C.</li>
</ol>
2. Cell harvest
<ol>
	<li>Aspirate media from a culture of cells at &#60;80% confluency. Cells are typically grown in a 35 mm or 60 mm dish, based on the number of cells needed, treatments, etc.</li>
	<li>Wash the cells with 1 mL or 3 mL of PBS.</li>
	<li>Add 0.5 mL or 1 mL of Trypsin and incubate for 2-3 min (depending on the cell types). Carefully use a 1 mL pipette to mix well.</li>
	<li>Add 1 mL or 2 mL of media to the plate and mix well. Transfer to a 15 mL conical tube. Centrifuge the cells in a 15 mL tube for 3 min at 300 x g. 
	NOTE: A swinging bucket rotor is recommended to minimize cell loss.</li>
	<li>Aspirate the media and resuspend the cells&#160;in 1 mL or 2 mL of medium. 
	NOTE: It is critical to prepare a homogenous single-cell solution. For tissues, a Dounce homogenizer can be used to homogenize tissues and minimize large cell clumps or aggregates. Some tissues require filtration (e.g., cell strainers) and/or FACS cell sorting to isolate viable cells and obtain a single cell solution.</li>
	<li>Count the cells using a cell counter. 
	NOTE: In this study, an automated cell counter (Table of Materials) was used.</li>
	<li>Centrifuge the required cell volume (e.g., a volume containing 1 x 106 cells based on cell count) at 300 x g for 3 min at room temperature (RT).</li>
	<li>Resuspend the cell pellet containing 1 x 106 cells in 1 mL of cold PBS. 
	NOTE: If cell numbers are less than 1 x 106&#160;cells, decrease the cold PBS volume to prepare the cell solution at the same concentration (1 x 106&#160;cells/mL).</li>
</ol>
3. Cell lysis
<ol>
	<li>Transfer 25 &#181;L (= 2.5 x 104 cells) to a new 1.7 mL microcentrifuge tube. Centrifuge&#160;for 5 min at 500 x g at 4 &#176;C and carefully discard the supernatant 
	NOTE: A swinging bucket rotor is recommended to minimize cell loss. Leave about 3 &#181;L of supernatant to ensure that the cells are not being discarded.</li>
	<li>Add 25 &#181;L of lysis buffer and resuspend the cells by gentle pipetting up and down. Incubate on ice for 5 min</li>
	<li>Centrifuge for 5 min at 500 x g at 4 &#176;C and carefully&#160;discard the supernatant. 
	NOTE: A swinging bucket rotor is recommended to minimize cell loss. Leave about 3 &#181;L of supernatant to ensure that the cells are not being discarded.</li>
</ol>
4. Tn5 tagmentation
<ol>
	<li>Immediately resuspend the pellet in 25 &#181;L of Tn5 reaction mixture. The Tn5 reaction mixture is as follows: 12.5 &#181;L of 2x Tagmentation DNA buffer (TD buffer), 1.25-5 &#181;L of Tn5 transposase, and 11.25-7.5 &#181;L of nuclease-free water. 
	NOTE: The standard concentration of Tn5 is 2.5 &#181;L for 2.5 x 104 cells.</li>
	<li>Incubate for 30 min at 37 &#176;C. 
	&#8203;NOTE: Mix the solution every 10 min.</li>
</ol>
5. DNA purification
NOTE: Purification is required before amplification. DNA purification is done using the MinElute PCR purification kit (Table of Materials).
<ol>
	<li>Add 5 &#181;L of 3 M sodium acetate (pH 5.2).</li>
	<li>Immediately add 125 &#181;L of Buffer PB and mix well.</li>
	<li>Follow the PCR purification kit protocol starting at step 2.
	<ol>
		<li>Place the spin column in a 2 mL collection tube.</li>
		<li>Apply the sample to the spin column and centrifuge for 1 min at 17,900 x g at RT.</li>
		<li>Discard the flow-through and place the spin column back in the same collection tube.</li>
		<li>Add 750 &#181;L of Buffer PE to the spin column and centrifuge for 1 min at 17,900 x g at RT.</li>
		<li>Discard the flow-through and place the spin column back in the same collection tube.</li>
		<li>Centrifuge the spin column for 5 min to dry the membrane completely.</li>
		<li>Discard the flow-through and place the spin column in a new 1.7 mL microcentrifuge tube.</li>
		<li>Elute the DNA fragments with 10 &#181;L of Buffer EB.</li>
		<li>Let the column stand for 1 min at RT.</li>
		<li>Centrifuge for 1 min at 17,900 x g at RT to elute the DNA.</li>
	</ol>
	</li>
</ol>
6. PCR amplification
NOTE: PCR amplification of transposed (tagmented) DNAs is necessary for sequencing. Nextera kit adapters (Table of Materials) were used in this example. The primers used in this study are listed in Table 1.
<ol>
	<li>Set up the following PCR reaction in 200 &#181;L PCR tubes (50 &#181;L for each sample). The PCR reaction mixture is as follows: 10 &#181;L of eluted DNA (step 5.), 2.5 &#181;L of 25 mM Adapter 1, 2.5 &#181;L of 25 mM Adapter 2, 25 &#181;L of 2x PCR master mix, and 10 &#181;L of nuclease-free water</li>
	<li>Perform PCR amplification program part 1 (Table 2). 
	NOTE: For initial amplification, the same conditions are used for all samples using a standard thermal cycler.</li>
	<li>Real-time qPCR (total of 14.5 &#181;L per sample)
	<ol>
		<li>Using 5 &#181;L of the product from the PCR amplification part 1 (step 6.2.), set up the following reaction, this time including SYBR gold and detection in a real-time PCR machine.</li>
		<li>Prepare the PCR reaction mixture as follows: 5 &#181;L of PCR product from step 6.2., 0.75 &#181;L of SYBR gold (1000x diluted), 5 &#181;L of 2x PCR master mix, and 3.75 &#181;L of nuclease-free water. 
		&#8203;NOTE: The precise cycle numbers for library amplification before reaching saturation for each sample are determined by qPCR (Table 3) to reduce the GC and size bias in PCR10. SYBR Gold was diluted with nuclease-free water. To make the diluted solution, 1 &#181;L of SYBR Gold (stock concentration 10,000x) was added to&#160;999 &#181;L of nuclease-free water. The run time of RT-qPCR mentioned in Table 3 is about 60 min.</li>
	</ol>
	</li>
	<li>Determine the required number of additional PCR cycles using the run parameters from step 6.3.
	<ol>
		<li>Number of cycles = 1/4 maximum (saturated) fluorescence intensity (typically 3 or 4 PCR cycles)</li>
	</ol>
	</li>
	<li>PCR amplification program part 2 (volume = 45 &#181;L; Table 4): Once you determine the cycle number, set up PCRs with additional cycles calculated at step 6.4.1. For instance, if the cycle number calculated is 3, set up 3 cycles in the program below. The total cycles would be 8 (5 in step 6.2., plus 3 additional cycles in step 6.5.)</li>
</ol>
7. Beads purification
NOTE: Here, AMPure XP (Table of Materials) beads were used.
<ol>
	<li>To the PCR products amplified in the previous step, add 150 &#181;L of beads at RT. 
	NOTE: Homemade AMPure beads14 can be used in this process.</li>
	<li>Incubate for 15 min at RT.</li>
	<li>Place the tubes on a magnetic stand for 5 min.</li>
	<li>Carefully remove the supernatant.</li>
	<li>Wash the beads with 200 &#181;L of 80% ethanol. 
	NOTE: 80% ethanol should be prepared fresh.</li>
	<li>Repeat step 7.4.</li>
	<li>Remove the ethanol completely.</li>
	<li>Air dry the samples for 10 min at RT.</li>
	<li>Resuspend with 50 &#181;L of elution buffer (10 mM Tris-HCL pH 8.0).</li>
	<li>Repeat the beads purification (steps 7.1.-7.9.) to further minimize primer contamination.</li>
</ol>
8. DNA concentration and quality check
<ol>
	<li>Measure the DNA concentration by using a nucleic acid quantification kit (Table of Materials).</li>
	<li>Check the amplified DNA fragments by gel electrophoresis using SYBR Gold (0.5x TBE, 1.5% agarose gel) or an automated electrophoresis system (e.g., TapeStation, bioanalyzer).</li>
</ol>
9. Sequencing
<ol>
	<li>Perform high-throughput sequencing using the amplified DNA samples12,13. 
	&#8203;NOTE: Amplified DNA samples are ready for high-throughput sequencing. To retain nucleosomal DNA fragment information, paired-end sequencing is recommended over single-end sequencing. Both ends of the DNA fragments indicate where the transposase bind. For mapping open chromatin regions, 30 million reads/sample is typically sufficient.</li>
</ol>
10. Data analysis
<ol>
	<li>Data filtration based on base call quality: Set the minimum average base quality score to 20 (99% accuracy).</li>
	<li>Adapter trimming: Remove the adapter sequences using an appropriate tool. 
	NOTE: The Trim Galore wrapper tool (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to remove adapter sequences. For ATAC-seq libraries prepared by Illumina Tn5, the following sequence is used as an adapter sequence: CTGTCTCTTATACACATCT. The minimum sequence length to recognize adapter contamination is set to 5.</li>
	<li>Genome mapping: Map the reads to the appropriate genome with Bowtie using the following parameters &#34;-I 0 -X 2000 -m 1&#34;15. An example of mapping quality from MDA-MB-231 basal breast cancer cells is below: 
	Reads with at least one reported alignment: 52.79% 
	Reads that failed to align: 13.10% 
	Reads with alignments suppressed due to -m: 34.11%</li>
	<li>Deduplication: Mark the PCR duplicates by Picard tools16.</li>
	<li>Generating a genome coverage file for data visualization: Convert the deduplicated paired-end reads to single-end read fragments. Genome coverage tracks are obtained using genomeCoverageBed from bedtools17.</li>
</ol>

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The authors declare that there are no relevant or material financial interests that relate to the research described in this paper.

ATAC-seq has been widely used for mapping open and active chromatin regions. Cancer cell progression is frequently driven by genetic alterations and epigenetic reprogramming, resulting in altered chromatin accessibility and gene expression. An example of this reprogramming is seen during the epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), which are known to be key cellular reprogramming processes during tumor metastasis30. Another example is the acquisition of drug resistance against hormone therapies is often observed in luminal breast tumors31. ATAC-seq is useful to monitor such cell phenotypic alterations at the chromatin level and can be used to predict a cell of origin and cancer subtypes4.
To precisely measure chromatin remodeling by ATAC-seq, the establishment of a reproducible and consistent protocol is necessary. In this manuscript, a standard strategy for ATAC-seq library optimization is described. The ATAC-seq data from MDA-MB-231 and NMuMG cell lines are used as examples of optimization processes. NMuMG cells have been used to study EMT, and MDA-MB-231 cells have been used to study its reverse process, MET. These cellular models have been previously used to study epigenetic alterations during EMT and MET13,32. The use of an appropriate buffer for cell lysis and Tn5 concentration is the key component of successful ATAC-seq library preparation. In addition to these components, a high percentage (&#62;90%) of cell viability, appropriate concentrations of detergent in the lysis buffer, and the preparation of the single-cell suspension are also very important. When Tn5 digestion efficiency is significantly different across samples, it is challenging to correct data variations. This is due to the lack of internal and external controls to quantitatively assess digestion efficiency. Cellular characteristics or properties might also be different between the cells from the control group versus the cells from the test group. Therefore, careful consideration of the experimental conditions, including the choice of lysis buffer, is necessary to obtain high-quality ATAC-seq data from both experimental groups (Figure 5 and Figure 6).
Besides open chromatin profiling, ATAC-seq has been used to identify transcription factor footprints and nucleosome positioning8,33. It is important to note that such information is mostly derived from open chromatin regions (Figure 1F). Therefore, results would be biased toward open chromatin regions. Nevertheless, ATAC-seq is a powerful tool for studying chromatin architecture. This revolutionary tool has more recently been adopted for single-cell genomics and continues to contribute to improving our understanding of gene regulation and chromatin biology.
DATA AVAILABILITY: 
The data presented in this protocol are available at Gene Expression Omnibus under Accession Number GSE202791. The ATAC-seq data from GSE72141 and GSE99479 were also used in the analysis.

Chromatin accessibility is a key requirement for the regulation of gene expression on a genome-wide scale1. Changes in chromatin accessibility are frequently associated with several disease states, including cancer2,3,4. Over the years, numerous techniques have been developed to enable researchers to probe the chromatin landscape by mapping regions of chromatin accessibility. Some of them include DNase-seq (DNase I hypersensitive sites sequencing)5, FAIRE-seq (formaldehyde-assisted isolation of regulatory elements)6, MAPit (methyltransferase accessibility protocol for individual templates)7, and the focus of this paper, ATAC-seq (assay for transposase-accessible chromatin)8. DNase-seq maps accessible regions by employing a key feature of DNase, namely the preferential digestion of naked DNA free from histones and other proteins such as transcription factors5. FAIRE-seq, similar to ChIP-seq, utilizes formaldehyde crosslinking and sonication, except no immunoprecipitation is involved, and the nucleosome-free regions are isolated by phenol-chloroform extraction6. The MAPit method uses a GC methyltransferase to probe chromatin structure at single-molecule resolution7. ATAC-seq relies on the hyperactive transposase, Tn58. The Tn5 transposase preferentially binds to open chromatin regions and inserts sequencing adapters into accessible regions. Tn5 operates through a DNA-mediated &#34;cut and paste&#34; mechanism, whereby the transposase preloaded with adapters binds to open chromatin sites, cuts DNA, and ligates the adapters8. Tn5 bound regions are recovered by PCR amplification using primers that anneal to these adapters. FAIRE-seq and DNase-seq require a large amount of starting material (~100,000 cells to 225,000 cells) and a separate library preparation step before sequencing9. On the other hand, the ATAC-seq protocol is relatively simple and requires a small number of cells (&#60;50,000 cells)10. Unlike the FAIRE-seq and DNase-seq techniques, the sequencing library preparation of ATAC-seq is relatively easy, as the isolated DNA sample is already being tagged with the sequencing adapters by Tn5. Therefore, only the PCR amplification step with appropriate primers is needed to complete the library preparation, and the prior processing steps such as end-repair and adapter ligation need not be performed, thus saving time11. Secondly, ATAC-seq avoids the need for bisulfite conversion, cloning, and amplification with region-specific primers required for MAPit7. Due to these advantages, ATAC-seq has become a hugely popular method for defining open chromatin regions. Although the ATAC-seq method is simple, multiple steps require optimization to obtain high-quality and reproducible data. This manuscript discusses optimization procedures for standard ATAC-seq library preparation, especially highlighting three parameters: (1) lysis buffer composition, (2) Tn5 transposase concentration, and (3) cell number. In addition, this paper provides example data from the optimization conditions using both cancerous and non-cancerous adherent epithelial cells.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.5 mL microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1615-5500</td>
			<td>Natural</td>
		</tr>
		<tr>
			<td>10 &#181;L XL TipOne tips</td>
			<td>USA Scientific</td>
			<td>1120-3810</td>
			<td>Filtered and low-retention</td>
		</tr>
		<tr>
			<td>100 &#181;L XL TipOne RPT tips</td>
			<td>USA Scientific</td>
			<td>1182-1830</td>
			<td>Filtered and low-retention</td>
		</tr>
		<tr>
			<td>100 &#181;L XL TipOne tips</td>
			<td>USA Scientific</td>
			<td>1120-1840</td>
			<td>Filtered and low-retention. Beveled Grade</td>
		</tr>
		<tr>
			<td>15 mL Conical Centrifuge Tubes</td>
			<td>Corning</td>
			<td>352096</td>
			<td></td>
		</tr>
		<tr>
			<td>20 &#181;L TipOne RPT tips</td>
			<td>USA Scientific</td>
			<td>1183-1810</td>
			<td>Filtered and low-retention</td>
		</tr>
		<tr>
			<td>200 &#181;L TipOne RPT tips</td>
			<td>USA Scientific</td>
			<td>1180-8810</td>
			<td>Filtered and low-retention</td>
		</tr>
		<tr>
			<td>50 mL Centrifuge Tubes</td>
			<td>Fisherbrand</td>
			<td>06-443-19</td>
			<td></td>
		</tr>
		<tr>
			<td>Agarose</td>
			<td>ThermoFisher Scientific</td>
			<td>YBP136010</td>
			<td>Genetic Analysis Grade</td>
		</tr>
		<tr>
			<td>All the cell lines used in this study are obtained from ATCC</td>
			<td>ATCC</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Allegra X-30R Centrifuge</td>
			<td>Beckman Coulter</td>
			<td>364658</td>
			<td>SX2415</td>
		</tr>
		<tr>
			<td>AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td>Bead purification kit</td>
		</tr>
		<tr>
			<td>CellDrop Cell Counter</td>
			<td>DeNovix</td>
			<td>CellDrop FL</td>
			<td>Cell counter</td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>MilliporeSigma</td>
			<td>EDS</td>
			<td>BioUltra, anhydrous, &#8805;99% (titration)</td>
		</tr>
		<tr>
			<td>EGTA</td>
			<td>MilliporeSigma</td>
			<td>E3889</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol 100%</td>
			<td>ThermoFisher Scientific</td>
			<td>AC615100020</td>
			<td>Anhydrous; Fisher Scientific - Decon Labs Sterilization Products</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum - TET Tested</td>
			<td>R&#38;D Systems</td>
			<td>S10350</td>
			<td>Triple 0.1 &#181;m filtered</td>
		</tr>
		<tr>
			<td>Gibco DMEM 1x</td>
			<td>ThermoFisher Scientific</td>
			<td>11965092</td>
			<td>[+] 4.5 g/L D-glucose; [+] L-Glutamine; [-] Sodium pyruvate</td>
		</tr>
		<tr>
			<td>Gibco PBS 1x</td>
			<td>ThermoFisher Scientific</td>
			<td>10010023</td>
			<td>pH 7.4</td>
		</tr>
		<tr>
			<td>Gibco Trypsin-EDTA 1x</td>
			<td>ThermoFisher Scientific</td>
			<td>25200056</td>
			<td>(0.25%), phenol red</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>IBI Scientific</td>
			<td>56-81-5</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycine</td>
			<td>MilliporeSigma</td>
			<td>G8898</td>
			<td></td>
		</tr>
		<tr>
			<td>HCl</td>
			<td>MilliporeSigma</td>
			<td>H1758</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>MilliporeSigma</td>
			<td>H3375</td>
			<td></td>
		</tr>
		<tr>
			<td>Invitrogen Qubit Fluorometer</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32857</td>
			<td></td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>MilliporeSigma</td>
			<td>M3634</td>
			<td></td>
		</tr>
		<tr>
			<td>MinElute PCR Purification kit</td>
			<td>Qiagen</td>
			<td>28004</td>
			<td>DNA purification kit</td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>IBI Scientific</td>
			<td>7647-14-5</td>
			<td></td>
		</tr>
		<tr>
			<td>NaOH</td>
			<td>MilliporeSigma</td>
			<td>S8045</td>
			<td>BioXtra, &#8805;98% (acidimetric), pellets (anhydrous)</td>
		</tr>
		<tr>
			<td>NEBNext High-Fidelity 2x PCR Master Mix</td>
			<td>New England Biolabs</td>
			<td>M0541</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera DNA Sample Preparation Kit</td>
			<td>Illumina</td>
			<td>FC-121-1030</td>
			<td>2x TD and Tn5 Transposase</td>
		</tr>
		<tr>
			<td>NP - 40 (IGEPAL CA-630)</td>
			<td>MilliporeSigma</td>
			<td>I8896</td>
			<td>for molecular biology</td>
		</tr>
		<tr>
			<td>PCR Detection System</td>
			<td>BioRad</td>
			<td>1855484</td>
			<td>CFX384 Real-Time System. C1000 Touch Thermal Cycler</td>
		</tr>
		<tr>
			<td>PIPES</td>
			<td>MilliporeSigma</td>
			<td>P1851</td>
			<td>BioPerformance Certified, suitable for cell culture</td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS Assay kit</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32854</td>
			<td>Invitrogen; Nucleic acid quantitation kit</td>
		</tr>
		<tr>
			<td>Quibit Assay Tubes</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32856</td>
			<td>Invitrogen</td>
		</tr>
		<tr>
			<td>SDS</td>
			<td>MilliporeSigma</td>
			<td>L3771</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Acetate</td>
			<td>Homemade</td>
			<td>-</td>
			<td>pH 5.2</td>
		</tr>
		<tr>
			<td>Sucrose</td>
			<td>IBI Scientific</td>
			<td>57-50-1</td>
			<td></td>
		</tr>
		<tr>
			<td>SYBR Gold</td>
			<td>ThermoFisher Scientific</td>
			<td>S11494</td>
			<td></td>
		</tr>
		<tr>
			<td>SYBR Green Supermix, 1.25 mL</td>
			<td>BioRad</td>
			<td>1708882</td>
			<td></td>
		</tr>
		<tr>
			<td>T100 Thermal Cycler</td>
			<td>BioRad</td>
			<td>1861096</td>
			<td></td>
		</tr>
		<tr>
			<td>TempAssure 0.2 mL PCR 8-Tube Strips</td>
			<td>USA Scientific</td>
			<td>1402-4700</td>
			<td>Flex-free, natural, polypropylene</td>
		</tr>
		<tr>
			<td>TempPlate 384-WELL PCR PLATE</td>
			<td>USA Scientific</td>
			<td>1438-4700</td>
			<td>Single notch. Natural polypropylene</td>
		</tr>
		<tr>
			<td>Tris Base</td>
			<td>MilliporeSigma</td>
			<td>648311</td>
			<td>ULTROL Grade</td>
		</tr>
		<tr>
			<td>Triton x-100</td>
			<td>IBI Scientific</td>
			<td>9002-93-1</td>
			<td></td>
		</tr>
		<tr>
			<td>TrueSeq Dual Index Sequencing Primer Kit</td>
			<td>Illumina</td>
			<td>PE-121-1003</td>
			<td>paired-end</td>
		</tr>
		<tr>
			<td>Trypan Blue Stain</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32851</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween-20</td>
			<td>MilliporeSigma</td>
			<td>P7949</td>
			<td>BioXtra, viscous liquid</td>
		</tr>
		<tr>
			<td>Water</td>
			<td>MilliporeSigma</td>
			<td>W3500</td>
			<td>sterile-filtered, BioReagent, suitable for cell culture</td>
		</tr>
	</tbody>
</table>

atac-seq optimization for cancer epigenetics research

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the hyperactive Tn5 transposase. Tn5 cuts and ligates adapters for high-throughput sequencing within accessible chromatin regions. In eukaryotic cells, genomic DNA is packaged into chromatin, a complex of DNA, histones, and other proteins, which acts as a physical barrier to the transcriptional machinery. In response to extrinsic signals, transcription factors recruit chromatin remodeling complexes to enable access to the transcriptional machinery for gene activation. Therefore, identifying open chromatin regions is useful when monitoring enhancer and gene promoter activities during biological events such as cancer progression. Since this protocol is easy to use and has a low cell input requirement, ATAC-seq has been widely adopted to define open chromatin regions in various cell types, including cancer cells. For successful data acquisition, several parameters need to be considered when preparing ATAC-seq libraries. Among them, the choice of cell lysis buffer, the titration of the Tn5 enzyme, and the starting volume of cells are crucial for ATAC-seq library preparation in cancer cells. Optimization is essential for generating high-quality data. Here, we provide a detailed description of the ATAC-seq optimization methods for epithelial cell types.

To obtain successful and high-quality ATAC-seq data, it is important to optimize the experimental conditions. ATAC-seq library preparation can be separated into the five major steps (Figure 1), namely cell lysis, tagmentation (fragmentation and adapter insertion by Tn5), genomic DNA purification, PCR amplification, and data analysis. As an initial process, the cell lysis (nuclear isolation) buffer must be first optimized for each cell type. Either the hypotonic buffer described in the original ATAC-seq paper8 or the CSK buffer12,13 was used to lyse breast cancer cells. Trypan blue staining can be used to confirm nuclei isolation18. For ATAC-seq library preparation in human cancer cell lines such as MDA-MB-231, T47D, MCF7, and A375 cells, CSK buffer has been used by multiple groups12,13,19,20. CSK buffer was also used for ATAC-seq library preparation in other cell types such as embryonic stem cells21,22 and Drosophila S2 cells23. For the lysis buffer optimization, Trypan blue staining is useful to assess the cell lysis efficiency. When NMuMG, non-cancerous mouse mammary gland epithelial cells, were treated with the hypotonic buffer8 (see step 1) and CSK buffer, higher cell lysis efficiency was observed in CSK-treated cells (Figure 2). For frozen tissues, Omni-ATAC has been shown to improve ATAC-seq signals24. In the Omni-ATAC, a buffer containing 0.1% NP40, 0.1% Tween-20, and 0.01% Digitonin is used for cell lysis. Although the Digitonin-based buffer is known to decrease the fraction of mitochondrial DNA, the signal-to-noise ratios and TSS read counts from ATAC-seq with CSK buffer were shown to be better than those from Omni-ATAC in breast cancer cells12. Therefore, the rest of this manuscript is mainly focused on the results from ATAC-seq data with CSK buffer.
Following the determination of the best cell lysis buffer (nuclear isolation) conditions, it is important to optimize the ratios between input cell number and Tn5 transposase concentration12,25. Typically, the Tn5 concentration can be titrated by varying the volume of Tn5 from 1.25 &#181;L, 2.5 &#181;L, to 5 &#181;L in a total volume of 25 &#181;L reaction mixture. Alternatively, input cell numbers can be changed (e.g., from 10,000 to 100,000), while maintaining the Tn5 enzyme unchanged at 2.5 &#181;L to optimize the Tn5 tagmentation reaction. To evaluate the quality of ATAC-seq libraries and the efficiency of Tn5-induced chromatin digestion, the library PCR products can be analyzed by agarose gel electrophoresis. Figure 3 shows examples of successful ATAC-seq libraries. Typically, PCR amplification of 8 or 9 cycles (total PCR cycles including initial PCR) results in 3-6 ng/&#181;L ATAC-seq library concentration. As Tn5 preferentially &#34;attacks&#34; nucleosome-free regions at open chromatin (Figure 1), the nucleosome ladders should be obvious on the gel image (Figure 3). Ethidium bromide or SYBR Gold can be used to detect amplified DNAs on the agarose gel.
Next, qPCR analysis using ATAC-seq libraries can be used to briefly check the quality of the libraries and the fragment enrichment at open chromatin regions such as promoters of active genes (Figure 4). Primers can be designed to generate &#60;80 bp amplicons using promoters of known active genes as positive controls and known &#34;closed&#34; (inactive) chromatin regions as negative controls (Figure 4A). Among the tested conditions, higher enrichment was observed in the T47D ATAC-seq libraries with CSK buffer (Figure 4B). As expected, we found more enrichment using primers K and L at the Estrogen Receptor alpha (ESR1) gene promoter region relative to a closed region upstream of ESR1 with primer C (Figure 4B)26. Figure 5 shows examples of ATAC-seq data with different Tn5 concentrations (25,000 cells were used). In this case, higher background noise was detected in the lowest (1.25 &#181;L of Tn5) Tn5 condition (Figure 5, vertical bars). ATAC-seq data from 2.5 &#181;L or 5 &#181;L of Tn5 showed similar levels of ATAC-seq signals at open chromatin regions with lower background signals. Considering the potential over-digestion by Tn5 at the higher concentration, it can be concluded that 2.5 &#181;L of Tn5 is suitable for this cell type.
We also performed the ATAC-seq using different cell numbers in NMuMG cells, frequently used to study the epithelial-mesenchymal transition (Figure 6). To optimize the starting volume, four different cell numbers (25,000, 50,000, 75,000 and 100,000) were used for the ATAC-seq library optimization. The cells were lysed with CSK buffer, and nuclei were incubated with 2.5 &#181;L of Tn5 transposase in 25 &#181;L of the reaction mixture. To explore the efficacy of Tn5 digestion and nucleosomal ladders, the size distribution of inserted DNAs was bioinformatically analyzed (Figure 6A). When lower cell numbers were used, nucleosome-free DNA fragments (&#60;100 bp) were more enriched in the sequencing data. On the other hand, the fraction of mono-nucleosomal DNA fragments (175-225 bp) was less in the low cell input samples (25,000 cells and 50,000 cells) compared to higher cell input conditions (75,000 cells and 100,000 cells). Although differential DNA fragment patterns were observed between the samples, input cell number appears to have minimal impact on the enrichment of ATAC-seq signals at promoters and other open chromatin regions (Figure 6B). To further investigate the impact of input cell numbers on downstream analysis, ATAC-seq peaks were defined. The ATAC-seq peaks were determined by the PeaKDEck peak calling program27 using the following parameters: -sig 0.0001 -bin 300 -back 3,000 -npBack 2,500,000. From ~4.9 million uniquely mapped reads, 38,878, 43,832, 41,509, and 45,530 peaks were observed in ATAC-seq data from 25,000, 50,000, 75,000, and 100,000 cell inputs, respectively. The 100,000-cell input condition showed the highest number of peaks. Since the Transcription Start Site (TSS) enrichment score has been used to assess the quality of ATAC-seq data, the TSS score of each ATAC-seq condition was calculated using the GitHub program: Jiananlin/TSS_enrichment_score_calculation28. The TSS scores for the 25,000, 50,000, 75,000, and 100,000 cell input conditions were 9.03, 9,02, 8.82, and 8.28, respectively (the TSS enrichment score is considered ideal if it is greater than 728). This indicated a gradual decrease in the TSS enrichment score with increasing cell numbers. While the largest peak number was observed in the 100,000 cell input condition, the 25,000 cell input condition gave a better TSS score. Peak annotation analysis by Homer29 indicated that the majority of the increased peaks are categorized as promoter distal peaks (Figure 6C). Based on these results, it can be concluded that the higher cell number inputs can produce a higher number of peaks and more frequently detect non-promoter peaks compared to the lower cell number inputs in this condition.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64242/64242fig01.jpg" /> 
Figure 1: Outline of ATAC-seq method. (A) ATAC-seq measures chromatin accessibility using a hyperactive Tn5 transposase preloaded with forward and reverse adapters. The various steps in the process include (B) cell lysis (including optimization of lysis buffer composition, titration of Tn5, and the number of cells used); (C) transposition (binding of Tn5 to open/accessible chromatin); (D) tagmentation of chromatin; (E) DNA fragment purification and amplification of libraries, and (F) sequencing of libraries and data analysis.&#160;Fragment length distribution analysis of ATAC-seq libraries typically shows an initial peak around ~50 bp (nucleosome-free regions) and another peak at ~200 bp (mono-nucleosome). Therefore, without computational or experimental size filtration, ATAC-seq signals contain both nucleosome-free and nucleosomal regions in open chromatin. Created with BioRender.com. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64242/64242fig02.jpg" /> 
Figure 2: Comparison of cell lysis efficiency using trypan blue. (A) NMuMG cells were treated with the hypotonic buffer8 and stained with trypan blue. (B) NMuMG cells were treated with CSK buffer and stained with trypan blue. The scale bars indicate 50 &#181;m. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64242/64242fig03.jpg" /> 
Figure 3: Amplified DNA fragment analysis of ATAC-seq libraries. ATAC-seq libraries were prepared from MDA-MB-231 breast cancer cells. (A) After PCR amplification, PCR products were analyzed on a 0.5x TBE, 1.5% agarose gel before and after beads purification. Primers are mostly removed by the initial beads purification. DNA band patterns from (B) 1x TAE, 1% agarose gel electrophoresis and (C) an automated electrophoresis are also indicated. SYBR Gold was used to visualize the DNA fragments shown in A and B. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64242/64242fig04.jpg" /> 
Figure 4: Enrichment analysis of ATAC-seq libraries. (A) Genome browser track showing the ESR1 locus. The genome coverage of the ATAC-seq data in T47D cells20 is shown. Primers from a previous publication were used26, and their target regions are highlighted in yellow. (B) Bar graph depicting primer amplicon enrichment in ATAC-seq libraries. ATAC-seq libraries were prepared by the hypotonic buffer or CSK buffer. Different concentrations of Tn5 were also used to generate ATAC-seq libraries. 1 ng of each library was used to perform qPCR. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64242/64242fig05.jpg" /> 
Figure 5: Genome browser visualization for ATAC-seq optimization. Different amounts of Tn5 transposase were added during library preparation. The 1.25 &#181;L of Tn5 condition shows higher background signals (highlighted in yellow), while the other two conditions look similar. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/64242/64242fig06.jpg" /> 
Figure 6: DNA fragment size distribution of ATAC-seq libraries. (A) ATAC-seq libraries were prepared by using the indicated cell numbers. The ATAC-seq data from the 25,000 cell input condition showed the highest enrichment of nucleosome-free fragments, while the 100,000 cell input condition presented the highest mono-nucleosomal DNAs. (B) Genome browser tracks showing ATAC-seq data from different cell numbers. (C) Homer peak annotation analysis. Each peak was classified as a promoter-proximal or distal peak by Homer29. <a href="https://www.jove.com/files/ftp_upload/64242/64242fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td>Sequence</td>
		</tr>
		<tr>
			<td>ESR1 C Forward</td>
			<td>TGGTGACTCATATTTGAACAAGCC</td>
		</tr>
		<tr>
			<td>ESR1 C Reverse</td>
			<td>CTCCTCCGTTGAATGTGTCTCC</td>
		</tr>
		<tr>
			<td>ESR1 K Forward</td>
			<td>TGTGGCTGGCTGCGTATG</td>
		</tr>
		<tr>
			<td>ESR1 K Reverse</td>
			<td>TGTCTCTCTTTCTGTTTGATTCCC</td>
		</tr>
		<tr>
			<td>ESR1 L Forward</td>
			<td>TGTGCCTGGAGTGATGTTTAAG</td>
		</tr>
		<tr>
			<td>ESR1 L Reverse</td>
			<td>CATTACAAAGGTGCTGGAGGAC</td>
		</tr>
	</tbody>
</table>
Table 1: List of primers used in this study.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Steps</td>
			<td>Temperature</td>
			<td>Time</td>
			<td>Cycle</td>
		</tr>
		<tr>
			<td>Gap filling</td>
			<td>72 &#176;C</td>
			<td>5 min</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Initial Denaturation</td>
			<td>98 &#176;C</td>
			<td>30 s</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Denaturation</td>
			<td>98 &#176;C</td>
			<td>10 s</td>
			<td rowspan="3">5</td>
		</tr>
		<tr>
			<td>Annealing</td>
			<td>63 &#176;C</td>
			<td>30 s</td>
		</tr>
		<tr>
			<td>Extension</td>
			<td>72 &#176;C</td>
			<td>1 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>4 &#176;C</td>
			<td>forever</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2: PCR amplification program part 1.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Steps</td>
			<td>Temperature</td>
			<td>Cycle</td>
		</tr>
		<tr>
			<td>Initial Denaturation</td>
			<td>98 &#176;C 30 s</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Denaturation</td>
			<td>98 &#176;C 10 s</td>
			<td rowspan="4">20</td>
		</tr>
		<tr>
			<td>Annealing</td>
			<td>63 &#176;C 30 s</td>
		</tr>
		<tr>
			<td>Extension</td>
			<td>72 &#176;C 1 min</td>
		</tr>
		<tr>
			<td>Plate read</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3: qPCR settings.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Steps</td>
			<td>Temperature</td>
			<td>Time</td>
			<td>Cycle</td>
		</tr>
		<tr>
			<td>Initial Denaturation</td>
			<td>98 &#176;C</td>
			<td>30 s</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Denaturation</td>
			<td>98 &#176;C</td>
			<td>10 s</td>
			<td rowspan="3">Total cycles calculated at step 6.4</td>
		</tr>
		<tr>
			<td>Annealing</td>
			<td>63 &#176;C</td>
			<td>30 s</td>
		</tr>
		<tr>
			<td>Extension</td>
			<td>72 &#176;C</td>
			<td>1 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>4 &#176;C</td>
			<td>forever</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 4: PCR amplification program part 2.

Watch this Scientific Journal Video about ATAC-Seq Optimization for Cancer Epigenetics Research at JoVE.com

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-seq is a DNA sequencing method that uses the hyperactive mutant transposase, Tn5, to map changes in chromatin accessibility mediated by transcription factors. ATAC-seq enables the discovery of the molecular mechanisms underlying phenotypic alterations in cancer cells. This protocol outlines optimization procedures for ATAC-seq in epithelial cell types, including cancer cells.

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the ...

atac-seq-optimization-for-cancer-epigenetics-research

Nanyang Technological University

Research

JoVE Journal

Cancer Research

4.0K Views.  University of North Dakota School of Medicine and Health Sciences. ATAC-seq is a DNA sequencing method that uses the hyperactive mutant transposase, Tn5, to map changes in chromatin accessibility mediated by transcription factors. ATAC-seq enables the discovery of the molecular mechanisms underlying phenotypic alterations in cancer cells. This protocol outlines optimization procedures for ATAC-seq in epithelial cell types, including cancer cells. 

Video: ATAC-Seq Optimization for Cancer Epigenetics Research

A Unified Methodological Framework for Vestibular Schwannoma Research

Vestibular schwannomas are the most common neoplasms of the cerebellopontine angle, making up 6-8% percent of all intracranial growths. Though these tumors cause sensorineural hearing loss in up to 95% of affected individuals, the molecular mechanisms underlying this hearing loss remain elusive. This article outlines the steps established in our laboratory to facilitate the collection and processing of various primary human tissue samples for downstream research applications integral to the study of vestibular schwannomas. Specifically, this work describes a unified methodological framework for the collection, processing, and culture of Schwann and schwannoma cells from surgical samples. This is integrated with parallel processing steps now considered essential for current research: the collection of tumor and nerve secretions, the preservation of RNA and the extraction of protein from collected tissues, the fixation of tissue for the preparation of sections, and the exposure of primary human cells to adeno-associated viruses for application to gene therapy. Additionally, this work highlights the translabyrinthine surgical approach to collect this tumor as a unique opportunity to obtain human sensory epithelium from the inner ear and perilymph. Tips to improve experimental quality are provided and common pitfalls highlighted.

The goal of this protocol is to outline the collection and processing of human surgical samples for multiple downstream applications in vestibular schwannoma and Schwann cell research.

Vestibular schwannomas are the most common neoplasms of the cerebellopontine angle, making up 6-8% percent of all intracranial growths. Though these ...

A Three-dimensional Thymic Culture System to Generate Murine Induced Pluripotent Stem Cell-derived Tumor Antigen-specific Thymic Emigrants

The inheritance of pre-rearranged T cell receptors (TCRs) and their epigenetic rejuvenation make induced pluripotent stem cell (iPSC)-derived T cells a promising source for adoptive T cell therapy (ACT). However, classical in vitro methods for producing regenerated T cells from iPSC result in either innate-like or terminally differentiated T cells, which are phenotypically and functionally distinct from na&#239;ve T cells. Recently, a novel three-dimensional (3D) thymic culture system was developed to generate a homogenous subset of CD8&#945;&#946;+ antigen-specific T cells with a na&#239;ve T cell-like functional phenotype, including the capacity for proliferation, memory formation, and tumor suppression in vivo. This protocol avoids aberrant developmental fates, allowing for the generation of clinically relevant iPSC-derived T cells, designated as iPSC-derived thymic emigrants (iTE), while also providing a potent tool to elucidate the subsequent functions necessary for T cell maturation after thymic selection.

This article describes a novel method to generate tumor antigen-specific induced pluripotent stem cell-derived thymic emigrants (iTE) by a three-dimensional (3D) thymic culture system. iTE are a homogenous subset of T cells closely related to na&#239;ve T cells with the capacity for proliferation, memory formation, and tumor suppression.

The inheritance of pre-rearranged T cell receptors (TCRs) and their epigenetic rejuvenation make induced pluripotent stem cell (iPSC)-derived T cells a ...

Preparation of Exosomes for siRNA Delivery to Cancer Cells

Extracellular vesicles, in particular exosomes, have recently gained interest as novel drug delivery vectors due to their biological origin, abundance, and intrinsic capability in intercellular delivery of various biomolecules. This work establishes an isolation protocol to achieve high yield and high purity of exosomes for siRNA delivery. Human Embryonic Kidney cells (HEK-293 cells) are cultured in bioreactor flasks and the culture supernatant (hereon referred to as conditioned medium) is harvested on a weekly basis to allow for enrichment of HEK-293 exosomes. The conditioned medium (CM) is pre-cleared of dead cells and cellular debris by differential centrifugation and is subjected to ultracentrifugation onto a sucrose cushion followed by a washing step, to collect the exosomes. Isolated HEK-293 exosomes are characterized for yield, morphology and exosomal marker expression by nanoparticle tracking analysis, protein quantification, electron microscopy and flow cytometry, respectively. Small interfering RNA (siRNA), fluorescently labeled with Atto655, is loaded into exosomes by electroporation and excess siRNA is removed by gel filtration. Cell uptake in PANC-1 cancer cells, after 24 h incubation at 37 &#176;C, is confirmed by flow cytometry. HEK-293 exosomes are 107.0 &#177; 8.2 nm in diameter. The exosome yield and particle-to-protein ratio (P:P) ratio are 6.99 &#177; 0.22 &#215; 1012 particle/mL and 8.3 &#177; 1.7 &#215; 1010 particle/&#181;g, respectively. The encapsulation efficiency of siRNA in exosomes is ~ 10-20%. Forty percent of the cells show positive signals for Atto655 at 24 h post-incubation. In conclusion, exosome isolation by ultracentrifugation onto sucrose cushion offers a combination of good yield and purity. siRNA could be successfully loaded into exosomes by electroporation and subsequently delivered into cancer cells in vitro. This protocol offers a standard procedure for developing siRNA-loaded exosomes for efficient delivery to cancer cells.

An exosome is a new generation of drug delivery carriers. We established an exosome isolation protocol with high yield and purity for siRNA delivery. We also encapsulated fluorescently labelled non-specific siRNA into exosomes and investigated the cellular uptake of siRNA-loaded exosomes in cancer cells.

Extracellular vesicles, in particular exosomes, have recently gained interest as novel drug delivery vectors due to their biological origin, abundance, ...

In Vivo Immunofluorescence Localization for Assessment of Therapeutic and Diagnostic Antibody Biodistribution in Cancer Research

Monoclonal antibodies (mAbs) are important tools in cancer detection, diagnosis, and treatment. They are used to unravel the role of proteins in tumorigenesis, can be directed to cancer biomarkers enabling tumor detection and characterization, and can be used for cancer therapy as mAbs or antibody-drug conjugates to activate immune effector cells, to inhibit signaling pathways, or directly kill cells carrying the specific antigen. Despite clinical advancements in the development and production of novel and highly specific mAbs, diagnostic and therapeutic applications can be impaired by the complexity and heterogeneity of the tumor microenvironment. Thus, for the development of efficient antibody-based therapies and diagnostics, it is crucial to assess the biodistribution and interaction of the antibody-based conjugate with the living tumor microenvironment. Here, we describe In Vivo Immunofluorescence Localization (IVIL) as a new approach to study interactions of antibody-based therapeutics and diagnostics in the in vivo physiological and pathological conditions. In this technique, a therapeutic or diagnostic antigen-specific antibody is intravenously injected in&#160;vivo and localized ex vivo with a secondary antibody in isolated tumors. IVIL, therefore, reflects the in vivo biodistribution of antibody-based drugs and targeting agents. Two IVIL applications are described assessing the biodistribution and accessibility of antibody-based contrast agents for molecular imaging of breast cancer. This protocol will allow future users to adapt the IVIL method for their own antibody-based research applications.

The in vivo immunofluorescence localization (IVIL) method can be used to examine in vivo biodistribution of antibodies and antibody conjugates for oncological purposes in living organisms using a combination of in vivo tumor targeting and ex vivo immunostaining methods.

Monoclonal antibodies (mAbs) are important tools in cancer detection, diagnosis, and treatment. They are used to unravel the role of proteins in ...

Optimization, Design and Avoiding Pitfalls in Manual Multiplex Fluorescent Immunohistochemistry

Microenvironment evaluation of intact tissue for analysis of cell infiltration and spatial organization are essential in understanding the complexity of disease processes. The principle techniques used in the past include immunohistochemistry (IHC) and immunofluorescence (IF) which enable visualization of cells as a snapshot in time using between 1 and 4 markers. Both techniques have shortcomings including difficulty staining poorly antigenic targets and limitations related to cross-species reactivity. IHC is reliable and reproducible, but the nature of the chemistry and reliance on the visible light spectrum allows for only a few markers to be used and makes co-localization challenging. Use of IF broadens potential markers but typically relies on frozen tissue due to the extensive tissue autofluorescence following formalin fixation. Flow cytometry, a technique that enables simultaneous labeling of multiple epitopes, abrogates many of the deficiencies of IF and IHC, however, the need to examine cells as a single cell suspension loses the spatial context of cells discarding important biologic relationships. Multiplex fluorescent immunohistochemistry (mfIHC) bridges these technologies allowing for multi-epitope cellular phenotyping in formalin fixed paraffin embedded (FFPE) tissue while preserving the overall microenvironment architecture and spatial relationship of cells within intact undisrupted tissue. High fluorescent intensity fluorophores that covalently bond to the tissue epitope enables multiple applications of primary antibodies without worry of species specific cross-reactivity by secondary antibodies. Although this technology has been proven to produce reliable and accurate images for the study of disease, the process of creating a useful mfIHC staining strategy can be time consuming and exacting due to extensive optimization and design. In order to make robust images that represent accurate cellular interactions in-situ and to mitigate the optimization period for manual analysis, presented here are methods for slide preparation, optimizing antibodies, multiplex design as well as errors commonly encountered during the staining process.

Multiplex fluorescent immunohistochemistry is an emerging technology that enables the visualization of multiple cell types within intact formalin-fixed, paraffin embedded (FFPE) tissue. Presented are guidelines for ensuring a successful 7-color multiplex with instructions for optimizing antibodies and reagents, preparing slides, design and tips for avoiding common problems.

Microenvironment evaluation of intact tissue for analysis of cell infiltration and spatial organization are essential in understanding the complexity of ...

Obtaining Cancer Stem Cell Spheres from Gynecological and Breast Cancer Tumors

Cancer stem cells (CSC) are a small population with self-renewal and plasticity which are responsible for tumorigenesis, resistance to treatment and recurrent disease. This population can be identified by surface markers, enzymatic activity and a functional profile. These approaches per se are limited, due to phenotypic heterogeneity and CSC plasticity. Here, we update the sphere-forming protocol to obtain CSC spheres from breast and gynecological cancers, assessing functional properties, CSC markers and protein expression. The spheres are obtained with single-cell seeding at low density in suspension culture, using a semi-solid methylcellulose medium to avoid migration and aggregates. This profitable protocol can be used in cancer cell lines but also in primary tumors. The tridimensional non-adherent suspension culture thought to mimic the tumor microenvironment, particularly the CSC-niche, is supplemented with epidermal growth factor and basic fibroblast growth factor to ensure CSC signaling. Aiming for robust identification of CSC, we propose a complementary approach, combining functional and phenotypic evaluation. Sphere-forming capacity, self-renewal and sphere projection area establish CSC functional properties. Additionally, characterization comprises flow cytometry evaluation of the markers, represented by CD44+/CD24- and CD133, and Western blot, considering ALDH. The presented protocol was also optimized for primary tumor samples, following a sample digestion procedure, useful for translational research.

The aim of this methodology is to identify cancer stem cells (CSC) in cancer cell lines and primary human tumor samples with the sphere-forming protocol, in a robust manner, using functional assays and phenotypic characterization with flow cytometry and Western blot.

Cancer stem cells (CSC) are a small population with self-renewal and plasticity which are responsible for tumorigenesis, resistance to treatment and ...

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less aggressive breast tumors, the 5-year survival rate of TNBC patients is 77% due to their characteristic drug-resistant phenotype and metastatic burden. Toward this end, murine models have been established aimed at identifying novel therapeutic strategies limiting TNBC tumor growth and metastatic spread. This work describes a practical guide for the TNBC orthotopic model where MDA-MB-231 breast cancer cells suspended in a basement membrane matrix are implanted in the fourth mammary fat pad, which closely mimics the cancer cell behavior in humans. Measurement of tumors by caliper, lung metastasis assessment via in vivo and ex vivo imaging, and molecular detection are discussed. This model provides an excellent platform to study therapeutic efficacy and is especially suitable for the study of the interaction between the primary tumor and distal metastatic sites.

This work presets an advanced protocol to accurately assess tumor loading by detection of green fluorescent protein and bioluminescence signals as well as the integration of quantitative molecular detection technique.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less ...

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 ...

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 ...

Circulating Tumor Cell Lines: an Innovative Tool for Fundamental and Translational Research

Metastasis is a leading cause of cancer death. Despite improvements in treatment strategies, metastatic cancer has a poor prognosis. We thus face an urgent need to understand the mechanisms behind metastasis development, and thus to propose efficient treatments for advanced cancer. Metastatic cancers are hard to treat, as biopsies are invasive and inaccessible. Recently, there has been considerable interest in liquid biopsies including both cell-free circulating deoxyribonucleic acid (DNA) and circulating tumor cells from peripheral blood and we have established several circulating tumor cell lines from metastatic colorectal cancer patients to participate in their characterization. Indeed, to functionally characterize these rare and poorly described cells, the crucial step is to expand them. Once established, circulating tumor cell (CTC) lines can then be cultured in suspension or adherent conditions. At the molecular level, CTC lines can be further used to assess the expression of specific markers of interest (such as differentiation, epithelial or cancer stem cells) by immunofluorescence or cytometry analysis. In addition, CTC lines can be used to assess drug sensitivity to gold-standard chemotherapies as well as to targeted therapies. The ability of CTC lines to initiate tumors can also be tested by subcutaneous injection of CTCs in immunodeficient mice.
Finally, it is possible to test the role of specific genes of interest that might be involved in cancer dissemination by editing CTC genes, by short hairpin ribonucleic acid (shRNA) or Crispr/Cas9. Modified CTCs can thus be injected into immunodeficient mouse spleens, to experimentally mimic part of the metastatic development process in vivo.
In conclusion, CTC lines are a precious tool for future research and for personalized medicine, where they will allow prediction of treatment efficiency using the very cells that are originally responsible for metastasis.

Culturing CTCs allows a deeper functional characterization of cancer, through assaying specific marker expression, and assessing drug resistance and the ability to colonize the liver among other possibilities. Overall, CTC culture could be a promising clinical tool for personalized medicine to improve patient outcome.

Metastasis is a leading cause of cancer death. Despite improvements in treatment strategies, metastatic cancer has a poor prognosis. We thus face an ...