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>3M NaOAc pH 5.5</td>
			<td>USB Corp.</td>
			<td>75897 100 mL</td>
			<td></td>
		</tr>
		<tr>
			<td>5 &#181;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&#39; Primer</td>
			<td></td>
			<td></td>
			<td>GUUCAGAGUUCUACAGUCCGACGAUC</td>
		</tr>
		<tr>
			<td>RNA 5&#39; 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 ...

sequencing-small-non-coding-rna-from-formalin-fixed-tissues-serum

Research

JoVE Journal

Cancer Research

6.7K Views.  Augusta University. 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.

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

Four-color Fluorescence Immunohistochemistry of T-cell Subpopulations in Archival Formalin-fixed, Paraffin-embedded Human Oropharyngeal Squamous Cell Carcinoma Samples

The four-color fluorescence immunohistochemistry (IHC) technique is a method to quantify cell populations of interest while taking into account their relative distribution and their localization in the tissue. This technique has been extensively applied to study the immune infiltrate in various tumor types. The tumor microenvironment is infiltrated by immune cells that are attracted to the tumor site. Different immune cell populations have been found to play different roles in the tumor microenvironment and to have a different impact on the outcome of disease. This manuscript describes the use of multiparameter fluorescence IHC on oropharyngeal squamous cell carcinoma (OPSCC) as an example. This technique can be extended to other tissue samples and cell types of interest. In the presented study, we analyzed the intraepithelial and stromal compartment of a large OPSCC cohort (n = 162). We focused on total T lymphocytes (CD3+), immunosuppressive regulatory T cells (Tregs; i.e., FoxP3+), and T helper 17 (Th17) cells (i.e., IL-17+CD3+) using a nuclear counterstain to distinguish tumor epithelium from stroma. A high number of T cells was found to be correlated with improved disease-free survival in patients with a low number of intratumoral IL-17+ non-T cells. This suggests that IL-17+ non-T cells may be correlated with a poor immune response in OPSCC, which is in agreement with the correlation described between IL-17 and poor survival in cancer patients. Currently, novel multiparameter fluorescence IHC techniques are being developed using up to 7 different fluorochromes and will enable the more precise characterization and localization of immune cells in the tumor microenvironment.

Multiparameter fluorescence immunohistochemistry can be used to assess the number, relative distribution, and localization of immune cell populations in the tumor microenvironment. This manuscript describes the use of this technique to analyze T-cell subpopulations in oropharyngeal cancer.

The four-color fluorescence immunohistochemistry (IHC) technique is a method to quantify cell populations of interest while taking into account their ...

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

Orthotopic tumor modeling is a valuable tool for pre-clinical prostate cancer research, as it has multiple advantages over both subcutaneous and transgenic genetically engineered mouse models. Unlike subcutaneous tumors, orthotopic tumors contain more clinically accurate vasculature, tumor microenvironment, and responses to multiple therapies. In contrast to genetically engineered mouse models, orthotopic models can be performed with lower cost and in less time, involve the use of highly complex and heterogeneous mouse or human cancer cell lines, rather that single genetic alterations, and these cell lines can be genetically modified, such as to express imaging agents. Here, we present a protocol to surgically injecting a luciferase- and mCherry-expressing murine prostate cancer cell line into the anterior prostate lobe of mice. These mice developed orthotopic tumors that were non-invasively monitored in vivo and further analyzed for tumor volume, weight, mouse survival, and immune infiltration. Further, orthotopic tumor-bearing mice were surgically castrated, leading to immediate tumor regression and subsequent recurrence, representing castration-resistant prostate cancer. Although technical skill is required to carry out this procedure, this syngeneic orthotopic model of both androgen-dependent and castration-resistant prostate cancer is of great use to all investigators in the field.

The goal of this protocol is to demonstrate the intra-prostatic injection of prostate cancer cells, with subsequent castration. Orthotopic pre-clinical models of androgen-dependent and castration-resistant prostate cancer are critical to study the disease in the context of a clinically relevant tumor microenvironment and an immunocompetent host.

Orthotopic tumor modeling is a valuable tool for pre-clinical prostate cancer research, as it has multiple advantages over both subcutaneous and ...

Using 22C3 Anti-PD-L1 Antibody Concentrate on Biopsy and Cytology Samples from Non-small Cell Lung Cancer Patients

Pembrolizumab monotherapy has been approved for the first- and second-line treatment of patients with PD-L1-expressing advanced non-small cell lung cancer (NSCLC). Testing for PD-L1 expression with the PD-L1 immunohistochemistry (IHC) 22C3 companion diagnostic assay, which gives a tumor proportion score (TPS), has been validated on tumor tissue. We developed an optimized laboratory-developed test (LDT) that uses the 22C3 antibody (Ab) concentrate on a widely available IHC autostainer for biopsy and cytology specimens. The PD-L1 TPS was evaluated with 120 paired whole-tumor tissue sections and biopsy samples and with 70 paired biopsy and cytology samples (bronchial washes, n = 40; pleural effusions, n = 30). The 22C3 Ab concentrate-based LDT showed a high concordance rate between biopsy (~100%) and cytology (~95%) specimens when compared to PD-L1 IHC expression determined using the PD-L1 IHC 22C3 companion assay at both TPS cut points (&#8805;1%, &#8805;50%). The optimized LDT presented here, using the 22C3 Ab concentrate to determine the PD-L1 expression in both tumor tissue and in cytology specimens, will expand the ability of laboratories worldwide to assess the eligibility of patients with NSCLC for treatment with pembrolizumab monotherapy in a reliable and reproducible manner.

To expand the ability of laboratories worldwide to assess the eligibility of patients with lung cancer for treatment with pembrolizumab, in a reliable and reproducible manner, we developed an assay that uses the 22C3 antibody concentrate on a widely available immunohistochemical autostainer, for both biopsy and cytology specimens.

Pembrolizumab monotherapy has been approved for the first- and second-line treatment of patients with PD-L1-expressing advanced non-small cell lung cancer ...

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4&#8242;,6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.

A detailed protocol for a six-marker multiplex immunofluorescence panel is optimized and performed, using an automated stainer for more consistent results and a shorter procedure time. This approach can be directly adapted by any laboratory for immuno-oncology studies.

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of ...

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL of blood) warrant a poor prognosis and are correlated with shorter overall survival in several cancers (e.g., breast, prostate and colorectal). Currently, the anti-EpCAM-coated magnetic bead-based CTC capturing system is the gold standard test approved by the U.S. Food and Drug Administration (FDA) for enumerating CTCs in the bloodstream. This test is based on the use of magnetic beads coated with anti-EpCAM markers, which specifically target epithelial cancer cells. Many studies have illustrated that EpCAM is not the optimal marker for CTC detection. Indeed, CTCs are a heterogeneous subpopulation of cancer cells and are able to undergo an epithelial-to-mesenchymal transition (EMT) associated with metastatic proliferation and invasion. These CTCs are able to reduce the expression of cell surface epithelial marker EpCAM, while increasing mesenchymal markers such as vimentin. To address this technical hurdle, other isolation methods based on physical properties of CTCs have been developed. Microfluidic technologies enable a label-free approach to CTC enrichment from whole blood samples. The spiral microfluidic technology uses the inertial and Dean drag forces with continuous flow in curved channels generated within a spiral microfluidic chip. The cells are separated based on the differences in size and plasticity between normal blood cells and tumoral cells. This protocol details the different steps to characterize the programmed death-ligand 1 (PD-L1) expression of CTCs, combining a spiral microfluidic device with customizable immunofluorescence (IF) marker set.

The characterization of circulating tumor cells (CTCs) is a popular topic in translational research. This protocol describes a semi-automatic immunofluorescence (IF) assay for PD-L1 characterization and enumeration of CTCs in non-small cell lung cancer (NSCLC) patient samples.

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL ...

Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced stage, which seriously hampers therapy. Mitochondrial changes are a hallmark of human ovarian cancers, and mitochondria are the centers of energy metabolism, cell signaling, and oxidative stress. In-depth insights into the changes of the mitochondrial proteome in ovarian cancers compared to control ovarian tissue will benefit in-depth understanding of the molecular mechanisms of ovarian cancer, and the discovery of effective and reliable biomarkers and therapeutic targets. An effective mitochondrial preparation method coupled with an isobaric tag for relative and absolute quantification (iTRAQ) quantitative proteomics are presented here to analyze human ovarian cancer and control mitochondrial proteomes, including differential-speed centrifugation, density gradient centrifugation, quality assessment of mitochondrial samples, protein digestion with trypsin, iTRAQ labeling, strong cation exchange fractionation (SCX), liquid chromatography (LC), tandem mass spectrometry (MS/MS), database analysis, and quantitative analysis of mitochondrial proteins. Many proteins have been successfully identified to maximize the coverage of the human ovarian cancer mitochondrial proteome and to achieve the differentially expressed mitochondrial protein profile in human ovarian cancers.

This article presents a protocol of differential-speed centrifugation in combination with density gradient centrifugation to separate mitochondria from human ovarian cancer tissues and control ovarian tissues for quantitative proteomics analysis, resulting in a high-quality mitochondrial sample and high-throughput and high-reproducibility quantitative proteomics analysis of a human ovarian cancer mitochondrial proteome.

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced ...

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education.
Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2.
Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.

A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration ...

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the tumor immune landscape. Although standard immunohistochemistry is suitable for studying tissue architecture, it is limited to the analysis of a small number of markers. Conversely, techniques such as flow cytometry can evaluate multiple markers simultaneously, although information about tissue morphology is lost. In recent years, multiplexed strategies that integrate phenotypic and spatial analysis have emerged as comprehensive approaches to the characterization of the tumor immune landscape. Herein, we discuss an innovative technology combining metal-labeled antibodies and secondary ion mass spectrometry focusing on the technical steps in assay development and optimization, tissue preparation, and image acquisition and processing. Before staining, a metal-labeled antibody panel must be developed and optimized. This hi-plex image system supports up to 40 metal-tagged antibodies in a single tissue section. Of note, the risk of signal interference increases with the number of markers included in the panel. After panel design, particular attention should be given to the metal isotope assignment to the antibody to minimize this interference. Preliminary panel testing is performed using a small subset of antibodies and subsequent testing of the entire panel in control tissues. Formalin-fixed, paraffin-embedded tissue sections are obtained and mounted on gold-coated slides and further stained. The staining takes 2 days and closely resembles standard immunohistochemical staining. Once samples are stained, they are placed in the image acquisition instrument. Fields of view are selected, and images are acquired, uploaded, and stored. The final stage is image preparation for the filtering and removal of interference using the system&#39;s image processing software. A disadvantage of this platform is the lack of analytical software. However, the images generated are supported by different computational pathology software.

In the era of cancer immunotherapy, interest in elucidating tumor microenvironment dynamics has increased strikingly. This protocol details a mass spectrometry imaging technique with respect to its staining and imaging steps, which allow for highly multiplexed spatial analysis.

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the ...

FISH for Long Non-Coding RNA Localization in Human Osteosarcoma Cells

RNA Fluorescence In Situ Hybridization for Long Non-Coding RNA Localization in Human Osteosarcoma Cells

The important roles of long non-coding RNAs (lncRNAs) in cancer have been studied, such as regulating the proliferation, epithelial-mesenchymal transition (EMT), migration, infiltration, and autophagy of cancer cells. Localization detection of lncRNAs in cells can provide insight into their functions. By designing the lncRNA-specific antisense chain sequence followed by labeling with fluorescent dyes, RNA fluorescence in situ hybridization (FISH) can be applied to detect the cellular localization of lncRNAs. Together with the development of microscopy, the RNA FISH techniques now even allow for visualization of the poorly expressed lncRNAs. This method can not only detect the localization of lncRNAs alone, but also detect the colocalization of other RNAs, DNA, or proteins by using double-color or multicolor immunofluorescence. Here, we have included the detailed experimental operation procedure and precautions of RNA FISH by using lncRNA small nucleolar RNA host gene 6 (SNHG6) in human osteosarcoma cells (143B) as an example, to provide a reference for researchers who want to perform RNA FISH experiments, especially lncRNA FISH.

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells 

The present protocol describes a method of RNA fluorescence in situ hybridization to localize the lncRNAs in human osteosarcoma cells.

The important roles of long non-coding RNAs (lncRNAs) in cancer have been studied, such as regulating the proliferation, epithelial-mesenchymal transition ...