Name: a complete pipeline for isolating and sequencing micrornas, and analyzing them using open source tools
Uploaded: 2019-08-21T13:30:00
Description: Watch this Scientific Journal Video about A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools at JoVE.com

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>
	...

<ol>
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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>

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</td>
			<td>NEB</td>
			<td>M0242S</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 ...

We have developed a defined, reproducible, and longstanding protocol for small RNA sequencing and for analyzing normalized reads using opensource bioinformatics tools. The main advantages of our strategy are that it accurately collects microRNAs through gel purification and that the bioinformatics analysis can be applied regardless of how the microRNAs are collected. High throughput sequencing of microRNAs can reveal insight into disease mechanisms, the processing of microRNAs, and the accurate quantification of gene therapy approaches that deliver small RNAs.Make sure to work in a RNase free environment and to keep all of the RNA products on ice throughout the procedure. Visualizing the gel cutting steps will help viewers to identify the correct size of the products required for downstream applications. For three prime adaptor ligation, combine 11 microliters of RNA with 1.5 microliters of ATP free 10X T4RNA ligase reaction buffer, one microliter of polyethylene glycol, and 0.5 microliters of three prime linker.Heat the samples at 95 degrees Celsius on a thermocycler for 30 to 40 seconds, followed by cooling on ice for one minute. Add one microliter of T4RNA ligase two and incubate the reaction at room temperature for two hours. While the samples are incubating, pour a 15%polyacyrilamide gel between 0.8 millimeter separated glass plates in a plastic cast and insert a comb.When the gel has solidified, add 0.5 5X TBE to the tank and pipette vigorously to wash the wells of residual urea. After pre-running the polyacrylamide gel for 25 minutes at 375 volts, load the samples leaving at least one lane in between each sample. Load 20 microliters of at least two sets of markers in an asymmetrical pattern to keep track of the gel orientation and run the gel at a constant 375 volts.After 15 minutes, increase to a constant 425 volts for the rest of the run and run the gel for about two hours. At the end of the run, use a plate separator to remove the gel from the glass plates and place the gel on a plastic page protector. Dilute five microliters of ethidium bromide in 500 microliters of distilled water and add the dye onto the marker lanes just above the top light blue marker.Next, use a clean razor blade to cut the gels from the upper to lower marker in each lane under ultraviolet light and transfer the pieces to a four by four centimeter square of laboratory sealing film. Cut the gel with about three cuts horizontally and two cuts vertically to produce 12 small squares. Add 400 microliters of 0.3 molar sodium chloride onto the sealing film and guide the gel pieces into 1.5 milliliter siliconized tubes.Agitate the samples on a nutator at four degrees Celsius overnight. The next morning, transfer 400 microliters of each supernatant into new tubes. Add one milliliter of 100%ethanol and one microliter of 15 milligrams per milliliter glycogen co-precipitant per tube.Then store the tubes at minus 80 degrees Celsius for one hour. For five prime linker ligation, first spin down the thawed samples and resuspend the pellets in water. Next, add 0.5 microliters of 100 micromolar five prime linker, one microliter of T4RNA ligase buffer, one microliter of 10 millimolar ATP, and one microliter of polyethylene glycol to each sample.Heat the reactions at 90 degrees Celsius for 30 seconds before placing them on ice. Then add one microliter of T4RNA ligase one to each tube and incubate the reactions at room temperature for two hours. For reverse transcription, collect the samples by centrifugation and resuspend the air dried pellet in 8.25 microliters of nuclease free water.Add 0.5 microliters of 100 micromolar reverse transcriptase primer and five microliters of 2X reverse transcriptase reaction mix from a complementary DNA synthesis kit. After three minutes at 42 degrees Celsius, add 1.5 microliters of 10X reverse transcriptase enzyme to each sample for a 30 minute incubation at 42 degrees Celsius in a thermocycler. After neutralization, prepare a PCR reaction with 29.5 microliters of water, five microliters of 10X taq buffer, one microliter of nucleoside triphosphate, two microliters of 25 micromolar forward primer, two microliters of 25 micromolar reverse primer, 0.5 microliters of taq polymerase, and 10 microliters of the reverse transcribed complementary DNA.Then run two PCR reactions. For agarose gel purification, prepare a 4%agarose gel with low melting agarose and load 40 microliters or more of the PCR product onto the gel with loading dye and 100 base pair and 25 base pair size markers. After running the gel, cut the band above the 125 base pair band and use a gel extraction kit according to the manufacturer's instructions.Shake to dissolve the gel band in buffer at room temperature before using the appropriate equipment to quantify the final sequence library. Then use the appropriate opensource bioinformatics tools to analyze the sequence data. In this representative PCR reaction on low melt agarose gel, the acquisition of the correct cloned product compared to the obtained linker linker product and unsaturated versus saturated samples can be observed.After alignment to human microRNA hairpins, a strong concordance between the microRNA read counts in each replicate can be observed. A total of 306 microRNA species were detected with the greatest number of reads mapping to microRNA 122. It is important to remember to cut the gels at the appropriate size to enable an accurate recovery of the microRNAs.After sequencing the microRNAs, users can apply a bioinformatics analysis to characterize the distribution of microRNAs within their tissues of interest. This technique has been used to identify how microRNAs are processed and which sets of microRNAs are altered in certain cancers. Be sure to exercise care when handling the ethidium bromide and when looking at the gels under ultraviolet light.

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Research

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JoVE Core

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Cell Biology

Genetics

Unit 1

Control of Gene Expression

SCIENTISTS IN ACTION

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been co-opted as a powerful tool for precise eukaryotic genome engineering. Here, we present a rapid and simple method using chimeric single guide RNAs (sgRNA) and CRISPR-Cas9 Ribonucleoproteins (RNPs) for the efficient and precise generation of genomic point mutations in C. elegans. We describe a pipeline for sgRNA target selection, homology-directed repair (HDR) template design, CRISPR-Cas9-RNP complexing and delivery, and a genotyping strategy that enables the robust and rapid identification of correctly edited animals. Our approach not only permits the facile generation and identification of desired genomic point mutant animals, but also facilitates the detection of other complex indel alleles in approximately 4 - 5 days with high efficiency and a reduced screening workload.

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

Here, we present a method to engineer the genome of C. elegans using CRISPR-Cas9 ribonucleoproteins and homology dependent repair templates.

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

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, ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...