The authors would like to acknowledge funding sources from the National Institutes of Health (NIH) (NCI-R00-CA197672-01A1). Small cell lung cancer lines (SHP77 and H82) were a generous gift from Drs. John Minna and Adi Gazdar from the UT Southwestern Medical Center.

1. Buffer preparation and storage
<ol>
	<li>Prepare 50 mL of 1x stock RNase-/DNase-free NP-40 lysis buffer (10 mM Tris-HCl [pH 8.0], 1 mM MgCl2, 1 mM ethylenediaminetetraacetic acid (EDTA), 1% [vol/vol] NP-40, 10% [vol/vol] glycerol, 150 mM NaCl, 5 mM &#946;-mercaptoethanol, and 0.1 mM 4-benzenesulfonyl fluoride hydrochloride (AEBSF)). This buffer can be aliquoted and stored at -20 &#176;C for future use. Avoid freeze/thaw cycles in order to obtain an optimal lysis of cells.</li>
	<li>Prepare 50 mL of 10x stock RNase-/DNase-free TRAP extension buffer (200 mM Tris-HCl [pH 8.0], 15 mM MgCl2, 630 mM KCl, 0.5% [vol/vol] Tween 20, and 10 mM ethylene glycol-bis(&#946;-aminoethyl ether)-N,N,N&#8242;,N&#8242;-tetraacetic acid (EGTA)). This buffer can be aliquoted into 1 mL aliquots and stored at -20 &#176;C for future use.</li>
</ol>
2. Cell lysis
<ol>
	<li>Preparation of the cells
	<ol>
		<li>Thaw a frozen aliquot of NP-40 lysis buffer. Once thawed, place the lysis buffer on ice. Add phenylmethylsulfonyl fluoride (PMSF protease inhibitor) to the NP-40 lysis buffer to reach a final concentration of 0.2 mM. 
		NOTE: This should be done immediately prior to lysing the cells.</li>
		<li>Remove any excess liquid from the collected cell pellets to ensure a dry cell pellet. Note that cell pellets, whether freshly collected or previously frozen (-80 &#176;C or flash frozen in liquid N2), must not contain any leftover solutions from previous centrifugations as these solutions may interfere with downstream procedures.</li>
		<li>Grow, collect, and freeze both telomerase-positive and telomerase-negative cell lines (BJ fibroblasts, IMR-90 or U2OS) in order to use them as positive and negative controls. Use new control lysates every time the assay is performed to ensure assay reproducibility. 
		NOTE: It is advised that a large culture of telomerase-negative cells is grown and aliquoted prior to freezing to remove any tissue-culture-related artifacts that may be introduced during the long-term culture of cell lines to help ensure reproducible results of the negative control sample.</li>
		<li>Place the cell pellets on ice. If the cells were frozen, allow them to thaw briefly on ice. 
		NOTE: Typical cell pellet sizes for the ddTRAP are between 500,000 and 1,000,000 cells. Smaller cell pellets may also be used if necessary, but the cell number must/ideally should be known. The ddTRAP can also be performed based on protein input using a BCA protein assay (input usually is 1 &#181;g).</li>
	</ol>
	</li>
	<li>Lysing of the cells
	<ol>
		<li>Lyse the cells in the NP-40 lysis buffer. Maintain a cell equivalence of 25,000 cells/&#181;L of buffer. For example, lyse a pellet (containing 1,000,000 cells) in 40 &#181;L of NP-40 lysis buffer. Gently pipet the lysate up and down in order to break open the cells. Try to avoid making bubbles.</li>
		<li>Allow the cells to lyse on ice for 30 min. Make sure to gently vortex the lysate to prevent clusters of cell debris from forming. This can be done every 10 min and is meant to keep the lysate a homogenized mixture.</li>
		<li>Dilute the cell lysate (25,000 cells/&#181;L) 1:20 in NP-40 lysis buffer, making the new cell equivalence 1,250 cells/&#181;L. For example, dilute 5 &#181;L of cell lysate into 95 &#181;L of lysis buffer.</li>
	</ol>
	</li>
</ol>
3. Telomerase extension reaction
<ol>
	<li>Prepare a master mix (Table 1) for the telomerase extension reaction (per reaction). 
	NOTE: It is best to prepare the extension reaction master mix during the cell lysis and store it on ice.
	<ol>
		<li>Pipet 48 &#181;L of extension master mix into each PCR tube.</li>
		<li>Add 2 &#181;L of the diluted (1,250 cells/&#181;L) lysate to the extension reaction. The total volume should now be 50 &#181;L with a final cell equivalence of 50 cells/&#181;L.</li>
	</ol>
	</li>
	<li>Perform the telomerase extension reaction (Table 2).</li>
	<li>Store the telomerase extension products at 4 &#176;C for up to 3&#8211;5 days; however, it is optimal to use the extension products within the first 24 h.</li>
</ol>
4. Droplet digital PCR setup
<ol>
	<li>Prepare a master mix (Table 3) for the ddTRAP (per reaction). 
	NOTE: Once the reagents are at room temperature, do not place them or the master mix on ice. Placing the mix on ice may increase viscosity and lead to poor droplet formation. The DNA polymerase in the ddPCR supermix is a hot start and should be stable at room temperature. The ddPCR supermix may be stored at 4 &#176;C after an initial thaw from -20 &#176;C.
	<ol>
		<li>Pipet 19.8 &#181;L of ddPCR master mix into each PCR tube.</li>
		<li>Add 2.2 &#181;L of the extension reaction to each tube. Note that the total volume should now be 22&#160;&#181;L. 
		NOTE: The total amount of sample needed for a ddTRAP is 20 &#181;L (cell equivalence of 100 cells). The extra volume is a precaution for pipet error or sample loss.</li>
	</ol>
	</li>
	<li>Set up the droplet generation cartridge. 
	NOTE: The cartridge contains three different columns of wells that are labeled.
	<ol>
		<li>Load 20 &#181;L of the reaction prepared according to step 4.1.2 into the sample well (middle well) in the cartridge. Avoid bubbles when pipetting the sample. 
		NOTE: If there are bubbles, gently tap the side of the cartridge so that these bubbles come to the top of the solution.</li>
		<li>Load 70 &#181;L of droplet generation oil into the oil well (left well). 
		NOTE: In order to avoid contamination, strictly use a separate set of pipettes and tips for ddPCR droplet generation steps. The order of loading the cartridge is important. The sample must be loaded prior to loading oil as oil is heavier and will fill the microfluidic chambers and lead to poor droplet formation. The minimum number of samples that can be run is eight. All wells of the cartridge must be loaded with the sample as any empty well will lead to a halt in the droplet generation from the droplet generator. If adequate samples are not available to load all eight wells within a cartridge, 20 &#181;L of ddTRAP master mix (step 4.1) can be loaded into the remaining cartridges.</li>
		<li>Secure the gasket in place by tethering it to the ends of the cartridge. 
		NOTE: The lack or improper placement of the gasket will prevent the generator from generating droplets.</li>
		<li>Place the loaded and assembled cartridge into the droplet generator. 
		NOTE: The cartridge is recognized by a magnet in the generator, which will inform the user when the cartridge is placed properly.</li>
	</ol>
	</li>
	<li>Remove the cartridge once the droplets are generated and the cycle is complete (~60&#8211;90 s).
	<ol>
		<li>Gently remove the gasket from the cartridge. Pipet the newly generated droplets (right well), using a multichannel pipette, into a 96-well PCR plate. 
		NOTE: The approximate volume for the newly generated droplet emulsion should be 40&#8211;43 &#181;L.</li>
		<li>Heat seal the plate with aluminum foil PCR plate seals once all the samples are loaded in the 96-well plate in order to prevent evaporation during the PCR steps.</li>
	</ol>
	</li>
	<li>Load the 96-well plate into the thermocycler and perform the following PCR reaction (Table 4). 
	NOTE: All ramp rates between the temperature steps must be set to 2.5 &#176;C/s in order to properly heat the reactions.</li>
</ol>
5. Detection of telomerase extension products
<ol>
	<li>Load the 96-well plate in the droplet reader. 
	NOTE: Make sure to orient the plate properly so that the A1 sample matches with that of the holder.
	<ol>
		<li>Open the software associated with the droplet reader. Double-click the first well A1 to open the sample/well editor screen.</li>
		<li>Click Experiment and select ABS from the drop-down menu. 
		NOTE: Experiment defines the type of assay to be used (i.e., absolute quantification or gene copy number assay). ABS stands for absolute quantification.</li>
		<li>Select QX200 ddPCR Evagreen Supermix to ensure that the correct detection method is employed by the reader. Click Apply in the lower right-hand side of the well editor screen to save the user-defined settings to all of the highlighted wells. 
		NOTE: SUPERMIX defines the type of PCR mix and detection chemistry that will be read by the reader.</li>
		<li>Click on TARGET in the well editor screen of the software in order to define the sample.</li>
		<li>Define the TYPE of sample by clicking the TARGET drop-down menu and selecting either unknown, reference, or NTC (no-template control). 
		NOTE: Unknown would refer to an experimental sample, reference could be a control sample of known telomerase activity, and an NTC is a critical control for the determination of assay validity in terms of contamination and background signal.</li>
		<li>Label all the samples in the Sample Name section and click Apply to ensure the highlighted wells are edited appropriately.</li>
	</ol>
	</li>
	<li>Click Run to run/read the plate. Select either Columns or Rows in the RUN OPTIONS screen when prompted to inform the machine about the orientation the plate should be read in.</li>
</ol>
6. Data analysis
<ol>
	<li>Determine the number of accepted droplets for each sample by clicking on individual wells. Double-click the individual wells or column/row headers to view and analyze the sample data. 
	NOTE: The most important criteria on whether or not to proceed with the analysis of a particular sample is the number of &#8220;accepted droplets&#8221;. For the ddTRAP, samples with 10,000 or more accepted droplets are valid for further analysis. Data can be provided to the user in many formats, including a .csv table, .jpg images, histograms, etc. There are many resources available for the in-depth analysis of ddPCR data, including the ddTRAP11,13. These resources offer a guide to analyzing ddTRAP data, from selecting thresholds11,13 to choosing samples for further analysis11,13, identifying false positives and negatives13, and general troubleshooting13.</li>
	<li>Highlight the wells representing sample replicates and NTC samples. Analyze the samples in comparison to either NTC or negative control lines, such as BJ cells (as listed above in step 2.1.2).
	<ol>
		<li>Manually set the threshold for the samples by clicking on the icon for setting thresholds on the bottom left of the screen. Set thresholds for each individual well or for multiple wells at a time (recommended). 
		NOTE: If background is detected in the NTC, it can be subtracted from all of the other samples to &#8216;normalize&#8217; the signal and ensure that only the true positive signal is analyzed. NTC values are typically in the 0.2&#8211;1 molecule/&#181;L range for the NP-40 lysis buffer system. Other buffer systems and components would need to be tested (for example, CHAPS buffers).</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Frenck, R. W., Blackburn, E. H., Shannon, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+rate+of+telomere+sequence+loss+in+human+leukocytes+varies+with+age.">The rate of telomere sequence loss in human leukocytes varies with age.</a> Proceedings of the National Academy of Sciences of the United States of America. 95 (10), 5607-5610 (1998).</li><li>Morin, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+human+telomere+terminal+transferase+enzyme+is+a+ribonucleoprotein+that+synthesizes+TTAGGG+repeats.">The human telomere terminal transferase enzyme is a ribonucleoprotein that synthesizes TTAGGG repeats.</a> Cell. 59 (3), 521-529 (1989).</li><li>de Lange, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+telomeres+solve+the+end-protection+problem.">How telomeres solve the end-protection problem.</a> Science. 326 (5955), 948-952 (2009).</li><li>Shay, J. W., Wright, W. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+telomeres+and+telomerase+in+cancer.">Role of telomeres and telomerase in cancer.</a> Seminars in Cancer Biology. 21 (6), 349-353 (2011).</li><li>Kim, W., Shay, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-range+telomere+regulation+of+gene+expression:+Telomere+looping+and+telomere+position+effect+over+long+distances+(TPE-OLD).">Long-range telomere regulation of gene expression: Telomere looping and telomere position effect over long distances (TPE-OLD).</a> Differentiation. 99, 1-9 (2018).</li><li>Robin, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Telomere+position+effect:+regulation+of+gene+expression+with+progressive+telomere+shortening+over+long+distances.">Telomere position effect: regulation of gene expression with progressive telomere shortening over long distances.</a> Genes Development. 28 (22), 2464-2476 (2014).</li><li>Shay, J. W., Wright, W. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Senescence+and+immortalization:+role+of+telomeres+and+telomerase.">Senescence and immortalization: role of telomeres and telomerase.</a> Carcinogenesis. 26 (5), 867-874 (2005).</li><li>Norton, J. C., Holt, S. E., Wright, W. E., Shay, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhanced+detection+of+human+telomerase+activity.">Enhanced detection of human telomerase activity.</a> DNA Cell Biology. 17 (3), 217-219 (1998).</li><li>Herbert, B. S., Hochreiter, A. E., Wright, W. E., Shay, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonradioactive+detection+of+telomerase+activity+using+the+telomeric+repeat+amplification+protocol.">Nonradioactive detection of telomerase activity using the telomeric repeat amplification protocol.</a> Nature Protocols. 1 (3), 1583-1590 (2006).</li><li>Vogelstein, B., Kinzler, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+PCR.">Digital PCR.</a> Proceedings of the National Academy of Sciences of the United States of America. 96 (16), 9236-9241 (1999).</li><li>Ludlow, A. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+telomerase+enzyme+activity+determination+using+droplet+digital+PCR+with+single+cell+resolution.">Quantitative telomerase enzyme activity determination using droplet digital PCR with single cell resolution.</a> Nucleic Acids Research. 42 (13), e104 (2014).</li><li>Huang, E. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Maintenance+of+Telomere+Length+in+CD28++T+Cells+During+T+Lymphocyte+Stimulation.">The Maintenance of Telomere Length in CD28+ T Cells During T Lymphocyte Stimulation.</a> Scientific Reports. 7 (1), 6785 (2017).</li><li>Ludlow, A. T., Shelton, D., Wright, W. E., Shay, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ddTRAP:+A+Method+for+Sensitive+and+Precise+Quantification+of+Telomerase+Activity.">ddTRAP: A Method for Sensitive and Precise Quantification of Telomerase Activity.</a> Methods Molecular Biology. 1768, 513-529 (2018).</li></ol>

The authors have nothing to disclose.

The measurement of telomerase activity is critical to a plethora of research topics including, but not limited to, cancer, telomere biology, aging, regenerative medicine, and structure-based drug design. Telomerase RNPs are low abundant, even in cancer cells, making the detection and study of this enzyme challenging. In this paper, we described the step-by-step procedures for the newly developed ddTRAP assay to robustly quantify telomerase activity in cells. By combining the traditional telomerase extension reaction with...

Telomeres are dynamic DNA-protein complexes at the ends of linear chromosomes. Human telomeres are composed of an array of 5&#39;-TTAGGGn hexameric repeats which vary in length between 12&#8211;15 kilobases (kb) at birth1. Human telomerase, the ribonucleoprotein enzyme that maintains the telomeres, was first identified in HeLa cell lysates (cancer cell line)2. Together, telomeres and telomerase play a major role in a spectrum of biological processes such as genome protection, gene regulation, and cancer cell immortality3,4,5,6.
Human telomerase is comprised primarily of two key components, namely telomerase reverse transcriptase and telomerase RNA (hTERT and hTERC, respectively). The protein subunit, hTERT, is the catalytically active reverse transcriptase component of the telomerase enzyme. The RNA template, hTERC, provides telomerase with the template to extend and/or maintain telomeres. Most human somatic tissues have no detectable telomerase activity. The inability of DNA polymerase to extend the end of the lagging strand of DNA along with the lack of telomerase leads to the progressive shortening of telomeres after every round of cellular division. These phenomena lead to telomere shortening in most somatic cells until they reach a critically shortened length, whereby cells enter a state of replicative senescence. The maximal number of times a cell can divide is dictated by its telomere length and this block to continued cell division is thought to prevent progression to oncogenesis7. Cancer cells are able to overcome telomere-induced replicative senescence and continue to proliferate by utilizing telomerase to maintain their telomeres. Approximately 90% of cancers activate telomerase, making telomerase activity critically important in both cancer detection and treatment.
The development of the TRAP assay in the 1990s was instrumental in the identification of the necessary components of the telomerase enzyme, as well as for the measurement of telomerase in a wide range of cells and tissues, both normal and cancerous. The original gel-based PCR assay used radioactively labeled DNA substrates to detect telomerase activity. In 2006, the assay was adapted into a nonradioactive form using fluorescently labeled substrates8,9. By using fluorescently labeled substrates, users were able to visualize the telomerase extension products as bands on a gel by exposing it to the correct excitation wavelength. The sensitivity of the TRAP assay and its ability to detect telomerase activity in crude cell lysates has made this assay the most widely used method for telomerase activity detection. However, the TRAP assay has limitations. The assay is gel-based, making it difficult to perform the necessary replicates in moderate to high-throughput studies, and thus, proper statistical analysis is rarely achieved. Furthermore, the gel-based assay is difficult to quantify reliably due to the inability of detecting less than twofold differences in telomerase activity between samples. Overcoming these two limitations is critical for enzymatic activity assays such as the TRAP to move to clinical or industry settings for the detection of telomerase activity in patient samples or drug design studies.
Digital PCR was initially developed in 1999 as a means to convert the exponential and analog nature of PCR into a linear and digital assay10. Droplet digital PCR (ddPCR) is the most recent innovation of the original digital PCR methodology. Droplet digital PCR came about with the advent of advanced microfluidics and oil-in-water emulsion chemistry to reliably generate stable and equally sized droplets. Unlike gel-based and even quantitative PCR (qPCR), ddPCR generates absolute quantification of the input material. The key to ddPCR is the generation of ~20,000 individual reactions by partitioning samples into droplets. Following end-point PCR, the droplet reader scans each droplet in a flow-cytometer-like fashion, counting, sizing, and recording the presence or absence of fluorescence in each individual droplet (i.e., absence or presence of PCR amplicons in each droplet). Then, using Poisson&#8217;s distribution, input molecules are estimated based on the ratio of positive droplets to the total number of droplets. This number represents an estimate of the number of input molecules in each PCR. Furthermore, ddPCR is performed and analyzed on a 96-well plate which allows the user to run many samples, as well as perform biological and technical replicates for proper statistical analysis. As a result, we have combined the powerful quantification and moderate-throughput nature of ddPCR with the TRAP assay to develop the ddTRAP assay11. This assay is designed for users to study and robustly quantify absolute telomerase activity from biological samples11,12. The sensitivity of the ddTRAP allows the quantification of telomerase activity from limited and precious samples, including single-cell measurements. Furthermore, users can also study the effects of telomerase manipulations and/or drugs with absolute quantification of less than twofold changes (~50% differences). The ddTRAP is the natural evolution of the TRAP assay into the digital and higher-throughput nature of modern laboratory experiments and clinical settings.

<table>
	<tbody>
		<tr>
			<td>1 M Tris-HCl pH 8.0</td>
			<td>Ambion</td>
			<td>AM9855G</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>1 M MgCl2</td>
			<td>Ambion</td>
			<td>AM9530G</td>
			<td>RnAse/DNAse free</td>
		</tr>
		<tr>
			<td>0.5 M EDTA pH 8.0</td>
			<td>Ambion</td>
			<td>AM9261</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>Surfact- Amps NP-40</td>
			<td>Thermo Scientific</td>
			<td>28324</td>
			<td></td>
		</tr>
		<tr>
			<td>100% Ultrapure Glycerol</td>
			<td>Invitrogen</td>
			<td>15514011</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>phenylmethylsulfonyl fluoride</td>
			<td>Thermo Scientific</td>
			<td>36978</td>
			<td>Powder</td>
		</tr>
		<tr>
			<td>2-Mercaptoethanol</td>
			<td>SIGMA-ALDRICH</td>
			<td>516732</td>
			<td></td>
		</tr>
		<tr>
			<td>Nuclease Free H20</td>
			<td>Ambion</td>
			<td>AM9932</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>2.5 mM dNTP mix</td>
			<td>Thermo Scientific</td>
			<td>R72501</td>
			<td>2.5 mM of each dATP, dCTP, dGTP and dTTP</td>
		</tr>
		<tr>
			<td>2 M KCl</td>
			<td>Ambion</td>
			<td>AM9640G</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>100% Tween-20</td>
			<td>Fisher</td>
			<td>9005-64-5</td>
			<td></td>
		</tr>
		<tr>
			<td>0.5 M EGTA pH 8.0</td>
			<td>Fisher</td>
			<td>50-255-956</td>
			<td>RNAse/DNAse free</td>
		</tr>
		<tr>
			<td>Telomerase Substrate (TS) Primer</td>
			<td>Integrated DNA Technology (IDT)</td>
			<td>Custom Primer (HPLC Purified)</td>
			<td>5&#39;- AATCCGTCGAGCAGAGTT-3&#39;</td>
		</tr>
		<tr>
			<td>ACX (Revers) Primer</td>
			<td>Integrated DNA Technology (IDT)</td>
			<td>Custom Primer (HPLC Purified)</td>
			<td>5&#39;- GCGCGGCTTACCCTTACCCTTACCCTAACC -3&#39;</td>
		</tr>
		<tr>
			<td>Thin walled (250 ul) PCR grade tubes</td>
			<td>USA Scientific</td>
			<td>1402-2900</td>
			<td>strips, plates, tubes etc.</td>
		</tr>
		<tr>
			<td>QX200 ddPCR EvaGreen Supermix</td>
			<td>Bio Rad</td>
			<td>1864034</td>
			<td></td>
		</tr>
		<tr>
			<td>Twin-Tec 96 Well Plate</td>
			<td>Fisher</td>
			<td>Eppendorf 951020362</td>
			<td></td>
		</tr>
		<tr>
			<td>Piercable foil heat seal</td>
			<td>Bio Rad</td>
			<td>1814040</td>
			<td></td>
		</tr>
		<tr>
			<td>Droplet generator cartidges (DG8)</td>
			<td>Bio Rad</td>
			<td>1863008</td>
			<td></td>
		</tr>
		<tr>
			<td>Droplet generator oil</td>
			<td>Bio Rad</td>
			<td>1863005</td>
			<td></td>
		</tr>
		<tr>
			<td>Droplet generator gasket</td>
			<td>Bio Rad</td>
			<td>1863009</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well Thermocycler T100</td>
			<td>Bio Rad</td>
			<td>1861096</td>
			<td></td>
		</tr>
		<tr>
			<td>PX1 PCR Plate Sealer</td>
			<td>Bio Rad</td>
			<td>1814000</td>
			<td></td>
		</tr>
		<tr>
			<td>QX200 Droplet Reader and Quantasoft Software</td>
			<td>Bio Rad</td>
			<td>1864001 and 1864003</td>
			<td></td>
		</tr>
		<tr>
			<td>ddPCR Droplet Reader Oil</td>
			<td>Bio Rad</td>
			<td>1863004</td>
			<td></td>
		</tr>
		<tr>
			<td>Nuclease Free Filtered Pipette Tips</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td>10 ul, 20 ul , 200 ul and 1000 ul</td>
		</tr>
	</tbody>
</table>

droplet digital trap (ddtrap): adaptation of the telomere repeat amplification protocol to droplet digital polymerase chain reaction

The telomere repeat amplification protocol (TRAP) is the most widely used assay to detect telomerase activity within a given a sample. The polymerase chain reaction (PCR)-based method allows for robust measurements of enzyme activity from most cell lysates. The gel-based TRAP with fluorescently labeled primers limits sample throughput, and the ability to detect differences in samples is restricted to two fold or greater changes in enzyme activity. The droplet digital TRAP, ddTRAP, is a highly sensitive approach that has been modified from the traditional TRAP assay, enabling the user to perform a robust analysis on 96 samples per run and obtain absolute quantification of the DNA (telomerase extension products) input within each PCR. Therefore, the newly developed ddTRAP assay overcomes the limitations of the traditional gel-based TRAP assay and provides a more efficient, accurate, and quantitative approach to measuring telomerase activity within laboratory and clinical settings.

Using the ddTRAP, telomerase activity was measured in a cell panel consisting of the following cell lines (Figure 1): nonsmall cell lung cancer (H2882, H1299, Calu6, H920, A549, and H2887), small cell lung cancer (H82 and SHP77), and telomerase-negative fibroblasts (BJ). One million cell pellets were lysed in NP-40 buffer, and telomerase extension reactions were performed in biological triplicates. A common and highly recommended negative control is the &#822...

Watch this Scientific Journal Video about Droplet Digital TRAP ddTRAP: Adaptation of the Telomere Repeat Amplification Protocol to Droplet Digital Polymerase Chain Reaction at JoVE.com

Droplet Digital TRAP ddTRAP: Adaptation of the Telomere Repeat Amplification Protocol to Droplet Digital Polymerase Chain Reaction

We successfully converted the standard telomere repeat amplification protocol (TRAP) assay to be employed in droplet digital polymerase chain reactions. This new assay, called ddTRAP, is more sensitive and quantitative, allowing for better detection and statistical analysis of telomerase activity within various human cells.

Droplet Digital TRAP (ddTRAP): Adaptation of the Telomere Repeat Amplification Protocol to Droplet Digital Polymerase Chain Reaction

The telomere repeat amplification protocol (TRAP) is the most widely used assay to detect telomerase activity within a given a sample. The polymerase ...

droplet-digital-trap-ddtrap-adaptation-telomere-repeat-amplification

Research

JoVE Journal

Cancer Research

8.7K Views.  University of Michigan. We successfully converted the standard telomere repeat amplification protocol (TRAP) assay to be employed in droplet digital polymerase chain reactions. This new assay, called ddTRAP, is more sensitive and quantitative, allowing for better detection and statistical analysis of telomerase activity within various human cells.

Video: Droplet Digital TRAP ddTRAP: Adaptation of the Telomere Repeat Amplification Protocol to Droplet Digital Polymerase Chain Reaction

Modified Terminal Restriction Fragment Analysis for Quantifying Telomere Length Using In-gel Hybridization

There are several different techniques for measuring telomere length, each with their own advantages and disadvantages. The traditional approach, Telomere Restriction Fragment (TRF) analysis, utilizes a DNA hybridization technique whereby genomic DNA samples are digested with restriction enzymes, leaving behind telomere DNA repeats and some sub-telomeric DNA. These are separated by agarose gel electrophoresis, transferred to a filter membrane and hybridized to oligonucleotide probes tagged with either chemiluminescence or radioactivity to visualize telomere restriction fragments. This approach, while requiring a larger quantity of DNA than other techniques such as PCR, can measure the telomere length distribution of a population of cells and allows measurement expressed in absolute kilobases. This manuscript demonstrates a modified DNA hybridization procedure for determining telomere length. Genomic DNA is first digested with restriction enzymes (that do not cut telomeres) and separated by agarose gel electrophoresis. The gel is then dried and the DNA is denatured and hybridized in situ to a radiolabeled oligonucleotide probe. This in situ hybridization avoids loss of telomere DNA and improves signal intensity. Following hybridization, the gels are imaged utilizing phosphor screens and the telomere length is quantified using a graphing program. This procedure was developed by the laboratories of Drs. Woodring Wright and Jerry Shay at the University of Texas Southwestern1,2. Here, we present a detailed description of this procedure, with some modifications.

In this article, a detailed protocol for quantifying telomere length using a modified terminal restriction fragment analysis is discussed that provides fast and efficient direct measurement of telomere length. This technique can be applied to a variety of cell sources of DNA for quantifying telomere length.

There are several different techniques for measuring telomere length, each with their own advantages and disadvantages. The traditional approach, Telomere ...

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average result masking the rich single cell behavior within a complex population. Single cell protein detection and quantification technologies have made a remarkable impact in recent years. Here we describe a practical and flexible single cell analysis platform based on addressable droplet microarrays. This study describes how the absolute copy numbers of target proteins may be measured with single cell resolution. The tumor suppressor p53 is the most commonly mutated gene in human cancer, with more than 50% of total cancer cases exhibiting a non-healthy p53 expression pattern. The protocol describes steps to create 10 nL droplets within which single human cancer cells are isolated and the copy number of p53 protein is measured with single molecule resolution to precisely determine the variability in expression. The method may be applied to any cell type including primary material to determine the absolute copy number of any target proteins of interest.

Here we present addressable droplet microarrays (ADMs), a droplet array based method able to determine absolute protein abundance in single cells. We demonstrate the capability of ADMs to characterize the heterogeneity in expression of the tumor suppressor protein p53 in a human cancer cell line.

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average ...

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal gastric mucosa. Recently, we detected CDH1a transcript variant in fresh-frozen tumor tissues obtained from patients with GC. The expression of this variant in tissue samples was investigated by the chip-based digital PCR (dPCR) approach presented here. dPCR offers the potential for an accurate, robust, and highly sensitive measurement of nucleic acids and is increasingly utilized for many applications in different fields. dPCR is capable of detecting rare targets; in addition, dPCR offers the possibility for absolute and precise quantification of nucleic acids without the need for calibrators and standard curves. In fact, the reaction partitioning enriches the target from the background, which improves amplification efficiency and tolerance to inhibitors. Such characteristics make dPCR an optimal tool for the detection of the CDH1a rare transcript.

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

The manuscript describes a chip-based digital PCR assay to detect a rare CDH1 transcript variant (CDH1a) in fresh-frozen normal and tumor tissues obtained from patients with gastric cancer.

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image analysis. Digital scanning is a fast way to scan a stack of slides and produce digital images with high quality. It can produce concordant results with conventional light microscopy (CLM) by pathologists. Furthermore, the availability of digital images makes it possible that the same slide can be concurrently observed by multiple people. Moreover, digital images of slides can be stored in a database, which means the long-term deterioration of glass slides is avoided. The limitations of this technique are as follows. First, it needs high-quality prepared tissue and the original immunohistochemistry (IHC) slides without any damage or excess sealant residue. Second, tumor or nontumor areas should be specified by experienced pathologists before the analysis using software, in order to avoid any confusion about the tumor or nontumor areas during scoring. Third, the operator needs to control the color reproduction throughout the digitization process in whole-slide imaging.

ZW10 interacting protein (ZWINT) participates in the mitotic spindle checkpoint and the pathogenesis of carcinoma. Here, we introduce a methodology of the immunostaining of ZWINT in human lung cancer tissues, followed by the digital scanning of whole slides and image analysis. This methodology can provide high-quality digital images and reliable results.

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image ...

Important Endpoints and Proliferative Markers to Assess Small Intestinal Injury and Adaptation using a Mouse Model of Chemotherapy-Induced Mucositis

Intestinal adaptation is the natural compensatory mechanism that occurs when the bowel is lost due to trauma. The adaptive responses, such as crypt cell proliferation and increased nutrient absorption, are critical in recovery, yet poorly understood. Understanding the molecular mechanism behind the adaptive responses is crucial to facilitate the identification of nutrients or drugs to enhance adaptation. Different approaches and models have been described throughout the literature, but a detailed descriptive way to essentially perform the procedures is needed to obtain reproducible data. Here, we describe a method to estimate important endpoints and proliferative markers of small intestinal injury and compensatory hyperproliferation using a model of chemotherapy-induced mucositis in mice. We demonstrate the detection of proliferating cells using a cell cycle specific marker, as well as using small intestinal weight, crypt depth, and villus height as endpoints. Some of the critical steps within the described method are the removal and weighing of the small intestine and the rather complex software system suggested for the measurement of this technique. These methods have the advantages that they are not time-consuming, and that they are cost-effective and easy to carry out and measure.

Here, we present a protocol to establish important endpoints and proliferative markers of small intestinal injury and compensatory hyperproliferation using a model of chemotherapy-induced mucositis. We demonstrate the detection of proliferating cells using a cell cycle specific marker and using small intestinal weight, crypt depth, and villus height as endpoints.

Intestinal adaptation is the natural compensatory mechanism that occurs when the bowel is lost due to trauma. The adaptive responses, such as crypt cell ...

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples from cancer patients often directly influences diagnosis, prognosis, and/or therapy selection. As a result, the accurate detection of gene fusions has become a critical component of clinical management for many disease types. Until recently, clinical gene fusion detection was predominantly accomplished through the use of single-gene assays. However, the ever-growing list of gene fusions with clinical significance has created a need for assessing fusion status of multiple genes simultaneously. Next generation sequencing (NGS)-based testing has met this demand through the ability to sequence nucleic acid in massively parallel fashion. Multiple NGS-based approaches that employ different strategies for gene target enrichment are now available for use in clinical molecular diagnostics, each with its own strengths and weaknesses. This article describes the use of anchored multiplex PCR (AMP)-based target enrichment and library preparation followed by NGS to assess for gene fusions in clinical solid tumor specimens. AMP is unique among amplicon-based enrichment approaches in that it identifies gene fusions regardless of the identity of the fusion partner. Detailed here are both the wet-bench and data analysis steps that ensure accurate gene fusion detection from clinical samples.

This article details the use of an anchored multiplex polymerase chain reaction-based library preparation kit followed by next-generation sequencing to assess for oncogenic gene fusions in clinical solid tumor samples. Both wet-bench and data analysis steps are described.

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples ...

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous metastasis models are available. Thus, the experimental metastasis model involving tail-vein injection of suitable cell lines is a mainstay of metastasis research. When cancer cells are injected into the lateral tail-vein, the lung is their preferred site of colonization. A potential limitation of this technique is the accurate quantification of the metastatic lung tumor burden. While some investigators count macrometastases of a pre-defined size and/or include micrometastases following sectioning of tissue, others determine the area of metastatic lesions relative to normal tissue area. Both of these quantification methods can be exceedingly difficult when the metastatic burden is high. Herein, we demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor burden using image analysis software. This process allows for investigation of multiple end-point parameters, including average metastasis size, total number of metastases, and total metastasis area, to provide a comprehensive analysis. Furthermore, this method has been reviewed by a veterinary pathologist board-certified by the American College of Veterinary Pathologists (SEK) to ensure accuracy.

Intravenous injection of cancer cells is often used in metastasis research, but the metastatic tumor burden can be difficult to analyze. Herein, we demonstrate a tail-vein injection model of metastasis and include a novel approach to analyze the resulting metastatic lung tumor burden.

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous ...

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

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses.
DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Proteomic dysregulation plays an important role in the spread of diffusely infiltrating gliomas, but several relevant proteins remain unidentified. Digital spatial processing (DSP) offers an efficient, high-throughput approach for characterizing the differential expression of candidate proteins that may contribute to the invasion and migration of infiltrative gliomas.

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically ...

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobanks are key research infrastructures aimed at the collection, storage, processing, and sharing of high-quality human biological samples and associated data for research, diagnosis, and personalized medicine. The Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology (IEO) is a landmark in this field. Biobanks collaborate with clinical divisions, internal and external research groups, and industry, supporting patients' treatment and scientific progress, including innovative diagnostics, biomarker discovery, and clinical trial design. Given the central role of biobanks in modern research, biobanking standard operating procedures (SOPs) should be extremely precise. SOPs and controls by certified specialists ensure the highest quality of samples for the implementation of science-based, diagnostic, prognostic, and therapeutic personalized strategies. However, despite numerous efforts to standardize and harmonize biobanks, these protocols, which follow a strict set of rules, quality controls, and guidelines based on ethical and legal principles, are not easily accessible. This paper presents the biobank standard operating procedures of a large cancer center.

Biobanks are crucial resources for biomedical research and the Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology is a model in this field. Here, we provide a detailed description of biobanks' standard operating procedures for the management of different types of human biological samples.

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Droplet Digital TRAP (ddTRAP): Adaptation of the Telomere Repeat Amplification Protocol to Droplet Digital Polymerase Chain Reaction