We would like to thank members of the laboratories of Andrew Fire and Mark Kay for guidance and suggestions.

Animal experiments were authorized by the Institutional Animal Care and Use Committee of the University of Washington.
Small RNA library preparation
1. RNA isolation
<ol>
	<li>Isolate RNA from a biological source using a standard RNA isolation reagent, or a kit that enriches for microRNAs. For tissues, it is best to start with samples snap-frozen in liquid nitrogen and ground to a powder using a pre-chilled mortar and pestle.</li>
	<li>Measure each sample&#8217;s RNA integrity on an instrument that can quantify RNA and provide an RNA integrity number (RIN). RINs should be &#62;7.</li>
</ol>
2. 3&#39; adaptor ligation
<ol>
	<li>Prepare a ligation reaction in PCR strip tubes, by combining: 11 &#956;L of RNA (1-3 &#956;g, using the same amount for each sample), 1.5 &#956;L of 10x T4 RNA ligase reaction buffer, ATP-free, 1 &#956;L of poly-ethylene glycol (PEG), and 0.5 &#956;L of 3&#39;-linker (100 &#956;M Universal miRNA cloning linker). 
	NOTE: The absence of ATP helps enrich for miRNAs and minimizes cloning of mRNA degradation products. PEG acts as a molecular crowding agent, enhancing successful ligation. The Universal miRNA cloning linker has a 3&#39; blocking group (amine) to prevent self-ligation, circularization, and ligation to RNA at the 5&#39; end.</li>
	<li>Heat the samples at 95 &#176;C on a thermocycler for 30-40 s. Cool on ice for 1 min. Add 1 &#956;L of T4 RNA ligase 2 and incubate at room temperature for 2 h. Prepare the gel (step 2.3) while the samples are incubating. 
	NOTE: Incubation at room temperature helps prevent linker-linker ligation. We have also successfully used T4 RNA ligase 1.</li>
	<li>Prepare 30 mL of a 15% polyacrylamide gel with 8 M urea (for a 20x20 cm gel): 14.4 g of urea, 3 mL of 10x Tris-buffered ethylenediaminetetraacetic acid (TBE), 11.2 mL of 40% 19:1 acrylamide, and H2O to 30 mL. Solution is best dissolved at 42 &#176;C. Immediately before casting, add 150 &#956;L of 10% ammonium persulfate (APS) and 30 &#956;L of tetramethylethylenediamine (TEMED) for polymerization.</li>
	<li>Pour between 0.8 mm separated glass plates in a plastic cast and insert comb. Once the gel is solidified (about 20 min), add 0.5x TBE to tank and wash wells of residual urea by pipetting vigorously.</li>
	<li>Pre-run the gel at a constant 375 V for 25 min without samples so urea can enter the gel, then wash the wells again. 
	NOTE: The amount of voltage may need to be reduced depending on the type of power supply and electrophoresis system that is used.</li>
	<li>Once the samples are done ligating, add 15 &#956;L of acrylamide loading dye to the samples (for a 1:1 ratio), then denature for 5 min at 95 &#176;C on a thermocycler.</li>
	<li>Prepare 25 ng/&#956;L of 37 and 44 bp size markers, diluted with one part acrylamide loading dye. Sequences are listed in Table 1.</li>
	<li>Load the samples on the polyacrylamide gel, leaving at least one lane in between each sample. Load 20 &#956;L of at least two sets of markers, in an asymmetrical pattern to keep track of gel orientation.</li>
	<li>Run the gel at a constant 375 V for the first 15 min and then increase to a constant 425 V for the remaining run. Run until bromophenol blue is about 1-4 cm from the bottom, which takes approximately 2 h. 
	NOTE: If necessary, the gel may be run at a lower constant voltage for a longer period of time until the bromophenol blue is about 1-4 cm from the bottom.</li>
	<li>Remove the gel from the glass plates using a plate separator, and place the gel on a plastic page protector. Dilute 5 &#956;L of ethidium bromide in 500 &#956;L of distilled water, and pipette onto marker lanes just above the top light blue marker (see Figure 1A). 
	CAUTION: Use gloves for ethidium bromide and dispose of waste in accordance with local regulations. Let sit for 5 min.</li>
	<li>Under ultraviolet (UV) light, cut the gels from upper to lower marker in each lane using clean razor blade (see Figure 1A). Transfer to a 4 x 4 cm square of laboratory sealing film, then cut the gel with about 4 cuts horizontally and 3 vertically to produce 12 small squares (see Figure 1B).</li>
	<li>Pipette 400 &#956;L of 0.3 M NaCl on to the sealing film square, and funnel gel pieces into 1.5 mL siliconized tubes (see Figure 1C). Agitate the samples on a nutator at 4 &#176;C overnight. 
	NOTE: Other low-retention 1.5 mL tubes can be substituted for siliconized tubes.</li>
	<li>After at least 12 h of agitation at 4 &#176;C, retrieve the samples and put them on ice, along with a conical tube of 100% ethanol.</li>
	<li>Transfer 400 &#956;L of supernatant to a new tube, and then add 1 mL of 100% ethanol and 1 &#956;L of 15 mg/mL glycogen coprecipitant. Ensure to collect as much supernatant as possible, spinning down at 4 &#176;C and pipetting more as necessary. Place at -80 &#176;C for 1 h, or -20 &#176;C for 2 h or longer. Glycogen coprecipitant improves pellet visibility and recovery.</li>
	<li>Spin at 4 &#176;C at 17,000 x g for 20-30 min. Remove all traces of ethanol and let pellet air dry for 5 min.</li>
</ol>
3. 5&#39; linker ligation
<ol>
	<li>Re-suspend the pellet by pipetting in 6.5 &#956;L of nuclease-free water. Letting the pellet sit in water for a few minutes first will help with re-suspension.</li>
	<li>After spinning down the pellet and resuspending in water, add 0.5 &#956;L of 100 &#956;M 5&#39;-linker (with barcodes; see Table 1), 1 &#956;L of T4 RNA ligase buffer, 1 &#956;L of 10 mM ATP, and 1 &#956;L of PEG. Heat at 90 &#176;C for 30 s, then place on ice. Add 1 &#956;L of T4 RNA ligase 1 and let incubate at room temperature for 2 h.</li>
	<li>Add 400 &#956;L of 0.3 M NaCl, followed by 400 &#956;L of acid phenol/chloroform. Vortex 30 s - 1 min (solution will look cloudy), and then spin at 4 &#176;C for 10-15 min at max speed in a microcentrifuge (~17,000 x g). Draw off the top layer and place in new 1.5 mL tube. 
	NOTE: Avoid pipetting any of the bottom layer.</li>
	<li>Add 350 &#956;L of chloroform, vortex briefly, and then spin at 4 &#176;C for 10 min at max speed (~17,000 x g). Draw off the top later and place in new 1.5 mL tube. Add 1.5 &#956;L of glycogen coprecipitant and 1 mL of 100% ethanol. 
	NOTE: Again, avoid pipetting any of the bottom layer.</li>
	<li>Vortex briefly, then place at -80 &#176;C for at least 1 h, or -20 &#176;C overnight.</li>
</ol>
4. Reverse transcription (RT)
<ol>
	<li>Turn on the 42 &#176;C heat block. Spin the samples at 4 &#176;C and ~17,000 x g for 20-30 min. Remove all supernatant and let the pellet air dry for 5 min.</li>
	<li>Re-suspend pelleted sample in 8.25 &#956;L of nuclease-free water, then add: 0.5 &#956;L of 100 &#956;M RT primer (Table 1), and 5 &#956;L of 2x RT Reaction Mix from a cDNA synthesis kit. Incubate at 42 &#176;C for 3 min.</li>
	<li>Add 1.5 &#956;L of 10x RT enzyme to each sample and incubate at 42 &#176;C for 30 min in a thermocycler. Place at -20 &#176;C or continue with hydrolysis and neutralization. 
	NOTE: Several RT kits can be utilized for steps 4.2 and 4.3.</li>
	<li>Perform alkaline hydrolysis and neutralization: Make 1 mL of 150 mM potassium hydroxide (KOH) solution (150 &#956;L of 1 M KOH, 20 &#956;L of 1 M Tris Base pH 7.5, and 830 &#956;L of H2O) and 1 mL of 150 mM hydrochloric acid (HCl) (150 &#956;L of 1 M HCl and 850 &#956;L of H2O).</li>
	<li>Before adding to the samples, determine the amount of HCl needed to neutralize the KOH solution. Generally, around 20-24 &#956;L of HCl will neutralize the 25 &#956;L of KOH. Check the combination on a pH strip to ensure it is in the right range (pH 7.0 to 9.5).</li>
	<li>Hydrolyze the samples by adding 25 &#956;L of 150 mM KOH solution and incubate for 10 min at 95 &#176;C.</li>
	<li>Neutralize the samples by adding the amount of 150 mM HCl determined in step 4.4, to obtain a final sample pH between 7.0 and 9.5.</li>
</ol>
5. PCR amplification
<ol>
	<li>After neutralization, prepare a PCR reaction with: 29.5 &#956;L of water, 5 &#956;L of 10x Taq buffer, 1 &#956;L of dNTP, 2 &#956;L of 25 &#956;M forward primer (Table 1), 2 &#956;L of 25 &#956;M reverse primer (Table 1), 0.5 &#956;L of Taq, and 10 &#956;L of the reverse transcribed cDNA from step 4.6.</li>
	<li>Run the following PCR reaction: 94 &#176;C for 2 min, then 20 cycles of 94 &#176;C for 45 s, 50 &#176;C for 75 s and 72 &#176;C for 60 s.</li>
	<li>Run a second PCR reaction of about 2-4 more cycles using 5 &#956;L of product from step 5.2. Mix: 34.8 &#956;L of water, 5 &#956;L of 10x Taq buffer, 1 &#956;L of dNTP, 1 &#956;L of 25 &#956;M forward primer (Table 1), 1 &#956;L of 25 &#956;M reverse primer (Table 1), and 0.2 &#956;L of Taq polymerase. Follow the same thermocycler parameters outlined in step 5.2. 
	NOTE: The purpose of doing two PCR reactions &#8211; with the first for 20 cycles and the second for just 2-4 more &#8211; is to ensure that the amount of cDNA amplified is in a dynamic range (i.e., not a saturated amount).</li>
</ol>
6. Agarose gel purification
<ol>
	<li>Prepare a 4% agarose gel with low-melting agarose. Load 40 &#956;L or more of the PCR product on the gel, along with loading dye. Load 100 bp and 25 bp size markers. 
	NOTE: The 25 bp ladder helps to distinguish amplified product from linker-linker ligation products. Low-melting agarose gels must be cast with greater care than traditional agarose gels, so closely follow the instructions from the manufacturer.</li>
	<li>For gel extraction, select the cycle number that is visible on the gel but is not saturated (usually 22&#8211;24 cycles). Choose similar intensity bands when running multiple samples.</li>
	<li>Cut the band that is above the 125 bp band (the darker band on 25 bp ladder; see Figure 1D). Using a gel extraction kit, follow the manufacturer&#8217;s instructions for adding buffer based on a 4% gel, then shake to dissolve agarose in buffer at room temperature. 
	NOTE: Dissolving at 55 &#176;C increases the potential for linker-linker ligation.</li>
	<li>Follow the manufacturer&#8217;s gel extraction instructions and elute in 30 &#956;L of elution buffer or water. If the product looked weak on the gel, then reduce elution to 20 &#956;L.</li>
	<li>Measure cDNA concentration using a sensitive technique, and prepare sample library for sequencing. Preparation will depend on the type of sequencing used. 
	NOTE: Minimum requirements for a sequencing library are typically a 10 &#956;L volume of 10 &#956;M product. If concentrations are too low, pool and ethanol precipitate samples to bring library to the desired concentration.</li>
	<li>Sequence the samples using available equipment. A common example would be to run samples using a kit for 50 bp single reads, to obtain approximately 15-25 million reads in a FASTQ output format.</li>
</ol>
Small RNA sequence alignment and bioinformatics
7. Data upload
<ol>
	<li>Download FASTQ files generated from each sequencing run. Download a list of microRNA hairpin sequences from miRbase.org8,9,10.</li>
	<li>Generate a Galaxy account at www.usegalaxy.org and upload a FASTQ file of sequence reads to this account.</li>
	<li>Upload a text file of barcode sequences to the Galaxy account, such as barcodes.txt, which is available as a text file (Supplementary Table 1).</li>
	<li>Upload a FASTA file of microRNA hairpins to the Galaxy account from a database like miRBase.org. Examples of mouse (mousehairpins.fa) or human (humanhairpins.fa) microRNA precursors are provided in Supplementary Table 2 and Supplementary Table 3.</li>
</ol>
8. Adaptor removal, barcode sort, and trim
<ol>
	<li>In the left-hand tab, navigate to Genomic file manipulation &#62; FASTA/FASTQ &#62; Clip adapter sequences.</li>
	<li>In Input file in FASTA or FASTQ format, enter FASTQ file from the drop-down list. Change Minimum sequence length to 18. Change Source to Enter custom sequence. Enter CTGTAGGC. Keep all other default parameters. Click Execute. 
	NOTE: Sequence reads that are shorter than 18 nucleotides are difficult to map uniquely to microRNAs and contain many degradation products.</li>
	<li>In the left-hand tab, navigate to Genomic file manipulation &#62; FASTA/FASTQ &#62; Barcode Splitter. 
	NOTE: Galaxy functions and headers are updated periodically, so the search function may be necessary to find an equivalent tool or its location. Commercial kits using indexed primers are often already sorted by barcode. Therefore, this step and the barcode trim step are not necessary if starting from a commercial kit.</li>
	<li>For Barcodes to use, point to barcodes.txt. For Library to split, use Clip on data file produced in the previous step. In Number of allowed mismatches, enter 1. Click Execute.</li>
	<li>Trim the first 4 nucleotides: navigate to Text Manipulations &#62; Trim leading or trailing characters. For Input dataset, click on the folder icon, which is a dataset collection. Select the batch file of samples, which includes the label Barcode splitter on data. In Trim from the beginning up to this position, enter 5. In Is input dataset in FASTQ format? enter Yes. Click Execute. Execution may take several minutes.</li>
</ol>
9. Alignment of reads to microRNAs
<ol>
	<li>In Galaxy, navigate to Genomics Analysis &#62; RNA-Seq &#62; Sailfish transcript quantification11.</li>
	<li>For the question Select a reference transcriptome from your history or use a built-in index? select Use one from the history. Enter the uploaded file mousehairpins.fa from the drop-down list. In FASTA/Q file, click the folder icon to use a dataset collection and select the file that includes Trim on collection. Click Execute. Execution may take several minutes.</li>
	<li>In the history tab on the right, click on Sailfish on collection&#8230; which is a list with 19 items. Click on each individual file and click the disk icon to save to local computer. Individually downloaded files first need to be uncompressed. They also may need to be re-saved with a .txt extension for the purpose of importing into a spreadsheet.</li>
	<li>Open each spreadsheet file in and re-name the NumReads column to the treatment condition. Merge the columns together to generate a matrix of microRNAs in the first column and read counts per condition in subsequent columns.</li>
	<li>To calculate differentially expressed microRNAs for each treatment condition, use the file with raw microRNA read counts as input for programs such as DESeq212. DESeq2 is present in Galaxy in the Genomics Analysis &#62; RNA-seq &#62; DESeq2 tab.</li>
	<li>Convert raw reads to normalized microRNA read counts. Counts are normalized to the library sequence depth of the library through the following calculation: [(raw reads/total microRNA reads) * (1,000,000 &#8211; number of microRNAs counted) + 1]. 
	NOTE: This calculation provides a normalized reads per million (RPM) mapped microRNAs that can be compared across datasets and biological conditions. The output is a tab delimited file. Sailfish provides a tpm output column, though this value is normalized by microRNA hairpin length, which is not necessary in this context.</li>
	<li>If relevant, repeat alignment with custom input sequences (e.g., a vector) to identify reads that map to delivered constructs, like shRNAs.</li>
</ol>

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The authors have nothing to disclose.

Despite the identification of microRNAs over 20 years ago13, the process of microRNA sequencing remains laborious and requires specialized equipment, hindering laboratories from routinely adopting in-house protocols14. Other techniques can simultaneously evaluate microRNAs, like microRNA microarrays and multiplexed expression panels; however, these approaches are limited in that they only quantify the microRNAs present in their probe set. Because of this, they miss importan...

First discovered in 19931, it is now estimated that nearly 2000 microRNAs are present in the human genome2. MicroRNAs are small non-coding RNAs that are typically 21-24 nucleotides long. They are post-transcriptional regulators of gene expression, often binding to complementary sites in the 3-untranslated region (3-UTR) of target genes to repress protein expression and degrade mRNA. Quantifying microRNAs can give valuable insight into gene expression and several protocols have been developed for this purpose3.
We have developed a defined, reproducible, and long-standing protocol for small RNA sequencing, and for analyzing normalized reads using open source bioinformatics tools. Importantly, our protocol enables the simultaneous identification of both endogenous microRNAs and exogenously delivered constructs that produce microRNA-like species, while minimizing reads that map to other small RNA species, including ribosomal RNAs (rRNAs), transfer RNA-derived small RNAs (tsRNAs), repeat-derived small RNAs, and mRNA degradation products. Fortunately, microRNAs are 5-phosphorylated and 2-3 hydroxylated4, a feature that can be leveraged to separate them from these other small RNAs and mRNA degradation products. Several commercial options exist for microRNA cloning and sequencing that are often quicker and easier to multiplex; however, the proprietary nature of kit reagents and their frequent modifications makes comparing sample runs challenging. Our strategy optimizes collecting only the correct size of microRNAs through acrylamide and agarose gel purification steps. In this protocol, we also describe a procedure for aligning sequence reads to microRNAs using open source tools. This set of instructions will be especially useful for novice informatics users, regardless of whether our library preparation method or a commercial method is used.
This protocol has been used in several published studies. For example, it was used to identify the mechanism by which the Dicer enzyme cleaves small hairpin RNAs at a distance of two nucleotides from the internal loop of the stem-loop structure - the so-called &#34;loop-counting rule&#34;5. We also followed these methods to identify the relative abundance of delivered small hairpin RNAs (shRNA) expressed from recombinant adeno-associated viral vectors (rAAVs), to identify the threshold of shRNA expression that can be tolerated prior to liver toxicity associated with excess shRNA expression6. Using this protocol, we also identified microRNAs in the liver that respond to the absence of microRNA-122 - a highly expressed hepatic microRNA - while also characterizing the degradation pattern of this microRNA7. Because we have used our protocol consistently in numerous experiments, we have been able to observe sample preparations longitudinally, and see that there are no discernible batch effects.
In sharing this protocol, our goal is to enable users to generate high quality, reproducible quantification of microRNAs in virtually any tissue or cell line, using affordable equipment and reagents, and free bioinformatics tools.

<table><tbody><tr><td>100 bp DNA ladder</td><td>NEB</td><td>N3231</td><td></td></tr><tr><td>19:1 bis-acrylamide</td><td>Millipore Sigma</td><td>A9926</td><td></td></tr><tr><td>25 bp DNA step ladder</td><td>Promega</td><td>G4511</td><td></td></tr><tr><td>Acid phenol/chloroform</td><td>ThermoFisher</td><td>AM9720</td><td></td></tr><tr><td>Acrylamide RNA loading dye</td><td>ThermoFisher</td><td>R0641</td><td></td></tr><tr><td>Ammonium persulfate (APS)</td><td>Biorad</td><td>161-0700</td><td></td></tr><tr><td>Bioanalyzer instrument</td><td>Agilent</td><td>G2991AA</td><td>For assessing RNA quality and concentration</td></tr><tr><td>Chloroform</td><td>Fisher Scientific</td><td>C298-500</td><td></td></tr><tr><td>Ethanol (100%)</td><td>Sigma</td><td>E7023</td><td></td></tr><tr><td>Gel Loading Buffer II</td><td>ThermoFisher</td><td>AM8547</td><td></td></tr><tr><td>GlycoBlue</td><td>ThermoFisher</td><td>AM9516</td><td>Blue color helps in visualizing pellet</td></tr><tr><td>HCl</td><td>Sigma</td><td>320331</td><td></td></tr><tr><td>KOH</td><td>Sigma</td><td>P5958</td><td></td></tr><tr><td>Maxi Vertical Gel Box 20 x 20cm</td><td>Genesee</td><td>45-109</td><td></td></tr><tr><td>miRVana microRNA isolation kit</td><td>ThermoFisher</td><td>AM1560</td><td></td></tr><tr><td>miSeq system</td><td>Illumina</td><td>SY-410-1003</td><td>For generating small RNA sequencing data</td></tr><tr><td>NaCl</td><td>Fisher Scientific</td><td>S271-500</td><td></td></tr><tr><td>Nusieve low-melting agarose</td><td>Lonza</td><td>50081</td><td></td></tr><tr><td>Parafilm (laboratory sealing film)</td><td>Millipore Sigma</td><td>P7793</td><td></td></tr><tr><td>Poly-ethylene glycol 8000</td><td>NEB</td><td>included with M0204</td><td></td></tr><tr><td>ProtoScript II First strand cDNA Synthesis Kit</td><td>NEB</td><td>E6560S</td><td></td></tr><tr><td>QIAquick Gel Extraction kit</td><td>Qiagen</td><td>28704</td><td></td></tr><tr><td>Qubit Fluorometer</td><td>ThermoFisher</td><td>Q33226</td><td>For quantifying DNA concentration</td></tr><tr><td>Qubit RNA HS Assay kit</td><td>ThermoFisher</td><td>Q32855</td><td></td></tr><tr><td>Razor Blades</td><td>Fisher Scientific</td><td>12640</td><td></td></tr><tr><td>Siliconized Low-Retention 1.5 ml tubes</td><td>Fisher Scientific</td><td>02-681-331</td><td></td></tr><tr><td>T4 RNA ligase 1</td><td>NEB</td><td>M0204</td><td></td></tr><tr><td>T4 RNA Ligase 2, truncated K227Q</td><td>NEB</td><td>M0351S</td><td></td></tr><tr><td>TapeStation</td><td>Agilent</td><td>G2939BA</td><td>For assessing RNA quality and concentration</td></tr><tr><td>Taq DNA Polymerase</td><td>NEB</td><td>M0273X</td><td></td></tr><tr><td>TEMED</td><td>Biorad</td><td>161-0800</td><td></td></tr><tr><td>Tris Base pH 7.5</td><td>Sigma</td><td>10708976001</td><td></td></tr><tr><td>Tris-buffered EDTA</td><td>Sigma</td><td>T9285</td><td></td></tr><tr><td>Trizol</td><td>ThermoFisher</td><td>15596026</td><td></td></tr><tr><td>UltraPure Ethidium bromide (10 mg/ml)</td><td>Invitrogen</td><td>15585-011</td><td></td></tr><tr><td>Universal miRNA cloning linker</td><td>NEB</td><td>S1315S</td><td></td></tr><tr><td>Urea</td><td>Sigma</td><td>U5378</td><td></td></tr></tbody></table>

a complete pipeline for isolating and sequencing micrornas, and analyzing them using open source tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Schematic of steps involved in library preparation 
An overall schematic of small RNA extraction, sequencing, and alignment is outlined in Figure 2. 
Liver samples from one male and one female mouse were collected and snap frozen in liquid nitrogen. Total RNA was extracted and evaluated for quality and concentration.
Small RNA sequencing yields sufficient RNA for s...

Watch this Scientific Journal Video about A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools at JoVE.com

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

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Control of Gene Expression

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Video: A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the research and pharmaceutical communities. To study miRNAs, it is essential that the quantification of microRNA levels is accurate and robust. By comparing wild-type to small RNA deficient mouse embryonic stem cells (mESC), we revealed a lack of accuracy and robustness in previous published multiplex qRT-PCR techniques. Here, we describe an optimized method, including purifying away excessive primers from previous multiplex steps before singleplex real time detection, which dramatically increases the accuracy and robustness of the technique. Furthermore, we explain how performing the technique on a microfluidic chip at nanoliter volumes significantly reduces reagent costs and permits time effective high throughput miRNA expression profiling.

Here we describe an optimized multiplex reverse transcriptase quantitative PCR (qRT-PCR) protocol in combination with a microfluidic platform as a cost and time effective high-throughput screening tool for microRNA (miRNA) expression levels, especially when working with limited amounts of sample.

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the ...

Bioengineering

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

There is increasing interest in liquid biopsy for cancer diagnosis, prognosis and therapeutic monitoring, creating the need for reliable and useful biomarkers for clinical practice. Here, we present a protocol to extract, reverse transcribe and evaluate the expression levels of circulating miRNAs from plasma samples of patients with colorectal cancer (CRC). microRNAs (miRNAs) are a class of non-coding RNAs of 18-25 nucleotides in length that regulate the expression of target genes at the translational level and play an important role, including that of pro- and anti-angiogenic function, in the physiopathology of various organs. miRNAs are stable in biological fluids such as serum and plasma, which renders them ideal circulating biomarkers for cancer diagnosis, prognosis and treatment decision-making and monitoring. Circulating miRNA extraction was performed using a rapid and effective method that involves both organic and column-based methodologies. For miRNA retrotranscription, we used a multi-step procedure that considers polyadenylation at 3&#39; and the ligation of an adapter at 5&#39; of the mature miRNAs, followed by random miRNA pre-amplification. We selected a 24-miRNA custom panel to be tested by quantitative real-time polymerase chain reaction (qRT-PCR) and spotted the miRNA probes on array custom plates. We performed qRT-PCR plate runs on a real-time PCR System. Housekeeping miRNAs for normalization were selected using GeNorm software (v. 3.2). Data were analyzed using Expression suite software (v 1.1) and statistical analyses were performed. The method proved to be reliable and technically robust and could be useful to evaluate biomarker levels in liquid samples such as plasma and/or serum.

We present a protocol to evaluate the expression levels of circulating microRNA (miRNAs) in plasma samples from cancer patients. In particular, we used a commercially available kit for circulating miRNA extraction and reverse transcription. Finally, we analyzed a panel of 24 selected miRNAs using real-time pre-spotted probe custom plates.

There is increasing interest in liquid biopsy for cancer diagnosis, prognosis and therapeutic monitoring, creating the need for reliable and useful ...

Cancer Research

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, ...

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.

A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in ...

Analyzing tRNA Dynamics Using AQRNA-seq

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate quantification of the pool of small RNAs. The AQRNA-seq library preparation procedure described here involves the use of custom-designed sequencing linkers and a step for reducing methylation RNA modifications that block reverse transcription processivity, which results in an increased yield of full-length cDNAs. In addition, a detailed implementation of the accompanying bioinformatics pipeline is presented. This demonstration of AQRNA-seq was conducted through a quantitative analysis of the 45 tRNAs in Mycobacterium bovis BCG harvested on 5 selected days across a 20-day time course of nutrient deprivation and 6 days of resuscitation. Ongoing efforts to improve the efficiency and rigor of AQRNA-seq will also be discussed here. This includes exploring methods to obviate gel purification for mitigating primer dimer issues after PCR amplification and to increase the proportion of full-length reads to enable more accurate read mapping. Future enhancements to AQRNA-seq will be focused on facilitating automation and high-throughput implementation of this technology for quantifying all small RNA species in cell and tissue samples from diverse organisms.

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Absolute quantification RNA sequencing (AQRNA-seq) is a technology developed to quantify the landscape of all small RNAs in biological mixtures. Here, both the library preparation and data processing steps of AQRNA-seq are demonstrated, quantifying changes in the transfer RNA (tRNA) pool in Mycobacterium bovis BCG during starvation-induced dormancy.

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

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

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

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

Medicine

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a promising future of using miRNAs in the diagnosis and care of lung diseases. Here, we define a protocol for miRNA profiling to measure the relative abundance of a group of miRNAs predicted to regulate inflammatory genes in the lung tissue from of an ozone-induced airway inflammation mouse model. Because it has been shown that circulating sex hormone levels can affect the regulation of lung innate immunity in females, the purpose of this method is to describe an inflammatory miRNA profiling protocol in female mice, taking into consideration the estrous cycle stage of each animal at the time of ozone exposure. We also address applicable bioinformatics approaches to miRNA discovery and target identification methods using limma, an R/Bioconductor software, and functional analysis software to understand the biological context and pathways associated with differential miRNA expression.

Here we describe a method to assess lung expression of miRNAs that are predicted to regulate inflammatory genes using mice exposed to ozone or filtered air at different stages of the estrous cycle.

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a ...

Immunology and Infection

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...

Biology

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs

Probe-based quantitative PCR (qPCR) is a favoured method for measuring transcript abundance, since it is one of the most sensitive detection methods that provides an accurate and reproducible analysis. Probe-based chemistry offers the least background fluorescence as compared to other (dye-based) chemistries. Presently, there are several platforms available that use probe-based chemistry to quantitate transcript abundance. qPCR in a 96 well plate is the most routinely used method, however only a maximum of 96 samples or miRNAs can be tested in a single run. This is time-consuming and tedious if a large number of samples/miRNAs are to be analyzed. High-throughput probe-based platforms such as microfluidics (e.g. TaqMan Array Card) and nanofluidics arrays (e.g. OpenArray) offer ease to reproducibly and efficiently detect the abundance of multiple microRNAs in a large number of samples in a short time. Here, we demonstrate the experimental setup and protocol for miRNA quantitation from serum or plasma-EDTA samples, using probe-based chemistry and three different platforms (96 well plate, microfluidics and nanofluidics arrays) offering increasing levels of throughput.

Circulating microRNAs have recently emerged as promising and novel biomarkers for various cancers and other diseases. The goal of this article is to discuss three different probe-based real-time PCR platforms and methods that are available to quantify and determine the abundance of circulating microRNAs.

Probe-based quantitative PCR (qPCR) is a favoured method for measuring transcript abundance, since it is one of the most sensitive detection methods that ...