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>

<ol>
	<li>Lee, R. C., Feinbaum, R. L., Ambros, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+heterochronic+gene+lin-4+encodes+small+RNAs+with+antisense+complementarity+to+lin-14.">The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.</a> Cell. 75 (5), 843-854 (1993).</li><li>Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metazoan+MicroRNAs.">Metazoan MicroRNAs.</a> Cell. 173 (1), 20-51 (2018).</li><li>Lau, N. C., Lim, L. P., Weinstein, E. G., Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+abundant+class+of+tiny+RNAs+with+probable+regulatory+roles+in+Caenorhabditis+elegans.">An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans.</a> Science. 294 (5543), 858-862 (2001).</li><li>Kim, V. N., Han, J., Siomi, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biogenesis+of+small+RNAs+in+animals.">Biogenesis of small RNAs in animals.</a> Nature Reviews Molecular Cell Biology. 10 (2), 126-139 (2009).</li><li>Gu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+loop+position+of+shRNAs+and+pre-miRNAs+is+critical+for+the+accuracy+of+dicer+processing+in+vivo.">The loop position of shRNAs and pre-miRNAs is critical for the accuracy of dicer processing in vivo.</a> Cell. 151 (4), 900-911 (2012).</li><li>Valdmanis, P. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+interference-induced+hepatotoxicity+results+from+loss+of+the+first+synthesized+isoform+of+microRNA-122+in+mice.">RNA interference-induced hepatotoxicity results from loss of the first synthesized isoform of microRNA-122 in mice.</a> Nature Medicine. 22 (5), 557-562 (2016).</li><li>Valdmanis, P. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miR-122+removal+in+the+liver+activates+imprinted+microRNAs+and+enables+more+effective+microRNA-mediated+gene+repression.">miR-122 removal in the liver activates imprinted microRNAs and enables more effective microRNA-mediated gene repression.</a> Nature Communications. 9 (1), 5321 (2018).</li><li>Griffiths-Jones, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microRNA+Registry.">The microRNA Registry.</a> Nucleic Acids Research. 32, D109-D111 (2004).</li><li>Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., Enright, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+microRNA+sequences,+targets+and+gene+nomenclature.">miRBase: microRNA sequences, targets and gene nomenclature.</a> Nucleic Acids Research. 34 (Database issue), D140-D144 (2006).</li><li>Griffiths-Jones, S., Saini, H. K., van Dongen, S., Enright, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+tools+for+microRNA+genomics.">miRBase: tools for microRNA genomics.</a> Nucleic Acids Research. 36 (Database issue), D154-D158 (2008).</li><li>Patro, R., Mount, S. M., Kingsford, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sailfish+enables+alignment-free+isoform+quantification+from+RNA-seq+reads+using+lightweight+algorithms.">Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.</a> Nature Biotechnology. 32 (5), 462-464 (2014).</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biology. 15 (12), 550 (2014).</li><li>Fire, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potent+and+specific+genetic+interference+by+double-stranded+RNA+in+Caenorhabditis+elegans.">Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans.</a> Nature. 391 (6669), 806-811 (1998).</li><li>Etheridge, A., Wang, K., Baxter, D., Galas, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+Small+RNA+NGS+Libraries+from+Biofluids.">Preparation of Small RNA NGS Libraries from Biofluids.</a> Methods in Molecular Biology. 1740, 163-175 (2018).</li><li>Yamane, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+hepatitis+C+virus+RNA+target+site+selection+and+host+factor+activities+of+naturally+occurring+miR-122+3+variants.">Differential hepatitis C virus RNA target site selection and host factor activities of naturally occurring miR-122 3 variants.</a> Nucleic Acids Research. 45 (8), 4743-4755 (2017).</li><li>Giraldez, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+multi-center+assessment+of+small+RNA-seq+methods+for+quantitative+miRNA+profiling.">Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling.</a> Nature Biotechnology. 36 (8), 746-757 (2018).</li><li>Dard-Dascot, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+comparison+of+small+RNA+library+preparation+protocols+for+next-generation+sequencing.">Systematic comparison of small RNA library preparation protocols for next-generation sequencing.</a> BMC Genomics. 19 (1), 118 (2018).</li><li>Yeri, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+commercially+available+small+RNASeq+library+preparation+kits+using+low+input+RNA.">Evaluation of commercially available small RNASeq library preparation kits using low input RNA.</a> BMC Genomics. 19 (1), 331 (2018).</li><li>Coenen-Stass, A. M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+methodologies+for+microRNA+biomarker+detection+by+next+generation+sequencing.">Evaluation of methodologies for microRNA biomarker detection by next generation sequencing.</a> RNA Biology. 15 (8), 1133-1145 (2018).</li><li>Baran-Gale, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Addressing+Bias+in+Small+RNA+Library+Preparation+for+Sequencing:+A+New+Protocol+Recovers+MicroRNAs+that+Evade+Capture+by+Current+Methods.">Addressing Bias in Small RNA Library Preparation for Sequencing: A New Protocol Recovers MicroRNAs that Evade Capture by Current Methods.</a> Frontiers in Genetics. 6, 352 (2015).</li><li>Jayaprakash, A. D., Jabado, O., Brown, B. D., Sachidanandam, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+remediation+of+biases+in+the+activity+of+RNA+ligases+in+small-RNA+deep+sequencing.">Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing.</a> Nucleic Acids Research. 39 (21), e141 (2011).</li><li>Van Nieuwerburgh, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+bias+in+Illumina+TruSeq+and+a+novel+post+amplification+barcoding+strategy+for+multiplexed+DNA+and+small+RNA+deep+sequencing.">Quantitative bias in Illumina TruSeq and a novel post amplification barcoding strategy for multiplexed DNA and small RNA deep sequencing.</a> PLoS One. 6 (10), e26969 (2011).</li><li>Valdmanis, P. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Upregulation+of+the+microRNA+cluster+at+the+Dlk1-Dio3+locus+in+lung+adenocarcinoma.">Upregulation of the microRNA cluster at the Dlk1-Dio3 locus in lung adenocarcinoma.</a> Oncogene. 34 (1), 94-103 (2015).</li><li>Schwarzenbach, H., da Silva, A. M., Calin, G., Pantel, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+Normalization+Strategies+for+MicroRNA+Quantification.">Data Normalization Strategies for MicroRNA Quantification.</a> Clinical Chemistry. 61 (11), 1333-1342 (2015).</li><li>Viollet, S., Fuchs, R. T., Munafo, D. B., Zhuang, F., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=T4+RNA+ligase+2+truncated+active+site+mutants:+improved+tools+for+RNA+analysis.">T4 RNA ligase 2 truncated active site mutants: improved tools for RNA analysis.</a> BMC Biotechnology. 11, 72 (2011).</li><li>Chiang, H. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mammalian+microRNAs:+experimental+evaluation+of+novel+and+previously+annotated+genes.">Mammalian microRNAs: experimental evaluation of novel and previously annotated genes.</a> Genes &amp; Development. 24 (10), 992-1009 (2010).</li><li>Fromm, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Uniform+System+for+the+Annotation+of+Vertebrate+microRNA+Genes+and+the+Evolution+of+the+Human+microRNAome.">A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome.</a> Annual Review of Genetics. 49, 213-242 (2015).</li></ol>

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 ...

a-complete-pipeline-for-isolating-sequencing-micrornas-analyzing-them

Universidad San Sebastian

Research

Education

JoVE Core

JoVE Journal

Cell Biology

Genetics

Unit 1

Control of Gene Expression

SCIENTISTS IN ACTION

7.2K Views.  University of Washington School of Medicine. μ

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

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

Neural stem cells (NSCs) are capable of self-renewal and differentiation into neurons, astrocytes and oligodendrocytes under specific local microenvironments. In here, we present a set of methods used for three dimensional (3D) differentiation and miRNA analysis of a clonal human neural stem cell (hNSC) line, currently in clinical trials for stroke disability (NCT01151124 and NCT02117635, Clinicaltrials.gov). HNSCs were derived from an ethical approved first trimester human fetal cortex and conditionally immortalized using retroviral integration of a single copy of the c-mycERTAMconstruct. We describe how to measure axon process outgrowth of hNSCs differentiated on 3D scaffolds and how to quantify associated changes in miRNA expression using PCR array. Furthermore we exemplify computational analysis with the aim of selecting miRNA putative targets. SOX5 and NR4A3 were identified as suitable miRNA putative target of selected significantly down-regulated miRNAs in differentiated hNSC. MiRNA target validation was performed on SOX5 and NR4A3 3&#8217;UTRs by dual reporter plasmid transfection and dual luciferase assay.

With the intent of characterizing changes in miRNAs on differentiated human neural stem cells (hNSCs) we describe hNSC differentiation on a three dimensional system,the evaluation of changes in microRNA expression by miRNA PCR array, and computational analysis for miRNA target prediction and its validation by dual luciferase assay.

Neural stem cells (NSCs) are capable of self-renewal and differentiation into neurons, astrocytes and oligodendrocytes under specific local ...

Developmental Biology

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 ...

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil and gas. This process revolutionized the U.S. energy industry, as it gives access to resources that were previously unobtainable and now produces two-thirds of the total natural gas in the United States. Although fracking has had a positive impact on the U.S. economy, several studies have highlighted its detrimental environmental effects. Of particular concern is the effect of fracking on headwater streams, which are especially important due to their disproportionately large impact on the health of the entire watershed. The bacteria within those streams can be used as indicators of stream health, as the bacteria present and their abundance in a disturbed stream would be expected to differ from those in an otherwise comparable but undisturbed stream. Therefore, this protocol aims to use the bacterial community to determine if streams have been impacted by fracking. To this end, sediment, and water samples, from streams near fracking (potentially impacted) and upstream or in a different watershed of fracking activity (unimpacted) must be collected. Those samples are then subjected to nucleic acid extraction, library preparation, and sequencing to investigate microbial community composition. Correlational analysis and machine learning models can subsequently be employed to identify which features are explanative of variation in the community, as well as identification of predictive biomarkers for fracking&#39;s impact. These methods can reveal a variety of differences in the microbial communities among headwater streams, based on the proximity to fracking, and serve as a foundation for future investigations on the environmental impact of fracking activities.

Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil ...

Environment

Performing Custom MicroRNA Microarray Experiments

microRNAs (miRNAs) are a large family of &tilde; 22 nucleotides (nt) long RNA molecules that are widely expressed in eukaryotes 1. Complex genomes encode at least hundreds of miRNAs, which primarily inhibit the expression of a vast number of target genes post-transcriptionally 2, 3. miRNAs control a broad range of biological processes 1. In addition, altered miRNA expression has been associated with human diseases such as cancers, and miRNAs may serve as biomarkers for diseases and prognosis 4, 5. It is important, therefore, to understand the expression and functions of miRNAs under many different conditions.
Three major approaches have been employed to profile miRNA expression: real-time PCR, microarray, and deep sequencing. The technique of miRNA microarray has the advantage of being high-throughput, generally less expensive, and most of the experimental and analysis steps can be carried out in a molecular biology laboratory at most universities, medical schools and associated hospitals. Here, we describe a method for performing custom miRNA microarray experiments. A miRNA probe set will be printed on glass slides to produce miRNA microarrays. RNA is isolated using a method or reagent that preserves small RNA species, and then labeled with a fluorescence dye. As a control, reference DNA oligonucleotides corresponding to a subset of miRNAs are also labeled with a different fluorescence dye. The reference DNA will serve to demonstrate the quality of the slide and hybridization and will also be used for data normalization. The RNA and DNA are mixed and hybridized to a microarray slide containing probes for most of the miRNAs in the database. After washing, the slide is scanned to obtain images, and intensities of the individual spots quantified. These raw signals will be further processed and analyzed as the expression data of the corresponding miRNAs. Microarray slides can be stripped and regenerated to reduce the cost of microarrays and to enhance the consistency of microarray experiments. The same principles and procedures are applicable to other types of custom microarray experiments.

A simple procedure of performing custom microRNA microarray experiments is described. The steps include isolating RNA, labeling RNA and reference DNA, hybridizing the samples to microarrays, scanning the microarrays, quantifying and analyzing hybridization signals.

microRNAs (miRNAs) are a large family of &tilde; 22 nucleotides (nt) long RNA molecules that are widely expressed in eukaryotes 1.  Complex genomes encode ...

Biology

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

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive human diseases. Considerable advances have been made to uncover the molecular signatures of homeostatic microglia as well as alterations of their gene expression in response to environmental stimuli. With the advent and maturation of single-cell genomic methodologies, it is increasingly recognized that heterogenous microglia may underlie the diverse roles they play in different developmental and pathological conditions. Further dissection of such heterogeneity can be achieved through efficient isolation of microglia from a given region of interest, followed by sensitive profiling of individual cells. Here, we provide a detailed protocol for the rapid isolation of microglia from different brain regions in a single adult mouse brain hemisphere. We also demonstrate how to use these sorted microglia for plate-based deep single-cell RNA sequencing. We discuss the adaptability of this method to other scenarios and provide guidelines for improving the system to accommodate large-scale studies.

We provide a protocol for isolation of microglia from different dissected regions of an adult mouse brain hemisphere, followed by semi-automated library preparation for deep single-cell RNA sequencing of full-length transcriptomes. This method will help to elucidate functional heterogeneity of microglia in health and disease.

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive ...

Neuroscience

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 ...

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved. As computational miRNA identification and annotation processes are mostly error-prone processes, homology-based predictions increase prediction accuracy. We developed and have improved the miRNA annotation pipeline, SUmir, in the last decade, which has been used for several plant genomes since then. 
This study presents a fully automated, new miRNA pipeline, mirMachine (miRNA Machine), by (i) adding an additional filtering step on the secondary structure predictions, (ii) making it fully automated, and (iii) introducing new options to predict either known miRNA based on homology or novel miRNAs based on small RNA sequencing reads using the previous pipeline. The new miRNA pipeline, mirMachine, was tested using The Arabidopsis Information Resource, TAIR10, release of the Arabidopsis genome and the International Wheat Genome Sequencing Consortium (IWGSC) wheat reference genome v2.

Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of ...

Isolation of microRNAs from Tick Ex Vivo Salivary Gland Cultures and Extracellular Vesicles

Ticks are important ectoparasites that can vector multiple pathogens. The salivary glands of ticks are essential for feeding as their saliva contains many effectors with pharmaceutical properties that can diminish host immune responses and enhance pathogen transmission. One group of such effectors are microRNAs (miRNAs). miRNAs are short non-coding sequences that regulate host gene expression at the tick-host interface and within the organs of the tick. These small RNAs are transported in the tick saliva via extracellular vesicles (EVs), which serve inter-and intracellular communication. Vesicles containing miRNAs have been identified in the saliva of ticks. However, little is known about the roles and profiles of the miRNAs in tick salivary vesicles and glands. Furthermore, the study of vesicles and miRNAs in tick saliva requires tedious procedures to collect tick saliva. This protocol aims to develop and validate a method for isolating miRNAs from purified extracellular vesicles produced by ex vivo organ cultures. The materials and methodology needed to extract miRNAs from extracellular vesicles and tick salivary glands are described herein.

Isolation of microRNAs from Tick Ex Vivo Salivary Gland Cultures and Extracellular Vesicles

The present protocol describes the isolation of microRNAs from tick salivary glands and purified extracellular vesicles. This is a universal procedure that combines commonly used reagents and supplies. The method also allows the use of a small number of ticks, resulting in quality microRNAs that can be readily sequenced.

Ticks are important ectoparasites that can vector multiple pathogens. The salivary glands of ticks are essential for feeding as their saliva contains many ...

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

Transkrypcja

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

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

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

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Performing Custom MicroRNA Microarray Experiments

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

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

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

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Isolation of microRNAs from Tick Ex Vivo Salivary Gland Cultures and Extracellular Vesicles