This work is supported by the US Army Medical Research Acquisition Activity (USAMRAA) through the Idea Development Award underAward No. W81XWH-18-1-303 and W81XWH-18-2-0013 and additionally by Award no. W81XWH-18-2-0015, W81XWH-18-2-0016, W81XWH-18-2-0017, W81XWH-18-2-0018, and W81XWH-18-2-0019 Prostate Cancer Biorepository Network (PCBN). Funding support to authors' lab by the National Cancer Institute at the National Institutes of Health (Grant Number RO1CA177984) is also acknowledged. Rajvir Dahiya is a Senior Research Career Scientist at the Department of Veterans Affairs, BX004473 and funded by NIH-UO1CA199694 (RD). Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense or U.S. Army. We are thankful to Judy Shigenaga, director of core facility at San Francisco VAMC, for her assistance with the NextSeq 500 sequencer.

This study was conducted in accordance with the ethical guidelines of US Common Rule and was approved by the Institutional Committee on Human Research.
1. Microdissection
NOTE: FFPE metastatic CRPC tissues with adenocarcinoma features (CRPC-Adeno) or NE differentiation (CRPC-NE) were obtained from the Prostate Cancer Biorepository Network (PCBN). Ten-micron PCa sections on glass slides were prepared for manual microdissection as discussed previously18. To briefly summarize:
<ol>
	<li>Deparaffinize the tissue sections by soaking the slides in xylene (2x, 10 min each). Then rehydrate the sections by incubating the slides for 5 min each in graded ethanol (100%, 95%, 90%, 80%, and 70%), followed by a distilled water wash and staining with hematoxylin (30 s soak).</li>
	<li>Following hematoxylin staining, wash the tissue sections with water. Then dehydrate the tissue sections by placing them in graded ethanol (70%, 80% 90%, 95%, 100%) (5 min each) followed by xylene (5 min).</li>
	<li>Analyze the corresponding hematoxylin and eosin (H&#38;E) slides to identify the tumor areas (i.e., adenocarcinomas or NED) and mark these tumor areas and normal adjacent areas with the assistance of a board-certified pathologist. Using these H&#38;E slides as roadmaps, mark the hematoxylin-stained tissues obtained in the above step to distinguish between the tumor and normal areas.</li>
	<li>Dissect the marked tissue slide carefully under the microscope using a scalpel blade.</li>
	<li>Collect the dissected tissue from normal and cancerous areas in separate tubes using electrostatically charged gel loading pipette tips. The charged tip attracts the dissected tissue and allows for maximum tissue recovery after dissection. Once the tissue sticks to the charged tip, cut the tip into an empty 2 mL collection tube.</li>
</ol>
2. Isolating Serum-derived EVs
NOTE: Frozen patient serum samples collected from patients with metastatic CRPC with adenocarcinoma characteristics (CRPC-adeno) or NE differentiation (CRPC-NE) were procured from PCBN using an approved IRB protocol and stored at -80 &#176;C prior to their use. Serum samples were collected from the same set of patients as those used for microdissected tissues to allow comparison between the corresponding tissues and the serum-derived EVs. A commercially available serum EV isolation reagent was used to collect the EVs.
<ol>
	<li>Thaw frozen patient serum samples at 4 &#176;C.</li>
	<li>Use 110 &#956;L of thawed serum sample and spin at 2,000 x g for 30 min.</li>
	<li>Collect the supernatant for subsequent EV/exosome isolation and discard the debris pellet. Add 30 &#956;L of serum exosome isolation reagent to the supernatant and incubate at 4 &#176;C for 30 min.</li>
	<li>Spin at 10,000 x g for 10 min. Collect the EV-depleted serum carefully without disturbing the pellet in a separate 1.5 mL tube and store at -80 &#176;C for future analysis, if needed.</li>
	<li>Resuspend the EV pellet in 70 &#956;L of phosphate buffered saline (PBS) prepared in nuclease-free water. Keep an aliquot for the particle tracking analyses system to assess the quality and number of particles. Store the rest of the sample at -80 &#176;C until further analysis.</li>
</ol>
3. RNA Isolation from Microdissected Prostate Tissues and EVs
NOTE: Total RNA was isolated from microdissected tissues and purified EVs using a commercially available kit (Table of Materials) per the manufacturer&#39;s instructions with a few modifications. The following sections briefly describe these procedures with special emphasis on the important steps:
<ol>
	<li>Isolate the RNA from the microdissected tissues.
	<ol>
		<li>Resuspend the microdissected FFPE tissue in 150 &#956;L of proteinase K buffer. Mix by vortexing and pipette out the residual tissue from the tip. Spin at 11,000 x g for 1 min.</li>
		<li>Add 10 &#956;L of proteinase K to the pellet. Wait for 2 min until the pellet turns white. Pipette up and down a few times.</li>
		<li>Incubate at 56 &#176;C for 16 min. Vortex every 3 min.</li>
		<li>Remove samples and let the heat block reach 80 &#176;C. Incubate the samples on the block for 16 min. Vortex every 3 min.</li>
		<li>Incubate on ice for 3 min.</li>
		<li>Spin at 20,000 x g for 15 min. Transfer the supernatant to a new 2 mL tube.</li>
		<li>Add 16 &#956;L of DNase booster buffer followed by 10 &#956;L of DNase I. Mix by inverting the tube gently and incubate at room temperature (RT) for 15 min.</li>
		<li>Add 320 &#956;L of red blood cell (RBC) lysis buffer. Mix by inverting the tube and then add 1,120 &#956;L of 100% ethanol. Immediately transfer on spin columns stored at 4 &#176;C. Spin at 8,000 x g for 30 s.</li>
		<li>Wash the columns 2x with wash buffer and spin the columns at 20,000 x g for 5 min to remove residual wash buffer.</li>
		<li>Allow the spin column to air dry for 2&#8722;3 min, then elute with 19 &#956;L of RNase-free water. Quantitate the purified RNA on a spectrophotometer and normalize the sample to 40 ng/&#956;L.</li>
	</ol>
	</li>
	<li>RNA isolation from serum-derived EVs.
	<ol>
		<li>Isolate the exosomal RNA using a kit. Add 75 &#956;L of lysis buffer A and 9.3 &#956;L of lysis buffer B to 50 &#956;L of the purified EV suspension. 
		NOTE: Keep mixing the sample by inverting the tube for about 10 min to allow complete lysis of the EVs.</li>
		<li>Add 130 &#956;L of 100% ethanol and immediately mix by inverting the sample. Transfer on a spin column and then spin at 3,300 x g for 1 min.</li>
		<li>Wash the column 2x with the wash buffer. Spin the column at 1,300 x g for 3 min to completely remove the wash buffer.</li>
		<li>Allow the column to air dry for 2 min. Add 18 &#956;L of elution buffer to the column and let it sit for 2 min. Spin at 400 x g for 1 min followed by a second spin at 5,800 x g for 3 min.</li>
		<li>Repeat step 3.2.4 with the eluted buffer included in the kit to increase the yield of the RNA from the sample.</li>
		<li>Quantify the sample on a spectrophotometer and normalize it to 40 ng/&#956;L.</li>
	</ol>
	</li>
</ol>
4. Library Preparation for Small RNA Sequencing
NOTE: A small RNA library prep kit (Table of Materials) was used to generate cDNA libraries from the isolated RNA samples. The steps to generate libraries, purify, and quality check them prior to running on a sequencer are crucial to any sequencing protocol as they eventually determine the quality of output data from a run. Therefore, proceed carefully with each step. The guidelines from the manufacturer suggest using a minimum of 50 ng of RNA. Given the poor quality and low amounts of total RNA usually obtained from these two sample sources, this protocol is optimized for RNA concentrations as low as 100 ng. The protocol below is for one sample of a library.
<ol>
	<li>Generate adapter-ligated cDNA.
	<ol>
		<li>Use 5 &#956;L of a normalized (40 ng/&#956;L) RNA sample. Add 1 &#956;L of 3' adaptor. Preheat the thermal cycler to 70 &#176;C prior to incubating the samples. Incubate for 2 min. Immediately transfer the samples on ice prior to moving onto the next step.</li>
		<li>In a separate tube, mix 2 &#956;L of ligation buffer, 1 &#956;L of RNase inhibitor, and 1 &#956;L of T4 RNA Ligase 2, a deletion mutant. Mix by pipetting up and down and add 4 &#956;L of this mix to the tube with the RNA/3' adaptor.</li>
		<li>Transfer to the thermal cycler that has been preheated to 28 &#176;C and incubate for 1 h.</li>
		<li>Add 1 &#956;L of stop solution while keeping the samples on the thermal block. Incubate at 28 &#176;C for another 15 min.</li>
		<li>Remove the tubes and keep on ice immediately after this step.</li>
		<li>In a separate tube, add 1 &#956;L of 5' adaptor.</li>
		<li>Transfer to a thermal cycler that has been preheated to 70 &#176;C and incubate for 2 min.</li>
		<li>Immediately place the tube on ice to prevent re-annealing of the adaptors.</li>
		<li>Add 1 &#956;L of 10 mM ATP to the tube of denatured 5' adaptor. Mix by pipetting and add 1 &#956;L of T4 RNA ligase to the mix. Mix by pipetting and add 3 &#956;L of this mix to the tube containing the 3' adaptor-ligated RNA. Transfer on the thermal cycler that has been preheated to 28 &#176;C and incubate for 1 h.</li>
		<li>Immediately transfer to ice and either proceed further with the samples or store at -20 &#176;C for future use.</li>
		<li>To perform RT-PCR, use 6 &#956;L of each adaptor-ligated RNA and add 1 &#956;L of RNA RT primer to it. Transfer to the thermal cycler that has been preheated to 70 &#176;C. Incubate for 2 min.</li>
		<li>In a separate tube, mix 2 &#956;L of 5x strand buffer, 0.5 &#956;L of 12.5 mM dNTPs, 1 &#956;L of 100 mM DTT, 1 &#956;L of RNase inhibitor, and 1 &#956;L of reverse transcriptase. Add 5.5 &#956;L of this mix to the adaptor-ligated RNA and transfer to the thermal cycler that has been preheated to 50&#176;C. Incubate for 1 h.</li>
		<li>Transfer the adaptor-ligated cDNA immediately to ice.</li>
		<li>In a separate tube, mix 25 &#956;L of PCR mix, 2 &#956;L of RNA PCR primer, 2 &#956;L of RNA PCR primer index, and 8.5 &#956;L of RNase-free water. Add 37.5 &#956;L of this mix to 12.5 &#956;L of adaptor-ligated cDNA. Transfer to the thermal cycler that has been preheated to 98 &#176;C. Program the thermal cycler as suggested in the manufacturer's protocol with the cycle number modified to 15 instead of 11. 
		NOTE: Sequences of all the primers used are listed under Table of Materials.</li>
		<li>Keep the samples at 4 &#176;C after completion. Proceed with them or store at -80 &#176;C for future use.</li>
	</ol>
	</li>
	<li>Purify and perform quality control for the ligated cDNA. 
	NOTE: Amplified cDNA was gel-purified by following the manufacturer's protocol except for a few modifications to improve the quality and yield of purified cDNA samples as explained below.
	<ol>
		<li>Use 6% TBE polyacrylamide gels to purify the amplified cDNA. Dilute the high-resolution ladder (HRL) and custom RNA ladder (CRL) with equal volumes of DNA loading dye.</li>
		<li>Add 10 &#956;L of DNA loading dye to each cDNA sample. Run 30 &#956;L of each sample in two separate lanes adjacent to each other with CRL and HRL in the intermediate space between the two different samples.</li>
		<li>Run the gel in 1x TBE buffer at 145 V for approximately 30&#8722;40 min. 
		NOTE: Keep track of the lower blue front of the loading dye. Stop the electrophoresis when it approaches the bottom of gel.</li>
		<li>Transfer the gel in 1x TBE buffer with Ethidium Bromide (EtBr) at 0.5 &#181;g EtBr/1 mL 1x TBE. 
		CAUTION: EtBr is a known carcinogen and each step from here until the excision from the gel should be performed with extreme caution.</li>
		<li>Visualize the gel in a transilluminator and cut the gel precisely between the 145 bp and 160 bp marker. 
		NOTE: The adaptor dimers run very close to the 145 bp marker. Therefore, cut the gel slightly above the 145 bp marker to avoid contamination with adaptor dimer.</li>
		<li>Transfer the gel to DNA breaking tubes kept in 2 mL collecting tubes. Spin at 20,000 x g for 2 min. Add 300 &#956;L of RNase-free water and allow it to rotate gently on a rocker overnight at RT.</li>
		<li>To perform the DNA precipitation, on the following day transfer the contents of the tube to 5 &#956;m filter tubes. Spin at 600 x g for 10 s. 
		NOTE: Do not let tubes spin longer than 10 s as the gel passes through the filter.</li>
		<li>Add 2 &#956;L of glycogen, 30 &#956;L of 3 M NaOAc, 2 &#956;L of 0.1x pellet paint, and 975 &#956;L of 100% ethanol to the eluted DNA. Spin at 17,000 x g at 4&#176;C for 20 min.</li>
		<li>Discard the supernatant and add 75% ethanol to wash the pellet. Spin at 17,000 x g at 4 &#176;C for 5 min. Discard the supernatant and incubate the tubes at 37 &#176;C to completely evaporate the ethanol.</li>
		<li>Resuspend the pellet with 12 &#956;L of 10 mM Tris-HCl pH 8.5.</li>
		<li>Perform a library quality check by diluting the purified cDNA by 10x (1:10) and using 1 &#956;L of diluted cDNA to run on a bio-analyzer.</li>
		<li>Make sure the cDNA product size corresponds to the range of small RNA (136&#8722;160 bp) in the electropherogram. Calculate the total molarity for each sample. Normalize each sample to 2 nM using Tris-HCl pH 8.5.</li>
	</ol>
	</li>
	<li>Denature and sequence the purified cDNA.
	<ol>
		<li>Pool the libraries by mixing equal volumes of each 2 nM sample in a single 200 &#956;L PCR tube. Add 10 &#956;L of freshly prepared 0.2 N NaOH to 10 &#956;L of pooled library.</li>
		<li>Mix immediately by vortexing, and spin at 280 x g for 1 min. Incubate at RT for 5 min.</li>
		<li>Add 10 &#956;L of 200 mM Tris-HCl pH 7 to 20 &#956;L of the denatured library. Mix by vortexing and spin at 280 x g for 1 min.</li>
		<li>Add 970 &#956;L of prechilled hybridization buffer to the library and mix well by vortexing. The library is at a concentration of 20 pM. Keep on ice until it is finally diluted. 
		NOTE: The final dilution is done right before running on sequencer.</li>
		<li>Add 117 &#956;L of the denatured library to 1,183 &#956;L of prechilled hybridization buffer. Mix by inverting the tube and pulse centrifuge. The final concentration of the library is now 1.8 pM.</li>
		<li>Add 1.3 &#956;L of PhiX to the 1.3 mL of final library. To initiate a run on a sequencer, follow the on-screen steps on the software interface, review the run parameters, including read type (single read), read length (number of cycles for each read), library ID, and select Start.</li>
	</ol>
	</li>
</ol>
5. Data Analysis
<ol>
	<li>At the end of the sequencer run, obtain the resulting sequencing data as fastQ files. Use the appropriate software to analyze the resulting sequencing data.</li>
	<li>Open the fastQ toolkit application and press the Launch button.</li>
	<li>Select the files from the list of samples in the software and select where the data will be saved.</li>
	<li>Add a string name that applies to each trimmed sequence. Keep the stringency to 0.9. Input the adapter sequence to trim as "TGGAATTCTCGGGTGCCAAGG" from the 3' end of each sequenced sample. Expand the Read Filtering tab and select a minimum read length of "5". Select the Acknowledge box and press the Continue button to start trimming. The trimmed files will be saved in the project folder upon the end of the analysis.</li>
	<li>Open the small RNA v.1.0 application on the software and press the Launch button.</li>
	<li>Select the project where the files will be saved and sent after the analysis.</li>
	<li>Select the Novel miRs and Differential miRs function in the application. Segregate the groups as "Control" and "Test" for differential analysis and add the trimmed files to be analyzed in their respective groups.</li>
	<li>Select the Launch button for initiating the analysis. Following the analyses, download the resulting files.</li>
</ol>

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The authors have no conflict of interest to declare.

In this article, we describe a protocol to isolate RNA from FFPE tissues and serum-derived EVs using kits that were optimized to increase the yield and quality of isolated RNAs. Further, the purified RNAs were used to generate cDNA libraries for small RNA sequencing. Both of the listed steps are essential in determining the quality and depth of the sequencing reads that are the final result from the run24. Therefore, optimizing these crucial steps is critical to a successful sequencing run.
<p...

Prostate cancer is driven by androgens acting via AR signaling. Therefore, targeting the AR pathway is the mainstay treatment for the disease1. However, resistance often ensues as a result of targeted therapies and the disease progresses to an advanced metastatic CRPC2. CRPC is treated by second generation of AR therapy that includes enzalutamide and abiraterone2,3 which improves survival initially. However, lethal variants such as NEPC often emerge in 25&#8722;30% of treated CRPC cases that have limited treatment options, leading to increased mortality3. NEPC arises via a reversible transdifferentiation process known as NED, wherein PCa cells undergo a lineage switch from adenocarcinomas and show decreased expression of AR and increased expression of NE lineage markers including enolase 2 (ENO2), chromogranin A (CHGA), and synaptophysin (SYP)4. Given that these resistance variants arise as a result of therapeutic interventions, it is essential to decipher pathways that lead to generation of this deadly, hard to treat form of PCa.
The genomics of NEPC was recently deciphered in an exhaustive study done by Beltran et al. whereby copy number alterations, mutations, single nucleotide polymorphisms (SNPs), and DNA methylation from patient-derived metastatic tissues were analyzed5. Despite significant advancement in the understanding of the genomics of this aggressive form of PCa, little is known about the epigenetic factors, including the small non-coding RNAs (microRNAs) that are involved in the transition of CRPC-adenocarcinoma to NEPC. MicroRNAs (miRNAs) are 22 bp long, double-stranded RNAs that act primarily by suppressing gene expression post-transcriptionally by sequence-specific interactions with the 3'-untranslated regions (UTRs) of cognate mRNA targets6. Several oncomiRs and tumor suppressors have now been identified, and their role in regulating disease onset and metastasis has been well studied in different cancers6,7. These small non-coding RNAs often serve as very important targets in controlling disease mortality6,8,9. More recent research has been focused on understanding the paracrine effects of miRNAs in cancer metastasis via their transport in EVs, such as exosomes, that flow in the bloodstream and allow tumor cells to send these secondary messengers to metastatic locations in a nuclease-free environment10,11,12. EVs carry miRNAs from the tumor cells to transfer the transforming effects from the host cells12. Identification of EVs as secondary messengers of tumor cells can therefore be useful in noninvasive detection of disease severity.
The miR-1246 is highly upregulated in EVs from aggressive PCa, and it indicates the disease severity13. These EV-associated miRNAs not only serve as noninvasive biomarkers of the disease, but also play functionally important roles in driving tumorigenesis. Thus, it essential to understand the significance of the miRNA repertoire that is associated with resistant forms of PCa to allow better identification of noninvasive biomarkers as well as their functional significance.
The advent of next generation sequencing has offered the most comprehensive platform to study the details of tumor landscape involving alterations in the genome such as mutations, chromosomal relocations, chromothripsis, and methylation, all of which contribute significantly to the form and nature of cancer14,15,16. Likewise, it is also an essential tool to understand the vast epigenomic changes that occur in a tumor cell and that are often important players in disease severity17. With the aim of understanding the miRNA repertoire associated with the generation of NEPC, small RNA sequencing was performed on FFPE metastatic CRPC tissues and their corresponding serum-derived EVs. RNA derived from either of these two sample sources is (1) low in yield and (2) of bad quality due to the degradation that often happens due to formalin fixation and EV isolation. Furthermore, generating cDNA libraries is a critical, but cumbersome, step of a sequencing run. Thus, methods for isolating these RNAs and using them to generate libraries for small RNA sequencing require optimization to generate accurate and reliable data.
There are several methods to profile miRNA expression in different samples, including RT-PCR, microarrays, and in-situ hybridization (ISH). A protocol using FFPE tissue-derived RNA to evaluate miRNA expression by RT-PCR and ISH was recently published18. More recent technologies offer more exhaustive and comprehensive platforms for profiling miRNA expression in a sample. NanoString nCounter offers a sensitive miRNA detection platform19, but the detection is often limited by the number of miRNAs that are available in the array (~2,000). In such a scenario, a more sensitive and exhaustive platform such as next generation sequencing offers much wider depth of miRNA identification and simultaneous profiling in different samples20. The method has been used to determine the miRNA signatures in urine or plasma from PCa patients21,22,23. In the current article, a protocol is presented to use a next generation sequencing platform to study miRNA profiles associated with aggressive CRPC using FFPE tissues and serum-derived EV RNAs.

<table><tbody><tr><td>3 M NaOAc pH 5.5</td><td>USB Corp.</td><td>75897 100 mL</td><td></td></tr><tr><td>5 &amp;micro;m filter tubes</td><td>IST Engineering Inc.</td><td>5388-50</td><td></td></tr><tr><td>6% TBE polyacrylamide gels</td><td>Novex</td><td>EC6265BOX</td><td></td></tr><tr><td>BaseSpace</td><td>Illumina</td><td></td><td>Analysis software</td></tr><tr><td>Bio-analyzer</td><td>Agilent</td><td></td><td></td></tr><tr><td>DNA loading dye</td><td>Novex</td><td>LC6678</td><td></td></tr><tr><td>Eppendorf Thermostat plus</td><td>Eppendorf 1.5 mL</td><td></td><td></td></tr><tr><td>EtBr</td><td>Pierce</td><td>17898</td><td></td></tr><tr><td>Ethyl alcohol 200 proof</td><td>Pharmco</td><td>111000200</td><td></td></tr><tr><td>Exosomal RNA isolation kit</td><td>Norgen</td><td>58000</td><td></td></tr><tr><td>Gel breaking tubes</td><td>IST Engineering Inc.</td><td>3388-100</td><td></td></tr><tr><td>Glycogen molecular biology grade</td><td>Thermo Scientifc</td><td>R0561</td><td></td></tr><tr><td>Hematoxylin Select</td><td>Stat lab</td><td>SL401</td><td></td></tr><tr><td>Microcentrifuge</td><td>Fisher Scientific</td><td>13-100-676</td><td>accuSpin Micro 17R</td></tr><tr><td>miRNeasy FFPE kit</td><td>Qiagen</td><td>217504</td><td></td></tr><tr><td>Nanodrop</td><td>Thermo Scientifc</td><td>Nano Drop 1000</td><td></td></tr><tr><td>Nanosight NTA</td><td>Malvern</td><td>LM14</td><td></td></tr><tr><td>NextSeq 500 Sequencer</td><td>Illumina</td><td></td><td></td></tr><tr><td>NextSeq 500/550 Mid Output Kit v2 (150 cycles)</td><td>Illumina</td><td>FC-404-2001</td><td></td></tr><tr><td>Pellet paint</td><td>Millipore</td><td>70748-3</td><td></td></tr><tr><td>Superscript II Reverse Transcriptase</td><td>Invitogen</td><td>18064014</td><td></td></tr><tr><td>T4 RNA Ligase 2 Deletion Mutant</td><td>Lucigen</td><td>LR2D11310K</td><td></td></tr><tr><td>TBE Running buffer (5x)</td><td>Novex</td><td>LC6675</td><td></td></tr><tr><td>Thermal cycler</td><td>MJ Research</td><td>PTC100</td><td></td></tr><tr><td>Total exosome isolation reagent (from serum)</td><td>Invitrogen</td><td>4478360</td><td></td></tr><tr><td>TrueSeq small RNA library Prep kit-Set A (Indexes 1-12)</td><td>Illumina</td><td>RS-200-0012</td><td></td></tr><tr><td>Xylene</td><td>Fisher Scientific</td><td>X3P- 1GAL</td><td></td></tr><tr><td>RNA 3&#039; Primer</td><td></td><td></td><td>GUUCAGAGUUCUACAGUCCGACGAUC</td></tr><tr><td>RNA 5&#039; Primer</td><td></td><td></td><td>TGGAATTCTCGGGTGCCAAGG</td></tr><tr><td>Stop Oligo</td><td></td><td></td><td>GAAUUCCACCACGUUCCCGUGG</td></tr><tr><td>RNA RT Primer</td><td></td><td></td><td>GCCTTGGCACCCGAGAATTCCA</td></tr><tr><td>RNA PCR Primer</td><td></td><td></td><td>AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA</td></tr><tr><td>RNA PCR Index Primer</td><td></td><td></td><td>CAAGCAGAAGACGGCATACGAGAT[6 bases]GTGACTGGAGTTCCTTGGCACCCGAGAATTCCA</td></tr><tr><td>-</td><td>-</td><td>-</td><td>6 bases in adapter</td></tr><tr><td>RNA PCR Index Primer 1</td><td></td><td></td><td>CGTGAT</td></tr><tr><td>RNA PCR Index Primer 2</td><td></td><td></td><td>ACATCG</td></tr><tr><td>RNA PCR Index Primer 3</td><td></td><td></td><td>GCCTAA</td></tr><tr><td>RNA PCR Index Primer 4</td><td></td><td></td><td>TGGTCA</td></tr><tr><td>RNA PCR Index Primer 5</td><td></td><td></td><td>CACTGT</td></tr><tr><td>RNA PCR Index Primer 6</td><td></td><td></td><td>ATTGGC</td></tr><tr><td>RNA PCR Index Primer 7</td><td></td><td></td><td>GATCTG</td></tr><tr><td>RNA PCR Index Primer 8</td><td></td><td></td><td>TCAAGT</td></tr><tr><td>RNA PCR Index Primer 9</td><td></td><td></td><td>CTGATC</td></tr><tr><td>RNA PCR Index Primer 10</td><td></td><td></td><td>AAGCTA</td></tr><tr><td>RNA PCR Index Primer 11</td><td></td><td></td><td>GTAGCC</td></tr><tr><td>RNA PCR Index Primer 12</td><td></td><td></td><td>TACAAG</td></tr></tbody></table>

sequencing small non-coding rna from formalin-fixed tissues and serum-derived exosomes from castration-resistant prostate cancer patients

Ablation of androgen receptor (AR) signaling by androgen deprivation is the goal of the first line of therapy for prostate cancer that initially results in cancer regression. However, in a significant number of cases, the disease progresses to advanced, castration-resistant prostate cancer (CRPC), which has limited therapeutic options and is often aggressive. Distant metastasis is mostly observed at this stage of the aggressive disease. CRPC is treated by a second generation of AR pathway inhibitors that improve survival initially, followed by the emergence of therapy resistance. Neuroendocrine prostate cancer (NEPC) is a rare variant of prostate cancer (PCa) that often develops as a result of therapy resistance via a transdifferentiation process known as neuroendocrine differentiation (NED), wherein PCa cells undergo a lineage switch from adenocarcinomas and show increased expression of neuroendocrine (NE) lineage markers. In addition to the genomic alterations that drive the progression and transdifferentiation to NEPC, epigenetic factors and microenvironmental cues are considered essential players in driving disease progression. This manuscript provides a detailed protocol to identify the epigenetic drivers (i.e., small non-coding RNAs) that are associated with advanced PCa. Using purified microRNAs from formalin-fixed paraffin-embedded (FFPE) metastatic tissues and corresponding serum-derived extracellular vesicles (EVs), the protocol describes how to prepare libraries with appropriate quality control for sequencing microRNAs from these sample sources. Isolating RNA from both FFPE and EVs is often challenging because most of it is either degraded or is limited in quantity. This protocol will elaborate on different methods to optimize the RNA inputs and cDNA libraries to yield most specific reads and high-quality data upon sequencing.

The library was prepared after RNA isolation and a quality check was performed. Gel purification for the amplified cDNA library prepared from RNA isolated from microdissected tissues and serum-derived EVs is shown in Figure 1 and Figure 2. The product size for adaptor-ligated miRNAs corresponded to about 136&#8722;160 bp for each sample. Figure 1A-B are marked to show the range of the gel that should be precisely ex...

Watch this Scientific Journal Video about Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients at JoVE.com

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Therapy resistance often develops in patients with advanced prostate cancer, and in some cases, cancer progresses to a lethal subtype called neuroendocrine prostate cancer. Assessing the small non-coding RNA-mediated molecular changes that facilitate this transition would allow better disease stratification and identification of causal mechanisms that lead to development of neuroendocrine prostate cancer.

Ablation of androgen receptor (AR) signaling by androgen deprivation is the goal of the first line of therapy for prostate cancer that initially results ...

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Research

JoVE Journal

Cancer Research

6.7K Views.  Augusta University. Met het gebruik van dit protocol onderzoekers kunnen identificeren nieuwe exoom of extracellulaire vesicle gebaseerd en weefsel gebaseerd micro cellulair voor gevorderde prostaatkanker. Het isoleren van RNA uit zowel F-F-B-weefsels als extracellulaire vikjes is vaak een uitdaging, omdat het grootste deel ervan ofwel de created is of in beperkte hoeveelheid is Dit deeltje zal verschillende methoden uitwerken om de R-N-A-ingangen en C-D-N-A-bibliotheken te optimaliseren om de meest specifieke v...

Video: Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Serum and Plasma Copy Number Detection Using Real-time PCR

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, and prediction of treatment resistance. Starting from the hypothesis that androgen receptor (AR) gene copy number (CN) gain is a frequent event in metastatic castration resistance prostate cancer (mCRPC), we propose to analyze this event in cfDNA as a potential predictive biomarker.
We evaluated AR CN in cfDNA using 2 different real-time PCR assays and 2 reference genes (RNaseP and AGO1). DNA amount of 60 ng was used for each assay combination. AR CN gain was confirmed using Digital PCR as a more accurate method. CN variation analysis has already been demonstrated to be informative for the prediction of treatment resistance in the setting of mCRPC, but it could be useful also for other purposes in different patient settings. CN analysis on cfDNA has several advantages: it is non-invasive, rapid and easy to perform, and it starts from a small volume of serum or plasma material.

This manuscript describes the copy number variation analysis performed in serum or plasma DNA using real-time PCR approach. This method is suitable for the prediction of drug resistance in castration resistant prostate cancer patients, but it could be informative also for other diseases.

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, ...

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of cancer development. By using non-invasive or pre-malignant lesions from patients who later develop invasive disease, gene expression analyses may help identify early molecular alterations that predispose to cancer risk. It has been well described that nucleic acids recovered from FFPE tissues have undergone severe physical damage and chemical modifications, which make their analysis difficult and generally requires adapted assays. MicroRNAs (miRNAs), however, which represent a small class of RNA molecules spanning only up to ~18&#8211;24 nucleotides, have been shown to withstand long-term storage and have been successfully analyzed in FFPE samples. Here we present a 3&#39; barcoded complementary DNA (cDNA) library preparation protocol specifically optimized for the analysis of small RNAs extracted from archived tissues, which was recently demonstrated to be robust and highly reproducible when using archived clinical specimens stored for up to 35 years. This library preparation is well adapted to the multiplex analysis of compromised/degraded material where RNA samples (up to 18) are ligated with individual 3&#39; barcoded adapters and then pooled together for subsequent enzymatic and biochemical preparations prior to analysis. All purifications are performed by polyacrylamide gel electrophoresis (PAGE), which allows size-specific selections and enrichments of barcoded small RNA species. This cDNA library preparation is well adapted to minute RNA inputs, as a pilot polymerase chain reaction (PCR) allows determination of a specific amplification cycle to produce optimal amounts of material for next-generation sequencing (NGS). This approach was optimized for the use of degraded FFPE RNA from specimens archived for up to 35 years and provides highly reproducible NGS data.

Formalin-fixed paraffin-embedded specimens represent a valuable source of molecular biomarkers of human diseases. Here we present a laboratory-based cDNA library preparation protocol, initially designed with fresh frozen RNA, and optimized for the analysis of archived microRNAs from tissues stored up to 35 years.

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of ...

Genetics

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 ...

Medicine

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1&#8203;&#8203;. &#8216;How to use PSA&#39; remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1).

Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, ...

An Enrichment Method for Small Extracellular Vesicles Derived from Liver Cancer Tissue

Small extracellular vesicles (sEVs) derived from tissue can reflect the functional status of the source cells and the characteristics of the tissue's interstitial space. The efficient enrichment of these sEVs is an important prerequisite to the study of their biological function and a key to the development of clinical detection techniques and therapeutic carrier technology. It is difficult to isolate sEVs from tissue because they are usually heavily contaminated. This study provides a method for the rapid enrichment of high-quality sEVs from liver cancer tissue. The method involves a four-step process: the incubation of digestive enzymes (collagenase D and DNase &#921;) with tissue, filtration through a 70 &#181;m cell strainer, differential ultracentrifugation, and filtration through a 0.22 &#181;m membrane filter. Owing to the optimization of the differential ultracentrifugation step and the addition of a filtration step, the purity of the sEVs obtained by this method is higher than that achieved by classic differential ultracentrifugation. It provides an important methodology and supporting data for the study of tissue-derived sEVs.

Here we describe the enrichment of small extracellular vesicles derived from liver cancer tissue through an optimized differential ultracentrifugation method.

Small extracellular vesicles (sEVs) derived from tissue can reflect the functional status of the source cells and the characteristics of the tissue's ...

Protocol for Prostatectomy Specimen Mapping and Dissection

Enhancing Prostate Tumor Biobanking Reliability with Improved Sampling Technique and Histological Characterization

Acquiring fresh and well-characterized tumor tissue samples is critical for conducting high-quality &#34;omics&#34; studies. However, it can be particularly challenging in the context of prostate cancer (PC) due to the unique nature of this organ and the high heterogeneity associated with this tumor. On the other hand, histopathologically characterizing samples before their storage without causing significant tissue alterations is also an intriguing challenge. In this context, we present a new method for acquiring, mapping, characterizing, and micro-dissecting resected prostate tissue based on anatomopathological criteria.
Unlike previously published protocols, this method reduces the time required for histopathological analysis of the prostate specimen without compromising its structure, which is crucial for assessing surgical margins. Furthermore, it enables the delineation and micro-macro dissection of fresh prostate tissue samples, with a focus on histological tumor areas defined by pathological criteria such as Gleason score, precursor lesions (high-grade prostatic intraepithelial neoplasia - PIN), and inflammatory lesions (prostatitis). These samples are then stored in a Biobank for subsequent research analyses.

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

This protocol describes a method for facilitating the collection of samples from radical prostatectomy specimens. The goal is to map, characterize, and micro-macro dissect tissue samples from the specimens based on anatomopathological criteria before storing them in a Biobank.

Acquiring fresh and well-characterized tumor tissue samples is critical for conducting high-quality &#34;omics&#34; studies. However, it can be ...

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, diagnosis, prognosis and treatment. In recent years, microRNAs (miRNAs) have emerged as promising alternate biomarkers for prostate cancer diagnosis and prognosis. However, the development of miRNAs as effective biomarkers for prostate cancer heavily relies on their accurate detection in clinical tissues. miRNA analyses in prostate cancer clinical specimens is often challenging owing to tumor heterogeneity, sampling errors, stromal contamination etc. The goal of this article is to describe a simplified workflow for miRNA analyses in archived FFPE or fresh frozen prostate cancer clinical specimens using a combination of quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH). Within this workflow, we optimize the existing methodologies for miRNA extraction from FFPE and frozen prostate tissues and expression analyses by Taqman-probe based miRNA RT-PCR. In addition, we describe an optimized method for ISH analyses formiRNA detection in prostate tissues using locked nucleic acid (LNA)- based probes. Our optimized miRNA ISH protocol can be applied to prostate cancer tissue slides or prostate cancer tissue microarrays (TMA).

Here we describe a simplified protocol for microRNA (miRNA) expression analyses in archived Formalin-Fixed, Paraffin-Embedded (FFPE) or fresh frozen prostate cancer (PCa) clinical tissues employing quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH).

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, ...

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types. Healthy prostate is comprised of fibromuscular stroma surrounding discrete epithelial-lined secretory lumens and a very small population of immune and neuroendocrine cells. In contrast, areas of prostate cancer have increased dysplastic luminal epithelium with greatly reduced or absent stromal population. Given the profound difference between stromal and epithelial cell types, it is imperative to separate the cell types for any type of downstream molecular analysis. Despite this knowledge, the bulk of gene expression studies compare benign prostate to cancer without micro-dissection, leading to stromal bias in the benign samples. Laser-capture micro-dissection (LCM) is an effective method to physically separate different cell types from a specimen section. The goal of this protocol is to show that RNA can be successfully isolated from LCM-collected human prostatic epithelium and used for downstream gene expression studies such as RT-qPCR and RNAseq.

The goal of this protocol is to use laser-capture micro-dissection as an effective method to isolate pure populations of cell types from heterogeneous prostate tissues for downstream RNA analysis.

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types.  Healthy prostate is comprised of ...

Exosomal miRNA Analysis in Non-small Cell Lung Cancer (NSCLC) Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool

The discovery of alterations in the EGFR and ALK genes, amongst others, in NSCLC has driven the development of targeted-drug therapy using selective tyrosine kinase inhibitors (TKIs). To optimize the use of these TKIs, the discovery of new biomarkers for early detection and disease progression is mandatory. These plasma-isolated exosomes can be used as a non-invasive and repeatable way for the detection and follow-up of these biomarkers. One ml of plasma from 12 NSCLC patients, with different mutations and treatments (and 6 healthy donors as controls), were used as exosome sources. After RNAse treatment, in order to degrade circulating miRNAs, the exosomes were isolated with a commercial kit and resuspended in specific buffers for further analysis. The exosomes were characterized by western blotting for ALIX and TSG101 and by transmission electron microscopy (TEM) analysis, the standard techniques to obtain biochemical and dimensional data of these nanovesicles. Total RNA extraction was performed with a high yield commercial kit. Due to the limited miRNA-content in exosomes, we decided to perform retro-transcription PCR using an individual assay for each selected miRNA. A panel of miRNAs (30b, 30c, 103, 122, 195, 203, 221, 222), all correlated with NSCLC disease, were analyzed taking advantage of the remarkable sensitivity and specificity of Real-Time PCR analysis; mir-1228-3p was used as endogenous control and data were processed according to the formula 2-&#916;&#916;ct 13. Control values were used as baseline and results are shown in logarithmic scale.

Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool

This protocol describes the feasibility to perform miRNA profiling in exosomes, released in plasma of NSCLC patients, through a commercial exosome isolation kit with Proteinase K and RNAse treatments, in order to avoid circulating miRNAs contamination and evaluate their biomarker features in NSCLC.

The discovery of alterations in the EGFR and ALK genes, amongst others, in NSCLC has driven the development of targeted-drug therapy using selective ...

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

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