The project was funded by Thermo Fisher Scientific Intramural grant.

All human samples were obtained from a commercial source following their guidelines for human research ethics.
1. Genomic DNA isolation
NOTE: Genomic DNA isolation is performed using a commercially available kit (see Table of Materials) from any hematopoietic cells, such as peripheral blood mononuclear cells (PBMCs), purified T cells, or any other hematopoietic cell type. In this experiment, peripheral blood mononuclear cells (PBMCs) and T cells from three different human donors were used.
<ol>
	<li>Place 1-2 x 106 hematopoietic cells in a conical tube and centrifuge at 400 x g for 10 min.</li>
	<li>Aspirate the supernatant completely.</li>
	<li>Add 350 &#181;L of lysis/binding buffer to the sample and incubate for 10 min at 55 &#176;C.</li>
	<li>Add 50 &#181;L of proteinase K (20 mg/mL) to 350 &#181;L of cell suspension and vortex for 30 s.</li>
	<li>Add 50 &#181;L of DNA binding paramagnetic beads and 400 &#181;L of 100% isopropanol and incubate for 3 min at room temperature (RT).</li>
	<li>Place the tube on a magnetic rack for 2 min and remove the supernatant, taking care not to disturb the beads. The DNA is bound to the magnetic beads at this step.</li>
	<li>Add 950 &#181;L of wash buffer 1. Vortex for 30 s and repeat step 1.6.</li>
	<li>Repeat step 1.7.</li>
	<li>Add 950 &#181;L of wash buffer 2. Vortex for 30 s and repeat step 1.6.</li>
	<li>Add 100 &#181;L of elution buffer and vortex for 2 min. Place the tube on a magnetic stand for 1 min and transfer the supernatant containing the gDNA to a new tube.</li>
	<li>Use 1 &#181;L of the gDNA isolated from the hematopoietic cells to measure the purity of the gDNA by spectrophotometry using a fluorimeter by obtaining the OD at 260 nm, 280 nm, and 230 nm. Ensure that the ratio of optical density (OD ratio) at 260 and 280 nm is between 1.7-2.0 and the OD ratio at 260 and 230 nm is between 1.5-2.4.</li>
</ol>
2. Sample preparation
<ol>
	<li>Preheat a thermomixer to 56 &#176;C.</li>
	<li>Label 2 mL tubes for all samples, calibrator, and reference material.</li>
	<li>Dilute the samples and calibrator with the elution buffer (available with the kit used for step 1) at RT.
	<ol>
		<li>For all experimental samples, dilute the gDNA to a total volume of 142 &#181;L. For example, dilute 4 &#181;L of gDNA at 100 ng/&#181;L into 138 &#181;L of elution buffer. 
		NOTE: 400-1,200 ng of gDNA can be used for the assay.</li>
		<li>Dilute 75 &#181;L of the calibrator to a total volume of 142 &#181;L.</li>
		<li>Check the reference gDNA concentration with a spectrophotometer, using TE (pH = 8.0) as a blank.</li>
		<li>Dilute 1,000-1,200 ng of the reference gDNA to a total volume of 142 &#181;L. For example, dilute 10 &#181;L of reference gDNA at 100 ng/&#181;L into 132 &#181;L of elution buffer.</li>
	</ol>
	</li>
	<li>Incubate the tubes at 56 &#176;C for 5 min at 900 rpm in a thermomixer. Briefly spin down the tubes to collect the samples.</li>
</ol>
3. Bisulfite conversion
NOTE: Ensure that the incubation times during the bisulfite conversion follow the recommended times. Over-incubation or under-incubation will affect the results of the assay.
<ol>
	<li>Set a thermomixer to 80 &#176;C.</li>
	<li>Add 270 &#181;L of ammonium bisulfite and 90 &#181;L of tetrahydrofurfuryl alcohol (THFA) to all the tubes. Vortex to mix completely and briefly spin down the samples to gather the liquid at the bottom.</li>
	<li>Incubate the tubes in a thermomixer at 80 &#176;C for 45 min at 900 rpm. If using a heat block, vortex the tubes for 1-2 s every 4.5 min during incubation.</li>
	<li>Briefly spin down the samples and allow to cool to RT for 3-5 min before continuing with step 4 or 5. The final volume should be ~500 &#181;L.</li>
</ol>
4. CD8 and Treg assays
<ol>
	<li>DNA purification following bisulfite conversion 
	NOTE: This step is performed using a commercially available DNA purification kit (see Table of Materials) on bisulfite-converted DNA. Ensure that the reagents are warmed to RT before use.
	<ol>
		<li>Before starting the purification, create a homogeneous mixture of the DNA isolation paramagnetic beads by vortexing for 30 s. Add ethanol and isopropanol to the wash buffers, following the volumes listed on the bottles, and set a heat block to 65 &#176;C.</li>
		<li>At RT, add 870 &#181;L of lysis/binding buffer and 105 &#181;L of DNA binding paramagnetic beads to each tube from step 3.4. Vortex to mix completely and briefly spin down the tubes.</li>
		<li>Add 570 &#181;L of 2-propanol and vortex to mix completely.</li>
		<li>Incubate the tubes at RT for 7 min at 50 rpm in a thermomixer or a standard rotating mixer.</li>
		<li>Briefly spin down the tubes. Place the tubes on the magnetic rack and incubate for 5 min.</li>
		<li>With the samples still on the magnetic rack, remove the supernatant without disturbing the beads. 
		NOTE: The DNA is bound to the beads.</li>
		<li>Remove the tubes from the magnetic rack and add 900 &#181;L of wash buffer 1. Vortex the tubes to completely resuspend the beads and then briefly spin down the tubes.</li>
		<li>Return the tubes to the magnetic rack and incubate for 3 min.</li>
		<li>With the samples still on the magnetic rack, remove the supernatant without disturbing the beads.</li>
		<li>Repeat the washes (4.1.7-4.1.9), once with wash buffer 1, then twice with wash buffer 2.</li>
		<li>After removing the supernatant, briefly spin down the tubes and return to the magnetic rack for 3 min.</li>
		<li>Remove all residual wash buffer 2 and remove the tubes from the magnetic rack.</li>
		<li>Dry the beads with the tube lids open at 65 &#176;C for 15 min.</li>
		<li>Add 60 &#181;L of elution buffer to each tube and incubate at RT for 7 min at 1,400 rpm. Alternatively, vortex at a moderate speed using a foam adaptor.</li>
		<li>Briefly spin down the tubes and place on the magnetic rack for 2 min.</li>
		<li>Transfer 55 &#181;L of eluate to a new tube without disturbing the beads. The eluate contains the bisulfite-converted DNA. Measuring the DNA concentration is not required.</li>
	</ol>
	</li>
	<li>Set up and run qPCR
	<ol>
		<li>Turn on the qPCR machine and the computer that operates its software.</li>
		<li>On the software user interface create a new experiment and select which block is being used to run the experiment.</li>
		<li>Select Standard Curve as the type of experiment.</li>
		<li>If applicable, select which detection chemistry you want to use. This assay uses the TaqMan reagents. Otherwise, select ROX as the passive reference.</li>
		<li>If applicable, select the properties of the instrument run as Standard.</li>
		<li>Define your experimental design by assigning targets (e.g., GAPDH or CD8 with FAM as the reporter), and a non-fluorescent quencher.</li>
		<li>Assign sample names for the standards, samples, and controls.</li>
		<li>Next, assign each well position according to the qPCR plate. Each well requires a target, such as CD8 or GAPDH, and a sample name. Each sample is run in triplicate and must be reflected on the plate layout for the instrument.</li>
		<li>Assign each standard the task Standard and specify the final copy numbers according to Table 1.</li>
		<li>Assign samples, calibrator, and reference the task Unknown.</li>
		<li>Designate wells for no template control as NTC or N.</li>
		<li>Prepare the serial dilutions of lambda DNA (10 ng/&#181;L) to be used as the standard (see Table 1).</li>
		<li>Prepare qPCR master mix cocktails, one for the target cell type and one for GAPDH (see Table 2). Run all the samples, standards, and the NTC control in triplicate. 
		NOTE: GAPDH is used to quantify the total number of cells and is run in parallel with the target amplification. Copies of unmethylated regulatory elements for the CD8 B gene are measured in the CD8 assay, FoxP3 in Treg, and IL-17 in Th17.</li>
		<li>Use a 96 well plate for qPCR. Load 3 &#181;L of template DNA into the wells first. Next, load 7 &#181;L of the master mix.</li>
		<li>Seal the plate with a qPCR film and briefly spin down the plate before placing it into the qPCR instrument.</li>
		<li>Run the qPCR as shown in Table 3.</li>
		<li>After the run completion, export the qPCR data as a .txt or .xlsx file.</li>
		<li>Analyze the data using the Analysis Templates for the CD8 assay and the Treg assay (see Table of Materials) to calculate the Ct Average, Ct Standard Deviation, and Copy Number.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Initial plasmid Copy Number/3 &#181;L</td>
			<td>Volume</td>
			<td>Diluent DNA [1]</td>
			<td>Final Copy Number per 3 &#181;L</td>
			<td>Label</td>
		</tr>
		<tr>
			<td>31250</td>
			<td>1000 &#181;L</td>
			<td>-</td>
			<td>31250</td>
			<td>STD#1</td>
		</tr>
		<tr>
			<td>31250</td>
			<td>200 &#181;L</td>
			<td>800 &#181;L</td>
			<td>6250</td>
			<td>STD#2</td>
		</tr>
		<tr>
			<td>6250</td>
			<td>200 &#181;L</td>
			<td>800 &#181;L</td>
			<td>1250</td>
			<td>STD#3</td>
		</tr>
		<tr>
			<td>1250</td>
			<td>200 &#181;L</td>
			<td>800 &#181;L</td>
			<td>250</td>
			<td>STD#4</td>
		</tr>
		<tr>
			<td>250</td>
			<td>200 &#181;L</td>
			<td>800 &#181;L</td>
			<td>50</td>
			<td>STD#5</td>
		</tr>
		<tr>
			<td>1250</td>
			<td>30 &#181;L</td>
			<td>1200 &#181;L</td>
			<td>30</td>
			<td>STD#6 [2]</td>
		</tr>
		<tr>
			<td colspan="5">[1] 10 ng/&#181;L Lambda DNA in TE (10 mM Tris, 1 mM EDTA, pH 8.0)</td>
		</tr>
		<tr>
			<td colspan="5">[2] Use STD#3 to prepare STD#6</td>
		</tr>
	</tbody>
</table>
Table 1: Preparation of standard dilutions. A 4-log dilution standards spanning 31250-30 copies were prepared according to the table using TE/lambda DNA as the diluent. The same standard dilutions were used for both CD8 and GAPDH. The standards were stored at -20 &#176;C.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagents</td>
			<td>Amount</td>
		</tr>
		<tr>
			<td>Lambda DNA (50 ng/&#181;L in TE, pH8.0)</td>
			<td>1 &#181;L</td>
		</tr>
		<tr>
			<td>TaqMan assay</td>
			<td>0.5 &#181;L</td>
		</tr>
		<tr>
			<td>Water, Nuclease-free</td>
			<td>0.5 &#181;L</td>
		</tr>
		<tr>
			<td>Master Mix</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>TOTAL</td>
			<td>7 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 2: Preparation of qPCR cocktail. Prepare the qPCR master mix in two separate tubes, one each for CD8 and GAPDH, excluding the template DNA according to the table.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Step</td>
			<td>Time</td>
			<td>Temp</td>
			<td>Cycles</td>
		</tr>
		<tr>
			<td>Pre-incubation</td>
			<td>35 min</td>
			<td>95&#176;C</td>
			<td>1X</td>
		</tr>
		<tr>
			<td rowspan="2">Amplification</td>
			<td>15 sec</td>
			<td>95&#176;C</td>
			<td rowspan="2">50X</td>
		</tr>
		<tr>
			<td>1 min</td>
			<td>61&#176;C</td>
		</tr>
		<tr>
			<td>Cooldown</td>
			<td>5 sec</td>
			<td>42&#176;C</td>
			<td>1X</td>
		</tr>
	</tbody>
</table>
Table 3: qPCR run parameters for the CD8 and Treg assay. The qPCR was run according to the parameters specified in the table.
5. Th17 assay
<ol>
	<li>DNA purification following bisulfite conversion
	<ol>
		<li>Perform the purification steps as described in section 4.1.</li>
		<li>To elute the samples, add 25 &#181;L of elution buffer to each tube and incubate at RT for 7 min at 1,400 rpm. Alternatively, vortex at a moderate speed using a foam adaptor.</li>
		<li>Briefly spin down the tubes and place on a magnetic rack for 2 min.</li>
		<li>Transfer 20 &#181;L of eluate to a new tube without disturbing the beads. The eluate contains the bisulfite-converted DNA.</li>
	</ol>
	</li>
	<li>DNA preamplification 
	NOTE: DNA preamplification is recommended for low abundance targets for accurate quantification via qPCR19. The Th17 methylation assay was designed to require preamplification for this reason.
	<ol>
		<li>Transfer 2 &#181;L of bisulfite-converted DNA into the PCR strip tubes. Create an NTC tube with 2 &#181;L of water.</li>
		<li>Create the preamplification master mix for all samples (see Table 4).</li>
		<li>Add 23 &#181;L of the master mix to each tube. Cap the tubes, vortex, and briefly spin down the tubes.</li>
		<li>Run the preamplification protocol on a thermocycler using a heated lid. (Table 5). After the run is complete, briefly spin down the tubes.</li>
		<li>Transfer 2 &#181;L of the amplified DNA to new tubes and add 78 &#181;L of water, diluting the samples 1:40.</li>
	</ol>
	</li>
	<li>qPCR set up and run
	<ol>
		<li>Follow section 4.2 to set up and run the qPCR.</li>
	</ol>
	</li>
	<li>Analyze data using the analysis template for the Th17 assay (see Table of Materials) to calculate the Ct Average, Ct Standard Deviation, and Copy Number.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagent</td>
			<td>Amount</td>
		</tr>
		<tr>
			<td>Water, Nuclease-free</td>
			<td>9.5 &#181;L</td>
		</tr>
		<tr>
			<td>Hot Start PCR Master Mix</td>
			<td>12.5 &#181;L</td>
		</tr>
		<tr>
			<td>Th17 PCR Primer</td>
			<td>1 &#181;L</td>
		</tr>
		<tr>
			<td>TOTAL</td>
			<td>23 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 4: Pre-amplify the DNA for the Th17 assay. The pre-amplification reaction master mix was prepared according to the table.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Step</td>
			<td>Time</td>
			<td>Temp</td>
			<td>Cycles</td>
		</tr>
		<tr>
			<td>Pre-incubation</td>
			<td>35 min</td>
			<td>95&#176;C</td>
			<td>1X</td>
		</tr>
		<tr>
			<td>Denaturation</td>
			<td>1 min</td>
			<td>95&#176;C</td>
			<td rowspan="3">12X</td>
		</tr>
		<tr>
			<td>Annealing</td>
			<td>45 sec</td>
			<td>55&#176;C</td>
		</tr>
		<tr>
			<td>Elongation</td>
			<td>30 sec</td>
			<td>72&#176;C</td>
		</tr>
		<tr>
			<td>Final elongation</td>
			<td>10 min</td>
			<td>72&#176;C</td>
			<td rowspan="2">1X</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>Indefinite</td>
			<td>4&#176;C</td>
		</tr>
	</tbody>
</table>
Table 5: Preamplify Th17 DNA. Th17 DNA was pre-amplified for 12 cycles before the actual qPCR.

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O., Yuan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+real-time+PCR+assisted+cell+counting+(qPACC)+for+epigenetic+-+based+immune+cell+quantification+in+blood+and+tissue.">Quantitative real-time PCR assisted cell counting (qPACC) for epigenetic - based immune cell quantification in blood and tissue.</a> Journal for ImmunoTherapy of Cancer. 3, 46 (2015).</li><li>Rapko, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+methylation+analysis+as+novel+tool+for+quality+control+in+regenerative+medicine.">DNA methylation analysis as novel tool for quality control in regenerative medicine.</a> Tissue Engineering. 13, 2271-2280 (2007).</li><li>Wieczorek, G., 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=Quantitative+DNA+Methylation+Analysis+of+FOXP3+as+a+New+Method+for+Counting+Regulatory+T+Cells+in+Peripheral+Blood+and+Solid+Tissue.">Quantitative DNA Methylation Analysis of FOXP3 as a New Method for Counting Regulatory T Cells in Peripheral Blood and Solid Tissue.</a> Cancer Research. 69, 599-608 (2009).</li><li>Andersson, 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=Properties+of+targeted+preamplification+in+DNA+and+cDNA+quantification.">Properties of targeted preamplification in DNA and cDNA quantification.</a> Expert Review of Molecular Diagnosis. 15, 1085-1100 (2015).</li><li>Jung, T., Schauer, U., Heusser, C., Neumann, C., Rieger, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+intracellular+cytokines+by+flow+cytometry.">Detection of intracellular cytokines by flow cytometry.</a> Journal of Immunological Methods. 159, 197-207 (1993).</li><li>Armbruster, D. A., Pry, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Limit+of+Blank,+Limit+of+Detection+and+Limit+of+Quantitation.">Limit of Blank, Limit of Detection and Limit of Quantitation.</a> Clinical Biochemistry Reviews. 29, S49-S52 (2008).</li><li>Bustin, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+MIQE+guidelines:+minimum+information+for+publication+of+quantitative+real-time+PCR+experiments.">The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments.</a> Clinical Chemistry. 55, 611-622 (2009).</li><li>Li, Y., Tollefsbol, T. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+methylation+detection:+Bisulfite+genomic+sequencing+analysis.">DNA methylation detection: Bisulfite genomic sequencing analysis.</a> Methods in Molecular Biology. 791, 11-21 (2011).</li><li>Warnecke, 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=Identification+and+resolution+of+artifacts+in+bisulfite+sequencing.">Identification and resolution of artifacts in bisulfite sequencing.</a> Methods. 27, 101-107 (2002).</li><li>Genereux, D. P., Johnson, W. C., Burden, A. F., St&#246;ger, R., Laird, C. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Errors+in+the+bisulfite+conversion+of+DNA:+modulating+inappropriate-+and+failed-conversion+frequencies.">Errors in the bisulfite conversion of DNA: modulating inappropriate- and failed-conversion frequencies.</a> Nucleic Acids Research. 36, e150 (2008).</li><li>Darst, R. P., Pardo, C. E., Ai, L., Brown, K. D., Kladde, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bisulfite+Sequencing+of+DNA.">Bisulfite Sequencing of DNA.</a> Current Protocols in Molecular Biology. 91, 7.9.1-7.9.7 (2010).</li><li>Taylor, S., Wakem, M., Dijkman, G., Alsarraj, M., Nguyen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+practical+approach+to+RT-qPCR-Publishing+data+that+conform+to+the+MIQE+guidelines.">A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines.</a> Methods. 50, S1-S5 (2010).</li><li>Baron, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+demethylation+in+the+human+FOXP3+locus+discriminates+regulatory+T+cells+from+activated+FOXP3(+)+conventional+T+cells.">DNA demethylation in the human FOXP3 locus discriminates regulatory T cells from activated FOXP3(+) conventional T cells.</a> European Journal of Immunology. 37, 2378-2389 (2007).</li><li>Yao, Z., 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=Human+IL-17:+a+novel+cytokine+derived+from+T+cells.">Human IL-17: a novel cytokine derived from T cells.</a> Journal of Immunology. 155, 5483-5486 (1995).</li><li>Lockhart, E., Green, A. M., Flynn, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IL-17+production+is+dominated+by+gammadelta+T+cells+rather+than+CD4+T+cells+during+Mycobacterium+tuberculosis+infection.">IL-17 production is dominated by gammadelta T cells rather than CD4 T cells during Mycobacterium tuberculosis infection.</a> Journal of Immunology. 177, 4662-4669 (2006).</li><li>Liang, S. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interleukin+(IL)-22+and+IL-17+are+coexpressed+by+Th17+cells+and+cooperatively+enhance+expression+of+antimicrobial+peptides.">Interleukin (IL)-22 and IL-17 are coexpressed by Th17 cells and cooperatively enhance expression of antimicrobial peptides.</a> Journal of Experimental Medicine. 203, 2271-2279 (2006).</li><li>Olsen, I., Sollid, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pitfalls+in+determining+the+cytokine+profile+of+human+T+cells.">Pitfalls in determining the cytokine profile of human T cells.</a> Journal of Immunological Methods. 390, 106-112 (2013).</li></ol>

Suman K. Pradhan, Jerry Guzman, Carl Dargitz, Mark Landon, and Uma Lakshmipathy are employed by Thermo Fisher Scientific. Sven Olek, Bjoern Samans, and Ulrich Hoffmueller are a founder and employees of the company Epiontis, respectively. No writing assistance was utilized in the production of this manuscript.

With the advent of new immunotherapeutics, there is a need for standardized methods of detecting immune cell identity and purity. Assessment methods for in-process and release testing that are robust, validated, and scalable remain to be established and pose a major challenge to the commercialization of cell-based therapies. While flow cytometry is currently the most common method for immune cell phenotyping, high sample quality and quantity requirements make it difficult for regular use. Further, implementing flow cytometry in a good manufacturing practice (GMP) environment is limited by operator-dependent gating strategies and the requirement of reference standards for each marker used13,14. Although automated gating has been shown to improve assay robustness, it is not yet a well-established quality control strategy. While gene expression profiling may also be used to characterize cell therapy product identity and purity, the data are semiquantitative and labor intensive. Additionally, RNA and microRNA are relatively less stable than DNA and can contribute to the lack of robust and reproducible results. Thus, utilizing the epigenetic DNA methylation status of a specific locus provides a stable, easy to perform, robust, and scalable method of identifying and quantifying a cell type of interest.
The detection of methylation patterns to phenotype cells relies on three critical steps: First, the assay requires the use of gDNA, which is isolated according to the described methods. High quality DNA is required and if not used, bisulfite conversion may be affected23,24. If low quality DNA is obtained, further purification is recommended to ensure the assay reliability. Second, successful bisulfite conversion of the unmethylated cytosine to uracil is required. The most critical step in bisulfite conversion is DNA denaturation23,25. High temperature denaturation using ammonium bisulfite, as opposed to sodium bisulfite, increases the conversion efficiency and consistency and is the recommended process for these assays25,26. Users must ensure that the reaction occurs at 80 &#176;C and that sample mixing is done frequently. For these reasons, a digital thermomixer is recommended (see Table of Materials). Third, proper pipetting technique must be followed during the qPCR preparation. Three technical replicates are required during the qPCR reaction to adhere to the minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines27. The inclusion of more technical replicates is acceptable but not required. Improper pipetting technique and/or not including technical replicates will lead to unreliable qPCR results.
If the critical steps are followed and desired results are still not obtained, multiple controls within the assay can be leveraged to pinpoint the issue. Proper bisulfite conversion is the most crucial step in this assay. Improper bisulfite conversion would be highlighted by a failed calibration factor and/or reference values calculated by the analysis template. If the bisulfite conversion was performed incorrectly (e.g., if the reaction was carried out at RT), there would be no amplification during qPCR. Also, no amplification would be seen if an error was made during the qPCR reaction preparation (e.g., if the qPCR master mix was not added). These two issues can be separated by investigating the standard samples. Amplification in the standard samples, but not the reference, calibrator, and experimental samples, would indicate that the bisulfite conversion was not performed correctly. No amplification in any of the samples would indicate that the qPCR was not performed correctly.
While this assay addresses the stringent sample requirement and analysis variability associated with other phenotyping methods, specifically flow cytometry, there are limitations that must be noted. This assay targets a single locus that is used as a unique identifier for the target cell, which prohibits the multiplexed analysis that is commonly performed with flow cytometry. This makes identifying complex cell types like Th17 difficult. However, the use of methylation patterns at a single locus has been shown to be an accurate phenotypic marker of multiple cells and has been used in multiple clinical trials15,16. This is true especially in Tregs where assessing FOXP3 methylation signatures is an accurate and unambiguous method for detecting true Tregs from transiently FOXP3 expressing cells28. The loss of multiplexed analysis is compensated by the accuracy of the loci interrogated. Future iterations of the assay could include multiple qPCR dyes and quenchers to allow for the detection of multiple loci in one qPCR reaction.
This assay can be used as an alternative method for flow cytometry in determining cell phenotype and identity. It should be noted that this assay does not yield the exact values that flow cytometry does (Figures 1-3). This is due in part to the variability of data analysis associated with flow cytometry. Depending on the gating strategy, results from flow cytometry can vary significantly. This is especially the case when using complex staining protocols to look at intracellular targets, such as IL-17, where the coefficient of variation (CV) can be as high as 15%14. Between multiple users, the assay presented consistently had a CV of &#60;15%, supporting the robustness of the assay and improved standardization capabilities. The largest differences between epigenetic and flow cytometry-based phenotyping is seen when cell stimulation is required (Figure 3). The detection of Th17 cells via flow cytometry was accomplished using a common protocol for T cell stimulation and intracellular cytokine staining29,30,31. The differences seen in Th17 phenotyping between epigenetic measurement and flow cytometry could be due to the kinetics of IL-17 production. While methylation signatures are steady, protein production takes time and must be present in sufficient quantities to be detected by fluorescent antibodies20,32. The 6 h incubation with protein transport inhibitor and cell stimulation cocktail may need to be extended to see the level of Th17 cells detected via epigenetic-based phenotyping methods. More studies are needed to determine the precise reason why the values do not match and determine the most accurate phenotyping method.
In this report, we detail how to identify and quantify immune cell types in a heterogeneous mixture of cells in a simple and robust manner. The assays are designed and optimized for potential use for in-process and release testing of cell-based therapeutics. The described assays meet the requirement for highly qualified raw materials to be used in cell therapy applications. By addressing the shortcomings of flow cytometry and other molecular methods such as stability, sample requirements, prior stimulation, cellular permeabilization for intracellular staining, and subjectivity of data analysis, the described assays are in line with the goals of commercialization of cell-based therapies.

The use of T cells in cellular immunotherapy has increased significantly over the last decade, especially with the advent of chimeric antigen receptor (CAR) T cell technology1. While T cell-based therapies have been met with great clinical success, they are heterogeneous and cell characteristics and identities can vary significantly between donors2. The heterogeneity of T cell populations can have major impacts on therapeutic efficacy and complicate conclusions from clinical trials. For this reason, it is crucial to understand T cell heterogeneity during product manufacturing and formulation to determine the optimal formulations of T cell immunotherapies.
In a broad sense, T cells can be divided into two groups: CD4+ helper T cells, which secrete immunomodulatory cytokines, and CD8+ cytotoxic T cells, which directly lyse cells presenting the cognate antigen on major histocompatibility complex (MHC) I molecules. CD4+ helper T cells can differentiate into a myriad of specific subsets that produce a unique set of cytokines. Some suggest that a defined ratio of CD4+ to CD8+ T cells in the final cell product maximize in vivo efficacy and persistence but could complicate cell manufacturing3. Regulatory T cells (Tregs), a subset of CD4+ T cells, are immunosuppressive and diminish the activation of immune cells. Regulatory T cells have been implicated in mediating tumor tolerance and, if present during CAR T cell manufacturing, can inhibit the antitumor immune response of CAR T cells4,5,6. Other CD4+ subsets such as T helper 17 (Th17) T cells promote tumor clearance and can be leveraged to increase the efficacy of T cell therapies. Thus, increasing Th17 CD4+ T cells is of special interest in solid tumor settings where increased production of interleukin 17 (IL-17) promotes antitumor immune response5,7,8,9. Understanding the importance of Th17 cells in tumor responses has prompted more investigation into strategies to generate these cells in vitro7,9. Therefore, methods for detecting these T cell subsets are crucial for optimizing T cell therapies and should be robust, easy, scalable, and reproducible.
Flow cytometry is the most common method to determine the identity of an immune cell. However, flow cytometry requires live, intact cells that must be analyzed on the same day they are harvested. For intracellular cytokine staining (ICS), protein secretion inhibition is required to retain cytokines within the T cells. However, different inhibitor compounds have differential effects on the secretion of specific cytokines, forcing users to create specialized cocktails for the detection of the cytokine of interest10,11. Additionally, the fidelity of flow cytometry relies on antibody clones that bind specifically and potently to their target. The use of different antibody clones can cause varying results and lead to imprecise conclusions, making the method difficult to standardize12. Further, analysis of data collected via flow cytometry, and thus conclusions drawn from the data, can vary greatly between users based on how gates are set13,14. For these reasons, flow cytometry is not ideal for precise quality control during cell therapy development.
The assessment of genomic methylation at specific gene loci is an alternative method of determining cell identity. The rationale for using DNA methylation to identify specific cell types has been described in multiple articles and relies on specific methylation patterns present in a given cell population15,16,17. In target cells, certain sites contain unmethylated nucleotides, while in non-target cells these sites are methylated. This pattern can be detected through bisulfite conversion, a process that exclusively converts unmethylated cytosine nucleotides (C) to uracil (U). Primers and probes can be designed against the converted sites, then amplified and detected through qPCR16.
The assay described herein measures the frequency of immune cell subtypes within heterogeneous populations by quantifying the number of specific unmethylated loci compared to a housekeeping gene. Copies of the unmethylated regulatory elements for the CD8 B gene are measured in the CD8 assay, FoxP3 in Treg, and IL-17 in Th17 in this assay. These loci were identified by isolating the specific cell population of interest (e.g., CD8+ T cells) and performing bisulfite sequencing to identify loci that are unmethylated in only that cell type15. Primers and probes are developed against the uniquely unmethylated loci and samples are analyzed via qPCR. A standard curve of known copy numbers is created from dilutions of a high copy number standard plasmid, allowing for the conversion of a Ct value to a transcript copy number.
To evaluate assay performance, control samples are required, including a reference gDNA and a calibrator plasmid. The reference genomic material is a pooled blood sample from multiple donors with known immune cell frequencies and is used for a quality control (QC) assay performance check. The calibrator sample is a synthesized plasmid that contains the CD8, FoxP3, and GAPDH gene sequences in an equimolar ratio. It is used as a measure of the bisulfite conversion efficiency, because bisulfite conversions within different genomic regions will vary in efficiency. The ratio of the target to GAPDH within the calibrator is 1, but due to differences of the bisulfite conversion efficiency, the ratio can vary. This calibration factor is applied to the CD8 and Treg assays. FoxP3, the gene used in the Treg assay, is located on the X chromosome. Because this assay measures only unmethylated copies of FoxP3, and only one copy of FoxP3 is unmethylated in Tregs, this assay is sex agnostic and can be used with both male and female samples18. The Th17 assay does not utilize a calibrator sample or GAPDH as a housekeeping gene. Instead, another primer set targeting the methylated loci present in non-Th17 cells is included and the total number of cells is reflected by the sum of the methylated and unmethylated copies of IL-17. The data obtained from the qPCR is entered into a preset analysis template that performs quality control checks on the data and calculates the percentage of the target cell within the starting population. This provides automated and unbiased data analysis, thereby removing user subjectivity and improving standardization capabilities.
This assay uses gDNA, which can be isolated by a leukapheresis procedure using cultured or fixed cells, or frozen cell pellets. The low sample requirement, flexibility in sample starting material, high accuracy, and standardized analysis address the limitations associated with flow cytometry and are ideal for quality control purposes during cell therapy process development.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2.0 ml Eppendorf Safe-Lock Tubes</td>
			<td>Eppendorf</td>
			<td>22363344</td>
			<td></td>
		</tr>
		<tr>
			<td>Analysis template for CD8 assay</td>
			<td></td>
			<td></td>
			<td><a href="https://assets.thermofisher.com/TFS-Assets/LSG/manuals/CTSPureQuantCD8plusTCellAssayAnalysisTemplate.xlsx">Click Here</a></td>
		</tr>
		<tr>
			<td>Analysis template for Th17 assay</td>
			<td></td>
			<td></td>
			<td><a href="https://assets.thermofisher.com/TFS-Assets/LSG/manuals/CTSPureQuantTh17AssayAnalysisTemplate.xlsx">Click Here</a></td>
		</tr>
		<tr>
			<td>Analysis template for Treg assay</td>
			<td></td>
			<td></td>
			<td><a href="https://assets.thermofisher.com/TFS-Assets/LSG/manuals/CTSPureQuantTregAssayAnalysisTemplate.xlsx">Click Here</a></td>
		</tr>
		<tr>
			<td>Attune NxT Acoustic Focusing Cytometer, blue/red/violet6/yellow</td>
			<td>Thermo Fisher Scientific</td>
			<td>A29004</td>
			<td></td>
		</tr>
		<tr>
			<td>CTS PureQuant CD8+ T-Cell Assay</td>
			<td>Thermo Fisher Scientific</td>
			<td>A43674</td>
			<td></td>
		</tr>
		<tr>
			<td>CTS PureQuant Th17 Assay</td>
			<td>Thermo Fisher Scientific</td>
			<td>A43676</td>
			<td></td>
		</tr>
		<tr>
			<td>CTS PureQuant Treg Assay</td>
			<td>Thermo Fisher Scientific</td>
			<td>A43675</td>
			<td></td>
		</tr>
		<tr>
			<td>Dynabeads SILANE Genomic DNA Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>37012D</td>
			<td>Used for genomic DNA isolation and purification after bisulfite conversion. Contains elution buffer to dilute samples.</td>
		</tr>
		<tr>
			<td>DynaMag-2 Magnet</td>
			<td>Thermo Fisher Scientific</td>
			<td>12321D</td>
			<td>Used as a magnetic rack during DNA isolation and purification</td>
		</tr>
		<tr>
			<td>eBioscience Essential Human T cell Phenotyping Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>A42923</td>
			<td></td>
		</tr>
		<tr>
			<td>eBioscience Essential Human Th1/Th17 Phenotyping Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>A42927</td>
			<td></td>
		</tr>
		<tr>
			<td>eBioscience Essential Human Treg Phenotyping Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>A42925</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf SmartBlock 2 mL</td>
			<td>Eppendorf</td>
			<td>5362000035</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf ThermoMixer C</td>
			<td>Eppendorf</td>
			<td>5382000023</td>
			<td></td>
		</tr>
		<tr>
			<td>HulaMixer Sample Mixer</td>
			<td>Thermo Fisher Scientific</td>
			<td>15920D</td>
			<td>Recommended sample mixer</td>
		</tr>
		<tr>
			<td>NanoDrop</td>
			<td>Thermo Fisher Scientific</td>
			<td>ND 1000</td>
			<td></td>
		</tr>
		<tr>
			<td>Nonstick, RNase-free Microfuge Tubes, 1.5 mL</td>
			<td>Applied Biosystems</td>
			<td>AM12450</td>
			<td></td>
		</tr>
		<tr>
			<td>QuantStudio 12S Flex</td>
			<td>Applied Biosystems</td>
			<td>4470661</td>
			<td></td>
		</tr>
		<tr>
			<td>TE, pH 8.0, RNase-free</td>
			<td>Thermo Fisher Scientific</td>
			<td>AM9849</td>
			<td></td>
		</tr>
		<tr>
			<td>Vortex Mixer</td>
			<td>Thermo Fisher Scientific</td>
			<td>H2KT17113</td>
			<td></td>
		</tr>
	</tbody>
</table>

determination of immune cell identity and purity using epigenetic-based quantitative pcr

Immune cell subtype population frequencies can have a large effect on the efficacy of T cell therapies. Current methods, like flow cytometry, have specific sample requirements, high sample input, are low throughput, and are difficult to standardize, all of which are detrimental to characterization of cell therapy products during their development and manufacturing.
The assays described herein accurately identify and quantify immune cell types in a heterogeneous mixture of cells using isolated genomic DNA (gDNA). DNA methylation patterns are revealed through bisulfite conversion, a process in which unmethylated cytosines are converted to uracils. Unmethylated DNA regions are detected through qPCR amplification using primers targeting converted areas. One unique locus per assay is measured and serves as an accurate identifier for a specific cell type. The assays are robust and identify CD8+, regulatory, and Th17 T cells in a high throughput manner. These optimized assays can potentially be used for in-process and product release testing for cell therapy process.

All three methylation assays begin with an input of gDNA and result in a percentage of CD8+ T cells, Treg, or Th17 cells within the entire cell population. The data generated from the qPCR following bisulfite conversion was analyzed using the provided analysis template. This template used the standard curves obtained from the standard samples to estimate the copy number of the targets and total cell counts in the test samples. The percent cell type was then calculated using the formulas integrated in the analysis templates. Figure 1 shows representative data from the CD8+ assay, obtained from analyzing both peripheral blood mononuclear cells (PBMCs) and T cells from three different donors using both flow cytometry and the described methylation assay. The trends between the two methods across all donors in both cell types were similar. Figure 2 shows representative data from the Treg assay. Three purified Treg donors were analyzed using both methylation-based qPCR and flow cytometry and the results were plotted on the graph shown. Results for both methods were relatively similar. However, in all cases flow cytometry yielded higher values. Figure 3 shows the comparison of Th17 cells detected via flow cytometry and methylation. In this graph, there are stimulated and unstimulated conditions for the experimental methods. Cells require stimulation with PMA and ionomycin and treatment with a protein transport inhibitor to increase the amount of IL-17A present within each cell so that it can be detected via flow cytometry20. Methylation status can be detected regardless of the stimulation status. Flow cytometry yielded lower levels of Th17 cells when compared to methylation. The sensitivity and specificity of the assay was demonstrated by the limit of blank (LOB), limit of detection (LOD), and limit of quantitation (LOQ) values displayed in Table 6.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60465/60465fig1.jpg" /> 
Figure 1: Flow and methylation comparison of CD8+ percentages for both PBMCs and T cells for three different donors. PBMCs and T cells from three different donors were assayed for the percentage of CD8+ T cells in the entire population using either flow cytometry or methylation. <a href="https://www.jove.com/files/ftp_upload/60465/60465fig1large.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/60465/60465fig2.jpg" /> 
Figure 2: Flow and methylation comparison of Treg percentages for purified Treg for three different donors. Purified Treg cells were assayed for the percentage of Treg cells in the entire population using either flow cytometry or methylation. <a href="https://www.jove.com/files/ftp_upload/60465/60465fig2large.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/60465/60465fig3.jpg" /> 
Figure 3: Flow and methylation comparison of Th17 percentages for stimulated and unstimulated experimental groups. Stimulated and unstimulated T cells were analyzed using both flow cytometry and methylation. <a href="https://www.jove.com/files/ftp_upload/60465/60465fig3large.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>Assay</td>
			<td>LoB</td>
			<td>LoD</td>
			<td>LoQ</td>
		</tr>
		<tr>
			<td>CD8</td>
			<td>0</td>
			<td>19</td>
			<td>52</td>
		</tr>
		<tr>
			<td>Gapdh</td>
			<td>0</td>
			<td>7</td>
			<td>21</td>
		</tr>
		<tr>
			<td>FoxP3</td>
			<td>0</td>
			<td>3</td>
			<td>12</td>
		</tr>
		<tr>
			<td>Th17 TpG</td>
			<td>0</td>
			<td>35</td>
			<td>73</td>
		</tr>
		<tr>
			<td>Th17 CpG</td>
			<td>0</td>
			<td>38</td>
			<td>73</td>
		</tr>
	</tbody>
</table>
Table 6: LoB, LoD, LoQ of copy numbers for methylation-based assays. Sensitivity and specificity of the assay is detailed by the LoB, LoD, and LoQ values obtained by MIQE guidelines21,22. In general, as few as 40 copies can be accurately detected by these assays.

Watch this Scientific Journal Video about Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR at JoVE.com

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Here we describe a robust method of determining immune cell identity and purity through epigenetic signatures detected using quantitative PCR (qPCR). DNA demethylation at a specific locus serves as a unique identifier for a particular cell type and allows for identification of CD8+, regulatory, or Th17 T cells.

Immune cell subtype population frequencies can have a large effect on the efficacy of T cell therapies. Current methods, like flow cytometry, have ...

determination-immune-cell-identity-purity-using-epigenetic-based

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Immunology and Infection

7.0K Views.  Thermo Fisher Scientific. Here we describe a robust method of determining immune cell identity and purity through epigenetic signatures detected using quantitative PCR (qPCR). DNA demethylation at a specific locus serves as a unique identifier for a particular cell type and allows for identification of CD8+, regulatory, or Th17 T cells.

Video: Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Quantitative Assessment of Immune Cells in the Injured Spinal Cord Tissue by Flow Cytometry:  a Novel Use for a Cell Purification Method

Detection of immune cells in the injured central nervous system (CNS) using morphological or histological techniques has not always provided true quantitative analysis of cellular inflammation. Flow cytometry is a quick alternative method to quantify immune cells in the injured brain or spinal cord tissue. Historically, flow cytometry has been used to quantify immune cells collected from blood or dissociated spleen or thymus, and only a few studies have attempted to quantify immune cells in the injured spinal cord by flow cytometry using fresh dissociated cord tissue. However, the dissociated spinal cord tissue is concentrated with myelin debris that can be mistaken for cells and reduce cell count reliability obtained by the flow cytometer. We have advanced a cell preparation method using the OptiPrep gradient system to effectively separate lipid/myelin debris from cells, providing sensitive and reliable quantifications of cellular inflammation in the injured spinal cord by flow cytometry. As described in our recent study (Beck &amp; Nguyen et al., Brain. 2010 Feb; 133 (Pt 2): 433-47), the OptiPrep cell preparation had increased sensitivity to detect cellular inflammation in the injured spinal cord, with counts of specific cell types correlating with injury severity. Critically, novel usage of this method provided the first characterization of acute and chronic cellular inflammation after SCI to include a complete time course for polymorphonuclear leukocytes (PMNs, neutrophils), macrophages/microglia, and T-cells over a period ranging from 2 hours to 180 days post-injury (dpi), identifying a surprising novel second phase of cellular inflammation. Thorough characterization of cellular inflammation using this method may provide a better understanding of neuroinflammation in the injured CNS, and reveal an important multiphasic component of neuroinflammation that may be critical for the design and implementation of rational therapeutic treatment strategies, including both cell-based and pharmacological interventions for SCI.

Quantification of cellular inflammation in the injured/pathological CNS by flow cytometry is complicated by lipid/myelin debris that can have similar size and granulation to cells, decreasing sensitivity/accuracy. We have advanced a cell preparation method to remove myelin debris and improve cell detection by flow cytometry in the injured spinal cord.

Detection of immune cells in the injured central nervous system (CNS) using morphological or histological techniques has not always provided true ...

Isolation of Functional Cardiac Immune Cells

Cardiac immune cells are gaining interest for the roles they play in the pathological remodeling in many cardiac diseases.1-5 These immune cells, which include mast cells, T-cells and macrophages; store and release a variety of biologically active mediators including cytokines and proteases such as tryptase.6-8 These mediators have been shown to be key players in extracellular matrix metabolism by activating matrix metalloproteinases or causing collagen accumulation by modulating the cardiac fibroblasts' function.9-11 However, available techniques for isolating cardiac immune cells have been problematic because they use bacterial collagenase to digest the myocardial tissue. This technique causes activation of the immune cells and thus a loss of function. For example, cardiac mast cells become significantly less responsive to compounds that cause degranulation.12 Therefore, we developed a technique that allows for the isolation of functional cardiac immune cells which would lead to a better understanding of the role of these cells in cardiac disease.13, 14
This method requires a familiarity with the anatomical location of the rat's xiphoid process, axilla and falciform ligament, and pericardium of the heart. These landmarks are important to increase success of the procedure and to ensure a higher yield of cardiac immune cells. These isolated cardiac immune cells can then be used for characterization of functionality, phenotype, maturity, and co-culture experiments with other cardiac cells to gain a better understanding of their interactions.

This method for isolating functional immune cells from the heart provides an alternative to the conventional methods of collagenase digestion, which causes unwanted immune cell activation, resulting in a decreased responsiveness of these cells. Our method of isolation yields functional cardiac immune cells by avoiding problems associated with enzymatic digestion.

Cardiac immune cells are gaining interest for the roles they play in the pathological remodeling in many cardiac diseases.1-5  These immune cells, which ...

Intravital Imaging of the Mouse Thymus using 2-Photon Microscopy

Two-photon Microscopy (TPM) provides image acquisition in deep areas inside tissues and organs. In combination with the development of new stereotactic tools and surgical procedures, TPM becomes a powerful technique to identify "niches" inside organs and to document cellular "behaviors" in live animals. While intravital imaging provides information that best resembles the real cellular behavior inside the organ, it is both more laborious and technically demanding in terms of required equipment/procedures than alternative ex vivo imaging acquisition. Thus, we describe a surgical procedure and novel "stereotactic" organ holder that allows us to follow the movements of Foxp3+ cells within the thymus.
Foxp3 is the master regulator for the generation of regulatory T cells (Tregs). Moreover, these cells can be classified according to their origin: ie. thymus-differentiated Tregs are called "naturally-occurring Tregs" (nTregs), as opposed to peripherally-converted Tregs (pTregs). Although significant amount of research has been reported in the literature concerning the phenotype and physiology of these T cells, very little is known about their in vivo interactions with other cells. This deficiency may be due to the absence of techniques that would permit such observations. The protocol described in this paper provides a remedy for this situation.
Our protocol consists of using nude mice that lack an endogenous thymus since they have a punctual mutation in the DNA sequence that compromises the differentiation of some epithelial cells, including thymic epithelial cells. Nude mice were gamma-irradiated and reconstituted with bone marrows (BM) from Foxp3-KIgfp/gfp mice. After BM recovery (6 weeks), each animal received embryonic thymus transplantation inside the kidney capsule. After thymus acceptance (6 weeks), the animals were anesthetized; the kidney containing the transplanted thymus was exposed, fixed in our organ holder, and kept under physiological conditions for in vivo imaging by TPM. We have been using this approach to study the influence of drugs in the generation of regulatory T cells.

We have developed novel laboratory tools and protocols for intravital imaging acquisition of the thymus. Our technique should help in the identification of &#x201C;niches&#x201D; within the thymus where T cell development occurs.

Two-photon Microscopy (TPM) provides image acquisition in deep areas inside tissues and organs. In combination with the development of new stereotactic ...

Quantitative Measurement of the Immune Response and Sleep in Drosophila

A complex interaction between the immune response and host behavior has been described in a wide range of species. Excess sleep, in particular, is known to occur as a response to infection in mammals 1 and has also recently been described in Drosophila melanogaster2. It is generally accepted that sleep is beneficial to the host during an infection and that it is important for the maintenance of a robust immune system3,4. However, experimental evidence that supports this hypothesis is limited4, and the function of excess sleep during an immune response remains unclear. We have used a multidisciplinary approach to address this complex problem, and have conducted studies in the simple genetic model system, the fruitfly Drosophila melanogaster. We use a standard assay for measuring locomotor behavior and sleep in flies, and demonstrate how this assay is used to measure behavior in flies infected with a pathogenic strain of bacteria. This assay is also useful for monitoring the duration of survival in individual flies during an infection. Additional measures of immune function include the ability of flies to clear an infection and the activation of NF&kappa;B, a key transcription factor that is central to the innate immune response in Drosophila. Both survival outcome and bacterial clearance during infection together are indicators of resistance and tolerance to infection. Resistance refers to the ability of flies to clear an infection, while tolerance is defined as the ability of the host to limit damage from an infection and thereby survive despite high levels of pathogen within the system5. Real-time monitoring of NF&kappa;B activity during infection provides insight into a molecular mechanism of survival during infection. The use of Drosophila in these straightforward assays facilitates the genetic and molecular analyses of sleep and the immune response and how these two complex systems are reciprocally influenced.

Quantitative Measurement of the Immune Response and Sleep in Drosophila

To understand a link between the immune response and behavior, we describe a method to measure locomotor behavior in Drosophila during bacterial infection as well as the ability of flies to mount an immune response by monitoring survival, bacterial load, and real-time activity of a key regulator of innate immunity, NF&kappa;B.

A complex interaction between the immune response and  host behavior has been described in a wide range of species. Excess sleep, in  particular, is known ...

Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin

The stroma is a key component of the lymph node structure and function. However, little is known about its origin, exact cellular composition and the mechanisms governing its formation. Lymph nodes are always encapsulated in adipose tissue and we recently demonstrated the importance of this relation for the formation of lymph node stroma. Adipocyte precursor cells migrate into the lymph node during its development and upon engagement of the Lymphotoxin-b receptor switch off adipogenesis and differentiate into lymphoid stromal cells (B&#233;n&#233;zech&#160;et al.14). Based on the lymphoid stroma potential of adipose tissue, we present a method using a lymph node/fat pad chimera that allows the lineage tracing of lymph node stromal cell precursors. We show how to isolate newborn lymph nodes and EYFP+ embryonic adipose tissue and make a LN/ EYFP+ fat pad chimera. After transfer under the kidney capsule of a host mouse, the lymph node incorporates local adipose tissue precursor cells and finishes its formation. Progeny analysis of EYFP+ fat pad cells in the resulting lymph nodes can be performed by flow-cytometric analysis of enzymatically digested lymph nodes or by immunofluorescence analysis of lymph nodes cryosections. By using fat pads from different knockout mouse models, this method will provide an efficient way of analyzing the origin of the different lymph node stromal cell populations.

Generation of lymph node/fat pad chimeras for the study of lymph node stromal cell origin is described. The method involves the isolation of lymph nodes from newborn mice and embryonic fat pads, the generation of chimeric lymph node-fat pads, and their transfer under the kidney capsule of a host mouse.

The stroma is a key component of the lymph node structure and function. However, little is known about its origin, exact cellular composition and the ...

Development and Validation of a Quantitative PCR Method for Equid Herpesvirus-2 Diagnostics in Respiratory Fluids

The protocol describes a quantitative RT-PCR method for the detection and quantification of EHV-2 in equine respiratory fluids according to the NF U47-600 norm. After the development and first validation step, two distinct characterization steps were performed according to the AFNOR norm: (a) characterization of the qRT-PCR assay alone and (b) characterization of the whole analytical method. The validation of the whole analytical method included the portrayal of all steps between the extraction of nucleic acids and the final PCR analysis. 
Validation of the whole method is very important for virus detection by qRT-PCR in order to get an accurate determination of the viral genome load. Since the extraction step is the primary source of loss of biological material, it may be considered the main source of error of quantification between one protocol and another. For this reason, the AFNOR norm NF-U-47-600 recommends including the range of plasmid dilution before the extraction step. In addition, the limits of quantification depend on the source from which the virus is extracted. Viral genome load results, which are expressed in international units (IU), are easier to use in order to compare results between different laboratories. 
This new method of characterization of qRT-PCR should facilitate the harmonization of data presentation and interpretation between laboratories.

Here, we present a protocol for the development and validation of a quantitative PCR method used for the detection and quantification of EHV-2 DNA in equine respiratory fluids. The EHV-2 qRT-PCR validation protocol involves a three-part procedure: development, characterization of qRT-PCR assay alone, and characterization of the whole analytical method.

The protocol describes a quantitative RT-PCR method for the detection and quantification of EHV-2 in equine respiratory fluids according to the NF U47-600 ...

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins towards the immune synapse to assure a stable binding and signal exchange. Classical confocal, TIRF, or super-resolution microscopy have been used to study the immune synapse. Since these methods require manual image acquisition and time-consuming quantification, the imaging of rare events is challenging. Here, we describe a workflow that enables the morphological analysis of tens of thousands of cells. Immune synapses are induced between primary human T cells in pan-leukocyte preparations and Staphylococcus aureus enterotoxin B (SEB)-loaded Raji cells as APCs. Image acquisition is performed with imaging flow cytometry, also called In-Flow microscopy, which combines features of a flow cytometer and a fluorescence microscope. A complete gating strategy for identifying T cell/APC couples and analyzing the immune synapses is provided. As this workflow allows the analysis of immune synapses in unpurified pan-leukocyte preparations and hence requires only a small volume of blood (i.e., 1 mL), it can be applied to samples from patients. Importantly, several samples can be prepared, measured, and analyzed in parallel.

Here, we describe a complete workflow for the qualitative and quantitative analysis of immune synapses between primary human T cells and antigen-presenting cells. The method is based on imaging flow cytometry, which allows the acquisition and evaluation of several thousand cell images within a relatively short period of time.

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins ...

Quantitative PCR of T7 Bacteriophage from Biopanning

This protocol describes the use of quantitative PCR (qPCR) to enumerate T7 phages from phage selection experiments (i.e., &#34;biopanning&#34;). qPCR is a fluorescence-based approach to quantify DNA, and here, it is adapted to quantify phage genomes as a proxy for phage particles. In this protocol, a facile phage DNA preparation method is described using high-temperature heating without additional DNA purification. The method only needs small volumes of heat-treated phages and small volumes of the qPCR reaction. qPCR is high-throughput and fast, able to process and obtain data from a 96-well plate of reactions in 2&#8211;4 h. Compared to other phage enumeration approaches, qPCR is more time-efficient. Here, qPCR is used to enumerate T7 phages identified from biopanning against in vitro cystic fibrosis-like mucus model. The qPCR method can be extended to quantify T7 phages from other experiments, including other types of biopanning (e.g.,&#160;immobilized protein binding, in vivo phage screening) and other sources (e.g., water systems or body fluids). In summary, this protocol can be modified to quantify any DNA-encapsulated viruses.

A reproducible, accurate, and time efficient quantitative PCR (qPCR) method to enumerate T7 bacteriophage is described here. The protocol clearly describes phage genomic DNA preparation, PCR reaction preparation, qPCR cycling conditions, and qPCR data analysis.

This protocol describes the use of quantitative PCR (qPCR) to enumerate T7 phages from phage selection experiments (i.e., &#34;biopanning&#34;). qPCR is a ...

Determination of Regulatory T Cell Subsets in Murine Thymus, Pancreatic Draining Lymph Node and Spleen Using Flow Cytometry

Our immune system consists of a number and variety of immune cells including regulatory T cells (Treg) cells. Treg cells can be divided into two subsets, thymic derived Treg (tTreg) cells and peripherally induced Treg (pTreg) cells. They are present in different organs of our body and can be distinguished by specific markers, such as Helios and Neuropilin 1. It has been reported that tTreg cells are functionally more suppressive than pTreg cells. Therefore, it is important to determine the proportion of both tTreg and pTreg cells when investigating heterogeneous cell populations. Herein, we collected thymic glands, pancreatic draining lymph nodes and spleens from normoglycemic non-obese diabetic mice to distinguish tTreg cells from pTreg cells using flow cytometry. We manually prepared single cell suspensions from these organs. Fluorochrome conjugated surface CD4, CD8, CD25, and Neuropilin 1 antibodies were used to stain the cells. They were kept in the fridge overnight. On the next day, the cells were stained with fluorochrome conjugated intracellular Foxp3 and Helios antibodies. These markers were used to characterize the two subsets of Treg cells. This protocol demonstrates a simple but practical way to prepare single cells from murine thymus, pancreatic draining lymph node and spleen and use them for subsequent flow cytometric analysis.

Herein, we present a protocol to prepare single cells from murine thymus, pancreatic draining lymph node and spleen to further study these cells using flow cytometry. In addition, this protocol was used for determining the subsets of regulatory T cells using flow cytometry.

Our immune system consists of a number and variety of immune cells including regulatory T cells (Treg) cells. Treg cells can be divided into two subsets, ...

Assessing the Cellular Immune Response of the Fruit Fly, Drosophila melanogaster, Using an In Vivo Phagocytosis Assay

In all animals, innate immunity provides an immediate and robust defense against a broad spectrum of pathogens. Humoral and cellular immune responses are the main branches of innate immunity, and many of the factors regulating these responses are evolutionarily conserved between invertebrates and mammals. Phagocytosis, the central component of cellular innate immunity, is carried out by specialized blood cells of the immune system. The fruit fly, Drosophila melanogaster, has emerged as a powerful genetic model to investigate the molecular mechanisms and physiological impacts of phagocytosis in whole animals. Here we demonstrate an injection-based in vivo phagocytosis assay to quantify the particle uptake and destruction by Drosophila blood cells, hemocytes. The procedure allows researchers to precisely control the particle concentration and dose, making it possible to obtain highly reproducible results in a short amount of time. The experiment is quantitative, easy to perform, and can be applied to screen for host factors that influence pathogen recognition, uptake, and clearance.

Assessing the Cellular Immune Response of the Fruit Fly, Drosophila melanogaster, Using an In Vivo Phagocytosis Assay

This protocol describes an in vivo phagocytosis assay in adult Drosophila melanogaster to quantify phagocyte recognition and clearance of microbial infections.

In all animals, innate immunity provides an immediate and robust defense against a broad spectrum of pathogens. Humoral and cellular immune responses are ...

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Summary

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Chapters in this video

Quantitative Assessment of Immune Cells in the Injured Spinal Cord Tissue by Flow Cytometry: a Novel Use for a Cell Purification Method

Isolation of Functional Cardiac Immune Cells

Intravital Imaging of the Mouse Thymus using 2-Photon Microscopy

Quantitative Measurement of the Immune Response and Sleep in Drosophila

Generation of Lymph Node-fat Pad Chimeras for the Study of Lymph Node Stromal Cell Origin

Development and Validation of a Quantitative PCR Method for Equid Herpesvirus-2 Diagnostics in Respiratory Fluids

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

Quantitative PCR of T7 Bacteriophage from Biopanning

Determination of Regulatory T Cell Subsets in Murine Thymus, Pancreatic Draining Lymph Node and Spleen Using Flow Cytometry

Assessing the Cellular Immune Response of the Fruit Fly, Drosophila melanogaster, Using an In Vivo Phagocytosis Assay