The project described was supported by Award Number R01MH079751 (PI: F. Peruzzi) from the National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institute of Health.

The following protocol describes the standard procedure to isolate RNA from CSF and profile microRNAs using ready PCR plates. Note that the organic phase extraction is optional, and that a carrier RNA is added to the sample during extraction ensuring maximal recovery of RNA. Consequently, there is no need to quantify the RNA.
Overall, an average of 6-8 plates can be run in one day if the cDNA is synthesized the day before (about 2 hr). Analysis of data is performed when all samples have been profiled and loaded into the software. Depending on the number of samples/groups or their combination for comparison, data analysis may require from one to several hours.
1. RNA Extraction
The RNA extraction was performed from CSF samples, stored frozen at -80 &#176;C in aliquots until analysis. For this procedure the mirVana Paris isolation kit was used, following the manufacturer&#39;s instructions for the total RNA isolation. Please note that, although enrichment for small RNA species is possible with this kit, this step is not done and the RNA extraction procedure is ended after the total RNA isolation step. In addition, although the mirVana Paris isolation kit (like other commercially available RNA isolation kits) does not require organic extraction, a protocol for this procedure can be found below. 
	The protocol for RNA extraction consists of two steps: 
	1.1. Organic Extraction (not required if using mirVana Paris RNA extraction kit) 
	1.2.2. Total RNA isolation
1.1. Organic extraction
1.1.1. Prepare reagents
<ol>
	<li>Add 375 &mu;l of 2-mercaptoethanol to 2x Denaturing solution, and store it at 4 &#176;C.</li>
</ol>
1.1.2. Prepare the CSF
<ol>
	<li>Thaw every aliquot of CSF on ice before the RNA extraction. Add 1 &#181;g of MS2 RNA carrier to 0.5 ml of thawed CSF and gently mix. Note that if the organic extraction is omitted, the MS2 carrier will be added to the sample prior RNA isolation described in 1.2.2.1 below.</li>
	<li>Mix 0.5 ml of 2x Denaturing Solution at room temperature to the CSF.</li>
	<li>Add 1 ml of acid-phenol:chloroform to the CSF plus the 2x Denaturing Solution: be sure to withdrawn the bottom phase containing acid-phenol:chloroform, not the aqueous buffer that lies on top of the mixture. Additionally, note that acid-phenol:chloroform contains phenol, which is a poison and an irritant, use gloves and other personal protection equipment when working with this reagent.</li>
	<li>Vortex for 30-60 sec to mix. For convenience, the 2 ml of sample/denaturing solution/acid-phenol:chloroform mix are divided into two 1.5 ml Eppendorf tubes.</li>
	<li>Centrifuge for 5 min at maximum speed (10,000 x g) at room temperature.</li>
	<li>Carefully remove the aqueous (upper) phase without disturbing the lower phase or the interphase and transfer it to a fresh tube. Note the volume recovered.</li>
</ol>
1.2. RNA isolation
It is recommended to have a dedicated bench, set of pipettes, and racks for handling RNA samples. The working area and tools are decontaminated by using RNase Zap spray and wipes prior each experiment.
1.2.1. Prepare reagents:
<ol>
	<li>Add 21 ml of 100% ethanol to miRNA Wash Solution 1 and 40 ml of 100 % ethanol to Wash Solution 2/3. Please note that miRNA Wash Solution 1 contains guanidium thiocyanate that is a potentially hazardous substance.</li>
</ol>
1.2.2. Total RNA isolation
<ol>
	<li>Add 1.25 volumes of room temperature 100% ethanol to the aqueous phase from the organic extraction and mix thoroughly.</li>
	<li>Place a Filter Cartridge into one of the Collection Tubes.</li>
	<li>Pipette not more than 700 &mu;l of the lysate/ethanol mixture onto the Filter Cartridge.</li>
	<li>Centrifuge for 30 sec at maximum speed. Apply the mixture exceeding 700 &mu;l in successive applications to the same filter. Discard the flow-through after every centrifugation and save the Collection Tube for the washing steps.</li>
	<li>Apply 700 &mu;l miRNA Wash Solution 1 to the Filter Cartridge and centrifuge for 15 sec at maximum speed. Discard the flow through from the Collection Tube and replace the Filter Cartridge into the same Collection Tube.</li>
	<li>Apply 500 &mu;l Wash Solution 2/3 and centrifuge for 15 sec at maximum speed. Discard the flow through from the Collection Tube and replace the Filter Cartridge into the same Collection Tube. Apply a second 500 &mu;l Wash Solution 2/3 and centrifuge for 15 sec at maximum speed. Discard the flow through from the Collection Tube and replace the Filter Cartridge into the same Collection Tube. Spin the assembly for 1 min at maximum speed to remove residual fluid from the filter.</li>
	<li>Transfer the Filter Cartridge into a fresh Collection Tube. Apply 100 &mu;l of preheated (to 95 &#176;C) nuclease-free water to the center of the filter, and close the cap. Centrifuge for 30 sec at maximum speed to recover the RNA.</li>
	<li>Collect the eluate containing RNA and store it at -80 &#176;C for subsequent applications.</li>
	<li>Quantify 1 &#181;l of RNA using the NanoDrop 2000c spectrophotometer. Note that RNA quantification can be skipped if an RNA carrier is added to the sample during RNA extraction. In that case 8 &#181;l of RNA are subjected to cDNA synthesis as detailed below.</li>
</ol>
2. miRNA Profile: qRT-PCR Protocol
The protocol for miRNA profiling consists of two steps:
<ol>
	<li>First-strand cDNA synthesis</li>
	<li>Real-time PCR amplification</li>
</ol>
2.1. First-strand cDNA synthesis
2.1.1. Dilute template RNA
<ol>
	<li>Adjust each of the template RNA samples to a concentration of 5 ng/&#181;l using nuclease free water. If the RNA has not been quantified (see step 1.2.2.9), then 8 &#181;l of RNA are utilized for cDNA synthesis</li>
</ol>
2.1.2. Prepare reagents
<ol>
	<li>Gently thaw the 5x Reaction buffer and nuclease free water, and immediately place on ice.</li>
	<li>Mix by vortexing. Resuspend the RNA spike-in by adding 40 &#181;l nuclease free water to the tube and mix by vortexing; leave it on ice for 15-20 min. Immediately before use, remove the enzyme mix from the freezer, mix by flicking the tubes and place on ice. Spin down all reagents.</li>
</ol>
2.1.3. Reverse transcription reaction setup
<ol>
	<li>Prepare the required amount of RT working solution in a 1.5 ml Eppendorf tube, or equivalent (in the proportions indicated in Table 1, except for the RNA template), mix by vortexing (1-2 sec at max speed), briefly spin down (we use a mini Eppendorf centrifuge with fixed speed, 10 sec) and place it on ice. Note that the UniSp6 RNA spike-in is added to the sample prior the RT reaction (see Table 1).</li>
	<li>Dispense RT working solution into nuclease free PCR tubes.</li>
	<li>Dispense template RNA in each tube. 
		Note: remember to calculate 10% excess volume/each reagent for pipetting and/or robotic dead volume.
		<table border="1" cellpadding="0" cellspacing="0">
			<tbody>
				<tr>
					<td>Reagent</td>
					<td>Panel I Volume (&#181;l)</td>
					<td>Panel I + II Volume (&#181;l)</td>
				</tr>
				<tr>
					<td>5x reaction buffer</td>
					<td>4.4</td>
					<td>8.8</td>
				</tr>
				<tr>
					<td>Nuclease free water</td>
					<td>9.9</td>
					<td>19.8</td>
				</tr>
				<tr>
					<td>Enzyme mix</td>
					<td>2.2</td>
					<td>4.4</td>
				</tr>
				<tr>
					<td>UniSp6 RNA Spike-in template</td>
					<td>1.1</td>
					<td>2.2</td>
				</tr>
				<tr>
					<td>Template total RNA (5 ng/&#181;l)</td>
					<td>4.4</td>
					<td>8.8</td>
				</tr>
				<tr>
					<td>Total Volume</td>
					<td>22</td>
					<td>44</td>
				</tr>
			</tbody>
		</table>
		 
		Table 1. Reverse transcription reaction setup.</li>
</ol>
2.1.4. Mix and spin reagents
<ol>
	<li>Mix the reaction by gentle (1-2 sec) vortexing to ensure that all reagents are thoroughly mixed. After mixing, spin down (mini Eppendorf centrifuge with fixed speed, 10 sec).</li>
</ol>
2.1.5. Incubate and heat inactivate
<ol>
	<li>Program a PCR machine as follow:</li>
	<li>Incubate for 60 min at 42 &#176;C</li>
	<li>Heat-inactivate the reverse transcriptase for 5 min at 95 &#176;C</li>
	<li>Immediately cool to 4 &#176;C</li>
	<li>Store at 4 &#176;C or freeze 
		Note: the protocol can be interrupted at this stage. The undiluted cDNA may be kept at -20 &#176;C for up to 5 weeks (optional store at 4 &#176;C for up to 4 days).</li>
</ol>
2.2. Real-time PCR amplification
2.2.1. Prepare reagents for Real-time PCR
<ol>
	<li>Place cDNA (from step 2.1.5) on ice.</li>
	<li>Thaw nuclease free water and PCR Master mix on ice. Protect the PCR Master mix vials from light by covering them with aluminum foil. Immediately before use, mix the PCR Master mix by vortexing 1-2 sec in a mini centrifuge and spin down at 1,500 x g for 1 min.</li>
</ol>
2.2.2. Combine SYBR Green master mix, water, and cDNA and add to PCR plates
The following procedure is recommended to avoid low concentrations of cDNA from adhering to tube surface:
<ol>
	<li>Before removing the plate seal, briefly spin down the plate(s) in a refrigerated centrifuge at 1,500 x g for 1 min.</li>
	<li>In a 15 ml conical tube, combine 2x PCR Master mix and water. Panel I: 2,000 &#181;l 2x master mix and 1,980 &#181;l water; Panel I+II: 4,000 &#181;l 2x master mix and 3,960 &#181;l water.</li>
	<li>Add 20 &#181;l cDNA (panel I) or 40 &#181;l cDNA (panel I+II) and mix.</li>
	<li>Mix gently by vortexing 1-2 sec and spin down in a refrigerated centrifuge at 1,500 x g for 1 min.</li>
	<li>Place the PCR Master mix/cDNA mix in a multichannel pipette reservoir. You may have to identify a reservoir that has the length of the multichannel pipette. This keeps the level of volume high enough allowing for equal volume aliquots throughout the multichannel. This is critical towards the last few aliquots.</li>
	<li>Dispense 10 &#181;l PCR Master mix/cDNA mix to each well of the 384-well plate using a 16-channel pipette.</li>
	<li>Seal the plate with optical sealing.</li>
	<li>Spin plate briefly in a refrigerated centrifuge (1,500 x g for 1 min), to mix the solutions and remove air bubbles. 
		 
		Note: the experiment can be paused at this point. Store the reactions protected from light at 4 &#176;C for up to 24 hr.</li>
</ol>
2.2.3. Real-Time PCR
Perform real-time PCR amplification and melting curve analysis following the parameters detailed in Table 2.
<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Process Step</td>
			<td>Settings, Roche LC480</td>
		</tr>
		<tr>
			<td>Polymerase Activation/Denaturation</td>
			<td>95 &#176;C, 10 min</td>
		</tr>
		<tr>
			<td>Amplification</td>
			<td>45 Amplification cycles at 
				95 &#176;C, 10 sec 
				60 &#176;C, 1 min 
				Ramp-rate 
				1.6 &#176;C/sec 
				Optical read</td>
		</tr>
		<tr>
			<td>Melting curve analysis</td>
			<td>Yes</td>
		</tr>
	</tbody>
</table>
Table 2. Real-time PCR cycle conditions using the Roche LightCycler 480.
2.3. Real-time PCR data analysis
Real time PCR data analysis is done with the Exiqon GenEx Professional 5.0, following the recommended instructions. The GenEx step-by-step manual can be downloaded at <a href="http://www.exiqon.com/ls/Documents/Scientific/Exiqon-data-analysis-guide.pdf">http://www.exiqon.com/ls/Documents/Scientific/Exiqon-data-analysis-guide.pdf</a> 
	Data analysis consists of three steps:
<ol>
	<li>Import of data</li>
	<li>Preprocessing of data</li>
	<li>Statistical analysis</li>
</ol>
2.3.1. Import of data
<ol>
	<li>In the Roche LightCycler 480 select the 2nd derivative analysis method and calculate the Cq values. Data are exported as a table and saved as text files.</li>
	<li>To import the data, open GenEx and click on the Exiqon import wizard button and click "Start". Follow the instructions to select format, instrument and plate layout file(s). Note that plate layout files (excel files) are downloaded from the Exiqon web site (<a href="http://www.exiqon.com/plate-layout-files">http://www.exiqon.com/plate-layout-files</a>).</li>
	<li>Import Panels I and II and click "Next".</li>
	<li>The table generated after file import contains predefined columns. Samples names can be edited and classification columns can be added or removed at this step. Click "Next" when you are done.</li>
	<li>Save data and load to Data Editor.</li>
</ol>
2.3.2. Preprocessing of data
<ol>
	<li>As recommended by Exiqon and GenEx, when comparing multiple plates the first thing to do is to calibrate the data between the plates by choosing inter-plate calibration from the preprocessing menu.</li>
	<li>It is recommended to run miRNA arrays in triplicate runs. If a miRNA is not present in at least two out of three replicas it will be set as nonexpressed.</li>
	<li>In the next step in the preprocessing, set a cut off. Defining a cut off value indicates that data with a threshold cycle (Ct) higher than this value are discarded. In the present experiments using cerebrospinal fluid specimen, the cut off was set at 39. Therefore, all samples with a Ct higher than 39 in the three replicas will be discarded. If a miRNA has a Ct &#62; 39 for one probe (one replica out of three), that reading will be replaced with the average of the Ct for the other two probes, provided they are both below 39.</li>
	<li>Define reference genes. GenEx Professional has the option to utilize geNorm and/or NormFinder to identify reference genes. Select a list of miRNAs that have similar Cq values across all sample and run geNorm and/or NormFinder. According to this analysis, in the example provided here, miR-622 and miR-1266 had the most uniform values across the samples and were chosen as reference genes (Figure 1).</li>
	<li>Next, data are normalized using the reference genes and the values obtained correspond to the &#916;Ct</li>
	<li>If cDNA synthesis reactions were performed in triplicates, at this point the values should be averaged.</li>
	<li>Validate sheet to remove empty and almost empty columns. In the preprocessing window select "Validate sheet" and than apply on the Remove empty columns line. In the line below, chose the desired percentage of valid data and click apply. For instance, select 100% if you wish to compare only miRNAs common to all samples.</li>
	<li>Handle missing data. 
		Select &#8220;Missing data&#8221; from the Preprocessing menu and chose one of the options to handle missing data. This step is required. Note that GenEx will give you error if you try to load files that contain unhandled missing data.</li>
	<li>Determine relative quantification between groups of samples (i.e. experiment versus control). Relative quantification among groups is calculated using the &#916;&#916; Ct method. In GenEx, click the preprocessing tab and select relative quantification. In the window select the reference group and hit apply. Note that for graphical representations and/or for further statistical analyses, expression data should be converted to a logarithmic scale. In the preprocessing menu, select Log2.</li>
</ol>
2.3.3. Statistical Analysis
GenEx software allows a wide range of statistical analyses, including T-test and ANOVA.
<ol>
	<li>Load the final mdf file into GenEx and open data manager. It is critical to select the correct samples or groups of samples for which statistical analysis is performed.</li>
	<li>Select "Statistics" and chose the icon corresponding to the desired test.</li>
	<li>Click "Run" in the control panel window. Save results as excel or mdf files.</li>
</ol>
2.3.4. Expression profiling
MicroRNAs and samples can be classified, clustered, and visualized on heat-maps and dendrograms, as follows
<ol>
	<li>Load the final mdf file into GenEx and open data manager. Use this window to select and create groups of samples or miRNAs. Click "Apply" when you are done.</li>
	<li>In the upper left corner select cluster and click on the heatmap icon</li>
	<li>In the control panel window click "Run". The heatmap can be saved in various formats such as tiff or bmp; it can also be copied and modified as needed.</li>
</ol>

<ol>
	<li>Pacifici, 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=Cerebrospinal+fluid+miRNA+profile+in+HIV-encephalitis.">Cerebrospinal fluid miRNA profile in HIV-encephalitis.</a> J. Cell. Physiol. 10, (2012).</li><li>Krol, J., Loedige, I., Filipowicz, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+widespread+regulation+of+microRNA+biogenesis,+function+and+decay.">The widespread regulation of microRNA biogenesis, function and decay.</a> Nat. Rev. Genet.. 11, 597-610 (2010).</li><li>Calin, G. A., Croce, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+signatures+in+human+cancers.">MicroRNA signatures in human cancers.</a> Nat. Rev. Cancer. 6, 857-866 (2006).</li><li>Chen, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+microRNAs+in+serum:+a+novel+class+of+biomarkers+for+diagnosis+of+cancer+and+other+diseases.">Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases.</a> Cell Res. 18, 997-1006 (2008).</li><li>De Smaele, E., Ferretti, E., Gulino, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs+as+biomarkers+for+CNS+cancer+and+other+disorders.">MicroRNAs as biomarkers for CNS cancer and other disorders.</a> Brain Res. 1338, 100-111 (2010).</li><li>Heneghan, H. M., Miller, N., Kerin, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MiRNAs+as+biomarkers+and+therapeutic+targets+in+cancer.">MiRNAs as biomarkers and therapeutic targets in cancer.</a> Curr. Opin. Pharmacol. 10, 543-550 (2010).</li><li>Kosaka, N., Iguchi, H., Ochiya, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Circulating+microRNA+in+body+fluid:+a+new+potential+biomarker+for+cancer+diagnosis+and+prognosis.">Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis.</a> Cancer Sci. 101, 2087-2092 (2010).</li><li>Lu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+expression+profiles+classify+human+cancers.">MicroRNA expression profiles classify human cancers.</a> Nature. 435, 834-838 (2005).</li><li>Shah, A. A., Leidinger, P., Blin, N., Meese, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRNA:+small+molecules+as+potential+novel+biomarkers+in+cancer.">miRNA: small molecules as potential novel biomarkers in cancer.</a> Curr. Med. Chem. 17, 4427-4432 (2010).</li><li>Blondal, 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=Assessing+sample+and+miRNA+profile+quality+in+serum+and+plasma+or+other+biofluids.">Assessing sample and miRNA profile quality in serum and plasma or other biofluids.</a> Methods. 59, 1-6 (2013).</li><li>Jensen, S. 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=Evaluation+of+two+commercial+global+miRNA+expression+profiling+platforms+for+detection+of+less+abundant+miRNAs.">Evaluation of two commercial global miRNA expression profiling platforms for detection of less abundant miRNAs.</a> BMC Genomics. 12, 435 (2011).</li></ol>

The authors have nothing to disclose.

MicroRNAs are small noncoding RNAs that regulate gene expression by inhibiting translation and/or promoting mRNA degradation2. Due to their high stability in cell-free conditions, microRNAs have been detected in many body fluids. Furthermore, distinct expression profile of microRNAs has been correlated with stage and/or progression in a variety of human cancers3-9.
There are several tools available to profile microRNAs, including classical chip arrays or the latest deep s...

MicroRNAs belong to the family of small (21-23 nt in length) noncoding RNAs that regulate gene expression post-transcriptionally. microRNAs can be secreted in the extracellular space where they appear to be relatively stable. While determining changes in miRNA expression can be an important step toward identification of biomarkers, performing miRNA profiles and handling a large amount of data may be intimidating.
Here, we describe a protocol to determine changes in miRNA expression in the cerebrospinal fluid (applicable to other body fluids) by a sensitive real time PCR. We also describe the use of software for data analysis, including statistical analysis and graphical representation of results. The entire procedure is relatively easy and straightforward and, depending on the number of samples to be profiled and the number of real time PCR machines available, also relatively quick. The experimental part requires accuracy in handling RNA and pipetting into 384-well plates, while the data analysis section using GenEx requires some basic knowledge in informatics and statistics.

Table III. List of equipment
<table border="1" cellpadding="0" cellspacing="0" style="width:655px;" width="655">
	<tbody>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:27px;">
			Equipment
			</td>
			<td nowrap="nowrap" style="width:150px;height:27px;">
			Company
			</td>
			<td nowrap="nowrap" style="width:204px;height:27px;">
			Catalog Number
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Finnpipette Novus Multichannel Pipetter
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Thermo Scientific
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			HH05279
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Finntip Pipette Tips
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Thermo Scientific
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			9400613
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Thermal Cycler C1000
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Biorad
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			0.2 ml Low Profile, Clear PCR tubes
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Biorad
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			TLS0801
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Flat Cap Strips
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Biorad
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			TLS0803
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			LightCycler&#174; 480 Real-Time PCR System
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Roche
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			LightCycler&#174; 480 Sealing Foil
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Roche
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			04 729 757 001
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Refrigerated Centrifuge 5804R
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Eppendorf
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Swing-bucket Rotor, 4-place
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Eppendorf
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			A-4-44
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Bench-Top Mini Centrifuge
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Fisher
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			05-090-100
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Bench-Top Vortex
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Fisher
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			1.5 ml Microcentrifuge Tubes, Sterilized
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Fisher
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			02-681-5
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Matrix Reagent Reservoir, 25 ml
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Thermo Scientific
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			8093
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Thermomixer Confort
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Eppendorf
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Bench-To Mini Centrifuge Sigma 1-15
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Sigma
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;"></td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:301px;height:21px;">
			Bench-Top Vortex
			</td>
			<td nowrap="nowrap" style="width:150px;height:21px;">
			Velp Scientifica
			</td>
			<td nowrap="nowrap" style="width:204px;height:21px;">
			F202A0173
			</td>
		</tr>
	</tbody>
</table>

Table IV. List of reagents
<table border="1" cellpadding="0" cellspacing="0" style="width:655px;" width="655">
	<tbody>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:25px;">
			Reagents
			</td>
			<td nowrap="nowrap" style="width:168px;height:25px;">
			Company
			</td>
			<td nowrap="nowrap" style="width:144px;height:25px;">
			Catalog Number
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			Universal c-DNA synthesis kit, 16-32 rxns
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Exiqon
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			203300
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			SYBR Green master mix, Universal RT, 25 ml
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Exiqon
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			203400
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			Ultrapure Distilled Water Dnase, Rnase free
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Invitrogen
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			10977-015
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			MicroRNA Ready to use PCR Human Panels (I+II) V2.R
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Exiqon
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			203608
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			mirVana&#8482; miRNA isolation Kit, 40 isolations
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Ambion
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			AM1556
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			MS2 RNA carrier
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Roche Applied Science
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			10165948001
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			2Betamercaptoethanol 98%
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Sigma Aldrich
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			M3148-25ml
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			Ethanol 100%
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Carlo Erba
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			64-17-5
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			Nuclease free water
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Qiagen
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			1039480
			</td>
		</tr>
		<tr>
			<td nowrap="nowrap" style="width:343px;height:21px;">
			RNAse Zap&#174;
			</td>
			<td nowrap="nowrap" style="width:168px;height:21px;">
			Ambion
			</td>
			<td nowrap="nowrap" style="width:144px;height:21px;">
			AM9780
			</td>
		</tr>
	</tbody>
</table>

cerebrospinal fluid microrna profiling using quantitative real time pcr

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their translational repression. Changes in miRNA expression have been shown to be involved in the development of all major complex diseases. Furthermore, recent findings showed that miRNAs can be secreted to the extracellular environment and enter the bloodstream and other body fluids where they can circulate with high stability. The function of such circulating miRNAs remains largely elusive, but systematic high throughput approaches, such as miRNA profiling arrays, have lead to the identification of miRNA signatures in several pathological conditions, including neurodegenerative disorders and several types of cancers. In this context, the identification of miRNA expression profile in the cerebrospinal fluid, as reported in our recent study, makes miRNAs attractive candidates for biomarker analysis.
There are several tools available for profiling microRNAs, such as microarrays, quantitative real-time PCR (qPCR), and deep sequencing. Here, we describe a sensitive method to profile microRNAs in cerebrospinal fluids by quantitative real-time PCR. We used the Exiqon microRNA ready-to-use PCR human panels I and II V2.R, which allows detection of 742 unique human microRNAs. We performed the arrays in triplicate runs and we processed and analyzed data using the GenEx Professional 5 software.
Using this protocol, we have successfully profiled microRNAs in various types of cell lines and primary cells, CSF, plasma, and formalin-fixed paraffin-embedded tissues.

Results from this study have been previously published1. Figure 1 shows results from the analysis of candidate reference microRNAs using geNorm application. Accordingly, two microRNAs, miR-622 and miR-1266, were identified as reference genes and were used to normalize miRNA values.
For the CSF study we had three groups of samples: HIV- (n=10), HIVE (n=4), and HIV+ without Encephalitis (HIV+, n=5). The two groups, HIVE and HIV+, were compar...

Watch this Scientific Journal Video about Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR at JoVE.com

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

We describe a protocol of real time PCR to profile microRNAs in the cerebrospinal fluid (CSF). With the exception of RNA extraction protocols, the procedure can be extended to RNA extracted from other body fluids, cultured cells, or tissue specimens.

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their ...

cerebrospinal-fluid-microrna-profiling-using-quantitative-real-time

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JoVE Journal

Medicine

15.3K Views.  LSU Health Sciences Center. We describe a protocol of real time PCR to profile microRNAs in the cerebrospinal fluid (CSF). With the exception of RNA extraction protocols, the procedure can be extended to RNA extracted from other body fluids, cultured cells, or tissue specimens.

Video: Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Evaluation of Nanoparticle Uptake in Tumors in Real Time Using Intravital Imaging

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle uptake in tumors at the microscopic level1,2,3, highlighting the utility of a suitable xenograft model in which to perform detailed uptake analyses. Here, we use high-resolution intravital imaging to evaluate nanoparticle uptake in human tumor xenografts in a modified, shell-less chicken embryo model. The chicken embryo model is particularly well-suited for these in vivo analyses because it supports the growth of human tumors, is relatively inexpensive and does not require anesthetization or surgery 4,5. Tumor cells form fully vascularized xenografts within 7 days when implanted into the chorioallantoic membrane (CAM) 6. The resulting tumors are visualized by non-invasive real-time, high-resolution imaging that can be maintained for up to 72 hours with little impact on either the host or tumor systems. Nanoparticles with a wide range of sizes and formulations administered distal to the tumor can be visualized and quantified as they flow through the bloodstream, extravasate from leaky tumor vasculature, and accumulate at the tumor site. We describe here the analysis of nanoparticles derived from Cowpea mosaic virus (CPMV) decorated with near-infrared fluorescent dyes and/or polyethylene glycol polymers (PEG) 7, 8, 9,10,11. Upon intravenous administration, these viral nanoparticles are rapidly internalized by endothelial cells, resulting in global labeling of the vasculature both outside and within the tumor7,12. PEGylation of the viral nanoparticles increases their plasma half-life, extends their time in the circulation, and ultimately enhances their accumulation in tumors via the enhanced permeability and retention (EPR) effect 7, 10,11. The rate and extent of accumulation of nanoparticles in a tumor is measured over time using image analysis software. This technique provides a method to both visualize and quantify nanoparticle dynamics in human tumors.

We present a novel approach to quantify nanoparticle localization in the vasculature of human xenografted tumors using dynamic, real-time intravital imaging in an avian embryo model.

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle ...

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR (qPCR)

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process including development1, apoptosis2, and cell cycle regulation3. MiRNAs are estimated to regulate the expression of 30% to 90% of human genes4 by binding to their target messenger RNAs (mRNAs)5. Widespread dysregulation of miRNAs has been reported in various diseases and cancer subtypes6. Due to their prevalence and unique structure, these small molecules are likely to be the next generation of biomarkers, therapeutic agents and/or targets.
Methods used to investigate miRNA expression include SYBR green I dye- based as well as Taqman-probe based qPCR. If miRNAs are to be effectively used in the clinical setting, it is imperative that their detection in fresh and/or archived clinical samples be accurate, reproducible, and specific. qPCR has been widely used for validating expression of miRNAs in whole genome analyses such as microarray studies7. The samples used in this protocol were from patients who underwent radical prostatectomy for clinically localized prostate cancer; however other tissues and cell lines can be substituted in. Prostate specimens were snap-frozen in liquid nitrogen after resection. Clinical variables and follow-up information for each patient were collected for subsequent analysis8.
Quantification of miRNA levels in prostate tumor samples. The main steps in qPCR analysis of tumors are: Total RNA extraction, cDNA synthesis, and detection of qPCR products using miRNA-specific primers. Total RNA, which includes mRNA, miRNA, and other small RNAs were extracted from specimens using TRIzol reagent. Qiagen's miScript System was used to synthesize cDNA and perform qPCR (Figure 1). Endogenous miRNAs are not polyadenylated, therefore during the reverse transcription process, a poly(A) polymerase polyadenylates the miRNA. The miRNA is used as a template to synthesize cDNA using oligo-dT and Reverse Transcriptase. A universal tag sequence on the 5' end of oligo-dT primers facilitates the amplification of cDNA in the PCR step. PCR product amplification is detected by the level of fluorescence emitted by SYBR Green, a dye which intercalates into double stranded DNA. Specific miRNA primers, along with a Universal Primer that binds to the universal tag sequence will amplify specific miRNA sequences. 
The miScript Primer Assays are available for over a thousand human-specific miRNAs, and hundreds of murine-specific miRNAs. Relative quantification method was used here to quantify the expression of miRNAs. To correct for variability amongst different samples, expression levels of a target miRNA is normalized to the expression levels of a reference gene. The choice of a gene on which to normalize the expression of targets is critical in relative quantification method of analysis. Examples of reference genes typically used in this capacity are the small RNAs RNU6B, RNU44, and RNU48 as they are considered to be stably expressed across most samples. In this protocol, RNU6B is used as the reference gene.

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR qPCR

Quantitative Real Time polymerase chain reaction (qPCR) is a rapid and sensitive method to investigate the expression levels of various microRNA (miRNA) molecules in tumor samples. Using this method expression of hundreds of different miRNA molecules can be amplified, quantified, and analyzed from the same cDNA template.

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process ...

Movement Retraining using Real-time Feedback of Performance

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular patterns responsible for governing the movement performance. This motor learning can be enhanced through a number of methods that are utilized in research and clinical settings alike. In general, verbal feedback of performance in real-time or knowledge of results following movement is commonly used clinically as a preliminary means of instilling motor learning. Depending on patient preference and learning style, visual feedback (e.g. through use of a mirror or different types of video) or proprioceptive guidance utilizing therapist touch, are used to supplement verbal instructions from the therapist. Indeed, a combination of these forms of feedback is commonplace in the clinical setting to facilitate motor learning and optimize outcomes.
Laboratory-based, quantitative motion analysis has been a mainstay in research settings to provide accurate and objective analysis of a variety of movements in healthy and injured populations. While the actual mechanisms of capturing the movements may differ, all current motion analysis systems rely on the ability to track the movement of body segments and joints and to use established equations of motion to quantify key movement patterns. Due to limitations in acquisition and processing speed, analysis and description of the movements has traditionally occurred offline after completion of a given testing session.
This paper will highlight a new supplement to standard motion analysis techniques that relies on the near instantaneous assessment and quantification of movement patterns and the display of specific movement characteristics to the patient during a movement analysis session. As a result, this novel technique can provide a new method of feedback delivery that has advantages over currently used feedback methods.

Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular ...

Using Quantitative Real-time PCR to Determine Donor Cell Engraftment in a Competitive Murine Bone Marrow Transplantation Model

Murine bone marrow transplantation models provide an important tool in measuring hematopoietic stem cell (HSC) functions and determining genes/molecules that regulate HSCs. In these transplant model systems, the function of HSCs is determined by the ability of these cells to engraft and reconstitute lethally irradiated recipient mice. Commonly, the donor cell contribution/engraftment is measured by antibodies to donor- specific cell surface proteins using flow cytometry. However, this method heavily depends on the specificity and the ability of the cell surface marker to differentiate donor-derived cells from recipient-originated cells, which may not be available for all mouse strains. Considering the various backgrounds of genetically modified mouse strains in the market, this cell surface/ flow cytometry-based method has significant limitations especially in mouse strains that lack well-defined surface markers to separate donor cells from congenic recipient cells. Here, we reported a PCR-based technique to determine donor cell engraftment/contribution in transplant recipient mice. We transplanted male donor bone marrow HSCs to lethally irradiated congenic female mice. Peripheral blood samples were collected at different time points post transplantation. Bone marrow samples were obtained at the end of the experiments. Genomic DNA was isolated and the Y chromosome specific gene, Zfy1, was amplified using quantitative Real time PCR. The engraftment of male donor-derived cells in the female recipient mice was calculated against standard curve with known percentage of male vs. female DNAs. Bcl2 was used as a reference gene to normalize the total DNA amount. Our data suggested that this approach reliably determines donor cell engraftment and provides a useful, yet simple method in measuring hematopoietic cell reconstitution in murine bone marrow transplantation models. Our method can be routinely performed in most laboratories because no costly equipment such as flow cytometry is required.

Determining donor cell engraftment presents a challenge in mouse bone marrow transplant models that lack well-defined phenotypical markers. We described a methodology to quantify male donor cell engraftment in female transplant recipient mice. This method can be used in all mouse strains for the study of HSC functions.

Murine bone marrow transplantation models provide an important tool in measuring hematopoietic stem cell (HSC) functions and determining genes/molecules ...

Real-time Cytotoxicity Assays in Human Whole Blood

A live cell-based whole blood cytotoxicity assay (WCA) that allows access to temporal information of the overall cell cytotoxicity is developed with high-throughput cell positioning technology. The targeted tumor cell populations are first preprogrammed to immobilization into an array format, and labeled with green fluorescent cytosolic dyes. Following the cell array formation, antibody drugs are added in combination with human whole blood. Propidium iodide (PI) is then added to assess cell death. The cell array is analyzed with an automatic imaging system. While cytosolic dye labels the targeted tumor cell populations, PI labels the dead tumor cell populations. Thus, the percentage of target cancer cell killing can be quantified by calculating the number of surviving targeted cells to the number of dead targeted cells. With this method, researchers are able to access time-dependent and dose-dependent cell cytotoxicity information. Remarkably, no hazardous radiochemicals are used. The WCA presented here has been tested with lymphoma, leukemia, and solid tumor cell lines. Therefore, WCA allows researchers to assess drug efficacy in a highly relevant ex&#160;vivo condition.

The whole blood cytotoxicity assay (WCA) is a cytotoxicity assay developed by incorporating high-throughput cell positioning technology with fluorescence microscopy and automated image processing. Here, we describe how lymphoma cells treated with an anti-CD20 antibody can be analyzed real-time in human whole blood to provide quantitative cellular cytotoxicity analysis.

A live cell-based whole blood cytotoxicity assay (WCA) that allows access to temporal information of the overall cell cytotoxicity is developed with ...

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic ...

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung

At birth, the lung undergoes a profound phenotypic switch from secretion to absorption, which allows for adaptation to breathing independently. Promoting and sustaining this phenotype is critically important in normal alveolar growth and gas exchange throughout life. Several in vitro studies have characterized the role of key regulatory proteins, signaling molecules, and steroid hormones that can influence the rate of lung fluid clearance. However, in vivo examinations must be performed to evaluate whether these regulatory factors play important physiological roles in regulating perinatal lung liquid absorption. As such, the utilization of real time X-ray imaging to determine perinatal lung fluid clearance, or pulmonary edema, represents a technological advancement in the field. Herein, we explain and illustrate an approach to assess the rate of alveolar lung fluid clearance and alveolar flooding in C57BL/6 mice at post natal day 10 using X-ray imaging and analysis. Successful implementation of this protocol requires prior approval from institutional animal care and use committees (IACUC), an in vivo small animal X-ray imaging system, and compatible molecular imaging software.

We present a protocol to assess the rate of alveolar fluid clearance or pulmonary edema in neonatal mouse lung using X-ray imaging technology.

At birth, the lung undergoes a profound phenotypic switch from secretion to absorption, which allows for adaptation to breathing independently. Promoting ...

Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated death 1. Common sites of metastatic spread include lung, lymph node, brain, and bone 2. Mechanisms that drive metastasis are intense areas of cancer research. Consequently, effective assays to measure metastatic burden in distant sites of metastasis are instrumental for cancer research. Evaluation of lung metastases in mammary tumor models is generally performed by gross qualitative observation of lung tissue following dissection. Quantitative methods of evaluating metastasis are currently limited to ex vivo and in vivo imaging based techniques that require user defined parameters. Many of these techniques are at the whole organism level rather than the cellular level 3-6. Although newer imaging methods utilizing multi-photon microscopy are able to evaluate metastasis at the cellular level 7, these highly elegant procedures are more suited to evaluating mechanisms of dissemination rather than quantitative assessment of metastatic burden. Here, a simple in vitro method to quantitatively assess metastasis is presented. Using quantitative Real-time PCR (QRT-PCR), tumor cell specific mRNA can be detected within the mouse lung tissue.

This protocol describes how to use quantitative Real-time PCR (QRT-PCR) to detect tumor cell specific mRNA representing metastasis within the mouse lung tissue.

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated ...

Isolation and Profiling of MicroRNA-containing Exosomes from Human Bile

Exosome research in the last three years has greatly extended the scope towards identification and characterization of biomarkers and their therapeutic uses.
Exosomes have recently been shown to contain microRNAs (miRs). MiRs themselves have arisen as valuable biomarkers for diagnostic purposes. As specimen collection in clinics and hospitals is quite variable, miRNA isolation from whole bile varies substantially. To achieve robust, accurate and reproducible miRNA profiles from collected bile samples in a simple manner required the development of a high-quality protocol to isolate and characterize exosomes from bile. The method requires several centrifugations and a filtration step with a final ultracentrifugation step to pellet the isolated exosomes. Electron microscopy, Western blots, flow cytometry and multi-parameter nanoparticle optical analysis, where available, are crucial characterization steps to validate the quality of the exosomes. For the isolation of miRNA from these exosomes, spiking the lysate with a non-specific, synthetic miRNA from a species like Caenorhabditis elegans, i.e., Cel-miR-39, is important for normalization of RNA extraction efficiency. The isolation of exosome from bile fluid following this method allows the successful miRNA profiling from bile samples stored for several years at -80 &#176;C.

Bile fluid is a valuable source of extracellular vesicles/exosomes that contain potentially important biomarkers. This protocol represents a robust method to isolate exosomes from human bile for further analyses including miRNA profiling.

Exosome research in the last three years has greatly extended the scope towards identification and characterization of biomarkers and their therapeutic ...

Extended Time-lapse Intravital Imaging of Real-time Multicellular Dynamics in the Tumor Microenvironment

In the tumor microenvironment, host stromal cells interact with tumor cells to promote tumor progression, angiogenesis, tumor cell dissemination and metastasis. Multicellular interactions in the tumor microenvironment can lead to transient events including directional tumor cell motility and vascular permeability. Quantification of tumor vascular permeability has frequently used end-point experiments to measure extravasation of vascular dyes. However, due to the transient nature of multicellular interactions and vascular permeability, the kinetics of these dynamic events cannot be discerned. By labeling cells and vasculature with injectable dyes or fluorescent proteins, high-resolution time-lapse intravital microscopy has allowed the direct, real-time visualization of transient events in the tumor microenvironment. Here we describe a method for using multiphoton microscopy to perform extended intravital imaging in live mice to directly visualize multicellular dynamics in the tumor microenvironment. This method details cellular labeling strategies, the surgical preparation of a mammary skin flap, the administration of injectable dyes or proteins by tail vein catheter and the acquisition of time-lapse images. The time-lapse sequences obtained from this method facilitate the visualization and quantitation of the kinetics of cellular events of motility and vascular permeability in the tumor microenvironment.

This protocol describes the use of multiphoton microscopy to perform extended time-lapse imaging of multicellular interactions in real time, in vivo at single cell resolution.

In the tumor microenvironment, host stromal cells interact with tumor cells to promote tumor progression, angiogenesis, tumor cell dissemination and ...